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\n  \n 2021\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Forecasting copper electrorefining cathode rejection by means of recurrent neural networks with attention mechanism.\n \n \n \n\n\n \n Correa, P. P.; Cipriano, A.; Nuñez, F.; Salas, J. C.; and Lobel, H.\n\n\n \n\n\n\n IEEE Access,1-1. 2021.\n \n\n\n\n
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@Article{\t  9410222,\n  author\t= {Correa, Pedro Pablo and Cipriano, Aldo and Nuñez, Felipe\n\t\t  and Salas, Juan Carlos and Lobel, Hans},\n  journal\t= {IEEE Access},\n  title\t\t= {Forecasting copper electrorefining cathode rejection by\n\t\t  means of recurrent neural networks with attention\n\t\t  mechanism},\n  year\t\t= {2021},\n  volume\t= {},\n  number\t= {},\n  pages\t\t= {1-1},\n  doi\t\t= {10.1109/ACCESS.2021.3074780}\n}\n\n
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\n \n\n \n \n \n \n \n \n Evaluating a Learning Analytics Dashboard to Visualize Student Self-Reports of Time-on-Task: A Case Study in a Latin American University.\n \n \n \n \n\n\n \n Hilliger, I.; Miranda, C.; Schuit, G.; Duarte, F.; Anselmo, M.; and Parra, D.\n\n\n \n\n\n\n In LAK21: 11th International Learning Analytics and Knowledge Conference, of LAK21, pages 592–598, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"EvaluatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  10.1145/3448139.3448203,\n  author\t= {Hilliger, Isabel and Miranda, Constanza and Schuit,\n\t\t  Gregory and Duarte, Fernando and Anselmo, Martin and Parra,\n\t\t  Denis},\n  title\t\t= {Evaluating a Learning Analytics Dashboard to Visualize\n\t\t  Student Self-Reports of Time-on-Task: A Case Study in a\n\t\t  Latin American University},\n  year\t\t= {2021},\n  isbn\t\t= {9781450389358},\n  url\t\t= {https://doi.org/10.1145/3448139.3448203},\n  doi\t\t= {10.1145/3448139.3448203},\n  booktitle\t= {LAK21: 11th International Learning Analytics and Knowledge\n\t\t  Conference},\n  pages\t\t= {592–598},\n  series\t= {LAK21}\n}\n\n
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\n \n\n \n \n \n \n \n Continual Learning of Microscopic Traffic Models Using Neural Networks.\n \n \n \n\n\n \n Farid, Y. Z.; Kreidieh, A. R.; Khalighi, F.; Lobel, H.; and Bayen, A. M\n\n\n \n\n\n\n Technical Report 2021.\n \n\n\n\n
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@TechReport{\t  farid2021continual,\n  title\t\t= {Continual Learning of Microscopic Traffic Models Using\n\t\t  Neural Networks},\n  author\t= {Farid, Yashar Zeinali and Kreidieh, Abdul Rahman and\n\t\t  Khalighi, Farnoush and Lobel, Hans and Bayen, Alexandre M},\n  year\t\t= {2021}\n}\n\n
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\n \n\n \n \n \n \n \n \n Measuring heterogeneous perception of urban space with massive data and machine learning: An application to safety.\n \n \n \n \n\n\n \n Ramírez, T.; Hurtubia, R.; Lobel, H.; and Rossetti, T.\n\n\n \n\n\n\n Landscape and Urban Planning, 208: 104002. April 2021.\n \n\n\n\n
\n\n\n\n \n \n \"MeasuringPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  ramrez2021,\n  doi\t\t= {10.1016/j.landurbplan.2020.104002},\n  url\t\t= {https://doi.org/10.1016/j.landurbplan.2020.104002},\n  year\t\t= {2021},\n  month\t\t= apr,\n  publisher\t= {Elsevier {BV}},\n  volume\t= {208},\n  pages\t\t= {104002},\n  author\t= {Tom{\\'{a}}s Ram{\\'{\\i}}rez and Ricardo Hurtubia and Hans\n\t\t  Lobel and Tom{\\'{a}}s Rossetti},\n  title\t\t= {Measuring heterogeneous perception of urban space with\n\t\t  massive data and machine learning: An application to\n\t\t  safety},\n  journal\t= {Landscape and Urban Planning}\n}\n\n
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\n  \n 2020\n \n \n (15)\n \n \n
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\n \n\n \n \n \n \n \n \n Automatic document screening of medical literature using word and text embeddings in an active learning setting.\n \n \n \n \n\n\n \n Carvallo, A.; Parra, D.; Lobel, H.; and Soto, A.\n\n\n \n\n\n\n Scientometrics. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  activecarvallo2020,\n  title\t\t= "Automatic document screening of medical literature using\n\t\t  word and text embeddings in an active learning setting",\n  journal\t= "Scientometrics",\n  year\t\t= "2020",\n  doi\t\t= "10.1007/s11192-020-03648-6",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/Pre_print_Carvallo_Document_Screening_Active_Learning.pdf",\n  author\t= "Andres Carvallo and Denis Parra and Hans Lobel and Alvaro\n\t\t  Soto",\n  keywords\t= "Active learning, Document screening, Natural language\n\t\t  processing"\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic document screening of medical literature using word and text embeddings in an active learning setting.\n \n \n \n \n\n\n \n Carvallo, A.; Parra, D.; Lobel, H.; and Soto, A.\n\n\n \n\n\n\n Scientometrics, 125(3): 3047–3084. September 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  carvallo2020,\n  doi\t\t= {10.1007/s11192-020-03648-6},\n  url\t\t= {https://doi.org/10.1007/s11192-020-03648-6},\n  year\t\t= {2020},\n  month\t\t= sep,\n  publisher\t= {Springer Science and Business Media {LLC}},\n  volume\t= {125},\n  number\t= {3},\n  pages\t\t= {3047--3084},\n  author\t= {Andres Carvallo and Denis Parra and Hans Lobel and Alvaro\n\t\t  Soto},\n  title\t\t= {Automatic document screening of medical literature using\n\t\t  word and text embeddings in an active learning setting},\n  journal\t= {Scientometrics}\n}\n\n
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\n \n\n \n \n \n \n \n Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism.\n \n \n \n\n\n \n Cerda-Mardini, P.; Araujo, V.; and Soto, A.\n\n\n \n\n\n\n arXiv preprint arXiv:2006.00697. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  cerda2020translating,\n  title\t\t= {Translating Natural Language Instructions for Behavioral\n\t\t  Robot Navigation with a Multi-Head Attention Mechanism},\n  author\t= {Cerda-Mardini, Patricio and Araujo, Vladimir and Soto,\n\t\t  Alvaro},\n  journal\t= {arXiv preprint arXiv:2006.00697},\n  year\t\t= {2020}\n}\n\n
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\n \n\n \n \n \n \n \n \n Algorithmic and HCI aspects for explaining recommendations of artistic images.\n \n \n \n \n\n\n \n Dominguez, V.; Donoso-Guzmán, I. N.; Messina, P.; and Parra, D.\n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems (TiiS). 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AlgorithmicPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dominguez2020algorithmic,\n  title\t\t= {Algorithmic and HCI aspects for explaining recommendations\n\t\t  of artistic images},\n  author\t= {Dominguez, Vicente and Donoso-Guzm{\\'a}n, Ivania Nadine\n\t\t  and Messina, Pablo and Parra, Denis},\n  journal\t= {ACM Transactions on Interactive Intelligent Systems\n\t\t  (TiiS)},\n  year\t\t= {2020},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/pre-print-alg_hci_explainable_recsys_ACMT-TiiS-2020.pdf},\n  doi\t\t= "10.1145/3369396",\n  publisher\t= {ACM New York, NY, USA}\n}\n\n
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\n \n\n \n \n \n \n \n Differentiable Adaptive Computation Time for Visual Reasoning.\n \n \n \n\n\n \n Eyzaguirre, C.; and Soto, A.\n\n\n \n\n\n\n In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  eyzaguirre_2020_cvpr,\n  author\t= {Eyzaguirre, Cristobal and Soto, Alvaro},\n  title\t\t= {Differentiable Adaptive Computation Time for Visual\n\t\t  Reasoning},\n  booktitle\t= {Proceedings of the IEEE/CVF Conference on Computer Vision\n\t\t  and Pattern Recognition (CVPR)},\n  month\t\t= {June},\n  year\t\t= {2020}\n}\n\n
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\n \n\n \n \n \n \n \n \n GENE: Graph generation conditioned on named entities for polarity and controversy detection in social media.\n \n \n \n \n\n\n \n Mendoza, M.; Parra, D.; and Soto, Á.\n\n\n \n\n\n\n Information Processing & Management,102366. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"GENE:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  genemendoza2020,\n  title\t\t= "GENE: Graph generation conditioned on named entities for\n\t\t  polarity and controversy detection in social media",\n  journal\t= "Information Processing & Management",\n  pages\t\t= "102366",\n  year\t\t= "2020",\n  issn\t\t= "0306-4573",\n  doi\t\t= "10.1016/j.ipm.2020.102366",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/PRE_PRINT_GENE_Graph_generation.pdf",\n  author\t= "Marcelo Mendoza and Denis Parra and Álvaro Soto",\n  keywords\t= "Graph-based representations, Controversy detection,\n\t\t  Polarity dynamics"\n}\n\n
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\n \n\n \n \n \n \n \n \n Social QA in non-CQA platforms.\n \n \n \n \n\n\n \n Herrera, J.; Parra, D.; and Poblete, B.\n\n\n \n\n\n\n Future Generation Computer Systems, 105: 631–649. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"SocialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  herrera2019social,\n  title\t\t= {Social QA in non-CQA platforms},\n  author\t= {Herrera, Jos{\\'e} and Parra, Denis and Poblete, Barbara},\n  journal\t= {Future Generation Computer Systems},\n  year\t\t= {2020},\n  volume\t= {105},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/Pre_print_Journal__Social_QA_in_non_CQA_platforms_.pdf},\n  doi\t\t= {https://doi.org/10.1016/j.future.2019.12.023},\n  pages\t\t= {631--649},\n  publisher\t= {Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n \n CompactNets: Compact Hierarchical Compositional Networks for Visual Recognition.\n \n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n Computer Vision and Image Understanding, 191: 102841. February 2020.\n \n\n\n\n
\n\n\n\n \n \n \"CompactNets:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  lobel2020,\n  doi\t\t= {10.1016/j.cviu.2019.102841},\n  url\t\t= {https://doi.org/10.1016/j.cviu.2019.102841},\n  year\t\t= {2020},\n  month\t\t= feb,\n  publisher\t= {Elsevier {BV}},\n  volume\t= {191},\n  pages\t\t= {102841},\n  author\t= {Hans Lobel and Ren{\\'{e}} Vidal and Alvaro Soto},\n  title\t\t= {{CompactNets}: Compact Hierarchical Compositional Networks\n\t\t  for Visual Recognition},\n  journal\t= {Computer Vision and Image Understanding}\n}\n\n
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\n \n\n \n \n \n \n \n \n CompactNets: Compact Hierarchical Compositional Networks for Visual Recognition.\n \n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n Computer Vision and Image Understanding, 191: 102841. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"CompactNets:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  lobel2020102841,\n  title\t\t= {CompactNets: Compact Hierarchical Compositional Networks\n\t\t  for Visual Recognition},\n  journal\t= {Computer Vision and Image Understanding},\n  volume\t= {191},\n  pages\t\t= {102841},\n  year\t\t= {2020},\n  issn\t\t= {1077-3142},\n  doi\t\t= {https://doi.org/10.1016/j.cviu.2019.102841},\n  url\t\t= {https://www.sciencedirect.com/science/article/pii/S1077314218301905},\n  author\t= {Hans Lobel and René Vidal and Alvaro Soto},\n  keywords\t= {Deep learning, Regularization, Group sparsity, Image\n\t\t  categorization},\n  abstract\t= {CNN-based models currently provide state-of-the-art\n\t\t  performance in image categorization tasks. While these\n\t\t  methods are powerful in terms of representational capacity,\n\t\t  they are generally not conceived with explicit means to\n\t\t  control complexity. This might lead to scenarios where\n\t\t  resources are used in a non-optimal manner, increasing the\n\t\t  number of unspecialized or repeated neurons, and\n\t\t  overfitting to data. In this work we propose CompactNets, a\n\t\t  new approach to visual recognition that learns a hierarchy\n\t\t  of shared, discriminative, specialized, and compact\n\t\t  representations. CompactNets naturally capture the notion\n\t\t  of compositional compactness, a characterization of\n\t\t  complexity in compositional models, consisting on using the\n\t\t  smallest number of patterns to build a suitable visual\n\t\t  representation. We employ a structural regularizer with\n\t\t  group-sparse terms in the objective function, that induces\n\t\t  on each layer, an efficient and effective use of elements\n\t\t  from the layer below. In particular, this allows groups of\n\t\t  top-level features to be specialized based on category\n\t\t  information. We evaluate CompactNets on the ILSVRC12\n\t\t  dataset, obtaining compact representations and competitive\n\t\t  performance, using an order of magnitude less parameters\n\t\t  than common CNN-based approaches. We show that CompactNets\n\t\t  are able to outperform other group-sparse-based approaches,\n\t\t  in terms of performance and compactness. Finally,\n\t\t  transfer-learning experiments on small-scale datasets\n\t\t  demonstrate high generalization power, providing remarkable\n\t\t  categorization performance with respect to alternative\n\t\t  approaches.}\n}\n\n
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\n CNN-based models currently provide state-of-the-art performance in image categorization tasks. While these methods are powerful in terms of representational capacity, they are generally not conceived with explicit means to control complexity. This might lead to scenarios where resources are used in a non-optimal manner, increasing the number of unspecialized or repeated neurons, and overfitting to data. In this work we propose CompactNets, a new approach to visual recognition that learns a hierarchy of shared, discriminative, specialized, and compact representations. CompactNets naturally capture the notion of compositional compactness, a characterization of complexity in compositional models, consisting on using the smallest number of patterns to build a suitable visual representation. We employ a structural regularizer with group-sparse terms in the objective function, that induces on each layer, an efficient and effective use of elements from the layer below. In particular, this allows groups of top-level features to be specialized based on category information. We evaluate CompactNets on the ILSVRC12 dataset, obtaining compact representations and competitive performance, using an order of magnitude less parameters than common CNN-based approaches. We show that CompactNets are able to outperform other group-sparse-based approaches, in terms of performance and compactness. Finally, transfer-learning experiments on small-scale datasets demonstrate high generalization power, providing remarkable categorization performance with respect to alternative approaches.\n
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\n \n\n \n \n \n \n \n \n Latent Chords: Generative Piano Chord Synthesis with Variational Autoencoders.\n \n \n \n \n\n\n \n Macaya, A.; Cadiz, R.; Cartagena, M.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the HAI-GEN 2020 - IUI 2020 Workshop on Human-AI Co-Creation with Generative Models, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"LatentPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  macaya2020,\n  author\t= {Macaya, Agustin and Cadiz, Rodrigo and Cartagena, Manuel\n\t\t  and Parra, Denis},\n  title\t\t= {Latent Chords: Generative Piano Chord Synthesis with\n\t\t  Variational Autoencoders},\n  year\t\t= {2020},\n  booktitle\t= {Proceedings of the HAI-GEN 2020 - IUI 2020 Workshop on\n\t\t  Human-AI Co-Creation with Generative Models},\n  location\t= {Calgary, Italy},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/latentchords2020-preprint.pdf},\n  keywords\t= {VAE, variational autoencoder, music, chors, creative AI}\n}\n\n
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\n \n\n \n \n \n \n \n \n GENE: Graph generation conditioned on named entities for polarity and controversy detection in social media.\n \n \n \n \n\n\n \n Mendoza, M.; Parra, D.; and Soto, Á.\n\n\n \n\n\n\n Information Processing & Management, 57(6): 102366. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"GENE:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  mendoza2020102366,\n  title\t\t= {GENE: Graph generation conditioned on named entities for\n\t\t  polarity and controversy detection in social media},\n  journal\t= {Information Processing & Management},\n  volume\t= {57},\n  number\t= {6},\n  pages\t\t= {102366},\n  year\t\t= {2020},\n  issn\t\t= {0306-4573},\n  doi\t\t= {https://doi.org/10.1016/j.ipm.2020.102366},\n  url\t\t= {https://www.sciencedirect.com/science/article/pii/S030645732030861X},\n  author\t= {Marcelo Mendoza and Denis Parra and Álvaro Soto},\n  keywords\t= {Graph-based representations, Controversy detection,\n\t\t  Polarity dynamics},\n  abstract\t= {Many of the interactions between users on social networks\n\t\t  are controversial, specially in polarized environments. In\n\t\t  effect, rather than producing a space for deliberation,\n\t\t  these environments foster the emergence of users that\n\t\t  disqualify the position of others. On news sites, comments\n\t\t  on the news are characterized by such interactions. This is\n\t\t  detrimental to the construction of a deliberative and\n\t\t  democratic climate, stressing the need for automatic tools\n\t\t  that can provide an early detection of polarization and\n\t\t  controversy. We introduce GENE (graph generation\n\t\t  conditioned on named entities), a representation of user\n\t\t  networks conditioned on the named entities (personalities,\n\t\t  brands, organizations) which users comment upon. GENE\n\t\t  models the leaning that each user has concerning entities\n\t\t  mentioned in the news. GENE graphs is able to segment the\n\t\t  user network according to their polarity. Using the\n\t\t  segmented network, we study the performance of two\n\t\t  controversy indices, the existing Random Walks Controversy\n\t\t  (RWC) and another one we introduce, Relative Closeness\n\t\t  Controversy (RCC). These indices measure the interaction\n\t\t  between the network’s poles providing a metric to\n\t\t  quantify the emergence of controversy. To evaluate the\n\t\t  performance of GENE, we model the network of users of a\n\t\t  popular news site in Chile, collecting data in an\n\t\t  observation window of more than three years. A large-scale\n\t\t  evaluation using GENE, on thousands of news, allows us to\n\t\t  conclude that over 60% of user comments have a predictable\n\t\t  polarity. This predictability of the user interaction\n\t\t  scenario allows both controversy indices to detect a\n\t\t  controversy successfully. In particular, our introduced RCC\n\t\t  index shows satisfactory performance in the early detection\n\t\t  of controversies using partial information collected during\n\t\t  the first hours of the news event, with a sensitivity to\n\t\t  the target class exceeding 90%.}\n}\n\n
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\n Many of the interactions between users on social networks are controversial, specially in polarized environments. In effect, rather than producing a space for deliberation, these environments foster the emergence of users that disqualify the position of others. On news sites, comments on the news are characterized by such interactions. This is detrimental to the construction of a deliberative and democratic climate, stressing the need for automatic tools that can provide an early detection of polarization and controversy. We introduce GENE (graph generation conditioned on named entities), a representation of user networks conditioned on the named entities (personalities, brands, organizations) which users comment upon. GENE models the leaning that each user has concerning entities mentioned in the news. GENE graphs is able to segment the user network according to their polarity. Using the segmented network, we study the performance of two controversy indices, the existing Random Walks Controversy (RWC) and another one we introduce, Relative Closeness Controversy (RCC). These indices measure the interaction between the network’s poles providing a metric to quantify the emergence of controversy. To evaluate the performance of GENE, we model the network of users of a popular news site in Chile, collecting data in an observation window of more than three years. A large-scale evaluation using GENE, on thousands of news, allows us to conclude that over 60% of user comments have a predictable polarity. This predictability of the user interaction scenario allows both controversy indices to detect a controversy successfully. In particular, our introduced RCC index shows satisfactory performance in the early detection of controversies using partial information collected during the first hours of the news event, with a sensitivity to the target class exceeding 90%.\n
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\n \n\n \n \n \n \n \n A Survey on Deep Learning and Explainability for Automatic Image-based Medical Report Generation.\n \n \n \n\n\n \n Messina, P.; Pino, P.; Parra, D.; Soto, A.; Besa, C.; Uribe, S.; Tejos, C.; Prieto, C.; Capurro, D.; and others\n\n\n \n\n\n\n arXiv preprint arXiv:2010.10563. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{\t  messina2020survey,\n  title\t\t= {A Survey on Deep Learning and Explainability for Automatic\n\t\t  Image-based Medical Report Generation},\n  author\t= {Messina, Pablo and Pino, Pablo and Parra, Denis and Soto,\n\t\t  Alvaro and Besa, Cecilia and Uribe, Sergio and Tejos,\n\t\t  Cristian and Prieto, Claudia and Capurro, Daniel and\n\t\t  others},\n  journal\t= {arXiv preprint arXiv:2010.10563},\n  year\t\t= {2020}\n}\n\n
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\n \n\n \n \n \n \n \n \n Explaining VQA predictions using visual grounding and a knowledge base.\n \n \n \n \n\n\n \n Riquelme, F.; De Goyeneche, A.; Zhang, Y.; Niebles, J. C.; and Soto, A.\n\n\n \n\n\n\n Image and Vision Computing, 101: 103968. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ExplainingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  riquelme2020103968,\n  title\t\t= {Explaining VQA predictions using visual grounding and a\n\t\t  knowledge base},\n  journal\t= {Image and Vision Computing},\n  volume\t= {101},\n  pages\t\t= {103968},\n  year\t\t= {2020},\n  issn\t\t= {0262-8856},\n  doi\t\t= {https://doi.org/10.1016/j.imavis.2020.103968},\n  url\t\t= {https://www.sciencedirect.com/science/article/pii/S0262885620301001},\n  author\t= {Felipe Riquelme and Alfredo {De Goyeneche} and Yundong\n\t\t  Zhang and Juan Carlos Niebles and Alvaro Soto},\n  keywords\t= {Deep Learning, Attention, Supervision, Knowledge Base,\n\t\t  Interpretability, Explainability},\n  abstract\t= {In this work, we focus on the Visual Question Answering\n\t\t  (VQA) task, where a model must answer a question based on\n\t\t  an image, and the VQA-Explanations task, where an\n\t\t  explanation is produced to support the answer. We introduce\n\t\t  an interpretable model capable of pointing out and\n\t\t  consuming information from a novel Knowledge Base (KB)\n\t\t  composed of real-world relationships between objects, along\n\t\t  with labels mined from available region descriptions and\n\t\t  object annotations. Furthermore, this model provides a\n\t\t  visual and textual explanations to complement the KB\n\t\t  visualization. The use of a KB brings two important\n\t\t  consequences: enhance predictions and improve\n\t\t  interpretability. We achieve this by introducing a\n\t\t  mechanism that can extract relevant information from this\n\t\t  KB, and can point out the relations better suited for\n\t\t  predicting the answer. A supervised attention map is\n\t\t  generated over the KB to select the relevant relationships\n\t\t  from it for each question-image pair. Moreover, we add\n\t\t  image attention supervision on the explanations module to\n\t\t  generate better visual and textual explanations. We\n\t\t  quantitatively show that the predicted answers improve when\n\t\t  using the KB; similarly, explanations improve with this and\n\t\t  when adding image attention supervision. Also, we\n\t\t  qualitatively show that the KB attention helps to improve\n\t\t  interpretability and enhance explanations. Overall, the\n\t\t  results support the benefits of having multiple tasks to\n\t\t  enhance the interpretability and performance of the\n\t\t  model.}\n}\n\n
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\n In this work, we focus on the Visual Question Answering (VQA) task, where a model must answer a question based on an image, and the VQA-Explanations task, where an explanation is produced to support the answer. We introduce an interpretable model capable of pointing out and consuming information from a novel Knowledge Base (KB) composed of real-world relationships between objects, along with labels mined from available region descriptions and object annotations. Furthermore, this model provides a visual and textual explanations to complement the KB visualization. The use of a KB brings two important consequences: enhance predictions and improve interpretability. We achieve this by introducing a mechanism that can extract relevant information from this KB, and can point out the relations better suited for predicting the answer. A supervised attention map is generated over the KB to select the relevant relationships from it for each question-image pair. Moreover, we add image attention supervision on the explanations module to generate better visual and textual explanations. We quantitatively show that the predicted answers improve when using the KB; similarly, explanations improve with this and when adding image attention supervision. Also, we qualitatively show that the KB attention helps to improve interpretability and enhance explanations. Overall, the results support the benefits of having multiple tasks to enhance the interpretability and performance of the model.\n
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\n \n\n \n \n \n \n \n \n Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games.\n \n \n \n \n\n\n \n Villa, A.; Araujo, V.; Cattan, F.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 14th ACM Conference on Recommender Systems, of RecSys '20, 2020. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"InterpretablePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  ttir2020,\n  author\t= {Villa, Andrés and Araujo, Vladimir and Cattan, Francisca\n\t\t  and Parra, Denis},\n  title\t\t= {Interpretable Contextual Team-aware Item Recommendation:\n\t\t  Application in Multiplayer Online Battle Arena Games},\n  year\t\t= {2020},\n  isbn\t\t= {9781450375832},\n  publisher\t= {Association for Computing Machinery},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/TTIR-2020.pdf},\n  doi\t\t= {10.1145/3383313.3412211},\n  booktitle\t= {Proceedings of the 14th ACM Conference on Recommender\n\t\t  Systems},\n  keywords\t= {item recommendation, deep learning, MOBA games},\n  location\t= {Virtual Event, Brazil},\n  series\t= {RecSys '20}\n}\n\n
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\n \n\n \n \n \n \n \n ResiliNet: Failure-Resilient Inference in Distributed Neural Networks.\n \n \n \n\n\n \n Yousefpour, A.; Nguyen, B. Q; Devic, S.; Wang, G.; Kreidieh, A.; Lobel, H.; Bayen, A. M; and Jue, J. P\n\n\n \n\n\n\n arXiv e-prints,arXiv–2002. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  yousefpour2020resilinet,\n  title\t\t= {ResiliNet: Failure-Resilient Inference in Distributed\n\t\t  Neural Networks},\n  author\t= {Yousefpour, Ashkan and Nguyen, Brian Q and Devic,\n\t\t  Siddartha and Wang, Guanhua and Kreidieh, Aboudy and Lobel,\n\t\t  Hans and Bayen, Alexandre M and Jue, Jason P},\n  journal\t= {arXiv e-prints},\n  pages\t\t= {arXiv--2002},\n  year\t\t= {2020}\n}\n\n
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\n  \n 2019\n \n \n (13)\n \n \n
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\n \n\n \n \n \n \n \n Using Twitter to Infer User Satisfaction With Public Transport: The Case of Santiago, Chile.\n \n \n \n\n\n \n Méndez, J. T.; Lobel, H.; Parra, D.; and Herrera, J. C.\n\n\n \n\n\n\n IEEE Access, 7: 60255-60263. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  8708245,\n  author\t= {Méndez, José Tomás and Lobel, Hans and Parra, Denis and\n\t\t  Herrera, Juan Carlos},\n  journal\t= {IEEE Access},\n  title\t\t= {Using Twitter to Infer User Satisfaction With Public\n\t\t  Transport: The Case of Santiago, Chile},\n  year\t\t= {2019},\n  volume\t= {7},\n  number\t= {},\n  pages\t\t= {60255-60263},\n  doi\t\t= {10.1109/ACCESS.2019.2915107}\n}\n\n
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\n \n\n \n \n \n \n \n \n Data Mining for Item Recommendation in MOBA Games.\n \n \n \n \n\n\n \n Araujo, V.; Rios, F.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 13th ACM Conference on Recommender Systems, of RecSys '19, pages 393–397, 2019. ACM\n \n\n\n\n
\n\n\n\n \n \n \"DataPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  araujo2019,\n  author\t= {Araujo, Vladimir and Rios, Felipe and Parra, Denis},\n  title\t\t= {Data Mining for Item Recommendation in MOBA Games},\n  booktitle\t= {Proceedings of the 13th ACM Conference on Recommender\n\t\t  Systems},\n  series\t= {RecSys '19},\n  year\t\t= {2019},\n  location\t= {Copenhagen, Denmark},\n  pages\t\t= {393--397},\n  numpages\t= {5},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/MOBArecsys2019-preprint.pdf},\n  doi\t\t= {10.1145/3298689.3346986},\n  publisher\t= {ACM},\n  keywords\t= {MOBA games, data mining, item recommendation}\n}\n\n
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\n \n\n \n \n \n \n \n \n Interpretable Visual Question Answering by Visual Grounding from Automatic Attention Annotations.\n \n \n \n \n\n\n \n Zhang, B.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n In WACV, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"InterpretablePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  ben:etal:2018,\n  author\t= {B. Zhang and JC. Niebles and A. Soto},\n  title\t\t= {Interpretable Visual Question Answering by Visual\n\t\t  Grounding from Automatic Attention Annotations},\n  booktitle\t= {WACV},\n  year\t\t= {2019},\n  abstract\t= {A key aspect of VQA models that are interpretable is their\n\t\t  ability to ground their answers to relevant regions in the\n\t\t  image. Current approaches with this capability rely on\n\t\t  supervised learning and human annotated groundings to train\n\t\t  attention mechanisms inside the VQA architecture.\n\t\t  Unfortunately, obtaining human annotations specific for\n\t\t  visual grounding is difficult and expensive. In this work,\n\t\t  we demonstrate that we can effectively train a VQA\n\t\t  architecture with grounding supervision that can be\n\t\t  automatically obtained from available region descriptions\n\t\t  and object annotations. We also show that our model trained\n\t\t  with this mined supervision generates visual groundings\n\t\t  that achieve higher correlation to manually-annotated\n\t\t  groundings than alternative approaches, even in the case of\n\t\t  state-of-the-art algorithms that are directly trained with\n\t\t  human grounding annotations.},\n  url\t\t= {https://arxiv.org/abs/1808.00265}\n}\n\n
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\n A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that our model trained with this mined supervision generates visual groundings that achieve higher correlation to manually-annotated groundings than alternative approaches, even in the case of state-of-the-art algorithms that are directly trained with human grounding annotations.\n
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\n \n\n \n \n \n \n \n \n Comparing Word Embeddings for Document Screening based on Active Learning.\n \n \n \n \n\n\n \n Carvallo, A.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019), 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ComparingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  carvallo2019,\n  author\t= {Carvallo, Andres and Parra, Denis},\n  title\t\t= {Comparing Word Embeddings for Document Screening based on\n\t\t  Active Learning},\n  year\t\t= {2019},\n  booktitle\t= {Proceedings of the 4th Joint Workshop on\n\t\t  Bibliometric-enhanced Information Retrieval and Natural\n\t\t  Language Processing for Digital Libraries (BIRNDL 2019)},\n  location\t= {Paris, France},\n  url\t\t= {http://ceur-ws.org/Vol-2414/paper10.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Recommender Systems for Online Video Game Platforms: The Case of STEAM.\n \n \n \n \n\n\n \n Cheuque, G.; Guzmán, J.; and Parra, D.\n\n\n \n\n\n\n In Companion Proceedings of The 2019 World Wide Web Conference, of WWW '19, pages 763–771, New York, NY, USA, 2019. ACM\n \n\n\n\n
\n\n\n\n \n \n \"RecommenderPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  cheuque:2019:rso:3308560.3316457,\n  author\t= {Cheuque, Germ\\'{a}n and Guzm\\'{a}n, Jos{\\'e} and Parra,\n\t\t  Denis},\n  title\t\t= {Recommender Systems for Online Video Game Platforms: The\n\t\t  Case of STEAM},\n  booktitle\t= {Companion Proceedings of The 2019 World Wide Web\n\t\t  Conference},\n  series\t= {WWW '19},\n  year\t\t= {2019},\n  isbn\t\t= {978-1-4503-6675-5},\n  location\t= {San Francisco, USA},\n  pages\t\t= {763--771},\n  numpages\t= {9},\n  url\t\t= {http://doi.acm.org/10.1145/3308560.3316457},\n  doi\t\t= {10.1145/3308560.3316457},\n  acmid\t\t= {3316457},\n  publisher\t= {ACM},\n  address\t= {New York, NY, USA},\n  keywords\t= {Deep Factorization Machines, Deep Neural Networks,\n\t\t  Diversity, Factorization Machines, Novelty, Recommender\n\t\t  System}\n}\n\n
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\n \n\n \n \n \n \n \n \n The Effect of Explanations and Algorithmic Accuracy on Visual Recommender Systems of Artistic Images.\n \n \n \n \n\n\n \n Dominguez, V.; Messina, P.; Donoso-Guzmán; and Parra, D.\n\n\n \n\n\n\n In 24th Conference on Intelligent User interfaces, of IUI '19, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dominguez2019,\n  author\t= "Dominguez, Vicente and Messina, Pablo and Donoso-Guzm\\'an\n\t\t  and Parra, Denis",\n  title\t\t= "The Effect of Explanations and Algorithmic Accuracy on\n\t\t  Visual Recommender Systems of Artistic Images",\n  booktitle\t= {24th Conference on Intelligent User interfaces},\n  series\t= {IUI '19},\n  location\t= {Los Angeles, California, USA},\n  year\t\t= "2019",\n  doi\t\t= "10.1145/3301275.3302274",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/dominguez_IUI_2019_camera_ready.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n #Default #Interactiveart #Audiencexperience.\n \n \n \n \n\n\n \n Garretón, M.; Rihm, A.; and Parra, D.\n\n\n \n\n\n\n In Companion Proceedings of The 2019 World Wide Web Conference, of WWW '19, pages 791–798, New York, NY, USA, 2019. ACM\n \n\n\n\n
\n\n\n\n \n \n \"#DefaultPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  garreton:2019:dia:3308560.3316453,\n  author\t= {Garret\\'{o}n, Manuela and Rihm, Andrea and Parra, Denis},\n  title\t\t= {\\#Default \\#Interactiveart \\#Audiencexperience},\n  booktitle\t= {Companion Proceedings of The 2019 World Wide Web\n\t\t  Conference},\n  series\t= {WWW '19},\n  year\t\t= {2019},\n  isbn\t\t= {978-1-4503-6675-5},\n  location\t= {San Francisco, USA},\n  pages\t\t= {791--798},\n  numpages\t= {8},\n  url\t\t= {http://doi.acm.org/10.1145/3308560.3316453},\n  doi\t\t= {10.1145/3308560.3316453},\n  acmid\t\t= {3316453},\n  publisher\t= {ACM},\n  address\t= {New York, NY, USA},\n  keywords\t= {Arts, Instagram, Interactive art evaluation, Social Media,\n\t\t  and culture on the web}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Behavioral Approach to Visual Navigation with Graph Localization Networks.\n \n \n \n \n\n\n \n Chen, K.; Vicente, J. D.; Sepulveda, G.; Xia, F.; Soto, A.; Vazquez, M.; and Savarese, S.\n\n\n \n\n\n\n In RSS, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  kevin:etal:2018,\n  author\t= {K. Chen and J.P De Vicente and G. Sepulveda and F. Xia and\n\t\t  A. Soto and M. Vazquez and S. Savarese},\n  title\t\t= {A Behavioral Approach to Visual Navigation with Graph\n\t\t  Localization Networks},\n  booktitle\t= {RSS},\n  year\t\t= {2019},\n  abstract\t= {Inspired by research in psychology, we introduce a\n\t\t  behavioral approach for visual navigation using topological\n\t\t  maps. Our goal is to enable a robot to navigate from one\n\t\t  location to another, relying only on its visual\n\t\t  observations and the topological map of the environment. To\n\t\t  this end, we propose using graph neural networks for\n\t\t  localizing the agent in the map, and decompose the action\n\t\t  space into primitive behaviors implemented as convolutional\n\t\t  or recurrent neural networks. Using the Gibson simulator\n\t\t  and the Stanford 2D-3D-S dataset, we verify that our\n\t\t  approach outperforms relevant baselines and is able to\n\t\t  navigate in both seen and unseen indoor environments.},\n  url\t\t= {https://graphnav.stanford.edu/}\n}\n\n
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\n Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual observations and the topological map of the environment. To this end, we propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator and the Stanford 2D-3D-S dataset, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen indoor environments.\n
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\n \n\n \n \n \n \n \n Tag-based information access in image collections: insights from log and eye-gaze analyses.\n \n \n \n\n\n \n Lin, Y.; Parra, D.; Trattner, C.; and Brusilovsky, P.\n\n\n \n\n\n\n Knowledge and Information Systems,1–28. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  lin2019tag,\n  title\t\t= {Tag-based information access in image collections:\n\t\t  insights from log and eye-gaze analyses},\n  author\t= {Lin, Yi-Ling and Parra, Denis and Trattner, Christoph and\n\t\t  Brusilovsky, Peter},\n  journal\t= {Knowledge and Information Systems},\n  pages\t\t= {1--28},\n  year\t\t= {2019},\n  publisher\t= {Springer},\n  doi\t\t= {10.1007/s10115-019-01343-4}\n}\n\n
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\n \n\n \n \n \n \n \n Using Twitter to Infer User Satisfaction With Public Transport: The Case of Santiago, Chile.\n \n \n \n\n\n \n Méndez, J. T.; Lobel, H.; Parra, D.; and Herrera, J. C.\n\n\n \n\n\n\n IEEE Access, 7: 60255–60263. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mendez2019using,\n  title\t\t= {Using Twitter to Infer User Satisfaction With Public\n\t\t  Transport: The Case of Santiago, Chile},\n  author\t= {M{\\'e}ndez, Jos{\\'e} Tom{\\'a}s and Lobel, Hans and Parra,\n\t\t  Denis and Herrera, Juan Carlos},\n  journal\t= {IEEE Access},\n  volume\t= {7},\n  pages\t\t= {60255--60263},\n  year\t\t= {2019},\n  publisher\t= {IEEE},\n  doi\t\t= {10.1109/ACCESS.2019.2915107}\n}\n\n
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\n \n\n \n \n \n \n \n \n Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features.\n \n \n \n \n\n\n \n Messina, P.; Dominguez, V.; Parra, D.; Trattner, C.; and Soto, A.\n\n\n \n\n\n\n User Modeling and User-Adapted Interaction, 29(2). 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Content-basedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  messina:etal:2019,\n  author\t= {Pablo Messina and Vicente Dominguez and Denis Parra and\n\t\t  Christoph Trattner and Alvaro Soto},\n  title\t\t= {Content-based artwork recommendation: integrating painting\n\t\t  metadata with neural and manually-engineered visual\n\t\t  features},\n  journal\t= {User Modeling and User-Adapted Interaction},\n  volume\t= {29},\n  number\t= {2},\n  year\t\t= {2019},\n  abstract\t= {Recommender Systems help us deal with information overload\n\t\t  by suggesting relevant items based on our personal\n\t\t  preferences. Although there is a large body of research in\n\t\t  areas such as movies or music, artwork recommendation has\n\t\t  received comparatively little attention, despite the\n\t\t  continuous growth of the artwork market. Most previous\n\t\t  research has relied on ratings and metadata, and a few\n\t\t  recent works have exploited visual features extracted with\n\t\t  deep neural networks (DNN) to recommend digital art. In\n\t\t  this work, we contribute to the area of content-based\n\t\t  artwork recommendation of physical paintings by studying\n\t\t  the impact of the aforementioned features (artwork\n\t\t  metadata, neural visual features), as well as\n\t\t  manually-engineered visual features, such as naturalness,\n\t\t  brightness and contrast. We implement and evaluate our\n\t\t  method using transactional data from UGallery.com, an\n\t\t  online artwork store. Our results show that artwork\n\t\t  recommendations based on a hybrid combination of artist\n\t\t  preference, curated attributes, deep neural visual features\n\t\t  and manually-engineered visual features produce the best\n\t\t  performance. Moreover, we discuss the trade-off between\n\t\t  automatically obtained DNN features and manually-engineered\n\t\t  visual features for the purpose of explainability, as well\n\t\t  as the impact of user profile size on predictions. Our\n\t\t  research informs the development of next-generation\n\t\t  content-based artwork recommenders which rely on different\n\t\t  types of data, from text to multimedia.},\n  url\t\t= {https://link.springer.com/article/10.1007/s11257-018-9206-9}\n}\n\n
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\n Recommender Systems help us deal with information overload by suggesting relevant items based on our personal preferences. Although there is a large body of research in areas such as movies or music, artwork recommendation has received comparatively little attention, despite the continuous growth of the artwork market. Most previous research has relied on ratings and metadata, and a few recent works have exploited visual features extracted with deep neural networks (DNN) to recommend digital art. In this work, we contribute to the area of content-based artwork recommendation of physical paintings by studying the impact of the aforementioned features (artwork metadata, neural visual features), as well as manually-engineered visual features, such as naturalness, brightness and contrast. We implement and evaluate our method using transactional data from UGallery.com, an online artwork store. Our results show that artwork recommendations based on a hybrid combination of artist preference, curated attributes, deep neural visual features and manually-engineered visual features produce the best performance. Moreover, we discuss the trade-off between automatically obtained DNN features and manually-engineered visual features for the purpose of explainability, as well as the impact of user profile size on predictions. Our research informs the development of next-generation content-based artwork recommenders which rely on different types of data, from text to multimedia.\n
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\n \n\n \n \n \n \n \n \n Mixture of Experts with Entropic Regularization for Data Classification.\n \n \n \n \n\n\n \n B. Peralta, A. S.; and L. Caro, A. S.\n\n\n \n\n\n\n Entropy, 21(2). 2019.\n \n\n\n\n
\n\n\n\n \n \n \"MixturePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  peralta:etal:2019,\n  author\t= {B. Peralta, A. Saavedra, L. Caro, A. Soto},\n  title\t\t= {Mixture of Experts with Entropic Regularization for Data\n\t\t  Classification},\n  journal\t= {Entropy},\n  volume\t= {21},\n  number\t= {2},\n  year\t\t= {2019},\n  abstract\t= {Today, there is growing interest in the automatic\n\t\t  classification of a variety of tasks, such as weather\n\t\t  forecasting, product recommendations, intrusion detection,\n\t\t  and people recognition.“Mixture-of-experts” is a\n\t\t  well-known classification technique; it is a probabilistic\n\t\t  model consisting of local expert classifiers weighted by a\n\t\t  gate network that is typically based on softmax functions,\n\t\t  combined with learnable complex patterns in data. In this\n\t\t  scheme, one data point is influenced by only one expert; as\n\t\t  a result, the training process can be misguided in real\n\t\t  datasets for which complex data need to be explained by\n\t\t  multiple experts. In this work, we propose a variant of the\n\t\t  regular mixture-of-experts model. In the proposed model,\n\t\t  the cost classification is penalized by the Shannon entropy\n\t\t  of the gating network in order to avoid a\n\t\t  “winner-takes-all” output for the gating network.\n\t\t  Experiments show the advantage of our approach using\n\t\t  several real datasets, with improvements in mean accuracy\n\t\t  of 3–6\\% in some datasets. In future work, we plan to\n\t\t  embed feature selection into this model.},\n  url\t\t= {https://www.mdpi.com/1099-4300/21/2/190}\n}\n\n
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\n Today, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition.“Mixture-of-experts” is a well-known classification technique; it is a probabilistic model consisting of local expert classifiers weighted by a gate network that is typically based on softmax functions, combined with learnable complex patterns in data. In this scheme, one data point is influenced by only one expert; as a result, the training process can be misguided in real datasets for which complex data need to be explained by multiple experts. In this work, we propose a variant of the regular mixture-of-experts model. In the proposed model, the cost classification is penalized by the Shannon entropy of the gating network in order to avoid a “winner-takes-all” output for the gating network. Experiments show the advantage of our approach using several real datasets, with improvements in mean accuracy of 3–6% in some datasets. In future work, we plan to embed feature selection into this model.\n
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\n \n\n \n \n \n \n \n \n Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach.\n \n \n \n \n\n\n \n Rossetti, T.; Lobel, H.; Rocco, V.; and Hurtubia, R.\n\n\n \n\n\n\n Landscape and Urban Planning, 181: 169–178. January 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ExplainingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  rossetti2019,\n  doi\t\t= {10.1016/j.landurbplan.2018.09.020},\n  url\t\t= {https://doi.org/10.1016/j.landurbplan.2018.09.020},\n  year\t\t= {2019},\n  month\t\t= jan,\n  publisher\t= {Elsevier {BV}},\n  volume\t= {181},\n  pages\t\t= {169--178},\n  author\t= {Tom{\\'{a}}s Rossetti and Hans Lobel and V{\\'{\\i}}ctor\n\t\t  Rocco and Ricardo Hurtubia},\n  title\t\t= {Explaining subjective perceptions of public spaces as a\n\t\t  function of the built environment: A massive data\n\t\t  approach},\n  journal\t= {Landscape and Urban Planning}\n}\n\n
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\n  \n 2018\n \n \n (26)\n \n \n
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\n \n\n \n \n \n \n \n Classifying Drivers' Behavior in Public Transport using Inertial Measurement Units and Decision Trees.\n \n \n \n\n\n \n Catalán, H. F.; Lobel, H.; and Herrera, J. C.\n\n\n \n\n\n\n In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 217-222, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  8569404,\n  author\t= {Catalán, Hernán F. and Lobel, Hans and Herrera, Juan\n\t\t  C.},\n  booktitle\t= {2018 21st International Conference on Intelligent\n\t\t  Transportation Systems (ITSC)},\n  title\t\t= {Classifying Drivers' Behavior in Public Transport using\n\t\t  Inertial Measurement Units and Decision Trees},\n  year\t\t= {2018},\n  volume\t= {},\n  number\t= {},\n  pages\t\t= {217-222},\n  doi\t\t= {10.1109/ITSC.2018.8569404}\n}\n\n
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\n \n\n \n \n \n \n \n \n Do Better ImageNet Models Transfer Better... for Image Recommendation?.\n \n \n \n \n\n\n \n del Rio, F.; Messina, P.; Dominguez, V.; and Parra, D.\n\n\n \n\n\n\n In 2nd workshop on Intelligent Recommender Systems by Knowledge Transfer and Learning, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"DoPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  1807.09870,\n  author\t= {Felipe del Rio and Pablo Messina and Vicente Dominguez and\n\t\t  Denis Parra},\n  title\t\t= {Do Better ImageNet Models Transfer Better... for Image\n\t\t  Recommendation?},\n  year\t\t= {2018},\n  eprint\t= {arXiv:1807.09870},\n  url\t\t= "https://arxiv.org/abs/1807.09870",\n  booktitle\t= {2nd workshop on Intelligent Recommender Systems by\n\t\t  Knowledge Transfer and Learning}\n}\n\n
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\n \n\n \n \n \n \n \n \n Moodplay: Interactive Music Recommendation based on Artists’ Mood Similarity .\n \n \n \n \n\n\n \n Andjelkovic, I.; Parra, D.; and O'Donovan, J.\n\n\n \n\n\n\n International Journal of Human-Computer Studies ,- . 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Moodplay:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  andjelkovic2018,\n  title\t\t= "Moodplay: Interactive Music Recommendation based on\n\t\t  Artists’ Mood Similarity ",\n  author\t= "Andjelkovic, Ivana and Parra, Denis and O'Donovan, John",\n  journal\t= "International Journal of Human-Computer Studies ",\n  volume\t= "",\n  number\t= "",\n  pages\t\t= " - ",\n  year\t\t= "2018",\n  issn\t\t= "1071-5819",\n  doi\t\t= "10.1016/j.ijhcs.2018.04.004",\n  keywords\t= "interactive visualization, recommender systems, artists'\n\t\t  emotion, music recommendation, user study",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/preprint-andjelkovic-IJCHS_Moodplay.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n CNVis: A Web-Based Visual Analytics Tool for Exploring Conference Navigator Data.\n \n \n \n \n\n\n \n Bailey, S. M.; Wei, J. A.; Wang, C.; Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In IS&T Electronic Imaging 2018 Symposium , 2018. \n \n\n\n\n
\n\n\n\n \n \n \"CNVis:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  bailey2018,\n  author\t= "Bailey, Samuel M. and Wei, Justin A. and Wang, Chaoli and\n\t\t  Parra, Denis and Brusilovsky, Peter",\n  title\t\t= "CNVis: A Web-Based Visual Analytics Tool for Exploring\n\t\t  Conference Navigator Data",\n  booktitle\t= {IS&T Electronic Imaging 2018 Symposium },\n  year\t\t= "2018",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/vda18-cnvis.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n IntersectionExplorer, a Multi-Perspective Approach for Exploring Recommendations.\n \n \n \n \n\n\n \n Cardoso, B.; Sedrakyan, G.; Gutiérrez, F.; Parra, D.; Brusilovsky, P.; and Verbert, K.\n\n\n \n\n\n\n International Journal of Human-Computer Studies. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"IntersectionExplorer,Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  cardoso2018,\n  title\t\t= "IntersectionExplorer, a Multi-Perspective Approach for\n\t\t  Exploring Recommendations",\n  journal\t= "International Journal of Human-Computer Studies",\n  year\t\t= "2018",\n  issn\t\t= "1071-5819",\n  doi\t\t= "10.1016/j.ijhcs.2018.04.008",\n  author\t= "Bruno Cardoso and Gayane Sedrakyan and Francisco\n\t\t  Gutiérrez and Denis Parra and Peter Brusilovsky and\n\t\t  Katrien Verbert",\n  keywords\t= "interactive visualization, exploration of recommendations,\n\t\t  recommender systems, set visualization, scalability, user\n\t\t  study",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/preprint-iexplorer-2018.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Advances in Artificial Intelligence - 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8-11, 2018, Proceedings.\n \n \n \n \n\n\n \n Bagheri, E.; and Cheung, J. C. K.,\n editors.\n \n\n\n \n\n\n\n Volume 10832, of Lecture Notes in Computer Science.Springer. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"AdvancesPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ai/2018,\n  editor\t= {Ebrahim Bagheri and Jackie Chi Kit Cheung},\n  title\t\t= {Advances in Artificial Intelligence - 31st Canadian\n\t\t  Conference on Artificial Intelligence, Canadian {AI} 2018,\n\t\t  Toronto, ON, Canada, May 8-11, 2018, Proceedings},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {10832},\n  publisher\t= {Springer},\n  year\t\t= {2018},\n  url\t\t= {https://doi.org/10.1007/978-3-319-89656-4},\n  doi\t\t= {10.1007/978-3-319-89656-4},\n  isbn\t\t= {978-3-319-89655-7},\n  timestamp\t= {Mon, 23 Apr 2018 18:08:42 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ai/2018},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-deterministic Planning.\n \n \n \n \n\n\n \n Camacho, A.; Baier, J. A.; Muise, C. J.; and McIlraith, S. A.\n\n\n \n\n\n\n In Advances in Artificial Intelligence - 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8-11, 2018, Proceedings, pages 45–59, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"SynthesizingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ai/camachobmm18,\n  author\t= {Alberto Camacho and Jorge A. Baier and Christian J. Muise\n\t\t  and Sheila A. McIlraith},\n  title\t\t= {Synthesizing Controllers: On the Correspondence Between\n\t\t  {LTL} Synthesis and Non-deterministic Planning},\n  booktitle\t= {Advances in Artificial Intelligence - 31st Canadian\n\t\t  Conference on Artificial Intelligence, Canadian {AI} 2018,\n\t\t  Toronto, ON, Canada, May 8-11, 2018, Proceedings},\n  pages\t\t= {45--59},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/ai/2018},\n  url\t\t= {https://doi.org/10.1007/978-3-319-89656-4\\_4},\n  doi\t\t= {10.1007/978-3-319-89656-4\\_4},\n  timestamp\t= {Mon, 23 Apr 2018 18:08:42 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ai/CamachoBMM18},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS 2018, Delft, The Netherlands, June 24-29, 2018.\n \n \n \n \n\n\n \n de Weerdt, M.; Koenig, S.; Röger, G.; and Spaan, M. T. J.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2018.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2018,\n  editor\t= {Mathijs de Weerdt and Sven Koenig and Gabriele R{\\"{o}}ger\n\t\t  and Matthijs T. J. Spaan},\n  title\t\t= {Proceedings of the Twenty-Eighth International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2018, Delft,\n\t\t  The Netherlands, June 24-29, 2018},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2018},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/icaps18contents.php},\n  isbn\t\t= {978-1-57735-797-1},\n  timestamp\t= {Mon, 25 Jun 2018 13:32:06 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2018},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Finite LTL Synthesis as Planning.\n \n \n \n \n\n\n \n Camacho, A.; Baier, J. A.; Muise, C. J.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, ICAPS 2018, Delft, The Netherlands, June 24-29, 2018., pages 29–38, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"FinitePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 42 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/camachobmm18,\n  author\t= {Alberto Camacho and Jorge A. Baier and Christian J. Muise\n\t\t  and Sheila A. McIlraith},\n  title\t\t= {Finite {LTL} Synthesis as Planning},\n  booktitle\t= {Proceedings of the Twenty-Eighth International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2018, Delft,\n\t\t  The Netherlands, June 24-29, 2018.},\n  pages\t\t= {29--38},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/aips/2018},\n  url\t\t= {https://aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17790},\n  timestamp\t= {Mon, 25 Jun 2018 13:32:06 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/CamachoBMM18},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.\n \n \n \n \n\n\n \n Lang, J.,\n editor.\n \n\n\n \n\n\n\n ijcai.org. 2018.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2018,\n  editor\t= {J{\\'{e}}r{\\^{o}}me Lang},\n  title\t\t= {Proceedings of the Twenty-Seventh International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2018, July\n\t\t  13-19, 2018, Stockholm, Sweden},\n  publisher\t= {ijcai.org},\n  year\t\t= {2018},\n  url\t\t= {http://www.ijcai.org/proceedings/2018/},\n  isbn\t\t= {978-0-9992411-2-7},\n  timestamp\t= {Sat, 28 Jul 2018 14:39:21 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2018},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n LTL Realizability via Safety and Reachability Games.\n \n \n \n \n\n\n \n Camacho, A.; Muise, C. J.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden., pages 4683–4691, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"LTLPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/camachombm18,\n  author\t= {Alberto Camacho and Christian J. Muise and Jorge A. Baier\n\t\t  and Sheila A. McIlraith},\n  title\t\t= {{LTL} Realizability via Safety and Reachability Games},\n  booktitle\t= {Proceedings of the Twenty-Seventh International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2018, July\n\t\t  13-19, 2018, Stockholm, Sweden.},\n  pages\t\t= {4683--4691},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/ijcai/2018},\n  url\t\t= {https://doi.org/10.24963/ijcai.2018/651},\n  doi\t\t= {10.24963/ijcai.2018/651},\n  timestamp\t= {Sat, 28 Jul 2018 14:39:21 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/CamachoMBM18},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n SynKit: LTL Synthesis as a Service.\n \n \n \n \n\n\n \n Camacho, A.; Muise, C. J.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden., pages 5817–5819, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"SynKit:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/camachombm18a,\n  author\t= {Alberto Camacho and Christian J. Muise and Jorge A. Baier\n\t\t  and Sheila A. McIlraith},\n  title\t\t= {SynKit: {LTL} Synthesis as a Service},\n  booktitle\t= {Proceedings of the Twenty-Seventh International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2018, July\n\t\t  13-19, 2018, Stockholm, Sweden.},\n  pages\t\t= {5817--5819},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/ijcai/2018},\n  url\t\t= {https://doi.org/10.24963/ijcai.2018/848},\n  doi\t\t= {10.24963/ijcai.2018/848},\n  timestamp\t= {Sat, 28 Jul 2018 14:39:21 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/CamachoMBM18a},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Eleventh International Symposium on Combinatorial Search, SOCS 2018, Stockholm, Sweden - 14-15 July 2018.\n \n \n \n \n\n\n \n Bulitko, V.; and Storandt, S.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2018.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2018,\n  editor\t= {Vadim Bulitko and Sabine Storandt},\n  title\t\t= {Proceedings of the Eleventh International Symposium on\n\t\t  Combinatorial Search, {SOCS} 2018, Stockholm, Sweden -\n\t\t  14-15 July 2018},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2018},\n  url\t\t= {http://www.aaai.org/Library/SOCS/socs18contents.php},\n  isbn\t\t= {978-1-57735-802-2},\n  timestamp\t= {Tue, 24 Jul 2018 20:17:24 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2018},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Suboptimality Bound for 2\\(^\\mboxk\\) Grid Path Planning.\n \n \n \n \n\n\n \n Kramm, B.; Rivera, N.; Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Eleventh International Symposium on Combinatorial Search, SOCS 2018, Stockholm, Sweden - 14-15 July 2018, pages 63–71, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/krammrhb18,\n  author\t= {Benjam{\\'{\\i}}n Kramm and Nicolas Rivera and Carlos\n\t\t  Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {A Suboptimality Bound for 2\\({}^{\\mbox{k}}\\) Grid Path\n\t\t  Planning},\n  booktitle\t= {Proceedings of the Eleventh International Symposium on\n\t\t  Combinatorial Search, {SOCS} 2018, Stockholm, Sweden -\n\t\t  14-15 July 2018},\n  pages\t\t= {63--71},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/socs/2018},\n  url\t\t= {https://aaai.org/ocs/index.php/SOCS/SOCS18/paper/view/17975},\n  timestamp\t= {Tue, 24 Jul 2018 20:17:24 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/KrammRHB18},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Neural Network for Decision Making in Real-Time Heuristic Search.\n \n \n \n \n\n\n \n Muñoz, F.; Fadic, M.; Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Eleventh International Symposium on Combinatorial Search, SOCS 2018, Stockholm, Sweden - 14-15 July 2018, pages 173–177, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/munozfhb18,\n  author\t= {Franco Mu{\\~{n}}oz and Miguel Fadic and Carlos\n\t\t  Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {A Neural Network for Decision Making in Real-Time\n\t\t  Heuristic Search},\n  booktitle\t= {Proceedings of the Eleventh International Symposium on\n\t\t  Combinatorial Search, {SOCS} 2018, Stockholm, Sweden -\n\t\t  14-15 July 2018},\n  pages\t\t= {173--177},\n  year\t\t= {2018},\n  crossref\t= {DBLP:conf/socs/2018},\n  url\t\t= {https://aaai.org/ocs/index.php/SOCS/SOCS18/paper/view/17976},\n  timestamp\t= {Tue, 24 Jul 2018 20:17:24 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/MunozFHB18},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Towards Explanations for Visual Recommender Systems of Artistic Images.\n \n \n \n \n\n\n \n Dominguez, V.; Messina, P.; Trattner, C.; and Parra, D.\n\n\n \n\n\n\n In Joint Workshop on Interfaces and Human Decision Making for Recommender Systems , 2018. \n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dominguez2018intrs,\n  author\t= {Vicente Dominguez and Pablo Messina and Christoph Trattner\n\t\t  and Denis Parra},\n  title\t\t= {Towards Explanations for Visual Recommender Systems of\n\t\t  Artistic Images},\n  year\t\t= {2018},\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/pre-print_ugallery_intrs_RecSys_2018.pdf",\n  booktitle\t= {Joint Workshop on Interfaces and Human Decision Making for\n\t\t  Recommender Systems }\n}\n\n
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\n \n\n \n \n \n \n \n \n An Interactive Relevance Feedback Interface for Evidence-Based Health Care.\n \n \n \n \n\n\n \n Donoso-Guzman, I.; and Parra, D.\n\n\n \n\n\n\n In 23rd Conference on Intelligent User interfaces, of IUI '18, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  donoso2018,\n  author\t= "Donoso-Guzman, Ivania and Parra, Denis",\n  title\t\t= "An Interactive Relevance Feedback Interface for\n\t\t  Evidence-Based Health Care",\n  booktitle\t= {23rd Conference on Intelligent User interfaces},\n  series\t= {IUI '18},\n  location\t= {Tokyo, Japan},\n  year\t\t= "2018",\n  issn\t\t= "1943-4294",\n  doi\t\t= "10.1007/s40558-017-0100-9",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/pre-print-EpistAid-IUI-2018.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n Inferring modes of transportation using mobile phone data.\n \n \n \n \n\n\n \n Graells-Garrido, E.; Caro, D.; and Parra, D.\n\n\n \n\n\n\n EPJ Data Science, 7(1): 49. Dec 2018.\n \n\n\n\n
\n\n\n\n \n \n \"InferringPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  graells-garrido2018,\n  author\t= "Graells-Garrido, Eduardo and Caro, Diego and Parra, Denis",\n  title\t\t= "Inferring modes of transportation using mobile phone data",\n  journal\t= "EPJ Data Science",\n  year\t\t= "2018",\n  month\t\t= "Dec",\n  day\t\t= "04",\n  volume\t= "7",\n  number\t= "1",\n  pages\t\t= "49",\n  issn\t\t= "2193-1127",\n  doi\t\t= "10.1140/epjds/s13688-018-0177-1",\n  url\t\t= "https://doi.org/10.1140/epjds/s13688-018-0177-1"\n}\n\n
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\n \n\n \n \n \n \n \n \n Learning to Leverage Microblog Information for QA Retrieval.\n \n \n \n \n\n\n \n Herrera, J.; Poblete, B.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the European Conference in Information Retrieval, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  herrera2018,\n  author\t= "Herrera, Jose and Poblete, Barbara and Parra, Denis",\n  title\t\t= "Learning to Leverage Microblog Information for QA\n\t\t  Retrieval",\n  booktitle\t= {Proceedings of the European Conference in Information\n\t\t  Retrieval},\n  year\t\t= "2018",\n  doi\t\t= "10.1007/978-3-319-76941-7_38",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/Herrera2018ECIR.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n End-to-End Joint Semantic Segmentation of Actors and Actions in Video.\n \n \n \n \n\n\n \n Ji, J.; Buch, S.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n In ECCV, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"End-to-EndPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  jingwei:etal:2018,\n  author\t= {J. Ji and S. Buch and JC. Niebles and A. Soto},\n  title\t\t= {End-to-End Joint Semantic Segmentation of Actors and\n\t\t  Actions in Video},\n  booktitle\t= {{ECCV}},\n  year\t\t= {2018},\n  abstract\t= {Traditional video understanding tasks include human action\n\t\t  recognition and actor-object semantic segmentation.\n\t\t  However, the joint task of providing semantic segmentation\n\t\t  for different actor classes simultaneously with their\n\t\t  action class remains a challenging but necessary task for\n\t\t  many applications. In this work, we propose a new\n\t\t  end-to-end architecture for tackling this joint task in\n\t\t  videos. Our model effectively leverages multiple input\n\t\t  modalities, contextual information, and joint multitask\n\t\t  learning in the video to directly output semantic\n\t\t  segmentations in a single unified framework. We train and\n\t\t  benchmark our model on the large-scale Actor-Action Dataset\n\t\t  (A2D) for joint actor-action semantic segmentation, and\n\t\t  demonstrate state-of-the-art performance for both\n\t\t  segmentation and detection. We also perform experiments\n\t\t  verifying our joint approach improves performance for\n\t\t  zero-shot understanding, indicating generalizability of our\n\t\t  jointly learned feature space.},\n  url\t\t= {http://svl.stanford.edu/assets/papers/ji2018eccv.pdf}\n}\n\n
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\n Traditional video understanding tasks include human action recognition and actor-object semantic segmentation. However, the joint task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this joint task in videos. Our model effectively leverages multiple input modalities, contextual information, and joint multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the large-scale Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our joint approach improves performance for zero-shot understanding, indicating generalizability of our jointly learned feature space.\n
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\n \n\n \n \n \n \n \n \n Predicting Process Behavior Meets Factorization Machines.\n \n \n \n \n\n\n \n Lee, W. L. J.; Parra, D.; Munoz-Gama, J.; and Sepúlveda, M.\n\n\n \n\n\n\n Expert Systems with Applications. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"PredictingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  lee2018,\n  title\t\t= "Predicting Process Behavior Meets Factorization Machines",\n  journal\t= "Expert Systems with Applications",\n  year\t\t= "2018",\n  issn\t\t= "0957-4174",\n  doi\t\t= "10.1016/j.eswa.2018.05.035",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/preprint-ESWA-PMRec-Lee-2018.pdf",\n  author\t= "Wai Lam Jonathan Lee and Denis Parra and Jorge Munoz-Gama\n\t\t  and Marcos Sepúlveda",\n  keywords\t= "Recommender Systems, Business Process Management,\n\t\t  Predictive Business Process Monitoring"\n}\n\n
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\n \n\n \n \n \n \n \n \n Content-based Artwork Recommendation: Integrating Painting Metadata with Neural and Manually-Engineered Visual Features.\n \n \n \n \n\n\n \n Messina, P.; Dominguez, V.; Parra, D.; Trattner, C.; and Soto, A.\n\n\n \n\n\n\n User Modeling and User-Adapted Interaction. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Content-basedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 11 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  messina2018,\n  author\t= "Messina, Pablo and Dominguez, Vicente and Parra, Denis and\n\t\t  Trattner, Christoph and Soto, Alvaro",\n  title\t\t= "Content-based Artwork Recommendation: Integrating Painting\n\t\t  Metadata with Neural and Manually-Engineered Visual\n\t\t  Features",\n  journal\t= "User Modeling and User-Adapted Interaction",\n  year\t\t= "2018",\n  issn\t\t= "1573-1391",\n  doi\t\t= "10.1007/s11257-018-9206-9",\n  url\t\t= "http://dparra.sitios.ing.uc.cl/pdfs/preprint-ugallery-UMUAI-2018.pdf"\n}\n\n
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\n \n\n \n \n \n \n \n \n A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation.\n \n \n \n \n\n\n \n Sepulveda, G.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n In ICRA, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  sepulveda:etal:2018,\n  author\t= {G. Sepulveda and JC. Niebles and A. Soto},\n  title\t\t= {A Deep Learning Based Behavioral Approach to Indoor\n\t\t  Autonomous Navigation},\n  booktitle\t= {{ICRA}},\n  year\t\t= {2018},\n  abstract\t= {We present a semantically rich graph representa- tion for\n\t\t  indoor robotic navigation. Our graph representation\n\t\t  encodes: semantic locations such as offices or corridors as\n\t\t  nodes, and navigational behaviors such as enter office or\n\t\t  cross a corridor as edges. In particular, our navigational\n\t\t  behaviors operate directly from visual inputs to produce\n\t\t  motor controls and are implemented with deep learning\n\t\t  architectures. This enables the robot to avoid explicit\n\t\t  computation of its precise location or the geometry of the\n\t\t  environment, and enables navigation at a higher level of\n\t\t  semantic abstraction. We evaluate the effectiveness of our\n\t\t  representation by simulating navigation tasks in a large\n\t\t  number of virtual environments. Our results show that using\n\t\t  a simple sets of perceptual and navigational behaviors, the\n\t\t  proposed approach can successfully guide the way of the\n\t\t  robot as it completes navigational missions such as going\n\t\t  to a specific office. Furthermore, our implementation shows\n\t\t  to be effective to control the selection and switching of\n\t\t  behaviors. },\n  url\t\t= {https://arxiv.org/pdf/1803.04119v1.pdf}\n}\n\n
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\n We present a semantically rich graph representa- tion for indoor robotic navigation. Our graph representation encodes: semantic locations such as offices or corridors as nodes, and navigational behaviors such as enter office or cross a corridor as edges. In particular, our navigational behaviors operate directly from visual inputs to produce motor controls and are implemented with deep learning architectures. This enables the robot to avoid explicit computation of its precise location or the geometry of the environment, and enables navigation at a higher level of semantic abstraction. We evaluate the effectiveness of our representation by simulating navigation tasks in a large number of virtual environments. Our results show that using a simple sets of perceptual and navigational behaviors, the proposed approach can successfully guide the way of the robot as it completes navigational missions such as going to a specific office. Furthermore, our implementation shows to be effective to control the selection and switching of behaviors. \n
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\n \n\n \n \n \n \n \n \n Investigating the utility of the weather context for point of interest recommendations.\n \n \n \n \n\n\n \n Trattner, C.; Oberegger, A.; Marinho, L.; and Parra, D.\n\n\n \n\n\n\n Information Technology & Tourism. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"InvestigatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{\t  trattner2018,\n  author\t= "Trattner, Christoph and Oberegger, Alexander and Marinho,\n\t\t  Leandro and Parra, Denis",\n  title\t\t= "Investigating the utility of the weather context for point\n\t\t  of interest recommendations",\n  journal\t= "Information Technology {\\&} Tourism",\n  year\t\t= "2018",\n  issn\t\t= "1943-4294",\n  doi\t\t= "10.1007/s40558-017-0100-9",\n  url\t\t= "https://doi.org/10.1007/s40558-017-0100-9"\n}\n\n
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\n \n\n \n \n \n \n \n \n Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation.\n \n \n \n \n\n\n \n Zang, X.; Pokle, A.; Chen, K.; Vazquez, M.; Niebles, J.; Soto, A.; and Savaresse, S.\n\n\n \n\n\n\n In EMNLP, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"TranslatingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  xiaoxue:etal:2018,\n  author\t= {X. Zang and Ashwini Pokle and Kevin Chen and M. Vazquez\n\t\t  and JC. Niebles and A. Soto and S. Savaresse},\n  title\t\t= {Translating Navigation Instructions in Natural Language to\n\t\t  a High-Level Plan for Behavioral Robot Navigation},\n  booktitle\t= {EMNLP},\n  year\t\t= {2018},\n  abstract\t= {We propose an end-to-end deep learning model for\n\t\t  translating free-form natural language instructions to a\n\t\t  high-level plan for behavioral robot navigation. We use\n\t\t  attention models to connect information from both the user\n\t\t  instructions and a topological representation of the\n\t\t  environment. We evaluate our model's performance on a new\n\t\t  dataset containing 10,050 pairs of navigation instructions.\n\t\t  Our model significantly outperforms baseline approaches.\n\t\t  Furthermore, our results suggest that it is possible to\n\t\t  leverage the environment map as a relevant knowledge base\n\t\t  to facilitate the translation of free-form navigational\n\t\t  instruction.},\n  url\t\t= {https://arxiv.org/abs/1810.00663}\n}\n\n
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\n We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.\n
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\n \n\n \n \n \n \n \n \n Behavioral Indoor Navigation With Natural Language Directions.\n \n \n \n \n\n\n \n Zang, X.; Vázquez, M.; Niebles, J.; Soto, A.; and Savarese, S.\n\n\n \n\n\n\n In HRI, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"BehavioralPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  zang:etal:2018,\n  author\t= {X. Zang and M. Vázquez and JC. Niebles and A. Soto and S.\n\t\t  Savarese},\n  title\t\t= {Behavioral Indoor Navigation With Natural Language\n\t\t  Directions},\n  booktitle\t= {{HRI}},\n  year\t\t= {2018},\n  abstract\t= {We describe a behavioral navigation approach that\n\t\t  leverages the rich semantic structure of human environments\n\t\t  to enable robots to navigate without an explicit geometric\n\t\t  representation of the world. Based on this approach, we\n\t\t  then present our efforts to allow robots to follow\n\t\t  navigation instructions in natural language. With our\n\t\t  proof-of-concept implementation, we were able to translate\n\t\t  natural language navigation commands into a sequence of\n\t\t  behaviors that could then be executed by a robot to reach a\n\t\t  desired goal.},\n  url\t\t= {http://www.marynel.net/static/pdfs/zang-HRI18.pdf}\n}\n
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\n We describe a behavioral navigation approach that leverages the rich semantic structure of human environments to enable robots to navigate without an explicit geometric representation of the world. Based on this approach, we then present our efforts to allow robots to follow navigation instructions in natural language. With our proof-of-concept implementation, we were able to translate natural language navigation commands into a sequence of behaviors that could then be executed by a robot to reach a desired goal.\n
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\n \n\n \n \n \n \n \n \n Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation.\n \n \n \n \n\n\n \n Dominguez, V.; Messina, P.; Parra, D.; Mery, D.; Trattner, C.; and Soto, A.\n\n\n \n\n\n\n In Proceedings of the Workshop on Deep Learning for Recommender Systems, co-located at RecSys 2017, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"ComparingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  1706.07515,\n  author\t= {Vicente Dominguez and Pablo Messina and Denis Parra and\n\t\t  Domingo Mery and Christoph Trattner and Alvaro Soto},\n  title\t\t= {Comparing Neural and Attractiveness-based Visual Features\n\t\t  for Artwork Recommendation},\n  year\t\t= {2017},\n  eprint\t= {arXiv:1706.07515},\n  url\t\t= {https://arxiv.org/pdf/1706.07515.pdf},\n  doi\t\t= {10.1145/3125486.3125495},\n  booktitle\t= {Proceedings of the Workshop on Deep Learning for\n\t\t  Recommender Systems, co-located at RecSys 2017}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA.\n \n \n \n \n\n\n \n Singh, S. P.; and Markovitch, S.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2017,\n  editor\t= {Satinder P. Singh and Shaul Markovitch},\n  title\t\t= {Proceedings of the Thirty-First {AAAI} Conference on\n\t\t  Artificial Intelligence, February 4-9, 2017, San Francisco,\n\t\t  California, {USA}},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2017},\n  url\t\t= {http://www.aaai.org/Library/AAAI/aaai17contents.php},\n  timestamp\t= {Mon, 06 Mar 2017 08:17:31 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2017},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces.\n \n \n \n \n\n\n \n Camacho, A.; Triantafillou, E.; Muise, C. J.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., pages 3716–3724, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Non-DeterministicPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 31 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/camachotmbm17,\n  author\t= {Alberto Camacho and Eleni Triantafillou and Christian J.\n\t\t  Muise and Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {Non-Deterministic Planning with Temporally Extended Goals:\n\t\t  {LTL} over Finite and Infinite Traces},\n  booktitle\t= {Proceedings of the Thirty-First {AAAI} Conference on\n\t\t  Artificial Intelligence, February 4-9, 2017, San Francisco,\n\t\t  California, {USA.}},\n  pages\t\t= {3716--3724},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/aaai/2017},\n  url\t\t= {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15026},\n  timestamp\t= {Mon, 06 Mar 2017 08:17:31 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/CamachoTMBM17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Grid Pathfinding on the 2\\emphk Neighborhoods.\n \n \n \n \n\n\n \n Rivera, N.; Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., pages 891–897, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"GridPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/riverahb17,\n  author\t= {Nicolas Rivera and Carlos Hern{\\'{a}}ndez and Jorge A.\n\t\t  Baier},\n  title\t\t= {Grid Pathfinding on the 2\\emph{k} Neighborhoods},\n  booktitle\t= {Proceedings of the Thirty-First {AAAI} Conference on\n\t\t  Artificial Intelligence, February 4-9, 2017, San Francisco,\n\t\t  California, {USA.}},\n  pages\t\t= {891--897},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/aaai/2017},\n  url\t\t= {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15014},\n  timestamp\t= {Mon, 06 Mar 2017 08:17:31 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/RiveraHB17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, ICAPS 2017, Pittsburgh, Pennsylvania, USA, June 18-23, 2017.\n \n \n \n \n\n\n \n Barbulescu, L.; Frank, J.; Mausam; and Smith, S. F.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2017,\n  editor\t= {Laura Barbulescu and Jeremy Frank and Mausam and Stephen\n\t\t  F. Smith},\n  title\t\t= {Proceedings of the Twenty-Seventh International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2017,\n\t\t  Pittsburgh, Pennsylvania, USA, June 18-23, 2017},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2017},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/icaps17contents.php},\n  timestamp\t= {Tue, 26 Sep 2017 07:30:03 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2017},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Improving MPGAA* for Extended Visibility Ranges.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling, ICAPS 2017, Pittsburgh, Pennsylvania, USA, June 18-23, 2017., pages 149–153, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/hernandezb17,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Improving MPGAA* for Extended Visibility Ranges},\n  booktitle\t= {Proceedings of the Twenty-Seventh International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2017,\n\t\t  Pittsburgh, Pennsylvania, USA, June 18-23, 2017.},\n  pages\t\t= {149--153},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/aips/2017},\n  url\t\t= {https://aaai.org/ocs/index.php/ICAPS/ICAPS17/paper/view/15764},\n  timestamp\t= {Tue, 26 Sep 2017 07:30:03 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/HernandezB17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017.\n \n \n \n \n\n\n \n Sierra, C.,\n editor.\n \n\n\n \n\n\n\n ijcai.org. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2017,\n  editor\t= {Carles Sierra},\n  title\t\t= {Proceedings of the Twenty-Sixth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2017,\n\t\t  Melbourne, Australia, August 19-25, 2017},\n  publisher\t= {ijcai.org},\n  year\t\t= {2017},\n  url\t\t= {http://www.ijcai.org/Proceedings/2017/},\n  isbn\t\t= {978-0-9992411-0-3},\n  timestamp\t= {Wed, 27 Jun 2018 12:24:11 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2017},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads.\n \n \n \n \n\n\n \n Hernández, C.; Botea, A.; Baier, J. A.; and Bulitko, V.\n\n\n \n\n\n\n In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 510–516, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"OnlinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/hernandezbbb17,\n  author\t= {Carlos Hern{\\'{a}}ndez and Adi Botea and Jorge A. Baier\n\t\t  and Vadim Bulitko},\n  title\t\t= {Online Bridged Pruning for Real-Time Search with Arbitrary\n\t\t  Lookaheads},\n  booktitle\t= {Proceedings of the Twenty-Sixth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2017,\n\t\t  Melbourne, Australia, August 19-25, 2017},\n  pages\t\t= {510--516},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/ijcai/2017},\n  url\t\t= {https://doi.org/10.24963/ijcai.2017/72},\n  doi\t\t= {10.24963/ijcai.2017/72},\n  timestamp\t= {Wed, 27 Jun 2018 12:24:11 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/HernandezBBB17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval.\n \n \n \n \n\n\n \n Icarte, R. T.; Baier, J. A.; Ruz, C.; and Soto, A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 1283–1289, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"HowPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/icartebrs17,\n  author\t= {Rodrigo Toro Icarte and Jorge A. Baier and Cristian Ruz\n\t\t  and Alvaro Soto},\n  title\t\t= {How a General-Purpose Commonsense Ontology can Improve\n\t\t  Performance of Learning-Based Image Retrieval},\n  booktitle\t= {Proceedings of the Twenty-Sixth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2017,\n\t\t  Melbourne, Australia, August 19-25, 2017},\n  pages\t\t= {1283--1289},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/ijcai/2017},\n  url\t\t= {https://doi.org/10.24963/ijcai.2017/178},\n  doi\t\t= {10.24963/ijcai.2017/178},\n  timestamp\t= {Wed, 27 Jun 2018 12:24:11 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/IcarteBRS17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Tenth International Symposium on Combinatorial Search, SOCS 2017, 16-17 June 2017, Pittsburgh, Pennsylvania, USA.\n \n \n \n \n\n\n \n Fukunaga, A.; and Kishimoto, A.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2017,\n  editor\t= {Alex Fukunaga and Akihiro Kishimoto},\n  title\t\t= {Proceedings of the Tenth International Symposium on\n\t\t  Combinatorial Search, {SOCS} 2017, 16-17 June 2017,\n\t\t  Pittsburgh, Pennsylvania, {USA}},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2017},\n  url\t\t= {http://www.aaai.org/Library/SOCS/socs17contents.php},\n  timestamp\t= {Tue, 24 Jul 2018 08:02:34 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2017},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fast and Almost Optimal Any-Angle Pathfinding Using the 2\\(^\\mboxk\\) Neighborhoods.\n \n \n \n \n\n\n \n Hormazábal, N.; Díaz, A.; Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Tenth International Symposium on Combinatorial Search, SOCS 2017, 16-17 June 2017, Pittsburgh, Pennsylvania, USA., pages 139–143, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hormazabaldhb17,\n  author\t= {Nicol{\\'{a}}s Hormaz{\\'{a}}bal and Antonio D{\\'{\\i}}az and\n\t\t  Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Fast and Almost Optimal Any-Angle Pathfinding Using the\n\t\t  2\\({}^{\\mbox{k}}\\) Neighborhoods},\n  booktitle\t= {Proceedings of the Tenth International Symposium on\n\t\t  Combinatorial Search, {SOCS} 2017, 16-17 June 2017,\n\t\t  Pittsburgh, Pennsylvania, {USA.}},\n  pages\t\t= {139--143},\n  year\t\t= {2017},\n  crossref\t= {DBLP:conf/socs/2017},\n  url\t\t= {https://aaai.org/ocs/index.php/SOCS/SOCS17/paper/view/15806},\n  timestamp\t= {Tue, 24 Jul 2018 08:02:34 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HormazabalDHB17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval.\n \n \n \n \n\n\n \n Icarte, R. T.; Baier, J. A.; Ruz, C.; and Soto, A.\n\n\n \n\n\n\n CoRR, abs/1705.08844. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"HowPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/corr/icartebrs17,\n  author\t= {Rodrigo Toro Icarte and Jorge A. Baier and Cristian Ruz\n\t\t  and Alvaro Soto},\n  title\t\t= {How a General-Purpose Commonsense Ontology can Improve\n\t\t  Performance of Learning-Based Image Retrieval},\n  journal\t= {CoRR},\n  volume\t= {abs/1705.08844},\n  year\t\t= {2017},\n  url\t\t= {http://arxiv.org/abs/1705.08844},\n  archiveprefix\t= {arXiv},\n  eprint\t= {1705.08844},\n  timestamp\t= {Mon, 13 Aug 2018 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/corr/IcarteBRS17},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Towards a Recommender System for Undergraduate Research.\n \n \n \n \n\n\n \n del-Rio , F.; Parra, D.; Kuzmicic, J.; and Svec, E.\n\n\n \n\n\n\n In Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) , 2017. \n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{\t  del2017towards,\n  title\t\t= {Towards a Recommender System for Undergraduate Research},\n  author\t= {del-Rio, Felipe and Parra, Denis and Kuzmicic, Jovan and\n\t\t  Svec, Erick},\n  journal\t= {arXiv preprint arXiv:1706.06701},\n  year\t\t= {2017},\n  booktitle\t= {Proceedings of the Poster Track of the 11th ACM Conference\n\t\t  on Recommender Systems (RecSys 2017) },\n  url\t\t= {http://ceur-ws.org/Vol-1905/recsys2017_poster8.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Enriching capstone project-based learning experiences using a crowdsourcing recommender engine.\n \n \n \n\n\n \n Diaz-Mosquera, J. D; Sanabria, P.; Neyem, A.; Parra, D.; and Navon, J.\n\n\n \n\n\n\n In Proceedings of the 4th International Workshop on CrowdSourcing in Software Engineering, pages 25–29, 2017. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  diaz2017enriching,\n  title\t\t= {Enriching capstone project-based learning experiences\n\t\t  using a crowdsourcing recommender engine},\n  author\t= {Diaz-Mosquera, Juan D and Sanabria, Pablo and Neyem,\n\t\t  Andres and Parra, Denis and Navon, Jaime},\n  booktitle\t= {Proceedings of the 4th International Workshop on\n\t\t  CrowdSourcing in Software Engineering},\n  pages\t\t= {25--29},\n  year\t\t= {2017},\n  organization\t= {IEEE Press}\n}\n\n
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\n \n\n \n \n \n \n \n \n Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation.\n \n \n \n \n\n\n \n Dominguez, V.; Messina, P.; Parra, D.; Mery, D.; Trattner, C.; and Soto, A.\n\n\n \n\n\n\n In Workshop on Deep Learning for Recommender Systems, co-located at RecSys 2017, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"ComparingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dominguez:etal:2017,\n  author\t= {V. Dominguez and P. Messina and D. Parra and D. Mery and\n\t\t  C. Trattner and A. Soto},\n  title\t\t= {Comparing Neural and Attractiveness-based Visual Features\n\t\t  for Artwork Recommendation},\n  year\t\t= {2017},\n  url\t\t= {https://arxiv.org/pdf/1706.07515.pdf},\n  booktitle\t= {Workshop on Deep Learning for Recommender Systems,\n\t\t  co-located at RecSys 2017}\n}\n\n
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\n \n\n \n \n \n \n \n \n Sparse composition of body poses and atomic actions for human activity recognition in RGB-D videos.\n \n \n \n \n\n\n \n Lillo, I.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n Image and Vision Computing, 59(March): 63-75. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"SparsePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  lillo:etal:2017,\n  author\t= {I. Lillo and JC. Niebles and A. Soto},\n  title\t\t= {Sparse composition of body poses and atomic actions for\n\t\t  human activity recognition in RGB-D videos},\n  journal\t= {Image and Vision Computing},\n  volume\t= {59},\n  number\t= {March},\n  pages\t\t= {63-75},\n  year\t\t= {2017},\n  abstract\t= {This paper presents an approach to recognize human\n\t\t  activities using body poses estimated from RGB-D data. We\n\t\t  focus on recognizing complex activities composed of\n\t\t  sequential or simultaneous atomic actions characterized by\n\t\t  body motions of a single actor. We tackle this problem by\n\t\t  introducing a hierarchical compositional model that\n\t\t  operates at three levels of abstraction. At the lowest\n\t\t  level, geometric and motion descriptors are used to learn a\n\t\t  dictionary of body poses. At the intermediate level, sparse\n\t\t  compositions of these body poses are used to obtain\n\t\t  meaningful representations for atomic human actions.\n\t\t  Finally, at the highest level, spatial and temporal\n\t\t  compositions of these atomic actions are used to represent\n\t\t  complex human activities. Our results show the benefits of\n\t\t  using a hierarchical model that exploits the sharing and\n\t\t  composition of body poses into atomic actions, and atomic\n\t\t  actions into activities. A quantitative evaluation using\n\t\t  two benchmark datasets illustrates the advantages of our\n\t\t  model to perform action and activity recognition.},\n  url\t\t= {http://www.sciencedirect.com/science/article/pii/S0262885616301949}\n}\n\n
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\n This paper presents an approach to recognize human activities using body poses estimated from RGB-D data. We focus on recognizing complex activities composed of sequential or simultaneous atomic actions characterized by body motions of a single actor. We tackle this problem by introducing a hierarchical compositional model that operates at three levels of abstraction. At the lowest level, geometric and motion descriptors are used to learn a dictionary of body poses. At the intermediate level, sparse compositions of these body poses are used to obtain meaningful representations for atomic human actions. Finally, at the highest level, spatial and temporal compositions of these atomic actions are used to represent complex human activities. Our results show the benefits of using a hierarchical model that exploits the sharing and composition of body poses into atomic actions, and atomic actions into activities. A quantitative evaluation using two benchmark datasets illustrates the advantages of our model to perform action and activity recognition.\n
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\n \n\n \n \n \n \n \n \n Gaining historical and international relations insights from social media: spatio-temporal real-world news analysis using Twitter.\n \n \n \n \n\n\n \n Pena-Araya, V.; Quezada, M.; Poblete, B.; and Parra, D.\n\n\n \n\n\n\n EPJ Data Science. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"GainingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{\t  pena-araya2017,\n  author\t= {Pena-Araya, Vanessa and Quezada, Mauricio and Poblete,\n\t\t  Barbara and Parra, Denis},\n  title\t\t= {Gaining historical and international relations insights\n\t\t  from social media: spatio-temporal real-world news analysis\n\t\t  using Twitter},\n  journal\t= {EPJ Data Science},\n  year\t\t= {2017},\n  issn\t\t= {2193-1127},\n  doi\t\t= {10.1140/epjds/s13688-017-0122-8},\n  url\t\t= {https://doi.org/10.1140/epjds/s13688-017-0122-8}\n}\n\n
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\n \n\n \n \n \n \n \n \n Unsupervised Local Regressive Attributes for Pedestrian Re-Identification.\n \n \n \n \n\n\n \n Peralta, B.; Caro, L.; and Soto, A.\n\n\n \n\n\n\n In CIARP, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"UnsupervisedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  peralta:etal:2017,\n  author\t= {B. Peralta and L. Caro and A. Soto},\n  title\t\t= {Unsupervised Local Regressive Attributes for Pedestrian\n\t\t  Re-Identification},\n  booktitle\t= {{CIARP}},\n  year\t\t= {2017},\n  abstract\t= {.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/}\n}\n\n
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\n \n\n \n \n \n \n \n \n GENIUS: web server to predict local gene networks and key genes for biological functions.\n \n \n \n \n\n\n \n Puelma, T.; Araus, V.; Canales, J.; Vidal, E.; Cabello, J.; Soto, A.; and Gutierrez, R.\n\n\n \n\n\n\n Bioinformatics. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"GENIUS:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  puelma:etal:2017,\n  author\t= {T. Puelma and V. Araus and J. Canales and E. Vidal and J.\n\t\t  Cabello and A. Soto and R. Gutierrez},\n  title\t\t= {GENIUS: web server to predict local gene networks and key\n\t\t  genes for biological functions},\n  journal\t= {Bioinformatics},\n  year\t\t= {2017},\n  abstract\t= {GENIUS is a user-friendly web server that uses a novel\n\t\t  machine learning algorithm to infer functional gene\n\t\t  networks focused on specific genes and experimental\n\t\t  conditions that are relevant to biological functions of\n\t\t  interest. These functions may have different levels of\n\t\t  complexity, from specific biological processes to complex\n\t\t  traits that involve several interacting processes. GENIUS\n\t\t  also enriches the network with new genes related to the\n\t\t  biological function of interest, with accuracies comparable\n\t\t  to highly discriminative Support Vector Machine methods},\n  url\t\t= {https://doi.org/10.1093/bioinformatics/btw702}\n}\n\n
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\n GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods\n
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\n \n\n \n \n \n \n \n \n pyRecLab: A Software Library for Quick Prototyping of Recommender Systems.\n \n \n \n \n\n\n \n Sepulveda, G.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) , 2017. \n \n\n\n\n
\n\n\n\n \n \n \"pyRecLab:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{\t  sepulveda2017pyreclab,\n  title\t\t= {pyRecLab: A Software Library for Quick Prototyping of\n\t\t  Recommender Systems},\n  author\t= {Sepulveda, Gabriel and Parra, Denis},\n  journal\t= {arXiv preprint arXiv:1706.06291},\n  year\t\t= {2017},\n  booktitle\t= {Proceedings of the Poster Track of the 11th ACM Conference\n\t\t  on Recommender Systems (RecSys 2017) },\n  url\t\t= {http://ceur-ws.org/Vol-1905/recsys2017_poster23.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval.\n \n \n \n \n\n\n \n R. Toro, J. B.; and C. Ruz, A. S.\n\n\n \n\n\n\n In IJCAI, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"HowPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{\t  toro:etal:2017,\n  author\t= {R. Toro, J. Baier, C. Ruz, A. Soto},\n  title\t\t= {How a General-Purpose Commonsense Ontology can Improve\n\t\t  Performance of Learning-Based Image Retrieval},\n  booktitle\t= {{IJCAI}},\n  year\t\t= {2017},\n  abstract\t= {The knowledge representation community has built\n\t\t  general-purpose ontologies which contain large amounts of\n\t\t  commonsense knowledge over relevant aspects of the world,\n\t\t  including useful visual information, e.g.: "a ball is used\n\t\t  by a football player", "a tennis player is located at a\n\t\t  tennis court". Current state-of-the-art approaches for\n\t\t  visual recognition do not exploit these rule-based\n\t\t  knowledge sources. Instead, they learn recognition models\n\t\t  directly from training examples. In this paper, we study\n\t\t  how general-purpose ontologies---specifically, MIT's\n\t\t  ConceptNet ontology---can improve the performance of\n\t\t  state-of-the-art vision systems. As a testbed, we tackle\n\t\t  the problem of sentence-based image retrieval. Our\n\t\t  retrieval approach incorporates knowledge from ConceptNet\n\t\t  on top of a large pool of object detectors derived from a\n\t\t  deep learning technique. In our experiments, we show that\n\t\t  ConceptNet can improve performance on a common benchmark\n\t\t  dataset. Key to our performance is the use of the ESPGAME\n\t\t  dataset to select visually relevant relations from\n\t\t  ConceptNet. Consequently, a main conclusion of this work is\n\t\t  that general-purpose commonsense ontologies improve\n\t\t  performance on visual reasoning tasks when properly\n\t\t  filtered to select meaningful visual relations. },\n  url\t\t= {https://arxiv.org/abs/1705.08844}\n}\n\n
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\n The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: \"a ball is used by a football player\", \"a tennis player is located at a tennis court\". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies—specifically, MIT's ConceptNet ontology—can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations. \n
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\n \n\n \n \n \n \n \n \n Monitoring obesity prevalence in the United States through bookmarking activities in online food portals.\n \n \n \n \n\n\n \n Trattner, C.; Parra, D.; and Elsweiler, D.\n\n\n \n\n\n\n PloS one, 12(6): e0179144. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"MonitoringPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  trattner2017monitoring,\n  title\t\t= {Monitoring obesity prevalence in the United States through\n\t\t  bookmarking activities in online food portals},\n  author\t= {Trattner, Christoph and Parra, Denis and Elsweiler,\n\t\t  David},\n  journal\t= {PloS one},\n  volume\t= {12},\n  number\t= {6},\n  pages\t\t= {e0179144},\n  year\t\t= {2017},\n  publisher\t= {Public Library of Science},\n  url\t\t= {https://doi.org/10.1371/journal.pone.0179144}\n}\n\n
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\n \n\n \n \n \n \n \n Supporting Conference Attendees with Visual Decision Making Interfaces.\n \n \n \n\n\n \n Verbert, K.; Brusilovsky, P.; Wongchokprasitti, C.; Parra, D.; and Cardoso, B.\n\n\n \n\n\n\n In Proceedings of the 22nd International Conference on Intelligent User Interfaces Companion, pages 161–164, 2017. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  verbert2017supporting,\n  title\t\t= {Supporting Conference Attendees with Visual Decision\n\t\t  Making Interfaces},\n  author\t= {Verbert, Katrien and Brusilovsky, Peter and\n\t\t  Wongchokprasitti, Chirayu and Parra, Denis and Cardoso,\n\t\t  Bruno},\n  booktitle\t= {Proceedings of the 22nd International Conference on\n\t\t  Intelligent User Interfaces Companion},\n  pages\t\t= {161--164},\n  year\t\t= {2017},\n  organization\t= {ACM}\n}\n\n
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\n \n\n \n \n \n \n \n \n Action Recognition in Video Using Sparse Coding and Relative Features.\n \n \n \n \n\n\n \n Alfaro, A.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In CVPR, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"ActionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  anali:etal:2016,\n  author\t= {A. Alfaro and D. Mery and A. Soto},\n  title\t\t= {Action Recognition in Video Using Sparse Coding and\n\t\t  Relative Features},\n  booktitle\t= {{CVPR}},\n  year\t\t= {2016},\n  abstract\t= {This work presents an approach to category-based action\n\t\t  recognition in video using sparse coding techniques. The\n\t\t  proposed approach includes two main contributions: i) A new\n\t\t  method to handle intra-class variations by decomposing each\n\t\t  video into a reduced set of representative atomic action\n\t\t  acts or key-sequences, and ii) A new video descriptor,\n\t\t  ITRA: Inter-Temporal Relational Act Descriptor, that\n\t\t  exploits the power of comparative reasoning to capture\n\t\t  relative similarity relations among key-sequences. In terms\n\t\t  of the method to obtain key-sequences, we introduce a loss\n\t\t  function that, for each video, leads to the identification\n\t\t  of a sparse set of representative key-frames capturing\n\t\t  both, relevant particularities arising in the input video,\n\t\t  as well as relevant generalities arising in the complete\n\t\t  class collection. In terms of the method to obtain the ITRA\n\t\t  descriptor, we introduce a novel scheme to quantify\n\t\t  relative intra and inter-class similarities among local\n\t\t  temporal patterns arising in the videos. The resulting ITRA\n\t\t  descriptor demonstrates to be highly effective to\n\t\t  discriminate among action categories. As a result, the\n\t\t  proposed approach reaches remarkable action recognition\n\t\t  performance on several popular benchmark datasets,\n\t\t  outperforming alternative state-of-the-art techniques by a\n\t\t  large margin.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/FinalVersion-Anali-CVPR-2016.pdf}\n}\n\n
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\n This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of-the-art techniques by a large margin.\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016.\n \n \n \n \n\n\n \n Kambhampati, S.,\n editor.\n \n\n\n \n\n\n\n IJCAI/AAAI Press. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2016,\n  editor\t= {Subbarao Kambhampati},\n  title\t\t= {Proceedings of the Twenty-Fifth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2016, New\n\t\t  York, NY, USA, 9-15 July 2016},\n  publisher\t= {{IJCAI/AAAI} Press},\n  year\t\t= {2016},\n  url\t\t= {http://www.ijcai.org/Proceedings/2016},\n  isbn\t\t= {978-1-57735-770-4},\n  timestamp\t= {Fri, 15 Jul 2016 15:25:58 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2016},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning co-located with 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 10, 2016.\n \n \n \n \n\n\n \n Barták, R.; McCluskey, T. L.; and Pontelli, E.,\n editors.\n \n\n\n \n\n\n\n Volume 1648, of CEUR Workshop Proceedings.CEUR-WS.org. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2016knowpros,\n  editor\t= {Roman Bart{\\'{a}}k and Thomas Leo McCluskey and Enrico\n\t\t  Pontelli},\n  title\t\t= {Proceedings of the Workshop on Knowledge-based Techniques\n\t\t  for Problem Solving and Reasoning co-located with 25th\n\t\t  International Joint Conference on Artificial Intelligence\n\t\t  {(IJCAI} 2016), New York City, USA, July 10, 2016},\n  series\t= {{CEUR} Workshop Proceedings},\n  volume\t= {1648},\n  publisher\t= {CEUR-WS.org},\n  year\t\t= {2016},\n  url\t\t= {http://ceur-ws.org/Vol-1648},\n  urn\t\t= {urn:nbn:de:0074-1648-1},\n  timestamp\t= {Fri, 05 Aug 2016 13:04:57 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2016knowpros},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Incomplete Causal Laws in the Situation Calculus Using Free Fluents.\n \n \n \n \n\n\n \n Arenas, M.; Baier, J. A.; Navarro, J. S.; and Sardiña, S.\n\n\n \n\n\n\n In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 907–914, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"IncompletePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/arenasbns16,\n  author\t= {Marcelo Arenas and Jorge A. Baier and Juan S. Navarro and\n\t\t  Sebastian Sardi{\\~{n}}a},\n  title\t\t= {Incomplete Causal Laws in the Situation Calculus Using\n\t\t  Free Fluents},\n  booktitle\t= {Proceedings of the Twenty-Fifth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2016, New\n\t\t  York, NY, USA, 9-15 July 2016},\n  pages\t\t= {907--914},\n  year\t\t= {2016},\n  crossref\t= {DBLP:conf/ijcai/2016},\n  url\t\t= {http://www.ijcai.org/Abstract/16/133},\n  timestamp\t= {Fri, 15 Jul 2016 15:25:58 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/ArenasBNS16},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Assumption-Based Planning with Sensing via Contingent Planning.\n \n \n \n \n\n\n \n Calvo, P.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning co-located with 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 10, 2016., 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Assumption-BasedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/calvob16,\n  author\t= {Pamela Calvo and Jorge A. Baier},\n  title\t\t= {Assumption-Based Planning with Sensing via Contingent\n\t\t  Planning},\n  booktitle\t= {Proceedings of the Workshop on Knowledge-based Techniques\n\t\t  for Problem Solving and Reasoning co-located with 25th\n\t\t  International Joint Conference on Artificial Intelligence\n\t\t  {(IJCAI} 2016), New York City, USA, July 10, 2016.},\n  year\t\t= {2016},\n  crossref\t= {DBLP:conf/ijcai/2016knowpros},\n  url\t\t= {http://ceur-ws.org/Vol-1648/paper7.pdf},\n  timestamp\t= {Fri, 05 Aug 2016 13:04:57 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/CalvoB16},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Non-Deterministic Planning with Temporally Extended Goals: Completing the Story for Finite and Infinite LTL (Amended Version).\n \n \n \n \n\n\n \n Camacho, A.; Triantafillou, E.; Muise, C. J.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Workshop on Knowledge-based Techniques for Problem Solving and Reasoning co-located with 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 10, 2016., 2016. \n \n\n\n\n
\n\n\n\n \n \n \"Non-DeterministicPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/camachotmbm16,\n  author\t= {Alberto Camacho and Eleni Triantafillou and Christian J.\n\t\t  Muise and Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {Non-Deterministic Planning with Temporally Extended Goals:\n\t\t  Completing the Story for Finite and Infinite {LTL} (Amended\n\t\t  Version)},\n  booktitle\t= {Proceedings of the Workshop on Knowledge-based Techniques\n\t\t  for Problem Solving and Reasoning co-located with 25th\n\t\t  International Joint Conference on Artificial Intelligence\n\t\t  {(IJCAI} 2016), New York City, USA, July 10, 2016.},\n  year\t\t= {2016},\n  crossref\t= {DBLP:conf/ijcai/2016knowpros},\n  url\t\t= {http://ceur-ws.org/Vol-1648/paper10.pdf},\n  timestamp\t= {Fri, 05 Aug 2016 13:04:57 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/CamachoTMBM16},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Ninth Annual Symposium on Combinatorial Search, SOCS 2016, Tarrytown, NY, USA, July 6-8, 2016.\n \n \n \n \n\n\n \n Baier, J. A.; and Botea, A.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2016,\n  editor\t= {Jorge A. Baier and Adi Botea},\n  title\t\t= {Proceedings of the Ninth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2016, Tarrytown, NY, USA, July 6-8, 2016},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2016},\n  url\t\t= {http://www.aaai.org/Library/SOCS/socs16contents.php},\n  isbn\t\t= {978-1-57735-769-8},\n  timestamp\t= {Fri, 12 Aug 2016 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2016},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Reconfigurable Applications Using GCMScript.\n \n \n \n\n\n \n Ibanez, M.; Ruz, C.; Henrio, L.; and Bustos-Jiménez, J.\n\n\n \n\n\n\n IEEE Cloud Computing, 3(3): 30–39. 2016.\n \n\n\n\n
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@Article{\t  dblp:journals/cloudcomp/ibanezrhb16,\n  author\t= {Matias Ibanez and Cristian Ruz and Ludovic Henrio and\n\t\t  Javier Bustos{-}Jim{\\'{e}}nez},\n  title\t\t= {Reconfigurable Applications Using GCMScript},\n  journal\t= {{IEEE} Cloud Computing},\n  volume\t= {3},\n  number\t= {3},\n  pages\t\t= {30--39},\n  year\t\t= {2016}\n}\n\n
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\n \n\n \n \n \n \n \n \n Time-Bounded Best-First Search for Reversible and Non-reversible Search Graphs.\n \n \n \n \n\n\n \n Hernández, C.; Baier, J. A.; and Asín, R.\n\n\n \n\n\n\n J. Artif. Intell. Res., 56: 547–571. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Time-BoundedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/jair/hernandezba16,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier and Roberto\n\t\t  As{\\'{\\i}}n},\n  title\t\t= {Time-Bounded Best-First Search for Reversible and\n\t\t  Non-reversible Search Graphs},\n  journal\t= {J. Artif. Intell. Res.},\n  volume\t= {56},\n  pages\t\t= {547--571},\n  year\t\t= {2016},\n  url\t\t= {https://doi.org/10.1613/jair.5073},\n  doi\t\t= {10.1613/jair.5073},\n  timestamp\t= {Wed, 21 Jun 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/jair/HernandezBA16},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Visualization and Recommendation of Large Image and Text Collections Toward Effective Sensemaking.\n \n \n \n \n\n\n \n Gu, Y.; Wang, C.; Ma, J.; Nemiroff, R. J.; Kao, D. L.; and Parra, D.\n\n\n \n\n\n\n Information Visualization. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"VisualizationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  gu2016,\n  author\t= {Yi Gu and Chaoli Wang and Jun Ma and Robert J. Nemiroff\n\t\t  and David L. Kao and Denis Parra},\n  journal\t= {Information Visualization},\n  keywords\t= {Graph models, Visualization, Recommender Systems},\n  title\t\t= {Visualization and Recommendation of Large Image and Text\n\t\t  Collections Toward Effective Sensemaking},\n  publisher\t= {SAGE Publications},\n  year\t\t= {2016},\n  doi\t\t= {10.1177/1473871616630778},\n  url\t\t= {http://www.nd.edu/~cwang11/research/iv16-igraph.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities.\n \n \n \n \n\n\n \n He, C.; Parra, D.; and Verbert, K.\n\n\n \n\n\n\n Expert Systems with Applications. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"InteractivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  he2016survey,\n  title\t\t= {Interactive recommender systems: a survey of the state of\n\t\t  the art and future research challenges and opportunities},\n  author\t= {He, Chen and Parra, Denis and Verbert, Katrien},\n  journal\t= {Expert Systems with Applications},\n  doi\t\t= {10.1016/j.eswa.2016.02.013},\n  year\t\t= {2016},\n  publisher\t= {Elsevier},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/pre-print_ESWA_He_Parra_Verbert_2016.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets.\n \n \n \n \n\n\n \n Lillo, I.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n In CVPR, 2016. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lillo:etal:2016,\n  author\t= {I. Lillo and JC. Niebles and A. Soto},\n  title\t\t= {A Hierarchical Pose-Based Approach to Complex Action\n\t\t  Understanding Using Dictionaries of Actionlets and Motion\n\t\t  Poselets},\n  booktitle\t= {{CVPR}},\n  year\t\t= {2016},\n  abstract\t= {In this paper, we introduce a new hierarchical model for\n\t\t  human action recognition using body joint locations. Our\n\t\t  model can categorize complex actions in videos, and perform\n\t\t  spatio-temporal annotations of the atomic actions that\n\t\t  compose the complex action being performed. That is, for\n\t\t  each atomic action, the model generates temporal action\n\t\t  annotations by estimating its starting and ending times, as\n\t\t  well as, spatial annotations by inferring the human body\n\t\t  parts that are involved in executing the action. Our model\n\t\t  includes three key novel properties: (i) it can be trained\n\t\t  with no spatial supervision, as it can automatically\n\t\t  discover active body parts from temporal action annotations\n\t\t  only; (ii) it jointly learns flexible representations for\n\t\t  motion poselets and actionlets that encode the visual\n\t\t  variability of body parts and atomic actions; (iii) a\n\t\t  mechanism to discard idle or non-informative body parts\n\t\t  which increases its robustness to common pose estimation\n\t\t  errors. We evaluate the performance of our method using\n\t\t  multiple action recognition benchmarks. Our model\n\t\t  consistently outperforms baselines and state-of-the-art\n\t\t  action recognition methods.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/FinalVersionActivities-CVPR-2016.pdf}\n}\n\n
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\n In this paper, we introduce a new hierarchical model for human action recognition using body joint locations. Our model can categorize complex actions in videos, and perform spatio-temporal annotations of the atomic actions that compose the complex action being performed. That is, for each atomic action, the model generates temporal action annotations by estimating its starting and ending times, as well as, spatial annotations by inferring the human body parts that are involved in executing the action. Our model includes three key novel properties: (i) it can be trained with no spatial supervision, as it can automatically discover active body parts from temporal action annotations only; (ii) it jointly learns flexible representations for motion poselets and actionlets that encode the visual variability of body parts and atomic actions; (iii) a mechanism to discard idle or non-informative body parts which increases its robustness to common pose estimation errors. We evaluate the performance of our method using multiple action recognition benchmarks. Our model consistently outperforms baselines and state-of-the-art action recognition methods.\n
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\n \n\n \n \n \n \n \n \n Moodplay: Interactive Mood-based Music Discovery andRecommendation.\n \n \n \n \n\n\n \n Andjelkovic, I.; Parra, D.; and O'Donovan, J.\n\n\n \n\n\n\n In Proceedings of the UMAP Conference, 2016. ACM\n \n\n\n\n
\n\n\n\n \n \n \"Moodplay:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  moodplay2016,\n  author\t= {Andjelkovic, Ivana and Parra, Denis and O'Donovan, John},\n  booktitle\t= {Proceedings of the UMAP Conference},\n  keywords\t= {recommender systems, emotion, music recommendation},\n  location\t= {Halifax, Canada},\n  publisher\t= {ACM},\n  title\t\t= {Moodplay: Interactive Mood-based Music Discovery\n\t\t  andRecommendation},\n  year\t\t= {2016},\n  url\t\t= {http://dparra.sitios.ing.uc.cl/pdfs/Moodplay_UMAP_2016.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Linking information and people in a social system for academic conferences.\n \n \n \n\n\n \n Brusilovsky, P.; Oh, J. S.; Lopez, C.; Parra, D.; and Jeng, W.\n\n\n \n\n\n\n New Review of Hypertext and Hypermedia (in press). 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  oh2016nrhh,\n  title\t\t= {Linking information and people in a social system for\n\t\t  academic conferences},\n  author\t= {Brusilovsky, Peter and Oh, Jung Sun and Lopez, Claudia and\n\t\t  Parra, Denis and Jeng, Wei},\n  journal\t= {New Review of Hypertext and Hypermedia (in press)},\n  year\t\t= {2016},\n  publisher\t= {Taylor and Francis}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Proposal for Supervised Clustering with Dirichlet Process Using Labels.\n \n \n \n \n\n\n \n Peralta, B.; Caro, A.; and Soto, A.\n\n\n \n\n\n\n Pattern Recognition Letters, 80: 52-57. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  peralta:etal:2016,\n  author\t= {B. Peralta and A. Caro and A. Soto},\n  title\t\t= {A Proposal for Supervised Clustering with Dirichlet\n\t\t  Process Using Labels},\n  journal\t= {Pattern Recognition Letters},\n  volume\t= {80},\n  pages\t\t= {52-57},\n  year\t\t= {2016},\n  abstract\t= {Supervised clustering is an emerging area of machine\n\t\t  learning, where the goal is to find class-uniform clusters.\n\t\t  However, typical state-of-the-art algorithms use a fixed\n\t\t  number of clusters. In this work, we propose a variation of\n\t\t  a non-parametric Bayesian modeling for supervised\n\t\t  clustering. Our approach consists of modeling the clusters\n\t\t  as a mixture of Gaussians with the constraint of\n\t\t  encouraging clusters of points with the same label. In\n\t\t  order to estimate the number of clusters, we assume\n\t\t  a-priori a countably infinite number of clusters using a\n\t\t  variation of Dirichlet Process model over the prior\n\t\t  distribution. In our experiments, we show that our\n\t\t  technique typically outperforms the results of other\n\t\t  clustering techniques.},\n  url\t\t= {http://www.sciencedirect.com/science/article/pii/S0167865516300976}\n}\n\n
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\n Supervised clustering is an emerging area of machine learning, where the goal is to find class-uniform clusters. However, typical state-of-the-art algorithms use a fixed number of clusters. In this work, we propose a variation of a non-parametric Bayesian modeling for supervised clustering. Our approach consists of modeling the clusters as a mixture of Gaussians with the constraint of encouraging clusters of points with the same label. In order to estimate the number of clusters, we assume a-priori a countably infinite number of clusters using a variation of Dirichlet Process model over the prior distribution. In our experiments, we show that our technique typically outperforms the results of other clustering techniques.\n
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\n \n\n \n \n \n \n \n \n Using QR codes to increase user engagement in museum-like spaces.\n \n \n \n \n\n\n \n Perez-Sanagustin, M.; Parra, D.; Verdugo, R.; Garcia-Galleguillos, G.; and Nussbaum, M.\n\n\n \n\n\n\n Computers in Human Behavior. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@Article{\t  qr2016,\n  author\t= {Perez-Sanagustin, Mar and Parra, Denis and Verdugo, Renato\n\t\t  and Garcia-Galleguillos, Gonzalo and Nussbaum, Miguel},\n  journal\t= {Computers in Human Behavior},\n  keywords\t= {QR codes, museums, user engagement},\n  title\t\t= {Using QR codes to increase user engagement in museum-like\n\t\t  spaces},\n  publisher\t= {Elsevier},\n  year\t\t= {2016},\n  url\t\t= {https://mperezsanagustin.files.wordpress.com/2016/02/preprint-pecc81rezsanagustin-etal16.pdf},\n  doi\t\t= {10.1016/j.chb.2016.02.012}\n}\n\n
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\n \n\n \n \n \n \n \n \n Agents vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance.\n \n \n \n \n\n\n \n Verbert, K.; Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems (in press). 2016.\n \n\n\n\n
\n\n\n\n \n \n \"AgentsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  verbert2016tiis,\n  title\t\t= {Agents vs. Users: Visual Recommendation of Research Talks\n\t\t  with Multiple Dimension of Relevance},\n  author\t= {Verbert, Katrien and Parra, Denis and Brusilovsky, Peter},\n  journal\t= {ACM Transactions on Interactive Intelligent Systems (in\n\t\t  press)},\n  year\t\t= {2016},\n  publisher\t= {in press},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/preprint-TiiS-AgentsvsUsers-2016.pdf}\n}\n\n
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\n  \n 2015\n \n \n (21)\n \n \n
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\n \n\n \n \n \n \n \n \n Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?.\n \n \n \n \n\n\n \n Balby Marinho, L.; Trattner, C.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 9th ACM Conference on Recommender Systems, of RecSys '15, pages 245–248, New York, NY, USA, 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"ArePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  balbymarinho:2015:rpr:2792838.2799674,\n  acmid\t\t= {2799674},\n  address\t= {New York, NY, USA},\n  author\t= {Balby Marinho, Leandro and Trattner, Christoph and Parra,\n\t\t  Denis},\n  booktitle\t= {Proceedings of the 9th ACM Conference on Recommender\n\t\t  Systems},\n  date-added\t= {2015-12-30 15:00:53 +0000},\n  date-modified\t= {2015-12-30 15:05:38 +0000},\n  doi\t\t= {10.1145/2792838.2799674},\n  isbn\t\t= {978-1-4503-3692-5},\n  keywords\t= {location-based recommendations, virtual environments},\n  location\t= {Vienna, Austria},\n  numpages\t= {4},\n  pages\t\t= {245--248},\n  publisher\t= {ACM},\n  series\t= {RecSys '15},\n  title\t\t= {Are Real-World Place Recommender Algorithms Useful in\n\t\t  Virtual World Environments?},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Parra-Marinho-Trattner-Recsys2015-VirtualWorlds-LBSN-preprint.pdf},\n  year\t\t= {2015},\n  bdsk-url-1\t= {http://doi.acm.org/10.1145/2792838.2799674},\n  bdsk-url-2\t= {http://dx.doi.org/10.1145/2792838.2799674}\n}\n\n
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\n \n\n \n \n \n \n \n \n Identifying Relevant Messages in a Twitter-based Citizen Channel for Natural Disaster Situations.\n \n \n \n \n\n\n \n Cobo, A.; Parra, D.; and Navón, J.\n\n\n \n\n\n\n In Proceedings of the 24th International Conference on World Wide Web, of WWW '15 Companion, pages 1189–1194, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee\n \n\n\n\n
\n\n\n\n \n \n \"IdentifyingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  cobo:2015:irm:2740908.2741719,\n  acmid\t\t= {2741719},\n  address\t= {Republic and Canton of Geneva, Switzerland},\n  author\t= {Cobo, Alfredo and Parra, Denis and Nav\\'{o}n, Jaime},\n  booktitle\t= {Proceedings of the 24th International Conference on World\n\t\t  Wide Web},\n  date-added\t= {2015-12-30 15:21:21 +0000},\n  date-modified\t= {2015-12-30 15:24:32 +0000},\n  doi\t\t= {10.1145/2740908.2741719},\n  isbn\t\t= {978-1-4503-3473-0},\n  keywords\t= {class imbalance, machine learning, natural disaster,\n\t\t  twitter},\n  location\t= {Florence, Italy},\n  numpages\t= {6},\n  pages\t\t= {1189--1194},\n  publisher\t= {International World Wide Web Conferences Steering\n\t\t  Committee},\n  series\t= {WWW '15 Companion},\n  title\t\t= {Identifying Relevant Messages in a Twitter-based Citizen\n\t\t  Channel for Natural Disaster Situations},\n  year\t\t= {2015},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Cobo20151503.05784v1.pdf},\n  bdsk-url-1\t= {http://dx.doi.org/10.1145/2740908.2741719}\n}\n\n
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\n \n\n \n \n \n \n \n \n Twitter in academic events: A study of temporal usage, communication, sentimental and topical patterns in 16 Computer Science conferences.\n \n \n \n \n\n\n \n Parra, D.; Trattner, C.; Gómez, D.; Matı́as Hurtado; Wen, X.; and Lin, Y.\n\n\n \n\n\n\n Computer Communications , 73, Part B: 301–314. 2015.\n Online Social Networks \n\n\n\n
\n\n\n\n \n \n \"TwitterPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@Article{\t  comcomparra2015,\n  author\t= {Denis Parra and Christoph Trattner and Diego G{\\'o}mez and\n\t\t  Mat{\\'\\i}as Hurtado and Xidao Wen and Yu-Ru Lin},\n  doi\t\t= {10.1016/j.comcom.2015.07.001},\n  issn\t\t= "0140-3664",\n  journal\t= "Computer Communications ",\n  volume\t= "73, Part B",\n  pages\t\t= "301--314",\n  year\t\t= {2015},\n  note\t\t= "Online Social Networks ",\n  keywords\t= {Sentiment analysis},\n  title\t\t= {Twitter in academic events: A study of temporal usage,\n\t\t  communication, sentimental and topical patterns in 16\n\t\t  Computer Science conferences},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/preprint-TwitterConferences__ComCom.pdf},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/preprint-TwitterConferences__ComCom.pdf},\n  bdsk-url-2\t= {http://dx.doi.org/10.1016/j.comcom.2015.07.001}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA.\n \n \n \n \n\n\n \n Bonet, B.; and Koenig, S.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2015,\n  editor\t= {Blai Bonet and Sven Koenig},\n  title\t\t= {Proceedings of the Twenty-Ninth {AAAI} Conference on\n\t\t  Artificial Intelligence, January 25-30, 2015, Austin,\n\t\t  Texas, {USA}},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2015},\n  url\t\t= {http://www.aaai.org/Library/AAAI/aaai15contents.php},\n  isbn\t\t= {978-1-57735-698-1},\n  timestamp\t= {Sun, 12 Apr 2015 12:16:43 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2015},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Reusing Previously Found A* Paths for Fast Goal-Directed Navigation in Dynamic Terrain.\n \n \n \n \n\n\n \n Hernández, C.; Asín, R.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA., pages 1158–1164, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"ReusingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/hernandezab15,\n  author\t= {Carlos Hern{\\'{a}}ndez and Roberto As{\\'{\\i}}n and Jorge\n\t\t  A. Baier},\n  title\t\t= {Reusing Previously Found A* Paths for Fast Goal-Directed\n\t\t  Navigation in Dynamic Terrain},\n  booktitle\t= {Proceedings of the Twenty-Ninth {AAAI} Conference on\n\t\t  Artificial Intelligence, January 25-30, 2015, Austin,\n\t\t  Texas, {USA.}},\n  pages\t\t= {1158--1164},\n  year\t\t= {2015},\n  crossref\t= {DBLP:conf/aaai/2015},\n  url\t\t= {http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/10053},\n  timestamp\t= {Sun, 12 Apr 2015 12:16:43 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/HernandezAB15},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015.\n \n \n \n \n\n\n \n Yang, Q.; and Wooldridge, M.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2015,\n  editor\t= {Qiang Yang and Michael Wooldridge},\n  title\t\t= {Proceedings of the Twenty-Fourth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2015, Buenos\n\t\t  Aires, Argentina, July 25-31, 2015},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2015},\n  url\t\t= {http://ijcai.org/proceedings/2015},\n  isbn\t\t= {978-1-57735-738-4},\n  timestamp\t= {Wed, 20 Jul 2016 15:18:06 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2015},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Polynomial-Time Reformulations of LTL Temporally Extended Goals into Final-State Goals.\n \n \n \n \n\n\n \n Torres, J.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 1696–1703, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Polynomial-TimePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/torresb15,\n  author\t= {Jorge Torres and Jorge A. Baier},\n  title\t\t= {Polynomial-Time Reformulations of {LTL} Temporally\n\t\t  Extended Goals into Final-State Goals},\n  booktitle\t= {Proceedings of the Twenty-Fourth International Joint\n\t\t  Conference on Artificial Intelligence, {IJCAI} 2015, Buenos\n\t\t  Aires, Argentina, July 25-31, 2015},\n  pages\t\t= {1696--1703},\n  year\t\t= {2015},\n  crossref\t= {DBLP:conf/ijcai/2015},\n  url\t\t= {http://ijcai.org/Abstract/15/242},\n  timestamp\t= {Wed, 20 Jul 2016 15:18:06 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/TorresB15},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments.\n \n \n \n\n\n \n Ruz, C.; Pieringer, C.; Peralta, B.; Lillo, I.; Espinace, P.; Gonzalez, R.; Wendt, B.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In WACV, pages 1–8, 2015. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/wacv/ruzpplegwms15,\n  author\t= {Cristian Ruz and Christian Pieringer and Billy Peralta and\n\t\t  Ivan Lillo and Pablo Espinace and R. Gonzalez and B. Wendt\n\t\t  and Domingo Mery and Alvaro Soto},\n  title\t\t= {Visual Recognition to Access and Analyze People Density\n\t\t  and Flow Patterns in Indoor Environments},\n  booktitle\t= {{WACV}},\n  pages\t\t= {1--8},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {2015}\n}\n\n
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\n \n\n \n \n \n \n \n \n Reusing cost-minimal paths for goal-directed navigation in partially known terrains.\n \n \n \n \n\n\n \n Hernández, C.; Uras, T.; Koenig, S.; Baier, J. A.; Sun, X.; and Meseguer, P.\n\n\n \n\n\n\n Autonomous Agents and Multi-Agent Systems, 29(5): 850–895. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"ReusingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/aamas/hernandezukbsm15,\n  author\t= {Carlos Hern{\\'{a}}ndez and Tansel Uras and Sven Koenig and\n\t\t  Jorge A. Baier and Xiaoxun Sun and Pedro Meseguer},\n  title\t\t= {Reusing cost-minimal paths for goal-directed navigation in\n\t\t  partially known terrains},\n  journal\t= {Autonomous Agents and Multi-Agent Systems},\n  volume\t= {29},\n  number\t= {5},\n  pages\t\t= {850--895},\n  year\t\t= {2015},\n  url\t\t= {https://doi.org/10.1007/s10458-014-9266-0},\n  doi\t\t= {10.1007/s10458-014-9266-0},\n  timestamp\t= {Sat, 20 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/aamas/HernandezUKBSM15},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Incorporating weights into real-time heuristic search.\n \n \n \n \n\n\n \n Rivera, N.; Baier, J. A.; and Hernández, C.\n\n\n \n\n\n\n Artif. Intell., 225: 1–23. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"IncorporatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/ai/riverabh15,\n  author\t= {Nicolas Rivera and Jorge A. Baier and Carlos\n\t\t  Hern{\\'{a}}ndez},\n  title\t\t= {Incorporating weights into real-time heuristic search},\n  journal\t= {Artif. Intell.},\n  volume\t= {225},\n  pages\t\t= {1--23},\n  year\t\t= {2015},\n  url\t\t= {https://doi.org/10.1016/j.artint.2015.03.008},\n  doi\t\t= {10.1016/j.artint.2015.03.008},\n  timestamp\t= {Sat, 27 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/ai/RiveraBH15},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Management of service compositionbased on self-controlled components.\n \n \n \n\n\n \n Aubonnet, T.; Henrio, L.; Kessal, S.; Kulankhina, O.; Lemoine, F.; Madelaine, E.; Ruz, C.; and Simoni, N.\n\n\n \n\n\n\n J. Internet Services and Applications, 6(1): 15:1–15:17. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/jisa/aubonnethkklmrs15,\n  author\t= {Tatiana Aubonnet and Ludovic Henrio and Soumia Kessal and\n\t\t  Oleksandra Kulankhina and Fr{\\'{e}}d{\\'{e}}ric Lemoine and\n\t\t  Eric Madelaine and Cristian Ruz and No{\\"{e}}mie Simoni},\n  title\t\t= {Management of service compositionbased on self-controlled\n\t\t  components},\n  journal\t= {J. Internet Services and Applications},\n  volume\t= {6},\n  number\t= {1},\n  pages\t\t= {15:1--15:17},\n  year\t\t= {2015}\n}\n\n
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\n \n\n \n \n \n \n \n Learning Shared, Discriminative, and Compact Representations for Visual Recognition.\n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n IEEE Trans. Pattern Anal. Mach. Intell., 37(11): 2218–2231. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/pami/lobelvs15,\n  author\t= {Hans Lobel and Ren{\\'{e}} Vidal and Alvaro Soto},\n  title\t\t= {Learning Shared, Discriminative, and Compact\n\t\t  Representations for Visual Recognition},\n  journal\t= {{IEEE} Trans. Pattern Anal. Mach. Intell.},\n  volume\t= {37},\n  number\t= {11},\n  pages\t\t= {2218--2231},\n  year\t\t= {2015}\n}\n\n
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\n \n\n \n \n \n \n \n Programming distributed and adaptable autonomous components - the GCM/ProActive framework.\n \n \n \n\n\n \n Baude, F.; Henrio, L.; and Ruz, C.\n\n\n \n\n\n\n Softw., Pract. Exper., 45(9): 1189–1227. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/spe/baudehr15,\n  author\t= {Fran{\\c{c}}oise Baude and Ludovic Henrio and Cristian Ruz},\n  title\t\t= {Programming distributed and adaptable autonomous\n\t\t  components - the GCM/ProActive framework},\n  journal\t= {Softw., Pract. Exper.},\n  volume\t= {45},\n  number\t= {9},\n  pages\t\t= {1189--1227},\n  year\t\t= {2015}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fast Algorithm for Catching a Prey Quickly in Known and Partially Known Game Maps.\n \n \n \n \n\n\n \n Baier, J. A.; Botea, A.; Harabor, D.; and Hernández, C.\n\n\n \n\n\n\n IEEE Trans. Comput. Intellig. and AI in Games, 7(2): 193–199. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/tciaig/baierbhh15,\n  author\t= {Jorge A. Baier and Adi Botea and Daniel Harabor and Carlos\n\t\t  Hern{\\'{a}}ndez},\n  title\t\t= {Fast Algorithm for Catching a Prey Quickly in Known and\n\t\t  Partially Known Game Maps},\n  journal\t= {{IEEE} Trans. Comput. Intellig. and {AI} in Games},\n  volume\t= {7},\n  number\t= {2},\n  pages\t\t= {193--199},\n  year\t\t= {2015},\n  url\t\t= {https://doi.org/10.1109/TCIAIG.2014.2337889},\n  doi\t\t= {10.1109/TCIAIG.2014.2337889},\n  timestamp\t= {Fri, 26 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/tciaig/BaierBHH15},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Language, Twitter and Academic Conferences.\n \n \n \n \n\n\n \n Gavilanes, R. O. G.; Gomez, D.; Parra Santander, D.; Trattner, C.; Kaltenbrunner, A.; and Graells, E.\n\n\n \n\n\n\n In Proceedings of the 26th ACM Conference on Hypertext & Social Media, of HT '15, pages 159–163, New York, NY, USA, 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"Language,Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  gavilanes:2015:lta:2700171.2791059,\n  acmid\t\t= {2791059},\n  address\t= {New York, NY, USA},\n  author\t= {Gavilanes, Ruth Olimpia G. and Gomez, Diego and Parra\n\t\t  Santander, Denis and Trattner, Christoph and Kaltenbrunner,\n\t\t  Andreas and Graells, Eduardo},\n  booktitle\t= {Proceedings of the 26th ACM Conference on Hypertext\n\t\t  \\&\\#38; Social Media},\n  date-added\t= {2015-12-30 15:01:12 +0000},\n  date-modified\t= {2015-12-30 15:08:02 +0000},\n  doi\t\t= {10.1145/2700171.2791059},\n  isbn\t\t= {978-1-4503-3395-5},\n  keywords\t= {dynamics, interactions, sociolinguistics, twitter},\n  location\t= {Guzelyurt, Northern Cyprus},\n  numpages\t= {5},\n  pages\t\t= {159--163},\n  publisher\t= {ACM},\n  series\t= {HT '15},\n  title\t\t= {Language, Twitter and Academic Conferences},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Language-Twitter-HT2015.pdf},\n  year\t\t= {2015},\n  bdsk-url-1\t= {http://doi.acm.org/10.1145/2700171.2791059},\n  bdsk-url-2\t= {http://dx.doi.org/10.1145/2700171.2791059}\n}\n\n
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\n \n\n \n \n \n \n \n \n Towards Improving Top-N Recommendation by Generalization of SLIM.\n \n \n \n \n\n\n \n Larrain, S.; Parra, D.; and Soto, A.\n\n\n \n\n\n\n In Poster Proceedings of ACM RecSys 2015, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{\t  gslim,\n  author\t= {Larrain, Santiago and Parra, Denis and Soto, Alvaro},\n  booktitle\t= {Poster Proceedings of ACM RecSys 2015},\n  date-added\t= {2015-12-02 04:37:53 +0000},\n  title\t\t= {Towards Improving Top-N Recommendation by Generalization\n\t\t  of SLIM},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Improving_SLIM_Recommendation.pdf},\n  year\t\t= {2015},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/Improving_SLIM_Recommendation.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging.\n \n \n \n \n\n\n \n Larrain, S.; Trattner, C.; Parra, D.; Graells-Garrido, E.; and Nørvåg, K.\n\n\n \n\n\n\n In Proceedings of the 9th ACM Conference on Recommender Systems, of RecSys '15, pages 269–272, New York, NY, USA, 2015. ACM\n \n\n\n\n
\n\n\n\n \n \n \"GoodPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  larrain:2015:gtb:2792838.2799682,\n  acmid\t\t= {2799682},\n  address\t= {New York, NY, USA},\n  author\t= {Larrain, Santiago and Trattner, Christoph and Parra, Denis\n\t\t  and Graells-Garrido, Eduardo and N{\\o}rv{\\aa}g, Kjetil},\n  booktitle\t= {Proceedings of the 9th ACM Conference on Recommender\n\t\t  Systems},\n  date-added\t= {2015-12-30 15:02:05 +0000},\n  date-modified\t= {2015-12-30 15:05:45 +0000},\n  doi\t\t= {10.1145/2792838.2799682},\n  isbn\t\t= {978-1-4503-3692-5},\n  keywords\t= {collaborative filtering, social tagging, time-aware\n\t\t  recommendations},\n  location\t= {Vienna, Austria},\n  numpages\t= {4},\n  pages\t\t= {269--272},\n  publisher\t= {ACM},\n  series\t= {RecSys '15},\n  title\t\t= {Good Times Bad Times: A Study on Recency Effects in\n\t\t  Collaborative Filtering for Social Tagging},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Larrain_Parra_etal_RecSys_2015_Time_in_TagBased_RecSys.pdf},\n  year\t\t= {2015},\n  bdsk-url-1\t= {http://doi.acm.org/10.1145/2792838.2799682},\n  bdsk-url-2\t= {http://dx.doi.org/10.1145/2792838.2799682}\n}\n\n
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\n \n\n \n \n \n \n \n \n Learning Shared, Discriminative, and Compact Representations for Visual Recognition.\n \n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11). 2015.\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  lobel:etal:2015,\n  author\t= {H. Lobel and R. Vidal and A. Soto},\n  title\t\t= {Learning Shared, Discriminative, and Compact\n\t\t  Representations for Visual Recognition},\n  journal\t= {{IEEE} Transactions on Pattern Analysis and Machine\n\t\t  Intelligence},\n  volume\t= {37},\n  number\t= {11},\n  year\t\t= {2015},\n  abstract\t= {Dictionary-based and part-based methods are among the most\n\t\t  popular approaches to visual recognition. In both methods,\n\t\t  a mid-level representation is built on top of low-level\n\t\t  image descriptors and high-level classifiers are trained on\n\t\t  top of the mid-level representation. While earlier methods\n\t\t  built the mid-level representation without supervision,\n\t\t  there is currently great interest in learning both\n\t\t  representations jointly to make the mid-level\n\t\t  representation more discriminative. In this work we propose\n\t\t  a new approach to visual recognition that jointly learns a\n\t\t  shared, discriminative, and compact mid-level\n\t\t  representation and a compact high-level representation. By\n\t\t  using a structured output learning framework, our approach\n\t\t  directly handles the multiclass case at both levels of\n\t\t  abstraction. Moreover, by using a group-sparse prior in the\n\t\t  structured output learning framework, our approach\n\t\t  encourages sharing of visual words and thus reduces the\n\t\t  number of words used to represent each class. We test our\n\t\t  proposed method on several popular benchmarks. Our results\n\t\t  show that, by jointly learning mid- and high-level\n\t\t  representations, and fostering the sharing of\n\t\t  discriminative visual words among target classes, we are\n\t\t  able to achieve state-of-the-art recognition performance\n\t\t  using far less visual words than previous approaches.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Hans-FINAL-PAMI-2015.pdf}\n}\n\n
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\n Dictionary-based and part-based methods are among the most popular approaches to visual recognition. In both methods, a mid-level representation is built on top of low-level image descriptors and high-level classifiers are trained on top of the mid-level representation. While earlier methods built the mid-level representation without supervision, there is currently great interest in learning both representations jointly to make the mid-level representation more discriminative. In this work we propose a new approach to visual recognition that jointly learns a shared, discriminative, and compact mid-level representation and a compact high-level representation. By using a structured output learning framework, our approach directly handles the multiclass case at both levels of abstraction. Moreover, by using a group-sparse prior in the structured output learning framework, our approach encourages sharing of visual words and thus reduces the number of words used to represent each class. We test our proposed method on several popular benchmarks. Our results show that, by jointly learning mid- and high-level representations, and fostering the sharing of discriminative visual words among target classes, we are able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.\n
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\n \n\n \n \n \n \n \n \n GDXray: The Database of X-ray Images for Nondestructive Testing.\n \n \n \n \n\n\n \n Mery, D.; Riffo, V.; Zscherpel, U.; Mondragón, G.; Lillo, I.; Zuccar, I.; Lobel, H.; and Carrasco, M.\n\n\n \n\n\n\n Journal of Nondestructive Evaluation, 34(4). November 2015.\n \n\n\n\n
\n\n\n\n \n \n \"GDXray:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mery2015,\n  doi\t\t= {10.1007/s10921-015-0315-7},\n  url\t\t= {https://doi.org/10.1007/s10921-015-0315-7},\n  year\t\t= {2015},\n  month\t\t= nov,\n  publisher\t= {Springer Science and Business Media {LLC}},\n  volume\t= {34},\n  number\t= {4},\n  author\t= {Domingo Mery and Vladimir Riffo and Uwe Zscherpel and\n\t\t  German Mondrag{\\'{o}}n and Iv{\\'{a}}n Lillo and Irene\n\t\t  Zuccar and Hans Lobel and Miguel Carrasco},\n  title\t\t= {{GDXray}: The Database of X-ray Images for Nondestructive\n\t\t  Testing},\n  journal\t= {Journal of Nondestructive Evaluation}\n}\n\n
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\n \n\n \n \n \n \n \n \n Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments.\n \n \n \n \n\n\n \n C. Ruz, C. P.; B. Peralta, I. L.; P. Espinace, R. G.; and B. Wendt, D. M.\n\n\n \n\n\n\n In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"VisualPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  ruz:etal:2015,\n  author\t= {C. Ruz, C. Pieringer, B. Peralta, I. Lillo, P. Espinace,\n\t\t  R. Gonzalez, B. Wendt, D. Mery, A. Soto},\n  title\t\t= {Visual Recognition to Access and Analyze People Density\n\t\t  and Flow Patterns in Indoor Environments},\n  booktitle\t= {IEEE Winter Conference on Applications of Computer Vision\n\t\t  (WACV)},\n  year\t\t= {2015},\n  abstract\t= {This work describes our experience developing a system to\n\t\t  access density and flow of people in large indoor spaces\n\t\t  using a network of RGB cameras. The proposed system is\n\t\t  based on a set of overlapped and calibrated cameras. This\n\t\t  facilitates the use of geometric constraints that help to\n\t\t  reduce visual ambiguities. These constraints are combined\n\t\t  with classifiers based on visual appearance to produce an\n\t\t  efficient and robust method to detect and track humans. In\n\t\t  this work, we argue that flow and density of people are low\n\t\t  level measurements that need to be complemented with\n\t\t  suitable analytic tools to bridge semantic gaps and become\n\t\t  useful information for a target application. Consequently,\n\t\t  we also propose a set of analytic tools that help a human\n\t\t  user to effectively take advantage of the measurements\n\t\t  provided by the system. Finally, we report results that\n\t\t  demonstrate the relevance of the proposed ideas.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/WACV-2015-VersionFinal.pdf}\n}\n\n
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\n This work describes our experience developing a system to access density and flow of people in large indoor spaces using a network of RGB cameras. The proposed system is based on a set of overlapped and calibrated cameras. This facilitates the use of geometric constraints that help to reduce visual ambiguities. These constraints are combined with classifiers based on visual appearance to produce an efficient and robust method to detect and track humans. In this work, we argue that flow and density of people are low level measurements that need to be complemented with suitable analytic tools to bridge semantic gaps and become useful information for a target application. Consequently, we also propose a set of analytic tools that help a human user to effectively take advantage of the measurements provided by the system. Finally, we report results that demonstrate the relevance of the proposed ideas.\n
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\n \n\n \n \n \n \n \n \n User-controllable personalization: A case study with SetFusion.\n \n \n \n \n\n\n \n Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n International Journal of Human Computer Studies, 78: 43-67. June 2015.\n \n\n\n\n
\n\n\n\n \n \n \"User-controllablePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  setfusion2015,\n  author\t= {Parra, Denis and Brusilovsky, Peter},\n  date-added\t= {2015-11-17 04:37:53 +0000},\n  date-modified\t= {2015-11-17 04:40:23 +0000},\n  doi\t\t= {10.1016/j.ijhcs.2015.01.007},\n  journal\t= {International Journal of Human Computer Studies},\n  month\t\t= {June},\n  pages\t\t= {43-67},\n  title\t\t= {User-controllable personalization: A case study with\n\t\t  SetFusion},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/SetFusion-IJHCS-pre-print.pdf},\n  volume\t= {78},\n  year\t\t= {2015}\n}\n\n
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\n  \n 2014\n \n \n (22)\n \n \n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014.\n \n \n \n \n\n\n \n Chien, S. A.; Do, M. B.; Fern, A.; and Ruml, W.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2014,\n  editor\t= {Steve A. Chien and Minh Binh Do and Alan Fern and Wheeler\n\t\t  Ruml},\n  title\t\t= {Proceedings of the Twenty-Fourth International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2014,\n\t\t  Portsmouth, New Hampshire, USA, June 21-26, 2014},\n  publisher\t= {{AAAI}},\n  year\t\t= {2014},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/icaps14contents.php},\n  isbn\t\t= {978-1-57735-660-8},\n  timestamp\t= {Thu, 19 Nov 2015 08:52:28 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2014},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Making A* Run Faster than D*-Lite for Path-Planning in Partially Known Terrain.\n \n \n \n \n\n\n \n Hernández, C.; Baier, J. A.; and Asín, R.\n\n\n \n\n\n\n In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS 2014, Portsmouth, New Hampshire, USA, June 21-26, 2014, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"MakingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/hernandezba14,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier and Roberto\n\t\t  As{\\'{\\i}}n},\n  title\t\t= {Making A* Run Faster than D*-Lite for Path-Planning in\n\t\t  Partially Known Terrain},\n  booktitle\t= {Proceedings of the Twenty-Fourth International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2014,\n\t\t  Portsmouth, New Hampshire, USA, June 21-26, 2014},\n  year\t\t= {2014},\n  crossref\t= {DBLP:conf/aips/2014},\n  url\t\t= {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7944},\n  timestamp\t= {Thu, 19 Nov 2015 08:52:28 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/HernandezBA14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Advances in Artificial Intelligence - IBERAMIA 2014 - 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, Proceedings.\n \n \n \n \n\n\n \n Bazzan, A. L. C.; and Pichara, K.,\n editors.\n \n\n\n \n\n\n\n Volume 8864, of Lecture Notes in Computer Science.Springer. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"AdvancesPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/iberamia/2014,\n  editor\t= {Ana L. C. Bazzan and Karim Pichara},\n  title\t\t= {Advances in Artificial Intelligence - {IBERAMIA} 2014 -\n\t\t  14th Ibero-American Conference on AI, Santiago de Chile,\n\t\t  Chile, November 24-27, 2014, Proceedings},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {8864},\n  publisher\t= {Springer},\n  year\t\t= {2014},\n  url\t\t= {https://doi.org/10.1007/978-3-319-12027-0},\n  doi\t\t= {10.1007/978-3-319-12027-0},\n  isbn\t\t= {978-3-319-12026-3},\n  timestamp\t= {Sun, 21 May 2017 00:22:36 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/iberamia/2014},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Real-Time Pathfinding in Unknown Terrain via Reconnection with an Ideal Tree.\n \n \n \n \n\n\n \n Rivera, N.; Illanes, L.; and Baier, J. A.\n\n\n \n\n\n\n In Advances in Artificial Intelligence - IBERAMIA 2014 - 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, Proceedings, pages 69–80, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"Real-TimePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 16 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/iberamia/riveraib14,\n  author\t= {Nicolas Rivera and Leon Illanes and Jorge A. Baier},\n  title\t\t= {Real-Time Pathfinding in Unknown Terrain via Reconnection\n\t\t  with an Ideal Tree},\n  booktitle\t= {Advances in Artificial Intelligence - {IBERAMIA} 2014 -\n\t\t  14th Ibero-American Conference on AI, Santiago de Chile,\n\t\t  Chile, November 24-27, 2014, Proceedings},\n  pages\t\t= {69--80},\n  year\t\t= {2014},\n  crossref\t= {DBLP:conf/iberamia/2014},\n  url\t\t= {https://doi.org/10.1007/978-3-319-12027-0\\_6},\n  doi\t\t= {10.1007/978-3-319-12027-0\\_6},\n  timestamp\t= {Sun, 21 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/iberamia/RiveraIB14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, Vienna, Austria, July 20-24, 2014.\n \n \n \n \n\n\n \n Baral, C.; Giacomo, G. D.; and Eiter, T.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"PrinciplesPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/kr/2014,\n  editor\t= {Chitta Baral and Giuseppe De Giacomo and Thomas Eiter},\n  title\t\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Fourteenth International Conference,\n\t\t  {KR} 2014, Vienna, Austria, July 20-24, 2014},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2014},\n  url\t\t= {http://www.aaai.org/Library/KR/kr14contents.php},\n  isbn\t\t= {978-1-57735-657-8},\n  timestamp\t= {Thu, 31 Jul 2014 10:24:17 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/2014},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Diagnostic Problem Solving via Planning with Ontic and Epistemic Goals.\n \n \n \n \n\n\n \n Baier, J. A.; Mombourquette, B.; and McIlraith, S. A.\n\n\n \n\n\n\n In Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, Vienna, Austria, July 20-24, 2014, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"DiagnosticPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 72 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/kr/baiermm14,\n  author\t= {Jorge A. Baier and Brent Mombourquette and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {Diagnostic Problem Solving via Planning with Ontic and\n\t\t  Epistemic Goals},\n  booktitle\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Fourteenth International Conference,\n\t\t  {KR} 2014, Vienna, Austria, July 20-24, 2014},\n  year\t\t= {2014},\n  crossref\t= {DBLP:conf/kr/2014},\n  url\t\t= {http://www.aaai.org/ocs/index.php/KR/KR14/paper/view/8019},\n  timestamp\t= {Thu, 31 Jul 2014 10:24:17 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/BaierMM14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Seventh Annual Symposium on Combinatorial Search, SOCS 2014, Prague, Czech Republic, 15-17 August 2014.\n \n \n \n \n\n\n \n Edelkamp, S.; and Barták, R.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2014.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2014,\n  editor\t= {Stefan Edelkamp and Roman Bart{\\'{a}}k},\n  title\t\t= {Proceedings of the Seventh Annual Symposium on\n\t\t  Combinatorial Search, {SOCS} 2014, Prague, Czech Republic,\n\t\t  15-17 August 2014},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2014},\n  url\t\t= {http://www.aaai.org/Library/SOCS/socs14contents.php},\n  isbn\t\t= {978-1-57735-676-9},\n  timestamp\t= {Thu, 31 Jul 2014 13:17:58 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2014},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Time-Bounded Best-First Search.\n \n \n \n \n\n\n \n Hernández, C.; Asín, R.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Seventh Annual Symposium on Combinatorial Search, SOCS 2014, Prague, Czech Republic, 15-17 August 2014., 2014. \n \n\n\n\n
\n\n\n\n \n \n \"Time-BoundedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hernandezab14,\n  author\t= {Carlos Hern{\\'{a}}ndez and Roberto As{\\'{\\i}}n and Jorge\n\t\t  A. Baier},\n  title\t\t= {Time-Bounded Best-First Search},\n  booktitle\t= {Proceedings of the Seventh Annual Symposium on\n\t\t  Combinatorial Search, {SOCS} 2014, Prague, Czech Republic,\n\t\t  15-17 August 2014.},\n  year\t\t= {2014},\n  crossref\t= {DBLP:conf/socs/2014},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS14/paper/view/8932},\n  timestamp\t= {Thu, 31 Jul 2014 13:17:58 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HernandezAB14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Toward a Search Strategy for Anytime Search in Linear Space Using Depth-First Branch and Bound.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Seventh Annual Symposium on Combinatorial Search, SOCS 2014, Prague, Czech Republic, 15-17 August 2014., 2014. \n \n\n\n\n
\n\n\n\n \n \n \"TowardPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 20 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hernandezb14,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Toward a Search Strategy for Anytime Search in Linear\n\t\t  Space Using Depth-First Branch and Bound},\n  booktitle\t= {Proceedings of the Seventh Annual Symposium on\n\t\t  Combinatorial Search, {SOCS} 2014, Prague, Czech Republic,\n\t\t  15-17 August 2014.},\n  year\t\t= {2014},\n  crossref\t= {DBLP:conf/socs/2014},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS14/paper/view/8935},\n  timestamp\t= {Thu, 31 Jul 2014 13:17:58 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HernandezB14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Avoiding and Escaping Depressions in Real-Time Heuristic Search.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n CoRR, abs/1401.5854. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"AvoidingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/corr/hernandezb14,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Avoiding and Escaping Depressions in Real-Time Heuristic\n\t\t  Search},\n  journal\t= {CoRR},\n  volume\t= {abs/1401.5854},\n  year\t\t= {2014},\n  url\t\t= {http://arxiv.org/abs/1401.5854},\n  archiveprefix\t= {arXiv},\n  eprint\t= {1401.5854},\n  timestamp\t= {Mon, 13 Aug 2018 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/corr/HernandezB14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Reconnection with the Ideal Tree: A New Approach to Real-Time Search.\n \n \n \n \n\n\n \n Rivera, N.; Illanes, L.; Baier, J. A.; and Hernández, C.\n\n\n \n\n\n\n J. Artif. Intell. Res., 50: 235–264. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"ReconnectionPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/jair/riveraibh14,\n  author\t= {Nicolas Rivera and Leon Illanes and Jorge A. Baier and\n\t\t  Carlos Hern{\\'{a}}ndez},\n  title\t\t= {Reconnection with the Ideal Tree: {A} New Approach to\n\t\t  Real-Time Search},\n  journal\t= {J. Artif. Intell. Res.},\n  volume\t= {50},\n  pages\t\t= {235--264},\n  year\t\t= {2014},\n  url\t\t= {https://doi.org/10.1613/jair.4292},\n  doi\t\t= {10.1613/jair.4292},\n  timestamp\t= {Wed, 21 Jun 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/jair/RiveraIBH14},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Study of Mobile Information Exploration with Multi-Touch Interactions.\n \n \n \n \n\n\n \n Han, S.; Hsiao, I.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 2014 International Social Computing, Behavioral Modeling and Prediction Conference, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  han2014study,\n  author\t= {Han, Shuguang and Hsiao, I-Han and Parra, Denis},\n  booktitle\t= {Proceedings of the 2014 International Social Computing,\n\t\t  Behavioral Modeling and Prediction Conference},\n  title\t\t= {A Study of Mobile Information Exploration with Multi-Touch\n\t\t  Interactions},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/SBP_2014.pdf},\n  year\t\t= {2014},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/SBP_2014.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Recommending Items in Social Tagging Systems Using Tag and Time Information.\n \n \n \n\n\n \n Lacic, E.; Kowald, D.; Seitlinger, P.; Trattner, C.; and Parra, D.\n\n\n \n\n\n\n In 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lacic2014recommending,\n  author\t= {Lacic, Emanuel and Kowald, Dominik and Seitlinger, Paul\n\t\t  and Trattner, Christoph and Parra, Denis},\n  journal\t= {arXiv preprint arXiv:1406.7727},\n  title\t\t= {Recommending Items in Social Tagging Systems Using Tag and\n\t\t  Time Information},\n  year\t\t= {2014}\n}\n\n
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\n \n\n \n \n \n \n \n Utilizing Online Social Network and Location-Based Data to Recommend Items in an Online Marketplace.\n \n \n \n\n\n \n Lacic, E.; Kowald, D.; Eberhard, L.; Trattner, C.; Parra, D.; and Marinho, L.\n\n\n \n\n\n\n In 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lacic2014utilizing,\n  author\t= {Lacic, Emanuel and Kowald, Dominik and Eberhard, Lukas and\n\t\t  Trattner, Christoph and Parra, Denis and Marinho, Leandro},\n  journal\t= {arXiv preprint arXiv:1405.1837},\n  title\t\t= {Utilizing Online Social Network and Location-Based Data to\n\t\t  Recommend Items in an Online Marketplace},\n  year\t\t= {2014}\n}\n\n
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\n \n\n \n \n \n \n \n \n Towards a Scalable Social Recommender Engine for Online Marketplaces: The Case of Apache Solr.\n \n \n \n \n\n\n \n Lacic, E.; Kowald, D.; Parra, D.; Kahr, M.; and Trattner, C.\n\n\n \n\n\n\n In Proceedings of the 23rd International Conference on World Wide Web, of WWW '14 Companion, pages 817–822, Republic and Canton of Geneva, Switzerland, 2014. International World Wide Web Conferences Steering Committee\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  lacic:2014:tss:2567948.2579245,\n  author\t= {Lacic, Emanuel and Kowald, Dominik and Parra, Denis and\n\t\t  Kahr, Martin and Trattner, Christoph},\n  title\t\t= {Towards a Scalable Social Recommender Engine for Online\n\t\t  Marketplaces: The Case of Apache Solr},\n  booktitle\t= {Proceedings of the 23rd International Conference on World\n\t\t  Wide Web},\n  series\t= {WWW '14 Companion},\n  year\t\t= {2014},\n  isbn\t\t= {978-1-4503-2745-9},\n  location\t= {Seoul, Korea},\n  pages\t\t= {817--822},\n  numpages\t= {6},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/p817-lacic.pdf},\n  doi\t\t= {10.1145/2567948.2579245},\n  acmid\t\t= {2579245},\n  publisher\t= {International World Wide Web Conferences Steering\n\t\t  Committee},\n  address\t= {Republic and Canton of Geneva, Switzerland},\n  keywords\t= {apache solr, online marketplaces, scalability, social\n\t\t  recommender engine}\n}\n\n
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\n \n\n \n \n \n \n \n \n Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities.\n \n \n \n \n\n\n \n Lillo, I.; Niebles, J.; and Soto, A.\n\n\n \n\n\n\n In CVPR, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"DiscriminativePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 33 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lillo:etal:2014,\n  author\t= {I. Lillo and JC. Niebles and A. Soto},\n  title\t\t= {Discriminative Hierarchical Modeling of Spatio-Temporally\n\t\t  Composable Human Activities},\n  booktitle\t= {{CVPR}},\n  year\t\t= {2014},\n  abstract\t= {This paper proposes a framework for recognizing complex\n\t\t  human activities in videos. Our method describes human\n\t\t  activities in a hierarchical discriminative model that\n\t\t  operates at three semantic levels. At the lower level, body\n\t\t  poses are encoded in a representative but discriminative\n\t\t  pose dictionary. At the intermediate level, encoded poses\n\t\t  span a space where simple human actions are composed. At\n\t\t  the highest level, our model captures temporal and spatial\n\t\t  compositions of actions into complex human activities. Our\n\t\t  human activity classifier simultaneously models which body\n\t\t  parts are relevant to the action of interest as well as\n\t\t  their appearance and composition using a discriminative\n\t\t  approach. By formulating model learning in a max-margin\n\t\t  framework, our approach achieves powerful multi-class\n\t\t  discrimination while providing useful annotations at the\n\t\t  intermediate semantic level. We show how our hierarchical\n\t\t  compositional model provides natural handling of\n\t\t  occlusions. To evaluate the effectiveness of our proposed\n\t\t  framework, we introduce a new dataset of composed human\n\t\t  activities. We provide empirical evidence that our method\n\t\t  achieves state-of-the-art activity classification\n\t\t  performance on several benchmark datasets.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/activities-CVPR-14.pdf}\n}\n\n
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\n This paper proposes a framework for recognizing complex human activities in videos. Our method describes human activities in a hierarchical discriminative model that operates at three semantic levels. At the lower level, body poses are encoded in a representative but discriminative pose dictionary. At the intermediate level, encoded poses span a space where simple human actions are composed. At the highest level, our model captures temporal and spatial compositions of actions into complex human activities. Our human activity classifier simultaneously models which body parts are relevant to the action of interest as well as their appearance and composition using a discriminative approach. By formulating model learning in a max-margin framework, our approach achieves powerful multi-class discrimination while providing useful annotations at the intermediate semantic level. We show how our hierarchical compositional model provides natural handling of occlusions. To evaluate the effectiveness of our proposed framework, we introduce a new dataset of composed human activities. We provide empirical evidence that our method achieves state-of-the-art activity classification performance on several benchmark datasets.\n
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\n \n\n \n \n \n \n \n \n See What You Want to See: Visual User-Driven Approach for Hybrid Recommendation.\n \n \n \n \n\n\n \n Parra, D.; Brusilovsky, P.; and Trattner, C.\n\n\n \n\n\n\n In Proceedings of the 19th international conference on Intelligent User Interfaces, pages 235–240, 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"SeePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  parra2014see,\n  author\t= {Parra, Denis and Brusilovsky, Peter and Trattner,\n\t\t  Christoph},\n  booktitle\t= {Proceedings of the 19th international conference on\n\t\t  Intelligent User Interfaces},\n  organization\t= {ACM},\n  pages\t\t= {235--240},\n  title\t\t= {See What You Want to See: Visual User-Driven Approach for\n\t\t  Hybrid Recommendation},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/IUI2014Setfusion.pdf},\n  year\t\t= {2014},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/IUI2014Setfusion.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Embedded local feature selection within mixture of experts.\n \n \n \n \n\n\n \n Peralta, B.; and Soto, A.\n\n\n \n\n\n\n Information Sciences, 269: 176-187. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"EmbeddedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  peralta:soto:2014,\n  author\t= {B. Peralta and A. Soto},\n  title\t\t= {Embedded local feature selection within mixture of\n\t\t  experts},\n  journal\t= {Information Sciences},\n  volume\t= {269},\n  pages\t\t= {176-187},\n  year\t\t= {2014},\n  abstract\t= {A useful strategy to deal with complex classification\n\t\t  scenarios is the divide and conquer approach. The mixture\n\t\t  of experts (MoE) technique makes use of this strategy by\n\t\t  jointly training a set of classifiers, or experts, that are\n\t\t  specialized in different regions of the input space. A\n\t\t  global model, or gate function, complements the experts by\n\t\t  learning a function that weighs their relevance in\n\t\t  different parts of the input space. Local feature selection\n\t\t  appears as an attractive alternative to improve the\n\t\t  specialization of experts and gate function, particularly,\n\t\t  in the case of high dimensional data. In general, subsets\n\t\t  of dimensions, or subspaces, are usually more appropriate\n\t\t  to classify instances located in different regions of the\n\t\t  input space. Accordingly, this work contributes with a\n\t\t  regularized variant of MoE that incorporates an embedded\n\t\t  process for local feature selection using\n\t\t  L1-regularization. Experiments using artificial and\n\t\t  real-world datasets provide evidence that the proposed\n\t\t  method improves the classical MoE technique, in terms of\n\t\t  accuracy and sparseness of the solution. Furthermore, our\n\t\t  results indicate that the advantages of the proposed\n\t\t  technique increase with the dimensionality of the data.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/RMoE.pdf}\n}\n\n
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\n A useful strategy to deal with complex classification scenarios is the divide and conquer approach. The mixture of experts (MoE) technique makes use of this strategy by jointly training a set of classifiers, or experts, that are specialized in different regions of the input space. A global model, or gate function, complements the experts by learning a function that weighs their relevance in different parts of the input space. Local feature selection appears as an attractive alternative to improve the specialization of experts and gate function, particularly, in the case of high dimensional data. In general, subsets of dimensions, or subspaces, are usually more appropriate to classify instances located in different regions of the input space. Accordingly, this work contributes with a regularized variant of MoE that incorporates an embedded process for local feature selection using L1-regularization. Experiments using artificial and real-world datasets provide evidence that the proposed method improves the classical MoE technique, in terms of accuracy and sparseness of the solution. Furthermore, our results indicate that the advantages of the proposed technique increase with the dimensionality of the data.\n
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\n \n\n \n \n \n \n \n \n Local Feature Selection Using Gaussian Process Regression.\n \n \n \n \n\n\n \n Pichara, K.; and Soto, A.\n\n\n \n\n\n\n Intelligent Data Analysis (IDA), 18(3). 2014.\n \n\n\n\n
\n\n\n\n \n \n \"LocalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 22 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  pichara:etal:2014,\n  author\t= {K. Pichara and A. Soto},\n  title\t\t= {Local Feature Selection Using Gaussian Process\n\t\t  Regression},\n  journal\t= {Intelligent Data Analysis (IDA)},\n  volume\t= {18},\n  number\t= {3},\n  year\t\t= {2014},\n  abstract\t= {Most feature selection algorithms determine a global\n\t\t  subset of features, where all data instances are projected\n\t\t  in order to improve classification accuracy. An attractive\n\t\t  alternative solution is to adaptively find a local subset\n\t\t  of features for each data instance, such that, the\n\t\t  classification of each instance is performed according to\n\t\t  its own selective subspace. This paper presents a novel\n\t\t  application of Gaussian Processes that improves\n\t\t  classification performance by learning discriminative local\n\t\t  subsets of features for each instance in a dataset.\n\t\t  Gaussian Processes are used to build for each available\n\t\t  feature a function that estimates the discriminative power\n\t\t  of the feature over all the input space. Using these\n\t\t  functions, we are able to determine a discriminative\n\t\t  subspace for each possible instance by locally joining the\n\t\t  features that present the highest levels of discriminative\n\t\t  power. New instances are then classified by using a K-NN\n\t\t  classifier that operates in the local subspaces.\n\t\t  Experimental results show that by using local\n\t\t  discriminative subspaces, we are able to reach higher\n\t\t  levels of accuracy than alternative state-of-the-art\n\t\t  feature selection approaches. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Karim-IDA-2014.pdf}\n}\n\n
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\n Most feature selection algorithms determine a global subset of features, where all data instances are projected in order to improve classification accuracy. An attractive alternative solution is to adaptively find a local subset of features for each data instance, such that, the classification of each instance is performed according to its own selective subspace. This paper presents a novel application of Gaussian Processes that improves classification performance by learning discriminative local subsets of features for each instance in a dataset. Gaussian Processes are used to build for each available feature a function that estimates the discriminative power of the feature over all the input space. Using these functions, we are able to determine a discriminative subspace for each possible instance by locally joining the features that present the highest levels of discriminative power. New instances are then classified by using a K-NN classifier that operates in the local subspaces. Experimental results show that by using local discriminative subspaces, we are able to reach higher levels of accuracy than alternative state-of-the-art feature selection approaches. \n
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\n \n\n \n \n \n \n \n \n Who will Trade with Whom? Predicting Buyer-Seller Interactions in Online Trading Platforms through Social Networks.\n \n \n \n \n\n\n \n Trattner, C.; Parra, D.; Eberhard, L.; and Wen, X.\n\n\n \n\n\n\n In Proceedings of the 23rd international conference companion on World wide web, 2014. \n \n\n\n\n
\n\n\n\n \n \n \"WhoPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  trattner2014will,\n  author\t= {Trattner, Christoph and Parra, Denis and Eberhard, Lukas\n\t\t  and Wen, Xidao},\n  booktitle\t= {Proceedings of the 23rd international conference companion\n\t\t  on World wide web},\n  title\t\t= {Who will Trade with Whom? Predicting Buyer-Seller\n\t\t  Interactions in Online Trading Platforms through Social\n\t\t  Networks},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/whowilltrade_www14.pdf},\n  year\t\t= {2014},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/whowilltrade_www14.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n How groups of people interact with each other on Twitter during academic conferences.\n \n \n \n \n\n\n \n Wen, X.; Parra, D.; and Trattner, C.\n\n\n \n\n\n\n In Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing, pages 253–256, 2014. ACM\n \n\n\n\n
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@InProceedings{\t  wen2014groups,\n  author\t= {Wen, Xidao and Parra, Denis and Trattner, Christoph},\n  booktitle\t= {Proceedings of the companion publication of the 17th ACM\n\t\t  conference on Computer supported cooperative work \\& social\n\t\t  computing},\n  organization\t= {ACM},\n  pages\t\t= {253--256},\n  title\t\t= {How groups of people interact with each other on Twitter\n\t\t  during academic conferences},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/Wen_CSCW14.pdf},\n  year\t\t= {2014},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/Wen_CSCW14.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Twitter in Academic Conferences: Usage, Networking and Participation over Time.\n \n \n \n \n\n\n \n Wen, X.; Lin, Y.; Trattner, C.; and Parra, D.\n\n\n \n\n\n\n In Proceedings of the 25th ACM Conference on Hypertext and Social Media, of HT '14, pages 285–290, New York, NY, USA, 2014. ACM\n \n\n\n\n
\n\n\n\n \n \n \"TwitterPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@InProceedings{\t  wen:2014:tac:2631775.2631826,\n  author\t= {Wen, Xidao and Lin, Yu-Ru and Trattner, Christoph and\n\t\t  Parra, Denis},\n  title\t\t= {Twitter in Academic Conferences: Usage, Networking and\n\t\t  Participation over Time},\n  booktitle\t= {Proceedings of the 25th ACM Conference on Hypertext and\n\t\t  Social Media},\n  series\t= {HT '14},\n  year\t\t= {2014},\n  isbn\t\t= {978-1-4503-2954-5},\n  location\t= {Santiago, Chile},\n  pages\t\t= {285--290},\n  numpages\t= {6},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/ht75s-wen.pdf},\n  doi\t\t= {10.1145/2631775.2631826},\n  acmid\t\t= {2631826},\n  publisher\t= {ACM},\n  address\t= {New York, NY, USA},\n  keywords\t= {academic conferences, interactions, retention, twitter,\n\t\t  usage}\n}\n\n
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\n  \n 2013\n \n \n (20)\n \n \n
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\n \n\n \n \n \n \n \n \n Human Action Recognition from Inter-Temporal Dictionaries of Key-Sequences.\n \n \n \n \n\n\n \n Alfaro, A.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"HumanPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  alfaro:etal:2013,\n  author\t= { A. Alfaro and D. Mery and A. Soto},\n  title\t\t= {Human Action Recognition from Inter-Temporal Dictionaries\n\t\t  of Key-Sequences},\n  booktitle\t= {6th Pacific-Rim Symposium on Image and Video Technology,\n\t\t  PSIVT},\n  year\t\t= {2013},\n  abstract\t= {This paper addresses the human action recognition in video\n\t\t  by proposing a method based on three main processing steps.\n\t\t  First, we tackle problems related to intraclass variations\n\t\t  and differences in video lengths. We achieve this by\n\t\t  reducing an input video to a set of key-sequences that\n\t\t  represent atomic meaningful acts of each action class.\n\t\t  Second, we use sparse coding techniques to learn a\n\t\t  representation for each key-sequence. We then join these\n\t\t  representations still preserving information about temporal\n\t\t  relationships. We believe that this is a key step of our\n\t\t  approach because it provides not only a suitable shared rep\n\t\t  resentation to characterize atomic acts, but it also\n\t\t  encodes global tem poral consistency among these acts.\n\t\t  Accordingly, we call this represen tation inter-temporal\n\t\t  acts descriptor. Third, we use this representation and\n\t\t  sparse coding techniques to classify new videos. Finally,\n\t\t  we show that, our approach outperforms several\n\t\t  state-of-the-art methods when is tested using common\n\t\t  benchmarks.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Anali-PSIVT-13.pdf}\n}\n\n
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\n This paper addresses the human action recognition in video by proposing a method based on three main processing steps. First, we tackle problems related to intraclass variations and differences in video lengths. We achieve this by reducing an input video to a set of key-sequences that represent atomic meaningful acts of each action class. Second, we use sparse coding techniques to learn a representation for each key-sequence. We then join these representations still preserving information about temporal relationships. We believe that this is a key step of our approach because it provides not only a suitable shared rep resentation to characterize atomic acts, but it also encodes global tem poral consistency among these acts. Accordingly, we call this represen tation inter-temporal acts descriptor. Third, we use this representation and sparse coding techniques to classify new videos. Finally, we show that, our approach outperforms several state-of-the-art methods when is tested using common benchmarks.\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA.\n \n \n \n \n\n\n \n desJardins , M.; and Littman, M. L.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2013.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2013,\n  editor\t= {Marie desJardins and Michael L. Littman},\n  title\t\t= {Proceedings of the Twenty-Seventh {AAAI} Conference on\n\t\t  Artificial Intelligence, July 14-18, 2013, Bellevue,\n\t\t  Washington, {USA}},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2013},\n  url\t\t= {http://www.aaai.org/Library/AAAI/aaai13contents.php},\n  isbn\t\t= {978-1-57735-615-8},\n  timestamp\t= {Tue, 17 Dec 2013 19:26:12 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2013},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Assumption-Based Planning: Generating Plans and Explanations under Incomplete Knowledge.\n \n \n \n \n\n\n \n Davis-Mendelow, S.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2013, Bellevue, Washington, USA., 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Assumption-BasedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 38 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/davis-mendelowbm13,\n  author\t= {Sammy Davis{-}Mendelow and Jorge A. Baier and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {Assumption-Based Planning: Generating Plans and\n\t\t  Explanations under Incomplete Knowledge},\n  booktitle\t= {Proceedings of the Twenty-Seventh {AAAI} Conference on\n\t\t  Artificial Intelligence, July 14-18, 2013, Bellevue,\n\t\t  Washington, {USA.}},\n  year\t\t= {2013},\n  crossref\t= {DBLP:conf/aaai/2013},\n  url\t\t= {http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6466},\n  timestamp\t= {Tue, 17 Dec 2013 19:26:12 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/Davis-MendelowBM13},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, ICAPS 2013, Rome, Italy, June 10-14, 2013.\n \n \n \n \n\n\n \n Borrajo, D.; Kambhampati, S.; Oddi, A.; and Fratini, S.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2013.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2013,\n  editor\t= {Daniel Borrajo and Subbarao Kambhampati and Angelo Oddi\n\t\t  and Simone Fratini},\n  title\t\t= {Proceedings of the Twenty-Third International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2013, Rome,\n\t\t  Italy, June 10-14, 2013},\n  publisher\t= {{AAAI}},\n  year\t\t= {2013},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/icaps13contents.php},\n  isbn\t\t= {978-1-57735-609-7},\n  timestamp\t= {Wed, 29 Mar 2017 16:45:27 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2013},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Moving Target Search with Compressed Path Databases.\n \n \n \n \n\n\n \n Botea, A.; Baier, J. A.; Harabor, D.; and Hernández, C.\n\n\n \n\n\n\n In Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, ICAPS 2013, Rome, Italy, June 10-14, 2013, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"MovingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/boteabhh13,\n  author\t= {Adi Botea and Jorge A. Baier and Daniel Harabor and Carlos\n\t\t  Hern{\\'{a}}ndez},\n  title\t\t= {Moving Target Search with Compressed Path Databases},\n  booktitle\t= {Proceedings of the Twenty-Third International Conference\n\t\t  on Automated Planning and Scheduling, {ICAPS} 2013, Rome,\n\t\t  Italy, June 10-14, 2013},\n  year\t\t= {2013},\n  crossref\t= {DBLP:conf/aips/2013},\n  url\t\t= {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS13/paper/view/6001},\n  timestamp\t= {Wed, 29 Mar 2017 16:45:27 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/BoteaBHH13},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n International conference on Autonomous Agents and Multi-Agent Systems, AAMAS '13, Saint Paul, MN, USA, May 6-10, 2013.\n \n \n \n \n\n\n \n Gini, M. L.; Shehory, O.; Ito, T.; and Jonker, C. M.,\n editors.\n \n\n\n \n\n\n\n IFAAMAS. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"InternationalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/atal/2013,\n  editor\t= {Maria L. Gini and Onn Shehory and Takayuki Ito and\n\t\t  Catholijn M. Jonker},\n  title\t\t= {International conference on Autonomous Agents and\n\t\t  Multi-Agent Systems, {AAMAS} '13, Saint Paul, MN, USA, May\n\t\t  6-10, 2013},\n  publisher\t= {{IFAAMAS}},\n  year\t\t= {2013},\n  url\t\t= {http://dl.acm.org/citation.cfm?id=2484920},\n  isbn\t\t= {978-1-4503-1993-5},\n  timestamp\t= {Fri, 28 Jun 2013 12:19:40 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/atal/2013},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Weighted real-time heuristic search.\n \n \n \n \n\n\n \n Rivera, N.; Baier, J. A.; and Hernández, C.\n\n\n \n\n\n\n In International conference on Autonomous Agents and Multi-Agent Systems, AAMAS '13, Saint Paul, MN, USA, May 6-10, 2013, pages 579–586, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"WeightedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/atal/riverabh13,\n  author\t= {Nicolas Rivera and Jorge A. Baier and Carlos\n\t\t  Hern{\\'{a}}ndez},\n  title\t\t= {Weighted real-time heuristic search},\n  booktitle\t= {International conference on Autonomous Agents and\n\t\t  Multi-Agent Systems, {AAMAS} '13, Saint Paul, MN, USA, May\n\t\t  6-10, 2013},\n  pages\t\t= {579--586},\n  year\t\t= {2013},\n  crossref\t= {DBLP:conf/atal/2013},\n  url\t\t= {http://dl.acm.org/citation.cfm?id=2485012},\n  timestamp\t= {Fri, 28 Jun 2013 12:19:40 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/atal/RiveraBH13},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition.\n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n In ICCV, pages 1697–1704, 2013. IEEE Computer Society\n \n\n\n\n
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@InProceedings{\t  dblp:conf/iccv/lobelvs13,\n  author\t= {Hans Lobel and Ren{\\'{e}} Vidal and Alvaro Soto},\n  title\t\t= {Hierarchical Joint Max-Margin Learning of Mid and Top\n\t\t  Level Representations for Visual Recognition},\n  booktitle\t= {{ICCV}},\n  pages\t\t= {1697--1704},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {2013}\n}\n\n
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\n \n\n \n \n \n \n \n Joint Dictionary and Classifier Learning for Categorization of Images Using a Max-margin Framework.\n \n \n \n\n\n \n Lobel, H.; Vidal, R.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In PSIVT, volume 8333, of Lecture Notes in Computer Science, pages 87–98, 2013. Springer\n \n\n\n\n
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@InProceedings{\t  dblp:conf/psivt/lobelvms13,\n  author\t= {Hans Lobel and Ren{\\'{e}} Vidal and Domingo Mery and\n\t\t  Alvaro Soto},\n  title\t\t= {Joint Dictionary and Classifier Learning for\n\t\t  Categorization of Images Using a Max-margin Framework},\n  booktitle\t= {{PSIVT}},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {8333},\n  pages\t\t= {87--98},\n  publisher\t= {Springer},\n  year\t\t= {2013}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Sixth Annual Symposium on Combinatorial Search, SOCS 2013, Leavenworth, Washington, USA, July 11-13, 2013.\n \n \n \n \n\n\n \n Helmert, M.; and Röger, G.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2013.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2013,\n  editor\t= {Malte Helmert and Gabriele R{\\"{o}}ger},\n  title\t\t= {Proceedings of the Sixth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2013, Leavenworth, Washington, USA, July\n\t\t  11-13, 2013},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2013},\n  url\t\t= {http://www.aaai.org/Library/SOCS/socs13contents.php},\n  isbn\t\t= {978-1-57735-584-7},\n  timestamp\t= {Tue, 20 Aug 2013 16:04:32 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2013},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Reconnecting with the Ideal Tree: An Alternative to Heuristic Learning in Real-Time Search.\n \n \n \n \n\n\n \n Rivera, N.; Illanes, L.; Baier, J. A.; and Hernández, C.\n\n\n \n\n\n\n In Proceedings of the Sixth Annual Symposium on Combinatorial Search, SOCS 2013, Leavenworth, Washington, USA, July 11-13, 2013., 2013. \n \n\n\n\n
\n\n\n\n \n \n \"ReconnectingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/riveraibh13,\n  author\t= {Nicolas Rivera and Leon Illanes and Jorge A. Baier and\n\t\t  Carlos Hern{\\'{a}}ndez},\n  title\t\t= {Reconnecting with the Ideal Tree: An Alternative to\n\t\t  Heuristic Learning in Real-Time Search},\n  booktitle\t= {Proceedings of the Sixth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2013, Leavenworth, Washington, USA, July\n\t\t  11-13, 2013.},\n  year\t\t= {2013},\n  crossref\t= {DBLP:conf/socs/2013},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS13/paper/view/7270},\n  timestamp\t= {Tue, 20 Aug 2013 16:04:32 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/RiveraIBH13},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Indoor Scene Recognition by a Mobile Robot Through Adaptive Object Detection.\n \n \n \n \n\n\n \n Espinace, P.; Kollar, T.; Roy, N.; and Soto, A.\n\n\n \n\n\n\n Robotics and Autonomous Systems, 61(9). 2013.\n \n\n\n\n
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@Article{\t  espinace:etal:2013,\n  author\t= {P. Espinace and T. Kollar and N. Roy and A. Soto},\n  title\t\t= {Indoor Scene Recognition by a Mobile Robot Through\n\t\t  Adaptive Object Detection},\n  journal\t= {Robotics and Autonomous Systems},\n  volume\t= {61},\n  number\t= {9},\n  year\t\t= {2013},\n  abstract\t= {Mobile Robotics has achieved notably progress, however, to\n\t\t  increase the complexity of the tasks that mobile robots can\n\t\t  perform in natural environments, we need to provide them\n\t\t  with a greater semantic understanding of their surrounding.\n\t\t  In particular, identifying indoor scenes, such as an office\n\t\t  or a kitchen, is a highly valuable perceptual ability for\n\t\t  an indoor mobile robot, and in this paper we propose a new\n\t\t  technique to achieve this goal. As a distinguishing\n\t\t  feature, we use common objects, such as doors or\n\t\t  furnitures, as a key intermediate representation to\n\t\t  recognize indoor scenes. We frame our method as a\n\t\t  generative probabilistic hierarchical model, where we use\n\t\t  object category classifiers to associate low-level visual\n\t\t  features to objects, and contextual relations to associate\n\t\t  objects to scenes. The inherent seman- tic interpretation\n\t\t  of common objects allows us to use rich sources of online\n\t\t  data to populate the probabilistic terms of our model. In\n\t\t  contrast to alterna- tive computer vision based methods, we\n\t\t  boost performance by exploiting the embedded and dynamic\n\t\t  nature of a mobile robot. In particular, we increase\n\t\t  detection accuracy and efficiency by using a 3D range\n\t\t  sensor that allows us to implement a focus of attention\n\t\t  mechanism based on geometric and struc- tural information.\n\t\t  Furthermore, we use concepts from information theory to\n\t\t  propose an adaptive scheme that limits computational load\n\t\t  by selectively guiding the search for informative objects.\n\t\t  The operation of this scheme is facilitated by the dynamic\n\t\t  nature of a mobile robot that is constantly changing its\n\t\t  field of view. We test our approach using real data\n\t\t  captured by a mo- bile robot navigating in office and home\n\t\t  environments. Our results indicate that the proposed\n\t\t  approach outperforms several state-of-the-art techniques },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Final-RAS-2013.pdf}\n}\n\n
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\n Mobile Robotics has achieved notably progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an office or a kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as doors or furnitures, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent seman- tic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alterna- tive computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and struc- tural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mo- bile robot navigating in office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques \n
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\n \n\n \n \n \n \n \n Encouraging Online Student Reading with Social Visualization Support.\n \n \n \n\n\n \n Guerra, J.; Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In 2nd Workshop on Intelligent Support for Learning in Groups - AIED 2013, 2013. \n \n\n\n\n
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@InProceedings{\t  guerra2013encouraging,\n  author\t= {Guerra, Julio and Parra, Denis and Brusilovsky, Peter},\n  booktitle\t= {2nd Workshop on Intelligent Support for Learning in Groups\n\t\t  - AIED 2013},\n  title\t\t= {Encouraging Online Student Reading with Social\n\t\t  Visualization Support},\n  year\t\t= {2013}\n}\n\n
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\n \n\n \n \n \n \n \n \n Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition.\n \n \n \n \n\n\n \n Lobel, H.; Vidal, R.; and Soto, A.\n\n\n \n\n\n\n In ICCV, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"HierarchicalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lobel-a:etal:2013,\n  author\t= {H. Lobel and R. Vidal and A. Soto},\n  title\t\t= {Hierarchical Joint Max-Margin Learning of Mid and Top\n\t\t  Level Representations for Visual Recognition},\n  booktitle\t= {{ICCV}},\n  year\t\t= {2013},\n  abstract\t= {Currently, Bag-of-Visual-Words (BoVW) and part-based\n\t\t  methods are the most popular approaches for visual\n\t\t  recognition. In both cases, a mid-level representation is\n\t\t  build on top of low level image descriptors while top\n\t\t  levels classifiers use this mid-level representation to\n\t\t  achieve visual recognition. While in current part-based\n\t\t  approaches, mid and top level representations are usually\n\t\t  jointly trained, this is not the usual case for BoVW\n\t\t  schemes. A main reason is the complex data association\n\t\t  problem associated to the larger size of the visual\n\t\t  dictionary usually needed by BoVW approaches at the\n\t\t  mid-level layer. As a further observation, typical\n\t\t  solutions based on BoVW and part-based representations are\n\t\t  usually limited to binary classification problems, a\n\t\t  strategy that ignores relevant correlations among classes.\n\t\t  In this work we propose a novel hierarchical approach for\n\t\t  visual recognition that, in the context of a BoVW scheme,\n\t\t  jointly learns suitable mid and top level representations.\n\t\t  Furthermore, using a max-margin learning framework, the\n\t\t  proposed approach directly handles the multiclass case at\n\t\t  both levels of abstraction. We test our proposed method\n\t\t  using several popular benchmarks datasets. As our main\n\t\t  result, we demonstrate that by coupling learning of mid and\n\t\t  top level representations, the proposed approach fosters\n\t\t  sharing of discriminativity words among target classes,\n\t\t  being able to achieve state-of-the-art recognition\n\t\t  performance using far less visual words than previous\n\t\t  approaches.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/finalHans-ICCV-13.pdf}\n}\n\n
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\n Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recognition. In both cases, a mid-level representation is build on top of low level image descriptors while top levels classifiers use this mid-level representation to achieve visual recognition. While in current part-based approaches, mid and top level representations are usually jointly trained, this is not the usual case for BoVW schemes. A main reason is the complex data association problem associated to the larger size of the visual dictionary usually needed by BoVW approaches at the mid-level layer. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to binary classification problems, a strategy that ignores relevant correlations among classes. In this work we propose a novel hierarchical approach for visual recognition that, in the context of a BoVW scheme, jointly learns suitable mid and top level representations. Furthermore, using a max-margin learning framework, the proposed approach directly handles the multiclass case at both levels of abstraction. We test our proposed method using several popular benchmarks datasets. As our main result, we demonstrate that by coupling learning of mid and top level representations, the proposed approach fosters sharing of discriminativity words among target classes, being able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.\n
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\n \n\n \n \n \n \n \n \n Joint Dictionary and Classifier learning for Categorization of Images using a Max-margin Framework.\n \n \n \n \n\n\n \n Lobel, H.; Vidal, R.; Mery, D.; and Soto., A.\n\n\n \n\n\n\n In 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT, 2013. \n \n\n\n\n
\n\n\n\n \n \n \"JointPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  lobel-b:etal:2013,\n  author\t= {H. Lobel and R. Vidal and D. Mery and A. Soto.},\n  title\t\t= {Joint Dictionary and Classifier learning for\n\t\t  Categorization of Images using a Max-margin Framework},\n  booktitle\t= {6th Pacific-Rim Symposium on Image and Video Technology,\n\t\t  PSIVT},\n  year\t\t= {2013},\n  abstract\t= {The Bag-of-Visual-Words (BoVW) model is a popular approach\n\t\t  for visual recognition. Used successfully in many different\n\t\t  tasks, simplicity and good performance are the main reasons\n\t\t  for its popularity. The central aspect of this model, the\n\t\t  visual dictionary, is used to build mid-level\n\t\t  representations based on low level image descriptors.\n\t\t  Classifiers are then trained using these mid-level\n\t\t  representations to perform categorization. While most works\n\t\t  based on BoVW models have been focused on learning a\n\t\t  suitable dictionary or on proposing a suitable pooling\n\t\t  strategy, little effort has been devoted to explore and\n\t\t  improve the coupling between the dictionary and the\n\t\t  top-level classifiers, in order to gen- erate more\n\t\t  discriminative models. This problem can be highly complex\n\t\t  due to the large dictionary size usually needed by these\n\t\t  methods. Also, most BoVW based systems usually perform\n\t\t  multiclass categorization using a one-vs-all strat- egy,\n\t\t  ignoring relevant correlations among classes. To tackle the\n\t\t  previous issues, we propose a novel approach that jointly\n\t\t  learns dictionary words and a proper top- level multiclass\n\t\t  classifier. We use a max-margin learning framework to\n\t\t  minimize a regularized energy formulation, allowing us to\n\t\t  propagate labeled information to guide the commonly\n\t\t  unsupervised dictionary learning process. As a result we\n\t\t  produce a dictionary that is more compact and\n\t\t  discriminative. We test our method on several popular\n\t\t  datasets, where we demonstrate that our joint optimization\n\t\t  strategy induces a word sharing behavior among the target\n\t\t  classes, being able to achieve state-of-the-art performance\n\t\t  using far less visual words than previous approaches. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Hans-PSIVT-13.pdf}\n}\n\n
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\n The Bag-of-Visual-Words (BoVW) model is a popular approach for visual recognition. Used successfully in many different tasks, simplicity and good performance are the main reasons for its popularity. The central aspect of this model, the visual dictionary, is used to build mid-level representations based on low level image descriptors. Classifiers are then trained using these mid-level representations to perform categorization. While most works based on BoVW models have been focused on learning a suitable dictionary or on proposing a suitable pooling strategy, little effort has been devoted to explore and improve the coupling between the dictionary and the top-level classifiers, in order to gen- erate more discriminative models. This problem can be highly complex due to the large dictionary size usually needed by these methods. Also, most BoVW based systems usually perform multiclass categorization using a one-vs-all strat- egy, ignoring relevant correlations among classes. To tackle the previous issues, we propose a novel approach that jointly learns dictionary words and a proper top- level multiclass classifier. We use a max-margin learning framework to minimize a regularized energy formulation, allowing us to propagate labeled information to guide the commonly unsupervised dictionary learning process. As a result we produce a dictionary that is more compact and discriminative. We test our method on several popular datasets, where we demonstrate that our joint optimization strategy induces a word sharing behavior among the target classes, being able to achieve state-of-the-art performance using far less visual words than previous approaches. \n
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\n \n\n \n \n \n \n \n \n Automated Design of a Computer Vision System for Food Quality Evaluation.\n \n \n \n \n\n\n \n Mery, D.; Pedreschi, F.; and Soto, A.\n\n\n \n\n\n\n Food and Bioprocess Technology, 6(8): 2093-2108. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"AutomatedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mery:etal:2013,\n  author\t= {D. Mery and F. Pedreschi and A. Soto},\n  title\t\t= {Automated Design of a Computer Vision System for Food\n\t\t  Quality Evaluation},\n  journal\t= {Food and Bioprocess Technology},\n  volume\t= {6},\n  number\t= {8},\n  pages\t\t= {2093-2108},\n  year\t\t= {2013},\n  abstract\t= {Considerable research efforts in computer classifiers for\n\t\t  a given application avoiding the classical vision applied\n\t\t  to food quality evaluation have been trial and error\n\t\t  framework commonly used by human developed in the last\n\t\t  years; however, they have been designers. The key idea of\n\t\t  the proposed framework concentrated on using or developing\n\t\t  tailored methods is to select—automatically—from a\n\t\t  large set of fea- based on visual features that are able to\n\t\t  solve a specific tures and a bank of classifiers those\n\t\t  features and clas- task. Nevertheless, today’s computer\n\t\t  capabilities are sifiers that achieve the highest\n\t\t  performance. We tested giving us new ways to solve complex\n\t\t  computer vision our framework on eight different food\n\t\t  quality evalua- problems. In particular, a new paradigm on\n\t\t  machine tion problems yielding a classification performance\n\t\t  of learning techniques has emerged posing the task of 95 %\n\t\t  or more in every case. The proposed framework recognizing\n\t\t  visual patterns as a search problem based was implemented\n\t\t  as a Matlab Toolbox available for on training data and a\n\t\t  hypothesis space composed by noncommercial purposes. visual\n\t\t  features and suitable classifiers. Furthermore, now we are\n\t\t  able to extract, process, and test in the same time more\n\t\t  image features and classifiers than before. Thus, we\n\t\t  propose a general framework that designs a computer vision\n\t\t  system automatically, i.e., it finds— without human\n\t\t  interaction—the features and the classifiers for a given\n\t\t  application avoiding the classical trial and error\n\t\t  framework commonly used by human designers. The key idea of\n\t\t  the proposed framework is to select—automatically—from\n\t\t  a large set of fea- tures and a bank of classifiers those\n\t\t  features and clas- sifiers that achieve the highest\n\t\t  performance. We tested our framework on eight different\n\t\t  food quality evalua- tion problems yielding a\n\t\t  classification performance of 95% or more in every case.\n\t\t  The proposed framework was implemented as a Matlab Toolbox\n\t\t  available for noncommercial purposes.\n\t\t  \n\t\t  },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Food-Mery-2012.pdf}\n}\n\n
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\n Considerable research efforts in computer classifiers for a given application avoiding the classical vision applied to food quality evaluation have been trial and error framework commonly used by human developed in the last years; however, they have been designers. The key idea of the proposed framework concentrated on using or developing tailored methods is to select—automatically—from a large set of fea- based on visual features that are able to solve a specific tures and a bank of classifiers those features and clas- task. Nevertheless, today’s computer capabilities are sifiers that achieve the highest performance. We tested giving us new ways to solve complex computer vision our framework on eight different food quality evalua- problems. In particular, a new paradigm on machine tion problems yielding a classification performance of learning techniques has emerged posing the task of 95 % or more in every case. The proposed framework recognizing visual patterns as a search problem based was implemented as a Matlab Toolbox available for on training data and a hypothesis space composed by noncommercial purposes. visual features and suitable classifiers. Furthermore, now we are able to extract, process, and test in the same time more image features and classifiers than before. Thus, we propose a general framework that designs a computer vision system automatically, i.e., it finds— without human interaction—the features and the classifiers for a given application avoiding the classical trial and error framework commonly used by human designers. The key idea of the proposed framework is to select—automatically—from a large set of fea- tures and a bank of classifiers those features and clas- sifiers that achieve the highest performance. We tested our framework on eight different food quality evalua- tion problems yielding a classification performance of 95% or more in every case. The proposed framework was implemented as a Matlab Toolbox available for noncommercial purposes. \n
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\n \n\n \n \n \n \n \n A field study of a visual controllable talk recommender.\n \n \n \n\n\n \n Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In Proceedings of the 2013 Chilean Conference on Human-Computer Interaction, pages 56–59, 2013. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  parra2013field,\n  author\t= {Parra, Denis and Brusilovsky, Peter},\n  booktitle\t= {Proceedings of the 2013 Chilean Conference on\n\t\t  Human-Computer Interaction},\n  organization\t= {ACM},\n  pages\t\t= {56--59},\n  title\t\t= {A field study of a visual controllable talk recommender},\n  year\t\t= {2013}\n}\n\n
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\n \n\n \n \n \n \n \n USER CONTROLLABILITY IN A HYBRID RECOMMENDER SYSTEM.\n \n \n \n\n\n \n Parra, D.\n\n\n \n\n\n\n Ph.D. Thesis, University of Pittsburgh, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@PhDThesis{\t  parra2013user,\n  author\t= {Parra, Denis},\n  school\t= {University of Pittsburgh},\n  title\t\t= {USER CONTROLLABILITY IN A HYBRID RECOMMENDER SYSTEM},\n  year\t\t= {2013}\n}\n\n
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\n \n\n \n \n \n \n \n \n Enhancing K-Means Using Class Labels.\n \n \n \n \n\n\n \n Peralta, B.; Espinace, P.; and Soto, A.\n\n\n \n\n\n\n Intelligent Data Analysis (IDA), 17(6): 1023-1039. 2013.\n \n\n\n\n
\n\n\n\n \n \n \"EnhancingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 9 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  peralta:etal:2013,\n  author\t= {B. Peralta and P. Espinace and A. Soto},\n  title\t\t= {Enhancing K-Means Using Class Labels},\n  journal\t= {Intelligent Data Analysis (IDA)},\n  volume\t= {17},\n  number\t= {6},\n  pages\t\t= {1023-1039},\n  year\t\t= {2013},\n  abstract\t= {Clustering is a relevant problem in machine learning where\n\t\t  the main goal is to locate meaningful partitions of\n\t\t  unlabeled data. In the case of labeled data, a related\n\t\t  problem is supervised clustering, where the objective is to\n\t\t  locate class- uniform clusters. Most current approaches to\n\t\t  supervised clustering optimize a score related to cluster\n\t\t  purity with respect to class labels. In particular, we\n\t\t  present Labeled K-Means (LK-Means), an algorithm for\n\t\t  supervised clustering based on a variant of K-Means that\n\t\t  incorporates information about class labels. LK-Means\n\t\t  replaces the classical cost function of K-Means by a convex\n\t\t  combination of the joint cost associated to: (i) A\n\t\t  discriminative score based on class labels, and (ii) A\n\t\t  generative score based on a traditional metric for\n\t\t  unsupervised clustering. We test the performance of\n\t\t  LK-Means using standard real datasets and an application\n\t\t  for object recognition. Moreover, we also compare its\n\t\t  performance against classical K-Means and a popular\n\t\t  K-Medoids-based supervised clustering method. Our\n\t\t  experiments show that, in most cases, LK-Means outperforms\n\t\t  the alternative techniques by a considerable margin.\n\t\t  Furthermore, LK-Means presents execution times considerably\n\t\t  lower than the alternative supervised clustering method\n\t\t  under evaluation. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/supClustering.pdf}\n}\n\n
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\n Clustering is a relevant problem in machine learning where the main goal is to locate meaningful partitions of unlabeled data. In the case of labeled data, a related problem is supervised clustering, where the objective is to locate class- uniform clusters. Most current approaches to supervised clustering optimize a score related to cluster purity with respect to class labels. In particular, we present Labeled K-Means (LK-Means), an algorithm for supervised clustering based on a variant of K-Means that incorporates information about class labels. LK-Means replaces the classical cost function of K-Means by a convex combination of the joint cost associated to: (i) A discriminative score based on class labels, and (ii) A generative score based on a traditional metric for unsupervised clustering. We test the performance of LK-Means using standard real datasets and an application for object recognition. Moreover, we also compare its performance against classical K-Means and a popular K-Medoids-based supervised clustering method. Our experiments show that, in most cases, LK-Means outperforms the alternative techniques by a considerable margin. Furthermore, LK-Means presents execution times considerably lower than the alternative supervised clustering method under evaluation. \n
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\n \n\n \n \n \n \n \n \n Visualizing recommendations to support exploration, transparency and controllability.\n \n \n \n \n\n\n \n Verbert, K.; Parra, D.; Brusilovsky, P.; and Duval, E.\n\n\n \n\n\n\n In Proceedings of the 2013 international conference on Intelligent user interfaces, pages 351–362, 2013. ACM\n \n\n\n\n
\n\n\n\n \n \n \"VisualizingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  verbert2013visualizing,\n  author\t= {Verbert, Katrien and Parra, Denis and Brusilovsky, Peter\n\t\t  and Duval, Erik},\n  booktitle\t= {Proceedings of the 2013 international conference on\n\t\t  Intelligent user interfaces},\n  organization\t= {ACM},\n  pages\t\t= {351--362},\n  title\t\t= {Visualizing recommendations to support exploration,\n\t\t  transparency and controllability},\n  url\t\t= {http://web.ing.puc.cl/~dparra/pdfs/IUI13-aduna-v5.5-notcameraready.pdf},\n  year\t\t= {2013},\n  bdsk-url-1\t= {http://web.ing.puc.cl/~dparra/pdfs/IUI13-aduna-v5.5-notcameraready.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Indoor Mobile Robotics at Grima, PUC.\n \n \n \n \n\n\n \n L. Caro, J. C.; P. Espinace, D. M.; R. Mitnik, S. M.; S. Pszszfolkowski, D. L.; A. Araneda, D. M.; and M. Torres, A. S.\n\n\n \n\n\n\n Journal of Intelligent and Robotic Systems, 66(1-2): 151-165. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"IndoorPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  caro:etal:2012,\n  author\t= {L. Caro, J. Correa, P. Espinace, D. Maturana, R. Mitnik,\n\t\t  S. Montabone, S. Pszszfolkowski, D. Langdon, A. Araneda, D.\n\t\t  Mery, M. Torres, A. Soto},\n  title\t\t= {Indoor Mobile Robotics at Grima, PUC},\n  journal\t= {Journal of Intelligent and Robotic Systems},\n  volume\t= {66},\n  pages\t\t= {151-165},\n  number\t= {1-2},\n  year\t\t= {2012},\n  abstract\t= {This paper describes the main activities and achievements\n\t\t  of our research group on Ma- chine Intelligence and\n\t\t  Robotics (Grima) at the Computer Science Department,\n\t\t  Pontificia Uni- versidad Catolica de Chile (PUC). Since\n\t\t  2002, we have been developing an active research in the\n\t\t  area of indoor autonomous social robots. Our main focus has\n\t\t  been the cognitive side of Robotics, where we have\n\t\t  developed algorithms for autonomous navigation using\n\t\t  wheeled robots, scene recognition using vision and 3D range\n\t\t  sen- sors, and social behaviors using Markov Deci- sion\n\t\t  Processes, among others. As a distinguish- ing feature, in\n\t\t  our research we have followed a probabilistic approach,\n\t\t  deeply rooted in ma- chine learning and Bayesian\n\t\t  statistical techniques. Among our main achievements are an\n\t\t  increasing list of publications in main Robotics conference\n\t\t  and journals, and the consolidation of a research group\n\t\t  with more than 25 people among full- time professors,\n\t\t  visiting researchers, and graduate students. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Latam-2012.pdf}\n}\n\n
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\n This paper describes the main activities and achievements of our research group on Ma- chine Intelligence and Robotics (Grima) at the Computer Science Department, Pontificia Uni- versidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive side of Robotics, where we have developed algorithms for autonomous navigation using wheeled robots, scene recognition using vision and 3D range sen- sors, and social behaviors using Markov Deci- sion Processes, among others. As a distinguish- ing feature, in our research we have followed a probabilistic approach, deeply rooted in ma- chine learning and Bayesian statistical techniques. Among our main achievements are an increasing list of publications in main Robotics conference and journals, and the consolidation of a research group with more than 25 people among full- time professors, visiting researchers, and graduate students. \n
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\n \n\n \n \n \n \n \n \n Time-bounded adaptive A.\n \n \n \n \n\n\n \n Hernández, C.; Baier, J. A.; Uras, T.; and Koenig, S.\n\n\n \n\n\n\n In International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, Valencia, Spain, June 4-8, 2012 (3 Volumes), pages 997–1006, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"Time-boundedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aamas/hernandezbuk12,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier and Tansel Uras\n\t\t  and Sven Koenig},\n  title\t\t= {Time-bounded adaptive {A}},\n  booktitle\t= {International Conference on Autonomous Agents and\n\t\t  Multiagent Systems, {AAMAS} 2012, Valencia, Spain, June\n\t\t  4-8, 2012 {(3} Volumes)},\n  pages\t\t= {997--1006},\n  year\t\t= {2012},\n  crossref\t= {DBLP:conf/atal/2012},\n  url\t\t= {http://dl.acm.org/citation.cfm?id=2343839},\n  timestamp\t= {Thu, 19 Mar 2015 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aamas/HernandezBUK12},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, Valencia, Spain, June 4-8, 2012 (3 Volumes).\n \n \n \n \n\n\n \n van der Hoek, W.; Padgham, L.; Conitzer, V.; and Winikoff, M.,\n editors.\n \n\n\n \n\n\n\n IFAAMAS. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"InternationalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/atal/2012,\n  editor\t= {Wiebe van der Hoek and Lin Padgham and Vincent Conitzer\n\t\t  and Michael Winikoff},\n  title\t\t= {International Conference on Autonomous Agents and\n\t\t  Multiagent Systems, {AAMAS} 2012, Valencia, Spain, June\n\t\t  4-8, 2012 {(3} Volumes)},\n  publisher\t= {{IFAAMAS}},\n  year\t\t= {2012},\n  url\t\t= {http://dl.acm.org/citation.cfm?id=2343576},\n  timestamp\t= {Sun, 09 Sep 2012 12:23:32 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/atal/2012},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings of the Fifth Annual Symposium on Combinatorial Search, SOCS 2012, Niagara Falls, Ontario, Canada, July 19-21, 2012.\n \n \n \n\n\n \n Borrajo, D.; Felner, A.; Korf, R. E.; Likhachev, M.; López, C. L.; Ruml, W.; and Sturtevant, N. R.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2012.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2012,\n  editor\t= {Daniel Borrajo and Ariel Felner and Richard E. Korf and\n\t\t  Maxim Likhachev and Carlos Linares L{\\'{o}}pez and Wheeler\n\t\t  Ruml and Nathan R. Sturtevant},\n  title\t\t= {Proceedings of the Fifth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2012, Niagara Falls, Ontario, Canada, July\n\t\t  19-21, 2012},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2012},\n  timestamp\t= {Fri, 17 Aug 2012 08:29:08 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2012},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Position Paper: Incremental Search Algorithms Considered Poorly Understood.\n \n \n \n \n\n\n \n Hernández, C.; Baier, J. A.; Uras, T.; and Koenig, S.\n\n\n \n\n\n\n In Proceedings of the Fifth Annual Symposium on Combinatorial Search, SOCS 2012, Niagara Falls, Ontario, Canada, July 19-21, 2012, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"PositionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hernandezbuk12,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier and Tansel Uras\n\t\t  and Sven Koenig},\n  title\t\t= {Position Paper: Incremental Search Algorithms Considered\n\t\t  Poorly Understood},\n  booktitle\t= {Proceedings of the Fifth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2012, Niagara Falls, Ontario, Canada, July\n\t\t  19-21, 2012},\n  year\t\t= {2012},\n  crossref\t= {DBLP:conf/socs/2012},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS12/paper/view/5414},\n  timestamp\t= {Fri, 17 Aug 2012 08:29:08 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HernandezBUK12},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Paper Summary: Time-Bounded Adaptive A.\n \n \n \n \n\n\n \n Hernández, C.; Baier, J. A.; Uras, T.; and Koenig, S.\n\n\n \n\n\n\n In Proceedings of the Fifth Annual Symposium on Combinatorial Search, SOCS 2012, Niagara Falls, Ontario, Canada, July 19-21, 2012, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"PaperPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hernandezbuk12a,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier and Tansel Uras\n\t\t  and Sven Koenig},\n  title\t\t= {Paper Summary: Time-Bounded Adaptive {A}},\n  booktitle\t= {Proceedings of the Fifth Annual Symposium on Combinatorial\n\t\t  Search, {SOCS} 2012, Niagara Falls, Ontario, Canada, July\n\t\t  19-21, 2012},\n  year\t\t= {2012},\n  crossref\t= {DBLP:conf/socs/2012},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS12/paper/view/5415},\n  timestamp\t= {Sat, 18 Aug 2012 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HernandezBUK12a},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Avoiding and Escaping Depressions in Real-Time Heuristic Search.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n J. Artif. Intell. Res., 43: 523–570. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"AvoidingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/jair/hernandezb12,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Avoiding and Escaping Depressions in Real-Time Heuristic\n\t\t  Search},\n  journal\t= {J. Artif. Intell. Res.},\n  volume\t= {43},\n  pages\t\t= {523--570},\n  year\t\t= {2012},\n  url\t\t= {https://doi.org/10.1613/jair.3590},\n  doi\t\t= {10.1613/jair.3590},\n  timestamp\t= {Wed, 21 Jun 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/jair/HernandezB12},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Comparative social visualization for personalized e-learning.\n \n \n \n\n\n \n Hsiao, I.; Guerra, J.; Parra, D.; Bakalov, F.; König-Ries, B.; and Brusilovsky, P.\n\n\n \n\n\n\n In Proceedings of the International Working Conference on Advanced Visual Interfaces, pages 303–307, 2012. ACM\n \n\n\n\n
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@InProceedings{\t  hsiao2012comparative,\n  author\t= {Hsiao, I-Han and Guerra, Julio and Parra, Denis and\n\t\t  Bakalov, Fedor and K{\\"o}nig-Ries, Birgitta and\n\t\t  Brusilovsky, Peter},\n  booktitle\t= {Proceedings of the International Working Conference on\n\t\t  Advanced Visual Interfaces},\n  organization\t= {ACM},\n  pages\t\t= {303--307},\n  title\t\t= {Comparative social visualization for personalized\n\t\t  e-learning},\n  year\t\t= {2012}\n}\n\n
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\n \n\n \n \n \n \n \n A hybrid peer recommender system for an online community of teachers.\n \n \n \n\n\n \n Miranda, C.; Guerra, J.; Parra, D.; and Scheihing, E.\n\n\n \n\n\n\n In UMAP Workshops, 2012. \n \n\n\n\n
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@InProceedings{\t  miranda2012hybrid,\n  author\t= {Miranda, Cristian and Guerra, Julio and Parra, Denis and\n\t\t  Scheihing, Eliana},\n  booktitle\t= {UMAP Workshops},\n  title\t\t= {A hybrid peer recommender system for an online community\n\t\t  of teachers.},\n  year\t\t= {2012}\n}\n\n
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\n \n\n \n \n \n \n \n Beyond Lists: Studying the Effect of Different Recommendation Visualizations.\n \n \n \n\n\n \n Parra, D.\n\n\n \n\n\n\n In Sixth ACM conference on Recommender systems (RecSys '12), 2012. \n \n\n\n\n
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@InProceedings{\t  parra2012beyond,\n  author\t= {Parra, Denis},\n  booktitle\t= {Sixth ACM conference on Recommender systems (RecSys '12)},\n  title\t\t= {Beyond Lists: Studying the Effect of Different\n\t\t  Recommendation Visualizations},\n  year\t\t= {2012}\n}\n\n
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\n \n\n \n \n \n \n \n Conference Navigator 3: An Online Social Conference Support System.\n \n \n \n\n\n \n Parra, D.; Jeng, W.; Brusilovsky, P.; López, C.; and Sahebi, S.\n\n\n \n\n\n\n In 2012. \n \n\n\n\n
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@InProceedings{\t  parra2012conference,\n  author\t= {Parra, Denis and Jeng, Wei and Brusilovsky, Peter and\n\t\t  L{\\'o}pez, Claudia and Sahebi, Shaghayegh},\n  title\t\t= {Conference Navigator 3: An Online Social Conference\n\t\t  Support System},\n  year\t\t= {2012}\n}\n\n
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\n \n\n \n \n \n \n \n Recommender Systems: Sources of Knowledge and Evaluation Metrics.\n \n \n \n\n\n \n Parra, D.; and Sahebi, S.\n\n\n \n\n\n\n In Advanced Techniques in Web Intelligence-2, pages 149–175. Springer Berlin/Heidelberg, 2012.\n \n\n\n\n
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@InCollection{\t  parra2012recommender,\n  author\t= {Parra, Denis and Sahebi, Shaghayegh},\n  booktitle\t= {Advanced Techniques in Web Intelligence-2},\n  pages\t\t= {149--175},\n  publisher\t= {Springer Berlin/Heidelberg},\n  title\t\t= {Recommender Systems: Sources of Knowledge and Evaluation\n\t\t  Metrics},\n  year\t\t= {2012}\n}\n\n
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\n \n\n \n \n \n \n \n \n Adaptive hierarchical contexts for object recognition with conditional mixture of trees.\n \n \n \n \n\n\n \n Peralta, B.; Espinace, P.; and Soto, A.\n\n\n \n\n\n\n In BMVC, 2012. \n \n\n\n\n
\n\n\n\n \n \n \"AdaptivePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  peralta:etal:2012,\n  author\t= {B. Peralta and P. Espinace and A. Soto},\n  title\t\t= {Adaptive hierarchical contexts for object recognition with\n\t\t  conditional mixture of trees},\n  booktitle\t= {{BMVC}},\n  year\t\t= {2012},\n  abstract\t= {Robust category-level object recognition is currently a\n\t\t  major goal for the computer vision community. Intra-class\n\t\t  and pose variations, as well as, background clutter and\n\t\t  partial occlusions are some of the main difficulties to\n\t\t  achieve this goal. Contextual in- formation, in the form of\n\t\t  object co-occurrences and spatial constraints, has been\n\t\t  suc- cessfully applied to improve object recognition\n\t\t  performance, however, previous work considers only fixed\n\t\t  contextual relations that do not depend of the type of\n\t\t  scene under inspection. In this work, we present a method\n\t\t  that learns adaptive conditional relation- ships that\n\t\t  depend on the type of scene being analyzed. In particular,\n\t\t  we propose a model based on a conditional mixture of trees\n\t\t  that is able to capture contextual relationships among\n\t\t  objects using global information about a scene. Our\n\t\t  experiments show that the adaptive specialization of\n\t\t  contextual relationships improves object recognition\n\t\t  accuracy outperforming previous state-of-the-art\n\t\t  approaches. },\n  url\t\t= {FinalBMVC-12.pdf}\n}\n\n
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\n Robust category-level object recognition is currently a major goal for the computer vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual in- formation, in the form of object co-occurrences and spatial constraints, has been suc- cessfully applied to improve object recognition performance, however, previous work considers only fixed contextual relations that do not depend of the type of scene under inspection. In this work, we present a method that learns adaptive conditional relation- ships that depend on the type of scene being analyzed. In particular, we propose a model based on a conditional mixture of trees that is able to capture contextual relationships among objects using global information about a scene. Our experiments show that the adaptive specialization of contextual relationships improves object recognition accuracy outperforming previous state-of-the-art approaches. \n
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\n \n\n \n \n \n \n \n \n Discriminative local subspaces in gene expression data for effective gene function prediction.\n \n \n \n \n\n\n \n Puelma, T.; Gutierrez, R.; and Soto, A.\n\n\n \n\n\n\n Bioinformatics, 28(17): 2256-64. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"DiscriminativePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  puelma:etal:2012,\n  author\t= {T. Puelma and R. Gutierrez and A. Soto},\n  title\t\t= {Discriminative local subspaces in gene expression data for\n\t\t  effective gene function prediction},\n  journal\t= {Bioinformatics},\n  volume\t= {28},\n  number\t= {17},\n  pages\t\t= {2256-64},\n  year\t\t= {2012},\n  abstract\t= {Motivation: Massive amounts of genome-wide gene expression\n\t\t  data have become available, motivating the development of\n\t\t  computatio- nal approaches that leverage this information\n\t\t  to predict gene func- tion. Among successful approaches,\n\t\t  supervised machine learning methods, such as Support Vector\n\t\t  Machines, have shown superior prediction accuracy. However,\n\t\t  these methods lack the simple biologi- cal intuition\n\t\t  provided by coexpression networks, limiting their practical\n\t\t  usefulness. Results: In this work we present Discriminative\n\t\t  Local Subspaces (DLS), a novel method that combines\n\t\t  supervised machine learning and coexpression techniques\n\t\t  with the goal of systematically predict genes involved in\n\t\t  specific biological processes of interest. Unlike tra-\n\t\t  ditional coexpression networks, DLS uses the knowledge\n\t\t  available in Gene Ontology (GO) to generate informative\n\t\t  training sets that guide the discovery of expression\n\t\t  signatures: expression patterns that are discriminative for\n\t\t  genes involved in the biological process of interest. By\n\t\t  linking genes coexpressed with these signatures, DLS is\n\t\t  able to construct a discriminative coexpression network\n\t\t  that links both, known and previously uncharacterized\n\t\t  genes, for the selected bio- logical process. This paper\n\t\t  focuses on the algorithm behind DLS and shows its\n\t\t  predictive power using an Arabidopsis thaliana dataset and\n\t\t  a representative set of 101 GO-terms from the Biological\n\t\t  Process Ontology. Our results show that DLS has a superior\n\t\t  average accuracy than both, Support Vector Machines and\n\t\t  Coexpression Networks. Thus, DLS is able to provide the\n\t\t  prediction accuracy of supervised learning methods, while\n\t\t  maintaining the intuitive understanding of coexpression\n\t\t  networks. Availability and Implementation: A MATLAB R\n\t\t  implementation of DLS is available at\n\t\t  http://virtualplant.bio.puc.cl/ cgi-bin/Lab/tools.cgi. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/DLS_Revised_Paper.pdf}\n}\n\n
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\n Motivation: Massive amounts of genome-wide gene expression data have become available, motivating the development of computatio- nal approaches that leverage this information to predict gene func- tion. Among successful approaches, supervised machine learning methods, such as Support Vector Machines, have shown superior prediction accuracy. However, these methods lack the simple biologi- cal intuition provided by coexpression networks, limiting their practical usefulness. Results: In this work we present Discriminative Local Subspaces (DLS), a novel method that combines supervised machine learning and coexpression techniques with the goal of systematically predict genes involved in specific biological processes of interest. Unlike tra- ditional coexpression networks, DLS uses the knowledge available in Gene Ontology (GO) to generate informative training sets that guide the discovery of expression signatures: expression patterns that are discriminative for genes involved in the biological process of interest. By linking genes coexpressed with these signatures, DLS is able to construct a discriminative coexpression network that links both, known and previously uncharacterized genes, for the selected bio- logical process. This paper focuses on the algorithm behind DLS and shows its predictive power using an Arabidopsis thaliana dataset and a representative set of 101 GO-terms from the Biological Process Ontology. Our results show that DLS has a superior average accuracy than both, Support Vector Machines and Coexpression Networks. Thus, DLS is able to provide the prediction accuracy of supervised learning methods, while maintaining the intuitive understanding of coexpression networks. Availability and Implementation: A MATLAB R implementation of DLS is available at http://virtualplant.bio.puc.cl/ cgi-bin/Lab/tools.cgi. \n
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\n \n\n \n \n \n \n \n Evaluating tag-based information access in image collections.\n \n \n \n\n\n \n Trattner, C.; Lin, Y.; Parra, D.; Yue, Z.; Real, W.; and Brusilovsky, P.\n\n\n \n\n\n\n In Proceedings of the 23rd ACM conference on Hypertext and social media, pages 113–122, 2012. ACM\n \n\n\n\n
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@InProceedings{\t  trattner2012evaluating,\n  author\t= {Trattner, Christoph and Lin, Yi-ling and Parra, Denis and\n\t\t  Yue, Zhen and Real, William and Brusilovsky, Peter},\n  booktitle\t= {Proceedings of the 23rd ACM conference on Hypertext and\n\t\t  social media},\n  organization\t= {ACM},\n  pages\t\t= {113--122},\n  title\t\t= {Evaluating tag-based information access in image\n\t\t  collections},\n  year\t\t= {2012}\n}\n\n
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\n  \n 2011\n \n \n (21)\n \n \n
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\n \n\n \n \n \n \n \n Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011.\n \n \n \n\n\n \n Burgard, W.; and Roth, D.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2011.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2011,\n  editor\t= {Wolfram Burgard and Dan Roth},\n  title\t\t= {Proceedings of the Twenty-Fifth {AAAI} Conference on\n\t\t  Artificial Intelligence, {AAAI} 2011, San Francisco,\n\t\t  California, USA, August 7-11, 2011},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2011},\n  timestamp\t= {Tue, 09 Aug 2011 07:56:46 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Preferred Explanations: Theory and Generation via Planning.\n \n \n \n \n\n\n \n Sohrabi, S.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011, 2011. \n \n\n\n\n
\n\n\n\n \n \n \"PreferredPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 45 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/sohrabibm11,\n  author\t= {Shirin Sohrabi and Jorge A. Baier and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {Preferred Explanations: Theory and Generation via\n\t\t  Planning},\n  booktitle\t= {Proceedings of the Twenty-Fifth {AAAI} Conference on\n\t\t  Artificial Intelligence, {AAAI} 2011, San Francisco,\n\t\t  California, USA, August 7-11, 2011},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/aaai/2011},\n  url\t\t= {http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3568},\n  timestamp\t= {Tue, 09 Aug 2011 07:56:46 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/SohrabiBM11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2011, October 10-14, 2011, Stanford, California, USA.\n \n \n \n \n\n\n \n Bulitko, V.; and Riedl, M. O.,\n editors.\n \n\n\n \n\n\n\n The AAAI Press. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aiide/2011,\n  editor\t= {Vadim Bulitko and Mark O. Riedl},\n  title\t\t= {Proceedings of the Seventh {AAAI} Conference on Artificial\n\t\t  Intelligence and Interactive Digital Entertainment, {AIIDE}\n\t\t  2011, October 10-14, 2011, Stanford, California, {USA}},\n  publisher\t= {The {AAAI} Press},\n  year\t\t= {2011},\n  url\t\t= {http://www.aaai.org/Library/AIIDE/aiide11contents.php},\n  timestamp\t= {Mon, 13 Feb 2012 19:36:33 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aiide/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Real-Time Adaptive A* with Depression Avoidance.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2011, October 10-14, 2011, Stanford, California, USA, 2011. \n \n\n\n\n
\n\n\n\n \n \n \"Real-TimePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aiide/hernandezb11,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Real-Time Adaptive A* with Depression Avoidance},\n  booktitle\t= {Proceedings of the Seventh {AAAI} Conference on Artificial\n\t\t  Intelligence and Interactive Digital Entertainment, {AIIDE}\n\t\t  2011, October 10-14, 2011, Stanford, California, {USA}},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/aiide/2011},\n  url\t\t= {http://www.aaai.org/ocs/index.php/AIIDE/AIIDE11/paper/view/4082},\n  timestamp\t= {Mon, 13 Feb 2012 19:36:33 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aiide/HernandezB11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings of the 21st International Conference on Automated Planning and Scheduling, ICAPS 2011, Freiburg, Germany June 11-16, 2011.\n \n \n \n\n\n \n Bacchus, F.; Domshlak, C.; Edelkamp, S.; and Helmert, M.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2011.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2011,\n  editor\t= {Fahiem Bacchus and Carmel Domshlak and Stefan Edelkamp and\n\t\t  Malte Helmert},\n  title\t\t= {Proceedings of the 21st International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2011, Freiburg,\n\t\t  Germany June 11-16, 2011},\n  publisher\t= {{AAAI}},\n  year\t\t= {2011},\n  timestamp\t= {Tue, 28 Jun 2011 14:20:04 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fast Subgoaling for Pathfinding via Real-Time Search.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the 21st International Conference on Automated Planning and Scheduling, ICAPS 2011, Freiburg, Germany June 11-16, 2011, 2011. \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/hernandezb11,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Fast Subgoaling for Pathfinding via Real-Time Search},\n  booktitle\t= {Proceedings of the 21st International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2011, Freiburg,\n\t\t  Germany June 11-16, 2011},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/aips/2011},\n  url\t\t= {http://aaai.org/ocs/index.php/ICAPS/ICAPS11/paper/view/2713},\n  timestamp\t= {Tue, 09 Aug 2011 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/HernandezB11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, Taiwan, May 2-6, 2011, Volume 1-3.\n \n \n \n\n\n \n Sonenberg, L.; Stone, P.; Tumer, K.; and Yolum, P.,\n editors.\n \n\n\n \n\n\n\n IFAAMAS. 2011.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/atal/2011,\n  editor\t= {Liz Sonenberg and Peter Stone and Kagan Tumer and Pinar\n\t\t  Yolum},\n  title\t\t= {10th International Conference on Autonomous Agents and\n\t\t  Multiagent Systems {(AAMAS} 2011), Taipei, Taiwan, May 2-6,\n\t\t  2011, Volume 1-3},\n  publisher\t= {{IFAAMAS}},\n  year\t\t= {2011},\n  isbn\t\t= {978-0-9826571-5-7},\n  timestamp\t= {Fri, 18 Nov 2011 09:09:28 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/atal/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Escaping heuristic depressions in real-time heuristic search.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, Taiwan, May 2-6, 2011, Volume 1-3, pages 1267–1268, 2011. \n \n\n\n\n
\n\n\n\n \n \n \"EscapingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/atal/hernandezb11,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Escaping heuristic depressions in real-time heuristic\n\t\t  search},\n  booktitle\t= {10th International Conference on Autonomous Agents and\n\t\t  Multiagent Systems {(AAMAS} 2011), Taipei, Taiwan, May 2-6,\n\t\t  2011, Volume 1-3},\n  pages\t\t= {1267--1268},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/atal/2011},\n  url\t\t= {http://portal.acm.org/citation.cfm?id=2034519\\&\\#38;CFID=69154334\\&\\#38;CFTOKEN=45298625},\n  timestamp\t= {Fri, 18 Nov 2011 09:09:28 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/atal/HernandezB11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Flexible SOA Lifecycle on the Cloud Using SCA.\n \n \n \n\n\n \n Ruz, C.; Baude, F.; Sauvan, B.; Mos, A.; and Boulze, A.\n\n\n \n\n\n\n In EDOCW, pages 275–282, 2011. IEEE Computer Society\n \n\n\n\n
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@InProceedings{\t  dblp:conf/edoc/ruzbsmb11,\n  author\t= {Cristian Ruz and Fran{\\c{c}}oise Baude and Bastien Sauvan\n\t\t  and Adrian Mos and Alain Boulze},\n  title\t\t= {Flexible {SOA} Lifecycle on the Cloud Using {SCA}},\n  booktitle\t= {{EDOCW}},\n  pages\t\t= {275--282},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {2011}\n}\n\n
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\n \n\n \n \n \n \n \n \n IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011.\n \n \n \n \n\n\n \n Walsh, T.,\n editor.\n \n\n\n \n\n\n\n IJCAI/AAAI. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"IJCAIPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2011,\n  editor\t= {Toby Walsh},\n  title\t\t= {{IJCAI} 2011, Proceedings of the 22nd International Joint\n\t\t  Conference on Artificial Intelligence, Barcelona,\n\t\t  Catalonia, Spain, July 16-22, 2011},\n  publisher\t= {{IJCAI/AAAI}},\n  year\t\t= {2011},\n  url\t\t= {http://ijcai.org/proceedings/2011},\n  isbn\t\t= {978-1-57735-516-8},\n  timestamp\t= {Wed, 20 Jul 2016 14:35:20 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Real-Time Heuristic Search with Depression Avoidance.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, pages 578–583, 2011. \n \n\n\n\n
\n\n\n\n \n \n \"Real-TimePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/hernandezb11,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Real-Time Heuristic Search with Depression Avoidance},\n  booktitle\t= {{IJCAI} 2011, Proceedings of the 22nd International Joint\n\t\t  Conference on Artificial Intelligence, Barcelona,\n\t\t  Catalonia, Spain, July 16-22, 2011},\n  pages\t\t= {578--583},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/ijcai/2011},\n  url\t\t= {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-104},\n  doi\t\t= {10.5591/978-1-57735-516-8/IJCAI11-104},\n  timestamp\t= {Tue, 23 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/HernandezB11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings of the Fourth Annual Symposium on Combinatorial Search, SOCS 2011, Castell de Cardona, Barcelona, Spain, July 15.16, 2011.\n \n \n \n\n\n \n Borrajo, D.; Likhachev, M.; and López, C. L.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2011.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/socs/2011,\n  editor\t= {Daniel Borrajo and Maxim Likhachev and Carlos Linares\n\t\t  L{\\'{o}}pez},\n  title\t\t= {Proceedings of the Fourth Annual Symposium on\n\t\t  Combinatorial Search, {SOCS} 2011, Castell de Cardona,\n\t\t  Barcelona, Spain, July 15.16, 2011},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2011},\n  timestamp\t= {Thu, 07 Jul 2011 18:00:20 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/2011},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Real-Time Adaptive A* with Depression Avoidance.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In Proceedings of the Fourth Annual Symposium on Combinatorial Search, SOCS 2011, Castell de Cardona, Barcelona, Spain, July 15.16, 2011, 2011. \n \n\n\n\n
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@InProceedings{\t  dblp:conf/socs/hernandezb11,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Real-Time Adaptive A* with Depression Avoidance},\n  booktitle\t= {Proceedings of the Fourth Annual Symposium on\n\t\t  Combinatorial Search, {SOCS} 2011, Castell de Cardona,\n\t\t  Barcelona, Spain, July 15.16, 2011},\n  year\t\t= {2011},\n  crossref\t= {DBLP:conf/socs/2011},\n  url\t\t= {http://www.aaai.org/ocs/index.php/SOCS/SOCS11/paper/view/4036},\n  timestamp\t= {Tue, 09 Aug 2011 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/socs/HernandezB11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning with rich goals, preferences and procedural operators via reformulation.\n \n \n \n \n\n\n \n Baier, J. A.\n\n\n \n\n\n\n AI Commun., 24(4): 347–348. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/aicom/baier11,\n  author\t= {Jorge A. Baier},\n  title\t\t= {Planning with rich goals, preferences and procedural\n\t\t  operators via reformulation},\n  journal\t= {{AI} Commun.},\n  volume\t= {24},\n  number\t= {4},\n  pages\t\t= {347--348},\n  year\t\t= {2011},\n  url\t\t= {https://doi.org/10.3233/AIC-2011-0507},\n  doi\t\t= {10.3233/AIC-2011-0507},\n  timestamp\t= {Thu, 18 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/aicom/Baier11},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Learning Discriminative Local Binary Patterns for Face Recognition.\n \n \n \n \n\n\n \n Maturana, D.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In 9th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2011. \n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  maturana:etal:2011,\n  author\t= {D. Maturana and D. Mery and A. Soto},\n  title\t\t= {Learning Discriminative Local Binary Patterns for Face\n\t\t  Recognition},\n  booktitle\t= {9th IEEE International Conference on Automatic Face and\n\t\t  Gesture Recognition (FG)},\n  year\t\t= {2011},\n  abstract\t= {Histograms of Local Binary Patterns (LBPs) and variations\n\t\t  thereof are a popular local visual descriptor for face\n\t\t  recognition. So far, most variations of LBP are designed by\n\t\t  hand or are learned with non-supervised methods. In this\n\t\t  work we propose a simple method to learn discriminative\n\t\t  LBPs in a supervised manner. The method represents an\n\t\t  LBP-like descriptor as a set of pixel comparisons within a\n\t\t  neighborhood and heuristically seeks for a set of pixel\n\t\t  comparisons so as to maximize a Fisher separability\n\t\t  criterion for the resulting his- tograms. Tests on standard\n\t\t  face recognition datasets show that this method can create\n\t\t  compact yet discriminative descriptors.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/FG-2011.pdf}\n}\n\n
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\n Histograms of Local Binary Patterns (LBPs) and variations thereof are a popular local visual descriptor for face recognition. So far, most variations of LBP are designed by hand or are learned with non-supervised methods. In this work we propose a simple method to learn discriminative LBPs in a supervised manner. The method represents an LBP-like descriptor as a set of pixel comparisons within a neighborhood and heuristically seeks for a set of pixel comparisons so as to maximize a Fisher separability criterion for the resulting his- tograms. Tests on standard face recognition datasets show that this method can create compact yet discriminative descriptors.\n
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\n \n\n \n \n \n \n \n \n Automated Fish Bone Detection using X-ray Testing.\n \n \n \n \n\n\n \n Mery, D.; Lillo, I.; Loebel, H.; V. Riffo, A. S.; Cipriano, A.; and Aguilera, J.\n\n\n \n\n\n\n Journal of Food Engineering, 105(3): 485-492. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"AutomatedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mery:etal:2011,\n  author\t= {D. Mery and I. Lillo and H. Loebel and V. Riffo, A. Soto\n\t\t  and A. Cipriano and JM. Aguilera},\n  title\t\t= {Automated Fish Bone Detection using X-ray Testing},\n  journal\t= {Journal of Food Engineering},\n  volume\t= {105},\n  number\t= {3},\n  pages\t\t= {485-492},\n  year\t\t= {2011},\n  abstract\t= {In countries where fish is often consumed, fish bones are\n\t\t  some of the most frequently ingested foreign bodies\n\t\t  encountered in foods. In the production of fish fillets,\n\t\t  fish bone detection is performed by human inspection using\n\t\t  their sense of touch and vision which can lead to\n\t\t  misclassification. Effective detection of fish bones in the\n\t\t  quality control process would help avoid this problem. For\n\t\t  this reason, an X-ray machine vision approach to\n\t\t  automatically detect fish bones in fish fillets was\n\t\t  developed. This paper describes our approach and the\n\t\t  corresponding experiments with salmon and trout fillets. In\n\t\t  the experiments, salmon X-ray images using 10×10 pixels\n\t\t  detection windows and 24 intensity features (selected from\n\t\t  279 features) were analyzed. The methodology was validated\n\t\t  using representative fish bones and trouts provided by a\n\t\t  salmon industry and yielded a detection performance of 99%.\n\t\t  We believe that the proposed approach opens new\n\t\t  possibilities in the field of automated visual inspection\n\t\t  of salmon, trout and other similar fish. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/2011-JFoodEng-SalmonX.pdf}\n}\n\n
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\n In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. In the production of fish fillets, fish bone detection is performed by human inspection using their sense of touch and vision which can lead to misclassification. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, an X-ray machine vision approach to automatically detect fish bones in fish fillets was developed. This paper describes our approach and the corresponding experiments with salmon and trout fillets. In the experiments, salmon X-ray images using 10×10 pixels detection windows and 24 intensity features (selected from 279 features) were analyzed. The methodology was validated using representative fish bones and trouts provided by a salmon industry and yielded a detection performance of 99%. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon, trout and other similar fish. \n
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\n \n\n \n \n \n \n \n Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping.\n \n \n \n\n\n \n Parra, D.; Karatzoglou, A.; Amatriain, X.; and Yavuz, I.\n\n\n \n\n\n\n In 2011. \n \n\n\n\n
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@InProceedings{\t  parra2011implicit,\n  author\t= {Parra, Denis and Karatzoglou, Alexandros and Amatriain,\n\t\t  Xavier and Yavuz, Idil},\n  title\t\t= {Implicit Feedback Recommendation via Implicit-to-Explicit\n\t\t  Ordinal Logistic Regression Mapping},\n  year\t\t= {2011}\n}\n\n
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\n \n\n \n \n \n \n \n Walk the Talk: analyzing the relation between implicit and explicit feedback for preference elicitation.\n \n \n \n\n\n \n Parra, D.; and Amatriain, X.\n\n\n \n\n\n\n In pages 255–268, 2011. Springer Berlin/Heidelberg\n \n\n\n\n
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@InProceedings{\t  parra2011walk,\n  author\t= {Parra, Denis and Amatriain, Xavier},\n  journal\t= {User Modeling, Adaption and Personalization},\n  pages\t\t= {255--268},\n  publisher\t= {Springer Berlin/Heidelberg},\n  title\t\t= {Walk the Talk: analyzing the relation between implicit and\n\t\t  explicit feedback for preference elicitation},\n  year\t\t= {2011}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mixing Hierarchical Contexts for Object Recognition.\n \n \n \n \n\n\n \n Peralta, B.; and Soto, A.\n\n\n \n\n\n\n In CIARP, 2011. \n \n\n\n\n
\n\n\n\n \n \n \"MixingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  peralta:etal:2011,\n  author\t= {B. Peralta and A. Soto},\n  title\t\t= {Mixing Hierarchical Contexts for Object Recognition},\n  booktitle\t= {{CIARP}},\n  year\t\t= {2011},\n  abstract\t= {Robust category-level object recognition is currently a\n\t\t  major goal for the Computer Vision community. Intra-class\n\t\t  and pose variations, as well as, background clutter and\n\t\t  partial occlusions are some of the main difficulties to\n\t\t  achieve this goal. Contextual information in the form of\n\t\t  ob- ject co-ocurrences and spatial contraints has been\n\t\t  successfully applied to reduce the inherent uncertainty of\n\t\t  the visual world. Recently, Choi et al. [5] propose the use\n\t\t  of a tree-structured graphical model to capture contextual\n\t\t  relations among objects. Under this model there is only one\n\t\t  possible fixed contextual relation among subsets of\n\t\t  objects. In this work we extent Choi et al. approach by\n\t\t  using a mixture model to consider the case that contextual\n\t\t  relations among objects depend on scene type. Our\n\t\t  experiments highlight the advantages of our proposal,\n\t\t  showing that the adaptive specialization of contextual\n\t\t  relations improves object recogni- tion and object\n\t\t  detection performances. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Peralta-2011.pdf}\n}\n\n
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\n Robust category-level object recognition is currently a major goal for the Computer Vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information in the form of ob- ject co-ocurrences and spatial contraints has been successfully applied to reduce the inherent uncertainty of the visual world. Recently, Choi et al. [5] propose the use of a tree-structured graphical model to capture contextual relations among objects. Under this model there is only one possible fixed contextual relation among subsets of objects. In this work we extent Choi et al. approach by using a mixture model to consider the case that contextual relations among objects depend on scene type. Our experiments highlight the advantages of our proposal, showing that the adaptive specialization of contextual relations improves object recogni- tion and object detection performances. \n
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\n \n\n \n \n \n \n \n \n Active Learning and Subspace Clustering for Anomaly Detection.\n \n \n \n \n\n\n \n Pichara, K.; and Soto, A.\n\n\n \n\n\n\n Intelligent Data Analysis (IDA), 15(2): 151-171. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"ActivePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  pichara:etal:2011,\n  author\t= {K. Pichara and A. Soto},\n  title\t\t= {Active Learning and Subspace Clustering for Anomaly\n\t\t  Detection},\n  journal\t= { Intelligent Data Analysis (IDA)},\n  volume\t= {15},\n  number\t= {2},\n  pages\t\t= {151-171},\n  year\t\t= {2011},\n  abstract\t= {Today, anomaly detection is a highly valuable application\n\t\t  in the analysis of current huge datasets. Insurance\n\t\t  companies, banks and many manufacturing industries need\n\t\t  systems to help humans to detect anomalies in their daily\n\t\t  information. In general, anomalies are a very small\n\t\t  fraction of the data, therefore their detection is not an\n\t\t  easy task. Usually real sources of an anomaly are given by\n\t\t  specific values expressed on selective dimensions of\n\t\t  datasets, furthermore, many anomalies are not really\n\t\t  interesting for humans, due to the fact that\n\t\t  interestingness of anomalies is categorized subjectively by\n\t\t  the human user. In this paper we propose a new\n\t\t  semi-supervised algorithm that actively learns to detect\n\t\t  relevant anomalies by interacting with an expert user in\n\t\t  order to obtain semantic information about user\n\t\t  preferences. Our approach is based on 3 main steps. First,\n\t\t  a Bayes network identifies an initial set of candidate\n\t\t  anomalies. Afterwards, a subspace clustering technique\n\t\t  identifies relevant subsets of dimensions. Finally, a\n\t\t  probabilistic active learning scheme, based on properties\n\t\t  of Dirichlet distribution, uses the feedback from an expert\n\t\t  user to efficiently search for relevant anomalies. Our\n\t\t  results, using synthetic and real datasets, indicate that,\n\t\t  under noisy data and anomalies presenting regular patterns,\n\t\t  our approach correctly identifies relevant anomalies. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/IDA-2011.pdf}\n}\n\n
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\n Today, anomaly detection is a highly valuable application in the analysis of current huge datasets. Insurance companies, banks and many manufacturing industries need systems to help humans to detect anomalies in their daily information. In general, anomalies are a very small fraction of the data, therefore their detection is not an easy task. Usually real sources of an anomaly are given by specific values expressed on selective dimensions of datasets, furthermore, many anomalies are not really interesting for humans, due to the fact that interestingness of anomalies is categorized subjectively by the human user. In this paper we propose a new semi-supervised algorithm that actively learns to detect relevant anomalies by interacting with an expert user in order to obtain semantic information about user preferences. Our approach is based on 3 main steps. First, a Bayes network identifies an initial set of candidate anomalies. Afterwards, a subspace clustering technique identifies relevant subsets of dimensions. Finally, a probabilistic active learning scheme, based on properties of Dirichlet distribution, uses the feedback from an expert user to efficiently search for relevant anomalies. Our results, using synthetic and real datasets, indicate that, under noisy data and anomalies presenting regular patterns, our approach correctly identifies relevant anomalies. \n
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\n \n\n \n \n \n \n \n Vectorised Spreading Activation Algorithm for Centrality Measurement.\n \n \n \n\n\n \n Troussov, A; Dařena, F; Žižka, J; Parra, D; and Brusilovsky, P\n\n\n \n\n\n\n Acta univ. agric. et silvic. Mendel. Brun.(Brno). 2011.\n \n\n\n\n
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@Article{\t  troussov2011vectorised,\n  author\t= {Troussov, A and Da{\\v{r}}ena, F and {\\v{Z}}i{\\v{z}}ka, J\n\t\t  and Parra, D and Brusilovsky, P},\n  journal\t= {Acta univ. agric. et silvic. Mendel. Brun.(Brno)},\n  title\t\t= {Vectorised Spreading Activation Algorithm for Centrality\n\t\t  Measurement},\n  year\t\t= {2011}\n}\n\n
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\n \n\n \n \n \n \n \n Collaborative information finding in smaller communities: The case of research talks.\n \n \n \n\n\n \n Brusilovsky, P.; Parra, D.; Sahebi, S.; and Wongchokprasitti, C.\n\n\n \n\n\n\n In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2010 6th International Conference on, pages 1–10, 2010. IEEE\n \n\n\n\n
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@InProceedings{\t  brusilovsky2010collaborative,\n  author\t= {Brusilovsky, Peter and Parra, Denis and Sahebi, Shaghayegh\n\t\t  and Wongchokprasitti, Chirayu},\n  booktitle\t= {Collaborative Computing: Networking, Applications and\n\t\t  Worksharing (CollaborateCom), 2010 6th International\n\t\t  Conference on},\n  organization\t= {IEEE},\n  pages\t\t= {1--10},\n  title\t\t= {Collaborative information finding in smaller communities:\n\t\t  The case of research talks},\n  year\t\t= {2010}\n}\n\n
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\n \n\n \n \n \n \n \n \n Active visual perception for mobile robot localization.\n \n \n \n \n\n\n \n Correa, J.; and Soto, A.\n\n\n \n\n\n\n Journal of Intelligent and Robotic Systems, 58(3-4): 339-354. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"ActivePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  correa:etal:2010,\n  author\t= {J. Correa and A. Soto},\n  title\t\t= {Active visual perception for mobile robot localization},\n  journal\t= {Journal of Intelligent and Robotic Systems},\n  volume\t= {58},\n  number\t= {3-4},\n  pages\t\t= {339-354},\n  year\t\t= {2010},\n  abstract\t= {Localization is a key issue for a mobile robot, in\n\t\t  particular in environments where a globally accurate\n\t\t  positioning system, such as GPS, is not available. In these\n\t\t  environments, accurate and efficient robot localization is\n\t\t  not a trivial task, as an increase in accuracy usually\n\t\t  leads to an impoverishment in efficiency and viceversa.\n\t\t  Active perception appears as an appealing way to improve\n\t\t  the localization process by increasing the richness of the\n\t\t  information acquired from the environment. In this paper,\n\t\t  we present an active perception strategy for a mobile robot\n\t\t  provided with a visual sensor mounted on a pan-tilt\n\t\t  mechanism. The visual sensor has a limited field of view,\n\t\t  so the goal of the active perception strategy is to use the\n\t\t  pan-tilt unit to direct the sensor to informative parts of\n\t\t  the environment. To achieve this goal, we use a topological\n\t\t  map of the environment and a Bayesian non-parametric\n\t\t  estimation of robot position based on a particle filter. We\n\t\t  slightly modify the regular implementation of this filter\n\t\t  by including an additional step that selects the best\n\t\t  perceptual action using Monte Carlo estimations. We\n\t\t  understand the best perceptual action as the one that\n\t\t  produces the greatest reduction in uncertainty about the\n\t\t  robot position. We also consider in our optimization\n\t\t  function a cost term that favors efficient perceptual\n\t\t  actions. Previous works have proposed active perception\n\t\t  strategies for robot localization, but mainly in the\n\t\t  context of range sensors, grid representations of the\n\t\t  environment, and parametric techniques, such as the\n\t\t  extended Kalman filter. Accordingly, the main contributions\n\t\t  of this work are: i) Development of a sound strategy for\n\t\t  active selection of perceptual actions in the context of a\n\t\t  visual sensor and a topological map; ii) Real time\n\t\t  operation using a modified version of the particle filter\n\t\t  and Monte Carlo based estimations; iii) Implementation and\n\t\t  testing of these ideas using simulations and a real case\n\t\t  scenario. Our results indicate that, in terms of accuracy\n\t\t  of robot localization, the proposed approach decreases mean\n\t\t  average error and standard deviation with respect to a\n\t\t  passive perception scheme. Furthermore, in terms of\n\t\t  efficiency, the active scheme is able to operate in real\n\t\t  time without adding a relevant overhead to the regular\n\t\t  robot operation. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Intell-Robots-2010.pdf}\n}\n\n
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\n Localization is a key issue for a mobile robot, in particular in environments where a globally accurate positioning system, such as GPS, is not available. In these environments, accurate and efficient robot localization is not a trivial task, as an increase in accuracy usually leads to an impoverishment in efficiency and viceversa. Active perception appears as an appealing way to improve the localization process by increasing the richness of the information acquired from the environment. In this paper, we present an active perception strategy for a mobile robot provided with a visual sensor mounted on a pan-tilt mechanism. The visual sensor has a limited field of view, so the goal of the active perception strategy is to use the pan-tilt unit to direct the sensor to informative parts of the environment. To achieve this goal, we use a topological map of the environment and a Bayesian non-parametric estimation of robot position based on a particle filter. We slightly modify the regular implementation of this filter by including an additional step that selects the best perceptual action using Monte Carlo estimations. We understand the best perceptual action as the one that produces the greatest reduction in uncertainty about the robot position. We also consider in our optimization function a cost term that favors efficient perceptual actions. Previous works have proposed active perception strategies for robot localization, but mainly in the context of range sensors, grid representations of the environment, and parametric techniques, such as the extended Kalman filter. Accordingly, the main contributions of this work are: i) Development of a sound strategy for active selection of perceptual actions in the context of a visual sensor and a topological map; ii) Real time operation using a modified version of the particle filter and Monte Carlo based estimations; iii) Implementation and testing of these ideas using simulations and a real case scenario. Our results indicate that, in terms of accuracy of robot localization, the proposed approach decreases mean average error and standard deviation with respect to a passive perception scheme. Furthermore, in terms of efficiency, the active scheme is able to operate in real time without adding a relevant overhead to the regular robot operation. \n
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\n \n\n \n \n \n \n \n Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, Toronto, Ontario, Canada, May 9-13, 2010.\n \n \n \n\n\n \n Lin, F.; Sattler, U.; and Truszczynski, M.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2010.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/kr/2010,\n  editor\t= {Fangzhen Lin and Ulrike Sattler and Miroslaw\n\t\t  Truszczynski},\n  title\t\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Twelfth International Conference, {KR}\n\t\t  2010, Toronto, Ontario, Canada, May 9-13, 2010},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2010},\n  timestamp\t= {Wed, 23 Jun 2010 11:56:48 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/2010},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Diagnosis as Planning Revisited.\n \n \n \n \n\n\n \n Sohrabi, S.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Principles of Knowledge Representation and Reasoning: Proceedings of the Twelfth International Conference, KR 2010, Toronto, Ontario, Canada, May 9-13, 2010, 2010. \n \n\n\n\n
\n\n\n\n \n \n \"DiagnosisPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 61 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/kr/sohrabibm10,\n  author\t= {Shirin Sohrabi and Jorge A. Baier and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {Diagnosis as Planning Revisited},\n  booktitle\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Twelfth International Conference, {KR}\n\t\t  2010, Toronto, Ontario, Canada, May 9-13, 2010},\n  year\t\t= {2010},\n  crossref\t= {DBLP:conf/kr/2010},\n  url\t\t= {http://aaai.org/ocs/index.php/KR/KR2010/paper/view/1300},\n  timestamp\t= {Wed, 23 Jun 2010 11:56:48 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/SohrabiBM10},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n ESB federation for large-scale SOA.\n \n \n \n\n\n \n Baude, F.; Filali, I.; Huet, F.; Contes, V. L.; Mathias, E. N.; Merle, P.; Ruz, C.; Krummenacher, R.; Simperl, E. P. B.; Hammerling, C.; and Lorré, J.\n\n\n \n\n\n\n In SAC, pages 2459–2466, 2010. ACM\n \n\n\n\n
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@InProceedings{\t  dblp:conf/sac/baudefhcmmrkshl10,\n  author\t= {Fran{\\c{c}}oise Baude and Imen Filali and Fabrice Huet and\n\t\t  Virginie Legrand Contes and Elton N. Mathias and Philippe\n\t\t  Merle and Cristian Ruz and Reto Krummenacher and Elena\n\t\t  Paslaru Bontas Simperl and Christophe Hammerling and\n\t\t  Jean{-}Pierre Lorr{\\'{e}}},\n  title\t\t= {{ESB} federation for large-scale {SOA}},\n  booktitle\t= {{SAC}},\n  pages\t\t= {2459--2466},\n  publisher\t= {{ACM}},\n  year\t\t= {2010}\n}\n\n
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\n \n\n \n \n \n \n \n \n SCCC 2010, Proceedings of the XXIX International Conference of the Chilean Computer Science Society, Antofagasta, Chile, 15-19 November 2010.\n \n \n \n \n\n\n \n Ochoa, S. F.; Meza, F.; Mery, D.; and Cubillos, C.,\n editors.\n \n\n\n \n\n\n\n IEEE Computer Society. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"SCCCPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/sccc/2010,\n  editor\t= {Sergio F. Ochoa and Federico Meza and Domingo Mery and\n\t\t  Claudio Cubillos},\n  title\t\t= {{SCCC} 2010, Proceedings of the {XXIX} International\n\t\t  Conference of the Chilean Computer Science Society,\n\t\t  Antofagasta, Chile, 15-19 November 2010},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {2010},\n  url\t\t= {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5750114},\n  isbn\t\t= {978-0-7695-4400-7},\n  timestamp\t= {Mon, 29 Jun 2015 17:51:30 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/2010},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Escaping Heuristic Hollows in Real-Time Search without Learning.\n \n \n \n \n\n\n \n Hernández, C.; and Baier, J. A.\n\n\n \n\n\n\n In SCCC 2010, Proceedings of the XXIX International Conference of the Chilean Computer Science Society, Antofagasta, Chile, 15-19 November 2010, pages 172–177, 2010. \n \n\n\n\n
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@InProceedings{\t  dblp:conf/sccc/hernandezb10,\n  author\t= {Carlos Hern{\\'{a}}ndez and Jorge A. Baier},\n  title\t\t= {Escaping Heuristic Hollows in Real-Time Search without\n\t\t  Learning},\n  booktitle\t= {{SCCC} 2010, Proceedings of the {XXIX} International\n\t\t  Conference of the Chilean Computer Science Society,\n\t\t  Antofagasta, Chile, 15-19 November 2010},\n  pages\t\t= {172--177},\n  year\t\t= {2010},\n  crossref\t= {DBLP:conf/sccc/2010},\n  url\t\t= {https://doi.org/10.1109/SCCC.2010.16},\n  doi\t\t= {10.1109/SCCC.2010.16},\n  timestamp\t= {Thu, 25 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/HernandezB10},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Indoor Scene Recognition Through Object Detection.\n \n \n \n \n\n\n \n Espinace, P.; Kollar, T.; Soto, A.; and Roy, N.\n\n\n \n\n\n\n In Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA), 2010. \n \n\n\n\n
\n\n\n\n \n \n \"IndoorPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  espinace:etal:2010,\n  author\t= {P. Espinace and T. Kollar and A. Soto and N. Roy},\n  title\t\t= {Indoor Scene Recognition Through Object Detection},\n  booktitle\t= {Proc. of IEEE Int. Conf. on Robotics and Automation\n\t\t  (ICRA)},\n  year\t\t= {2010},\n  abstract\t= {Scene recognition is a highly valuable percep- tual\n\t\t  ability for an indoor mobile robot, however, current\n\t\t  approaches for scene recognition present a significant drop\n\t\t  in performance for the case of indoor scenes. We believe\n\t\t  that this can be explained by the high appearance\n\t\t  variability of indoor environments. This stresses the need\n\t\t  to include high- level semantic information in the\n\t\t  recognition process. In this work we propose a new approach\n\t\t  for indoor scene recognition based on a generative\n\t\t  probabilistic hierarchical model that uses common objects\n\t\t  as an intermediate semantic representation. Under this\n\t\t  model, we use object classifiers to associate low- level\n\t\t  visual features to objects, and at the same time, we use\n\t\t  contextual relations to associate objects to scenes. As a\n\t\t  further contribution, we improve the performance of current\n\t\t  state-of- the-art category-level object classifiers by\n\t\t  including geometrical information obtained from a 3D range\n\t\t  sensor that facilitates the implementation of a focus of\n\t\t  attention mechanism within a Monte Carlo sampling scheme.\n\t\t  We test our approach using real data, showing significant\n\t\t  advantages with respect to previous state-of-the-art\n\t\t  methods. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Icra-2010.pdf}\n}\n\n
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\n Scene recognition is a highly valuable percep- tual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in performance for the case of indoor scenes. We believe that this can be explained by the high appearance variability of indoor environments. This stresses the need to include high- level semantic information in the recognition process. In this work we propose a new approach for indoor scene recognition based on a generative probabilistic hierarchical model that uses common objects as an intermediate semantic representation. Under this model, we use object classifiers to associate low- level visual features to objects, and at the same time, we use contextual relations to associate objects to scenes. As a further contribution, we improve the performance of current state-of- the-art category-level object classifiers by including geometrical information obtained from a 3D range sensor that facilitates the implementation of a focus of attention mechanism within a Monte Carlo sampling scheme. We test our approach using real data, showing significant advantages with respect to previous state-of-the-art methods. \n
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\n \n\n \n \n \n \n \n \n Face Recognition with Decision Tree-based Local Binary Patterns.\n \n \n \n \n\n\n \n Maturana, D.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In Proc. of Asian Conference on Computer Vision (ACCV-2010), 2010. \n \n\n\n\n
\n\n\n\n \n \n \"FacePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  maturana:etal:2010,\n  author\t= {D. Maturana and D. Mery and A. Soto},\n  title\t\t= {Face Recognition with Decision Tree-based Local Binary\n\t\t  Patterns},\n  booktitle\t= {Proc. of Asian Conference on Computer Vision (ACCV-2010)},\n  year\t\t= {2010},\n  abstract\t= {Many state-of-the-art face recognition algorithms use\n\t\t  image descriptors based on features known as Local Binary\n\t\t  Patterns (LBPs). While many variations of LBP exist, so far\n\t\t  none of them can automati- cally adapt to the training\n\t\t  data. We introduce and analyze a novel gen- eralization of\n\t\t  LBP that learns the most discriminative LBP-like features\n\t\t  for each facial region in a supervised manner. Since the\n\t\t  proposed method is based on Decision Trees, we call it\n\t\t  Decision Tree Local Binary Pat- terns or DT-LBPs. Tests on\n\t\t  standard face recognition datasets show the superiority of\n\t\t  DT-LBP with respect of several state-of-the-art feature\n\t\t  descriptors regularly used in face recognition\n\t\t  applications. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/ACCV-2010.pdf}\n}\n\n
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\n Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary Patterns (LBPs). While many variations of LBP exist, so far none of them can automati- cally adapt to the training data. We introduce and analyze a novel gen- eralization of LBP that learns the most discriminative LBP-like features for each facial region in a supervised manner. Since the proposed method is based on Decision Trees, we call it Decision Tree Local Binary Pat- terns or DT-LBPs. Tests on standard face recognition datasets show the superiority of DT-LBP with respect of several state-of-the-art feature descriptors regularly used in face recognition applications. \n
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\n \n\n \n \n \n \n \n \n Automated Detection of Fish Bones in Salmon Fillets using X-ray Testing.\n \n \n \n \n\n\n \n Mery, D.; Lillo, I.; Loebel, H.; Riffo, V.; Soto, A.; Cipriano, A.; and Aguilera, J.\n\n\n \n\n\n\n In Proc. of 4th Pacific-Rim Symposium on Image and Video Technology (PSIVT-2010), 2010. \n \n\n\n\n
\n\n\n\n \n \n \"AutomatedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  mery-psivt:etal:2010,\n  author\t= {D. Mery and I. Lillo and H. Loebel and V. Riffo and A.\n\t\t  Soto and A. Cipriano and JM. Aguilera},\n  title\t\t= {Automated Detection of Fish Bones in Salmon Fillets using\n\t\t  X-ray Testing},\n  booktitle\t= {Proc. of 4th Pacific-Rim Symposium on Image and Video\n\t\t  Technology (PSIVT-2010)},\n  year\t\t= {2010},\n  abstract\t= {X-ray testing is playing an increasingly important role in\n\t\t  food quality assurance. In the production of fish fillets,\n\t\t  however, fish bone detection is performed by human\n\t\t  operators using their sense of touch and vision which can\n\t\t  lead to misclassification. In countries where fish is often\n\t\t  consumed, fish bones are some of the most frequently\n\t\t  ingested foreign bodies encountered in foods. Effective\n\t\t  detection of fish bones in the quality control process\n\t\t  would help avoid this problem. For this reason, we\n\t\t  developed an X-ray machine vision approach to au-\n\t\t  tomatically detect fish bones in fish fillets. This paper\n\t\t  describes our approach and the corresponding validation\n\t\t  experiments with salmon fillets. The approach consists of\n\t\t  six steps: 1) A digital X-ray image is taken of the fish\n\t\t  fillet being tested. 2) The X-ray image is filtered and\n\t\t  enhanced to facilitate the detection of fish bones. 3)\n\t\t  Potential fish bones in the image are segmented using band\n\t\t  pass filtering, thresholding and morphological techniques.\n\t\t  4) Intensity features of the enhanced X-ray image are\n\t\t  extracted from small detection windows that are defined in\n\t\t  those regions where potential fish bones were segmented. 5)\n\t\t  A classifier is used to discriminate between ‘bones’\n\t\t  and ‘no-bones’ classes in the detection windows. 6)\n\t\t  Finally, fish bones in the X-ray image are isolated using\n\t\t  morphological operations applied on the corresponding\n\t\t  segments classified as ‘bones’. In the experiments we\n\t\t  used a high resolution flat panel detector with the\n\t\t  capacity to capture up to a 6 million pixel digital X-ray\n\t\t  image. In the training phase, we analyzed 20 representative\n\t\t  salmon fillets, 7700 detection windows (10×10 pixels) and\n\t\t  279 intensity features. Cross validation yielded a\n\t\t  detection performance of 95% using a support vector machine\n\t\t  classifier with only 24 selected features. We believe that\n\t\t  the proposed approach opens new possibilities in the field\n\t\t  of automated visual inspection of salmon and other similar\n\t\t  fish. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/PSIVT-2010.pdf}\n}\n\n
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\n X-ray testing is playing an increasingly important role in food quality assurance. In the production of fish fillets, however, fish bone detection is performed by human operators using their sense of touch and vision which can lead to misclassification. In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, we developed an X-ray machine vision approach to au- tomatically detect fish bones in fish fillets. This paper describes our approach and the corresponding validation experiments with salmon fillets. The approach consists of six steps: 1) A digital X-ray image is taken of the fish fillet being tested. 2) The X-ray image is filtered and enhanced to facilitate the detection of fish bones. 3) Potential fish bones in the image are segmented using band pass filtering, thresholding and morphological techniques. 4) Intensity features of the enhanced X-ray image are extracted from small detection windows that are defined in those regions where potential fish bones were segmented. 5) A classifier is used to discriminate between ‘bones’ and ‘no-bones’ classes in the detection windows. 6) Finally, fish bones in the X-ray image are isolated using morphological operations applied on the corresponding segments classified as ‘bones’. In the experiments we used a high resolution flat panel detector with the capacity to capture up to a 6 million pixel digital X-ray image. In the training phase, we analyzed 20 representative salmon fillets, 7700 detection windows (10×10 pixels) and 279 intensity features. Cross validation yielded a detection performance of 95% using a support vector machine classifier with only 24 selected features. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon and other similar fish. \n
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\n \n\n \n \n \n \n \n \n Quality Classification of Corn Tortillas using Computer Vision.\n \n \n \n \n\n\n \n Mery, D.; Chanona-Perez, J.; Soto, A.; Aguilera, J.; Cipriano, A.; Velez-Riverab, N.; Arzate-Vazquez, I.; and Gutierrez-Lopez, G.\n\n\n \n\n\n\n Journal of Food Engineering, 101(4): 357-364. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"QualityPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mery:etal:2010,\n  author\t= {D. Mery and J. Chanona-Perez and A. Soto and JM. Aguilera\n\t\t  and A. Cipriano and N. Velez-Riverab and I. Arzate-Vazquez\n\t\t  and G. Gutierrez-Lopez},\n  title\t\t= {Quality Classification of Corn Tortillas using Computer\n\t\t  Vision},\n  journal\t= {Journal of Food Engineering},\n  volume\t= {101},\n  number\t= {4},\n  pages\t\t= {357-364},\n  year\t\t= {2010},\n  abstract\t= {Computer vision is playing an increasingly important role\n\t\t  in automated visual food inspection. However quality\n\t\t  control in tortilla production is still performed by human\n\t\t  operators which may lead to misclassification due to their\n\t\t  subjectivity and fatigue. In order to reduce the need for\n\t\t  human operators and therefore misclassification, we\n\t\t  developed a computer vision framework to automatically\n\t\t  classify the quality of corn tortillas according to five\n\t\t  hedonic sub-classes given by a sensorial panel. The\n\t\t  proposed framework analyzed 750 corn tortillas obtained\n\t\t  from 15 different Mexican commercial stores which were\n\t\t  either small, medium or large in size. More than 2300\n\t\t  geometric and color features were extracted from 1500\n\t\t  images capturing both sides of the 750 tortillas. After\n\t\t  implementing a feature selection algorithm, in which the\n\t\t  most relevant features were selected for the classification\n\t\t  of the five sub-classes, only 64 features were required to\n\t\t  design a classifier based on support vector machines. Cross\n\t\t  validation yielded a performance of 95% in the\n\t\t  classification of the five hedonic sub-classes.\n\t\t  Additionally, using only 10 of the selected features and a\n\t\t  simple statistical classifier, it was possible to determine\n\t\t  the origin of the tortillas with a performance of 96%. We\n\t\t  believe that the proposed framework opens up new\n\t\t  possibilities in the field of automated visual inspection\n\t\t  of tortillas. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Tortillas-2010.pdf}\n}\n\n
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\n Computer vision is playing an increasingly important role in automated visual food inspection. However quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas. \n
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\n \n\n \n \n \n \n \n \n Human Detection Using a Mobile Platform and Novel Features Derived From a Visual Saliency Mechanism.\n \n \n \n \n\n\n \n Montabone, S.; and Soto, A.\n\n\n \n\n\n\n Image and Vision Computing, 28(3): 391-402. 2010.\n \n\n\n\n
\n\n\n\n \n \n \"HumanPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  montabone:etal:2010,\n  author\t= {S. Montabone and A. Soto},\n  title\t\t= {Human Detection Using a Mobile Platform and Novel Features\n\t\t  Derived From a Visual Saliency Mechanism},\n  journal\t= {Image and Vision Computing},\n  volume\t= {28},\n  number\t= {3},\n  pages\t\t= {391-402},\n  year\t\t= {2010},\n  abstract\t= {Human detection is a key ability to an increasing number\n\t\t  of applications that operates in human inhab- ited\n\t\t  environments or needs to interact with a human user.\n\t\t  Currently, most successful approaches to human detection\n\t\t  are based on background substraction techniques that apply\n\t\t  only to the case of static cameras or cameras with highly\n\t\t  constrained motions. Furthermore, many applications rely on\n\t\t  features derived from specific human poses, such as systems\n\t\t  based on features derived from the human face which is only\n\t\t  visible when a person is facing the detecting camera. In\n\t\t  this work, we present a new com- puter vision algorithm\n\t\t  designed to operate with moving cameras and to detect\n\t\t  humans in different poses under partial or complete view of\n\t\t  the human body. We follow a standard pattern recognition\n\t\t  approach based on four main steps: (i) preprocessing to\n\t\t  achieve color constancy and stereo pair calibration, (ii)\n\t\t  seg- mentation using depth continuity information, (iii)\n\t\t  feature extraction based on visual saliency, and (iv)\n\t\t  classification using a neural network. The main novelty of\n\t\t  our approach lies in the feature extraction step, where we\n\t\t  propose novel features derived from a visual saliency\n\t\t  mechanism. In contrast to previous works, we do not use a\n\t\t  pyramidal decomposition to run the saliency algorithm, but\n\t\t  we implement this at the original image resolution using\n\t\t  the so-called integral image. Our results indicate that our\n\t\t  method: (i) outperforms state-of-the-art techniques for\n\t\t  human detection based on face detectors, (ii) outperforms\n\t\t  state-of-the-art techniques for complete human body\n\t\t  detection based on different set of visual features, and\n\t\t  (iii) operates in real time onboard a mobile platform, such\n\t\t  as a mobile robot (15 fps). },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/ImageVisionComp-10.pdf}\n}\n\n
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\n Human detection is a key ability to an increasing number of applications that operates in human inhab- ited environments or needs to interact with a human user. Currently, most successful approaches to human detection are based on background substraction techniques that apply only to the case of static cameras or cameras with highly constrained motions. Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera. In this work, we present a new com- puter vision algorithm designed to operate with moving cameras and to detect humans in different poses under partial or complete view of the human body. We follow a standard pattern recognition approach based on four main steps: (i) preprocessing to achieve color constancy and stereo pair calibration, (ii) seg- mentation using depth continuity information, (iii) feature extraction based on visual saliency, and (iv) classification using a neural network. The main novelty of our approach lies in the feature extraction step, where we propose novel features derived from a visual saliency mechanism. In contrast to previous works, we do not use a pyramidal decomposition to run the saliency algorithm, but we implement this at the original image resolution using the so-called integral image. Our results indicate that our method: (i) outperforms state-of-the-art techniques for human detection based on face detectors, (ii) outperforms state-of-the-art techniques for complete human body detection based on different set of visual features, and (iii) operates in real time onboard a mobile platform, such as a mobile robot (15 fps). \n
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\n \n\n \n \n \n \n \n Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles.\n \n \n \n\n\n \n Parra-Santander, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, volume 1, pages 136–142, 2010. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  parra2010improving,\n  author\t= {Parra-Santander, Denis and Brusilovsky, Peter},\n  booktitle\t= {Web Intelligence and Intelligent Agent Technology\n\t\t  (WI-IAT), 2010 IEEE/WIC/ACM International Conference on},\n  organization\t= {IEEE},\n  pages\t\t= {136--142},\n  title\t\t= {Improving Collaborative Filtering in Social Tagging\n\t\t  Systems for the Recommendation of Scientific Articles},\n  volume\t= {1},\n  year\t\t= {2010}\n}\n\n
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\n \n\n \n \n \n \n \n Conference Navigator 2.0: Community-Based Recommendation for Academic Conferences.\n \n \n \n\n\n \n Wongchokprasitti, C.; Brusilovsky, P.; and Parra-Santander, D.\n\n\n \n\n\n\n In 2010. ACM\n \n\n\n\n
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@InProceedings{\t  wongchokprasitti2010conference,\n  author\t= {Wongchokprasitti, Chirayu and Brusilovsky, Peter and\n\t\t  Parra-Santander, Denis},\n  publisher\t= {ACM},\n  title\t\t= {Conference Navigator 2.0: Community-Based Recommendation\n\t\t  for Academic Conferences},\n  year\t\t= {2010}\n}\n\n
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\n \n\n \n \n \n \n \n \n Performance Evaluation of the Covariance Descriptor for Target Detection.\n \n \n \n \n\n\n \n Cortez-Cargill, P.; Undurraga-Rius, C.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In Proc. of XXVIII Int. Conf. of the Chilean Computer Science Society/IEEE CS Press, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"PerformancePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  cortez:etal:2009,\n  author\t= {P. Cortez-Cargill and C. Undurraga-Rius and D. Mery and A.\n\t\t  Soto},\n  title\t\t= {Performance Evaluation of the Covariance Descriptor for\n\t\t  Target Detection},\n  booktitle\t= {Proc. of XXVIII Int. Conf. of the Chilean Computer Science\n\t\t  Society/IEEE CS Press},\n  year\t\t= {2009},\n  abstract\t= {In computer vision, there has been a strong advance in\n\t\t  creating new image descriptors. A descriptor that has\n\t\t  recently appeared is the Covariance Descriptor, but there\n\t\t  have not been any studies about the different methodologies\n\t\t  for its construction. To address this problem we have\n\t\t  conducted an analysis on the contribution of diverse\n\t\t  features of an image to the descriptor and therefore their\n\t\t  contribution to the detection of varied targets, in our\n\t\t  case: faces and pedestrians. That is why we have defined a\n\t\t  methodology to determinate the performance of the\n\t\t  covariance matrix created from different characteristics.\n\t\t  Now we are able to determinate the best set of features for\n\t\t  face and people detection, for each problem. We have also\n\t\t  achieved to establish that not any kind of combination of\n\t\t  features can be used because it might not exist a\n\t\t  correlation between them. Finally, when an analysis is\n\t\t  performed with the best set of features, for the face\n\t\t  detection problem we reach a performance of 99%, meanwhile\n\t\t  for the pedestrian detection problem we reach a performance\n\t\t  of 85%. With this we hope we have built a more solid base\n\t\t  when choosing features for this descriptor, allowing to\n\t\t  move forward to other topics such as object recognition or\n\t\t  tracking. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Final-Proceedings-Cortez_Undurraga_Mery_Soto_SCCC2009.pdf}\n}\n\n
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\n In computer vision, there has been a strong advance in creating new image descriptors. A descriptor that has recently appeared is the Covariance Descriptor, but there have not been any studies about the different methodologies for its construction. To address this problem we have conducted an analysis on the contribution of diverse features of an image to the descriptor and therefore their contribution to the detection of varied targets, in our case: faces and pedestrians. That is why we have defined a methodology to determinate the performance of the covariance matrix created from different characteristics. Now we are able to determinate the best set of features for face and people detection, for each problem. We have also achieved to establish that not any kind of combination of features can be used because it might not exist a correlation between them. Finally, when an analysis is performed with the best set of features, for the face detection problem we reach a performance of 99%, meanwhile for the pedestrian detection problem we reach a performance of 85%. With this we hope we have built a more solid base when choosing features for this descriptor, allowing to move forward to other topics such as object recognition or tracking. \n
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\n \n\n \n \n \n \n \n Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009.\n \n \n \n\n\n \n Gerevini, A.; Howe, A. E.; Cesta, A.; and Refanidis, I.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2009.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2009,\n  editor\t= {Alfonso Gerevini and Adele E. Howe and Amedeo Cesta and\n\t\t  Ioannis Refanidis},\n  title\t\t= {Proceedings of the 19th International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2009,\n\t\t  Thessaloniki, Greece, September 19-23, 2009},\n  publisher\t= {{AAAI}},\n  year\t\t= {2009},\n  isbn\t\t= {978-1-57735-406-2},\n  timestamp\t= {Wed, 25 Nov 2009 09:51:15 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2009},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Improving Planning Performance Using Low-Conflict Relaxed Plans.\n \n \n \n \n\n\n \n Baier, J. A.; and Botea, A.\n\n\n \n\n\n\n In Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/baierb09,\n  author\t= {Jorge A. Baier and Adi Botea},\n  title\t\t= {Improving Planning Performance Using Low-Conflict Relaxed\n\t\t  Plans},\n  booktitle\t= {Proceedings of the 19th International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2009,\n\t\t  Thessaloniki, Greece, September 19-23, 2009},\n  year\t\t= {2009},\n  crossref\t= {DBLP:conf/aips/2009},\n  url\t\t= {http://aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/view/725},\n  timestamp\t= {Thu, 13 Dec 2012 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/BaierB09},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting N-Gram Analysis to Predict Operator Sequences.\n \n \n \n \n\n\n \n Muise, C. J.; McIlraith, S. A.; Baier, J. A.; and Reimer, M.\n\n\n \n\n\n\n In Proceedings of the 19th International Conference on Automated Planning and Scheduling, ICAPS 2009, Thessaloniki, Greece, September 19-23, 2009, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/muisembr09,\n  author\t= {Christian J. Muise and Sheila A. McIlraith and Jorge A.\n\t\t  Baier and Michael Reimer},\n  title\t\t= {Exploiting N-Gram Analysis to Predict Operator Sequences},\n  booktitle\t= {Proceedings of the 19th International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2009,\n\t\t  Thessaloniki, Greece, September 19-23, 2009},\n  year\t\t= {2009},\n  crossref\t= {DBLP:conf/aips/2009},\n  url\t\t= {http://aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/view/741},\n  timestamp\t= {Thu, 13 Dec 2012 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/MuiseMBR09},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009.\n \n \n \n \n\n\n \n Boutilier, C.,\n editor.\n \n\n\n \n\n\n\n 2009.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2009,\n  editor\t= {Craig Boutilier},\n  title\t\t= {{IJCAI} 2009, Proceedings of the 21st International Joint\n\t\t  Conference on Artificial Intelligence, Pasadena,\n\t\t  California, USA, July 11-17, 2009},\n  year\t\t= {2009},\n  url\t\t= {http://ijcai.org/proceedings/2009},\n  timestamp\t= {Wed, 20 Jul 2016 14:02:05 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2009},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n HTN Planning with Preferences.\n \n \n \n \n\n\n \n Sohrabi, S.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pages 1790–1797, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"HTNPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 45 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/sohrabibm09,\n  author\t= {Shirin Sohrabi and Jorge A. Baier and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {{HTN} Planning with Preferences},\n  booktitle\t= {{IJCAI} 2009, Proceedings of the 21st International Joint\n\t\t  Conference on Artificial Intelligence, Pasadena,\n\t\t  California, USA, July 11-17, 2009},\n  pages\t\t= {1790--1797},\n  year\t\t= {2009},\n  crossref\t= {DBLP:conf/ijcai/2009},\n  url\t\t= {http://ijcai.org/Proceedings/09/Papers/298.pdf},\n  timestamp\t= {Wed, 20 Jul 2016 14:02:05 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/SohrabiBM09},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A heuristic search approach to planning with temporally extended preferences.\n \n \n \n \n\n\n \n Baier, J. A.; Bacchus, F.; and McIlraith, S. A.\n\n\n \n\n\n\n Artif. Intell., 173(5-6): 593–618. 2009.\n \n\n\n\n
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@Article{\t  dblp:journals/ai/baierbm09,\n  author\t= {Jorge A. Baier and Fahiem Bacchus and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {A heuristic search approach to planning with temporally\n\t\t  extended preferences},\n  journal\t= {Artif. Intell.},\n  volume\t= {173},\n  number\t= {5-6},\n  pages\t\t= {593--618},\n  year\t\t= {2009},\n  url\t\t= {https://doi.org/10.1016/j.artint.2008.11.011},\n  doi\t\t= {10.1016/j.artint.2008.11.011},\n  timestamp\t= {Sat, 27 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/ai/BaierBM09},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor classification.\n \n \n \n \n\n\n \n Maturana, D.; Mery, D.; and Soto, A.\n\n\n \n\n\n\n In Proc. of XXVIII Int. Conf. of the Chilean Computer Science Society/IEEE CS Press, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"FacePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  maturana:etal:2009,\n  author\t= {D. Maturana and D. Mery and A. Soto},\n  title\t\t= {Face Recognition with Local Binary Patterns, Spatial\n\t\t  Pyramid Histograms and Naive Bayes Nearest Neighbor\n\t\t  classification},\n  booktitle\t= {Proc. of XXVIII Int. Conf. of the Chilean Computer Science\n\t\t  Society/IEEE CS Press},\n  year\t\t= {2009},\n  abstract\t= {Face recognition algorithms commonly assume that face\n\t\t  images are well aligned and have a similar pose – yet in\n\t\t  many practical applications it is impossible to meet these\n\t\t  conditions. Therefore extending face recognition to un-\n\t\t  constrained face images has become an active area of\n\t\t  research. To this end, histograms of Local Binary Patterns\n\t\t  (LBP) have proven to be highly discriminative descriptors\n\t\t  for face recognition. Nonetheless, most LBP-based\n\t\t  algorithms use a rigid descriptor matching strategy that is\n\t\t  not robust against pose variation and misalignment. We\n\t\t  propose two algorithms for face recognition that are de-\n\t\t  signed to deal with pose variations and misalignment. We\n\t\t  also incorporate an illumination normalization step that\n\t\t  increases robustness against lighting variations. The\n\t\t  proposed algorithms use descriptors based on histograms of\n\t\t  LBP and perform descriptor matching with spatial pyramid\n\t\t  matching (SPM) and Naive Bayes Nearest Neighbor (NBNN),\n\t\t  respectively. Our con- tribution is the inclusion of\n\t\t  flexible spatial matching schemes that use an\n\t\t  image-to-class relation to provide an improved robustness\n\t\t  with respect to intra-class variations. We compare the\n\t\t  accuracy of the proposed algorithms against Ahonen’s\n\t\t  original LBP-based face recognition system and two baseline\n\t\t  holistic classifiers on four standard datasets. Our results\n\t\t  indicate that the algorithm based on NBNN outperforms the\n\t\t  other solutions, and does so more markedly in presence of\n\t\t  pose variations. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Final-Daniel-09.pdf}\n}\n\n
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\n Face recognition algorithms commonly assume that face images are well aligned and have a similar pose – yet in many practical applications it is impossible to meet these conditions. Therefore extending face recognition to un- constrained face images has become an active area of research. To this end, histograms of Local Binary Patterns (LBP) have proven to be highly discriminative descriptors for face recognition. Nonetheless, most LBP-based algorithms use a rigid descriptor matching strategy that is not robust against pose variation and misalignment. We propose two algorithms for face recognition that are de- signed to deal with pose variations and misalignment. We also incorporate an illumination normalization step that increases robustness against lighting variations. The proposed algorithms use descriptors based on histograms of LBP and perform descriptor matching with spatial pyramid matching (SPM) and Naive Bayes Nearest Neighbor (NBNN), respectively. Our con- tribution is the inclusion of flexible spatial matching schemes that use an image-to-class relation to provide an improved robustness with respect to intra-class variations. We compare the accuracy of the proposed algorithms against Ahonen’s original LBP-based face recognition system and two baseline holistic classifiers on four standard datasets. Our results indicate that the algorithm based on NBNN outperforms the other solutions, and does so more markedly in presence of pose variations. \n
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\n \n\n \n \n \n \n \n \n Collaborative Robotic Instruction: A Graph Teaching Experience.\n \n \n \n \n\n\n \n Mitnik, R.; Recabarren, M.; Nussbaum, M.; and Soto, A.\n\n\n \n\n\n\n Computers and Education, 53(2): 330-342. 2009.\n \n\n\n\n
\n\n\n\n \n \n \"CollaborativePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mitnik:etal:2009,\n  author\t= {R. Mitnik and M. Recabarren and M. Nussbaum and A. Soto},\n  title\t\t= {Collaborative Robotic Instruction: A Graph Teaching\n\t\t  Experience},\n  journal\t= {Computers and Education},\n  volume\t= {53},\n  number\t= {2},\n  pages\t\t= {330-342},\n  year\t\t= {2009},\n  abstract\t= {Graphing is a key skill in the study of Physics. Drawing\n\t\t  and interpreting graphs play a key role in the\n\t\t  understanding of science, while the lack of these has\n\t\t  proved to be a handicap and a limiting factor in the\n\t\t  learning of scientific concepts. It has been observed that\n\t\t  despite the amount of previous graph-work- ing experience,\n\t\t  students of all ages experience a series of difficulties\n\t\t  when trying to comprehend graphs or when trying to relate\n\t\t  them with physical concepts such as position, velocity and\n\t\t  acceleration. Several computational tools have risen to\n\t\t  improve the students’ understanding of kinematical\n\t\t  graphs; however, these approaches fail to develop graph\n\t\t  construction skills. On the other hand, Robots have opened\n\t\t  new opportunities in learning. Nevertheless, most of their\n\t\t  educational applications focus on Robotics related\n\t\t  subjects, such as robot programming, robot construction,\n\t\t  and artificial intelligence. This paper describes a robotic\n\t\t  activity based on face-to-face computer supported\n\t\t  collaborative learning. By means of a set of handhelds and\n\t\t  a robot wirelessly interconnected, the aim of the activity\n\t\t  is to develop graph construction and graph interpretation\n\t\t  skills while also reinforcing kinematics concepts. Results\n\t\t  show that students using the robotic activity achieve a\n\t\t  significant increase in their graph interpreting skills.\n\t\t  Moreover, when compared with a similar computer-simulated\n\t\t  activity, it proved to be almost twice as effective.\n\t\t  Finally, the robotic application proved to be a highly\n\t\t  motivating activity for the students, fostering\n\t\t  collaboration among them.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Mitnik_2009_Computers-&amp;-Education.pdf}\n}\n\n
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\n Graphing is a key skill in the study of Physics. Drawing and interpreting graphs play a key role in the understanding of science, while the lack of these has proved to be a handicap and a limiting factor in the learning of scientific concepts. It has been observed that despite the amount of previous graph-work- ing experience, students of all ages experience a series of difficulties when trying to comprehend graphs or when trying to relate them with physical concepts such as position, velocity and acceleration. Several computational tools have risen to improve the students’ understanding of kinematical graphs; however, these approaches fail to develop graph construction skills. On the other hand, Robots have opened new opportunities in learning. Nevertheless, most of their educational applications focus on Robotics related subjects, such as robot programming, robot construction, and artificial intelligence. This paper describes a robotic activity based on face-to-face computer supported collaborative learning. By means of a set of handhelds and a robot wirelessly interconnected, the aim of the activity is to develop graph construction and graph interpretation skills while also reinforcing kinematics concepts. Results show that students using the robotic activity achieve a significant increase in their graph interpreting skills. Moreover, when compared with a similar computer-simulated activity, it proved to be almost twice as effective. Finally, the robotic application proved to be a highly motivating activity for the students, fostering collaboration among them.\n
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\n \n\n \n \n \n \n \n Collaborative filtering for social tagging systems: an experiment with CiteULike.\n \n \n \n\n\n \n Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In Proceedings of the third ACM conference on Recommender systems, pages 237–240, 2009. ACM\n \n\n\n\n
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@InProceedings{\t  parra2009collaborative,\n  author\t= {Parra, Denis and Brusilovsky, Peter},\n  booktitle\t= {Proceedings of the third ACM conference on Recommender\n\t\t  systems},\n  organization\t= {ACM},\n  pages\t\t= {237--240},\n  title\t\t= {Collaborative filtering for social tagging systems: an\n\t\t  experiment with CiteULike},\n  year\t\t= {2009}\n}\n\n
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\n \n\n \n \n \n \n \n Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike.\n \n \n \n\n\n \n Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In volume 3, 2009. \n \n\n\n\n
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@InProceedings{\t  parra2009evaluation,\n  author\t= {Parra, Denis and Brusilovsky, Peter},\n  journal\t= {Workshop: Web 3.0 Merging Semantic Web and Social Web at\n\t\t  HyperText'09, 3-6},\n  title\t\t= {Evaluation of Collaborative Filtering Algorithms for\n\t\t  Recommending Articles on CiteULike},\n  volume\t= {3},\n  year\t\t= {2009}\n}\n\n
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\n \n\n \n \n \n \n \n \n An ensemble of Discriminative Local Subspaces in Microarray Data for Gene Ontology Annotation Predictions.\n \n \n \n \n\n\n \n Puelma, T.; Soto, A.; and Gutierrez, R.\n\n\n \n\n\n\n In Proc. of 1st Chilean Workshop on Pattern Recognition (CWPR), pages 52-61, 2009. \n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  puelma:etal:2009,\n  author\t= {T. Puelma and A. Soto and R. Gutierrez},\n  title\t\t= {An ensemble of Discriminative Local Subspaces in\n\t\t  Microarray Data for Gene Ontology Annotation Predictions},\n  booktitle\t= {Proc. of 1st Chilean Workshop on Pattern Recognition\n\t\t  (CWPR)},\n  pages\t\t= {52-61},\n  year\t\t= {2009},\n  abstract\t= {Genome sequencing has allowed to know almost every gene of\n\t\t  many organisms. However, understanding the functions of\n\t\t  most genes is still an open problem. In this paper, we\n\t\t  present a novel machine learning method to predict\n\t\t  functions of unknown genes in base of gene expression data\n\t\t  and Gene Ontology annotations. Most function prediction al-\n\t\t  gorithms developed in the past don’t exploit the\n\t\t  discriminative power of supervised learning. In contrast,\n\t\t  our method uses this to find discriminative local subspaces\n\t\t  that are suitable to perform gene functional prediction.\n\t\t  Cross-validation test are done in artificial and real data\n\t\t  and compared with a state-of- the-art method. Preliminary\n\t\t  results shows that in overall, our method outperforms the\n\t\t  other approach in terms of precision and recall, giving\n\t\t  insights in the importance of a good selection of\n\t\t  discriminative experiments.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/DLS-Final-v2.pdf}\n}\n\n
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\n Genome sequencing has allowed to know almost every gene of many organisms. However, understanding the functions of most genes is still an open problem. In this paper, we present a novel machine learning method to predict functions of unknown genes in base of gene expression data and Gene Ontology annotations. Most function prediction al- gorithms developed in the past don’t exploit the discriminative power of supervised learning. In contrast, our method uses this to find discriminative local subspaces that are suitable to perform gene functional prediction. Cross-validation test are done in artificial and real data and compared with a state-of- the-art method. Preliminary results shows that in overall, our method outperforms the other approach in terms of precision and recall, giving insights in the importance of a good selection of discriminative experiments.\n
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\n \n\n \n \n \n \n \n Spreading Activation Approach to Tag-aware Recommenders: Modeling Similarity on Multidimensional Networks.\n \n \n \n\n\n \n Troussov, A.; Parra, D.; and Brusilovsky, P.\n\n\n \n\n\n\n In 2009. \n \n\n\n\n
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@InProceedings{\t  troussov2009spreading,\n  author\t= {Troussov, Alexander and Parra, Denis and Brusilovsky,\n\t\t  Peter},\n  journal\t= {Recommender Systems \\& the Social Web},\n  title\t\t= {Spreading Activation Approach to Tag-aware Recommenders:\n\t\t  Modeling Similarity on Multidimensional Networks},\n  year\t\t= {2009}\n}\n\n
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\n \n\n \n \n \n \n \n \n Unsupervised Anomaly Detection in Large Databases Using Bayesian Networks.\n \n \n \n \n\n\n \n Cansado, A.; and Soto, A.\n\n\n \n\n\n\n Applied Artificial Intelligence, 22(4): 309-330. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"UnsupervisedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  cansado:soto:2008,\n  author\t= {A. Cansado and A. Soto},\n  title\t\t= {Unsupervised Anomaly Detection in Large Databases Using\n\t\t  Bayesian Networks},\n  journal\t= {Applied Artificial Intelligence},\n  volume\t= {22},\n  number\t= {4},\n  pages\t\t= {309-330},\n  year\t\t= {2008},\n  abstract\t= {Today, there has been a massive proliferation of huge\n\t\t  databases storing valuable information. The opportunities\n\t\t  of an effective use of these new data sources are enormous,\n\t\t  however, the huge size and dimensionality of current large\n\t\t  databases call for new ideas to scale up current\n\t\t  statistical and computational approaches. This paper\n\t\t  presents an application of Ar- tificial Intelligence\n\t\t  technology to the problem of automatic detection of\n\t\t  candidate anomalous records in a large database. We build\n\t\t  our approach with three main goals in mind: 1)An effective\n\t\t  detection of the records that are potentially anomalous,\n\t\t  2)A suitable selection of the subset of at- tributes that\n\t\t  explains what makes a record anomalous, and 3)An efficient\n\t\t  implementation that allows us to scale the approach to\n\t\t  large databases. Our algorithm, called Bayesian Network\n\t\t  Anomaly Detector (BNAD), uses the joint probability density\n\t\t  function (pdf) provided by a Bayesian Net- work (BN) to\n\t\t  achieve these goals. By using appropriate data structures,\n\t\t  advanced caching techniques, the flexibility of Gaussian\n\t\t  Mixture models, and the efficiency of BNs to model joint\n\t\t  pdfs, BNAD manages to effi- ciently learn a suitable BN\n\t\t  from a large dataset. We test BNAD using synthetic and real\n\t\t  databases, the latter from the fields of manufacturing and\n\t\t  astronomy, obtaining encouraging results. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Cansado-Soto-AAI-2007.pdf}\n}\n\n
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\n Today, there has been a massive proliferation of huge databases storing valuable information. The opportunities of an effective use of these new data sources are enormous, however, the huge size and dimensionality of current large databases call for new ideas to scale up current statistical and computational approaches. This paper presents an application of Ar- tificial Intelligence technology to the problem of automatic detection of candidate anomalous records in a large database. We build our approach with three main goals in mind: 1)An effective detection of the records that are potentially anomalous, 2)A suitable selection of the subset of at- tributes that explains what makes a record anomalous, and 3)An efficient implementation that allows us to scale the approach to large databases. Our algorithm, called Bayesian Network Anomaly Detector (BNAD), uses the joint probability density function (pdf) provided by a Bayesian Net- work (BN) to achieve these goals. By using appropriate data structures, advanced caching techniques, the flexibility of Gaussian Mixture models, and the efficiency of BNs to model joint pdfs, BNAD manages to effi- ciently learn a suitable BN from a large dataset. We test BNAD using synthetic and real databases, the latter from the fields of manufacturing and astronomy, obtaining encouraging results. \n
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\n \n\n \n \n \n \n \n Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008.\n \n \n \n\n\n \n Fox, D.; and Gomes, C. P.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2008.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2008,\n  editor\t= {Dieter Fox and Carla P. Gomes},\n  title\t\t= {Proceedings of the Twenty-Third {AAAI} Conference on\n\t\t  Artificial Intelligence, {AAAI} 2008, Chicago, Illinois,\n\t\t  USA, July 13-17, 2008},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2008},\n  isbn\t\t= {978-1-57735-368-3},\n  timestamp\t= {Fri, 15 Aug 2008 11:13:22 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2008},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Beyond Classical Planning: Procedural Control Knowledge and Preferences in State-of-the-Art Planners.\n \n \n \n \n\n\n \n Baier, J. A.; Fritz, C.; Bienvenu, M.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008, pages 1509–1512, 2008. \n \n\n\n\n
\n\n\n\n \n \n \"BeyondPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/baierfbm08,\n  author\t= {Jorge A. Baier and Christian Fritz and Meghyn Bienvenu and\n\t\t  Sheila A. McIlraith},\n  title\t\t= {Beyond Classical Planning: Procedural Control Knowledge\n\t\t  and Preferences in State-of-the-Art Planners},\n  booktitle\t= {Proceedings of the Twenty-Third {AAAI} Conference on\n\t\t  Artificial Intelligence, {AAAI} 2008, Chicago, Illinois,\n\t\t  USA, July 13-17, 2008},\n  pages\t\t= {1509--1512},\n  year\t\t= {2008},\n  crossref\t= {DBLP:conf/aaai/2008},\n  url\t\t= {http://www.aaai.org/Library/AAAI/2008/aaai08-251.php},\n  timestamp\t= {Tue, 10 Jun 2014 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/BaierFBM08},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16-19, 2008.\n \n \n \n\n\n \n Brewka, G.; and Lang, J.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2008.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/kr/2008,\n  editor\t= {Gerhard Brewka and J{\\'{e}}r{\\^{o}}me Lang},\n  title\t\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Eleventh International Conference, {KR}\n\t\t  2008, Sydney, Australia, September 16-19, 2008},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2008},\n  isbn\t\t= {978-1-57735-384-3},\n  timestamp\t= {Fri, 21 Nov 2008 12:14:41 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/2008},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n ConGolog, Sin Trans: Compiling ConGolog into Basic Action Theories for Planning and Beyond.\n \n \n \n \n\n\n \n Fritz, C.; Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16-19, 2008, pages 600–610, 2008. \n \n\n\n\n
\n\n\n\n \n \n \"ConGolog,Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/kr/fritzbm08,\n  author\t= {Christian Fritz and Jorge A. Baier and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {ConGolog, Sin Trans: Compiling ConGolog into Basic Action\n\t\t  Theories for Planning and Beyond},\n  booktitle\t= {Principles of Knowledge Representation and Reasoning:\n\t\t  Proceedings of the Eleventh International Conference, {KR}\n\t\t  2008, Sydney, Australia, September 16-19, 2008},\n  pages\t\t= {600--610},\n  year\t\t= {2008},\n  crossref\t= {DBLP:conf/kr/2008},\n  url\t\t= {http://www.aaai.org/Library/KR/2008/kr08-059.php},\n  timestamp\t= {Tue, 10 Jun 2014 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/FritzBM08},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning with Preferences.\n \n \n \n \n\n\n \n Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n AI Magazine, 29(4): 25–36. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 23 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/aim/baierm08,\n  author\t= {Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {Planning with Preferences},\n  journal\t= {{AI} Magazine},\n  volume\t= {29},\n  number\t= {4},\n  pages\t\t= {25--36},\n  year\t\t= {2008},\n  url\t\t= {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2204},\n  timestamp\t= {Mon, 27 Dec 2010 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/journals/aim/BaierM08},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Unsupervised Identification of Useful Visual Landmarks Using Multiple Segmentations and Top-Down Feedback.\n \n \n \n \n\n\n \n Espinace, P.; Langdon, D.; and Soto, A.\n\n\n \n\n\n\n Robotics and Autonomous Systems, 56(6): 538-548. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"UnsupervisedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  espinace:soto:2008a,\n  author\t= {P. Espinace and D. Langdon and A. Soto},\n  title\t\t= {Unsupervised Identification of Useful Visual Landmarks\n\t\t  Using Multiple Segmentations and Top-Down Feedback},\n  journal\t= {Robotics and Autonomous Systems},\n  volume\t= {56},\n  number\t= {6},\n  pages\t\t= {538-548},\n  year\t\t= {2008},\n  abstract\t= {In this paper, we tackle the problem of unsupervised\n\t\t  selection and posterior recognition of visual landmarks in\n\t\t  images sequences acquired by an indoor mobile robot. This\n\t\t  is a highly valuable perceptual capability for a wide\n\t\t  variety of robotic applications, in particular autonomous\n\t\t  navigation. Our method combines a bottom-up data driven\n\t\t  approach with top-down feedback provided by high level\n\t\t  semantic representations. The bottom-up approach is based\n\t\t  on three main mechanisms: visual attention, area\n\t\t  segmentation, and landmark characterization. As there is no\n\t\t  segmentation method that works properly in every situation,\n\t\t  we integrate multiple segmentation algorithms in order to\n\t\t  increase the robustness of the approach. In terms of the\n\t\t  top-down feedback, this is provided by two information\n\t\t  sources: i) An estimation of the robot position that\n\t\t  reduces the searching scope for potential matches with\n\t\t  previously selected landmarks, ii) A set of weights that,\n\t\t  according to the results of previous recognitions, controls\n\t\t  the influence of each segmentation algorithm in the\n\t\t  recognition of each landmark. We test our approach with\n\t\t  encouraging results in three datasets corresponding to\n\t\t  real-world scenarios. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Pablo-RAS-08.pdf}\n}\n\n
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\n In this paper, we tackle the problem of unsupervised selection and posterior recognition of visual landmarks in images sequences acquired by an indoor mobile robot. This is a highly valuable perceptual capability for a wide variety of robotic applications, in particular autonomous navigation. Our method combines a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. As there is no segmentation method that works properly in every situation, we integrate multiple segmentation algorithms in order to increase the robustness of the approach. In terms of the top-down feedback, this is provided by two information sources: i) An estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, ii) A set of weights that, according to the results of previous recognitions, controls the influence of each segmentation algorithm in the recognition of each landmark. We test our approach with encouraging results in three datasets corresponding to real-world scenarios. \n
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\n \n\n \n \n \n \n \n \n Improving the Selection and Detection of Visual Landmarks Through Object Tracking.\n \n \n \n \n\n\n \n Espinace, P.; and Soto, A.\n\n\n \n\n\n\n In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Workshop on Visual Localization for Mobile Platforms, 2008. \n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  espinace:soto:2008b,\n  author\t= {P. Espinace and A. Soto},\n  title\t\t= {Improving the Selection and Detection of Visual Landmarks\n\t\t  Through Object Tracking},\n  booktitle\t= {IEEE Conf. on Computer Vision and Pattern Recognition\n\t\t  (CVPR), Workshop on Visual Localization for Mobile\n\t\t  Platforms},\n  pages\t\t= {},\n  year\t\t= {2008},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Real-Time Robot Localization In Indoor Environments Using Structural Information.\n \n \n \n \n\n\n \n Espinace, P.; Soto, A.; and Torres-Torriti, M.\n\n\n \n\n\n\n In IEEE Latin American Robotics Symposium (LARS), 2008. \n \n\n\n\n
\n\n\n\n \n \n \"Real-TimePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  espinace:soto:torres:2008,\n  author\t= {P. Espinace and A. Soto and M. Torres-Torriti},\n  title\t\t= {Real-Time Robot Localization In Indoor Environments Using\n\t\t  Structural Information},\n  booktitle\t= {IEEE Latin American Robotics Symposium (LARS)},\n  pages\t\t= {},\n  year\t\t= {2008},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n An autonomous educational mobile robot mediator.\n \n \n \n \n\n\n \n Mitnik, R.; Nussbaum, M.; and Soto, A.\n\n\n \n\n\n\n Autonomous Robots, 25(4): 367-382. 2008.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  mitnik:etal:2008,\n  author\t= {R. Mitnik and M. Nussbaum and A. Soto},\n  title\t\t= {An autonomous educational mobile robot mediator},\n  journal\t= {Autonomous Robots},\n  volume\t= {25},\n  number\t= {4},\n  pages\t\t= {367-382},\n  year\t\t= {2008},\n  abstract\t= {So far, most of the applications of robotic technology to\n\t\t  education have mainly focused on sup- porting the teaching\n\t\t  of subjects that are closely related to the Robotics field,\n\t\t  such as robot programming, robot construction, or\n\t\t  mechatronics. Moreover, most of the applications have used\n\t\t  the robot as an end or a passive tool of the learning\n\t\t  activity, where the robot has been constructed or\n\t\t  programmed. In this paper, we present a novel application\n\t\t  of robotic technologies to education, where we use the real\n\t\t  world situatedness of a robot to teach non-robotic related\n\t\t  subjects, such as math and physics. Furthermore, we also\n\t\t  provide the robot with a suitable degree of autonomy to\n\t\t  actively guide and mediate in the development of the\n\t\t  educational activ- ity. We present our approach as an\n\t\t  educational frame- work based on a collaborative and\n\t\t  constructivist learn- ing environment, where the robot is\n\t\t  able to act as an interaction mediator capable of managing\n\t\t  the interac- tions occurring among the working students. We\n\t\t  illus- trate the use of this framework by a 4-step\n\t\t  methodology that is used to implement two educational\n\t\t  activities. These activities were tested at local schools\n\t\t  with en- couraging results. Accordingly, the main\n\t\t  contributions of this work are: i) A novel use of a mobile\n\t\t  robot to illustrate and teach relevant concepts and\n\t\t  properties of the real world; ii) A novel use of robots as\n\t\t  mediators that autonomously guide an educational activity\n\t\t  using a collaborative and constructivist learning approach;\n\t\t  iii) The implementation and testing of these ideas in a\n\t\t  real scenario, working with students at local schools. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/EducationalRobot.pdf}\n}\n\n
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\n So far, most of the applications of robotic technology to education have mainly focused on sup- porting the teaching of subjects that are closely related to the Robotics field, such as robot programming, robot construction, or mechatronics. Moreover, most of the applications have used the robot as an end or a passive tool of the learning activity, where the robot has been constructed or programmed. In this paper, we present a novel application of robotic technologies to education, where we use the real world situatedness of a robot to teach non-robotic related subjects, such as math and physics. Furthermore, we also provide the robot with a suitable degree of autonomy to actively guide and mediate in the development of the educational activ- ity. We present our approach as an educational frame- work based on a collaborative and constructivist learn- ing environment, where the robot is able to act as an interaction mediator capable of managing the interac- tions occurring among the working students. We illus- trate the use of this framework by a 4-step methodology that is used to implement two educational activities. These activities were tested at local schools with en- couraging results. Accordingly, the main contributions of this work are: i) A novel use of a mobile robot to illustrate and teach relevant concepts and properties of the real world; ii) A novel use of robots as mediators that autonomously guide an educational activity using a collaborative and constructivist learning approach; iii) The implementation and testing of these ideas in a real scenario, working with students at local schools. \n
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\n \n\n \n \n \n \n \n \n Detection of Anomalies in Large Datasets Using an Active Learning Scheme Based on Dirichlet Distributions.\n \n \n \n \n\n\n \n Pichara, K.; Soto, A.; and Araneda, A.\n\n\n \n\n\n\n In Advances in Artificial Intelligence, Iberamia-08, LNCS 5290, pages 163-172, 2008. \n \n\n\n\n
\n\n\n\n \n \n \"DetectionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  pichara:etal:2008,\n  author\t= {K. Pichara and A. Soto and A. Araneda},\n  title\t\t= {Detection of Anomalies in Large Datasets Using an Active\n\t\t  Learning Scheme Based on Dirichlet Distributions},\n  booktitle\t= {Advances in Artificial Intelligence, Iberamia-08, LNCS\n\t\t  5290},\n  pages\t\t= {163-172},\n  year\t\t= {2008},\n  abstract\t= {Today, the detection of anomalous records is a highly\n\t\t  valu- able application in the analysis of current huge\n\t\t  datasets. In this paper we propose a new algorithm that,\n\t\t  with the help of a human expert, effi- ciently explores a\n\t\t  dataset with the goal of detecting relevant anomalous\n\t\t  records. Under this scheme the computer selectively asks\n\t\t  the expert for data labeling, looking for relevant semantic\n\t\t  feedback in order to improve its knowledge about what\n\t\t  characterizes a relevant anomaly. Our ratio- nale is that\n\t\t  while computers can process huge amounts of low level data,\n\t\t  an expert has high level semantic knowledge to efficiently\n\t\t  lead the search. We build upon our previous work based on\n\t\t  Bayesian networks that pro- vides an initial set of\n\t\t  potential anomalies. In this paper, we augment this\n\t\t  approach with an active learning scheme based on the\n\t\t  clustering proper- ties of Dirichlet distributions. We test\n\t\t  the performance of our algorithm using synthetic and real\n\t\t  datasets. Our results indicate that, under noisy data and\n\t\t  anomalies presenting regular patterns, our approach\n\t\t  signifi- cantly reduces the rate of false positives, while\n\t\t  decreasing the time to reach the relevant anomalies. },\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/ActiveLearning.pdf}\n}\n\n
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\n Today, the detection of anomalous records is a highly valu- able application in the analysis of current huge datasets. In this paper we propose a new algorithm that, with the help of a human expert, effi- ciently explores a dataset with the goal of detecting relevant anomalous records. Under this scheme the computer selectively asks the expert for data labeling, looking for relevant semantic feedback in order to improve its knowledge about what characterizes a relevant anomaly. Our ratio- nale is that while computers can process huge amounts of low level data, an expert has high level semantic knowledge to efficiently lead the search. We build upon our previous work based on Bayesian networks that pro- vides an initial set of potential anomalies. In this paper, we augment this approach with an active learning scheme based on the clustering proper- ties of Dirichlet distributions. We test the performance of our algorithm using synthetic and real datasets. Our results indicate that, under noisy data and anomalies presenting regular patterns, our approach signifi- cantly reduces the rate of false positives, while decreasing the time to reach the relevant anomalies. \n
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\n \n\n \n \n \n \n \n \n Human Detection in Indoor Environments Using Multiple Visual Cues and a Mobile Robot.\n \n \n \n \n\n\n \n Pszczolkowski, S.; and Soto, A.\n\n\n \n\n\n\n In Iberoamerican Congress on Pattern Recognition (CIARP), LNCS 4756, pages 350-359, 2008. \n \n\n\n\n
\n\n\n\n \n \n \"HumanPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  pszczolkowski:soto:2008,\n  author\t= {S. Pszczolkowski and A. Soto},\n  title\t\t= {Human Detection in Indoor Environments Using Multiple\n\t\t  Visual Cues and a Mobile Robot},\n  booktitle\t= {Iberoamerican Congress on Pattern Recognition (CIARP),\n\t\t  LNCS 4756},\n  pages\t\t= {350-359},\n  year\t\t= {2008},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Computer Vision for Quality Control in Latin American Food Industry, A Case Study.\n \n \n \n \n\n\n \n Aguilera, J.; Cipriano, A.; Erana, M.; Lillo, I.; Mery, D.; Soto, A.; and Valdivieso, C.\n\n\n \n\n\n\n In Int. Conf. on Computer Vision (ICCV): Workshop on Computer Vision Applications for Developing Countries, 2007. \n \n\n\n\n
\n\n\n\n \n \n \"ComputerPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  aguilera:etat:2007,\n  author\t= {JM. Aguilera and A. Cipriano and M. Erana and I. Lillo and\n\t\t  D. Mery and A. Soto and C. Valdivieso},\n  title\t\t= {Computer Vision for Quality Control in Latin American Food\n\t\t  Industry, A Case Study},\n  booktitle\t= {Int. Conf. on Computer Vision (ICCV): Workshop on Computer\n\t\t  Vision Applications for Developing Countries},\n  pages\t\t= {},\n  year\t\t= {2007},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Statistical approach to simultaneous mapping and localization for mobile robots.\n \n \n \n \n\n\n \n Araneda, A.; Fienberg, S.; and Soto, A.\n\n\n \n\n\n\n The Annals of Applied Statistics, 1(1): 66-84. 2007.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  araneda:fienberg:soto:2007,\n  author\t= {A. Araneda and S. Fienberg and A. Soto},\n  title\t\t= {A Statistical approach to simultaneous mapping and\n\t\t  localization for mobile robots},\n  journal\t= {The Annals of Applied Statistics},\n  volume\t= {1},\n  number\t= {1},\n  pages\t\t= {66-84},\n  year\t\t= {2007},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Logical Formalizations of Commonsense Reasoning, Papers from the 2007 AAAI Spring Symposium, Technical Report SS-07-05, Stanford, California, USA, March 26-28, 2007.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n AAAI. 2007.\n \n\n\n\n
\n\n\n\n \n \n \"LogicalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaaiss/2007-5,\n  title\t\t= {Logical Formalizations of Commonsense Reasoning, Papers\n\t\t  from the 2007 {AAAI} Spring Symposium, Technical Report\n\t\t  SS-07-05, Stanford, California, USA, March 26-28, 2007},\n  publisher\t= {{AAAI}},\n  year\t\t= {2007},\n  url\t\t= {http://www.aaai.org/Library/Symposia/Spring/ss07-05.php},\n  timestamp\t= {Fri, 17 Feb 2012 14:14:44 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaaiss/2007-5},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n On Domain-Independent Heuristics for Planning with Qualitative Preferences.\n \n \n \n \n\n\n \n Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Logical Formalizations of Commonsense Reasoning, Papers from the 2007 AAAI Spring Symposium, Technical Report SS-07-05, Stanford, California, USA, March 26-28, 2007, pages 7–12, 2007. \n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaaiss/baierm07,\n  author\t= {Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {On Domain-Independent Heuristics for Planning with\n\t\t  Qualitative Preferences},\n  booktitle\t= {Logical Formalizations of Commonsense Reasoning, Papers\n\t\t  from the 2007 {AAAI} Spring Symposium, Technical Report\n\t\t  SS-07-05, Stanford, California, USA, March 26-28, 2007},\n  pages\t\t= {7--12},\n  year\t\t= {2007},\n  crossref\t= {DBLP:conf/aaaiss/2007-5},\n  url\t\t= {http://www.aaai.org/Library/Symposia/Spring/2007/ss07-05-003.php},\n  timestamp\t= {Fri, 17 Feb 2012 14:14:44 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaaiss/BaierM07},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling, ICAPS 2007, Providence, Rhode Island, USA, September 22-26, 2007.\n \n \n \n\n\n \n Boddy, M. S.; Fox, M.; and Thiébaux, S.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2007,\n  editor\t= {Mark S. Boddy and Maria Fox and Sylvie Thi{\\'{e}}baux},\n  title\t\t= {Proceedings of the Seventeenth International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2007,\n\t\t  Providence, Rhode Island, USA, September 22-26, 2007},\n  publisher\t= {{AAAI}},\n  year\t\t= {2007},\n  isbn\t\t= {978-1-57735-344-7},\n  timestamp\t= {Fri, 15 Aug 2008 12:09:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2007},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Procedural Domain Control Knowledge in State-of-the-Art Planners.\n \n \n \n \n\n\n \n Baier, J. A.; Fritz, C.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling, ICAPS 2007, Providence, Rhode Island, USA, September 22-26, 2007, pages 26–33, 2007. \n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/baierfm07,\n  author\t= {Jorge A. Baier and Christian Fritz and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {Exploiting Procedural Domain Control Knowledge in\n\t\t  State-of-the-Art Planners},\n  booktitle\t= {Proceedings of the Seventeenth International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2007,\n\t\t  Providence, Rhode Island, USA, September 22-26, 2007},\n  pages\t\t= {26--33},\n  year\t\t= {2007},\n  crossref\t= {DBLP:conf/aips/2007},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/2007/icaps07-004.php},\n  timestamp\t= {Tue, 10 Jun 2014 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/BaierFM07},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n The Thread Migration Mechanism of DSM-PEPE.\n \n \n \n\n\n \n Meza, F.; and Ruz, C.\n\n\n \n\n\n\n In ICA3PP, volume 4494, of Lecture Notes in Computer Science, pages 177–187, 2007. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ica3pp/mezar07,\n  author\t= {Federico Meza and Cristian Ruz},\n  title\t\t= {The Thread Migration Mechanism of {DSM-PEPE}},\n  booktitle\t= {{ICA3PP}},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {4494},\n  pages\t\t= {177--187},\n  publisher\t= {Springer},\n  year\t\t= {2007}\n}\n\n
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\n \n\n \n \n \n \n \n \n IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007.\n \n \n \n \n\n\n \n Veloso, M. M.,\n editor.\n \n\n\n \n\n\n\n 2007.\n \n\n\n\n
\n\n\n\n \n \n \"IJCAIPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/ijcai/2007,\n  editor\t= {Manuela M. Veloso},\n  title\t\t= {{IJCAI} 2007, Proceedings of the 20th International Joint\n\t\t  Conference on Artificial Intelligence, Hyderabad, India,\n\t\t  January 6-12, 2007},\n  year\t\t= {2007},\n  url\t\t= {http://ijcai.org/proceedings/2007},\n  timestamp\t= {Wed, 20 Jul 2016 13:58:40 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/2007},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Heuristic Search Approach to Planning with Temporally Extended Preferences.\n \n \n \n \n\n\n \n Baier, J. A.; Bacchus, F.; and McIlraith, S. A.\n\n\n \n\n\n\n In IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007, pages 1808–1815, 2007. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/ijcai/baierbm07,\n  author\t= {Jorge A. Baier and Fahiem Bacchus and Sheila A.\n\t\t  McIlraith},\n  title\t\t= {A Heuristic Search Approach to Planning with Temporally\n\t\t  Extended Preferences},\n  booktitle\t= {{IJCAI} 2007, Proceedings of the 20th International Joint\n\t\t  Conference on Artificial Intelligence, Hyderabad, India,\n\t\t  January 6-12, 2007},\n  pages\t\t= {1808--1815},\n  year\t\t= {2007},\n  crossref\t= {DBLP:conf/ijcai/2007},\n  url\t\t= {http://ijcai.org/Proceedings/07/Papers/292.pdf},\n  timestamp\t= {Wed, 20 Jul 2016 13:58:40 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/ijcai/BaierBM07},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Towards Grid Monitoring and deployment in Jade, using ProActive.\n \n \n \n\n\n \n Ruz, C.; Baude, F.; and Contes, V. L.\n\n\n \n\n\n\n CoRR, abs/0710.5348. 2007.\n \n\n\n\n
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@Article{\t  dblp:journals/corr/abs-0710-5348,\n  author\t= {Cristian Ruz and Fran{\\c{c}}oise Baude and Virginie\n\t\t  Legrand Contes},\n  title\t\t= {Towards Grid Monitoring and deployment in Jade, using\n\t\t  ProActive},\n  journal\t= {CoRR},\n  volume\t= {abs/0710.5348},\n  year\t\t= {2007}\n}\n\n
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\n \n\n \n \n \n \n \n \n An Accelerated Algorithm for Density Estimation in Large Databases, Using Gaussian Mixtures.\n \n \n \n \n\n\n \n Soto, A.; Zavala, F.; and Araneda, A.\n\n\n \n\n\n\n Cybernetics and Systems: An International Journal, 38(2): 123-139. 2007.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  soto:zavala:araneda:2007,\n  author\t= {A. Soto and F. Zavala and A. Araneda},\n  title\t\t= {An Accelerated Algorithm for Density Estimation in Large\n\t\t  Databases, Using Gaussian Mixtures},\n  journal\t= {Cybernetics and Systems: An International Journal},\n  volume\t= {38},\n  number\t= {2},\n  pages\t\t= {123-139},\n  year\t\t= {2007},\n  abstract\t= {Today, with the advances of computer storage and\n\t\t  technology, there are huge datasets available, offering an\n\t\t  opportunity to extract valuable information. Probabilistic\n\t\t  approaches are specially suited to learn from data by\n\t\t  representing knowledge as density functions. In this paper,\n\t\t  we choose Gaussian Mixture Models (GMMs) to represent\n\t\t  densities, as they possess great flexibility to adequate to\n\t\t  a wide class of problems. The classical estimation approach\n\t\t  for GMMs corresponds to the iterative algorithm of\n\t\t  Expectation Maximization. This approach, however, does not\n\t\t  scale properly to meet the high demanding processing\n\t\t  requirements of large databases. In this paper we introduce\n\t\t  an EM-based algorithm, that solves the scalability problem.\n\t\t  Our approach is based on the concept of data condensation\n\t\t  which, in addition to substantially diminishing the\n\t\t  computational load, provides sound starting values that\n\t\t  allow the algorithm to reach convergence faster. We also\n\t\t  focus on the model selection problem. We test our algorithm\n\t\t  using synthetic and real databases, and find several\n\t\t  advantages, when compared to other standard existing\n\t\t  procedures.},\n  url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/Felipe-07.pdf}\n}\n\n
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\n Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian Mixture Models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of Expectation Maximization. This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept of data condensation which, in addition to substantially diminishing the computational load, provides sound starting values that allow the algorithm to reach convergence faster. We also focus on the model selection problem. We test our algorithm using synthetic and real databases, and find several advantages, when compared to other standard existing procedures.\n
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\n \n\n \n \n \n \n \n \n Using Data Mining Techniques to Predict Industrial Wine Problem Fermentations.\n \n \n \n \n\n\n \n Urtubia; Perez-Correa, J. R.; Soto, A.; and Pszczolkowski, P.\n\n\n \n\n\n\n Food Control, 18(12): 1512-1517. 2007.\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  urtubia:etal:2008,\n  author\t= {Urtubia and J. R. Perez-Correa and A. Soto and P.\n\t\t  Pszczolkowski},\n  title\t\t= {Using Data Mining Techniques to Predict Industrial Wine\n\t\t  Problem Fermentations},\n  journal\t= {Food Control},\n  volume\t= {18},\n  number\t= {12},\n  pages\t\t= {1512-1517},\n  year\t\t= {2007},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 2006\n \n \n (10)\n \n \n
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\n \n\n \n \n \n \n \n Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA.\n \n \n \n\n\n \n \n\n\n \n\n\n\n AAAI Press. 2006.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aaai/2006,\n  title\t\t= {Proceedings, The Twenty-First National Conference on\n\t\t  Artificial Intelligence and the Eighteenth Innovative\n\t\t  Applications of Artificial Intelligence Conference, July\n\t\t  16-20, 2006, Boston, Massachusetts, {USA}},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2006},\n  timestamp\t= {Thu, 13 Jul 2006 12:28:06 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/2006},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning with First-Order Temporally Extended Goals using Heuristic Search.\n \n \n \n \n\n\n \n Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, USA, pages 788–795, 2006. \n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 24 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aaai/baierm06,\n  author\t= {Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {Planning with First-Order Temporally Extended Goals using\n\t\t  Heuristic Search},\n  booktitle\t= {Proceedings, The Twenty-First National Conference on\n\t\t  Artificial Intelligence and the Eighteenth Innovative\n\t\t  Applications of Artificial Intelligence Conference, July\n\t\t  16-20, 2006, Boston, Massachusetts, {USA}},\n  pages\t\t= {788--795},\n  year\t\t= {2006},\n  crossref\t= {DBLP:conf/aaai/2006},\n  url\t\t= {http://www.aaai.org/Library/AAAI/2006/aaai06-125.php},\n  timestamp\t= {Mon, 19 Mar 2012 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aaai/BaierM06},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling, ICAPS 2006, Cumbria, UK, June 6-10, 2006.\n \n \n \n\n\n \n Long, D.; Smith, S. F.; Borrajo, D.; and McCluskey, L.,\n editors.\n \n\n\n \n\n\n\n AAAI. 2006.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/aips/2006,\n  editor\t= {Derek Long and Stephen F. Smith and Daniel Borrajo and Lee\n\t\t  McCluskey},\n  title\t\t= {Proceedings of the Sixteenth International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2006, Cumbria,\n\t\t  UK, June 6-10, 2006},\n  publisher\t= {{AAAI}},\n  year\t\t= {2006},\n  isbn\t\t= {978-1-57735-270-9},\n  timestamp\t= {Fri, 23 Nov 2007 13:41:37 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/2006},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning with Temporally Extended Goals Using Heuristic Search.\n \n \n \n \n\n\n \n Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling, ICAPS 2006, Cumbria, UK, June 6-10, 2006, pages 342–345, 2006. \n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 27 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/aips/baierm06,\n  author\t= {Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {Planning with Temporally Extended Goals Using Heuristic\n\t\t  Search},\n  booktitle\t= {Proceedings of the Sixteenth International Conference on\n\t\t  Automated Planning and Scheduling, {ICAPS} 2006, Cumbria,\n\t\t  UK, June 6-10, 2006},\n  pages\t\t= {342--345},\n  year\t\t= {2006},\n  crossref\t= {DBLP:conf/aips/2006},\n  url\t\t= {http://www.aaai.org/Library/ICAPS/2006/icaps06-036.php},\n  timestamp\t= {Thu, 13 Dec 2012 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/aips/BaierM06},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile.\n \n \n \n\n\n \n Ortega, P. A.; Figueroa, C. J.; and Ruz, G. A.\n\n\n \n\n\n\n In DMIN, pages 224–231, 2006. CSREA Press\n \n\n\n\n
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@InProceedings{\t  dblp:conf/dmin/ortegafr06,\n  author\t= {Pedro A. Ortega and Cristi{\\'{a}}n J. Figueroa and Gonzalo\n\t\t  A. Ruz},\n  title\t\t= {A Medical Claim Fraud/Abuse Detection System based on Data\n\t\t  Mining: {A} Case Study in Chile},\n  booktitle\t= {{DMIN}},\n  pages\t\t= {224--231},\n  publisher\t= {{CSREA} Press},\n  year\t\t= {2006}\n}\n\n
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\n \n\n \n \n \n \n \n Self-stabilizing Deadlock Detection Under the OR Requirement Model.\n \n \n \n\n\n \n Orellana, C. F.; Ruz, C.; and Eterovic, Y.\n\n\n \n\n\n\n In Euro-Par, volume 4128, of Lecture Notes in Computer Science, pages 559–570, 2006. Springer\n \n\n\n\n
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@InProceedings{\t  dblp:conf/europar/orellanare06,\n  author\t= {Christian F. Orellana and Cristian Ruz and Yadran\n\t\t  Eterovic},\n  title\t\t= {Self-stabilizing Deadlock Detection Under the {OR}\n\t\t  Requirement Model},\n  booktitle\t= {Euro-Par},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {4128},\n  pages\t\t= {559--570},\n  publisher\t= {Springer},\n  year\t\t= {2006}\n}\n\n
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\n \n\n \n \n \n \n \n Proceedings, Tenth International Conference on Principles of Knowledge Representation and Reasoning, Lake District of the United Kingdom, June 2-5, 2006.\n \n \n \n\n\n \n Doherty, P.; Mylopoulos, J.; and Welty, C. A.,\n editors.\n \n\n\n \n\n\n\n AAAI Press. 2006.\n \n\n\n\n
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@Proceedings{\t  dblp:conf/kr/2006,\n  editor\t= {Patrick Doherty and John Mylopoulos and Christopher A.\n\t\t  Welty},\n  title\t\t= {Proceedings, Tenth International Conference on Principles\n\t\t  of Knowledge Representation and Reasoning, Lake District of\n\t\t  the United Kingdom, June 2-5, 2006},\n  publisher\t= {{AAAI} Press},\n  year\t\t= {2006},\n  isbn\t\t= {978-1-57735-271-6},\n  timestamp\t= {Fri, 09 Jun 2006 11:56:44 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/2006},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n On Planning with Programs that Sense.\n \n \n \n \n\n\n \n Baier, J. A.; and McIlraith, S. A.\n\n\n \n\n\n\n In Proceedings, Tenth International Conference on Principles of Knowledge Representation and Reasoning, Lake District of the United Kingdom, June 2-5, 2006, pages 492–502, 2006. \n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/kr/baierm06,\n  author\t= {Jorge A. Baier and Sheila A. McIlraith},\n  title\t\t= {On Planning with Programs that Sense},\n  booktitle\t= {Proceedings, Tenth International Conference on Principles\n\t\t  of Knowledge Representation and Reasoning, Lake District of\n\t\t  the United Kingdom, June 2-5, 2006},\n  pages\t\t= {492--502},\n  year\t\t= {2006},\n  crossref\t= {DBLP:conf/kr/2006},\n  url\t\t= {http://www.aaai.org/Library/KR/2006/kr06-051.php},\n  timestamp\t= {Thu, 13 Dec 2012 00:00:00 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/kr/BaierM06},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Automatic Selection and Detection of Visual Landmarks Using Multiple Segmentations.\n \n \n \n \n\n\n \n Langdon, D.; Soto, A.; and Mery, D.\n\n\n \n\n\n\n In IEEE Pacific-Rim Symposium on Image and Video Technology (PSIVT), LNCS 4319, pages 601-610, 2006. \n \n\n\n\n
\n\n\n\n \n \n \"AutomaticPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  langdon:soto:mery:2006,\n  author\t= {D. Langdon and A. Soto and D. Mery},\n  title\t\t= {Automatic Selection and Detection of Visual Landmarks\n\t\t  Using Multiple Segmentations},\n  booktitle\t= {IEEE Pacific-Rim Symposium on Image and Video Technology\n\t\t  (PSIVT), LNCS 4319},\n  pages\t\t= {601-610},\n  year\t\t= {2006},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Mobile Robotics Course for Undergraduate Students in Computer Science.\n \n \n \n \n\n\n \n Soto, A.; Espinace, P.; and Mitnik, R.\n\n\n \n\n\n\n In IEEE Latin American Robotics Symposium, LARS, pages 187-192, 2006. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:espinace:mitnik:2006,\n  author\t= {A. Soto and P. Espinace and R. Mitnik},\n  title\t\t= {A Mobile Robotics Course for Undergraduate Students in\n\t\t  Computer Science},\n  booktitle\t= {IEEE Latin American Robotics Symposium, LARS},\n  pages\t\t= {187-192},\n  year\t\t= {2006},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 2005\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Important Sampling in Mapping and Localization by a Mobile Robot.\n \n \n \n \n\n\n \n Araneda, A.; and Soto, A.\n\n\n \n\n\n\n In Workshop on Case Studies of Bayesian Statistics, 2005. \n \n\n\n\n
\n\n\n\n \n \n \"ImportantPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  araneda:soto:2005,\n  author\t= {A. Araneda and A. Soto},\n  title\t\t= {Important Sampling in Mapping and Localization by a Mobile\n\t\t  Robot},\n  booktitle\t= {Workshop on Case Studies of Bayesian Statistics},\n  year\t\t= {2005},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n Searching an Optimal History Size for History-Based Page Prefetching on Software DSM Systems.\n \n \n \n\n\n \n Ruz, C.; and Piquer, J. M.\n\n\n \n\n\n\n In HPCC, volume 3726, of Lecture Notes in Computer Science, pages 133–142, 2005. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/hpcc/ruzp05,\n  author\t= {Cristian Ruz and Jos{\\'{e}} M. Piquer},\n  title\t\t= {Searching an Optimal History Size for History-Based Page\n\t\t  Prefetching on Software {DSM} Systems},\n  booktitle\t= {{HPCC}},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {3726},\n  pages\t\t= {133--142},\n  publisher\t= {Springer},\n  year\t\t= {2005}\n}\n\n
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\n \n\n \n \n \n \n \n An Adaptive Web Platform based and on a Multiagent System and Ontologies.\n \n \n \n\n\n \n Scheihing, E.; Carrasco, J.; Guerra, J.; and Parra, D.\n\n\n \n\n\n\n In TISE 2005, Nuevas Ideas en Informática Educativa, 2005. ISBN 956-299-954-8\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  scheihing2005adaptive,\n  author\t= {Scheihing, Eliana and Carrasco, Jos{\\'e} and Guerra, Julio\n\t\t  and Parra, Denis},\n  booktitle\t= {TISE 2005, Nuevas Ideas en Inform{\\'a}tica Educativa},\n  organization\t= {ISBN 956-299-954-8},\n  title\t\t= {An Adaptive Web Platform based and on a Multiagent System\n\t\t  and Ontologies},\n  year\t\t= {2005}\n}\n\n
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\n \n\n \n \n \n \n \n \n Self Adaptive Particle Filter.\n \n \n \n \n\n\n \n Soto, A.\n\n\n \n\n\n\n In Proceedings of International Join Conference on Artificial Intelligence (IJCAI), pages 1398-1406, 2005. \n \n\n\n\n
\n\n\n\n \n \n \"SelfPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:2005,\n  author\t= {A. Soto},\n  title\t\t= {Self Adaptive Particle Filter},\n  booktitle\t= {Proceedings of International Join Conference on Artificial\n\t\t  Intelligence (IJCAI)},\n  pages\t\t= {1398-1406},\n  year\t\t= {2005},\n  abstract\t= {The particle filter has emerged as a useful tool for\n\t\t  problems requiring dynamic state estimation. The efficiency\n\t\t  and accuracy of the filter depend mostly on the number of\n\t\t  particles used in the estimation and on the propagation\n\t\t  function used to re-allocate these particles at each\n\t\t  iteration. Both features are specified beforehand and are\n\t\t  kept fixed in the reg- ular implementation of the filter.\n\t\t  In practice this may be highly inappropriate since it\n\t\t  ignores errors in the models and the varying dynamics of\n\t\t  the pro- cesses. This work presents a self adaptive version\n\t\t  of the particle filter that uses statistical methods to\n\t\t  adapt the number of particles and the propagation function\n\t\t  at each iteration. Furthermore, our method presents similar\n\t\t  computational load than the stan- dard particle filter. We\n\t\t  show the advantages of the self adaptive filter by applying\n\t\t  it to a synthetic ex- ample and to the visual tracking of\n\t\t  targets in a real video sequence. },\n  url\t\t= {Soto-IJCAI-0 5.pdf}\n}\n\n
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\n The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the reg- ular implementation of the filter. In practice this may be highly inappropriate since it ignores errors in the models and the varying dynamics of the pro- cesses. This work presents a self adaptive version of the particle filter that uses statistical methods to adapt the number of particles and the propagation function at each iteration. Furthermore, our method presents similar computational load than the stan- dard particle filter. We show the advantages of the self adaptive filter by applying it to a synthetic ex- ample and to the visual tracking of targets in a real video sequence. \n
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\n  \n 2004\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Statistical Inference in Mapping and Localization for Mobile Robots.\n \n \n \n \n\n\n \n Araneda, A.; and A. Soto, undefined\n\n\n \n\n\n\n In Advances in Artificial Intelligence, Iberamia-04, LNAI 3315, pages 545-554, 2004. \n \n\n\n\n
\n\n\n\n \n \n \"StatisticalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  araneda:soto:2004,\n  author\t= {A. Araneda and A. Soto,},\n  title\t\t= {Statistical Inference in Mapping and Localization for\n\t\t  Mobile Robots},\n  booktitle\t= {Advances in Artificial Intelligence, Iberamia-04, LNAI\n\t\t  3315},\n  pages\t\t= {545-554},\n  year\t\t= {2004},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Advances in Web-Age Information Management: 5th International Conference, WAIM 2004, Dalian, China, July 15-17, 2004.\n \n \n \n \n\n\n \n Li, Q.; Wang, G.; and Feng, L.,\n editors.\n \n\n\n \n\n\n\n Volume 3129, of Lecture Notes in Computer Science.Springer. 2004.\n \n\n\n\n
\n\n\n\n \n \n \"AdvancesPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/waim/2004,\n  editor\t= {Qing Li and Guoren Wang and Ling Feng},\n  title\t\t= {Advances in Web-Age Information Management: 5th\n\t\t  International Conference, {WAIM} 2004, Dalian, China, July\n\t\t  15-17, 2004},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {3129},\n  publisher\t= {Springer},\n  year\t\t= {2004},\n  url\t\t= {https://doi.org/10.1007/b98703},\n  doi\t\t= {10.1007/b98703},\n  isbn\t\t= {3-540-22418-1},\n  timestamp\t= {Wed, 14 Mar 2018 16:00:06 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/waim/2004},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Semantic Search in the WWW Supported by a Cognitive Model.\n \n \n \n \n\n\n \n Wechsler, K.; Baier, J. A.; Nussbaum, M.; and Baeza-Yates, R. A.\n\n\n \n\n\n\n In Advances in Web-Age Information Management: 5th International Conference, WAIM 2004, Dalian, China, July 15-17, 2004, pages 315–324, 2004. \n \n\n\n\n
\n\n\n\n \n \n \"SemanticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/waim/wechslerbnb04,\n  author\t= {Katia Wechsler and Jorge A. Baier and Miguel Nussbaum and\n\t\t  Ricardo A. Baeza{-}Yates},\n  title\t\t= {Semantic Search in the {WWW} Supported by a Cognitive\n\t\t  Model},\n  booktitle\t= {Advances in Web-Age Information Management: 5th\n\t\t  International Conference, {WAIM} 2004, Dalian, China, July\n\t\t  15-17, 2004},\n  pages\t\t= {315--324},\n  year\t\t= {2004},\n  crossref\t= {DBLP:conf/waim/2004},\n  url\t\t= {https://doi.org/10.1007/978-3-540-27772-9\\_32},\n  doi\t\t= {10.1007/978-3-540-27772-9\\_32},\n  timestamp\t= {Wed, 14 Mar 2018 16:00:06 +0100},\n  biburl\t= {https://dblp.org/rec/bib/conf/waim/WechslerBNB04},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mobile Robotic Supported Collaborative Learning (MRSCL).\n \n \n \n \n\n\n \n Mitnik, R.; Nussbaum, M.; and Soto, A.\n\n\n \n\n\n\n In Advances in Artificial Intelligence, Iberamia-04, LNAI 3315, pages 912-921, 2004. \n \n\n\n\n
\n\n\n\n \n \n \"MobilePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  mitnik:nussbaum:soto:2004,\n  author\t= {R. Mitnik and M. Nussbaum and A. Soto},\n  title\t\t= {Mobile Robotic Supported Collaborative Learning (MRSCL)},\n  booktitle\t= {Advances in Artificial Intelligence, Iberamia-04, LNAI\n\t\t  3315},\n  pages\t\t= {912-921},\n  year\t\t= {2004},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Method to Adaptively Propagate the Set of Samples Used by Particle Filters.\n \n \n \n \n\n\n \n Soto, A.\n\n\n \n\n\n\n In Lectures Notes in Artificial Intelligence, LNAI 3040, pages 47-56, 2004. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:2004,\n  author\t= {A. Soto},\n  title\t\t= {A Method to Adaptively Propagate the Set of Samples Used\n\t\t  by Particle Filters},\n  booktitle\t= {Lectures Notes in Artificial Intelligence, LNAI 3040},\n  pages\t\t= {47-56},\n  year\t\t= {2004},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Detection of Rare Objects in Massive Astrophysical Data Sets Using Innovative Knowledge Discovery Technology.\n \n \n \n \n\n\n \n Soto, A.; A.Cansado; and Zavala, F.\n\n\n \n\n\n\n In Astronomical Data Analysis Software & Systems Conf. Series (ADASS), pages 66-72, 2004. \n \n\n\n\n
\n\n\n\n \n \n \"DetectionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:cansado:zavala:2008,\n  author\t= {A. Soto and A.Cansado and F. Zavala},\n  title\t\t= {Detection of Rare Objects in Massive Astrophysical Data\n\t\t  Sets Using Innovative Knowledge Discovery Technology},\n  booktitle\t= {Astronomical Data Analysis Software &amp; Systems Conf.\n\t\t  Series (ADASS)},\n  pages\t\t= {66-72},\n  year\t\t= {2004},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n On the Design and Implementation of a Portable DSM System for Low-Cost Multicomputers.\n \n \n \n\n\n \n Meza, F.; Campos, A. E.; and Ruz, C.\n\n\n \n\n\n\n In ICCSA (1), volume 2667, of Lecture Notes in Computer Science, pages 967–976, 2003. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/iccsa/mezacr03,\n  author\t= {Federico Meza and Alvaro E. Campos and Cristian Ruz},\n  title\t\t= {On the Design and Implementation of a Portable {DSM}\n\t\t  System for Low-Cost Multicomputers},\n  booktitle\t= {{ICCSA} {(1)}},\n  series\t= {Lecture Notes in Computer Science},\n  volume\t= {2667},\n  pages\t\t= {967--976},\n  publisher\t= {Springer},\n  year\t\t= {2003}\n}\n\n
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\n \n\n \n \n \n \n \n \n Planning under uncertainty as Golog programs.\n \n \n \n \n\n\n \n Baier, J. A.; and Pinto, J.\n\n\n \n\n\n\n J. Exp. Theor. Artif. Intell., 15(4): 383–405. 2003.\n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  dblp:journals/jetai/baierp03,\n  author\t= {Jorge A. Baier and Javier Pinto},\n  title\t\t= {Planning under uncertainty as Golog programs},\n  journal\t= {J. Exp. Theor. Artif. Intell.},\n  volume\t= {15},\n  number\t= {4},\n  pages\t\t= {383--405},\n  year\t\t= {2003},\n  url\t\t= {https://doi.org/10.1080/0952813031000064567},\n  doi\t\t= {10.1080/0952813031000064567},\n  timestamp\t= {Thu, 18 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/journals/jetai/BaierP03},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Sequential Monte Carlo Methods for the Creation of Adaptive Software.\n \n \n \n \n\n\n \n Soto, A.; and Khosla, P.\n\n\n \n\n\n\n In 3th Int. Workshop on Self-adaptive Software, 2003. \n \n\n\n\n
\n\n\n\n \n \n \"SequentialPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:khosla:2003a,\n  author\t= {A. Soto and P. Khosla},\n  title\t\t= {Sequential Monte Carlo Methods for the Creation of\n\t\t  Adaptive Software},\n  booktitle\t= {3th Int. Workshop on Self-adaptive Software},\n  pages\t\t= {},\n  year\t\t= {2003},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Probabilistic Approach for Dynamic State Estimation Using Visual Information.\n \n \n \n \n\n\n \n Soto, A.; and Khosla, P.\n\n\n \n\n\n\n In Lectures Notes in Computer Science, LNCS 2821, pages 421-435, 2003. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:khosla:2003b,\n  author\t= {A. Soto and P. Khosla},\n  title\t\t= {A Probabilistic Approach for Dynamic State Estimation\n\t\t  Using Visual Information},\n  booktitle\t= {Lectures Notes in Computer Science, LNCS 2821},\n  pages\t\t= {421-435},\n  year\t\t= {2003},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Adaptive Agent Based System for State Estimation Using Dynamic Multidimentional Information Sources.\n \n \n \n \n\n\n \n Soto, A.; and Khosla, P.\n\n\n \n\n\n\n In Lectures Notes in Computer Science, LNCS 2614, pages 66-83, 2003. \n \n\n\n\n
\n\n\n\n \n \n \"AdaptivePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:khosla:2003c,\n  author\t= {A. Soto and P. Khosla},\n  title\t\t= {Adaptive Agent Based System for State Estimation Using\n\t\t  Dynamic Multidimentional Information Sources},\n  booktitle\t= {Lectures Notes in Computer Science, LNCS 2614},\n  pages\t\t= {66-83},\n  year\t\t= {2003},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n Probabilistic Adaptive Agent Based System for Dynamic State Estimation Using Multiple Visual Cues.\n \n \n \n \n\n\n \n Soto, A.; and Khosla, P.\n\n\n \n\n\n\n In volume 6, pages 559-572, 2003. \n \n\n\n\n
\n\n\n\n \n \n \"ProbabilisticPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:khosla:2003d,\n  author\t= {A. Soto and P. Khosla},\n  title\t\t= {Probabilistic Adaptive Agent Based System for Dynamic\n\t\t  State Estimation Using Multiple Visual Cues},\n  journal\t= {Springer Tracts in Advanced Robotics (STAR)},\n  volume\t= {6},\n  pages\t\t= {559-572},\n  year\t\t= {2003},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 2002\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n 22nd International Conference of the Chilean Computer Science Society (SCCC 2002), 6-8 November 2002, Copiapo, Chile.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n IEEE Computer Society. 2002.\n \n\n\n\n
\n\n\n\n \n \n \"22ndPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/sccc/2002,\n  title\t\t= {22nd International Conference of the Chilean Computer\n\t\t  Science Society {(SCCC} 2002), 6-8 November 2002, Copiapo,\n\t\t  Chile},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {2002},\n  url\t\t= {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8377},\n  isbn\t\t= {0-7695-1867-2},\n  timestamp\t= {Thu, 23 Jun 2016 15:53:28 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/2002},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n On Procedure Recognition in the Situation Calculus.\n \n \n \n \n\n\n \n Baier, J. A.\n\n\n \n\n\n\n In 22nd International Conference of the Chilean Computer Science Society (SCCC 2002), 6-8 November 2002, Copiapo, Chile, pages 33–42, 2002. \n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/sccc/baier02,\n  author\t= {Jorge A. Baier},\n  title\t\t= {On Procedure Recognition in the Situation Calculus},\n  booktitle\t= {22nd International Conference of the Chilean Computer\n\t\t  Science Society {(SCCC} 2002), 6-8 November 2002, Copiapo,\n\t\t  Chile},\n  pages\t\t= {33--42},\n  year\t\t= {2002},\n  crossref\t= {DBLP:conf/sccc/2002},\n  url\t\t= {https://doi.org/10.1109/SCCC.2002.1173171},\n  doi\t\t= {10.1109/SCCC.2002.1173171},\n  timestamp\t= {Thu, 25 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/Baier02},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Recent Advances in Distributed Tactical Surveillance.\n \n \n \n \n\n\n \n Saptharishi, M.; Bhat, K.; Diehl, C.; Oliver, S.; Savvides, M.; Soto, A.; Dolan, J.; and Khosla, P.\n\n\n \n\n\n\n In SPIE on Unattended Ground Sensor Technologies and Applications, Aerosense, 2002. \n \n\n\n\n
\n\n\n\n \n \n \"RecentPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  saptharishi:etal:2002,\n  author\t= {M. Saptharishi and K. Bhat and C. Diehl and S. Oliver and\n\t\t  M. Savvides and A. Soto and J. Dolan and P. Khosla},\n  title\t\t= {Recent Advances in Distributed Tactical Surveillance},\n  booktitle\t= {SPIE on Unattended Ground Sensor Technologies and\n\t\t  Applications, Aerosense},\n  pages\t\t= {},\n  year\t\t= {2002},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Probabilistic Approach for the Adaptive Integration of Multiple Visual Cues Using an Agent Framework.\n \n \n \n \n\n\n \n Soto, A.\n\n\n \n\n\n\n Technical Report PhD. Thesis, Robotics Institute, School of Computer Science, Tech Report CMU-RI-TR-02-30, Carnegie Mellon University, 2002.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@TechReport{\t  soto:2002,\n  author\t= {A. Soto},\n  title\t\t= {A Probabilistic Approach for the Adaptive Integration of\n\t\t  Multiple Visual Cues Using an Agent Framework},\n  number\t= {PhD. Thesis, Robotics Institute, School of Computer\n\t\t  Science, Tech Report CMU-RI-TR-02-30},\n  institution\t= {Carnegie Mellon University},\n  year\t\t= {2002},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 1999\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n 19th International Conference of the Chilean Computer Science Society (SCCC '99), 11-13 November 1999, Talca, Chile.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n IEEE Computer Society. 1999.\n \n\n\n\n
\n\n\n\n \n \n \"19thPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{\t  dblp:conf/sccc/1999,\n  title\t\t= {19th International Conference of the Chilean Computer\n\t\t  Science Society {(SCCC} '99), 11-13 November 1999, Talca,\n\t\t  Chile},\n  publisher\t= {{IEEE} Computer Society},\n  year\t\t= {1999},\n  url\t\t= {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6588},\n  isbn\t\t= {0-7695-0296-2},\n  timestamp\t= {Thu, 23 Jun 2016 15:53:28 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/1999},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Integrating True Concurrency into the Robot Programming Language.\n \n \n \n \n\n\n \n Baier, J. A.; and Pinto, J.\n\n\n \n\n\n\n In 19th International Conference of the Chilean Computer Science Society (SCCC '99), 11-13 November 1999, Talca, Chile, pages 179–186, 1999. \n \n\n\n\n
\n\n\n\n \n \n \"IntegratingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  dblp:conf/sccc/baierp99,\n  author\t= {Jorge A. Baier and Javier Pinto},\n  title\t\t= {Integrating True Concurrency into the Robot Programming\n\t\t  Language},\n  booktitle\t= {19th International Conference of the Chilean Computer\n\t\t  Science Society {(SCCC} '99), 11-13 November 1999, Talca,\n\t\t  Chile},\n  pages\t\t= {179--186},\n  year\t\t= {1999},\n  crossref\t= {DBLP:conf/sccc/1999},\n  url\t\t= {https://doi.org/10.1109/SCCC.1999.810185},\n  doi\t\t= {10.1109/SCCC.1999.810185},\n  timestamp\t= {Thu, 25 May 2017 01:00:00 +0200},\n  biburl\t= {https://dblp.org/rec/bib/conf/sccc/BaierP99},\n  bibsource\t= {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Distributed Tactical Surveillance with ATVs.\n \n \n \n \n\n\n \n Dolan, J.; Trebi-Ollennu, A.; Soto, A.; and Khosla, P.\n\n\n \n\n\n\n In SPIE on Unattended Ground Sensor Technologies and Applications, Aerosense, Vol. 3693, 1999. \n \n\n\n\n
\n\n\n\n \n \n \"DistributedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{\t  dolan:etal:1999,\n  author\t= {J. Dolan and A. Trebi-Ollennu and A. Soto and P. Khosla},\n  title\t\t= {Distributed Tactical Surveillance with ATVs},\n  booktitle\t= {SPIE on Unattended Ground Sensor Technologies and\n\t\t  Applications, Aerosense, Vol. 3693},\n  pages\t\t= {},\n  year\t\t= {1999},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n An Effective Mobile Robot Educator with a Full-Time Job.\n \n \n \n \n\n\n \n Nourbakhsh, I.; Bobenage, J.; Grange, S.; Lutz, R.; Meyer, R.; and Soto, A.\n\n\n \n\n\n\n Artificial Intelligence, 114(1-2): 95-124. 1999.\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  nourbakhsh:etal:1999,\n  author\t= {I. Nourbakhsh and J. Bobenage and S. Grange and R. Lutz\n\t\t  and R. Meyer and A. Soto},\n  title\t\t= {An Effective Mobile Robot Educator with a Full-Time Job},\n  journal\t= {Artificial Intelligence},\n  volume\t= {114},\n  number\t= {1-2},\n  pages\t\t= {95-124},\n  year\t\t= {1999},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n CyberATVs: Dynamic and Distributed Reconnaissance and Surveillance Using All Terrain UGVs.\n \n \n \n \n\n\n \n Soto, A.; Saptharishi, M.; Dolan, J.; Trebi-Ollennu, A.; and Khosla, P.\n\n\n \n\n\n\n In Proceedings of the International Conference on Field and Service Robotics (FSR), 1999. \n \n\n\n\n
\n\n\n\n \n \n \"CyberATVs:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:etal:2002,\n  author\t= {A. Soto and M. Saptharishi and J. Dolan and A.\n\t\t  Trebi-Ollennu and P. Khosla},\n  title\t\t= {CyberATVs: Dynamic and Distributed Reconnaissance and\n\t\t  Surveillance Using All Terrain UGVs},\n  booktitle\t= {Proceedings of the International Conference on Field and\n\t\t  Service Robotics (FSR)},\n  pages\t\t= {},\n  year\t\t= {1999},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 1998\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n A Real Time Visual Sensor for Supervision of Flotations Cells.\n \n \n \n \n\n\n \n Cipriano, A.; Guarini, M.; Vidal, R.; Soto, A.; Sepúlveda, C.; Mery, D.; and Briseño, H.\n\n\n \n\n\n\n Minerals Engineering, 11(6): 489-499. 1998.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{\t  cipriano:etal:1998,\n  author\t= {A. Cipriano and M. Guarini and R. Vidal and A. Soto and C.\n\t\t  Sepúlveda and D. Mery and H. Briseño},\n  title\t\t= {A Real Time Visual Sensor for Supervision of Flotations\n\t\t  Cells},\n  journal\t= {Minerals Engineering},\n  volume\t= {11},\n  number\t= {6},\n  pages\t\t= {489-499},\n  year\t\t= {1998},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Scenario for Planning Visual Navigation of a Mobile Robot.\n \n \n \n \n\n\n \n Soto, A.; and Nourbakhsh, I.\n\n\n \n\n\n\n In American Association for Artificial Intelligence (AAAI), Fall Symposium Series, 1998. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:illah:1998,\n  author\t= {A. Soto and I. Nourbakhsh},\n  title\t\t= {A Scenario for Planning Visual Navigation of a Mobile\n\t\t  Robot},\n  booktitle\t= {American Association for Artificial Intelligence (AAAI),\n\t\t  Fall Symposium Series},\n  pages\t\t= {},\n  year\t\t= {1998},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 1997\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Expert supervision of flotation cells using digital image processing.\n \n \n \n \n\n\n \n Cipriano, A.; Guarini, M.; Soto, A.; Briseño, H.; and D. Mery, undefined\n\n\n \n\n\n\n In In Proc. of 20th Int. Mineral Processing Congress, pages 281-292, 1997. \n \n\n\n\n
\n\n\n\n \n \n \"ExpertPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  cipriano:et:al:1997,\n  author\t= {A. Cipriano and M. Guarini and A. Soto and H. Briseño and\n\t\t  D. Mery,},\n  title\t\t= {Expert supervision of flotation cells using digital image\n\t\t  processing},\n  booktitle\t= {In Proc. of 20th Int. Mineral Processing Congress},\n  pages\t\t= {281-292},\n  year\t\t= {1997},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 1996\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Image processing applied to real time measurement of traffic flow.\n \n \n \n \n\n\n \n Soto, A.; and Cipriano, A.\n\n\n \n\n\n\n In In Proc. of 28th Southeastern Symposium on System Theory, 1996. \n \n\n\n\n
\n\n\n\n \n \n \"ImagePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  soto:cipriano:1996,\n  author\t= {A. Soto and A. Cipriano},\n  title\t\t= {Image processing applied to real time measurement of\n\t\t  traffic flow},\n  booktitle\t= {In Proc. of 28th Southeastern Symposium on System Theory},\n  pages\t\t= {},\n  year\t\t= {1996},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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\n  \n 1995\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Measurement of physical characteristics of foam in flotation cells.\n \n \n \n \n\n\n \n Guarini, M.; Soto, A.; Cipriano, A.; Guesalaga, A.; and Caceres, J.\n\n\n \n\n\n\n In In Proc. of Int. Conference: Copper-95, 1995. \n \n\n\n\n
\n\n\n\n \n \n \"MeasurementPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{\t  guarini:etal:1995,\n  author\t= {M. Guarini and A. Soto and A. Cipriano and A. Guesalaga\n\t\t  and J. Caceres},\n  title\t\t= {Measurement of physical characteristics of foam in\n\t\t  flotation cells},\n  booktitle\t= {In Proc. of Int. Conference: Copper-95},\n  pages\t\t= {},\n  year\t\t= {1995},\n  abstract\t= {},\n  url\t\t= {}\n}\n\n
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