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\n  \n 2019\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n LatRec: Recognizing Goals in Latent Space (Demo).\n \n \n \n\n\n \n Amado, L.; Aires, J. P.; Pereira, R. F.; Magnaguagno, M. C.; Granada, R.; Licks, G. P.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS), 2019. AAAI 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 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{Amado2019,\n  author = {Leonardo Amado and Jo\\~{a}o Paulo Aires and Ramon F. Pereira and Maur\\'{i}cio C. Magnaguagno and Roger Granada and Gabriel Paludo Licks and Felipe Meneguzzi},\n  title = {{LatRec: Recognizing Goals in Latent Space (Demo)}},\n  booktitle = {Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS)},\n  year = {2019},\n  publisher = {AAAI Press},\n  abstract = {Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. \n  These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. LatRec applies modern goal recognition algorithms directly to real-world data (images) by building planning domain knowledge using an unsupervised learning algorithm that generates domain theories from raw images. We demonstrate this approach in an online simulation of simple games, such as the n-puzzle game.}\n}\n\n\n
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\n Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. LatRec applies modern goal recognition algorithms directly to real-world data (images) by building planning domain knowledge using an unsupervised learning algorithm that generates domain theories from raw images. We demonstrate this approach in an online simulation of simple games, such as the n-puzzle game.\n
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\n \n\n \n \n \n \n \n ConCon: A Contract Conflict Identifier.\n \n \n \n\n\n \n Aires, J. P.; Granada, R.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems, 2019. \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 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{Aires2019a,\n  author =    {Jo{\\~{a}}o Paulo Aires and Roger Granada and Felipe Meneguzzi},\n  title =     {ConCon: A Contract Conflict Identifier},\n  booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems},\n  year =      {2019},\n  abstract =  {Contracts are the main medium through which people and legal entities formalise their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still encoded in contracts written in natural language, necessitating substantial human revision effort to eliminate possible conflicting statements, especially for long and complex contracts. We demonstrate the ConCon (Contract Conflicts) tool, to automatically read natural language contracts and indicate potential conflicts among their clauses. Using our tool, legal professionals and the general public can benefit from a ranking of potential conflicts between the clauses in a contract, saving time and effort from legal experts in contract proof-reading.}\n}\n\n
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\n Contracts are the main medium through which people and legal entities formalise their trade relations, be they the exchange of goods or the specification of mutual obligations. While electronic contracts allow automated processes to verify their correctness, most agreements in the real world are still encoded in contracts written in natural language, necessitating substantial human revision effort to eliminate possible conflicting statements, especially for long and complex contracts. We demonstrate the ConCon (Contract Conflicts) tool, to automatically read natural language contracts and indicate potential conflicts among their clauses. Using our tool, legal professionals and the general public can benefit from a ranking of potential conflicts between the clauses in a contract, saving time and effort from legal experts in contract proof-reading.\n
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\n \n\n \n \n \n \n \n \n Classification of Contractual Conflicts via Learning of Semantic Representations.\n \n \n \n \n\n\n \n Aires, J. P.; Granada, R.; Monteiro, J.; Barros, R. C.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019, pages 1764–1766, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ClassificationPaper\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{DBLP:conf/atal/AiresGMBM19,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Roger Granada and\n               Juarez Monteiro and\n               Rodrigo Coelho Barros and\n               Felipe Meneguzzi},\n  title     = {Classification of Contractual Conflicts via Learning of Semantic Representations},\n  booktitle = {Proceedings of the 18th International Conference on Autonomous Agents\n               and MultiAgent Systems, {AAMAS} '19, Montreal, QC, Canada, May 13-17,\n               2019},\n  pages     = {1764--1766},\n  year      = {2019},\n  crossref  = {DBLP:conf/atal/2019},\n  url       = {http://dl.acm.org/citation.cfm?id=3331911},\n  timestamp = {Wed, 29 May 2019 16:36:58 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/atal/AiresGMBM19},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n ConCon: A Contract Conflict Identifier.\n \n \n \n \n\n\n \n Aires, J. P.; Granada, R.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '19, Montreal, QC, Canada, May 13-17, 2019, pages 2327–2329, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ConCon: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{DBLP:conf/atal/AiresGM19,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Roger Granada and\n               Felipe Meneguzzi},\n  title     = {ConCon: {A} Contract Conflict Identifier},\n  booktitle = {Proceedings of the 18th International Conference on Autonomous Agents\n               and MultiAgent Systems, {AAMAS} '19, Montreal, QC, Canada, May 13-17,\n               2019},\n  pages     = {2327--2329},\n  year      = {2019},\n  crossref  = {DBLP:conf/atal/2019},\n  url       = {http://dl.acm.org/citation.cfm?id=3332101},\n  timestamp = {Wed, 29 May 2019 16:36:58 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/atal/AiresGM19},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Automating News Summarization with Sentence Vectors Offset.\n \n \n \n \n\n\n \n Steinert, M.; Granada, R.; Aires, J. P.; and Meneguzzi, F.\n\n\n \n\n\n\n In 8th Brazilian Conference on Intelligent Systems, BRACIS 2019, Salvador, Brazil, October 15-18, 2019, pages 102–107, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"AutomatingPaper\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{DBLP:conf/bracis/SteinertGAM19,\n  author    = {Mauricio Steinert and\n               Roger Granada and\n               Jo{\\~{a}}o Paulo Aires and\n               Felipe Meneguzzi},\n  title     = {Automating News Summarization with Sentence Vectors Offset},\n  booktitle = {8th Brazilian Conference on Intelligent Systems, {BRACIS} 2019, Salvador,\n               Brazil, October 15-18, 2019},\n  pages     = {102--107},\n  year      = {2019},\n  crossref  = {DBLP:conf/bracis/2019},\n  url       = {https://doi.org/10.1109/BRACIS.2019.00027},\n  doi       = {10.1109/BRACIS.2019.00027},\n  timestamp = {Thu, 09 Jan 2020 12:36:19 +0100},\n  biburl    = {https://dblp.org/rec/bib/conf/bracis/SteinertGAM19},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Classifying Norm Conflicts using Learned Semantic Representations.\n \n \n \n \n\n\n \n Aires, J. P.; Granada, R.; Monteiro, J.; Barros, R. C.; and Meneguzzi, F.\n\n\n \n\n\n\n CoRR, abs/1906.02121. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ClassifyingPaper\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{DBLP:journals/corr/abs-1906-02121,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Roger Granada and\n               Juarez Monteiro and\n               Rodrigo C. Barros and\n               Felipe Meneguzzi},\n  title     = {Classifying Norm Conflicts using Learned Semantic Representations},\n  journal   = {CoRR},\n  volume    = {abs/1906.02121},\n  year      = {2019},\n  url       = {http://arxiv.org/abs/1906.02121},\n  archivePrefix = {arXiv},\n  eprint    = {1906.02121},\n  timestamp = {Thu, 13 Jun 2019 01:00:00 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1906-02121},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2018\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Norm Conflict Identification using Vector Space Offsets.\n \n \n \n \n\n\n \n Aires, J. P.; Monteiro, J.; Granada, R.; and Meneguzzi, F.\n\n\n \n\n\n\n In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"NormPaper\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{DBLP:conf/ijcnn/AiresMGM18,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Juarez Monteiro and\n               Roger Granada and\n               Felipe Meneguzzi},\n  title     = {Norm Conflict Identification using Vector Space Offsets},\n  booktitle = {2018 International Joint Conference on Neural Networks, {IJCNN} 2018,\n               Rio de Janeiro, Brazil, July 8-13, 2018},\n  pages     = {1--8},\n  year      = {2018},\n  crossref  = {DBLP:conf/ijcnn/2018},\n  url       = {https://doi.org/10.1109/IJCNN.2018.8489119},\n  doi       = {10.1109/IJCNN.2018.8489119},\n  timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/ijcnn/AiresMGM18},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets.\n \n \n \n \n\n\n \n Aires, J. P.; Padilha, C.; Quevedo, C.; and Meneguzzi, F.\n\n\n \n\n\n\n In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\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{DBLP:conf/ijcnn/AiresPQM18,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Carlos Padilha and\n               Christian Quevedo and\n               Felipe Meneguzzi},\n  title     = {A Deep Learning Approach to Classify Aspect-Level Sentiment using\n               Small Datasets},\n  booktitle = {2018 International Joint Conference on Neural Networks, {IJCNN} 2018,\n               Rio de Janeiro, Brazil, July 8-13, 2018},\n  pages     = {1--8},\n  year      = {2018},\n  crossref  = {DBLP:conf/ijcnn/2018},\n  url       = {https://doi.org/10.1109/IJCNN.2018.8489760},\n  doi       = {10.1109/IJCNN.2018.8489760},\n  timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/ijcnn/AiresPQM18},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Goal Recognition in Latent Space.\n \n \n \n \n\n\n \n Amado, L.; Pereira, R. F.; Aires, J. P.; Magnaguagno, M. C.; Granada, R.; and Meneguzzi, F.\n\n\n \n\n\n\n In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"GoalPaper\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{DBLP:conf/ijcnn/AmadoPAMGM18,\n  author    = {Leonardo Amado and\n               Ramon Fraga Pereira and\n               Jo{\\~{a}}o Paulo Aires and\n               Mauricio Cecilio Magnaguagno and\n               Roger Granada and\n               Felipe Meneguzzi},\n  title     = {Goal Recognition in Latent Space},\n  booktitle = {2018 International Joint Conference on Neural Networks, {IJCNN} 2018,\n               Rio de Janeiro, Brazil, July 8-13, 2018},\n  pages     = {1--8},\n  year      = {2018},\n  crossref  = {DBLP:conf/ijcnn/2018},\n  url       = {https://doi.org/10.1109/IJCNN.2018.8489653},\n  doi       = {10.1109/IJCNN.2018.8489653},\n  timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/ijcnn/AmadoPAMGM18},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Evaluating the Feasibility of Deep Learning for Action Recognition in Small Datasets.\n \n \n \n \n\n\n \n Monteiro, J.; Granada, R.; Aires, J. P.; and Barros, R. C.\n\n\n \n\n\n\n In 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pages 1–8, 2018. \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 \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{DBLP:conf/ijcnn/MonteiroGAB18,\n  author    = {Juarez Monteiro and\n               Roger Granada and\n               Jo{\\~{a}}o Paulo Aires and\n               Rodrigo C. Barros},\n  title     = {Evaluating the Feasibility of Deep Learning for Action Recognition\n               in Small Datasets},\n  booktitle = {2018 International Joint Conference on Neural Networks, {IJCNN} 2018,\n               Rio de Janeiro, Brazil, July 8-13, 2018},\n  pages     = {1--8},\n  year      = {2018},\n  crossref  = {DBLP:conf/ijcnn/2018},\n  url       = {https://doi.org/10.1109/IJCNN.2018.8489297},\n  doi       = {10.1109/IJCNN.2018.8489297},\n  timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/ijcnn/MonteiroGAB18},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n LSTM-Based Goal Recognition in Latent Space.\n \n \n \n \n\n\n \n Amado, L.; Aires, J. P.; Pereira, R. F.; Magnaguagno, M. C.; Granada, R.; and Meneguzzi, F.\n\n\n \n\n\n\n CoRR, abs/1808.05249. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"LSTM-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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@article{DBLP:journals/corr/abs-1808-05249,\n  author    = {Leonardo Amado and\n               Jo{\\~{a}}o Paulo Aires and\n               Ramon Fraga Pereira and\n               Mauricio Cecilio Magnaguagno and\n               Roger Granada and\n               Felipe Meneguzzi},\n  title     = {LSTM-Based Goal Recognition in Latent Space},\n  journal   = {CoRR},\n  volume    = {abs/1808.05249},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1808.05249},\n  archivePrefix = {arXiv},\n  eprint    = {1808.05249},\n  timestamp = {Sun, 02 Sep 2018 01:00:00 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1808-05249},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n  \n 2017\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Norm conflict identification in contracts.\n \n \n \n \n\n\n \n Aires, J. P.; Pinheiro, D.; de Lima, V. S.; and Meneguzzi, F.\n\n\n \n\n\n\n Artif. Intell. Law, 25(4): 397–428. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"NormPaper\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{DBLP:journals/ail/AiresPLM17,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Daniele Pinheiro and\n               Vera Strube de Lima and\n               Felipe Meneguzzi},\n  title     = {Norm conflict identification in contracts},\n  journal   = {Artif. Intell. Law},\n  volume    = {25},\n  number    = {4},\n  pages     = {397--428},\n  year      = {2017},\n  url       = {https://doi.org/10.1007/s10506-017-9205-x},\n  doi       = {10.1007/s10506-017-9205-x},\n  timestamp = {Fri, 24 Nov 2017 00:00:00 +0100},\n  biburl    = {https://dblp.org/rec/bib/journals/ail/AiresPLM17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Norm Conflict Identification Using Deep Learning.\n \n \n \n \n\n\n \n Aires, J. P.; and Meneguzzi, F.\n\n\n \n\n\n\n In Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Visionary Papers, São Paulo, Brazil, May 8-12, 2017, Revised Selected Papers, pages 194–207, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"NormPaper\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{DBLP:conf/atal/AiresM17a,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Felipe Meneguzzi},\n  title     = {Norm Conflict Identification Using Deep Learning},\n  booktitle = {Autonomous Agents and Multiagent Systems - {AAMAS} 2017 Workshops,\n               Visionary Papers, S{\\~{a}}o Paulo, Brazil, May 8-12, 2017, Revised\n               Selected Papers},\n  pages     = {194--207},\n  year      = {2017},\n  crossref  = {DBLP:conf/atal/2017v},\n  url       = {https://doi.org/10.1007/978-3-319-71679-4\\_13},\n  doi       = {10.1007/978-3-319-71679-4\\_13},\n  timestamp = {Tue, 14 May 2019 10:00:54 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/atal/AiresM17a},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Deep Learning Approach for Norm Conflict Identification.\n \n \n \n \n\n\n \n Aires, J. P.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, May 8-12, 2017, pages 1451–1453, 2017. \n \n\n\n\n
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@inproceedings{DBLP:conf/atal/AiresM17,\n  author    = {Jo{\\~{a}}o Paulo Aires and\n               Felipe Meneguzzi},\n  title     = {A Deep Learning Approach for Norm Conflict Identification},\n  booktitle = {Proceedings of the 16th Conference on Autonomous Agents and MultiAgent\n               Systems, {AAMAS} 2017, S{\\~{a}}o Paulo, Brazil, May 8-12, 2017},\n  pages     = {1451--1453},\n  year      = {2017},\n  crossref  = {DBLP:conf/atal/2017},\n  url       = {http://dl.acm.org/citation.cfm?id=3091326},\n  timestamp = {Wed, 27 Sep 2017 07:24:00 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/atal/AiresM17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n \n Virtual guide dog: An application to support visually-impaired people through deep convolutional neural networks.\n \n \n \n \n\n\n \n Monteiro, J.; Aires, J. P.; Granada, R.; Barros, R. C.; and Meneguzzi, F.\n\n\n \n\n\n\n In 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, AK, USA, May 14-19, 2017, pages 2267–2274, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"VirtualPaper\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{DBLP:conf/ijcnn/MonteiroAGBM17,\n  author    = {Juarez Monteiro and\n               Jo{\\~{a}}o Paulo Aires and\n               Roger Granada and\n               Rodrigo C. Barros and\n               Felipe Meneguzzi},\n  title     = {Virtual guide dog: An application to support visually-impaired people\n               through deep convolutional neural networks},\n  booktitle = {2017 International Joint Conference on Neural Networks, {IJCNN} 2017,\n               Anchorage, AK, USA, May 14-19, 2017},\n  pages     = {2267--2274},\n  year      = {2017},\n  crossref  = {DBLP:conf/ijcnn/2017},\n  url       = {https://doi.org/10.1109/IJCNN.2017.7966130},\n  doi       = {10.1109/IJCNN.2017.7966130},\n  timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},\n  biburl    = {https://dblp.org/rec/bib/conf/ijcnn/MonteiroAGBM17},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
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\n \n\n \n \n \n \n \n Temporal Regions for Activity Recognition.\n \n \n \n\n\n \n Monteiro, J.; Aires, J. P.; Granada, R.; Meneguzzi, F.; and Barros, R.\n\n\n \n\n\n\n In Proceedings of the 26th International Conference on Artificial Neural Networks, 2017. \n \n\n\n\n
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@inproceedings{Monteiro2017,\n  author =    {Juarez Monteiro and Jo{\\~{a}}o Paulo Aires and Roger Granada and Felipe Meneguzzi and Rodrigo Barros},\n  title =     {Temporal Regions for Activity Recognition},\n  booktitle = {Proceedings of the 26th International Conference on Artificial Neural Networks},\n  year =      {2017}\n}
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