var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"http://learn.inf.puc-rio.br/files/all.bib\",\"jsonp\":\"1\",\"theme\":\"side\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Latent Trees for Coreference Resolution\",\"volume\":\"40\",\"issn\":\"0891-2017\",\"url\":\"https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200\",\"doi\":\"10.1162/COLI_a_00200\",\"abstract\":\"We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5% error reduction, and is the best performing system for each of the three languages.\",\"number\":\"4\",\"urldate\":\"2020-03-02\",\"journal\":\"Computational Linguistics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fernandes\"],\"firstnames\":[\"Eraldo\",\"Rezende\"],\"suffixes\":[]},{\"propositions\":[\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cícero\",\"Nogueira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2014\",\"pages\":\"801–835\",\"bibtex\":\"@article{fernandes_latent_2014,\\n  title = {Latent {Trees} for {Coreference} {Resolution}},\\n  volume = {40},\\n  issn = {0891-2017},\\n  url = {https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200},\\n  doi = {10.1162/COLI_a_00200},\\n  abstract = {We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5\\\\% error reduction, and is the best performing system for each of the three languages.},\\n  number = {4},\\n  urldate = {2020-03-02},\\n  journal = {Computational Linguistics},\\n  author = {Fernandes, Eraldo Rezende and dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},\\n  month = apr,\\n  year = {2014},\\n  pages = {801--835}\\n}\\n\\n\",\"author_short\":[\"Fernandes, E. R.\",\"dos Santos, C. N.\",\"Milidiú, R. L.\"],\"key\":\"fernandes_latent_2014\",\"id\":\"fernandes_latent_2014\",\"bibbaseid\":\"fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Trading team composition for the intraday multistock market\",\"volume\":\"54\",\"issn\":\"0167-9236\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0167923612002448\",\"doi\":\"10.1016/j.dss.2012.09.009\",\"abstract\":\"Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM&FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26% annualized profit, outperforming by 380.07% the chosen baseline strategy with a 16.07% profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US$ 50,000, the MSR strategy provides a profit of at least 38.63%.\",\"language\":\"en\",\"number\":\"2\",\"urldate\":\"2020-03-02\",\"journal\":\"Decision Support Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Alvim\"],\"firstnames\":[\"Leandro\",\"G.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"L.\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2013\",\"pages\":\"838–845\",\"bibtex\":\"@article{alvim_trading_2013,\\n  title = {Trading team composition for the intraday multistock market},\\n  volume = {54},\\n  issn = {0167-9236},\\n  url = {http://www.sciencedirect.com/science/article/pii/S0167923612002448},\\n  doi = {10.1016/j.dss.2012.09.009},\\n  abstract = {Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM\\\\&FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26\\\\% annualized profit, outperforming by 380.07\\\\% the chosen baseline strategy with a 16.07\\\\% profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US\\\\$ 50,000, the MSR strategy provides a profit of at least 38.63\\\\%.},\\n  language = {en},\\n  number = {2},\\n  urldate = {2020-03-02},\\n  journal = {Decision Support Systems},\\n  author = {Alvim, Leandro G. M. and Milidiú, Ruy L.},\\n  month = jan,\\n  year = {2013},\\n  pages = {838--845}\\n}\\n\\n\",\"author_short\":[\"Alvim, L. G. M.\",\"Milidiú, R. L.\"],\"key\":\"alvim_trading_2013\",\"id\":\"alvim_trading_2013\",\"bibbaseid\":\"alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0167923612002448\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"RelHunter : a machine learning method for relation extraction from text\",\"volume\":\"16\",\"copyright\":\"2010 The Brazilian Computer Society\",\"issn\":\"1678-4804\",\"shorttitle\":\"RelHunter\",\"url\":\"https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y\",\"doi\":\"10.1007/s13173-010-0018-y\",\"abstract\":\"We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter’s key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.\",\"language\":\"en\",\"number\":\"3\",\"urldate\":\"2020-03-02\",\"journal\":\"Journal of the Brazilian Computer Society\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fernandes\"],\"firstnames\":[\"Eraldo\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rentería\"],\"firstnames\":[\"Raúl\",\"P.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2010\",\"pages\":\"191–199\",\"bibtex\":\"@article{fernandes_relhunter_2010,\\n  title = {{RelHunter} : a machine learning method for relation extraction from text},\\n  volume = {16},\\n  copyright = {2010 The Brazilian Computer Society},\\n  issn = {1678-4804},\\n  shorttitle = {{RelHunter}},\\n  url = {https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y},\\n  doi = {10.1007/s13173-010-0018-y},\\n  abstract = {We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter’s key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14\\\\% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.},\\n  language = {en},\\n  number = {3},\\n  urldate = {2020-03-02},\\n  journal = {Journal of the Brazilian Computer Society},\\n  author = {Fernandes, Eraldo R. and Milidiú, Ruy L. and Rentería, Raúl P.},\\n  month = sep,\\n  year = {2010},\\n  pages = {191--199}\\n}\\n\\n\",\"author_short\":[\"Fernandes, E. R.\",\"Milidiú, R. L.\",\"Rentería, R. P.\"],\"key\":\"fernandes_relhunter_2010\",\"id\":\"fernandes_relhunter_2010\",\"bibbaseid\":\"fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Portuguese corpus-based learning using ETL\",\"volume\":\"14\",\"issn\":\"0104-6500\",\"url\":\"http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0104-65002008000400003&lng=en&nrm=iso&tlng=en\",\"doi\":\"10.1007/BF03192569\",\"language\":\"en\",\"number\":\"4\",\"urldate\":\"2020-03-02\",\"journal\":\"Journal of the Brazilian Computer Society\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cícero\",\"Nogueira\",\"dos\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Duarte\"],\"firstnames\":[\"Julio\",\"Cesar\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2008\",\"pages\":\"17–27\",\"bibtex\":\"@article{milidiu_portuguese_2008,\\n  title = {Portuguese corpus-based learning using {ETL}},\\n  volume = {14},\\n  issn = {0104-6500},\\n  url = {http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0104-65002008000400003&lng=en&nrm=iso&tlng=en},\\n  doi = {10.1007/BF03192569},\\n  language = {en},\\n  number = {4},\\n  urldate = {2020-03-02},\\n  journal = {Journal of the Brazilian Computer Society},\\n  author = {Milidiú, Ruy Luiz and Santos, Cícero Nogueira dos and Duarte, Julio Cesar},\\n  month = dec,\\n  year = {2008},\\n  pages = {17--27}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Santos, C. N. d.\",\"Duarte, J. C.\"],\"key\":\"milidiu_portuguese_2008\",\"id\":\"milidiu_portuguese_2008\",\"bibbaseid\":\"milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0104-65002008000400003&lng=en&nrm=iso&tlng=en\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"MKPLS approach: switching strategies for the non-linear multi-kernel PLSR\",\"volume\":\"22\",\"issn\":\"1613-9658\",\"shorttitle\":\"MKPLS approach\",\"url\":\"https://doi.org/10.1007/s00180-007-0040-5\",\"doi\":\"10.1007/s00180-007-0040-5\",\"abstract\":\"We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.\",\"language\":\"en\",\"number\":\"2\",\"urldate\":\"2020-03-02\",\"journal\":\"Computational Statistics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Rentería\"],\"firstnames\":[\"Raúl\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Souza\"],\"firstnames\":[\"Rafael\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2007\",\"pages\":\"323–330\",\"bibtex\":\"@article{renteria_mkpls_2007,\\n  title = {{MKPLS} approach: switching strategies for the non-linear multi-kernel {PLSR}},\\n  volume = {22},\\n  issn = {1613-9658},\\n  shorttitle = {{MKPLS} approach},\\n  url = {https://doi.org/10.1007/s00180-007-0040-5},\\n  doi = {10.1007/s00180-007-0040-5},\\n  abstract = {We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.},\\n  language = {en},\\n  number = {2},\\n  urldate = {2020-03-02},\\n  journal = {Computational Statistics},\\n  author = {Rentería, Raúl and Milidiú, Ruy and Souza, Rafael},\\n  month = jul,\\n  year = {2007},\\n  pages = {323--330}\\n}\\n\\n\",\"author_short\":[\"Rentería, R.\",\"Milidiú, R.\",\"Souza, R.\"],\"key\":\"renteria_mkpls_2007\",\"id\":\"renteria_mkpls_2007\",\"bibbaseid\":\"rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s00180-007-0040-5\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Trento, Italy\",\"series\":\"SAC '12\",\"title\":\"Double dip map-reduce for processing cross validation jobs\",\"isbn\":\"978-1-4503-0857-1\",\"url\":\"https://doi.org/10.1145/2245276.2245367\",\"doi\":\"10.1145/2245276.2245367\",\"abstract\":\"Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.\",\"urldate\":\"2020-03-02\",\"booktitle\":\"Proceedings of the 27th Annual ACM Symposium on Applied Computing\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Moret\"],\"firstnames\":[\"Danilo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Breitman\"],\"firstnames\":[\"Karin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Amorim\"],\"firstnames\":[\"Evelin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Talavera\"],\"firstnames\":[\"Jose\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiu\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Viterbo\"],\"firstnames\":[\"Jose\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2012\",\"pages\":\"473–477\",\"bibtex\":\"@inproceedings{moret_double_2012,\\n  address = {Trento, Italy},\\n  series = {{SAC} '12},\\n  title = {Double dip map-reduce for processing cross validation jobs},\\n  isbn = {978-1-4503-0857-1},\\n  url = {https://doi.org/10.1145/2245276.2245367},\\n  doi = {10.1145/2245276.2245367},\\n  abstract = {Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.},\\n  urldate = {2020-03-02},\\n  booktitle = {Proceedings of the 27th {Annual} {ACM} {Symposium} on {Applied} {Computing}},\\n  publisher = {Association for Computing Machinery},\\n  author = {Moret, Danilo and Breitman, Karin and Amorim, Evelin and Talavera, Jose and Milidiu, Ruy and Viterbo, Jose},\\n  month = mar,\\n  year = {2012},\\n  pages = {473--477}\\n}\\n\\n\",\"author_short\":[\"Moret, D.\",\"Breitman, K.\",\"Amorim, E.\",\"Talavera, J.\",\"Milidiu, R.\",\"Viterbo, J.\"],\"key\":\"moret_double_2012\",\"id\":\"moret_double_2012\",\"bibbaseid\":\"moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1145/2245276.2245367\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Instance-Based Ontology Mapping\",\"doi\":\"10.1109/EASe.2008.18\",\"abstract\":\"We have explored the use of formal ontology and alignment techniques as a general approach to mediate and reconcile different representations. This approach proved to be very effective when fueled by ontologies rich in detail, (properties, restrictions, attributes and axioms that hold among classes), but performs poorly when available representations are incomplete or lacking, which is often the case in real life settings. In this paper we propose an enhancement to the original approach, based on a, instance-based matching technique.\",\"booktitle\":\"Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems (ease 2008)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Breitman\"],\"firstnames\":[\"Karin\",\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brauner\"],\"firstnames\":[\"Daniela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Casanova\"],\"firstnames\":[\"Marco\",\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gazola\"],\"firstnames\":[\"Alexandre\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perazolo\"],\"firstnames\":[\"Marcelo\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2008\",\"note\":\"ISSN: 2168-1872\",\"pages\":\"67–74\",\"bibtex\":\"@inproceedings{breitman_instance-based_2008,\\n  title = {Instance-{Based} {Ontology} {Mapping}},\\n  doi = {10.1109/EASe.2008.18},\\n  abstract = {We have explored the use of formal ontology and alignment techniques as a general approach to mediate and reconcile different representations. This approach proved to be very effective when fueled by ontologies rich in detail, (properties, restrictions, attributes and axioms that hold among classes), but performs poorly when available representations are incomplete or lacking, which is often the case in real life settings. In this paper we propose an enhancement to the original approach, based on a, instance-based matching technique.},\\n  booktitle = {Fifth {IEEE} {Workshop} on {Engineering} of {Autonomic} and {Autonomous} {Systems} (ease 2008)},\\n  author = {Breitman, Karin K. and Brauner, Daniela and Casanova, Marco Antonio and Milidiú, Ruy and Gazola, Alexandre and Perazolo, Marcelo},\\n  month = mar,\\n  year = {2008},\\n  note = {ISSN: 2168-1872},\\n  pages = {67--74}\\n}\\n\\n\",\"author_short\":[\"Breitman, K. K.\",\"Brauner, D.\",\"Casanova, M. A.\",\"Milidiú, R.\",\"Gazola, A.\",\"Perazolo, M.\"],\"key\":\"breitman_instance-based_2008\",\"id\":\"breitman_instance-based_2008\",\"bibbaseid\":\"breitman-brauner-casanova-milidi-gazola-perazolo-instancebasedontologymapping-2008\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Berlin, Heidelberg\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"TBL Template Selection: An Evolutionary Approach\",\"isbn\":\"978-3-540-75271-4\",\"shorttitle\":\"TBL Template Selection\",\"doi\":\"10.1007/978-3-540-75271-4_19\",\"abstract\":\"Transformation Based Learning (TBL) is an intensively Machine Learning algorithm frequently used in Natural Language Processing. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template selection process. We show some empirical evidence that our approach provides template sets with almost the same quality as human built templates.\",\"language\":\"en\",\"booktitle\":\"Current Topics in Artificial Intelligence\",\"publisher\":\"Springer\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Duarte\"],\"firstnames\":[\"Julio\",\"Cesar\"],\"suffixes\":[]},{\"propositions\":[\"Nogueira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cícero\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Borrajo\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Castillo\"],\"firstnames\":[\"Luis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Corchado\"],\"firstnames\":[\"Juan\",\"Manuel\"],\"suffixes\":[]}],\"year\":\"2007\",\"pages\":\"180–189\",\"bibtex\":\"@inproceedings{milidiu_tbl_2007,\\n  address = {Berlin, Heidelberg},\\n  series = {Lecture {Notes} in {Computer} {Science}},\\n  title = {{TBL} {Template} {Selection}: {An} {Evolutionary} {Approach}},\\n  isbn = {978-3-540-75271-4},\\n  shorttitle = {{TBL} {Template} {Selection}},\\n  doi = {10.1007/978-3-540-75271-4_19},\\n  abstract = {Transformation Based Learning (TBL) is an intensively Machine Learning algorithm frequently used in Natural Language Processing. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template selection process. We show some empirical evidence that our approach provides template sets with almost the same quality as human built templates.},\\n  language = {en},\\n  booktitle = {Current {Topics} in {Artificial} {Intelligence}},\\n  publisher = {Springer},\\n  author = {Milidiú, Ruy Luiz and Duarte, Julio Cesar and Nogueira dos Santos, Cícero},\\n  editor = {Borrajo, Daniel and Castillo, Luis and Corchado, Juan Manuel},\\n  year = {2007},\\n  pages = {180--189}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Duarte, J. C.\",\"Nogueira dos Santos, C.\"],\"editor_short\":[\"Borrajo, D.\",\"Castillo, L.\",\"Corchado, J. M.\"],\"key\":\"milidiu_tbl_2007\",\"id\":\"milidiu_tbl_2007\",\"bibbaseid\":\"milidi-duarte-nogueiradossantos-tbltemplateselectionanevolutionaryapproach-2007\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Phrase chunking using entropy guided transformation learning\",\"booktitle\":\"Proceedings of ACL-08: HLT\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cicero\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Duarte\"],\"firstnames\":[\"Julio\",\"C.\"],\"suffixes\":[]}],\"year\":\"2008\",\"pages\":\"647–655\",\"bibtex\":\"@inproceedings{milidiu_phrase_2008,\\n  title = {Phrase chunking using entropy guided transformation learning},\\n  booktitle = {Proceedings of {ACL}-08: {HLT}},\\n  author = {Milidiú, Ruy Luiz and dos Santos, Cicero and Duarte, Julio C.},\\n  year = {2008},\\n  pages = {647--655}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"dos Santos, C.\",\"Duarte, J. C.\"],\"key\":\"milidiu_phrase_2008\",\"id\":\"milidiu_phrase_2008\",\"bibbaseid\":\"milidi-dossantos-duarte-phrasechunkingusingentropyguidedtransformationlearning-2008\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Time-series forecasting through wavelets transformation and a mixture of expert models\",\"volume\":\"28\",\"number\":\"1-3\",\"journal\":\"Neurocomputing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Machado\"],\"firstnames\":[\"Ricardo\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Renterı́a\"],\"firstnames\":[\"Raúl\",\"P.\"],\"suffixes\":[]}],\"year\":\"1999\",\"pages\":\"145–156\",\"bibtex\":\"@article{milidiu_time-series_1999,\\n  title = {Time-series forecasting through wavelets transformation and a mixture of expert models},\\n  volume = {28},\\n  number = {1-3},\\n  journal = {Neurocomputing},\\n  author = {Milidiú, Ruy L. and Machado, Ricardo J. and Renterı́a, Raúl P.},\\n  year = {1999},\\n  pages = {145--156}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Machado, R. J.\",\"Renterı́a, R. P.\"],\"key\":\"milidiu_time-series_1999\",\"id\":\"milidiu_time-series_1999\",\"bibbaseid\":\"milidi-machado-rentera-timeseriesforecastingthroughwaveletstransformationandamixtureofexpertmodels-1999\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Data stream anomaly detection through principal subspace tracking\",\"booktitle\":\"Proceedings of the 2010 ACM Symposium on Applied Computing\",\"author\":[{\"propositions\":[\"dos\"],\"lastnames\":[\"Santos\",\"Teixeira\"],\"firstnames\":[\"Pedro\",\"Henriques\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"2010\",\"pages\":\"1609–1616\",\"bibtex\":\"@inproceedings{dos_santos_teixeira_data_2010,\\n  title = {Data stream anomaly detection through principal subspace tracking},\\n  booktitle = {Proceedings of the 2010 {ACM} {Symposium} on {Applied} {Computing}},\\n  author = {dos Santos Teixeira, Pedro Henriques and Milidiú, Ruy Luiz},\\n  year = {2010},\\n  pages = {1609--1616}\\n}\\n\\n\",\"author_short\":[\"dos Santos Teixeira, P. H.\",\"Milidiú, R. L.\"],\"key\":\"dos_santos_teixeira_data_2010\",\"id\":\"dos_santos_teixeira_data_2010\",\"bibbaseid\":\"dossantosteixeira-milidi-datastreamanomalydetectionthroughprincipalsubspacetracking-2010\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Belief function model for information retrieval\",\"volume\":\"44\",\"number\":\"1\",\"journal\":\"Journal of the American Society for Information Science\",\"author\":[{\"propositions\":[\"da\"],\"lastnames\":[\"Silva\"],\"firstnames\":[\"Wagner\",\"Teixeira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"1993\",\"pages\":\"10–18\",\"bibtex\":\"@article{da_silva_belief_1993,\\n  title = {Belief function model for information retrieval},\\n  volume = {44},\\n  number = {1},\\n  journal = {Journal of the American Society for Information Science},\\n  author = {da Silva, Wagner Teixeira and Milidiú, Ruy Luiz},\\n  year = {1993},\\n  pages = {10--18}\\n}\\n\\n\",\"author_short\":[\"da Silva, W. T.\",\"Milidiú, R. L.\"],\"key\":\"da_silva_belief_1993\",\"id\":\"da_silva_belief_1993\",\"bibbaseid\":\"dasilva-milidi-belieffunctionmodelforinformationretrieval-1993\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Entropy guided transformation learning\",\"booktitle\":\"Foundations of Computational, Intelligence Volume 1\",\"publisher\":\"Springer\",\"author\":[{\"propositions\":[\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cícero\",\"Nogueira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"2009\",\"pages\":\"159–184\",\"bibtex\":\"@incollection{dos_santos_entropy_2009,\\n  title = {Entropy guided transformation learning},\\n  booktitle = {Foundations of {Computational}, {Intelligence} {Volume} 1},\\n  publisher = {Springer},\\n  author = {dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},\\n  year = {2009},\\n  pages = {159--184}\\n}\\n\\n\",\"author_short\":[\"dos Santos, C. N.\",\"Milidiú, R. L.\"],\"key\":\"dos_santos_entropy_2009\",\"id\":\"dos_santos_entropy_2009\",\"bibbaseid\":\"dossantos-milidi-entropyguidedtransformationlearning-2009\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"An Object-Oriented Framework for Building Software Agents.\",\"volume\":\"2\",\"number\":\"1\",\"journal\":\"Journal of Object Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sardinha\"],\"firstnames\":[\"José\",\"Alberto\",\"Rodrigues\",\"Pereira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ribeiro\"],\"firstnames\":[\"Paula\",\"Clark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[\"de\"],\"lastnames\":[\"Lucena\"],\"firstnames\":[\"Carlos\",\"José\",\"Pereira\"],\"suffixes\":[]}],\"year\":\"2003\",\"pages\":\"85–97\",\"bibtex\":\"@article{sardinha_object-oriented_2003,\\n  title = {An {Object}-{Oriented} {Framework} for {Building} {Software} {Agents}.},\\n  volume = {2},\\n  number = {1},\\n  journal = {Journal of Object Technology},\\n  author = {Sardinha, José Alberto Rodrigues Pereira and Ribeiro, Paula Clark and Milidiú, Ruy Luiz and de Lucena, Carlos José Pereira},\\n  year = {2003},\\n  pages = {85--97}\\n}\\n\\n\",\"author_short\":[\"Sardinha, J. A. R. P.\",\"Ribeiro, P. C.\",\"Milidiú, R. L.\",\"de Lucena, C. J. P.\"],\"key\":\"sardinha_object-oriented_2003\",\"id\":\"sardinha_object-oriented_2003\",\"bibbaseid\":\"sardinha-ribeiro-milidi-delucena-anobjectorientedframeworkforbuildingsoftwareagents-2003\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Efficient implementation of the WARM-UP algorithm for the construction of length-restricted prefix codes\",\"booktitle\":\"Workshop on Algorithm Engineering and Experimentation\",\"publisher\":\"Springer\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pessoa\"],\"firstnames\":[\"Artur\",\"Alves\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]}],\"year\":\"1999\",\"pages\":\"1–17\",\"bibtex\":\"@inproceedings{milidiu_efficient_1999,\\n  title = {Efficient implementation of the {WARM}-{UP} algorithm for the construction of length-restricted prefix codes},\\n  booktitle = {Workshop on {Algorithm} {Engineering} and {Experimentation}},\\n  publisher = {Springer},\\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\\n  year = {1999},\\n  pages = {1--17}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Pessoa, A. A.\",\"Laber, E. S.\"],\"key\":\"milidiu_efficient_1999\",\"id\":\"milidiu_efficient_1999\",\"bibbaseid\":\"milidi-pessoa-laber-efficientimplementationofthewarmupalgorithmfortheconstructionoflengthrestrictedprefixcodes-1999\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Bounding the inefficiency of length-restricted prefix codes\",\"volume\":\"31\",\"number\":\"4\",\"journal\":\"Algorithmica\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]}],\"year\":\"2001\",\"pages\":\"513–529\",\"bibtex\":\"@article{milidiu_bounding_2001,\\n  title = {Bounding the inefficiency of length-restricted prefix codes},\\n  volume = {31},\\n  number = {4},\\n  journal = {Algorithmica},\\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},\\n  year = {2001},\\n  pages = {513--529}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Laber, E. S.\"],\"key\":\"milidiu_bounding_2001\",\"id\":\"milidiu_bounding_2001\",\"bibbaseid\":\"milidi-laber-boundingtheinefficiencyoflengthrestrictedprefixcodes-2001\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"An Agent Based Architecture for Highly Competitive Electronic Markets.\",\"booktitle\":\"FLAIRS Conference\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sardinha\"],\"firstnames\":[\"José\",\"Alberto\",\"RP\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Paranhos\"],\"firstnames\":[\"Patrick\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cunha\"],\"firstnames\":[\"Pedro\",\"M.\"],\"suffixes\":[]},{\"propositions\":[\"de\"],\"lastnames\":[\"Lucena\"],\"firstnames\":[\"Carlos\",\"José\",\"Pereira\"],\"suffixes\":[]}],\"year\":\"2005\",\"pages\":\"326–332\",\"bibtex\":\"@inproceedings{sardinha_agent_2005,\\n  title = {An {Agent} {Based} {Architecture} for {Highly} {Competitive} {Electronic} {Markets}.},\\n  booktitle = {{FLAIRS} {Conference}},\\n  author = {Sardinha, José Alberto RP and Milidiú, Ruy Luiz and Paranhos, Patrick M. and Cunha, Pedro M. and de Lucena, Carlos José Pereira},\\n  year = {2005},\\n  pages = {326--332}\\n}\\n\\n\",\"author_short\":[\"Sardinha, J. A. R.\",\"Milidiú, R. L.\",\"Paranhos, P. M.\",\"Cunha, P. M.\",\"de Lucena, C. J. P.\"],\"key\":\"sardinha_agent_2005\",\"id\":\"sardinha_agent_2005\",\"bibbaseid\":\"sardinha-milidi-paranhos-cunha-delucena-anagentbasedarchitectureforhighlycompetitiveelectronicmarkets-2005\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The warm-up algorithm: a Lagrangian construction of length restricted Huffman codes\",\"volume\":\"30\",\"shorttitle\":\"The warm-up algorithm\",\"number\":\"5\",\"journal\":\"SIAM Journal on Computing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]}],\"year\":\"2000\",\"pages\":\"1405–1426\",\"bibtex\":\"@article{milidiu_warm-up_2000,\\n  title = {The warm-up algorithm: a {Lagrangian} construction of length restricted {Huffman} codes},\\n  volume = {30},\\n  shorttitle = {The warm-up algorithm},\\n  number = {5},\\n  journal = {SIAM Journal on Computing},\\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},\\n  year = {2000},\\n  pages = {1405--1426}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Laber, E. S.\"],\"key\":\"milidiu_warm-up_2000\",\"id\":\"milidiu_warm-up_2000\",\"bibbaseid\":\"milidi-laber-thewarmupalgorithmalagrangianconstructionoflengthrestrictedhuffmancodes-2000\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Quotation extraction for portuguese\",\"booktitle\":\"Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fernandes\"],\"firstnames\":[\"William\",\"Paulo\",\"Ducca\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Motta\"],\"firstnames\":[\"Eduardo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"2011\",\"bibtex\":\"@inproceedings{fernandes_quotation_2011,\\n  title = {Quotation extraction for portuguese},\\n  booktitle = {Proceedings of the 8th {Brazilian} {Symposium} in {Information} and {Human} {Language} {Technology}},\\n  author = {Fernandes, William Paulo Ducca and Motta, Eduardo and Milidiú, Ruy Luiz},\\n  year = {2011}\\n}\\n\\n\",\"author_short\":[\"Fernandes, W. P. D.\",\"Motta, E.\",\"Milidiú, R. L.\"],\"key\":\"fernandes_quotation_2011\",\"id\":\"fernandes_quotation_2011\",\"bibbaseid\":\"fernandes-motta-milidi-quotationextractionforportuguese-2011\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Daily volume forecasting using high frequency predictors\",\"volume\":\"674\",\"booktitle\":\"Proceedings of the 10th IASTED International Conference\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Alvim\"],\"firstnames\":[\"Leandro\",\"GM\"],\"suffixes\":[]},{\"propositions\":[\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Cicero\",\"N.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiu\"],\"firstnames\":[\"Ruy\",\"L.\"],\"suffixes\":[]}],\"year\":\"2010\",\"pages\":\"248\",\"bibtex\":\"@inproceedings{alvim_daily_2010,\\n  title = {Daily volume forecasting using high frequency predictors},\\n  volume = {674},\\n  booktitle = {Proceedings of the 10th {IASTED} {International} {Conference}},\\n  author = {Alvim, Leandro GM and dos Santos, Cicero N. and Milidiu, Ruy L.},\\n  year = {2010},\\n  pages = {248}\\n}\\n\\n\",\"author_short\":[\"Alvim, L. G.\",\"dos Santos, C. N.\",\"Milidiu, R. L.\"],\"key\":\"alvim_daily_2010\",\"id\":\"alvim_daily_2010\",\"bibbaseid\":\"alvim-dossantos-milidiu-dailyvolumeforecastingusinghighfrequencypredictors-2010\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"DPLS and PPLS: two PLS algorithms for large data sets\",\"volume\":\"48\",\"shorttitle\":\"DPLS and PPLS\",\"number\":\"1\",\"journal\":\"Computational statistics & data analysis\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiu\"],\"firstnames\":[\"Ruy\",\"L.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Renterı́a\"],\"firstnames\":[\"Raúl\",\"P.\"],\"suffixes\":[]}],\"year\":\"2005\",\"pages\":\"125–138\",\"bibtex\":\"@article{milidiu_dpls_2005,\\n  title = {{DPLS} and {PPLS}: two {PLS} algorithms for large data sets},\\n  volume = {48},\\n  shorttitle = {{DPLS} and {PPLS}},\\n  number = {1},\\n  journal = {Computational statistics \\\\& data analysis},\\n  author = {Milidiu, Ruy L. and Renterı́a, Raúl P.},\\n  year = {2005},\\n  pages = {125--138}\\n}\\n\\n\",\"author_short\":[\"Milidiu, R. L.\",\"Renterı́a, R. P.\"],\"key\":\"milidiu_dpls_2005\",\"id\":\"milidiu_dpls_2005\",\"bibbaseid\":\"milidiu-rentera-dplsandpplstwoplsalgorithmsforlargedatasets-2005\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The complexity of makespan minimization for pipeline transportation\",\"volume\":\"306\",\"number\":\"1-3\",\"journal\":\"Theoretical Computer Science\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pessoa\"],\"firstnames\":[\"Artur\",\"Alves\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]}],\"year\":\"2003\",\"pages\":\"339–351\",\"bibtex\":\"@article{milidiu_complexity_2003,\\n  title = {The complexity of makespan minimization for pipeline transportation},\\n  volume = {306},\\n  number = {1-3},\\n  journal = {Theoretical Computer Science},\\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\\n  year = {2003},\\n  pages = {339--351}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Pessoa, A. A.\",\"Laber, E. S.\"],\"key\":\"milidiu_complexity_2003\",\"id\":\"milidiu_complexity_2003\",\"bibbaseid\":\"milidi-pessoa-laber-thecomplexityofmakespanminimizationforpipelinetransportation-2003\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Three space-economical algorithms for calculating minimum-redundancy prefix codes\",\"volume\":\"47\",\"number\":\"6\",\"journal\":\"IEEE Transactions on Information Theory\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pessoa\"],\"firstnames\":[\"Artur\",\"Alves\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]}],\"year\":\"2001\",\"pages\":\"2185–2198\",\"bibtex\":\"@article{milidiu_three_2001,\\n  title = {Three space-economical algorithms for calculating minimum-redundancy prefix codes},\\n  volume = {47},\\n  number = {6},\\n  journal = {IEEE Transactions on Information Theory},\\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\\n  year = {2001},\\n  pages = {2185--2198}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Pessoa, A. A.\",\"Laber, E. S.\"],\"key\":\"milidiu_three_2001\",\"id\":\"milidiu_three_2001\",\"bibbaseid\":\"milidi-pessoa-laber-threespaceeconomicalalgorithmsforcalculatingminimumredundancyprefixcodes-2001\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Bounding the compression loss of the FGK algorithm\",\"volume\":\"32\",\"number\":\"2\",\"journal\":\"Journal of Algorithms\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Laber\"],\"firstnames\":[\"Eduardo\",\"Sany\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pessoa\"],\"firstnames\":[\"Artur\",\"Alves\"],\"suffixes\":[]}],\"year\":\"1999\",\"pages\":\"195–211\",\"bibtex\":\"@article{milidiu_bounding_1999,\\n  title = {Bounding the compression loss of the {FGK} algorithm},\\n  volume = {32},\\n  number = {2},\\n  journal = {Journal of Algorithms},\\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany and Pessoa, Artur Alves},\\n  year = {1999},\\n  pages = {195--211}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R. L.\",\"Laber, E. S.\",\"Pessoa, A. A.\"],\"key\":\"milidiu_bounding_1999\",\"id\":\"milidiu_bounding_1999\",\"bibbaseid\":\"milidi-laber-pessoa-boundingthecompressionlossofthefgkalgorithm-1999\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Algorithms for combining belief functions\",\"volume\":\"7\",\"number\":\"1-2\",\"journal\":\"International Journal of Approximate Reasoning\",\"author\":[{\"propositions\":[\"da\"],\"lastnames\":[\"Silva\"],\"firstnames\":[\"Wagner\",\"Teixeira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiu\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"1992\",\"pages\":\"73–94\",\"bibtex\":\"@article{da_silva_algorithms_1992,\\n  title = {Algorithms for combining belief functions},\\n  volume = {7},\\n  number = {1-2},\\n  journal = {International Journal of Approximate Reasoning},\\n  author = {da Silva, Wagner Teixeira and Milidiu, Ruy Luiz},\\n  year = {1992},\\n  pages = {73--94}\\n}\\n\\n\",\"author_short\":[\"da Silva, W. T.\",\"Milidiu, R. L.\"],\"key\":\"da_silva_algorithms_1992\",\"id\":\"da_silva_algorithms_1992\",\"bibbaseid\":\"dasilva-milidiu-algorithmsforcombiningbelieffunctions-1992\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The Computation Of Compound Distributions With Two-Sided Severities (Negative-Binomial Iterative, Recursive, Asymptotic, Sum)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiu\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]}],\"year\":\"1987\",\"bibtex\":\"@article{milidiu_computation_1987,\\n  title = {The {Computation} {Of} {Compound} {Distributions} {With} {Two}-{Sided} {Severities} ({Negative}-{Binomial} {Iterative}, {Recursive}, {Asymptotic}, {Sum})},\\n  author = {Milidiu, Ruy Luiz},\\n  year = {1987}\\n}\\n\\n\",\"author_short\":[\"Milidiu, R. L.\"],\"key\":\"milidiu_computation_1987\",\"id\":\"milidiu_computation_1987\",\"bibbaseid\":\"milidiu-thecomputationofcompounddistributionswithtwosidedseveritiesnegativebinomialiterativerecursiveasymptoticsum-1987\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification\",\"abstract\":\"Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Betine\"],\"firstnames\":[\"Eduardo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dias\"],\"firstnames\":[\"Bruno\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bulcão\"],\"firstnames\":[\"André\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2019\",\"bibtex\":\"@misc{betine_deep_2019,\\n  title = {A {Deep} {Convolutional} {Network} for {Seismic} {Shot}-{Gather} {Image} {Quality} {Classification}},\\n  abstract = {Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56\\\\% in the test set.},\\n  author = {Betine, Eduardo and Busson, Antonio and Milidiú, Ruy and Colcher, Sérgio and Dias, Bruno and Bulcão, André},\\n  month = dec,\\n  year = {2019}\\n}\\n\\n\",\"author_short\":[\"Betine, E.\",\"Busson, A.\",\"Milidiú, R.\",\"Colcher, S.\",\"Dias, B.\",\"Bulcão, A.\"],\"key\":\"betine_deep_2019\",\"id\":\"betine_deep_2019\",\"bibbaseid\":\"betine-busson-milidi-colcher-dias-bulco-adeepconvolutionalnetworkforseismicshotgatherimagequalityclassification-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"A CNN-Based Tool to Index Emotion on Anime Character Stickers\",\"doi\":\"10.1109/ISM46123.2019.00071\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Jesus\"],\"firstnames\":[\"Ivan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cardoso\"],\"firstnames\":[\"Jessica\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Livio\"],\"firstnames\":[\"Álan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2019\",\"pages\":\"319–3193\",\"bibtex\":\"@inproceedings{jesus_cnn-based_2019,\\n  title = {A {CNN}-{Based} {Tool} to {Index} {Emotion} on {Anime} {Character} {Stickers}},\\n  doi = {10.1109/ISM46123.2019.00071},\\n  author = {Jesus, Ivan and Cardoso, Jessica and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\\n  month = dec,\\n  year = {2019},\\n  pages = {319--3193}\\n}\\n\\n\",\"author_short\":[\"Jesus, I.\",\"Cardoso, J.\",\"Busson, A.\",\"Livio, Á.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"jesus_cnn-based_2019\",\"id\":\"jesus_cnn-based_2019\",\"bibbaseid\":\"jesus-cardoso-busson-livio-colcher-milidi-acnnbasedtooltoindexemotiononanimecharacterstickers-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool\",\"shorttitle\":\"PVBR-Recog\",\"doi\":\"10.5753/webmedia_estendido.2019.8149\",\"abstract\":\"Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ferreira\",\"Alves\",\"Pinto\"],\"firstnames\":[\"Pedro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Melo\"],\"firstnames\":[\"João\",\"Paulo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2019\",\"bibtex\":\"@inproceedings{ferreira_alves_pinto_pvbr-recog_2019,\\n  title = {{PVBR}-{Recog}: {A} {YOLOv3}-based {Brazilian} {Automatic} {License} {Plate} {Recognition} {Tool}},\\n  shorttitle = {{PVBR}-{Recog}},\\n  doi = {10.5753/webmedia_estendido.2019.8149},\\n  abstract = {Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95\\\\% in plate detection and 96.2\\\\% accuracy in the recognition of the 7 characters of the license plate.},\\n  author = {Ferreira Alves Pinto, Pedro and Busson, Antonio and Melo, João Paulo and Colcher, Sérgio and Milidiú, Ruy},\\n  month = oct,\\n  year = {2019}\\n}\\n\\n\",\"author_short\":[\"Ferreira Alves Pinto, P.\",\"Busson, A.\",\"Melo, J. P.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"ferreira_alves_pinto_pvbr-recog_2019\",\"id\":\"ferreira_alves_pinto_pvbr-recog_2019\",\"bibbaseid\":\"ferreiraalvespinto-busson-melo-colcher-milidi-pvbrrecogayolov3basedbrazilianautomaticlicenseplaterecognitiontool-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Deep learning methods for video understanding\",\"isbn\":\"978-1-4503-6763-9\",\"doi\":\"10.1145/3323503.3345029\",\"abstract\":\"Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.\",\"author\":[{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[\"Almeida\",\"de\"],\"lastnames\":[\"Freitas\"],\"firstnames\":[\"Pedro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Livio\"],\"firstnames\":[\"Álan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2019\",\"pages\":\"21–23\",\"bibtex\":\"@inproceedings{noronha_pereira_dos_santos_deep_2019,\\n  title = {Deep learning methods for video understanding},\\n  isbn = {978-1-4503-6763-9},\\n  doi = {10.1145/3323503.3345029},\\n  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.},\\n  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Milidiú, Ruy and Colcher, Sérgio},\\n  month = oct,\\n  year = {2019},\\n  pages = {21--23}\\n}\\n\\n\",\"author_short\":[\"Noronha Pereira dos Santos, G.\",\"Almeida de Freitas, P.\",\"Busson, A.\",\"Livio, Á.\",\"Milidiú, R.\",\"Colcher, S.\"],\"key\":\"noronha_pereira_dos_santos_deep_2019\",\"id\":\"noronha_pereira_dos_santos_deep_2019\",\"bibbaseid\":\"noronhapereiradossantos-almeidadefreitas-busson-livio-milidi-colcher-deeplearningmethodsforvideounderstanding-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Métodos baseados em Deep Learning para Análise de Vídeo\",\"isbn\":\"978-85-7669-481-6\",\"abstract\":\"Os métodos baseados no Deep Learning tornaram-se state-of-the-art em vários desafios de multimídia. No entanto, existe uma lacuna de profissionais para realizar o Deep Learning na indústria. Este capítulo tem como foco apresentar os fundamentos e tecnologias para desenvolver tais métodos de DL para analise de vídeo. Em especial, buscamos capacitar o leitor a: (1) entender os principais modelos baseados em DL, mais espe-cificamente Convolutional Neural Networks (CNN); (2) aplicar os modelos de DL para resolver tarefas de vídeo como: classificação de vídeo, classificação de multi-etiquetas de vídeo, detecção de objetos e estimação de pose. A linguagem de programação Python é apresentada em conjunto com a biblioteca TensorFlow para implementação dos modelos de DL.\",\"author\":[{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[\"Almeida\",\"de\"],\"lastnames\":[\"Freitas\"],\"firstnames\":[\"Pedro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Livio\"],\"firstnames\":[\"Álan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2019\",\"doi\":\"10.5753/sbc.481.6.04\",\"pages\":\"119\",\"bibtex\":\"@incollection{noronha_pereira_dos_santos_metodos_2019,\\n  title = {Métodos baseados em {Deep} {Learning} para {Análise} de {Vídeo}},\\n  isbn = {978-85-7669-481-6},\\n  abstract = {Os métodos baseados no Deep Learning tornaram-se state-of-the-art em vários desafios de multimídia. No entanto, existe uma lacuna de profissionais para realizar o Deep Learning na indústria. Este capítulo tem como foco apresentar os fundamentos e tecnologias para desenvolver tais métodos de DL para analise de vídeo. Em especial, buscamos capacitar o leitor a: (1) entender os principais modelos baseados em DL, mais espe-cificamente Convolutional Neural Networks (CNN); (2) aplicar os modelos de DL para resolver tarefas de vídeo como: classificação de vídeo, classificação de multi-etiquetas de vídeo, detecção de objetos e estimação de pose. A linguagem de programação Python é apresentada em conjunto com a biblioteca TensorFlow para implementação dos modelos de DL.},\\n  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\\n  month = oct,\\n  year = {2019},\\n  doi = {10.5753/sbc.481.6.04},\\n  pages = {119}\\n}\\n\\n\",\"author_short\":[\"Noronha Pereira dos Santos, G.\",\"Almeida de Freitas, P.\",\"Busson, A.\",\"Livio, Á.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"noronha_pereira_dos_santos_metodos_2019\",\"id\":\"noronha_pereira_dos_santos_metodos_2019\",\"bibbaseid\":\"noronhapereiradossantos-almeidadefreitas-busson-livio-colcher-milidi-mtodosbaseadosemdeeplearningparaanlisedevdeo-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources Request PDF\",\"url\":\"https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources\",\"abstract\":\"Request PDF Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large... Find, read and cite all the research you need on ResearchGate\",\"language\":\"en\",\"year\":\"2019\",\"urldate\":\"2020-03-03\",\"journal\":\"ResearchGate\",\"bibtex\":\"@misc{noauthor_building_nodate,\\n  title = {Building a {Massive} {Corpus} for {Named} {Entity} {Recognition} using {Free} {Open} {Data} {Sources} {Request} {PDF}},\\n  url = {https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources},\\n  abstract = {Request PDF Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large... Find, read and cite all the research you need on ResearchGate},\\n  language = {en},\\n  year = {2019},\\n  urldate = {2020-03-03},\\n  journal = {ResearchGate}\\n}\\n\\n\",\"key\":\"noauthor_building_nodate\",\"id\":\"noauthor_building_nodate\",\"bibbaseid\":\"anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019\",\"role\":\"\",\"urls\":{\"Paper\":\"https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Structured Prediction Networks through Latent Cost Learning\",\"doi\":\"10.1109/SSCI.2018.8628625\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rocha\"],\"firstnames\":[\"Rafael\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2018\",\"pages\":\"645–649\",\"bibtex\":\"@inproceedings{milidiu_structured_2018,\\n  title = {Structured {Prediction} {Networks} through {Latent} {Cost} {Learning}},\\n  doi = {10.1109/SSCI.2018.8628625},\\n  author = {Milidiú, Ruy and Rocha, Rafael},\\n  month = nov,\\n  year = {2018},\\n  pages = {645--649}\\n}\\n\\n\",\"author_short\":[\"Milidiú, R.\",\"Rocha, R.\"],\"key\":\"milidiu_structured_2018\",\"id\":\"milidiu_structured_2018\",\"bibbaseid\":\"milidi-rocha-structuredpredictionnetworksthroughlatentcostlearning-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Developing Deep Learning Models for Multimedia Applications in TensorFlow\",\"doi\":\"10.1145/3243082.3264605\",\"abstract\":\"Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Figueiredo\"],\"firstnames\":[\"Lucas\"],\"suffixes\":[]},{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Damasceno\"],\"firstnames\":[\"Andre\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2018\",\"pages\":\"7–9\",\"bibtex\":\"@inproceedings{busson_developing_2018,\\n  title = {Developing {Deep} {Learning} {Models} for {Multimedia} {Applications} in {TensorFlow}},\\n  doi = {10.1145/3243082.3264605},\\n  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.},\\n  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},\\n  month = oct,\\n  year = {2018},\\n  pages = {7--9}\\n}\\n\\n\",\"author_short\":[\"Busson, A.\",\"Figueiredo, L.\",\"Noronha Pereira dos Santos, G.\",\"Damasceno, A.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"busson_developing_2018\",\"id\":\"busson_developing_2018\",\"bibbaseid\":\"busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-developingdeeplearningmodelsformultimediaapplicationsintensorflow-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"VideoRecognition - Uma Proposta de Serviço para Reconhecimento de Elementos de Vídeo em Larga Escala\",\"abstract\":\"Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Livio\"],\"firstnames\":[\"Álan\"],\"suffixes\":[]},{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Soares\",\"Neto\"],\"firstnames\":[\"Carlos\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2018\",\"doi\":\"10.13140/RG.2.2.32903.24481\",\"bibtex\":\"@inproceedings{busson_videorecognition_2018,\\n  title = {{VideoRecognition} - {Uma} {Proposta} de {Serviço} para {Reconhecimento} de {Elementos} de {Vídeo} em {Larga} {Escala}},\\n  abstract = {Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.},\\n  author = {Busson, Antonio and Livio, Álan and Noronha Pereira dos Santos, Gabriel and Soares Neto, Carlos and Milidiú, Ruy and Colcher, Sérgio},\\n  month = oct,\\n  year = {2018},\\n  doi = {10.13140/RG.2.2.32903.24481}\\n}\\n\\n\",\"author_short\":[\"Busson, A.\",\"Livio, Á.\",\"Noronha Pereira dos Santos, G.\",\"Soares Neto, C.\",\"Milidiú, R.\",\"Colcher, S.\"],\"key\":\"busson_videorecognition_2018\",\"id\":\"busson_videorecognition_2018\",\"bibbaseid\":\"busson-livio-noronhapereiradossantos-soaresneto-milidi-colcher-videorecognitionumapropostadeservioparareconhecimentodeelementosdevdeoemlargaescala-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Desenvolvendo Modelos de Deep Learning para Aplicações Multimídia no Tensorflow\",\"isbn\":\"978-85-7669-455-7\",\"abstract\":\"The availability of massive quantities of data, combined with increasing computational capabilities, makes it possible to develop more precise Machine Learning algorithms. These new tools provide advances in areas such as Natural Language Processing and Computer Vision, allowing efficient processing of images, text and audio. Now, cognitive functionalities, such as learning, recognition and detection, can be used in multimedia applications to create mechanisms beyond traditional capture, streaming and presenta- tion uses. Methods based on Deep Learning became state-of-the-art in several Multi- media challenges. This short course presents the grounds and ways to develop models using Deep Learning. It prepares the participant to: (1) understand and develop mod- els based on Deep Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, including LSTM and GRU; (2) apply the Deep Learning models to solve problems within the multimedia domain like Image Classification, Facial Recogni- tion, Object Detection, Video Scenes Classification. The Python programming language is shown alongside TensorFlow, a package for developing Deep Learning models.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Figueiredo\"],\"firstnames\":[\"Lucas\"],\"suffixes\":[]},{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Damasceno\"],\"firstnames\":[\"Andre\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2018\",\"doi\":\"10.5753/sbc.455.7.03\",\"pages\":\"67–116\",\"bibtex\":\"@incollection{busson_desenvolvendo_2018,\\n  title = {Desenvolvendo {Modelos} de {Deep} {Learning} para {Aplicações} {Multimídia} no {Tensorflow}},\\n  isbn = {978-85-7669-455-7},\\n  abstract = {The availability of massive quantities of data, combined with increasing computational capabilities, makes it possible to develop more precise Machine Learning algorithms. These new tools provide advances in areas such as Natural Language Processing and Computer Vision, allowing efficient processing of images, text and audio. Now, cognitive functionalities, such as learning, recognition and detection, can be used in multimedia applications to create mechanisms beyond traditional capture, streaming and presenta- tion uses. Methods based on Deep Learning became state-of-the-art in several Multi- media challenges. This short course presents the grounds and ways to develop models using Deep Learning. It prepares the participant to: (1) understand and develop mod- els based on Deep Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, including LSTM and GRU; (2) apply the Deep Learning models to solve problems within the multimedia domain like Image Classification, Facial Recogni- tion, Object Detection, Video Scenes Classification. The Python programming language is shown alongside TensorFlow, a package for developing Deep Learning models.},\\n  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},\\n  month = oct,\\n  year = {2018},\\n  doi = {10.5753/sbc.455.7.03},\\n  pages = {67--116}\\n}\\n\\n\",\"author_short\":[\"Busson, A.\",\"Figueiredo, L.\",\"Noronha Pereira dos Santos, G.\",\"Damasceno, A.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"busson_desenvolvendo_2018\",\"id\":\"busson_desenvolvendo_2018\",\"bibbaseid\":\"busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-desenvolvendomodelosdedeeplearningparaaplicaesmultimdianotensorflow-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Entropy-Guided Feature Generation for Large Margin Structured Learning\",\"doi\":\"10.17771/PUCRio.DImcc.24327\",\"journal\":\"Monografias em Ciência da Computação\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fernandes\"],\"firstnames\":[\"Eraldo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2018\",\"bibtex\":\"@article{fernandes_entropy-guided_2018,\\n  title = {Entropy-{Guided} {Feature} {Generation} for {Large} {Margin} {Structured} {Learning}},\\n  doi = {10.17771/PUCRio.DImcc.24327},\\n  journal = {Monografias em Ciência da Computação},\\n  author = {Fernandes, Eraldo and Milidiú, Ruy},\\n  month = feb,\\n  year = {2018}\\n}\\n\\n\",\"author_short\":[\"Fernandes, E.\",\"Milidiú, R.\"],\"key\":\"fernandes_entropy-guided_2018\",\"id\":\"fernandes_entropy-guided_2018\",\"bibbaseid\":\"fernandes-milidi-entropyguidedfeaturegenerationforlargemarginstructuredlearning-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes\",\"abstract\":\"Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal (using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95% and 98.94% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.\",\"author\":[{\"propositions\":[\"Almeida\",\"de\"],\"lastnames\":[\"Freitas\"],\"firstnames\":[\"Pedro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mendes\"],\"firstnames\":[\"Paulo\"],\"suffixes\":[]},{\"propositions\":[\"Noronha\",\"Pereira\",\"dos\"],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Gabriel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Busson\"],\"firstnames\":[\"Antonio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Livio\"],\"firstnames\":[\"Álan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Colcher\"],\"firstnames\":[\"Sérgio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2019\",\"bibtex\":\"@misc{almeida_de_freitas_multimodal_2019,\\n  title = {A {Multimodal} {CNN}-based {Tool} to {Censure} {Inappropriate} {Video} {Scenes}},\\n  abstract = {Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal (using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95\\\\% and 98.94\\\\% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.},\\n  author = {Almeida de Freitas, Pedro and Mendes, Paulo and Noronha Pereira dos Santos, Gabriel and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\\n  month = nov,\\n  year = {2019}\\n}\\n\\n\",\"author_short\":[\"Almeida de Freitas, P.\",\"Mendes, P.\",\"Noronha Pereira dos Santos, G.\",\"Busson, A.\",\"Livio, Á.\",\"Colcher, S.\",\"Milidiú, R.\"],\"key\":\"almeida_de_freitas_multimodal_2019\",\"id\":\"almeida_de_freitas_multimodal_2019\",\"bibbaseid\":\"almeidadefreitas-mendes-noronhapereiradossantos-busson-livio-colcher-milidi-amultimodalcnnbasedtooltocensureinappropriatevideoscenes-2019\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Cumulative Sum Ranking\",\"url\":\"http://arxiv.org/abs/1911.11255\",\"abstract\":\"The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.\",\"urldate\":\"2020-03-03\",\"journal\":\"arXiv:1911.11255 [cs, stat]\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Milidiú\"],\"firstnames\":[\"Ruy\",\"Luiz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rocha\"],\"firstnames\":[\"Rafael\",\"Henrique\",\"Santos\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2019\",\"note\":\"arXiv: 1911.11255\",\"bibtex\":\"@misc{milidiu_cumulative_2019,\\n  title = {Cumulative {Sum} {Ranking}},\\n  url = {http://arxiv.org/abs/1911.11255},\\n  abstract = {The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.},\\n  urldate = {2020-03-03},\\n  journal = {arXiv:1911.11255 [cs, stat]},\\n  author = {Milidiú, Ruy Luiz and Rocha, Rafael Henrique Santos},\\n  month = nov,\\n  year = {2019},\\n  note = {arXiv: 1911.11255}\\n}\",\"author_short\":[\"Milidiú, R. L.\",\"Rocha, R. H. S.\"],\"key\":\"milidiu_cumulative_2019\",\"id\":\"milidiu_cumulative_2019\",\"bibbaseid\":\"milidi-rocha-cumulativesumranking-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/1911.11255\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{fernandes_latent_2014,
  title = {Latent {Trees} for {Coreference} {Resolution}},
  volume = {40},
  issn = {0891-2017},
  url = {https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200},
  doi = {10.1162/COLI_a_00200},
  abstract = {We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5\% error reduction, and is the best performing system for each of the three languages.},
  number = {4},
  urldate = {2020-03-02},
  journal = {Computational Linguistics},
  author = {Fernandes, Eraldo Rezende and dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},
  month = apr,
  year = {2014},
  pages = {801--835}
}



@article{alvim_trading_2013,
  title = {Trading team composition for the intraday multistock market},
  volume = {54},
  issn = {0167-9236},
  url = {http://www.sciencedirect.com/science/article/pii/S0167923612002448},
  doi = {10.1016/j.dss.2012.09.009},
  abstract = {Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM\&FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26\% annualized profit, outperforming by 380.07\% the chosen baseline strategy with a 16.07\% profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US\$ 50,000, the MSR strategy provides a profit of at least 38.63\%.},
  language = {en},
  number = {2},
  urldate = {2020-03-02},
  journal = {Decision Support Systems},
  author = {Alvim, Leandro G. M. and Milidiú, Ruy L.},
  month = jan,
  year = {2013},
  pages = {838--845}
}



@article{fernandes_relhunter_2010,
  title = {{RelHunter} : a machine learning method for relation extraction from text},
  volume = {16},
  copyright = {2010 The Brazilian Computer Society},
  issn = {1678-4804},
  shorttitle = {{RelHunter}},
  url = {https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y},
  doi = {10.1007/s13173-010-0018-y},
  abstract = {We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter’s key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14\% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.},
  language = {en},
  number = {3},
  urldate = {2020-03-02},
  journal = {Journal of the Brazilian Computer Society},
  author = {Fernandes, Eraldo R. and Milidiú, Ruy L. and Rentería, Raúl P.},
  month = sep,
  year = {2010},
  pages = {191--199}
}



@article{milidiu_portuguese_2008,
  title = {Portuguese corpus-based learning using {ETL}},
  volume = {14},
  issn = {0104-6500},
  url = {http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0104-65002008000400003&lng=en&nrm=iso&tlng=en},
  doi = {10.1007/BF03192569},
  language = {en},
  number = {4},
  urldate = {2020-03-02},
  journal = {Journal of the Brazilian Computer Society},
  author = {Milidiú, Ruy Luiz and Santos, Cícero Nogueira dos and Duarte, Julio Cesar},
  month = dec,
  year = {2008},
  pages = {17--27}
}



@article{renteria_mkpls_2007,
  title = {{MKPLS} approach: switching strategies for the non-linear multi-kernel {PLSR}},
  volume = {22},
  issn = {1613-9658},
  shorttitle = {{MKPLS} approach},
  url = {https://doi.org/10.1007/s00180-007-0040-5},
  doi = {10.1007/s00180-007-0040-5},
  abstract = {We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.},
  language = {en},
  number = {2},
  urldate = {2020-03-02},
  journal = {Computational Statistics},
  author = {Rentería, Raúl and Milidiú, Ruy and Souza, Rafael},
  month = jul,
  year = {2007},
  pages = {323--330}
}



@inproceedings{moret_double_2012,
  address = {Trento, Italy},
  series = {{SAC} '12},
  title = {Double dip map-reduce for processing cross validation jobs},
  isbn = {978-1-4503-0857-1},
  url = {https://doi.org/10.1145/2245276.2245367},
  doi = {10.1145/2245276.2245367},
  abstract = {Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.},
  urldate = {2020-03-02},
  booktitle = {Proceedings of the 27th {Annual} {ACM} {Symposium} on {Applied} {Computing}},
  publisher = {Association for Computing Machinery},
  author = {Moret, Danilo and Breitman, Karin and Amorim, Evelin and Talavera, Jose and Milidiu, Ruy and Viterbo, Jose},
  month = mar,
  year = {2012},
  pages = {473--477}
}



@inproceedings{breitman_instance-based_2008,
  title = {Instance-{Based} {Ontology} {Mapping}},
  doi = {10.1109/EASe.2008.18},
  abstract = {We have explored the use of formal ontology and alignment techniques as a general approach to mediate and reconcile different representations. This approach proved to be very effective when fueled by ontologies rich in detail, (properties, restrictions, attributes and axioms that hold among classes), but performs poorly when available representations are incomplete or lacking, which is often the case in real life settings. In this paper we propose an enhancement to the original approach, based on a, instance-based matching technique.},
  booktitle = {Fifth {IEEE} {Workshop} on {Engineering} of {Autonomic} and {Autonomous} {Systems} (ease 2008)},
  author = {Breitman, Karin K. and Brauner, Daniela and Casanova, Marco Antonio and Milidiú, Ruy and Gazola, Alexandre and Perazolo, Marcelo},
  month = mar,
  year = {2008},
  note = {ISSN: 2168-1872},
  pages = {67--74}
}



@inproceedings{milidiu_tbl_2007,
  address = {Berlin, Heidelberg},
  series = {Lecture {Notes} in {Computer} {Science}},
  title = {{TBL} {Template} {Selection}: {An} {Evolutionary} {Approach}},
  isbn = {978-3-540-75271-4},
  shorttitle = {{TBL} {Template} {Selection}},
  doi = {10.1007/978-3-540-75271-4_19},
  abstract = {Transformation Based Learning (TBL) is an intensively Machine Learning algorithm frequently used in Natural Language Processing. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template selection process. We show some empirical evidence that our approach provides template sets with almost the same quality as human built templates.},
  language = {en},
  booktitle = {Current {Topics} in {Artificial} {Intelligence}},
  publisher = {Springer},
  author = {Milidiú, Ruy Luiz and Duarte, Julio Cesar and Nogueira dos Santos, Cícero},
  editor = {Borrajo, Daniel and Castillo, Luis and Corchado, Juan Manuel},
  year = {2007},
  pages = {180--189}
}



@inproceedings{milidiu_phrase_2008,
  title = {Phrase chunking using entropy guided transformation learning},
  booktitle = {Proceedings of {ACL}-08: {HLT}},
  author = {Milidiú, Ruy Luiz and dos Santos, Cicero and Duarte, Julio C.},
  year = {2008},
  pages = {647--655}
}



@article{milidiu_time-series_1999,
  title = {Time-series forecasting through wavelets transformation and a mixture of expert models},
  volume = {28},
  number = {1-3},
  journal = {Neurocomputing},
  author = {Milidiú, Ruy L. and Machado, Ricardo J. and Renterı́a, Raúl P.},
  year = {1999},
  pages = {145--156}
}



@inproceedings{dos_santos_teixeira_data_2010,
  title = {Data stream anomaly detection through principal subspace tracking},
  booktitle = {Proceedings of the 2010 {ACM} {Symposium} on {Applied} {Computing}},
  author = {dos Santos Teixeira, Pedro Henriques and Milidiú, Ruy Luiz},
  year = {2010},
  pages = {1609--1616}
}



@article{da_silva_belief_1993,
  title = {Belief function model for information retrieval},
  volume = {44},
  number = {1},
  journal = {Journal of the American Society for Information Science},
  author = {da Silva, Wagner Teixeira and Milidiú, Ruy Luiz},
  year = {1993},
  pages = {10--18}
}



@incollection{dos_santos_entropy_2009,
  title = {Entropy guided transformation learning},
  booktitle = {Foundations of {Computational}, {Intelligence} {Volume} 1},
  publisher = {Springer},
  author = {dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},
  year = {2009},
  pages = {159--184}
}



@article{sardinha_object-oriented_2003,
  title = {An {Object}-{Oriented} {Framework} for {Building} {Software} {Agents}.},
  volume = {2},
  number = {1},
  journal = {Journal of Object Technology},
  author = {Sardinha, José Alberto Rodrigues Pereira and Ribeiro, Paula Clark and Milidiú, Ruy Luiz and de Lucena, Carlos José Pereira},
  year = {2003},
  pages = {85--97}
}



@inproceedings{milidiu_efficient_1999,
  title = {Efficient implementation of the {WARM}-{UP} algorithm for the construction of length-restricted prefix codes},
  booktitle = {Workshop on {Algorithm} {Engineering} and {Experimentation}},
  publisher = {Springer},
  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},
  year = {1999},
  pages = {1--17}
}



@article{milidiu_bounding_2001,
  title = {Bounding the inefficiency of length-restricted prefix codes},
  volume = {31},
  number = {4},
  journal = {Algorithmica},
  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},
  year = {2001},
  pages = {513--529}
}



@inproceedings{sardinha_agent_2005,
  title = {An {Agent} {Based} {Architecture} for {Highly} {Competitive} {Electronic} {Markets}.},
  booktitle = {{FLAIRS} {Conference}},
  author = {Sardinha, José Alberto RP and Milidiú, Ruy Luiz and Paranhos, Patrick M. and Cunha, Pedro M. and de Lucena, Carlos José Pereira},
  year = {2005},
  pages = {326--332}
}



@article{milidiu_warm-up_2000,
  title = {The warm-up algorithm: a {Lagrangian} construction of length restricted {Huffman} codes},
  volume = {30},
  shorttitle = {The warm-up algorithm},
  number = {5},
  journal = {SIAM Journal on Computing},
  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},
  year = {2000},
  pages = {1405--1426}
}



@inproceedings{fernandes_quotation_2011,
  title = {Quotation extraction for portuguese},
  booktitle = {Proceedings of the 8th {Brazilian} {Symposium} in {Information} and {Human} {Language} {Technology}},
  author = {Fernandes, William Paulo Ducca and Motta, Eduardo and Milidiú, Ruy Luiz},
  year = {2011}
}



@inproceedings{alvim_daily_2010,
  title = {Daily volume forecasting using high frequency predictors},
  volume = {674},
  booktitle = {Proceedings of the 10th {IASTED} {International} {Conference}},
  author = {Alvim, Leandro GM and dos Santos, Cicero N. and Milidiu, Ruy L.},
  year = {2010},
  pages = {248}
}



@article{milidiu_dpls_2005,
  title = {{DPLS} and {PPLS}: two {PLS} algorithms for large data sets},
  volume = {48},
  shorttitle = {{DPLS} and {PPLS}},
  number = {1},
  journal = {Computational statistics \& data analysis},
  author = {Milidiu, Ruy L. and Renterı́a, Raúl P.},
  year = {2005},
  pages = {125--138}
}



@article{milidiu_complexity_2003,
  title = {The complexity of makespan minimization for pipeline transportation},
  volume = {306},
  number = {1-3},
  journal = {Theoretical Computer Science},
  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},
  year = {2003},
  pages = {339--351}
}



@article{milidiu_three_2001,
  title = {Three space-economical algorithms for calculating minimum-redundancy prefix codes},
  volume = {47},
  number = {6},
  journal = {IEEE Transactions on Information Theory},
  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},
  year = {2001},
  pages = {2185--2198}
}



@article{milidiu_bounding_1999,
  title = {Bounding the compression loss of the {FGK} algorithm},
  volume = {32},
  number = {2},
  journal = {Journal of Algorithms},
  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany and Pessoa, Artur Alves},
  year = {1999},
  pages = {195--211}
}



@article{da_silva_algorithms_1992,
  title = {Algorithms for combining belief functions},
  volume = {7},
  number = {1-2},
  journal = {International Journal of Approximate Reasoning},
  author = {da Silva, Wagner Teixeira and Milidiu, Ruy Luiz},
  year = {1992},
  pages = {73--94}
}



@article{milidiu_computation_1987,
  title = {The {Computation} {Of} {Compound} {Distributions} {With} {Two}-{Sided} {Severities} ({Negative}-{Binomial} {Iterative}, {Recursive}, {Asymptotic}, {Sum})},
  author = {Milidiu, Ruy Luiz},
  year = {1987}
}



@misc{betine_deep_2019,
  title = {A {Deep} {Convolutional} {Network} for {Seismic} {Shot}-{Gather} {Image} {Quality} {Classification}},
  abstract = {Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56\% in the test set.},
  author = {Betine, Eduardo and Busson, Antonio and Milidiú, Ruy and Colcher, Sérgio and Dias, Bruno and Bulcão, André},
  month = dec,
  year = {2019}
}



@inproceedings{jesus_cnn-based_2019,
  title = {A {CNN}-{Based} {Tool} to {Index} {Emotion} on {Anime} {Character} {Stickers}},
  doi = {10.1109/ISM46123.2019.00071},
  author = {Jesus, Ivan and Cardoso, Jessica and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},
  month = dec,
  year = {2019},
  pages = {319--3193}
}



@inproceedings{ferreira_alves_pinto_pvbr-recog_2019,
  title = {{PVBR}-{Recog}: {A} {YOLOv3}-based {Brazilian} {Automatic} {License} {Plate} {Recognition} {Tool}},
  shorttitle = {{PVBR}-{Recog}},
  doi = {10.5753/webmedia_estendido.2019.8149},
  abstract = {Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95\% in plate detection and 96.2\% accuracy in the recognition of the 7 characters of the license plate.},
  author = {Ferreira Alves Pinto, Pedro and Busson, Antonio and Melo, João Paulo and Colcher, Sérgio and Milidiú, Ruy},
  month = oct,
  year = {2019}
}



@inproceedings{noronha_pereira_dos_santos_deep_2019,
  title = {Deep learning methods for video understanding},
  isbn = {978-1-4503-6763-9},
  doi = {10.1145/3323503.3345029},
  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.},
  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Milidiú, Ruy and Colcher, Sérgio},
  month = oct,
  year = {2019},
  pages = {21--23}
}



@incollection{noronha_pereira_dos_santos_metodos_2019,
  title = {Métodos baseados em {Deep} {Learning} para {Análise} de {Vídeo}},
  isbn = {978-85-7669-481-6},
  abstract = {Os métodos baseados no Deep Learning tornaram-se state-of-the-art em vários desafios de multimídia. No entanto, existe uma lacuna de profissionais para realizar o Deep Learning na indústria. Este capítulo tem como foco apresentar os fundamentos e tecnologias para desenvolver tais métodos de DL para analise de vídeo. Em especial, buscamos capacitar o leitor a: (1) entender os principais modelos baseados em DL, mais espe-cificamente Convolutional Neural Networks (CNN); (2) aplicar os modelos de DL para resolver tarefas de vídeo como: classificação de vídeo, classificação de multi-etiquetas de vídeo, detecção de objetos e estimação de pose. A linguagem de programação Python é apresentada em conjunto com a biblioteca TensorFlow para implementação dos modelos de DL.},
  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},
  month = oct,
  year = {2019},
  doi = {10.5753/sbc.481.6.04},
  pages = {119}
}



@misc{noauthor_building_nodate,
  title = {Building a {Massive} {Corpus} for {Named} {Entity} {Recognition} using {Free} {Open} {Data} {Sources} {Request} {PDF}},
  url = {https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources},
  abstract = {Request PDF Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large... Find, read and cite all the research you need on ResearchGate},
  language = {en},
  year = {2019},
  urldate = {2020-03-03},
  journal = {ResearchGate}
}



@inproceedings{milidiu_structured_2018,
  title = {Structured {Prediction} {Networks} through {Latent} {Cost} {Learning}},
  doi = {10.1109/SSCI.2018.8628625},
  author = {Milidiú, Ruy and Rocha, Rafael},
  month = nov,
  year = {2018},
  pages = {645--649}
}



@inproceedings{busson_developing_2018,
  title = {Developing {Deep} {Learning} {Models} for {Multimedia} {Applications} in {TensorFlow}},
  doi = {10.1145/3243082.3264605},
  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.},
  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},
  month = oct,
  year = {2018},
  pages = {7--9}
}



@inproceedings{busson_videorecognition_2018,
  title = {{VideoRecognition} - {Uma} {Proposta} de {Serviço} para {Reconhecimento} de {Elementos} de {Vídeo} em {Larga} {Escala}},
  abstract = {Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.},
  author = {Busson, Antonio and Livio, Álan and Noronha Pereira dos Santos, Gabriel and Soares Neto, Carlos and Milidiú, Ruy and Colcher, Sérgio},
  month = oct,
  year = {2018},
  doi = {10.13140/RG.2.2.32903.24481}
}



@incollection{busson_desenvolvendo_2018,
  title = {Desenvolvendo {Modelos} de {Deep} {Learning} para {Aplicações} {Multimídia} no {Tensorflow}},
  isbn = {978-85-7669-455-7},
  abstract = {The availability of massive quantities of data, combined with increasing computational capabilities, makes it possible to develop more precise Machine Learning algorithms. These new tools provide advances in areas such as Natural Language Processing and Computer Vision, allowing efficient processing of images, text and audio. Now, cognitive functionalities, such as learning, recognition and detection, can be used in multimedia applications to create mechanisms beyond traditional capture, streaming and presenta- tion uses. Methods based on Deep Learning became state-of-the-art in several Multi- media challenges. This short course presents the grounds and ways to develop models using Deep Learning. It prepares the participant to: (1) understand and develop mod- els based on Deep Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, including LSTM and GRU; (2) apply the Deep Learning models to solve problems within the multimedia domain like Image Classification, Facial Recogni- tion, Object Detection, Video Scenes Classification. The Python programming language is shown alongside TensorFlow, a package for developing Deep Learning models.},
  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},
  month = oct,
  year = {2018},
  doi = {10.5753/sbc.455.7.03},
  pages = {67--116}
}



@article{fernandes_entropy-guided_2018,
  title = {Entropy-{Guided} {Feature} {Generation} for {Large} {Margin} {Structured} {Learning}},
  doi = {10.17771/PUCRio.DImcc.24327},
  journal = {Monografias em Ciência da Computação},
  author = {Fernandes, Eraldo and Milidiú, Ruy},
  month = feb,
  year = {2018}
}



@misc{almeida_de_freitas_multimodal_2019,
  title = {A {Multimodal} {CNN}-based {Tool} to {Censure} {Inappropriate} {Video} {Scenes}},
  abstract = {Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal (using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95\% and 98.94\% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.},
  author = {Almeida de Freitas, Pedro and Mendes, Paulo and Noronha Pereira dos Santos, Gabriel and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},
  month = nov,
  year = {2019}
}



@misc{milidiu_cumulative_2019,
  title = {Cumulative {Sum} {Ranking}},
  url = {http://arxiv.org/abs/1911.11255},
  abstract = {The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.},
  urldate = {2020-03-03},
  journal = {arXiv:1911.11255 [cs, stat]},
  author = {Milidiú, Ruy Luiz and Rocha, Rafael Henrique Santos},
  month = nov,
  year = {2019},
  note = {arXiv: 1911.11255}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=http://learn.inf.puc-rio.br/files/all.bib\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (8)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"betine-busson-milidi-colcher-dias-bulco-adeepconvolutionalnetworkforseismicshotgatherimagequalityclassification-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Betine, E.; Busson, A.; Milidiú, R.; Colcher, S.; Dias, B.;  and Bulcão, A.\n</span>\n\n  \n\n</span>\n\n  December 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/betine-busson-milidi-colcher-dias-bulco-adeepconvolutionalnetworkforseismicshotgatherimagequalityclassification-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('betine-busson-milidi-colcher-dias-bulco-adeepconvolutionalnetworkforseismicshotgatherimagequalityclassification-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{betine_deep_2019,\n  title = {A {Deep} {Convolutional} {Network} for {Seismic} {Shot}-{Gather} {Image} {Quality} {Classification}},\n  abstract = {Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56\\% in the test set.},\n  author = {Betine, Eduardo and Busson, Antonio and Milidiú, Ruy and Colcher, Sérgio and Dias, Bruno and Bulcão, André},\n  month = dec,\n  year = {2019}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"jesus-cardoso-busson-livio-colcher-milidi-acnnbasedtooltoindexemotiononanimecharacterstickers-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A CNN-Based Tool to Index Emotion on Anime Character Stickers.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Jesus, I.; Cardoso, J.; Busson, A.; Livio, Á.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  In pages 319–3193, December 2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ISM46123.2019.00071\"\n     onclick=\"log_download('jesus-cardoso-busson-livio-colcher-milidi-acnnbasedtooltoindexemotiononanimecharacterstickers-2019', 'http://doi.org/10.1109/ISM46123.2019.00071', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/jesus-cardoso-busson-livio-colcher-milidi-acnnbasedtooltoindexemotiononanimecharacterstickers-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('jesus-cardoso-busson-livio-colcher-milidi-acnnbasedtooltoindexemotiononanimecharacterstickers-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{jesus_cnn-based_2019,\n  title = {A {CNN}-{Based} {Tool} to {Index} {Emotion} on {Anime} {Character} {Stickers}},\n  doi = {10.1109/ISM46123.2019.00071},\n  author = {Jesus, Ivan and Cardoso, Jessica and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\n  month = dec,\n  year = {2019},\n  pages = {319--3193}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ferreiraalvespinto-busson-melo-colcher-milidi-pvbrrecogayolov3basedbrazilianautomaticlicenseplaterecognitiontool-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ferreira Alves Pinto, P.; Busson, A.; Melo, J. P.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  In October 2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5753/webmedia_estendido.2019.8149\"\n     onclick=\"log_download('ferreiraalvespinto-busson-melo-colcher-milidi-pvbrrecogayolov3basedbrazilianautomaticlicenseplaterecognitiontool-2019', 'http://doi.org/10.5753/webmedia_estendido.2019.8149', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ferreiraalvespinto-busson-melo-colcher-milidi-pvbrrecogayolov3basedbrazilianautomaticlicenseplaterecognitiontool-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ferreiraalvespinto-busson-melo-colcher-milidi-pvbrrecogayolov3basedbrazilianautomaticlicenseplaterecognitiontool-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ferreira_alves_pinto_pvbr-recog_2019,\n  title = {{PVBR}-{Recog}: {A} {YOLOv3}-based {Brazilian} {Automatic} {License} {Plate} {Recognition} {Tool}},\n  shorttitle = {{PVBR}-{Recog}},\n  doi = {10.5753/webmedia_estendido.2019.8149},\n  abstract = {Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95\\% in plate detection and 96.2\\% accuracy in the recognition of the 7 characters of the license plate.},\n  author = {Ferreira Alves Pinto, Pedro and Busson, Antonio and Melo, João Paulo and Colcher, Sérgio and Milidiú, Ruy},\n  month = oct,\n  year = {2019}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"noronhapereiradossantos-almeidadefreitas-busson-livio-milidi-colcher-deeplearningmethodsforvideounderstanding-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Deep learning methods for video understanding.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Noronha Pereira dos Santos, G.; Almeida de Freitas, P.; Busson, A.; Livio, Á.; Milidiú, R.;  and Colcher, S.\n</span>\n\n  \n\n</span>\n\n  In pages 21–23, October 2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3323503.3345029\"\n     onclick=\"log_download('noronhapereiradossantos-almeidadefreitas-busson-livio-milidi-colcher-deeplearningmethodsforvideounderstanding-2019', 'http://doi.org/10.1145/3323503.3345029', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/noronhapereiradossantos-almeidadefreitas-busson-livio-milidi-colcher-deeplearningmethodsforvideounderstanding-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('noronhapereiradossantos-almeidadefreitas-busson-livio-milidi-colcher-deeplearningmethodsforvideounderstanding-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{noronha_pereira_dos_santos_deep_2019,\n  title = {Deep learning methods for video understanding},\n  isbn = {978-1-4503-6763-9},\n  doi = {10.1145/3323503.3345029},\n  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.},\n  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Milidiú, Ruy and Colcher, Sérgio},\n  month = oct,\n  year = {2019},\n  pages = {21--23}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this tutorial aims to present the grounds and ways to develop applications that uses methods based on Deep Learning for video analysis tasks. Likewise, this tutorial is an opportunity for students and information technology professionals to qualify themselves. The main focus of this short course is to present fundamentals and technologies to develop such methods of DL.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"noronhapereiradossantos-almeidadefreitas-busson-livio-colcher-milidi-mtodosbaseadosemdeeplearningparaanlisedevdeo-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Métodos baseados em Deep Learning para Análise de Vídeo.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Noronha Pereira dos Santos, G.; Almeida de Freitas, P.; Busson, A.; Livio, Á.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  In pages 119. October 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5753/sbc.481.6.04\"\n     onclick=\"log_download('noronhapereiradossantos-almeidadefreitas-busson-livio-colcher-milidi-mtodosbaseadosemdeeplearningparaanlisedevdeo-2019', 'http://doi.org/10.5753/sbc.481.6.04', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/noronhapereiradossantos-almeidadefreitas-busson-livio-colcher-milidi-mtodosbaseadosemdeeplearningparaanlisedevdeo-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('noronhapereiradossantos-almeidadefreitas-busson-livio-colcher-milidi-mtodosbaseadosemdeeplearningparaanlisedevdeo-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{noronha_pereira_dos_santos_metodos_2019,\n  title = {Métodos baseados em {Deep} {Learning} para {Análise} de {Vídeo}},\n  isbn = {978-85-7669-481-6},\n  abstract = {Os métodos baseados no Deep Learning tornaram-se state-of-the-art em vários desafios de multimídia. No entanto, existe uma lacuna de profissionais para realizar o Deep Learning na indústria. Este capítulo tem como foco apresentar os fundamentos e tecnologias para desenvolver tais métodos de DL para analise de vídeo. Em especial, buscamos capacitar o leitor a: (1) entender os principais modelos baseados em DL, mais espe-cificamente Convolutional Neural Networks (CNN); (2) aplicar os modelos de DL para resolver tarefas de vídeo como: classificação de vídeo, classificação de multi-etiquetas de vídeo, detecção de objetos e estimação de pose. A linguagem de programação Python é apresentada em conjunto com a biblioteca TensorFlow para implementação dos modelos de DL.},\n  author = {Noronha Pereira dos Santos, Gabriel and Almeida de Freitas, Pedro and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\n  month = oct,\n  year = {2019},\n  doi = {10.5753/sbc.481.6.04},\n  pages = {119}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Os métodos baseados no Deep Learning tornaram-se state-of-the-art em vários desafios de multimídia. No entanto, existe uma lacuna de profissionais para realizar o Deep Learning na indústria. Este capítulo tem como foco apresentar os fundamentos e tecnologias para desenvolver tais métodos de DL para analise de vídeo. Em especial, buscamos capacitar o leitor a: (1) entender os principais modelos baseados em DL, mais espe-cificamente Convolutional Neural Networks (CNN); (2) aplicar os modelos de DL para resolver tarefas de vídeo como: classificação de vídeo, classificação de multi-etiquetas de vídeo, detecção de objetos e estimação de pose. A linguagem de programação Python é apresentada em conjunto com a biblioteca TensorFlow para implementação dos modelos de DL.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019', 'https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources', event);\">\n    Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources Request PDF.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  \n</span>\n\n  \n\n</span>\n\n   2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources\"\n     onclick=\"log_download('anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019', 'https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources Request PDF [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('anonymous-buildingamassivecorpusfornamedentityrecognitionusingfreeopendatasourcesrequestpdf-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{noauthor_building_nodate,\n  title = {Building a {Massive} {Corpus} for {Named} {Entity} {Recognition} using {Free} {Open} {Data} {Sources} {Request} {PDF}},\n  url = {https://www.researchgate.net/publication/335233220_Building_a_Massive_Corpus_for_Named_Entity_Recognition_using_Free_Open_Data_Sources},\n  abstract = {Request PDF Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large... Find, read and cite all the research you need on ResearchGate},\n  language = {en},\n  year = {2019},\n  urldate = {2020-03-03},\n  journal = {ResearchGate}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Request PDF Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources With the recent progress in machine learning, boosted by techniques such as deep learning, many tasks can be successfully solved once a large... Find, read and cite all the research you need on ResearchGate\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"almeidadefreitas-mendes-noronhapereiradossantos-busson-livio-colcher-milidi-amultimodalcnnbasedtooltocensureinappropriatevideoscenes-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Almeida de Freitas, P.; Mendes, P.; Noronha Pereira dos Santos, G.; Busson, A.; Livio, Á.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  November 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/almeidadefreitas-mendes-noronhapereiradossantos-busson-livio-colcher-milidi-amultimodalcnnbasedtooltocensureinappropriatevideoscenes-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('almeidadefreitas-mendes-noronhapereiradossantos-busson-livio-colcher-milidi-amultimodalcnnbasedtooltocensureinappropriatevideoscenes-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{almeida_de_freitas_multimodal_2019,\n  title = {A {Multimodal} {CNN}-based {Tool} to {Censure} {Inappropriate} {Video} {Scenes}},\n  abstract = {Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal (using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95\\% and 98.94\\% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.},\n  author = {Almeida de Freitas, Pedro and Mendes, Paulo and Noronha Pereira dos Santos, Gabriel and Busson, Antonio and Livio, Álan and Colcher, Sérgio and Milidiú, Ruy},\n  month = nov,\n  year = {2019}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal (using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95% and 98.94% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-rocha-cumulativesumranking-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/1911.11255\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'milidi-rocha-cumulativesumranking-2019', 'http://arxiv.org/abs/1911.11255', event);\">\n    Cumulative Sum Ranking.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.;  and Rocha, R. H. S.\n</span>\n\n  \n\n</span>\n\n  November 2019.\n  <span class=\"note\">arXiv: 1911.11255</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/1911.11255\"\n     onclick=\"log_download('milidi-rocha-cumulativesumranking-2019', 'http://arxiv.org/abs/1911.11255', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Cumulative Sum Ranking [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-rocha-cumulativesumranking-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-rocha-cumulativesumranking-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{milidiu_cumulative_2019,\n  title = {Cumulative {Sum} {Ranking}},\n  url = {http://arxiv.org/abs/1911.11255},\n  abstract = {The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item&#x27;s rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.},\n  urldate = {2020-03-03},\n  journal = {arXiv:1911.11255 [cs, stat]},\n  author = {Milidiú, Ruy Luiz and Rocha, Rafael Henrique Santos},\n  month = nov,\n  year = {2019},\n  note = {arXiv: 1911.11255}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The goal of Ordinal Regression is to find a rule that ranks items from a given set. Several learning algorithms to solve this prediction problem build an ensemble of binary classifiers. Ranking by Projecting uses interdependent binary perceptrons. These perceptrons share the same direction vector, but use different bias values. Similar approaches use independent direction vectors and biases. To combine the binary predictions, most of them adopt a simple counting heuristics. Here, we introduce a novel cumulative sum scoring function to combine the binary predictions. The proposed score value aggregates the strength of each one of the relevant binary classifications on how large is the item's rank. We show that our modeling casts ordinal regression as a Structured Perceptron problem. As a consequence, we simplify its formulation and description, which results in two simple online learning algorithms. The second algorithm is a Passive-Aggressive version of the first algorithm. We show that under some rank separability condition both algorithms converge. Furthermore, we provide mistake bounds for each one of the two online algorithms. For the Passive-Aggressive version, we assume the knowledge of a separation margin, what significantly improves the corresponding mistake bound. Additionally, we show that Ranking by Projecting is a special case of our prediction algorithm. From a neural network architecture point of view, our empirical findings suggest a layer of cusum units for ordinal regression, instead of the usual softmax layer of multiclass problems.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidi-rocha-structuredpredictionnetworksthroughlatentcostlearning-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Structured Prediction Networks through Latent Cost Learning.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R.;  and Rocha, R.\n</span>\n\n  \n\n</span>\n\n  In pages 645–649, November 2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/SSCI.2018.8628625\"\n     onclick=\"log_download('milidi-rocha-structuredpredictionnetworksthroughlatentcostlearning-2018', 'http://doi.org/10.1109/SSCI.2018.8628625', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-rocha-structuredpredictionnetworksthroughlatentcostlearning-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-rocha-structuredpredictionnetworksthroughlatentcostlearning-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{milidiu_structured_2018,\n  title = {Structured {Prediction} {Networks} through {Latent} {Cost} {Learning}},\n  doi = {10.1109/SSCI.2018.8628625},\n  author = {Milidiú, Ruy and Rocha, Rafael},\n  month = nov,\n  year = {2018},\n  pages = {645--649}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-developingdeeplearningmodelsformultimediaapplicationsintensorflow-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Developing Deep Learning Models for Multimedia Applications in TensorFlow.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Busson, A.; Figueiredo, L.; Noronha Pereira dos Santos, G.; Damasceno, A.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  In pages 7–9, October 2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3243082.3264605\"\n     onclick=\"log_download('busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-developingdeeplearningmodelsformultimediaapplicationsintensorflow-2018', 'http://doi.org/10.1145/3243082.3264605', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-developingdeeplearningmodelsformultimediaapplicationsintensorflow-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-developingdeeplearningmodelsformultimediaapplicationsintensorflow-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{busson_developing_2018,\n  title = {Developing {Deep} {Learning} {Models} for {Multimedia} {Applications} in {TensorFlow}},\n  doi = {10.1145/3243082.3264605},\n  abstract = {Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.},\n  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},\n  month = oct,\n  year = {2018},\n  pages = {7--9}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Methods based on Deep Learning became state-of-the-art in several Multimedia challenges. However, there is a gap of professionals to perform Deep Learning in the industry. Therefore, this short course aims to present the grounds and ways to develop multimedia applications using methods based on Deep Learning. Likewise, this short course is an opportunity for students and IT professionals can qualify yourselves.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"busson-livio-noronhapereiradossantos-soaresneto-milidi-colcher-videorecognitionumapropostadeservioparareconhecimentodeelementosdevdeoemlargaescala-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  VideoRecognition - Uma Proposta de Serviço para Reconhecimento de Elementos de Vídeo em Larga Escala.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Busson, A.; Livio, Á.; Noronha Pereira dos Santos, G.; Soares Neto, C.; Milidiú, R.;  and Colcher, S.\n</span>\n\n  \n\n</span>\n\n  In October 2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.13140/RG.2.2.32903.24481\"\n     onclick=\"log_download('busson-livio-noronhapereiradossantos-soaresneto-milidi-colcher-videorecognitionumapropostadeservioparareconhecimentodeelementosdevdeoemlargaescala-2018', 'http://doi.org/10.13140/RG.2.2.32903.24481', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/busson-livio-noronhapereiradossantos-soaresneto-milidi-colcher-videorecognitionumapropostadeservioparareconhecimentodeelementosdevdeoemlargaescala-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('busson-livio-noronhapereiradossantos-soaresneto-milidi-colcher-videorecognitionumapropostadeservioparareconhecimentodeelementosdevdeoemlargaescala-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{busson_videorecognition_2018,\n  title = {{VideoRecognition} - {Uma} {Proposta} de {Serviço} para {Reconhecimento} de {Elementos} de {Vídeo} em {Larga} {Escala}},\n  abstract = {Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.},\n  author = {Busson, Antonio and Livio, Álan and Noronha Pereira dos Santos, Gabriel and Soares Neto, Carlos and Milidiú, Ruy and Colcher, Sérgio},\n  month = oct,\n  year = {2018},\n  doi = {10.13140/RG.2.2.32903.24481}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Deep Learning research has allowed significant advancement of various segments of multimedia, especially in tasks related to speech processing, hearing and computational vision. However, some video services are still focused only on the traditional use of media (capture, storage, transmission and presentation). In this paper, we discuss our ongoing research towards a DLaS, i.e. Deep Learning as a Service. This way, we present the state of art in video classification and recognition. Then we propose the VideoRecognition as DLaS to support the tasks such as: image classification and video scenes, object detection and facial recognition. We discuss the usage of the proposed service in the context of the video@RNP repository. Our main contributions consist on dicussussions over the state of art and it usage in nowdays multimedia services.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-desenvolvendomodelosdedeeplearningparaaplicaesmultimdianotensorflow-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Desenvolvendo Modelos de Deep Learning para Aplicações Multimídia no Tensorflow.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Busson, A.; Figueiredo, L.; Noronha Pereira dos Santos, G.; Damasceno, A.; Colcher, S.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  In pages 67–116. October 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.5753/sbc.455.7.03\"\n     onclick=\"log_download('busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-desenvolvendomodelosdedeeplearningparaaplicaesmultimdianotensorflow-2018', 'http://doi.org/10.5753/sbc.455.7.03', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-desenvolvendomodelosdedeeplearningparaaplicaesmultimdianotensorflow-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('busson-figueiredo-noronhapereiradossantos-damasceno-colcher-milidi-desenvolvendomodelosdedeeplearningparaaplicaesmultimdianotensorflow-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{busson_desenvolvendo_2018,\n  title = {Desenvolvendo {Modelos} de {Deep} {Learning} para {Aplicações} {Multimídia} no {Tensorflow}},\n  isbn = {978-85-7669-455-7},\n  abstract = {The availability of massive quantities of data, combined with increasing computational capabilities, makes it possible to develop more precise Machine Learning algorithms. These new tools provide advances in areas such as Natural Language Processing and Computer Vision, allowing efficient processing of images, text and audio. Now, cognitive functionalities, such as learning, recognition and detection, can be used in multimedia applications to create mechanisms beyond traditional capture, streaming and presenta- tion uses. Methods based on Deep Learning became state-of-the-art in several Multi- media challenges. This short course presents the grounds and ways to develop models using Deep Learning. It prepares the participant to: (1) understand and develop mod- els based on Deep Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, including LSTM and GRU; (2) apply the Deep Learning models to solve problems within the multimedia domain like Image Classification, Facial Recogni- tion, Object Detection, Video Scenes Classification. The Python programming language is shown alongside TensorFlow, a package for developing Deep Learning models.},\n  author = {Busson, Antonio and Figueiredo, Lucas and Noronha Pereira dos Santos, Gabriel and Damasceno, Andre and Colcher, Sérgio and Milidiú, Ruy},\n  month = oct,\n  year = {2018},\n  doi = {10.5753/sbc.455.7.03},\n  pages = {67--116}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The availability of massive quantities of data, combined with increasing computational capabilities, makes it possible to develop more precise Machine Learning algorithms. These new tools provide advances in areas such as Natural Language Processing and Computer Vision, allowing efficient processing of images, text and audio. Now, cognitive functionalities, such as learning, recognition and detection, can be used in multimedia applications to create mechanisms beyond traditional capture, streaming and presenta- tion uses. Methods based on Deep Learning became state-of-the-art in several Multi- media challenges. This short course presents the grounds and ways to develop models using Deep Learning. It prepares the participant to: (1) understand and develop mod- els based on Deep Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, including LSTM and GRU; (2) apply the Deep Learning models to solve problems within the multimedia domain like Image Classification, Facial Recogni- tion, Object Detection, Video Scenes Classification. The Python programming language is shown alongside TensorFlow, a package for developing Deep Learning models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"fernandes-milidi-entropyguidedfeaturegenerationforlargemarginstructuredlearning-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Entropy-Guided Feature Generation for Large Margin Structured Learning.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fernandes, E.;  and Milidiú, R.\n</span>\n\n  \n\n</span>\n\n  <i>Monografias em Ciência da Computação</i>. February 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.17771/PUCRio.DImcc.24327\"\n     onclick=\"log_download('fernandes-milidi-entropyguidedfeaturegenerationforlargemarginstructuredlearning-2018', 'http://doi.org/10.17771/PUCRio.DImcc.24327', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fernandes-milidi-entropyguidedfeaturegenerationforlargemarginstructuredlearning-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fernandes-milidi-entropyguidedfeaturegenerationforlargemarginstructuredlearning-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{fernandes_entropy-guided_2018,\n  title = {Entropy-{Guided} {Feature} {Generation} for {Large} {Margin} {Structured} {Learning}},\n  doi = {10.17771/PUCRio.DImcc.24327},\n  journal = {Monografias em Ciência da Computação},\n  author = {Fernandes, Eraldo and Milidiú, Ruy},\n  month = feb,\n  year = {2018}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014', 'https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200', event);\">\n    Latent Trees for Coreference Resolution.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fernandes, E. R.; dos Santos, C. N.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  <i>Computational Linguistics</i>, 40(4): 801–835. April 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200\"\n     onclick=\"log_download('fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014', 'https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Latent Trees for Coreference Resolution [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1162/COLI_a_00200\"\n     onclick=\"log_download('fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014', 'http://doi.org/10.1162/COLI_a_00200', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fernandes-dossantos-milidi-latenttreesforcoreferenceresolution-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{fernandes_latent_2014,\n  title = {Latent {Trees} for {Coreference} {Resolution}},\n  volume = {40},\n  issn = {0891-2017},\n  url = {https://www.mitpressjournals.org/doi/10.1162/COLI_a_00200},\n  doi = {10.1162/COLI_a_00200},\n  abstract = {We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5\\% error reduction, and is the best performing system for each of the three languages.},\n  number = {4},\n  urldate = {2020-03-02},\n  journal = {Computational Linguistics},\n  author = {Fernandes, Eraldo Rezende and dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},\n  month = apr,\n  year = {2014},\n  pages = {801--835}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task datasets, which comprise three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58.69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60.15, which corresponds to a 3.5% error reduction, and is the best performing system for each of the three languages.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167923612002448\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013', 'http://www.sciencedirect.com/science/article/pii/S0167923612002448', event);\">\n    Trading team composition for the intraday multistock market.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Alvim, L. G. M.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  <i>Decision Support Systems</i>, 54(2): 838–845. January 2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0167923612002448\"\n     onclick=\"log_download('alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013', 'http://www.sciencedirect.com/science/article/pii/S0167923612002448', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Trading team composition for the intraday multistock market [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.dss.2012.09.009\"\n     onclick=\"log_download('alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013', 'http://doi.org/10.1016/j.dss.2012.09.009', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('alvim-milidi-tradingteamcompositionfortheintradaymultistockmarket-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{alvim_trading_2013,\n  title = {Trading team composition for the intraday multistock market},\n  volume = {54},\n  issn = {0167-9236},\n  url = {http://www.sciencedirect.com/science/article/pii/S0167923612002448},\n  doi = {10.1016/j.dss.2012.09.009},\n  abstract = {Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM\\&amp;FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26\\% annualized profit, outperforming by 380.07\\% the chosen baseline strategy with a 16.07\\% profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US\\$ 50,000, the MSR strategy provides a profit of at least 38.63\\%.},\n  language = {en},\n  number = {2},\n  urldate = {2020-03-02},\n  journal = {Decision Support Systems},\n  author = {Alvim, Leandro G. M. and Milidiú, Ruy L.},\n  month = jan,\n  year = {2013},\n  pages = {838--845}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM&FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26% annualized profit, outperforming by 380.07% the chosen baseline strategy with a 16.07% profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US$ 50,000, the MSR strategy provides a profit of at least 38.63%.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1145/2245276.2245367\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012', 'https://doi.org/10.1145/2245276.2245367', event);\">\n    Double dip map-reduce for processing cross validation jobs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Moret, D.; Breitman, K.; Amorim, E.; Talavera, J.; Milidiu, R.;  and Viterbo, J.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 27th Annual ACM Symposium on Applied Computing</i>, of <i>SAC '12</i>, pages 473–477, Trento, Italy, March 2012. Association for Computing Machinery\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1145/2245276.2245367\"\n     onclick=\"log_download('moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012', 'https://doi.org/10.1145/2245276.2245367', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Double dip map-reduce for processing cross validation jobs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2245276.2245367\"\n     onclick=\"log_download('moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012', 'http://doi.org/10.1145/2245276.2245367', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('moret-breitman-amorim-talavera-milidiu-viterbo-doubledipmapreduceforprocessingcrossvalidationjobs-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{moret_double_2012,\n  address = {Trento, Italy},\n  series = {{SAC} &#x27;12},\n  title = {Double dip map-reduce for processing cross validation jobs},\n  isbn = {978-1-4503-0857-1},\n  url = {https://doi.org/10.1145/2245276.2245367},\n  doi = {10.1145/2245276.2245367},\n  abstract = {Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.},\n  urldate = {2020-03-02},\n  booktitle = {Proceedings of the 27th {Annual} {ACM} {Symposium} on {Applied} {Computing}},\n  publisher = {Association for Computing Machinery},\n  author = {Moret, Danilo and Breitman, Karin and Amorim, Evelin and Talavera, Jose and Milidiu, Ruy and Viterbo, Jose},\n  month = mar,\n  year = {2012},\n  pages = {473--477}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fernandes-motta-milidi-quotationextractionforportuguese-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Quotation extraction for portuguese.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fernandes, W. P. D.; Motta, E.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology</i>,  2011. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fernandes-motta-milidi-quotationextractionforportuguese-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fernandes-motta-milidi-quotationextractionforportuguese-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{fernandes_quotation_2011,\n  title = {Quotation extraction for portuguese},\n  booktitle = {Proceedings of the 8th {Brazilian} {Symposium} in {Information} and {Human} {Language} {Technology}},\n  author = {Fernandes, William Paulo Ducca and Motta, Eduardo and Milidiú, Ruy Luiz},\n  year = {2011}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010', 'https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y', event);\">\n    RelHunter : a machine learning method for relation extraction from text.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fernandes, E. R.; Milidiú, R. L.;  and Rentería, R. P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of the Brazilian Computer Society</i>, 16(3): 191–199. September 2010.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y\"\n     onclick=\"log_download('fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010', 'https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"RelHunter : a machine learning method for relation extraction from text [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s13173-010-0018-y\"\n     onclick=\"log_download('fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010', 'http://doi.org/10.1007/s13173-010-0018-y', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fernandes-milidi-rentera-relhunteramachinelearningmethodforrelationextractionfromtext-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{fernandes_relhunter_2010,\n  title = {{RelHunter} : a machine learning method for relation extraction from text},\n  volume = {16},\n  copyright = {2010 The Brazilian Computer Society},\n  issn = {1678-4804},\n  shorttitle = {{RelHunter}},\n  url = {https://journal-bcs.springeropen.com/articles/10.1007/s13173-010-0018-y},\n  doi = {10.1007/s13173-010-0018-y},\n  abstract = {We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter’s key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14\\% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.},\n  language = {en},\n  number = {3},\n  urldate = {2020-03-02},\n  journal = {Journal of the Brazilian Computer Society},\n  author = {Fernandes, Eraldo R. and Milidiú, Ruy L. and Rentería, Raúl P.},\n  month = sep,\n  year = {2010},\n  pages = {191--199}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We propose RelHunter, a machine learning-based method for the extraction of structured information from text. RelHunter’s key idea is to model the target structures as a relation over entities. Hence, the modeling effort is reduced to the identification of entities and the generation of a candidate relation, which are simpler problems than the original one. RelHunter fits a very broad spectrum of complex computational linguistic problems. We apply it to five tasks: phrase chunking, clause identification, hedge detection, quotation extraction, and dependency parsing. We compare RelHunter to token classification approaches through several computational experiments on seven multilingual corpora. RelHunter outperforms the token classification approaches by 2.14% on average. Moreover, we compare the derived systems against state-of-the-art systems for each corpus. Our systems achieve state-of-the-art performances for three corpora: Portuguese phrase chunking, Portuguese clause identification, and English quotation extraction. Additionally, the derived systems show good quality performance for the other four corpora.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dossantosteixeira-milidi-datastreamanomalydetectionthroughprincipalsubspacetracking-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Data stream anomaly detection through principal subspace tracking.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  dos Santos Teixeira, P. H.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 2010 ACM Symposium on Applied Computing</i>, pages 1609–1616,  2010. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dossantosteixeira-milidi-datastreamanomalydetectionthroughprincipalsubspacetracking-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dossantosteixeira-milidi-datastreamanomalydetectionthroughprincipalsubspacetracking-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{dos_santos_teixeira_data_2010,\n  title = {Data stream anomaly detection through principal subspace tracking},\n  booktitle = {Proceedings of the 2010 {ACM} {Symposium} on {Applied} {Computing}},\n  author = {dos Santos Teixeira, Pedro Henriques and Milidiú, Ruy Luiz},\n  year = {2010},\n  pages = {1609--1616}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"alvim-dossantos-milidiu-dailyvolumeforecastingusinghighfrequencypredictors-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Daily volume forecasting using high frequency predictors.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Alvim, L. G.; dos Santos, C. N.;  and Milidiu, R. L.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 10th IASTED International Conference</i>, volume 674, pages 248,  2010. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/alvim-dossantos-milidiu-dailyvolumeforecastingusinghighfrequencypredictors-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('alvim-dossantos-milidiu-dailyvolumeforecastingusinghighfrequencypredictors-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{alvim_daily_2010,\n  title = {Daily volume forecasting using high frequency predictors},\n  volume = {674},\n  booktitle = {Proceedings of the 10th {IASTED} {International} {Conference}},\n  author = {Alvim, Leandro GM and dos Santos, Cicero N. and Milidiu, Ruy L.},\n  year = {2010},\n  pages = {248}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2009\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2009')\"\n      onkeypress=\"toggleGroup('group_2009')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2009</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dossantos-milidi-entropyguidedtransformationlearning-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Entropy guided transformation learning.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  dos Santos, C. N.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  In <i>Foundations of Computational, Intelligence Volume 1</i>, pages 159–184. Springer,  2009.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dossantos-milidi-entropyguidedtransformationlearning-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dossantos-milidi-entropyguidedtransformationlearning-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{dos_santos_entropy_2009,\n  title = {Entropy guided transformation learning},\n  booktitle = {Foundations of {Computational}, {Intelligence} {Volume} 1},\n  publisher = {Springer},\n  author = {dos Santos, Cícero Nogueira and Milidiú, Ruy Luiz},\n  year = {2009},\n  pages = {159--184}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2008\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2008')\"\n      onkeypress=\"toggleGroup('group_2008')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2008</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.scielo.br/scielo.php?script=sci_abstract&amp;pid=S0104-65002008000400003&amp;lng=en&amp;nrm=iso&amp;tlng=en\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008', 'http://www.scielo.br/scielo.php?script=sci_abstract&amp;pid=S0104-65002008000400003&amp;lng=en&amp;nrm=iso&amp;tlng=en', event);\">\n    Portuguese corpus-based learning using ETL.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Santos, C. N. d.;  and Duarte, J. C.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of the Brazilian Computer Society</i>, 14(4): 17–27. December 2008.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.scielo.br/scielo.php?script=sci_abstract&amp;pid=S0104-65002008000400003&amp;lng=en&amp;nrm=iso&amp;tlng=en\"\n     onclick=\"log_download('milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008', 'http://www.scielo.br/scielo.php?script=sci_abstract&amp;pid=S0104-65002008000400003&amp;lng=en&amp;nrm=iso&amp;tlng=en', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Portuguese corpus-based learning using ETL [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/BF03192569\"\n     onclick=\"log_download('milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008', 'http://doi.org/10.1007/BF03192569', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-santos-duarte-portuguesecorpusbasedlearningusingetl-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_portuguese_2008,\n  title = {Portuguese corpus-based learning using {ETL}},\n  volume = {14},\n  issn = {0104-6500},\n  url = {http://www.scielo.br/scielo.php?script=sci_abstract&amp;pid=S0104-65002008000400003&amp;lng=en&amp;nrm=iso&amp;tlng=en},\n  doi = {10.1007/BF03192569},\n  language = {en},\n  number = {4},\n  urldate = {2020-03-02},\n  journal = {Journal of the Brazilian Computer Society},\n  author = {Milidiú, Ruy Luiz and Santos, Cícero Nogueira dos and Duarte, Julio Cesar},\n  month = dec,\n  year = {2008},\n  pages = {17--27}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"breitman-brauner-casanova-milidi-gazola-perazolo-instancebasedontologymapping-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Instance-Based Ontology Mapping.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Breitman, K. K.; Brauner, D.; Casanova, M. A.; Milidiú, R.; Gazola, A.;  and Perazolo, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems (ease 2008)</i>, pages 67–74, March 2008. \n  <span class=\"note\">ISSN: 2168-1872</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/EASe.2008.18\"\n     onclick=\"log_download('breitman-brauner-casanova-milidi-gazola-perazolo-instancebasedontologymapping-2008', 'http://doi.org/10.1109/EASe.2008.18', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/breitman-brauner-casanova-milidi-gazola-perazolo-instancebasedontologymapping-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('breitman-brauner-casanova-milidi-gazola-perazolo-instancebasedontologymapping-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{breitman_instance-based_2008,\n  title = {Instance-{Based} {Ontology} {Mapping}},\n  doi = {10.1109/EASe.2008.18},\n  abstract = {We have explored the use of formal ontology and alignment techniques as a general approach to mediate and reconcile different representations. This approach proved to be very effective when fueled by ontologies rich in detail, (properties, restrictions, attributes and axioms that hold among classes), but performs poorly when available representations are incomplete or lacking, which is often the case in real life settings. In this paper we propose an enhancement to the original approach, based on a, instance-based matching technique.},\n  booktitle = {Fifth {IEEE} {Workshop} on {Engineering} of {Autonomic} and {Autonomous} {Systems} (ease 2008)},\n  author = {Breitman, Karin K. and Brauner, Daniela and Casanova, Marco Antonio and Milidiú, Ruy and Gazola, Alexandre and Perazolo, Marcelo},\n  month = mar,\n  year = {2008},\n  note = {ISSN: 2168-1872},\n  pages = {67--74}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We have explored the use of formal ontology and alignment techniques as a general approach to mediate and reconcile different representations. This approach proved to be very effective when fueled by ontologies rich in detail, (properties, restrictions, attributes and axioms that hold among classes), but performs poorly when available representations are incomplete or lacking, which is often the case in real life settings. In this paper we propose an enhancement to the original approach, based on a, instance-based matching technique.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-dossantos-duarte-phrasechunkingusingentropyguidedtransformationlearning-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Phrase chunking using entropy guided transformation learning.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; dos Santos, C.;  and Duarte, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of ACL-08: HLT</i>, pages 647–655,  2008. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-dossantos-duarte-phrasechunkingusingentropyguidedtransformationlearning-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-dossantos-duarte-phrasechunkingusingentropyguidedtransformationlearning-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{milidiu_phrase_2008,\n  title = {Phrase chunking using entropy guided transformation learning},\n  booktitle = {Proceedings of {ACL}-08: {HLT}},\n  author = {Milidiú, Ruy Luiz and dos Santos, Cicero and Duarte, Julio C.},\n  year = {2008},\n  pages = {647--655}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2007\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2007')\"\n      onkeypress=\"toggleGroup('group_2007')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2007</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s00180-007-0040-5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007', 'https://doi.org/10.1007/s00180-007-0040-5', event);\">\n    MKPLS approach: switching strategies for the non-linear multi-kernel PLSR.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Rentería, R.; Milidiú, R.;  and Souza, R.\n</span>\n\n  \n\n</span>\n\n  <i>Computational Statistics</i>, 22(2): 323–330. July 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s00180-007-0040-5\"\n     onclick=\"log_download('rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007', 'https://doi.org/10.1007/s00180-007-0040-5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"MKPLS approach: switching strategies for the non-linear multi-kernel PLSR [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s00180-007-0040-5\"\n     onclick=\"log_download('rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007', 'http://doi.org/10.1007/s00180-007-0040-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('rentera-milidi-souza-mkplsapproachswitchingstrategiesforthenonlinearmultikernelplsr-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{renteria_mkpls_2007,\n  title = {{MKPLS} approach: switching strategies for the non-linear multi-kernel {PLSR}},\n  volume = {22},\n  issn = {1613-9658},\n  shorttitle = {{MKPLS} approach},\n  url = {https://doi.org/10.1007/s00180-007-0040-5},\n  doi = {10.1007/s00180-007-0040-5},\n  abstract = {We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.},\n  language = {en},\n  number = {2},\n  urldate = {2020-03-02},\n  journal = {Computational Statistics},\n  author = {Rentería, Raúl and Milidiú, Ruy and Souza, Rafael},\n  month = jul,\n  year = {2007},\n  pages = {323--330}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present two strategies to determine the kernel switching order for the non-linear multi-kernel PLSR algorithm. The multi-kernel PLS (MKPLS) algorithm builds upon a one kernel PLSR which uses a kernel matrix to hold the inner products of the projection of the independent data set onto a feature space. After building a PLSR model, MKPLS deflates the kernel matrix so that only that part which cannot be predicted by the model remains. This remainder is projected onto a different feature space and a new PLSR model is built. The switching algorithms presented for this approach address two questions: which kernel should be used at each iteration and; how many factors should be extracted before switching to another kernel.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-duarte-nogueiradossantos-tbltemplateselectionanevolutionaryapproach-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  TBL Template Selection: An Evolutionary Approach.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Duarte, J. C.;  and Nogueira dos Santos, C.\n</span>\n\n  \n\n</span>\n\n  In Borrajo, D.; Castillo, L.;  and Corchado, J. M., editor(s), <i>Current Topics in Artificial Intelligence</i>, of <i>Lecture Notes in Computer Science</i>, pages 180–189, Berlin, Heidelberg,  2007. Springer\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-540-75271-4_19\"\n     onclick=\"log_download('milidi-duarte-nogueiradossantos-tbltemplateselectionanevolutionaryapproach-2007', 'http://doi.org/10.1007/978-3-540-75271-4_19', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-duarte-nogueiradossantos-tbltemplateselectionanevolutionaryapproach-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-duarte-nogueiradossantos-tbltemplateselectionanevolutionaryapproach-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{milidiu_tbl_2007,\n  address = {Berlin, Heidelberg},\n  series = {Lecture {Notes} in {Computer} {Science}},\n  title = {{TBL} {Template} {Selection}: {An} {Evolutionary} {Approach}},\n  isbn = {978-3-540-75271-4},\n  shorttitle = {{TBL} {Template} {Selection}},\n  doi = {10.1007/978-3-540-75271-4_19},\n  abstract = {Transformation Based Learning (TBL) is an intensively Machine Learning algorithm frequently used in Natural Language Processing. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template selection process. We show some empirical evidence that our approach provides template sets with almost the same quality as human built templates.},\n  language = {en},\n  booktitle = {Current {Topics} in {Artificial} {Intelligence}},\n  publisher = {Springer},\n  author = {Milidiú, Ruy Luiz and Duarte, Julio Cesar and Nogueira dos Santos, Cícero},\n  editor = {Borrajo, Daniel and Castillo, Luis and Corchado, Juan Manuel},\n  year = {2007},\n  pages = {180--189}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Transformation Based Learning (TBL) is an intensively Machine Learning algorithm frequently used in Natural Language Processing. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template selection process. We show some empirical evidence that our approach provides template sets with almost the same quality as human built templates.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2005\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2005')\"\n      onkeypress=\"toggleGroup('group_2005')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2005</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"sardinha-milidi-paranhos-cunha-delucena-anagentbasedarchitectureforhighlycompetitiveelectronicmarkets-2005\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  An Agent Based Architecture for Highly Competitive Electronic Markets.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sardinha, J. A. R.; Milidiú, R. L.; Paranhos, P. M.; Cunha, P. M.;  and de Lucena, C. J. P.\n</span>\n\n  \n\n</span>\n\n  In <i>FLAIRS Conference</i>, pages 326–332,  2005. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sardinha-milidi-paranhos-cunha-delucena-anagentbasedarchitectureforhighlycompetitiveelectronicmarkets-2005\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sardinha-milidi-paranhos-cunha-delucena-anagentbasedarchitectureforhighlycompetitiveelectronicmarkets-2005')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sardinha_agent_2005,\n  title = {An {Agent} {Based} {Architecture} for {Highly} {Competitive} {Electronic} {Markets}.},\n  booktitle = {{FLAIRS} {Conference}},\n  author = {Sardinha, José Alberto RP and Milidiú, Ruy Luiz and Paranhos, Patrick M. and Cunha, Pedro M. and de Lucena, Carlos José Pereira},\n  year = {2005},\n  pages = {326--332}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidiu-rentera-dplsandpplstwoplsalgorithmsforlargedatasets-2005\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  DPLS and PPLS: two PLS algorithms for large data sets.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiu, R. L.;  and Renterı́a, R. P.\n</span>\n\n  \n\n</span>\n\n  <i>Computational statistics & data analysis</i>, 48(1): 125–138.  2005.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidiu-rentera-dplsandpplstwoplsalgorithmsforlargedatasets-2005\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidiu-rentera-dplsandpplstwoplsalgorithmsforlargedatasets-2005')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_dpls_2005,\n  title = {{DPLS} and {PPLS}: two {PLS} algorithms for large data sets},\n  volume = {48},\n  shorttitle = {{DPLS} and {PPLS}},\n  number = {1},\n  journal = {Computational statistics \\&amp; data analysis},\n  author = {Milidiu, Ruy L. and Renterı́a, Raúl P.},\n  year = {2005},\n  pages = {125--138}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2003\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2003')\"\n      onkeypress=\"toggleGroup('group_2003')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2003</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"sardinha-ribeiro-milidi-delucena-anobjectorientedframeworkforbuildingsoftwareagents-2003\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  An Object-Oriented Framework for Building Software Agents.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sardinha, J. A. R. P.; Ribeiro, P. C.; Milidiú, R. L.;  and de Lucena, C. J. P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Object Technology</i>, 2(1): 85–97.  2003.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sardinha-ribeiro-milidi-delucena-anobjectorientedframeworkforbuildingsoftwareagents-2003\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sardinha-ribeiro-milidi-delucena-anobjectorientedframeworkforbuildingsoftwareagents-2003')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{sardinha_object-oriented_2003,\n  title = {An {Object}-{Oriented} {Framework} for {Building} {Software} {Agents}.},\n  volume = {2},\n  number = {1},\n  journal = {Journal of Object Technology},\n  author = {Sardinha, José Alberto Rodrigues Pereira and Ribeiro, Paula Clark and Milidiú, Ruy Luiz and de Lucena, Carlos José Pereira},\n  year = {2003},\n  pages = {85--97}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-pessoa-laber-thecomplexityofmakespanminimizationforpipelinetransportation-2003\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  The complexity of makespan minimization for pipeline transportation.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Pessoa, A. A.;  and Laber, E. S.\n</span>\n\n  \n\n</span>\n\n  <i>Theoretical Computer Science</i>, 306(1-3): 339–351.  2003.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-pessoa-laber-thecomplexityofmakespanminimizationforpipelinetransportation-2003\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-pessoa-laber-thecomplexityofmakespanminimizationforpipelinetransportation-2003')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_complexity_2003,\n  title = {The complexity of makespan minimization for pipeline transportation},\n  volume = {306},\n  number = {1-3},\n  journal = {Theoretical Computer Science},\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\n  year = {2003},\n  pages = {339--351}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2001\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2001')\"\n      onkeypress=\"toggleGroup('group_2001')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2001</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidi-laber-boundingtheinefficiencyoflengthrestrictedprefixcodes-2001\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Bounding the inefficiency of length-restricted prefix codes.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.;  and Laber, E. S.\n</span>\n\n  \n\n</span>\n\n  <i>Algorithmica</i>, 31(4): 513–529.  2001.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-laber-boundingtheinefficiencyoflengthrestrictedprefixcodes-2001\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-laber-boundingtheinefficiencyoflengthrestrictedprefixcodes-2001')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_bounding_2001,\n  title = {Bounding the inefficiency of length-restricted prefix codes},\n  volume = {31},\n  number = {4},\n  journal = {Algorithmica},\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},\n  year = {2001},\n  pages = {513--529}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-pessoa-laber-threespaceeconomicalalgorithmsforcalculatingminimumredundancyprefixcodes-2001\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Three space-economical algorithms for calculating minimum-redundancy prefix codes.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Pessoa, A. A.;  and Laber, E. S.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Transactions on Information Theory</i>, 47(6): 2185–2198.  2001.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-pessoa-laber-threespaceeconomicalalgorithmsforcalculatingminimumredundancyprefixcodes-2001\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-pessoa-laber-threespaceeconomicalalgorithmsforcalculatingminimumredundancyprefixcodes-2001')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_three_2001,\n  title = {Three space-economical algorithms for calculating minimum-redundancy prefix codes},\n  volume = {47},\n  number = {6},\n  journal = {IEEE Transactions on Information Theory},\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\n  year = {2001},\n  pages = {2185--2198}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2000\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2000')\"\n      onkeypress=\"toggleGroup('group_2000')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2000</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidi-laber-thewarmupalgorithmalagrangianconstructionoflengthrestrictedhuffmancodes-2000\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  The warm-up algorithm: a Lagrangian construction of length restricted Huffman codes.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.;  and Laber, E. S.\n</span>\n\n  \n\n</span>\n\n  <i>SIAM Journal on Computing</i>, 30(5): 1405–1426.  2000.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-laber-thewarmupalgorithmalagrangianconstructionoflengthrestrictedhuffmancodes-2000\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-laber-thewarmupalgorithmalagrangianconstructionoflengthrestrictedhuffmancodes-2000')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_warm-up_2000,\n  title = {The warm-up algorithm: a {Lagrangian} construction of length restricted {Huffman} codes},\n  volume = {30},\n  shorttitle = {The warm-up algorithm},\n  number = {5},\n  journal = {SIAM Journal on Computing},\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany},\n  year = {2000},\n  pages = {1405--1426}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1999\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1999')\"\n      onkeypress=\"toggleGroup('group_1999')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1999</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidi-machado-rentera-timeseriesforecastingthroughwaveletstransformationandamixtureofexpertmodels-1999\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Time-series forecasting through wavelets transformation and a mixture of expert models.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Machado, R. J.;  and Renterı́a, R. P.\n</span>\n\n  \n\n</span>\n\n  <i>Neurocomputing</i>, 28(1-3): 145–156.  1999.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-machado-rentera-timeseriesforecastingthroughwaveletstransformationandamixtureofexpertmodels-1999\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-machado-rentera-timeseriesforecastingthroughwaveletstransformationandamixtureofexpertmodels-1999')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_time-series_1999,\n  title = {Time-series forecasting through wavelets transformation and a mixture of expert models},\n  volume = {28},\n  number = {1-3},\n  journal = {Neurocomputing},\n  author = {Milidiú, Ruy L. and Machado, Ricardo J. and Renterı́a, Raúl P.},\n  year = {1999},\n  pages = {145--156}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-pessoa-laber-efficientimplementationofthewarmupalgorithmfortheconstructionoflengthrestrictedprefixcodes-1999\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Efficient implementation of the WARM-UP algorithm for the construction of length-restricted prefix codes.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Pessoa, A. A.;  and Laber, E. S.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Algorithm Engineering and Experimentation</i>, pages 1–17,  1999. Springer\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-pessoa-laber-efficientimplementationofthewarmupalgorithmfortheconstructionoflengthrestrictedprefixcodes-1999\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-pessoa-laber-efficientimplementationofthewarmupalgorithmfortheconstructionoflengthrestrictedprefixcodes-1999')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{milidiu_efficient_1999,\n  title = {Efficient implementation of the {WARM}-{UP} algorithm for the construction of length-restricted prefix codes},\n  booktitle = {Workshop on {Algorithm} {Engineering} and {Experimentation}},\n  publisher = {Springer},\n  author = {Milidiú, Ruy Luiz and Pessoa, Artur Alves and Laber, Eduardo Sany},\n  year = {1999},\n  pages = {1--17}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"milidi-laber-pessoa-boundingthecompressionlossofthefgkalgorithm-1999\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Bounding the compression loss of the FGK algorithm.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiú, R. L.; Laber, E. S.;  and Pessoa, A. A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Algorithms</i>, 32(2): 195–211.  1999.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidi-laber-pessoa-boundingthecompressionlossofthefgkalgorithm-1999\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidi-laber-pessoa-boundingthecompressionlossofthefgkalgorithm-1999')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_bounding_1999,\n  title = {Bounding the compression loss of the {FGK} algorithm},\n  volume = {32},\n  number = {2},\n  journal = {Journal of Algorithms},\n  author = {Milidiú, Ruy Luiz and Laber, Eduardo Sany and Pessoa, Artur Alves},\n  year = {1999},\n  pages = {195--211}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1993\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1993')\"\n      onkeypress=\"toggleGroup('group_1993')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1993</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dasilva-milidi-belieffunctionmodelforinformationretrieval-1993\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Belief function model for information retrieval.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  da Silva, W. T.;  and Milidiú, R. L.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of the American Society for Information Science</i>, 44(1): 10–18.  1993.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dasilva-milidi-belieffunctionmodelforinformationretrieval-1993\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dasilva-milidi-belieffunctionmodelforinformationretrieval-1993')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{da_silva_belief_1993,\n  title = {Belief function model for information retrieval},\n  volume = {44},\n  number = {1},\n  journal = {Journal of the American Society for Information Science},\n  author = {da Silva, Wagner Teixeira and Milidiú, Ruy Luiz},\n  year = {1993},\n  pages = {10--18}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1992\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1992')\"\n      onkeypress=\"toggleGroup('group_1992')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1992</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dasilva-milidiu-algorithmsforcombiningbelieffunctions-1992\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Algorithms for combining belief functions.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  da Silva, W. T.;  and Milidiu, R. L.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Approximate Reasoning</i>, 7(1-2): 73–94.  1992.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dasilva-milidiu-algorithmsforcombiningbelieffunctions-1992\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dasilva-milidiu-algorithmsforcombiningbelieffunctions-1992')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{da_silva_algorithms_1992,\n  title = {Algorithms for combining belief functions},\n  volume = {7},\n  number = {1-2},\n  journal = {International Journal of Approximate Reasoning},\n  author = {da Silva, Wagner Teixeira and Milidiu, Ruy Luiz},\n  year = {1992},\n  pages = {73--94}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1987\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1987')\"\n      onkeypress=\"toggleGroup('group_1987')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1987</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"milidiu-thecomputationofcompounddistributionswithtwosidedseveritiesnegativebinomialiterativerecursiveasymptoticsum-1987\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  The Computation Of Compound Distributions With Two-Sided Severities (Negative-Binomial Iterative, Recursive, Asymptotic, Sum).\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Milidiu, R. L.\n</span>\n\n  \n\n</span>\n\n  .  1987.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/milidiu-thecomputationofcompounddistributionswithtwosidedseveritiesnegativebinomialiterativerecursiveasymptoticsum-1987\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('milidiu-thecomputationofcompounddistributionswithtwosidedseveritiesnegativebinomialiterativerecursiveasymptoticsum-1987')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{milidiu_computation_1987,\n  title = {The {Computation} {Of} {Compound} {Distributions} {With} {Two}-{Sided} {Severities} ({Negative}-{Binomial} {Iterative}, {Recursive}, {Asymptotic}, {Sum})},\n  author = {Milidiu, Ruy Luiz},\n  year = {1987}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);