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ENIAC 2024<\/h2>\n
[\/et_pb_text][dsm_business_hours _builder_version=”4.25.0″ _module_preset=”default” global_colors_info=”{}”][\/dsm_business_hours][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.25.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/05\/financial-planning-20.png” title_text=”financial-planning-20″ _builder_version=”4.25.0″ _module_preset=”default” positioning=”absolute” position_origin_a=”center_left” custom_margin=”0px|-30%|0px||false|false” custom_margin_tablet=”|0%||0%|false|true” custom_margin_phone=”|0%||0%|false|true” custom_margin_last_edited=”on|tablet” custom_padding=”0px||0px||false|false” locked=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ bg_img_2=”https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/04\/business-05.jpg” admin_label=”About” _builder_version=”4.16″ background_image=”https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/04\/investing-15.jpg” background_size=”initial” background_position=”top_center” custom_padding=”||19px|||” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.25.0″ custom_margin=”0px||||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.27.0″ text_font=”||||||||” text_font_size=”15px” text_line_height=”2em” header_2_font=”Glegoo||||||||” header_2_font_size=”40px” header_2_line_height=”1.4em” header_4_font=”|700||on|||||” header_4_text_color=”#1d48c0″ header_4_font_size=”16px” header_4_letter_spacing=”2px” header_4_line_height=”1.6em” header_2_font_size_tablet=”30px” header_2_font_size_phone=”22px” header_2_font_size_last_edited=”on|phone” header_4_font_size_tablet=”” header_4_font_size_phone=”14px” header_4_font_size_last_edited=”on|phone” locked=”off” global_colors_info=”{}”]<\/p>\n
ENIAC<\/span>\u00a0<\/span>2024 is the 21st edition of a series of successful meetings bringing together Artificial Intelligence and Computational Intelligence, supported by Brazilian Special Interest Groups on Artificial Intelligence and Computational Intelligence from the Brazilian Computer Society.<\/p>\n<\/div>\n \n<\/div>\n ENIAC<\/span>\u00a0<\/span>is part of the 34th Brazilian Conference on Intelligent Systems (BRACIS) \u00a0to be held in Bel\u00e9m, PA, Brazil \u2013\u00a0 from November 17th to 21st, 2024. This Brazilian meeting provides a forum for researchers, practitioners, educators, and students to present and discuss innovations, trends, experiences, and developments in the \u00a0Artificial Intelligence and Computational Intelligence fields. In particular, it is the ideal event for postgraduate and undergraduate students to submit and present their ongoing results, even their first papers.<\/p>\n <\/p>\n Plenary Speakers<\/b><\/p>\n <\/p>\n<\/div>\n<\/div>\n Eulanda Miranda dos Santos (IComp\/UFAM) TITLE:<\/b> INNOVATIONS AND CHALLENGES OF APPLICATION-DRIVENmachine learning: AN ANIMAL SPECIES CLASSIFICATION USE CASE<\/p>\n ABSTRACT<\/b>: It is not unusual that machine learning methods considered state-of-the-art fail when used in real-world tasks. This happens because general methods are designed based on their performance on standardized benchmarks, while real-world data present several specific challenges. The focus of application-driven machine learning is developing methods to tackle a real-world problem, or a family of related problems, and their properties. Despite being focused on specific application tasks, application-driven machine learning may contribute to innovative methods, in turn advancing the state of the art in the entire research domain. In this talk, we will discuss the challenges and innovation possibilities of application-driven methods. We will present the problem of animal species classification in camera trap images as a use case to illustrate these issues more specifically.<\/p>\n<\/div>\n <\/p>\n Mauricio G. C. Resende, Ph.D. (UNIFESP-ITA PPG-PO and University of Washington) – S.J. dos Campos, Brazil and Seattle, USA<\/p>\n ———————————————————————————————————<\/p>\n Short Bio: <\/b>Mauricio G. C. Resende\u00a0is and an Affiliate Professor of Industrial and Systems Engineering at the University of Washington and a Senior Professor of the Graduate Program in Operations Research of UNIFESP-ITA.<\/p>\n He grew up in Rio de Janeiro (BR), West Lafayette (IN-US), and Amherst (MA-US). He did his undergraduate training in electrical engineering (systems engineering concentration) at the Pontifical Catholic U. of Rio de Janeiro.\u00a0 He obtained an MS in operations research from Georgia Tech and a PhD in operations research from the U. of California, Berkeley.<\/p>\n He is most known for his work with metaheuristics, in particular GRASP and biased random-key genetic algorithms, as well as for his work with interior point methods for linear programming and network flows.\u00a0 Dr. Resende has published over 200 papers on optimization and holds 15 U.S. patents.\u00a0 He edited the\u00a0Handbook of Heuristics\u00a0(Springer, 2018), the\u00a0Handbook of Optimization in Telecommunications\u00a0(Springer, 2006), the\u00a0Handbook of Massive Data Sets\u00a0(Kluwer, 2002), and the\u00a0Handbook of Applied Optimization\u00a0(Oxford, 2002), and is coauthor of the book\u00a0Optimization by GRASP\u00a0(Springer, 2016). He has a Google Scholar h-index of 87 (over 33,500 Google Scholar citations) and is on the List of Top 2% Scientists of the World (2020-23). He sits on editorial boards of several optimization journals.<\/p>\n He worked at Amazon Research as a Principal Research Scientist in the logistics area from 2014 to 2022. Prior to Amazon, Resende was a Lead Inventive Scientist at the Mathematical Foundations of Computing Department of AT&T Bell Labs and at the Algorithms and Optimization Research Department of AT&T Labs Research from 1988 to 2014. Since 2016, he is Affiliate Professor of Industrial and Systems Engineering at the University of Washington in Seattle and since 1994, a Permanent Member of the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) at Rutgers University as well as a Senior Professor in the UNIFESP-ITA graduate program in operations research since 2023. \u00a0He is a 2016 Fellow of INFORMS and was awarded the 2017 Constantine Carath\u00e9odory Prize.<\/p>\n TITLE: <\/b>RANDOM-KEY OPTIMIZERS (RKO): PROBLEM INDEPENDENT COMBINATORIAL OPTIMIZATION<\/span><\/p>\n ABSTRACT: <\/b>This talk introduces Random-Key Optimizers (RKO), a problem-independent approach for solving combinatorial optimization problems. Random keys are randomly generated real numbers in the interval (0,1]. A random-key vector is a vector of n<\/i><\/em>\u00a0random keys and corresponds to a point in n<\/i><\/em>-dimensional unit hypercube. Solutions of combinatorial optimization problems can be encoded as random-key vectors. By using a decoder, a solution can be retrieved from a random-key vector. For example, by sorting a random-key vector, a permutation will result from the indices of the sorted vector. In a TSP these indices correspond to the cities in a tour. In the random-key optimization problem we seek a point in the n<\/i><\/em>-dimensional unit hypercube that optimizes the decoder function. For a TSP, what is the point in the n<\/i><\/em>-dimensional unit hypercube that decoded with the sorting decoder results in the shortest tour on the n<\/i><\/em>\u00a0cities. We illustrate this concept describing several decoders for different combinational optimization problems, and by presenting several RKOs, including a hybrid system that combines several RKOs into one intelligent system.<\/p>\n <\/p>\n<\/div>\n PAPER FORMAT AND SUBMISSION<\/strong><\/p>\n Manuscripts are limited to twelve (12) pages including text, references, appendices, tables, and figures. Articles may be written either in Portuguese or English, using the SBC article style:<\/span><\/p>\n![\"\"](\"https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/09\/WhatsApp-Image-2024-09-19-at-08.36.56-300x244.jpeg\")
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Short Bio: <\/b>EULANDA MIRANDA DOS SANTOS received a B.Sc. degree in Informatics from Federal University of Para (Brazil), a M.Sc. degree in Informatics from Federal University of Paraiba (Brazil) and a Ph.D. degree in Engineering at \u00c9cole de Technologie Sup\u00e9rieure, University of Quebec (Canada) in 1999, 2002 and 2008, respectively. Currently, she is a Full Professor at Federal University of Amazonas (Brazil). Her research interests include machine learning and computer vision. She develops R&D projects aimed at environmental protection and medical applications.<\/p>\n![\"\"](\"https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/09\/imagem_2024-09-19_175420345-217x300.png\")
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