IJCCI is a joint conference composed of three concurrent conferences: ECTA, FCTA and NCTA. These three conferences are always co-located and held in parallel. Keynote lectures are plenary sessions and can be attended by all IJCCI participants.
Bridging the Emotional Gap - From Objective Representations to Subjective Interpretations
Marie-Jeanne Lesot, Independent Researcher, France
Computational Intelligence in Automotive R & D
Danil Prokhorov, Toyota Tech Center, United States
In Silico Cyclic Phenomena - Fascination through Computation
Bernard De Baets, Ghent University, Belgium
Learning in Non-stationary Environments
Cesare Alippi, Politecnico di Milano, Italy
Bridging the Emotional Gap - From Objective Representations to Subjective Interpretations
Marie-Jeanne Lesot
Independent Researcher
France
Brief Bio
Marie-Jeanne Lesot obtained her PhD in 2005 from the University Pierre et Marie Curie in Paris. Since 2006 she is an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration and, in particular, to model and manage subjective information; they include similarity measures, fuzzy clustering, linguistic summaries, affective computing and information scoring.
Abstract
In the framework of affective computing, emotion mining constitutes a classification task that aims at recognising the emotional content of various types of data including, but not limited to, texts, images or physiological signals. It adds to the traditional semantic gap, between low-level numerical data descriptions and their high-level conceptual interpretations, the difficulty of going from an objective to a subjective representation.
After discussing the difficulty of a computational model of the labels to be considered in this specific classification task, due to the essential ambiguity and imprecision of emotions, the talk will illustrate the shift from numerical data representations to the emotions the data convey, through the integration of intermediate subjectivity levels, exploiting either external knowledge to include emotional information in the objective representation, or a subjective non-emotional level.
Computational Intelligence in Automotive R & D
Danil Prokhorov
Toyota Tech Center
United States
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Brief Bio
Dr. Danil Prokhorov began his career in St. Petersburg, Russia, in 1992. He was a research engineer in St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences. He became involved in automotive research in 1995 when he was a Summer intern at Ford Scientific Research Lab in Dearborn, MI. In 1997 he became a Ford Research staff member involved in application-driven research on neural networks and other machine learning methods. While at Ford, he was involved in several production-bound projects including neural network based engine misfire detection. Since 2005 he is with Toyota Technical Center, Ann Arbor, MI. He is currently in charge of future mobility research department at Toyota Research Institute North America, a TTC division. He has more than 100 papers in various journals and conference proceedings, as well as 15 patents, to his credit. He is honored to serve in a number of capacities including the International Neural Network Society (INNS) President, the National Science Foundation (NSF) Expert, and the Associate Editor/Program Committee member of many international journals/conferences.
Abstract
Computational intelligence is traditionally understood as encompassing artificial neural, fuzzy and evolutionary methods and associated computational techniques. Different CI methodologies often get combined with each other and with non-CI methods to achieve superior results in various applications. I will discuss CI methodological issues and illustrate them with several applications. I will also discuss lessons learned about successful and yet-to-be-successful industrial applications of CI.
In Silico Cyclic Phenomena - Fascination through Computation
Bernard De Baets
Ghent University
Belgium
http://www.kermit.ugent.be
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Brief Bio
B. De Baets holds an M.Sc. degree in Mathematics (1988), a Postgraduate degree in Knowledge Technology (1991) and a Ph.D. degree in Mathematics (1995). He is a Full Professor (2008) in Applied Mathematics at Ghent University, Belgium, where he is leading the research unit Knowledge-based Systems (KERMIT, 2000) at the Faculty of Bioscience Engineering. He is an affiliated professor (2009) at the Anton de Kom Universiteit (Suriname) and an Honorary Professor (2006) of Budapest Tech (Hungary). He was a Government of Canada Award holder (1988-89) at the Intelligent Systems Research Laboratory of the University of Saskatchewan. He was elected Fellow of the International Fuzzy Systems Association in 2011 and has been nominated for the 2012 Ghent University Prometheus Award for Research. KERMIT is an interdisciplinary team of (bio-)engineers, computer scientists and mathematicians. Its current activities consist of three interwoven threads: knowledge-based, predictive and spatio-temporal modelling. B. De Baets has acted as supervisor of 44 Ph.D. students. At present, numerous Ph.D. students are involved in the research activities of KERMIT, either in-house, through affiliations or in the framework of joint projects. Due to its unique position, KERMIT serves as an attraction pole for applications in the applied biological sciences. The bibliography of B. De Baets comprises more than 350 publications in international peer-reviewed journals, 60 chapters in books and 270 contributions to proceedings of international conferences. He delivered over 200 lectures at conferences and research institutes. He has received several best paper awards (1994, 2006, 2007, 2009, 2010 and 2013). B. De Baets is co-editor-in-chief (2007) of Fuzzy Sets and Systems and member of the editorial board of several other journals.
Abstract
Artists and scientists alike share a fascination for cyclic phenomena (also called strange loops), such as Escher’s drawings, Shepard’s musical scale, Condorcet’s voting paradox (inspiring Arrow’s impossibility theorem), the liar paradox, Gödel’s incompleteness theorem, the Rock-Paper-Scissors children game, to name but a few.
In this lecture, we study cyclic phenomena associated with the winning probably relation of a random vector. We briefly introduce the cycle-transitivity framework, ideally suited for characterizing the transitivity of reciprocal relations, a generalization of crisp complete relations encompassing winning probability relations. Focusing on winning probability relations, we lay bare the link with the underlying dependence structure, and complement elegant theoretical results with remarkable observations made through massive computation involving all 9.30 E+10 sets of 4 dice with 6 faces (independent random variables), and all 1 104 891 746 non-isomorphic posets of 12 elements (intricately dependent random variables). Throughout, we point out connections with species competition, environmetrics and chemometrics, economics and finance, and machine learning.
Since most attention so far was limited to cycles of length three, we initiate the study of cycles of length four, introducing the Rock-Paper-Scissors-Lizard metaphor. Although the picture is still far from complete, it already offers some interesting insights and challenging open problems.
Learning in Non-stationary Environments
Cesare Alippi
Politecnico di Milano
Italy
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Brief Bio
Cesare Alippi received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), USI (CH).Alippi is an IEEE Fellow, Vice-President education of the IEEE Computational Intelligence Society (CIS), Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Tran. Neural Networks (2005-2012), IEEE-Trans Instrumentation and Measurements (2003-09) and member and chair of other IEEE committees including the IEEE Rosenblatt award.In 2013 he received the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2011 has been awarded Knight of the Order of Merit of the Italian Republic.Current research activity addresses adaptation and learning in non-stationary environments and Intelligent embedded systems.
Abstract
Most of machine learning applications assume the stationarity hypothesis for the process generating the data. This amenable assumption is so widely –and implicitly- accepted that sometimes we even forget that it does not generally hold in the practice due to concept drift (i.e., a structural change in the process generating the acquired datastreams).
The ability to detect concept drift and react accordingly is hence a major achievement for intelligent learning machines and constitutes one of the hottest research topics. This ability allows the machine for actively tuning the application to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few relevant tasks.
The talk will focus on “Learning in a non-stationary environments”, by introducing both passive and active approaches. The active approach will be deepened by presenting triggering mechanisms based on Change point methods and Change detection tests. Finally, the just-in-time detect&react mechanism is introduced where, following a detected change, the system immediately reacts with a strategy depending on the available information.