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Keynote Lectures

Physiological Profiling using Computational Intelligence
Keeley Crockett, Manchester Metropolitan University, United Kingdom

Learning Vector Quantization Methods as Multi-layer Networks for Interpretable Classification Learning
Thomas Villmann, University of Applied Sciences Mittweida, Germany

Available Soon
Oscar Cordón, University of Granada, Spain

An Approximation to the Problem of Information Fusion in Fuzzy Classification
Humberto Bustince, Public University of Navarra, Spain

 

Physiological Profiling using Computational Intelligence

Keeley Crockett
Manchester Metropolitan University
United Kingdom
 

Brief Bio
Keeley Crockett is a Reader in Computational Intelligence in the School of Computing, Mathematics and Digital Technology at Manchester Metropolitan University in the UK. She gained a BSc Degree (Hons) in Computation from UMIST in 1993, and a PhD in the field of machine learning from the Manchester Metropolitan University in 1998 entitled "Fuzzy Rule Induction from Data Domains". She is A Senior Fellow of the Higher Education Academy. She is a knowledge engineer and has worked with companies to provide business rule automation with natural language interfaces using conversational agents. She is Senior Artificial Intelligence Scientist consultant for Silent Talker Ltd. She leads the Intelligent Systems Group (Computational Intelligence Lab – launch in 2018) that has established a strong international presence in its research into Conversational Agents and Adaptive Psychological Profiling including an international patent on "Silent Talker". She is currently a member of the IEEE Task Force on Ethical and Social Implications of Computational Intelligence and has a strong focus on ethically aligned design in the context of intelligent systems development. Her main research interests include fuzzy decision trees, semantic text based clustering, conversational agents, fuzzy natural language processing, semantic similarity measures, and AI for psychological profiling. Currently the Principal Investigator (MMU) on the H2020 funded project iBorderCtrl – Intelligent Smart Border Control and CI a UK Knowledge Transfer Partnership with Service Power. She is the currently Chair of IEEE WIE UKI, Chair of the IEEE CIS Webinars, Vice-Chair of the IEEE Women into Computational Intelligence Society. She s also a IEEE WCCI Tutorials 2018 Co-chair. She has authored over 120 peer reviewed publications and is currently an Associate Editor for IEEE Transactions on Emerging Topics in Computational Intelligence.


Abstract
This keynote will focus on how computational intelligence techniques can be used to automatically physiological profile people. Silent Talker is a pioneering psychological profiling system which was developed by experts in Behavioural Neuroscience and Computational Intelligence. Designed for use in natural conversation, Silent Talker combines image processing and artificial intelligence to classify multiple visible  non-verbal signals of the head and face during verbal communication. From analysis, the system produces an accurate and comprehensive time-based profile of a subject’s psychological state.
The talk will give examples on how Silent Talker technology can be used. Firstly, to detect deception through providing risk scores to border guards in a border crossing scenario which is being developed as part of the European Union sponsored project known as iBorderCtrl. Secondly, to detect the comprehension level of a person in order to provide personalised  and adaptable online learning within a conversational intelligent tutoring system. Ethical considerations will also be touched on in line with the GDPR and how it is important to have a “human in the loop” when developing such systems.



 

 

Learning Vector Quantization Methods as Multi-layer Networks for Interpretable Classification Learning

Thomas Villmann
University of Applied Sciences Mittweida
Germany
 

Brief Bio
Prof. Thomas Villmann is with the University of Applied Sciences Mittweida (UASW), Germany. He holds a diploma degree in Mathematics, received his Ph.D. in Computer Science in 1996 and his habilitation as well as venia legendi in the same subject in 2005, all from the University of Leipzig, Germany. From 1997 to 2009 he led the computational intelligence group of the hospital for psychotherapy at Leipzig University. in 2006 he was visiting professor at the University Paris Panthéon-Sorbonne in the dpeartment for statistical analysis and mathematical modelling (SAMM) Since 2009 he is a full professor for Technomathematics/ Computational Intelligence at the UASW (Saxony), Germany. He is founding member of the German chapter of European Neural Network Society (GNNS) and its president since 2011 as well as board member of the European Neural network Society (ENNS). Further he leads the Institute of Computational Intelligence and Intelligent Data Analysis e.V. in Mittweida, Germany and the Computational Intelligence Group at the University of Applied Sciences Mittweida. Since 2017 he is director of the Saxony Institute for Computational Intelligence and machine Learning (SICIM.) Prof. Villmann published more than 90 articles in leading journals. He authored and co-authored more than 250 conference papers and book chapters. Under his supervision, 10 PhD completions were achieved, three more anticipated this year. He is editor in chief of the Machine Learning Reports (MLR) and acts as an associate editor for Neural Processing Letters and for Computational Intelligence and Neuroscience. His research focus includes the theory of prototype based clustering and classification, non-standard metrics, information theoretic and similarity based learning, statistical data analysis and their application in pattern recognition, data mining and knowledge discovery for use in medicine, bioinformatics, remote sensing, hyperspectral analysis, forensics and others. Peronally, Thomas Villmann is a mountaineer climbing all over the world. He is still active in judo (bronze medaillist at German Championship for Veterans this year) and likes jogging together with his border terrier dog Emmy.


Abstract
Learning Vector Quantization (LVQ) as introduced by T. Kohonen is an intuitive method for classification learning inspired by Bayesian decision learning and unsupervised neural vector quantization. Nowadays learning vector quantizers are trained using stochastic gradient descent learning according to a cost function approximating the overall classification error. Thereby, the geometric interpretation as a vector quantization model partitioning the data space into class regions gives one of the main advantages for this classifier model. Yet, the origins of LVQ are unsupervised neural learning models based on competing perceptrons. Thus LVQ can be comprehended also as multilayer neural networks.
The talk will consider recent developments of LVQ starting with the basic stochastic gradient learning variant. Several modifications and adaptations for specific aspects like matrix-relevance learning, classification-border sensitive learning or classification learning for imbalanced data, etc. will be explained. Further, we will also focus to the multilayer network description of LVQ classifiers allowing to draw close connections to deep networks. We will show, how successful ideas from there can be easily transferred to LVQ.



 

 

Keynote Lecture

Oscar Cordón
University of Granada
Spain
 

Brief Bio
Oscar Cordón was born in Cadiz (Spain) on August 11, 1972. He received his M.S. degree in Computer Science in 1994 and his Ph.D. in Computer Science in 1997, both from the University of Granada. He is professor at the Department of Computer Science and Artificial Intelligence of the University of Granada (UGR) since 1995, Associate Professor since 2001, and Full Professor since 2011. He is a member of the Soft Computing and Intelligent Information Systems research group, granted with the Outstanding UGR Research Group Award in 2013, where he heads the Soft Computing for Complex Environments (SOCCER) lab. Oscar Cordón was also the founder and leader of the Virtual Learning Centre of the University of Granada (CEVUG) between 2001 and 2005 and is currently Vice-Rector for Digital University since 2015. From April 2006 to December 2015, he was affiliated to the European Centre for Soft Computing, a private international research center, where he first acted as founding Principal Researcher of the Applications of Fuzzy Logic and Evolutionary Algorithms Research Unit until August 2011 and later as Distinguished Affiliated Researcher until December 2015. He also has R&D collaborations with R0D Brand Consultants and with the Prothius Industrial Engineering Chair. Dr. Cordón has coordinated 19 research projects and 10 research contracts composing an overall budget of 7,1M€. The research projects (14 national and 5 international) have been funded by the UNESCO, the European Commission, the Spanish and Andalusian Ministries, the Principality of Asturias, and the University of Granada. His 10 coordinated research contracts has been funded by the Puleva Food S.A., Tenneco Automotive Iberica S.A., EDP Renewables Europe S.L., Tecnologías Avanzadas Inspiralia S.A., R0D Brand Consultants, and Atarfil S.L. enterprises. Dr. Cordón is also the co-inventor of a granted international patent on a intelligent system to automatize a skeleton-based forensic identification method for missing people which is currently under exploitation in Mexico and South Africa. This system was recognized with the International Fuzzy Systems Association Award for Outstanding Applications of Fuzzy Technology in 2011. He has advised 16 PhD dissertations, one of them awarded with the European Society for Fuzzy Logic and Technologies Best Ph.D. Thesis Award in 2010. He has co-organized 19 special sessions in national and international Conferences, co-edited 12 special issues in different international journals and 3 edited books (see editorial activities). He is an IEEE member since 2004 (senior member since 2011) and has enjoyed many different representations for reputed international societies such as the IEEE Computational Intelligence Society: founder and chair of the Genetic Fuzzy Systems Task Force (2004-2007); member of the Fuzzy Systems Technical Committee (2004-2013, 2015-date); member of the Graduate Student Research Grants sub-committee (2009-2011); elected member of the Administrative Committee (AdCom, 2010-2012 term); member of the Outstanding Computational Intelligence Early Career Award sub-committee (2011-2015); member of the Awards Committee (2014-15); member of the Outstanding PhD Dissertation Award sub-committee (2016); and member of the Outstanding Chapter Award sub-committee (2017); among many others, as well as for the EUSFLAT Society: Treasurer (2005-2007) and Executive Board member (2005-07, 2009-2013). He has also been involved in the organization of many different conferences: IPC chair of IEEE EFS2006, GEFS2008 and ESTYLF2008; international co-chair of HIS2008; publicity co-chair of IEEE SCCI2009; finance co-chair of IFSA-EUSFLAT 2009; advisory board member of ISDA'09; evolutionary algorithms IPC area chair of IPMU2010; special session co-chair of 2010 IEEE CEC 2010 (WCCI 2010); Fuzzy image, speech, vision and signal processing IPC area chair of Fuzz-IEEE 2011; special session chair of Fuzz-IEEE 2013; program committe co-chair of IFSA2015, program committe co-chair of IEEE CEC 2015; General Chair of Fuzz-IEEE 2016 (WCCI 2016); technical co-chair of IEEE CEC 2017; and tutorial co-chair of FuzzIEEE 2017. His current main research interests are in the fields of: fuzzy rule-based systems; genetic fuzzy systems; fuzzy and linguistic modeling; evolutionary algorithms, ant colony optimization and other metaheuristics; soft computing applications to different topics (medical image registration, forensic anthropology, assembly line balancing, information retrieval etc.); visual science maps design and mining; multiobjective graph-based data mining; and agent-based modeling, social networks, and their applications in marketing science.


Abstract
Available Soon



 

 

An Approximation to the Problem of Information Fusion in Fuzzy Classification

Humberto Bustince
Public University of Navarra
Spain
 

Brief Bio
Humberto Bustince received his Bs. C. degree on Physics from the Salamanca University, Spain, in 1983 and his Ph.D. degree in Mathematics from the Public University of Navarra, Pamplona, Spain, in 1994. He has been a teacher at the Public University of Navarra since 1991, and he is currently a Full Professor with the Department of Automatics and Computation. He served as subdirector of the Technical School for Industrial Engineering and Telecommunications from 01/01/2003 to 30/10/2008 and he was involved in the implantation of Computer Science courses at the Public University of Navarra. He is currently involved in teaching artificial intelligence for students of computer sciences. Dr. Bustince has authored more than 120 journal papers (Web of Knowledge), and more than 100 contributions to international conferences. He has also been co-author of four books on fuzzy theory and extensions of fuzzy sets.


Abstract
Humberto Bustince received his Bs. C. degree on Physics from the Salamanca University, Spain, in 1983 and his Ph.D. degree in Mathematics from the Public University of Navarra, Pamplona, Spain, in 1994. He has been a teacher at the Public University of Navarra since 1991, and he is currently a Full Professor with the Department of Automatics and Computation. He served as subdirector of the Technical School for Industrial Engineering and Telecommunications from 01/01/2003 to 30/10/2008 and he was involved in the implantation of Computer Science courses at the Public University of Navarra. He is currently involved in teaching artificial intelligence for students of computer sciences.
Dr. Bustince has authored more than 120 journal papers (Web of Knowledge), and more than 100 contributions to international conferences. He has also been co-author of four books on fuzzy theory and extensions of fuzzy sets.



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