Keynote

Professor Mario Köppen

Editor in Chief, Applied Soft Computing (ASOC)
Editor in Chief, International Journal of Soft Computing and Networking (IJSCN)
Associate Editor, The International Journal of Hybrid Intelligent Systems (IJHIS)

Professor
Department of Computer Science and Electronics
Graduate School of Creative Informatics
Kyushu Institute of Technology. Japan
Speaker Bio

Mario Köppen was born in 1964. He studied physics at the Humboldt-University of Berlin and received his master degree in solid state physics in 1991. Afterwards, he worked as scientific assistant at the Central Institute for Cybernetics and Information Processing in Berlin and changed his main research interests to image processing and neural networks. From 1992 to 2006, he was working with the Fraunhofer Institute for Production Systems and Design Technology. He continued his works on the industrial applications of image processing, pattern recognition, and soft computing, esp. evolutionary computation. During this period, he achieved the doctoral degree at the Technical University Berlin with his thesis works: "Development of an intelligent image processing system by using soft computing" with honors. He has published more than 150 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, incl. the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, and also Editor of the Applied Soft Computing journal. In 2006, he became JSPS fellow at the Kyushu Institute of Technology in Japan, and in 2008 Professor at the Network Design and Reserach Center (NDRC) and 2013 Professor at the Graduate School of Creative Informatics of the Kyushu Institute of Technology, where he is conducting now research in the fields of multi-objective and relational optimization, digital convergence and multimodal content management.

Tentative Title : Human-Centered Computing: Paradigms, Applications and Products

Abstract

Human-Centered Computing (HCC) is a novel trans-disciplinary science linking the recently popular intelligent technologies (Artificial Intelligence, Computational Intelligence, Soft Computing, Machine Learning, Pattern Recognition) with all sensations of a human embodiment. HCC will usually integrate two or more techniques to tackle challenging problems or find a practical application, having the human or human sense expansion as a central theme: image and signal processing (the human visual system), networking and collaborative systems (the human as a social being), security, physiology/psychology, sentiment analysis, smart recommendation systems, science of preferences, up to aspects of political economy and complex systems theory. In this talk, starting with the more fancy discussion whether it's now better to say "human-centered" or "human-centric," it will be shown why a single technique isn't sufficient to solve a number of tasks and why the integration is necessary. The question whether there is a "human way" of computing, different from what a typical computer is doing these days, will find a positive and intuitive answer.


Professor Xiaoyi Jiang

Editor in Chief
International Journal of Pattern Recognition and Artificial Intelligence

Professor
Department of Computer Science
University of Münster, Germany

Tentative Title : Consensus Learning

Abstract

Consensus problems in various forms have a long history in computer science. In pattern recognition, for instance, there are no context- or problem-independent reasons to favor one classification method over another. Therefore, combining multiple classification methods towards a consensus decision can help compensate the erroneous decisions of one classifier by other classifiers. Practically, ensemble methods turned out to be an effective means of improving the classification performance in many applications. In general, this principle corresponds to combining multiple models into one consensus model, which helps among others reduce the uncertainty in the initial models. Consensus learning can be formulated and studied in numerous problem domains; ensemble classification is just one special instance.