Biography: Roman Słowiński is a Professor and Founding Chair of the Laboratory of Intelligent Decision Support Systems at the Institute of Computing Science, Poznań University of Technology, Poland. He is also Professor at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. He is a Full Member of the Polish Academy of Sciences, a society of 330 leading Polish scholars. He has been elected president of the Poznań Branch of the Polish Academy of Sciences for 2011-2018, and chairman of the Committee on Informatics of the Polish Academy of Sciences for the term 2016-2019. Since 2013 he is also a member of Academia Europaea. He is coordinator of the EURO Working Group on Multiple Criteria Decision Aiding, and Past President and Fellow of the International Rough Set Society. In years 2009-2013, he has been an expert of the panel (PE6–Computer Science) of the European Research Council. Since 1999, he is Coordinating Editor of the European Journal of Operational Research, a premier journal in Operational Research.
He is recipient of the EURO Gold Medal (1991), and Doctor Honoris Causa of Polytechnic Faculty of Mons (2000), University Paris Dauphine (2001) and Technical University of Crete (2008). In 1997 he was given the Edgeworth-Pareto Award by International Society on Multiple Criteria Decision Making, and in 2005 he received the Annual Prize of the Foundation for Polish Science - regarded as the highest scientific honor awarded in Poland. In 2016, he received the Scientific Award of the President of the Polish Academy of Sciences. He is an IEEE Fellow for "contributions to dominance-based rough set theory, robust ordinal regression and preference learning". He has published 14 books and more than 400 articles in major scientific reviews (Web of Science h-index=40, Scopus h-index=52, Google Scholar h-index=80.
Title of Speech: Constructive preference learning through robust ordinal regression
Abstract: We present a constructive preference learning methodology, called Robust Ordinal Regression, for Multiple Criteria Decision Aiding. Identification of Decision Maker’s (DM’s) preferences is a crucial step in decision aiding. It is known that the dominance relation established in the set of alternatives evaluated on multiple criteria is the only objective information that comes from the formulation of a multiple criteria decision problem (ordinal classification, or ranking, or choice – with multiobjective optimization being a particular case). While it permits to eliminate many irrelevant (i.e., dominated) alternatives, it does not compare completely all of them, resulting in a situation where many alternatives remain incomparable. This situation may be addressed by taking into account preferences of the DM. Therefore, decision aiding methods require some preference information elicited from a DM or a group of DMs. This information is used to build more or less explicit preference model, which is then applied on a non-dominated set of alternatives to arrive at a recommendation presented to the DM. In practical decision aiding, the process composed of preference elicitation, preference modeling, and DM’s analysis of a recommendation, loops until the DM accepts the recommendation or decides to change the problem setting. Such an interactive process is called constructive preference learning. We will focus on processing DM’s preference information concerning multiple criteria ranking and choice problems. This information has the form of pairwise comparisons of selected alternatives, and/or comparisons of intensities of preference between pairs of some alternatives. Research indicates that such preference elicitation requires less cognitive effort from the DM than direct assessment of preference model parameters (like criteria weights, comparison thresholds, or trade-offs between conflicting criteria). We will describe how to construct from this input information a preference model being a utility function or an outranking relation, via Robust Ordinal Regression (ROR). An important feature of ROR is identification and use of all instances of the preference model that are compatible with the input preference information – this permits to draw robust conclusions in terms of necessary and possible relations in the set of considered alternatives. The methodology will be presented along with some examples of their application.
Biography: Ying Tan is a professor of Peking University, and director of Computational Intelligence Laboratory at Peking University, and also a professor of Faculty of Design, Kyushu University, Japan. He worked at Chinese University of Hong Kong in 1999 and 2004-2005, and at University of Science and Technology of China in 1998, 2005-2006 as a professor under the 100-talent program of CAS, and visited many universities including Columbia University, Auckland University of Technology, etc. He is the inventor of Fireworks Algorithm (FWA). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), International Journal of Swarm Intelligence Research (IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He also served as an Editor of Springer’s Lecture Notes on Computer Science (LNCS) for 32+ volumes, and Guest Editors of several referred Journals, including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, Natural Computing, Swarm and Evolutionary Optimization, etc. He is a senior member of IEEE. He is the founder general chair of the ICSI International Conference series since 2010 and the DMBD conference series since 2016. He won the 2nd-Class Natural Science Award of China in 2009 and many best paper awards. His research interests include computational intelligence, swarm intelligence, swarm robotics, data mining, machine learning, intelligent information processing for information security and financial prediction, etc. He has published more than 300+ papers in refereed journals and conferences in these areas, and authored/co-authored 11 books, including “Fireworks Algorithm” by Springer-Nature in 2015, and “GPU-based Parallel Implementation of Swarm Intelligence Algorithms” by Morgan Kaufmann (Elsevier) in 2016, and 20 chapters in book, and received 4 invention patents.
Title of Speech: Research Advance in Swarm Intelligence and Fireworks Algorithm
Abstract: Recently, inspired from the collective behaviors of many swarm-based creatures in nature or social phenomena, swarm intelligence (SI) has been received attention and studied extensively, gradually becomes a class of efficiently intelligent optimization methods. Inspired by fireworks explosion at night, the fireworks algorithm (FWA) was developed in 2010. Since then, several improvements and some applications were proposed to improve the efficiency of FWA. In this talk, after brief introduction of swarm intelligence, the fireworks algorithm is reviewed in detail, then several effective improved fireworks algorithms are highlighted individually. By changing the ways of calculating numbers and amplitudes of sparks in fireworks’ explosion, the improved FWA algorithms become more reasonable and explainable. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based FWA are also briefly presented, particularly the GPU-based FWA is able to speed up the optimization process considerably. Extensive experiments on IEEE-CEC’s benchmark functions demonstrate that the improved fireworks algorithms significantly increase the accuracy of found solutions, yet decrease the running time dramatically. Finally, some applications of FWA are briefly described, in particular, the applications of FWA to some important optimization problems happened in material engineering filed are highlighted and reviewed in details. I believe that the FWA will find its right way in the field of material engineering in future.
Biography: Kaspar Riesen received his MSc and PhD degrees in Computer Science from the University of Bern in 2006 and 2009, respectively. His PhD thesis received the Alumni price for an outstanding work at the Institute of Computer Science of the University of Bern. In 2016 he received the Venia Docendi for Computer Science from the Faculty of Science of the University of Bern. His research interests cover the fields of artificial intelligence, pattern recognition, machine learning and data mining. In particular, he is working on the development of novel algorithms for solving graph matching problems in various domains of intelligent information processing. For instance, one of his current research projects is concerned with keyword spotting in historical documents, while another project pursues the question whether novel matching algorithms can be beneficially employed for user authentication.
An extended version of Kaspar Riesen’s PhD thesis appeared as a book by a well known international publisher. Moreover, he recently published a unique book that presents a thorough introduction to the field of structural pattern recognition. Kaspar Riesen has collaborated with more than 30 researchers since 2006. According to Google Scholar his publications are currently cited more than 2,600 times (i10-Index: 45 and h-Index: 25). His list of publications includes 20 papers in international peer-reviewed journals and more than 50 publications in refereed conference proceedings and edited books.
During his studies in Computer Science, Kaspar Riesen graduated at the Pedagogical College Bern and received his teaching diploma for grammar schools in 2009. Since 2010 Kaspar Riesen works as a lecturer and researcher at the University of Applied Sciences and Arts Northwestern Switzerland. (in autumn 2016 he became a full UAS professor). Since 2011 he regularly acts as lecturer at the University of Bern at the Institute of Computer Science. Last but not least, in 2014 he founded Intelligent Insights GmbH — a start-up company concerned with the transfer of research methods to practical applications.
Title of Speech: User Authentication by Means of Handwriting - Recent Advances and Open Problem
Abstract: User authentication is actually required in many situations in our everyday life. Examples include the login to e-banking or e-mail accounts, the registration to a webshop, or online payments with a credit card. Authentication systems can be broadly classified into the following three categories.
- Knowledge based systems rely on something the user knows (e.g. a password).
- Possession based systems rely on something the user has (e.g. an ID-Card).
- Inherence based systems rely on physiological traits of the user (e.g. a fingerprint).
The present talk is focused on an authentication systems based on handwritten signatures as a physiological trait of users (i.e. we focus on inherence based systems). Signature verification is often based on only few genuine specimens and is thus regarded as a challenging task (even for humans). Yet, the current state-of-the-art achieves a high level of accuracy which is comparable to that of other biometric authentication frameworks. Moreover, with the recent rise of mobile devices equipped with sophisticated touch-sensitive screens, signatures can easily be acquired in a noninvasive way. In this particular scenario several dynamic characteristics, such as writing speed, acceleration, or pen pressure are also available for recognition (known as online signatures). This talk gives a survey on recent methods for online signature verification and provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it presents a novel cost model for string matching which turns out to be particularly well suited for the task of signature verification. The talk also provides some use case scenarios of this particular framework and concludes with several open research problems in the field of user authentication by means of handwriting.