Keynote Speakers


Prof. Roman Słowiński

Poznań University of Technology, and Polish Academy of Sciences, Poland


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. 



Prof. Dr. Kaspar Riesen

University of Applied Sciences and Arts Northwestern Switzerland, Switzerland


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. 


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