Prof. Dr. Gia Sirbiladze New Fuzzy Technologies of Weakly Structured Processes' Modeling and Simulation
Dr. Fedor Krasnov Data Science Applications in Oil and Gas Industry
Prof. Nikos G. Bardis Remote Identification and Authentication for High Security Access in Multi User Systems
Gallery in Vienna - Leopoldstadt, Floßgasse 9, 1020 Wien, Österreich
Jose A. Lozano,
Scientific Director, Basque Centre for Applied Mathematics (BCAM)
Professor, University of the Basque Conuntry UPV/EHU
Jose A. Lozano received his M.Sc. degree in mathematics and PhD in computer science from the University of the Basque Country UPV/EHU, in Spain, in 1992 and 1998 respectively. He has been a full professor at the University of the Basque since 2008 where he leads the Intelligent Systems Group. Since January 2018 he is the scientific director of the Basque Center for Applied Mathematics (Spain). Dr. Lozano has authored more than 110 ISI journal papers, some of them have become highly cited papers. His current research interests include machine learning and its synergies with optimization in general and supervised classification, time series analysis and Bayesian inference in particular. Dr. Lozano has served on the organizing and program committee of over 60 international conferences being the general chair of IEEE Congress on Evolutionary Computation 2017. He also serves as Associate Editor of top journals such as IEEE Trans. on Evolutionary Computation, Evolutionary Computation and IEEE Trans. on Neural Network and Learning Systems, to name but a few.
Topic: Time Series Data Mining Challenges
Time series have gained much interest in the last decade. They appear naturally in industrial, medical or economical environments to name a few. Time series mining refers to the activities related with the extraction of knowledge from time series databases. Particularly, typical machine learning activities such as supervised classification or clustering are carried out from this kind of data. Furthermore, the timely nature of the data allows considering new problems. An example is the early classification of time series, where the objective is to classify the series as early as possible a before its end. In this talk we will review time series mining algorithms and pointed out to new avenues to do research in the area.
Prof. Nikos G. Bardis,PhD, Computer Engineering and Informatics
NIKOS G. BARDIS received the diploma of Computer Engineering and the PhD degree from National Technical University of Ukraine (Polytechnic Institute of Kiev) in 1995 and 1999 respectively. He is currently an Associate Professor at the Department of Mathematics and Engineering Sciences of the Hellenic Army Academy. Collaborates at the University of Athens - Department of Mathematics and entered the postgraduate course of Cryptography and Security of Information Systems. His research interests include cryptography and data security, information theory, coding theory, systems engineering and applications in defence. He has published in over 50 peer-reviewed journals and conferences. He is a member of Technical Program Committee (TPC) of the IEEE Communication Society (COMSOC), IEEE Computer Society Technical Committee on Computer Communications (TCCC), Technical Council on Software Engineering (TCSE) and IEEE Information Theory Society.
Topic: Remote Identification and Authentication for High Security Access in Multi User Systems
Information Society applications, such as e-Government, e-Banking, e-Commerce increasingly demand high security for remote user interaction. Processing platforms concerned include cloud computing resources, Internet of Things applications, smart card systems and other remote access paradigms. One of the fundamental security issues arising in such platforms is remote user identification and authentication. More specifically, this presentation is concerned with the problem of the implementation of high complexity identification and authentication processes in devices with restricted computational resources. The traditional approaches are based in asymmetric algorithms, such as the RSA, that demand significant computational effort which essentially prohibits their use. In this lecture innovative methodologies are presented that deal with this problem. A variety of methods are presented that aim to facilitate the design and implementation of high security user identification and authentication principles in multiuser systems. Security is achieved using proposed technical primitives that provide security in different contexts. The primitives include modified asymmetric algorithms, elliptic curves and innovative methodologies based on Boolean transformations.
Keywords: Galois Field, Modular exponentiation, modular multiplication, exponentiation cloud computing, zero-knowledge identification, cryptography, data security.
Paolo Rosso, Full Professor (Profesor Catedrático), Universitat Politècnica de València, Spain
Paolo Rosso is full professor at the Universitat Politècnica de València, Spain where he is also a member of the PRHLT research center. His research interests are focused on social media data analysis, mainly on author profiling, irony detection, opinion spam detection, and social copying. Since 2009 he has been involved in the organisation of PAN benchmark activities at CLEF, where he is also deputy steering committee chair for the conference, and at FIRE evaluation forum, mainly on plagiarism / text reuse detection and author profiling. At SemEval he has been co-organiser of shared tasks on sentiment analysis of figurative language in Twitter (2015), and on multilingual detection of hate speech against immigrants and women in Twitter (2019). He has been PI of several national and international research projects funded by EC, U.S. Army Research Office, and recently Qatar National Research Fund. He is associate editor of Information Processing & Management, and co-author of several papers in international journals and conferences.
Topic: Modeling and Profiling Users in Social Media
In the keynote we will address the importance of inferring demographic information from social media for marketing and security reasons. The aim is to model how language is shared among users of a certan demographic group. We will see how a shallow discourse analysis can be done on the basis of a graph-based representation. We will present some experiments for identifying gender and age, both in English and in Spanish social media data, and we will compare with state-of-the-art systems. Last, we will also address the importance of profiling whether the author of a Twitter feed, for instance, is a bot or a human. This problem will be investigated in the framework of the auhor profiling shared task at PAN 2019: https://pan.webis.de/clef19/pan19-web/author-profiling.html
Jan Mendling, Full Professor with the Institute for Information Business at Wirtschaftsuniversität Wien (WU Vienna), Austria
Prof. Dr. Jan Mendling is a Full Professor with the Institute for Information Business at Wirtschaftsuniversität Wien (WU Vienna), Austria. His research interests include various topics in the area of business process management and information systems. He has published more than 300 research papers and articles, among others in ACM Transactions on Software Engineering and Methodology, IEEE Transaction on Software Engineering, Information Systems, Data & Knowledge Engineering, and Decision Support Systems. He is member of the editorial board of six international journals, member of the board of the Austrian Society for Process Management (http://prozesse.at), one of the founders of the Berlin BPM Community of Practice (http://www.bpmb.de), organizer of several academic events on process management, and member of the IEEE Task Force on Process Mining. His co-authored textbook Fundamentals of BPM, now available in its second edition, is an important reference for this field of research.
Topic: Evaluation of Process Mining Algorithms
The BPM community makes increasingly use of shared benchmark data sets such as those available from the BPI Challenges. These offer an excellent basis for comparing algorithms. However, they also bear disadvantages. In this talk, we aim to provide a balanced account of the strengths and weaknesses of using such benchmark data to advance a field like process mining.