Prof. Witold Pedrycz, IEEE Life Fellow
University of Alberta, Edmonton, Canada
Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others.
Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).
(Online) Speech Title: A Unified Framework of Data and Knowledge Environment of Machine Learning
Abstract: Over the recent years, we have been witnessing truly remarkable progress in Machine Learning (ML) with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others.
Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models.
The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML. We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. We also elaborate on the central role of information granularity in this area.
Agung Trisetyarso, Bina Nusantara University, Indonesia
Agung Trisetyarso, Ph.D is a Faculty Member in School of Computer Science, Bina Nusantara University and previously a Lecturer in Graduate School of Informatics Engineering from Institut Teknologi Telkom (IT Telkom), Bandung, Indonesia. He received the B.S. degree in physics from the Institut Teknologi Bandung (ITB), Indonesia, in 2000, then the M.S. also in physics from ITB in June 2002. By March 2011, he received his Ph.D. degrees in applied physics and physico-informatics from the Keio Gijuku Daigaku or Keio University in Tokyo, Japan, in the field of quantum information and computation. He was a Monbukagakusho and also Keio Leading-edge Laboratory recipient (2007–2010) during the Ph.D study. Currently, he is interested in doing research in quantum computing, machine learning, intelligent system, theoretical computer science and physics, social media, smart city, and also internet of things.
(Onsite) Speech Title: Quantum Circuit of Acemoglu-Restrepo Model
Abstract: We study Acemoglu-Restrepo automation model on quantum coopetition by engineering and econophysics perspective. Automation production function network in the large scale of coopetition is analysed. We found that the task content of production function exhibits the quantum and resonator circuits model: the production function can be decomposed into classical and quantum resonant circuits. Metaphorical connections between automation production function and resonant circuit is quantitatively translated to find the time interval of an automation era. Quantum mechanical paradigm provides the the energy level of an automation through Schr\"odinger pictures, while Heisenberg pictures provides the evolution of automation production function. The proposed circuit of production function will be presented.
Prof. Dr. Sergei Gorlatch, University of Muenster, Germany
Sergei Gorlatch is Full Professor of Computer Science at the University of Muenster (Germany) since 2003. Earlier he was Associate Professor at the Technical University of Berlin, Assistant Professor at the University of Passau, and Humboldt Research Fellow at the Technical University of Munich, all in Germany. Prof. Gorlatch has more than 200 peer-reviewed publications in renowned international books, journals and conferences. He was principal investigator in several international research and development projects in the field of software for parallel, distributed, Grid and Cloud systems and networking, funded by the European Community and by German national bodies.
Speech Title: Future Distributed Applications Based on Mobile Clouds and Software-Defined Networks
We consider an emerging class of challenging software applications called Real-Time Online Interactive Applications (ROIA). ROIA are networked applications connecting a potentially very high number of users who interact with the application and with each other in real time, i.e., a response to a user’s action happens virtually immediately. Typical representatives of ROIA are multiplayer online computer games, advanced simulation-based e-learning and serious gaming. All these applications are characterized by high performance and QoS requirements, such as: short response times to user inputs (about 0.1-1.5 s); frequent state updates (up to 100 Hz); large and frequently changing numbers of users in a single application instance (up to tens of thousands simultaneous users). This talk will address two challenging aspects of software for future Internet-based ROIA applications: a) using Mobile Cloud Computing for allowing high application performance when a ROIA application is accessed from multiple mobile devices, and b) managing dynamic QoS requirements of ROIA applications by employing the emerging technology of Software-Defined Networking (SDN).