Professor Jerzy Swiatek

In the paper the abilities of system analysis techniques applied for two eHealth systems: eDiab and SmartFit are demonstrated. The first one is designed to support diabetes treatment and the second one serves to support physical training of sportsmen. These systems make use of measurements recorded by wireless devices such as glucometer, pressure gauge, scales, accelerometer, heart rate monitor and EMG recorder. Acquired measurements are transferred to mobile phone which makes them available all over the world for only authorized persons and supercomputing and networking centres. Therefore advanced processing may be performed on collected data. It means that we may propose additional functionalities for eHealth systems.

The eDiab basic role involves: - monitoring of glucose level, blood pressure, body mass; - classification of the user’s state; - current and historical treatment, storing results of ambulatory measurements i.e. HbA1C; - alerts for predefined glucose levels and appropriate advices. Moreover, system analysis techniques are employed in the following advanced tasks: - prediction of glucose level; - monitoring of physical activity.
The SmartFit basic role is:  - monitoring of heart rate, body mass, acceleration and EMG; - data filtering, - fitness assessment; and advanced ones: - on-line notifications about training intensity; - estimation of energy expenditure.

Advanced functionalities mentioned above, both for eDiab and SmartFit, require handling with various research tasks. For eDiab we formulated recognition of the patient’s health state and prediction of glucose level tasks in order to detect emergency situation and to call appropriate procedures. The glucose-insulin model, in the form of set of differential equations, is used to generate prediction and its parameters estimated from measurement data serve as feature vector for a classifier.

For SmartFit we formulated and solved the problem of assessment of physical activity and optimization of the energy expenditure. Assessment of physical activity involves a classifier design and is needed to estimate energy expenditure. Energy expenditure is optimized by generation of instructions (i.e. notifications) for sportsman during training. To generate instructions we apply a nonlinear model relating heart rate and training intensity together with decision making procedure.

Jerzy Swiatek (1953) graduated at Wroclaw University of Technology in 1977 – M.Sc. Electronic Engineering. He did his PhD at the Institute of Technical Cybernetics in 1979 and D.Sc. at University of Mining and Metallurgy in 1987 in Automatics and Robotics. He is a professor at the Institute of Computer Science, Wroclaw University of Technology. He is author and co-author of over 150 published research works. His areas of research are: identification and modeling of complex systems, identification of complex of operations, computer control systems, and adaptive control in complex systems. During last years he introduced the new idea modeling of systems described by relation.

Areas of Interest for Future International Research and Education Collaboration:

Prof Jerzy Swiatek Till march 2009 he was a President of Accreditation Commission of Polish Technical Universities. Now he is a member of State Accreditation Committee. He is also interested in relation: Education – Employers – Region. During the years 1993 -1999 he was a Dean of Computer Science and Management Faculty then in 1999 - 2005 he was a Vice – President for Education at the Wroclaw University of Technology and now since 2005 hi is a Dean of Computer Science and Management Faculty Professor Jerzy Swiatek is a member of the Committee of Control Engineering and Robotics of the Polish Academy of Sciences (PAS), the Commission of Computer Science and Control Engineering of PAS, branch in Wroclaw, the Section of Computer Aided Decision Systems at the Committee of Computer Science of PAS and Wroclaw Science Society.

Professor Shi-Kuo Chang

In this talk I will introduce the concept of slow intelligence. Not all intelligent systems have fast intelligence. There are a surprisingly large number of intelligent systems, quasi-intelligent systems and semi-intelligent systems that have slow intelligence. Such slow intelligence systems are often neglected in mainstream research on intelligent systems, but they are really worthy of our attention and emulation. I will discuss the general characteristics of slow intelligence systems and then concentrate on component-based slow intelligence systems using what I call super componennts. Applications such as evolutionary query processing for distributed multimedia systems, social network analysis, product and service customization and network optimization will also be discussed.

Dr. Chang received the B.S.E.E. degree from National Taiwan University in 1965. He received the M.S. and Ph.D. degrees from the University of California, Berkeley, in 1967 and 1969, respectively. He was a research scientist at IBM Watson Research Center from 1969 to 1975. From 1975 to 1982 he was Associate Professor and then Professor at the Department of Information Engineering, University of Illinois at Chicago. From 1982 to 1986 he was Professor and Chairman of the Department of Electrical and Computer Engineering, Illinois Institute of Technology. From 1986 to 1991 he was Professor and Chairman of the Department of Computer Science, University of Pittsburgh. He is currently Professor and Director of the Center for Parallel, Distributed and Intelligent Systems, University of Pittsburgh. Dr. Chang is a Fellow of IEEE. He published over 230 papers and 16 scientific books. He is the founder and co-editor-in-chief of the international journal, Visual Languages and Computing, published by Academic Press, the editor-in-chief of the international journal, Software Engineering & Knowledge Engineering, published by World Scientific Press, and the co-editor-in-chief of the international journal on Distance Education Technologies. Dr. Chang pioneered the development of Chinese language computers, and was the first to develop a picture grammar for Chinese ideographs, and invented the phonetic phrase Chinese input method.

Professor Jun Wang

Winner-take-all is a general rule in nature and society, and commonly used in many applications such as machine learning and data mining. K-winners-take-all is a generalization of winner-take-all with multiple winners. Over the last twenty years, many K-winners-take-all neural networks and circuits have been developed with varied complexity and performance. In this talk, I will start with several mathematical problem formulations of the K-winners-take-all solutions via neurodynamic optimization, then present several K winners-take-all networks with reducing model complexity based on our neurodynamic optimization models. Finally, we will introduce the best one with the simplest model complexity and maximum computational efficiency.  Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance. The applications to parallel sorting, rank-order filtering, and information retrieval will be also discussed.

Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National 1000-talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 140 journal papers, 12 book chapters, 8 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics – Part B since 2003 and a member of the Editorial Advisory Board of the International Journal of Neural System since 2006. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), and Neurocomputing (2008). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence and a Program Chair of the 2012 IEEE International Conference on Systems, Man and Cybernetics. He served as the President of the Asia Pacific Neural Network Assembly in 2006. He is an IEEE Fellow, an IEEE Computational Intelligence Society Distinguished Lecturer, and a recipient of a Research Excellence Award from the Chinese University of Hong Kong for 2008-2009, a Natural Science Award (first class) from Shanghai Government in 2009 and from the Ministry of Education of China in 2011, the IEEE Transactions on Neural Networks Outstanding Paper Award and the APNNA Outstanding Achievement Award in 2011.

Professor Rongsheng Xu

The earlier seminar in China on Digital Forensics Technology was in 2004 held at Beijing people’s Police College, Actually, Chinese Technology sector started paying attention on this technique area can be dated in 2000. For MPS, it was even early (around 1997 or so) by the cyber crime investigation in China.

The China Committee of Experts on Computer Forensics (CECF) was set up in 2005 under CIE as a key national academic organization to handle the national wide seminars in these years, it’s successes will be reviewed in this speech such as the current concerning topics, the innovation and development in the Challenges of Computer Forensics in China.

However, the contributions of CCFC and some of enterprises could not be ignored by their vitalities. CCFC (China Computer Forensics Conference) was originally formed by a group of technique lovers, and it’s now renamed CCFRC (the China Computer Forensic Research Center), CCFC has been hosting annual Conferences and Exhibitions since 2005, which were gathering of Chinese and foreign industry professionals and researchers with the aim of facilitating the development of advanced computer forensic technology both domestically and abroad. The event is now also co-organized with the Information Security & Forensics Society (ISFS) Hong Kong Chapter, it provided attendees the opportunity to share case studies and technical skills, as well as develop new contacts with fellow digital forensic practitioners, law enforcement professionals and industry experts.

In the Cloud Computing Era, it brings much difficulty on collecting E-evidence, especially the massive evidence data streams in real time collection will be mostly concerned, the Key Technologies in Distributed mass data storage and management, data analysis under the network super I / O, obtain the evidence under the virtual machine technology platform will be proposed.

Researcher of Computing center at Institute of High Energy Physics, Chinese Academy of Sciences, Chief scientist of Cyber Security Laboratory and Chairman of China Computer Forensics Research Center.