Keynote Speakers

 

       Prof. Guo Song, The Hong Kong Polytechnic University, Hong Kong

 

Bio: Song Guo received his Ph.D. in computer science from University of Ottawa. He is currently a full professor at Department of Computing, The Hong Kong Polytechnic University. Prior to joining PolyU, he was a full professor with the University of Aizu, Japan. His research interests are mainly in the areas of cloud and green computing, big data, wireless networks, and cyber-physical systems. He has published over 300 conference and journal papers in these areas and received multiple best paper awards from IEEE/ACM conferences. His research has been sponsored by JSPS, JST, MIC, NSF, NSFC, and industrial companies. Dr. Guo has served as an editor of several journals, including IEEE Transactions on Parallel and Distributed Systems (2011-2015), IEEE Transactions on Emerging Topics in Computing (2013-), IEEE Transactions on Green Communications and Networking (2016-), IEEE Communications Magazine (2015-), and Wireless Networks (2013-). He has been actively participating in international conferences as general chair and TPC chair. He is a senior member of IEEE, a senior member of ACM, and an IEEE Communications Society Distinguished Lecturer.

Title: Machine Intelligence in Geo-Distributed Systems: From Cloud to Edge

Abstract: When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. In this talk, we will analyze the methodology and limitations of popular approaches for supporting AI services on geo-distributed systems along the evolution from cloud computing to edge computing. In particular, we shall discuss how to deal with different sets of challenges in training and inference, the two phases of machine learning based applications, over heterogeneous geo-distributed systems. We shall also present our recent studies on data driven resource management among networked collaborative edges.

 

       Prof. Girija Chetty, University of Canberra, Australia

 

Bio: Dr. Girija Chetty has a Bachelors and Masters degree in Electrical Engineering and Computer Science, and PhD in Information Sciences and Engineering from Australia. She has more than 25 years of experience in Industry, Research and Teaching from Universities and Research and Development Companies from India and Australia, and has held several leadership positions including Head of Software Engineering and Computer Science, and Course Director for Master of Computing Course. Currently, she is the Head of Multimodal Systems and Information Fusion Group in University of Canberra, Australia, and leads a research group with several PhD students, Post Docs, research assistants and regular International and National visiting researchers. She is a Senior Member of IEEE, USA, and senior member of Australian Computer Society, and her research interests are in the area of multimodal systems, computer vision, pattern recognition and image processing. She has published extensively with more than 120 fully refereed publications in several invited book chapters, edited books, high quality conference and journals, and she is in the editorial boards, technical review committees and regular reviewer for several IEEE, Elsevier and IET journals in Computer Vision, Pattern Recognition and Image Processing.

Title: Multimodal Computational Framework Based on Deep Learning for Biomedical Image Analysis

Abstract: Traditional approaches in biology generally look at only a few aspects of an organism at a time and try to analyse diseases, by molecularly dissecting them, and studying them part by part with the expectation, that the sum of knowledge of parts can explain the underlying cause of disease. Seldom has this been a successful strategy to understand the causes and cures for complex diseases. Major advances in machine learning, artificial intelligence and data science are beginning to have an impact on how to objectively uncover the complex interactions between large data involving multiple data sources, and detect the biomarkers, and track the status and progression of disease. In this talk, the details of a multimodal computational framework and deep learning tools for biomedical image analysis being developed in our research laboratory will be presented.

 

       Prof. Harumi Watanabe, Tokai University, Japan

 

Bio: Dr. Harumi Watanabe received Doctor of Engineering from School of Computing, Tokyo Institute of Technology, Tokyo, Japan. She is currently a full professor and the Head of Department of Embedded Software (2014-), Tokai University. Her research interests are mainly in the areas of IoT, cyber-physical, and embedded system developments methods and their education. She is a chair of the Special Interest Group on Embedded Systems (SIG-EMB), Information Processing Society of Japan (IPSJ) (2016-). She has organized and managed several robot contests since 2004. Her students have participated in many robot contests and have received awards every year. She was the chair of the board of NHK robot contest, Tokai region (2006). NHK robot contest is broadcasting in NHK and is the most major contest related to robot developments. In inter-high school championships on electronics manufacturing Japan, she was the chair of the board of the award of Ministers of Education, Culture, Sports, Science and Technology (2007), and the chair of the board of the award of Minister of Health, Labour and Welfare Japan (2018). She is a steering committee of Asia Pacific Conference on Robot IoT System Development and Platform (APRIS) (2018-). This conference organizes a robot contest for providing education of IoT system development.

Title: How to Build a Project-Based Learning for IoT System Development

Abstract: Towards an era of IoT and Industry 4.0, the demand for embedded system engineers is rising, and the importance of education is increasing. During the past decade, the educational materials and the methods have been highly progressing to fulfill the demand. LEGO, Arduino, and Raspberry Pi contribute to learning IoT systems. Recently, many elementary schools and kindergartens adopt LEGO or small robots for learning computer science. In the early 2000s, to develop a small robot, we had needed to build electronic circuits and write programs for devices. Although LEGO Mindstorms had already been proposed, the functions were limited in the smallest. Regarding the educational methods, Project-Based Learning (PBL) has become to be popular, and Robot contests have been opening in anywhere. However, we cannot easily educate students who will be embedded system engineers, even though the educational materials and the methods are improving; because the engineers need knowledge of broad fields. We need to consider what we should educate the students in a limited period in a university curriculum, and how to raise the students' motivation. To solve such problems, we have organized a robot contest called ESS robot challenge and provided the education on PBL since 2004. In this talk, I would like to share the experience for fifteen years.

 

       Prof. Dong Hwa Kim, Hanbat National University, South Korea

 

Bio: Dr. Dong Hwa Kim received Ph.D from Ajou University in Korea and also got Ph.D from Dept. of Computational Intelligence and Systems Science, TIT (Tokyo Institute of Technology, K. Hirota Lab.), Tokyo, Japan.
He has experience in many areas such as Visiting Professor, Mechanical, Optic, Engineering Informatics, Budapest University of Technology and Economic (March 20, 2012-2013), President, Korea Institute HuCARE (President of Hu-CARE (Human-Centered Advanced Technology Research/Education, 2009 – ), EU-FP NCP (ICT) in Korea (Nov. 2009-), Korea Atomic Energy Research Institute (Nov., 1977-March, 1993), President, Daedeok Korea-India Forum (March 1, 2010 – ), Vice-president of the recognition board of the world congress of arts, sciences and communications, IBC (Sept. 1, 2007, UK), Co-editor, Japan Society for Fuzzy Theory and Intelligent Informatics, executive committees (June 2, 2007 -2009), Co-editor, Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII, Fujii press, Japan (2006-), Director of National Science Foundation (2006-2008).
He wrote many columns about science & technology strategy and policy in major newspaper in Korea. He also had ever have lecture and keynote speaker in over 100 University and conference or forum over the world. He was Book Author in Hybrid Evolutionary Algorithms (Computational Intelligence 75), Springer, Germany, 2007, and published 200 papers in international Journal and awards 2000 Outstanding Intellectuals of the 21st Century, Top 100 Engineers 2008 (UK), International Einstein Award for Scientific achievement.
He got also best innovation award from Hankook Illbo (Korea major daily newspaper) on 2009. He is now working at Hanbat National University (2009 – ).

Title: The Application of AI in Material Science, Social Pattern, Economy, and Blockchain

Abstract: The influence of AI is growing up more and more in everywhere. Game, medical, business, and etc. AI also is growing up for science areas by using AI’s learning technology. More recently blockchain is growing up as the new network technology into security, smart contracts, and networking. Therefore, many universities are permitting students to pay tuition fee as blockchain in Swiss. Also global company is establishing this related technolony as blockchain centrer. It is clear that the technology of blochchain is becoming seriously useful in security, distributed work, etc. On the other hand, existing AI is going to combine with blockchain and examining the 2nd Internet in terms fast combined development in AI and blochchain. This lecture will provide current application in material science and analysis. Also how the impact of AI is big now and future in social and economic area. Herein, young generation and researcher such as master course should recognize their research areas and topics for their job or future works. And this presentation will explore how AI and blochchain can be combined for the DCS (Distributed Control System) as one of example through research experience and its combined technology can be useful in innovative applications and how the results will influence social factors, platform architectures, distributed system in the future. The presentation is hoped to provide motivation for young researchers and students to explore novel research directions or advance their extending knowledge.