Keynote Speaker 1

Associate Professor Dr. Yuto Lim

  • School of Information Science, Security and Networks Area, Center for Trustworthy IoT Infrastructure
    Japan Advanced Institute of Science and Technology

Specialization: Smart Homes, Smart Cities, Cyber-Physical Systems, Internet of Things, Future Wireless Communication and Network, and Smart Energy Distribution.

Biography: Yuto Lim received the B.Eng. (Hons) and M.Inf. Tech. degrees from Universiti Malaysia Sarawak (UNIMAS), Malaysia in 1998 and 2000, respectively. He received the Ph.D. degree in communications and computer engineering from Kyoto University in 2005. In November 2005, he was an expert researcher at National Institute of Information and Communications Technology (NICT), Japan until September of 2009. In 2005, he is actively joining the standardization activities of IEEE 802.11s Mesh Networking. He and his team members have introduced two proposals, which currently adopted in the draft of IEEE 802.11s D1.04. He also led the RA-OLSR group in resolving a part of the comments. In 2006, he is also actively joining the standardization activities of next-generation home networks from Telecommunication Technology Committee (TTC), Japan. Since October 2009, he has been working at Japan Advanced Institute of Science and Technology (JAIST) as an associate professor. During the years 2017-2019, he is actively joining the standardization activities of smart cities from the Bridging the Standardization Gap (BSG) Working Group of the TTC, Japan. He is honorably appointed as TTC representative to report the "Smart City" activities in Asia Pacific Region in both APT Standardization Program Forum (ASTAP) and APT Telecommunication/ICT Development Forum (ADF). His research interests are Smart Homes, Smart Cities, Cyber-Physical Systems, Internet of Things, Future Wireless Communication and Network, and Smart Energy Distribution. He is a member of IEEE, IEICE, and IPSJ.

Title: Dependable Smart Homes with Cyber-Physical Human Centric Framework for Society 5.0 (View)

Abstract: Japan will take the lead to realize a super-smart society, Society 5.0 ahead of the rest of the world. Society 5.0 that aims to achieve a human-centered society utilizes the tight integration of cyber space and physical space to enable Internet of Things (IoT), Cyber-Physical Systems (CPS), Artificial Intelligence (AI) and Big Data to realize economic development while solving key social problems. In Japan, examples of Society 5.0 especially Super City will be delivered in this talk. Also, the domain of smart homes known as the best practice for realizing the mimic model of our future human-centered society will be introduced. In this talk, I will introduce a Cyber-Physical Human Centric Framework using the CPS approach, as a current trend of IoT for the promising smart homes. Besides that, the dependability is an essential measure of system ability to provide services such as availability, reliability, safety, security, and maintainability. Today, this dependability concept has been widely and usually used in design and analysis of information systems. Due to the rapid increase of devices and home appliances in the smart homes, its importance in control and management has increased drastically in order to guarantee the dependability of smart homes. The Cyber-Physical Human Centric Framework is highly expected to offer a dependable and unified network operation and management to facilitate efficient implementation and reduce processing load in the smart home domain.

Keynote Speaker 2

Associate Professor Dr. Dimitar Kazakov

  • Head of Artificial Intelligence Research Group, Department of Computer Science, University of York, United Kingdom

Specialization: Machine learning; natural language processing; machine learning of language, including unsupervised learning; language evolution modelling in multi-agent environments.

Biography: Dr Dimitar Kazakov is a Senior Lecturer (Associate Professor) in Computer Science at the University of York, and co-ordinator of the CS Artificial Intelligence group. His research encompasses the development of Machine Learning (ML) and Evolutionary Algorithms and their applications to Natural Language Processing, real-time systems, intelligent agents, function optimisation and financial forecasting. He has published over 120 peer-reviewed articles, supervised 7 and co-supervised another 3 PhD students to completion. He is currently leading a research team of 6 PhD students. Dr Kazakov is a former Vice-Chair of the UK Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB).

Title: Artificial Intelligence and Financial Forecasting (View)

Abstract: 

 

Keynote Speaker 3

Prof. Emeritus Dato Dr. Tengku Mohd Bin Tengku Sembok

  • Faculty of Defence Science and Technology, National Defence University of Malaysia

Specialization: computational linguistics (for Malay, English and Arabic), artificial intelligence, information retrieval, and multimedia courseware development

Biography: Tengku Mohd Tengku Sembok has over forty years of experience in various fields of Information Communication Technology. He has taught undergraduate and postgraduate programs and managed numerous R&D and consultancy projects. He obtained his B.Sc (Hons) in Computer Science from Brighton Polytechnic in 1977, MS from Iowa University in 1981, and PhD from Glasgow University in 1989. His last appointment is as Deputy Vice Chancellor (Academic and Internationalisation) at National Defence University Malaysia (NDUM:2007-2010;2015-2021). Prior to that, he was the Dean of Kulliyyah ICT, International Islamic University Malaysia. He holds a chair of senior fellow in Cyber Security Centre of NDUM. He had held several academic posts at Universiti Kebangsaan Malaysia. Among the posts held were Head of Computer Science Department, Founder and Dean of Faculty of Information Science and Technology. Before joining UKM he was with Malaysian Rubber Institute and Daresbury Nuclear Physics Laboratory (England) as a programmer, and Malaysian Prime Minister’s Department as a system analyst. He has contributed in various international and national committees on ICT such SIGIR Program Committee, UNESCO Ethics in Science and Technology, Terengganu State ICT Council, IRPA Steering Committee in Services Sector (MOSTE), Curriculum Development Committee for Computing Subject (Malaysia Examination Council), and Chairman for Malaysian Qualification Agency in ICT Cluster. Professor Tengku Mohd has involved in MSC Smart School Flagship Application as Project Director in the development of Mathematics courseware for secondary schools in Malaysia. His current research areas are in computational linguistics (for Malay, English and Arabic), artificial intelligence, information retrieval, and multimedia courseware development. He has published over 200 articles in these areas. He has also received numerous awards in international invention and innovation exhibitions for his research products in Geneve, Brussels, and London. He was awarded the national ICT Excellent Teacher 2004 jointly by Ministry of Science, Technology and Innovation, Ministry of Energy, Water and Communication, Malaysian National Computer Confederation, and MAXIS Bhd. He is Fellow of British Computer Society, Academy of Sciences Malaysia, Malaysian Scientific Association, Academy Professor Malaysia, and Founder-Fellow of Society of Information Retrieval and Knowledge Management. He was a commonwealth research fellow at the Sheffield University (1994-1995).

Title: Text to Natural Language Processing in Information Retrieval and Question Answering Systems (View)

Abstract: The levels-of-processing theory proposes that there are many ways to process and code information. The knowledge presentation adopted in information retrieval and question answering systems will determine the performance of the systems. The levels-of-processing applied can be classified as follows: string processing, morphological processing, syntactic processing and semantic processing. Each has their own knowledge representation approach. Conventional information retrieval models, such as Boolean and vector space models, rely on an extensive use of keywords and their frequencies as independent strings in storing and retrieving information. Thus, string processing and morphological processing are mainly adopted in these models. It is believed that such an approach has reached its upper limit of retrieval effectiveness. With advances made in artificial intelligence and natural language processing, there are many attempts made to include linguistic processing and knowledge representation techniques in information retrieval and question answering systems. We focus our research on the application of certain techniques on specific languages and domain areas. Besides English, we focus the application of certain techniques and models on Malay and Arabic documents. In this keynote we will highlight some of our research done in the area of information retrieval and question answering systems at the various levels of processing, and also expound the current research we are doing and the future direction that we would like to undertake.

Keynote Speaker 4

Professor Dae-Ki Kang

  • Director of Machine Learning/Deep Learning Research Lab, Full Professor at the Department of Computer Engineering, Dongseo University, South Korea

Specialization: Learning (artificial intelligence), Bayes methods, Web sites, electronic commerce, feature extraction, information retrieval, pattern classification, regression analysis, social networking (online), text analysis, Internet, cloud computing, collaborative filtering, computer games, consumer behaviour, data handling, data mining, language translation, maximum likelihood estimation, message authentication,mobile computing, natural language processing, optimisation, random processes, recommender systems

Biography: Dae-Ki Kang is a professor at Dongseo University in South Korea. He was a senior member of engineering staff at the attached Institute of Electronics and Telecommunications Research Institute in South Korea. He earned a Ph.D. in computer science from Iowa State University in 2006. His research interests include deep learning, machine learning, and intrusion detection. Prior to joining Iowa State, he worked at two Bay-area startup companies and at the Electronics and Telecommunication Research Institute in South Korea. He received a science master degree in computer science at Sogang University in 1994 and a bachelor of engineering (BE) degree in computer science and engineering at Hanyang University in 1992.

Title: Searching for Optimal: Network Architecture Search and Hyper-Parameter
Optimization (View Starting at 0:48:05)

Abstract: Now, AutoML is one of artificial intelligence core technologies that can be used universally to resolve the shortage problem of artificial intelligence experts, reduce the costs and improve efficiency in machine learning system development. Two key technologies in AutoML are Neural Architecture Search (NAS) and Hyper-Parameter Optimization (HPO). As the complexity of artificial neural networks and algorithms increases, the need for NAS/HPO technology also increases because it can not only optimize hyper-parameters but also automatically find network structures. In this talk, we will cover seminal research work in NAS and HPO. In analyzing the research work, we will summarize various research methodologies for NAS and HPO, including reinforcement learning, evolutionary algorithm, Bayesian optimization, sequential model based optimization, gradient descent algorithms, etc. And we introduce our ongoing project to incorporate AutoML technology into MLForte, a workflow based automated deep learning model construction tool.