Keynote Speakers

Keynote 1.

Prof. Ryuhei Uehara
Title: Introduction to Computational Origami


Keynote 2.

Prof. Ong Yew Soon
Title: Towards ‘General Optimization Intelligence’


Keynote 3.

Prof. Dr. Thanaruk Theeramunkong
Title: Natural Language Processing in Thai: Past, Present and Future


Keynote 4.

Prof. Akira Namatame
Title: Towards Real-Time Machine Learning and Edge Intelligence


Keynote 5.

Prof. Sung Wook Baik
Title: Video Summarization


Keynote 6.

Prof. Jonathan Garibaldi
Title: Type-2 Fuzzy Systems for Human Decision Making



Prof. Ryuhei Uehara

Japan Advanced Institute of Science and Technology (JAIST)



Introduction to Computational Origami


Computational origami has been attracted in computational geometry problem is to decide whether given polygon P can be folded to a given from the viewpoints of data structure and algorithms. Representative polyhedron Q or not. If Q is a regular tetrahedron, there is a beautiful mathematical theorem that characterizes P as a tiling. However, except that, the problem is quite difficult in general. One of the reasons is the problem is quite counterintuitive. For example, there exists a polygon P of area 22 that can fold to one box of size 1x1x5, and another box of size 1x2x3 just by changing folding lines. Our research group found a surprising polygon of area 30 that can fold to one box of size 1x1x7, another box of size 1x3x3, and the third cube of size sqrt{5}x sqrt{5}x sqrt{5}.  Moreover, the last cube has two different ways to fold to the cube. For finding such a polygon, we had to develop some new algorithms, and then we run our program on a supercomputer two months. However, recently, we can speed up it in only 10 days. Using these new techniques and algorithms, we find several interesting folding problems. In this talk, I give these interesting folding problems, related results for the other polyhedra, and open problems about them.



Ryuhei Uehara is the dean of School of Information Science, Japan Advanced Institute of Science and Technology (JAIST). He is also the director of JAIST Gallery, which has one of the biggest collections of mathematical games and puzzles. His research interests include computational complexity, algorithms and data structures, and graph algorithms. Especially, he is engrossed in computational origami, games and puzzles from the viewpoints of theoretical computer science. He is also the chair of the Japan chapter of European Association for Theoretical Computer Science (EATCS).




Prof. Ong Yew Soon

Nanyang Technological University, Singapore



Towards ‘General Optimization Intelligence’


Traditional Optimization tends to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of optimization solvers do not automatically grow with experience. In contrast however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either be repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that can replicate human cognitive capabilities, leveraging on lessons learned from the past to accelerate the search towards optimal solutions of never before seen tasks. With the above in mind, this talk aims to shed light on recent research advances in the field of global black-box optimization that champion the general theme of ‘General Optimization Intelligence’. A brief overview of associated algorithmic developments in memetic computation and Bayesian optimization shall be provided, with illustrative examples of adaptive knowledge transfer across problems from diverse areas, including, operations research, engineering design, and neuro-evolution.



Yew-soon, Ong is currently a President’s Chair Professor in Computer Science, Professor (Cross Appointment) with School of Physical and Mathematical Science at the Nanyang Technological University, Singapore. Concurrently he is Director of the Data Science and Artificial Intelligence Research Center, co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab and co-Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems. He was Chair of the School of Computer Science and Engineering, Nanyang Technological University from 2016-2018, Lead of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab from 2013-2016 and Director of the Centre for Computational Intelligence from 2008-2015. His research interest lies in artificial & computational Intelligence, mainly optimization intelligence and machine learning. He is a Fellow of the IEEE and Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence. He was listed among the World's Most Influential Scientific Minds and a Thomson Reuters Highly Cited Researcher.  Several of his research publications have received IEEE outstanding paper awards.




 Prof. Thanaruk Theeramunkong

Thammasat University, Thailand 




Natural Language Processing in Thai: Past, Present and Future


Processing Thai language is difficult due to its complex writing system, sophisticated phonological systems, and flexible syntactic structures, such as optional vowel marking, no word-boundary marking, no sentence-boundary marking, existence of tonal variant, flexible word composition and flexible grammar structure. So far, three comprehensive surveys on Thai language processing have been provided by Sornlertlamvanich et al. (2000) and by Koanantakool et al. (2009) and Kawtrakul and Praneetpolgrang (2014). This talk summarizes the state of the art on research and development in Thai language processing, starting from machine translation, information retrieval and information extraction, speech recognition and synthesis, optical character recognition, text categorization, text summarization, and text sentimental analysis. Moreover, three Thai corpora; a named-entity-tagged corpus, an emotional speech corpus, and a tree-bank corpus, are described and their potential applications are discussed.

Keywords: Knowledge Construction, Information Extraction, Thai language processing.


Thanaruk Theeramunkong is currently a professor at School of Information, Computer and Communication Technology at Sirindhorn International Institute of Technology (SIIT) at Thammasat University, Bangkok, Thailand. He is the Program Director of Information and Communication Technology for Embedded Systems (ICTES) at TAIST Tokyo Tech, National Science and Technology Development Agency (NSTDA). He is also an Associate Fellow, Academy of Science, the Royal Society of Thailand. As a professional society, He is the president of Artificial Intelligence Association of Thailand. He serves as an academic committee to the Industrial Section, National Research Council of Thailand (NRCT).

He received his bachelor degree in Electric and Electronics Engineering, master and doctoral degrees in Computer Science from Tokyo Institute of Technology. He was a research associate at Japan Advanced Institute of Science and Technology in Japan and a MIS manager at C.P. Seven Eleven Public Co., Ltd. in Thailand. He got several awards, including the Very Good Research Award in engineering field from Thammasat University in 2008, 2009 and 2010. Recently, in 2014, he has received the National Outstanding Researcher Award in the field of Information Technology and Communication Arts. He also got several best paper awards from conferences and societies, including the Japanese Society for Artificial Intelligence, PAKDD workshops, and KICSS. In 2015, he also got a Gold Medal with the Congratulations of the Jury from the 43rd International Exhibition of Inventions of Geneva for the inventions of automatic semantic-based multi-document summarization and application to public hearing. His research interests are natural language processing, data mining, text mining, machine learning and applications to service science. He was an associate editor of the Institute of Electronics, Information and Communication Engineers (IEICE). He is a member of the Steering Committee of the Pacific-Asia Conferences on Knowledge Discovery and Data Mining (PAKDD) and a member of the Steering Committee of the Pacific Rim International Conferences on Artificial Intelligence (PRICAI). He is the author of more than 48 papers in a number of journals with impact factors and more than 140 conference papers.



Prof. Emeritus, Akira Namatame 
National Defense Academy of Japan


Towards Real-Time Machine Learning and Edge Intelligence


Edge computing performs data processing near the source of the data at the edge of the network, and is more capable to respond to application latency, bandwidth, and other requirements. With the Internet of Things (IoT) becoming part of our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and with this growth of connected devices, edge computing paradigms are seen as promising solutions for handling the large volume of time-sensitive data. Edge computing is also necessarily for monitoring, diagnostics and prognostics of many complex connected systems. For instance, prognostics predict the future performance of a component, machine or system by analysis of failure modes, detection of early signs of fault conditions. These applications create new challenges for existing machine learning (ML) frameworks. Many of the AI based technological innovations come form the fields of ML. Most ML applications require huge computational powers and the demands of cloud computing in the data center are increasing. ML applications are also increasingly deployed not only to serve predictions using static models, but also as integrated components of feedback loops involving dynamic and real-time decision making.

In this talk, I provide a comprehensive survey and categorize the efforts on edge computing paradigms. Then I provide the framework of real-time machine learning and discuss some challenges and future directions for research in real-time machine learning that is likely to shape edge intelligence, intelligence at the edge levels.


Dr. Akira Namatame is Professor emeritus of National Defense Academy, Japan. He is now Scientific Advisor, Asian Office of Aerospace Research & Development of US Air Force Research Laboratory.

His research interests include multi-agent systems, complex networks, artificial intelligence, computational social science, and game theory. In the past ten years, he has given over 40 invited talks, and over 20 tutorial lectures in international conferences and workshops, and academic institutions. He has organized more that 30 international conferences and workshops, and special sessions. He is the editor-in-chief of Springer’s Journal of Economic Interaction and Coordination (JEIC), editor in Modeling and Simulation Society Letter. He has published more than 300 refereed scientific papers, together with eight books on multi-agent systems, agent modeling and network dynamics, collective systems and game theory. More detail information can be obtained through.



Prof. Sung Wook Baik

Sejong University, Korea



Smart Cities Surveillance: Current Research Challenges and Future Directions


The rapid advancements in communication technologies integrated with smart sensors yield huge amount of data, creating greater chances for making cities smarter. Vision sensor in particular, plays a prominent role in smart surveillance where the video data can be processed through computationally intelligent techniques to make cities smart and comfort human lives. In this presentation, we provide an overview of smart cities by considering Incheon Free Economic Zone (IFEZ) as a practical example. Further, we review state-of-the-art technologies for smart surveillance by highlighting their achievements and limitations. In addition, we focus on key challenges of multi-view video data and provide future directions for various surveillance applications in smart cities.


Sung Wook Baik received the B.S degree in computer science from Seoul National University, Seoul, Korea, in 1987, the M.S. degree in computer science from Northern Illinois University, Dekalb, in 1992, and the Ph.D. degree in information technology engineering from George Mason University, Far fax, VA, in 1999. He worked at Datamat Systems Research Inc. as a senior scientist of the Intelligent Systems Group from 1997 to 2002. In 2002, he joined the faculty of the College of Electronics and Information Engineering, Sejong University, Seoul, Korea, where he is currently a Full Professor and the Chief of Sejong Industry-Academy Cooperation Foundation. He is also the head of Intelligent Media Laboratory (IM Lab) at Sejong University. His research interests include computer vision, multimedia, pattern recognition, machine learning, data mining, virtual reality, and computer games.


Prof. John Garibaldi

University of Nottingham, UK



Type-2 Fuzzy Systems for Human Decision Making



Type-2 fuzzy sets and systems, including both interval and general type-2 sets, are now firmly established as tools for the fuzzy researcher that may be deployed on a wide range of applications and in a wide set of contexts. However, in many situations the output of type-2 systems are type-reduced and then defuzzified to an interval centroid, which are then often even simply averaged to obtain a single crisp output. Many successful applications of type-2 have been in control contexts, often focussing on reducing the RMSE. This is not taking full advantage of the extra modelling capabilities inherent in type-2 fuzzy sets. In this talk, I will present some of the current research being carried out within the LUCID group at Nottingham, and wider, into type-2 for modelling human reasoning. I will cover approaches and methodologies which make more use of type-2 capabilities, illustrating these with reference to practical applications such as classification of breast cancer tumours, modelling expert variability in cyber-security contexts, and other decision support problems.


Professor Jon Garibaldi received the BSc degree in Physics from University of Bristol, UK, in 1984, and MSc degree and PhD degree from the University of Plymouth, UK, in 1990 and 1997, respectively. Prof. Garibaldi is currently Head of School of Computer Science, University of Nottingham, Head of the Intelligent Modelling and Analysis (IMA) Research Group, Member of the Lab for Uncertainty in Data and Decision Making (LUCID) and joint Director of the Advanced Data Analysis Centre (ADAC). His main research interests include modelling uncertainty and variation in human reasoning, and in modelling and interpreting complex data to enable better decision making, particularly in medical domains. Prof. Garibaldi is the current Editor-in-Chief of IEEE Transactions on Fuzzy Systems. He has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN.