PROGRAMME


Keynote Speakers

Public Lectures/Tutorials

Ah-Hwee Tan, Dr., Prof.
School of Computer Science & Engineering
Nanyang Technological University
URL: http://www.ntu.edu.sg/home/asahtan/
Title: Towards Self-Awareness in Artificial Intelligence Systems
(View Abstract)
Laszlo T. Koczy, Dr., Prof.
Szechenyi Istvan University (Gyor, SZE).
Title: Rule Based Fuzzy Systems and Applications
(View Abstract)
Guang-Bin, Huang, Dr., Prof.
School of Electrical & Electronic Engineering
Nanyang Technological University
URL: http://www.ntu.edu.sg/home/egbhuang/
(View Title & Abstract)
Peter Haddawy, Dr., Prof.
Mahidol University, Thailand
Title: Intelligent Virtual Surgical Training Environments
(View Abstract)
Irwin King, Dr., Prof.
Department of Computer Science & Engineering
The Chinese University of Hong Kong
URL: http://www.cse.cuhk.edu.hk/irwin.king
(View Title & Abstract)
Junmo Kim, Dr., Prof.
Korea Advanced Institute of Science and Technology (KAIST)
Title: Deep Learning for Computer Vision
(View Abstract)

Soo-Young Lee, Dr., Prof.
Korea Advanced Institute of Science and Technology (KAIST)
Title: Deep Learning for Speech, Language Processing, and Emotion Recognition
(View Abstract)

Special Sessions

Creative Computing Creative Computing More ...
IOT Internet of Things More ...
Resilient Planning More ...
Heterogeneous Data Heterogeneous Data More ...
DEA Data-enabled Apps More ...
Intelligent Transportation Intelligent Transport More ...
Energy Informatics Energy Informatics More ...
CyberPhysicalSystems Cyber Physical Systems More ...

Accepted Papers


Title: Intelligent Virtual Surgical Training
1. Key challenges in surgical training.
2. Solutions in terms of systems that make use of Virtual Reality and AI techniques to provide virtual environments in which surgical students can practice procedures and receive intelligent tutorial feedback.
The lecture covers training of psychomotor skills and decision making skills. Examples are provided in the area of endodontic surgery. The technqiues discussed are applicable beyond surgery to any domain that involves training of high precision, high risk procedures.
Speaker Biography Professor Haddawy received MSc and PhD degrees in Computer Science from the University of Illinois-Urbana in 1986 and 1991, respectively. He has been a Fulbright Fellow, Hanse-Wissenschaftskolleg Fellow, Avery Brundage Scholar, and Shell Oil Company Fellow. His research falls broadly in the areas of Artificial Intelligence, Medical Informatics, and Scientometrics and he has published over 120 refereed papers with his work widely cited. His research in Artificial Intelligence has concentrated on the use of decision-theoretic principles to build intelligent systems and he has conducted seminal work in the areas of decision-theoretic planning and probability logic. His current work focuses on intelligent medical training systems and modeling of vector-borne disease. In the area of Scientometrics Prof. Haddawy has focused on development of novel analytical techniques motivated by and applied to practical policy issues. He currently holds a professorship in the Faculty of ICT at Mahidol University in Thailand.
Abstract: TBC
Title: Deep Learning for Computer Vision
Abstract: Recently deep learning has become one of the most powerful and popular machine learning techniques due to its record-breaking performances in a variety of recognition tasks including speech recognition and image classification. Deep learning also changed the paradigm of pattern recognition in that it allows us to automatically discover hierarchical features from data instead of relying on hand-crafted features. In this tutorial, I will provide an overview of deep learning discussing what have been the main difficulties of training deep neural networks and how these difficulties have been overcome by recent breakthroughs. I will also introduce several deep learning techniques such as restricted Boltzmann machine (RBM), deep belief network (DBN), deep neural network (DNN), and convolutional neural network (CNN) and talk about how they are applied to computer vision problems such as a large scale image classification.
Speaker Biography Dr. Junmo Kim received the B.S. degree from Seoul National University, Seoul, Korea, in 1998, and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), Cambridge, in 2000 and 2005, respectively. From 2005 to 2009, he was with the Samsung Advanced Institute of Technology (SAIT), Korea, as a Research Staff Member. He joined the faculty of KAIST in 2009, where he is currently an Associate Professor of electrical engineering. His research interests are in image processing, computer vision, statistical signal processing, machine learning, and information theory.
Area: Extreme Learning Machines
Abstract: TBC
Dr. Huang is a Full Professor (with tenure) in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His current research interests include big data analytics, human computer interface, brain computer interface, image processing/understanding, machine learning theories and algorithms, extreme learning machine, and pattern recognition.
Title: Towards Self-Awareness in Artificial Intelligence Systems
Abstract: Although recent development in machine learning techniques, in particular deep learning in neural networks, has raised much expectation in Artificial Intelligence (AI), current AI systems are typically designed to solve specific problems. In fact, they are still far from human-level performance in handling simple daily tasks, that we human are so adept in doing. One of the key functions noticeably missing in current AI systems is self-awareness, the ability to perceive and reflect upon one’s identity and behaviour. Self-awareness is a key aspect of human cognition and a critical feature in human-like AI systems, such as chat bots, virtual assistants, and social robots. In this talk, I shall share some of my work towards injecting self-awareness in AI systems. Firstly, I shall present a general framework for modelling self-awareness, which covers different aspects of self, namely identity, physical embodiment, mental states, experiential memory, and social relations. Then, I shall review a family of biologically-inspired self-organizing neural networks, collectively known as fusion Adaptive Resonance Theory (fusion ART). Following the notion of embodied cognition, this talk will show how fusion ART, encompassing a set of universal neural coding and adaptation principles, could be used as a building block of self-aware AI Systems, especially for autobiographical memory modelling and goal-driven reinforcement learning.
Speaker Biography Dr. Ah-Hwee Tan received a Ph.D. in Cognitive and Neural Systems from Boston University, a Master of Science and a Bachelor of Science (First Class Honors) in Computer and Information Science from the National University of Singapore. He is currently a Professor of Computer Science and the Associate Chair (Research) at the School of Computer Science and Engineering (SCE), Nanyang Technological University. Prior to joining NTU, he was a Research Manager at the A*STAR Institute for Infocomm Research (I2R), heading the Text Mining and Intelligent Agents research programmes. His current research interests include cognitive and neural systems, brain-inspired intelligent agents, machine learning, and text mining.

Title: Rule Based Fuzzy Systems and Applications
1. Basic concepts, operations and relations;
2. Classic fuzzy rule based system;
3. Decision making and control in fuzzy signature set rule bases; and
4. Example of applications.

Speaker Biography Laszlo T. Koczy received the M.Sc., M.Phil. and Ph.D. degrees from the Technical University of Budapest (BME) in 1975, 1976 and 1977, respectively; and the D.Sc. degree from the Hungarian Academy of Science in 1998. He spent his career at BME until 2001, and from 2002 at Szechenyi Istvan University (Gyor, SZE). He has been from 2002 to 2011 Dean of Engineering, and from 2013 to current President of the University Research Council and of the University Ph.D. Council. From 2012 he has been a member of the Hungarian Accreditation Committee (for higher education), appointed by the Prime Minister, and elected Chair of the Engineering and Computer Science sub-committee, member of the Professors and Ph.D. sub-committee, and has been a member of the National Doctoral Council since 2012.
Area: Web Intelligence & Social Computing
Abstract: TBC
Dr. King is Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles. His research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing.