Keynote Speakers

Public Lectures/Tutorials

Ah-Hwee Tan, Dr., Prof.
School of Computer Science & Engineering
Nanyang Technological University
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
(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
(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)

Accepted Papers

ID Authors Title Paper/Copyright/Registration
1 Darko Brodic, Alessia Amelio, Radmila Jankovic and Zoran Milivojevic. Analysis of the Reforming Languages by Image-based variations of LBP and NBP operators
2 Peigen Xu and Charibeth Cheng. Factors that Affect Emotion Elicited from News Readers
7 Tee Connie, Mundher Al-Shabi, Wooi Ping Cheah and Michael Goh. Facial Expression Recognition Using a Hybrid CNN–SIFT Aggregator
10 Ricardo Soto, Broderick Crawford, Leandro Alexander Vásquez Mora, Roberto Zulantay Arias, Ana Elizabeth Jaime Bernal, Maykol Ramirez and Boris Almonacid. Solving the Manufacturing Cell Design Problem using Artificial Bee Colony
14 Anvar Bahrampour and Omid Mohamad Nezami. Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)
17 Mariatul Ariffin, Shiqah Hadi and Somnuk Phon-Amnuaisuk. Evolving 3D Models Using Interactive Genetic Algorithms and L-systems
20 Bianca Trish Adolfo, Jerson Lao, Joanna Pauline Rivera, John Zem Talens and Ethel Ong. Generating Children’s Stories from Character and Event Models
22 Christian Freksa, Ana-Maria Olteteanu, Thomas Barkowsky, Jasper van de Ven and Holger Schultheis. Spatial Problem Solving in Spatial Structures
23 Peter Haddawy, Myat Su Yin, Tanawan Wisanrakkit, Rootrada Limsupavanich, Promporn Promrat and Saranath Lawpoolsri. AIC-Driven Spatial Hierarchical Clustering: Case Study for Malaria Prediction in Northern Thailand
24 Thuy Le Thi, Tho Quan Thanh and Tuoi Phan Thi.Ontology-based Conference Resolution in Sentiment Analysis
26 Narumol Vannaprathip, Peter Haddawy, Holger Schultheis and Siriwan Suebnukarn. Generating Tutorial Interventions for Teaching Situation Awareness in Dental Surgery – Preliminary Report
29 Chattrakul Sombattheera. An Anytime Algorithm for Scheduling task for Multiagent Systems
30 Ahmad Sabry, W. Z. W. Hasan, Mza Ab. Kadir, M. A. M. Radzi and S. Shafie. Processing and Monitoring Algorithm for Solar-Powered Smart Home in DC-environment System Based on RF-radio Node
31Owais Malik and Daphne Lai. Multivariate Time Series Clustering Analysis for Human Balance Data
32 Nguyen Duy Hung. Inference and Learning in Probabilistic Argumentation
37 How Siang Chuah, Li-Pei Wong and Fadratul Hafinaz Hassan. Swap-based Discrete Firefly Algorithm for Traveling Salesman Problem
38 K-Zin Phyo, Myint Myint Sein and Kzin Phyo. Optimal Route Assessment for Emergency Vehicles Travelling on Complex Road Network
41 Dk Nur Siti Khadhijah Pg Hj Ali Kumar, Thien Wan Au and Wida Susanty Hj Suhaili. A smart LED street lgith system: A Bruneian case study
42 Myat Thiri Khine and Myint Myint Sein. Location-based Services for Surrounding Area with Myanmar Language on Mobile Devices
43 Nguyen Minh Hai and Quan Thanh Tho. Packer Identification using Hidden Markov Model
45 Boldizsár Tüű-Szabó, Péter Földesi and László T. Kóczy. An efficient new memetic method for the Traveling Salesman Problem with Time Windows
46 Michiharu Maeda and Takahiro Hino. A Novel Approach of Set-Based Particle Swarm Optimization with Memory State
49 Parwat Singh Anjana, Rajeev Wankar and C. Raghavendra Rao. Design of a Cloud Brokerage Architecture using Fuzzy Rough Set Technique
54 Mohammad Darwich, Shahrul Azman Mohd Noah and Nazlia Omar. Malay Sentiment Analysis: Mining Kamus Dewan for Subjective Terms
55 Pan Yang, Sheng Han and Youfang Lin. Neural Network Control Method for Mobile Robot Trajectory Tracking
57 Pg Hj Asmali Pg Badarudin. Artificial Intelligence in Pasang Emas, a computer implementation of a Brunei traditional Game
58 Gajanan Gawde and Jyoti Pawar. Enhanced Minimum Description Length Preprocessing for Time Series Trajectories
59 Sadam Al-Azani and Jameleddine Hassine. Validation of Machine Learning Classifiers using Metamorphic Testing and Feature Selection Techniques
60 Sougata Deb, Dr. Cleta Milagros Libre Acebedo, Jun Yu, Gomathypriya Dhanapal and Niranchana Periasamy. Analysis of District-Level Monsoon Rainfall Patterns in India
62 Thanh Tri Pham, Chau Vo and Hua Phung Nguyen. Transfer Learning-based Case Base Preparation for a Case-Based Reasoning-based Decision Making Support Model in the Educational Domain
63 Darko Brodic, Alessia Amelio, Nadeem Ahmad and Syed Khurram Shahzad. Usability Analysis of the Image and Interactive CAPTCHA via Prediction of the Response Time
65 Mohd Hilmi Hasan, Izzatdin Abdul Aziz, Jafreezal Jaafar, Lukman Ab Rahim and Joseph Mabor Agany Manyiel. A Comparative Study of Mamdani and Sugeno Fuzzy Inference Systems in Quality of Web Services Compliance Monitoring
69 Ibraheem Al-Jadir, Kok Wai Wong, Chun Che Fung and Hong Xie. Text Dimensionality Reduction for Document Clustering using Hybrid Memetic Feature Selection
70 Stephen Akandwanaho and Serestina Viriri. A Spy Search Mechanism (SSM) for Memetic Algorithm (MA) in Dynamic Environments.
75 Bacha Rehman, Ong Wee Hong and Abby Tan Chee Hong. Hybrid Model with Margin-Based Real-Time Face Detection and Tracking
76 Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel and Hong Xie. Multi-level Search of a Knowledgebase for Semantic Parsing
77 Shakil Muhammad, Alaelddin Fuad Yousif Mohammed, Oh Hyeontaek and Jun Kyun Choi. DREAD-R: Severity assessment of ONOS SDN Controller
79 Somnuk Phon-Amnuaisuk. What does a Policy Network Learn after Mastering a Pong Game?
81 Phooi Yee Lau, Hock Woon Hon, Zulaikha Kadim and Kim Meng Liang. GuARD: A Real-time System for Detecting Aggressive Human Behavior in Cage Environment
82 Mohamed Saifullah Hussin and Thomas Stützle. Hybrid Simulated Annealing for the Bi-objective Quadratic Assignment Problem

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.