PROGRAMME


Keynote Speakers

Public Lectures

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: Applications of AI in Healthcare
(View Abstract)

Junmo Kim, Dr., Prof.
Korea Advanced Institute of Science and Technology (KAIST)
Title: Deep Learning for Computer Vision
(View Abstract)


Knowledge Representation & Reasoning

ID Authors Title
32 Nguyen Duy Hung. Inference and Learning in Probabilistic Argumentation
22 Christian Freksa, Ana-Maria Olteteanu, Thomas Barkowsky, Jasper van de Ven and Holger Schultheis. Spatial Problem Solving in Spatial Structures
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
76 Anupiya Nugaliyadde, Kok Wai Wong, Ferdous Sohel and Hong Xie. Multi-level Search of a Knowledgebase for Semantic Parsing
49 Parwat Singh Anjana, Rajeev Wankar and C. Raghavendra Rao. Design of a Cloud Brokerage Architecture using Fuzzy Rough Set Technique
26 Narumol Vannaprathip, Peter Haddawy, Holger Schultheis and Siriwan Suebnukarn. Generating Tutorial Interventions for Teaching Situation Awareness in Dental Surgery – Preliminary Report

Swarm Intelligence

ID Authors Title
37 How Siang Chuah, Li-Pei Wong and Fadratul Hafinaz Hassan. Swap-based Discrete Firefly Algorithm for Traveling Salesman Problem
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
70 Stephen Akandwanaho and Serestina Viriri. A Spy Search Mechanism (SSM) for Memetic Algorithm (MA) in Dynamic Environments.
82 Mohamed Saifullah Hussin and Thomas Stützle. Hybrid Simulated Annealing for the Bi-objective Quadratic Assignment Problem
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
17 Mariatul Ariffin, Shiqah Hadi and Somnuk Phon-Amnuaisuk. Evolving 3D Models Using Interactive Genetic Algorithms and L-systems

Deep Learning and its Applications

ID Authors Title
7 Tee Connie, Mundher Al-Shabi, Wooi Ping Cheah and Michael Goh. Facial Expression Recognition Using a Hybrid CNN–SIFT Aggregator
87Muhammad Rizki Aulia Rahman Maulana, Retno Larasati and Mohamad Ivan FananyVisual-Only Word Boundary Detection
88Khuong Vo, Dang Pham, Mao Nguyen, Trung Mai and Tho QuanCombination of Domain Knowledge and Deep Learning for Sentiment Analysis
89Derwin Suhartono, Aryo Pradipta Gema, Suhendro Winton, Theodorus David, Mohamad Ivan Fanany and Aniati Murni ArymurthyHierarchical Attention Network with XGBoost for Recognizing Insufficiently Supported Argument
85 Dileep Kumar, Wasim Ahmad and Jong-Myon KimSpeed Invariant Bearing Fault Characterization using Convolutional Neural Networks
55 Pan Yang, Sheng Han and Youfang Lin. Neural Network Control Method for Mobile Robot Trajectory Tracking
79 Somnuk Phon-Amnuaisuk. What does a Policy Network Learn after Mastering a Pong Game?

Data Mining & Machine learning

ID Authors Title
59 Sadam Al-Azani and Jameleddine Hassine. Validation of Machine Learning Classifiers using Metamorphic Testing and Feature Selection Techniques
43 Nguyen Minh Hai and Quan Thanh Tho. Packer Identification using Hidden Markov Model
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
31Owais Malik and Daphne Lai. Multivariate Time Series Clustering Analysis for Human Balance Data
60 Sougata Deb, Dr. Cleta Milagros Libre Acebedo, Jun Yu, Gomathypriya Dhanapal and Niranchana Periasamy. Analysis of District-Level Monsoon Rainfall Patterns in India

Intelligent Information Systems

ID Authors Title
42 Myat Thiri Khine and Myint Myint Sein. Location-based Services for Surrounding Area with Myanmar Language on Mobile Devices
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
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
77 Shakil Muhammad, Alaelddin Fuad Yousif Mohammed, Oh Hyeontaek and Jun Kyun Choi. DREAD-R: Severity assessment of ONOS SDN Controller
84 Azizi Ab Aziz, Hayder Mohammed and Ahmad Hanis Mohd Shabli. Formal Specifications and Analysis of an Agent-Based Model for Cognitive Aspects of Fear of Crime
29 Chattrakul Sombattheera. An Anytime Algorithm for Scheduling task for Multiagent Systems
75 Bacha Rehman, Ong Wee Hong and Abby Tan Chee Hong. Hybrid Model with Margin-Based Real-Time Face Detection and Tracking
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
38 K-Zin Phyo, Myint Myint Sein and Kzin Phyo. Optimal Route Assessment for Emergency Vehicles Travelling on Complex Road Network
57 Pg Hj Asmali Pg Badarudin. Artificial Intelligence in Pasang Emas, a computer implementation of a Brunei traditional Game

Document Analysis

ID Authors Title
54 Mohammad Darwich, Shahrul Azman Mohd Noah and Nazlia Omar. Malay Sentiment Analysis: Mining Kamus Dewan for Subjective Terms
1 Darko Brodic, Alessia Amelio, Radmila Jankovic and Zoran Milivojevic. Analysis of the Reforming Languages by Image-based variations of LBP and NBP operators
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
20 Bianca Trish Adolfo, Jerson Lao, Joanna Pauline Rivera, John Zem Talens and Ethel Ong. Generating Children’s Stories from Character and Event Models
69 Ibraheem Al-Jadir, Kok Wai Wong, Chun Che Fung and Hong Xie. Text Dimensionality Reduction for Document Clustering using Hybrid Memetic Feature Selection

Title: Applications of AI in Healthcare
The increasing availability of new diverse sources of data as well as AI techniques to exploit such diverse data are coming together to transform the way that information is used to support medical and public health practice. In this talk I illustrate this trend in medicine and public health through our research in the areas of medical training and public health decision support.
The teaching of surgical skills, which has traditionally followed an apprentice style approach, is facing a number of challenges that are driving medical schools to seek alternatives. Medical schools are increasingly introducing simulators into their curricula as a way to provide students with increased practice. The deployment of such systems provides a great opportunity to gather data on student performance and to use this data to personalize instruction. I describe our work on intelligent environments for surgical training that seeks to explore the potential of the integration of techniques from Virtual Reality and Intelligent Tutoring Systems to increase the effectiveness of surgical training by providing personalized instruction, assessment, and formative feedback in a form and on a scale not possible in the physical world. I describe techniques to train physical skills and to train decision making skills.
Effective control of infectious disease requires rapid intervention in order to prevent spread from local areas of transmission. Since many diseases begin in relatively remote areas where public health resources are scarce, targeted intervention and resource allocation become crucial. Disease prediction models can help support such decisions. I describe our work on spatiotemporal Bayesian networks for malaria prediction, which are able to model the complex relationships between environment and disease necessary for accurate prediction. I present techniques to automatically generate models tailored to the specifics of the geographic region being modeled. I then discuss the relationship between spatial resolution and prediction accuracy, which is well known but poorly studied. I present complexity-based spatial hierarchical clustering, a new technique that effectively exploits the tradeoff and provides guidance in selection of appropriate spatial resolution.
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.
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.
Title: Extreme Learning Machines (ELM) – Filling the Gap between Machine Learning and Biological Learning
Abstract: One of the most curious in the world is how brains produce intelligence. Brains have been considered one of the most complicated things in the universe. Machine learning and biological learning are often considered separate topics in the years. The objectives of this talk are three-folds: 1) It will analyse the differences and relationships between artificial intelligence and machine learning, and advocates that artificial intelligence and machine learning tend to become different, they have different focus and techniques; 2) There exists some convergence between machine learning and biological learning; 3) Although there exist many different types of techniques for machine learning and also many different types of learning mechanism in brains, Extreme Learning Machines (ELM) as a common learning mechanism may fill the gap between machine learning and biological learning, in fact, ELM theories have been validated by more and more direct biological evidences recently. ELM theories actually show that brains may be globally ordered but may be locally random. ELM theories further prove that such a learning system happens to have regression, classification, sparse coding, clustering, compression and feature learning capabilities, which are fundamental to cognition and reasoning. This talk also shows how ELM unifies SVM, PCA, NMF and a few other learning algorithms which indeed provide suboptimal solutions compared to ELM.
Speaker BiographyGuang-Bin Huang is a Full Professor in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He is a member of Elsevier's Research Data Management Advisory Board. He is one of three Expert Directors for Expert Committee of China Big Data Industry Ecological Alliance organized by China Ministry of Industry and Information Technology, and a member of International Robotic Expert Committee for China. He was a Nominee of 2016 Singapore President Science Award, was awarded by Thomson Reuters “Highly Cited Researcher” (in two fields: Engineering and Computer Science), and listed in Thomson Reuters’s “The World's Most Influential Scientific Minds.” He received the best paper award from IEEE Transactions on Neural Networks and Learning Systems (2013). His two works on Extreme Learning Machines (ELM) have been listed by Google Scholar in 2017 as Top 2 and Top 7, respectively in its “Classic Papers: Articles That Have Stood The Test of Time” - Top 10 in Artificial Intelligence.
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.