Keynote Speakers of ICACS 2018
Tok Wang Ling, IEEE Senior Life Member
National University of Singapore, Singapore
Dr. Tok Wang LING is a professor in Computer
Science Department at the National University of Singapore. He
was Head of IT Division, Deputy Head of the Department of
Information Systems and Computer Science, and Vice Dean of the
School of Computing of the University.
He received his PhD and M.Math, both in computer science, from University of Waterloo (Canada) and B.Sc. (1st class Hons) in Mathematics from Nanyang University (Singapore).
His current research interests include Database Modeling, Entity-Relationship Approach, Object-Oriented Data Model, Normalization Theory, Semi-Structured Data Model, XML Twig Pattern Query Processing, XML and Relational Database Keyword Query Processing, Temporal Database keyword Query Processing.
He serves/served on the steering committees of 5 international conferences, was the SC Chair of both ER and DASFAA, and currently is the SC Chair of BigComp. He served as Conference Co-chair of 12 international conferences, including ER 2004, DASFAA 2005, SIGMOD 2007, VLDB 2010, BigComp 2015, and ER 2018. He served as Program Committee Co-chair of 6 international conferences, including DASFAA 1995, ER 1998, ER 2003, and ER 2011.
He received the ACM Recognition of Service Award in 2007, the DASFAA Outstanding Contributions Award in 2010, and the Peter P. Chen Award in 2011.
Speech Title: Conceptual Modeling Views on SQL and NoSQL
Abstract: The three basic concepts in the
Entity Relationship Model are: object class, relationship type,
and attribute of object class and relationship type. They are
termed ORA-semantics. Without knowing the ORA-semantics in the
databases, the quality of some databases are low. The Relational
Model does not capture ORA-semantics.
We first present some restrictions in the relational model such as limitations of normalization theory and limitations of Relational Model using the Universal Relation Assumption. We then discuss the requirements for traditional database applications in RDBMS using SQL and the performance issues, review some of the existing methods which can be used to improve the performances of certain database applications, such as materialized view design, horizontal and vertical partitioning of data, the concepts of strong FD/MVD and weak FD in physical database design, etc.
We further present some rarely-mentioned but very important issues in data and schema integration, such as entity resolution vs relationship resolution, primary key vs object identifier (OID), local OID vs global OID, local FD/MVD vs global FD/MVD, semantic dependency vs FD/MVD, etc. All these concepts are related to ORA-semantics and they have significant impact on the quality of the integrated database and schema.
We briefly present the basic data models of the 4 major categories of NoSQL databases, i.e. key-value store, wide-column store, document store, and graph database. We then give a conceptual modeling view on SQL vs NoSQL using a set of characteristics and requirements of database applications. We finally present a set of criteria to aid decision-making regarding when to use SQL or NoSQL for database applications.
Chin-Chen Chang, IEEE and IET Fellow
Feng Chia University, Taiwan
Professor Chin-Chen Chang obtained his Ph.D. degree in computer engineering from National Chiao Tung University. His first degree is Bachelor of Science in Applied Mathematics and master degree is Master of Science in computer and decision sciences. Both were awarded in National TsingHua University. Dr. Chang served in National Chung Cheng University from 1989 to 2005. His current title is Chair Professor in Department of Information Engineering and Computer Science, Feng Chia University, from Feb. 2005. Prior to joining Feng Chia University, Professor Chang was an associate professor in Chiao Tung University, professor in National Chung Hsing University, chair professor in National Chung Cheng University. He had also been Visiting Researcher and Visiting Scientist to Tokyo University and Kyoto University, Japan. During his service in Chung Cheng, Professor Chang served as Chairman of the Institute of Computer Science and Information Engineering, Dean of College of Engineering, Provost and then Acting President of Chung Cheng University and Director of Advisory Office in Ministry of Education, Taiwan.
Professor Chang's specialties include, but not limited to, data engineering, database systems, computer cryptography and information security. A researcher of acclaimed and distinguished services and contributions to his country and advancing human knowledge in the field of information science, Professor Chang has won many research awards and honorary positions by and in prestigious organizations both nationally and internationally. He is currently a Fellow of IEEE and a Fellow of IEE, UK. And since his early years of career development, he consecutively won Institute of Information & Computing Machinery Medal of Honor, Outstanding Youth Award of Taiwan, Outstanding Talent in Information Sciences of Taiwan, AceR Dragon Award of the Ten Most Outstanding Talents, Outstanding Scholar Award of Taiwan, Outstanding Engineering Professor Award of Taiwan, Chung-Shan Academic Publication Awards, Distinguished Research Awards of National Science Council of Taiwan, Outstanding Scholarly Contribution Award of the International Institute for Advanced Studies in Systems Research and Cybernetics, Top Fifteen Scholars in Systems and Software Engineering of the Journal of Systems and Software, Top Cited Paper Award of Pattern Recognition Letters, and so on. On numerous occasions, he was invited to serve as Visiting Professor, Chair Professor, Honorary Professor, Honorary Director, Honorary Chairman, Distinguished Alumnus, Distinguished Researcher, Research Fellow by universities and research institutes. He also published over hundreds papers in Information Sciences. In the meantime, he participates actively in international academic organizations and performs advisory work to government agencies and academic organizations.
Speech Title: Applying De-clustering Concept to Information Hiding
Abstract: Reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image. In this talk, I will talk about a reversible scheme based on declustering strategy for VQ compressed images. The declustering can be regarded as a preprocessing step to make the proposed steganographic method more efficient. Our experimental results show that the time required for the embedding process in the proposed method is few. In addition, the reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image. In this talk, l will introduce a reversible scheme for VQ-compressed images that is based on a declustering strategy and takes advantage of the local spatial characteristics of the image. The main advantages of our method are ease of implementation, low computational demands,and no requirement for auxiliary data.
Prof. Sheng-Uei Guan
Xi’an Jiaotong-Liverpool University, China
Steven Guan received his BSc. from Tsinghua
University (1979) and M.Sc. (1987) & Ph.D. (1989) from the
University of North Carolina at Chapel Hill. He is currently a
Professor and the Director for Research Institute of Big Data
Analytics at Xi'an Jiaotong-Liverpool University (XJTLU). He
served the head of department position at XJTLU for 4.5 years,
creating the department from scratch and now in shape. Before
joining XJTLU, he was a tenured professor and chair in
intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined the academia for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor for 8 years.
Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, coding theory, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired, delivered keynote speech for 80+ international conferences and served in 180+ international conference committees and 20+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, Self-Modifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, Incremental Hyper-sphere Partitioning for Classification, Controllable Cellular Automata for Pseudorandom Number Generation, Self Programmable Cellular Automata, Configurable Cellular Automata, Layered Cellular Automata, Transformation Sequencing of Cellular Automata for Pseudorandom Number Generation, Open Communication with Self-Modifying Protocols, etc.
Speech Title: Opportunities and Challenges in Information Communications Technology
Abstract: This talk introduces the overall trends of Information Communications Technology (ICT) and presents an overview for opportunities and challenges in ICT. Critical issues, research problems and developments of ICT in various areas are addressed, such as green computing, Internet computing, mobile computing, and intelligent computing. Opportunities and challenges in relevant areas are also covered, for example, Internet of Things, cloud computing, big data analytics. Critical development of ICT in various aspects are proposed thereafter. Finally, the challenges faced by the higher education sector are also discussed.
Prof. Jun Xu
Chinese Academy of Sciences, China
Prof. Jun Xu received his B.E. and Ph.D. in Computer Science from Nankai University, in 2001 and 2006, respectively. He worked as an associate researcher, researcher, and senior researcher at Microsoft Research Asia and Huawei Noah's Ark Lab. In 2014, he joined Institute of Computing Technology, Chinese Academy of Sciences. His research interests are in information retrieval, machine learning, and big data analysis. I have worked on (i) learning to rank for information retrieval; (ii) large scale topic modeling; and (iii) semantic matching in search. (For more)
Speech Title: Reinforcement Learning to Rank for Search Result Diversification
Abstract: The goal of search result diversification is to construct a document ranking for satisfying as many different query subtopics as possible. Typically, the diverse ranking process can be formalized as greedy sequential document selection. At each position, the document that can provide the largest amount of additional information to the users is selected. Since the utility of a document depends on its preceding documents in search result diversification, constructing an optimal document ranking is NP-hard. The traditional greedy document selection usually leads to suboptimal solutions. In the talk, I will show that the problem can be alleviated with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model. Specifically, the sequential document selection process is fit into an MDP. At each time step the greedy action is further improved through the exploratory tree search by MCTS. Reinforcement learning algorithm was developed to learn the model parameters. Empirical evaluation clearly indicated the effectiveness of the approach. The MCTS enhanced MDP can also be applied to variant applications, including sequence tagging, text matching etc.