Keynote Speaker I

Prof. Xiaoli Li,
Nanyang Technological University, Singapore
 

Speech Title: Harnessing the Power of AI: Transforming Industries Through Advanced Computer Science and Engineering
Abstract: This presentation delves into the transformative potential of computer science and engineering across key industries, including manufacturing, aerospace, professional services, and transportation. In manufacturing and aerospace, AI-driven time series analytics emerge as a revolutionary force, enabling predictive maintenance and condition monitoring. Discover how these advancements optimize operations, minimize downtime, and elevate productivity. In the professional services sector, AI proves indispensable in enhancing auditor productivity, accurately predicting staff attrition, and developing advanced Cyber Threat Hunting Tools to bolster security. In the transportation industry, explore how AI can optimize traffic light systems for increased efficiency. Join us on a journey to uncover how computer science and engineering are reshaping industries, driving innovation, and paving the way for real-world transformation.

Biography: Dr. Xiaoli is currently a department head (Machine Intellection department, consisting of 100+ AI and data scientists, which is the largest AI and data science group in Singapore) and a principal scientist at the Institute for Infocomm Research, A*STAR, Singapore. He also holds adjunct professor position at Nanyang Technological University (He was holding adjunct position at National University of Singapore for 6 years). He is an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). Xiaoli is also serving as KPMG-I2R joint lab co-director. He has been a member of Information Technology Standards Committee (ITSC) from ESG Singapore and Infocomm Media Development Authority (IMDA) since 2020. Moreover, he serves as a health innovation expert panel member for the Ministry of Health (MOH), expert panel member for Ministry of Education (MOE), as well as an AI advisor for the Smart Nation and Digital Government Office (SNDGO), Prime Minister s Office, highlighting his extensive involvement in key Government and industry initiatives

Keynote Speaker II

Prof. Minghua Chen
City University of Hong Kong, Hong Kong, China

Speech Title: Synthesizing Distributed Algorithms for Combinatorial Network Optimization
Abstract: Many important network design problems are fundamentally combinatorial optimization problems. A large number of such problems, however, cannot readily be tackled by distributed algorithms. We develop a Markov approximation technique for synthesizing distributed algorithms for network combinatorial problems with near-optimal performance. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of time-reversible Markov chains. Selected Markov chains among this class, or their carefully perturbed versions, yield distributed algorithms that solve the log-sum-exp approximated problem. The Markov Approximation technique allows one to leverage the rich theories of Markov chains to design distributed schemes with performance guarantees. By case studies, we illustrate that it not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in other problem domains with provable performance, including cloud computing, edge computing, and IoT scheduling.

Biography: Dr. Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is currently a Professor of School of Data Science, City University of Hong Kong. He received the Eli Jury award from UC Berkeley (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, IEEE INFOCOM Best Poster Award in 2021, and ACM e-Energy Best Paper Award in 2023. He is currently a Senior Editor for IEEE Systems Journal and an Executive Member of ACM SIGEnergy (as the Award Chair). His recent research interests include online optimization and algorithms, machine learning in power systems, intelligent transportation systems, distributed optimization, and delay-critical networked systems. He is an ACM Distinguished Scientist and an IEEE Fellow.

Invited Speaker I

Dr. Bruno Carpentieri,
Università di Salerno, Italy
 

Speech Title: Data Compression Does Not Only Compress Data
Abstract: Digital data compression is the coding of digital data to minimize its representation. In compressed form digital data can be stored more compactly and transmitted more rapidly. While at the beginning data compression had as its main application the storage of files on disks and digital memories today digital data compression is the key protagonist in digital communication: we would not have, for example, digital TV, smartphones and satellite communications and even the AI engines without efficient data compression.Recent advances in compression span a wide range of applications.
For example Internet and the World Wide Web infrastructures benefits from compression, search engines can extend the idea of sketches that work for text files, image, speech or music data, etc.. Additionally, new general compression methods are always being developed, those that allow indexing over compressed data or error resilience. Compression also inspires information theoretic tools for pattern discovery and classification, especially for bio sequences.
Today we know that data compression, data prediction, data classification, learning and data mining are facets of the same (multidimensional) coin.
In this talk we will review some of the recent advances in the field and discover uncommon applications of data compression.

Biography: Bruno Carpentieri graduated in Computer Science at the University of Salerno, and then obtained the Master of Arts Degree and the Philosopy Doctorate Degree in Computer Science at the Brandeis University (Waltham, MA, USA).Since 1991, he was first Researcher, then Associate Professor and finally Full Professor of Computer Science at the University of Salerno (Italy).His research interests include data compression and information hiding.He was Associate Editor of IEEE Trans magazine. on Image Processing and is still Associate Editor of the international journals Algorithms and Security and Communication Networks. He was also chair and organizer of various international conferences including the International Conference on Data Compression, Communication and Processing, co-chair of the International Conference on Compression and Complexity of Sequences, and, for many years, a member of the program committee of the IEEE Data Compression Conference.He has been responsible for several European Commission contracts in the field of data compression (compression of digital images and videos).He directs the Data Compression Laboratory at the Computer Science Department of the University of Salerno.

Invited Speaker II

Dr. Sanghyuk Lee
New Uzbekistan University, Uzbekistan
 

Speech Title: Decision Making with Iterative Game with Semi-Perfect Information
Abstract: A decision making framework with an iterative game structure has been proposed. In the proposed structure, the maximum benefit for each player is resolved by the iteration process. Based on the payoff matrix, the optimal solution is sought by comparing it with the criterion set by the players. To maximize the benefit for each party (buyer and seller), an iterative game structure is proposed based on the given payoff matrix and iterative machine. For real-world application, practical example is considered, and a feasible solution is obtained. In comparison with the existing body of research on game theory under semi-perfect information, the provided solution is far from their payoff but the result would be acceptable for two parties.

Biography: Sanghyuk Lee (M'21-SM'21) received Doctorate degree from Seoul National University, Seoul, Korea, in Electrical Engineering in 1998. His main research interests include data evaluation with similarity measure, human signal analysis, high dimensional data analysis, controller design for linear/nonlinear system, and observer design for linear/nonlinear system. Dr. Lee is currently working as a Professor at School of Computing of New Uzbekistan University, Tashkent, Uzbekistan since 2023. He had been working as a founding director of the Centre for Smart Grid and Information Convergence (CeSGIC) in Xi’an Jiaotog-Liverpool University in Suzhou, China from 2014 to 2023. He also had been serving as a Vice President of Korean Convergence Society (KCS) from 2012 to 2019, and was appointed as an Adjunct Professor at Chiang Mai University, Chiang Mai, Thailand, in 2016. Dr. Lee organized several international conferences with KCS and was awarded multiple honors such as outstanding scholar/best paper award from KCS and Korean Fuzzy Society. Dr. Lee is a senior member of IEEE.

Invited Speaker III

Dr. Amirrudin Kamsin
University Malaya, Malaysia
 

Speech Title: Challenges in Blended Learning
Abstract: Blended learning is widely regarded as an approach that combines the benefits afforded by face-to-face and online learning components. However, this approach of combining online with face-to-face instructional components has raised concerns over the years. Several studies have highlighted the overall challenges of blended learning mode of instruction, but there has been no clear understanding of the challenges that exist in the online component of blended learning. Thus, a systematic review of literature was conducted with the aim of identifying the challenges in the online component of blended learning from students, teachers, and educational institutions perspectives. Self-regulation challenges and challenges in using learning technology are the key challenges that students face. Teachers’ challenges are mainly on the use of technology for teaching. Challenges in the provision of suitable instructional technology and effective training support to teachers are the main challenges faced by educational institutions. This review highlights the need for further investigations to address students, teachers, and educational institutions’ challenges in blended learning. In addition, we proposed some recommendations for future research.

Biography: Amirrudin Kamsin is a Senior Lecturer at the Faculty of Computer Science and Information Technology, and the Acting Director and Deputy Director (ODL and Professional Programme) at the University of Malaya Centre for Continuing Education (UMCCed), University of Malaya, Malaysia. He received his BIT (Management) in 2001 and MSc in Computer Animation in 2002 from University of Malaya and Bournemouth University, UK respectively. He obtained his PhD in Computer Science from University College London (UCL) in 2014. His research areas include human-computer interaction (HCI), authentication systems, e-learning, mobile applications, serious game, augmented reality and mobile health services.