Prof. William Yeoh 

Hong Kong Metropolitan University, China

Speech Title: Human-Centric Cybersecurity: My Journey and Insights. Speaker: Professor William Yeoh, Hong Kong Metropolitan University.

Abstract: Zero trust cybersecurity is beginning to replace traditional perimeter-based security strategies and is being adopted by organizations across a wide range of industries. However, the implementation of zero trust is a complex undertaking, different from traditional perimeter-based security, and requires a fresh approach in terms of its management. As such, a clear set of critical success factors (CSFs) will help organizations to better plan, assess, and manage their zero trust cybersecurity. In response, we investigated the CSFs for implementing zero trust cybersecurity by conducting a three-round Delphi study to obtain the consensus from a panel of 12 cybersecurity experts. We built a multi-dimensional CSFs framework that comprises eight dimensions, namely identity, endpoint, application and workload, data, network, infrastructure, visibility and analytics, and automation and orchestration. Based on the CSFs, we developed a maturity assessment framework enabling organizations to evaluate their zero trust maturity. This paper contributes to a theoretical understanding of how to deploy zero trust from multiple dimensions and offers a viable guidance framework for organizations from a practical perspective. This paper is useful for organizational stakeholders who are in the process of planning, reviewing, or implementing zero trust cybersecurity.

Prof. Liang Hu

TongJi University, China

Research Area: Recommender systems, machine learning, deep learning, data science, and cross-disciplinary-inspired and convergent smart technologies

Liang Hu is a professor in the School of Electronic and Information Engineering at Tongji University, selected as Shanghai Overseas High-level Talent and National Foundation of China Excellent Youth (Overseas). His research areas include recommender systems, machine learning, deep learning, data science, and cross-disciplinary-inspired and converged intelligent technologies. He has published more than 40 high-level academic papers in many research directions such as cross-domain learning, multimodal learning, complex relationship learning, behavioral sequence analysis and prediction, and multi-objective optimization, including more than 20 papers in Chinese Computer Federation (CCF) Class A journals, more than 10 papers in CCF B as well as JCR Q1 journals, including WWW, IJCAI, AAAI, ICDM, ICWS, TOIS, TKDE, IEEE IS, and so on.

Speech Title: New Paradigms of Generative Human-Computer Interaction and Collaboration in the Era of Large Models

Abstract: The development of generative large language models, represented by ChatGPT, is in full swing, and the cross-modal generative technologies represented by MidJourney and SORA have become cutting-edge hot topics. It is said the generative AI technology is rapidly emerging. In the era of current AI large models, the development of generative technology has also brought a brand new paradigm to the modes of human-computer interaction and collaboration. This report will narrate the new paradigms of human-computer interaction and collaboration brought to us by generative AI technology in common daily scenarios, including content recommendation, game production, and smart healthcare.

Assoc. Prof. Hongbo Li

School of Management, Shanghai University, China

Hongbo Li is Associate Professor of Information Systems and Management Science in the School of Management at Shanghai University, Shanghai, China. He obtained his PhD degree in Management Science in July 2014 from School of Economics and Management, Beihang University, Beijing, China. He was a visiting PhD student at Research Center for Operations Management, Faculty of Economics and Business, KU Leuven, Belgium from 2012 to 2013. His research interests include artificial intelligence, metaheuristics, project scheduling, robust scheduling, data science, business analytics, and information systems. He has published in a variety of refereed journals, such as Journal of Scheduling, International Journal of Production Research, Decision Support Systems, Expert Systems with Applications, and Electronic Commerce Research and Applications.

Speech Title: Data-driven analytics for student reviews in China's higher vocational education MOOCs

Abstract: The wide application of Massive Open Online Courses (MOOCs) has effectively improved the curriculum system of China's higher vocational education. In the meantime, some MOOCs suffer from poor course quality. Therefore, from the perspective of course quality improvement, we propose a data-driven framework for mining and analyzing student reviews in China's higher vocational education MOOCs. In our framework, we first mine multi-level student demands hidden in MOOC reviews by combining web crawlers and text mining. Then we use an artificial neural network and the KANO model to classify the extracted student demands, thereby designing effective MOOC quality improvement strategies. Based on the real data from China's higher vocational education MOOCs, we validate the effectiveness of the proposed data-driven framework.