| Prof. Maode MAShenzhen University of Advanced Technology Prof. Maode Ma, a Fellow of IET, received his Ph.D. from the Department of Computer Science at the Hong Kong University of Science and Technology in 1999. Prof. Ma is a Full Professor in the Faculty of Computer Science and Artificial Intelligence at Shenzhen University of Advanced Technology. Before joining SUAT, he had been a faculty member at Nanyang Technological University and Qatar University for over 25 years. He has extensive research interests in network security, AI security, and wireless networking. He has about 550 international academic publications, which include more than 280 journal papers. His publication has received close to 13,000 citations in Google Scholar. Prof. Ma currently serves as the Editor-in-Chief of the Journal of Communication and Network Security, the International Journal of Computer and Communication Engineering, and the Journal of Communications. He also serves as a Senior Editor for IEEE Communications Surveys and Tutorials, and an Associate Editor for the International Journal of Communication Systems. Prof. Ma is a senior member of the IEEE Communication Society. Prof. Ma has been a Distinguished Lecturer for the IEEE Communication Society from 2013 to 2016 and from 2023 to 2024. Speech Title: Design of Automatic Incremental Lifetime Learning IDSs Abstract: Traditional Intrusion Detection Systems(IDSs) provide limited defense against emerging threats, as they rely on static rules or machine learning (ML) models that lack the capacity for real-time updates. The Incremental Lifetime Learning IDS (ILL-IDS) is a new type of IDS to address this limitation by enabling adaptive learning of new attack types. However, ILL-IDS depends heavily on large volumes of high-quality labeled data, making the model update process costly and labor-intensive. In this talk, the Automatic Incremental Lifetime Learning IDS (AILL-IDS) is introduced, which is a novel IDS framework that can significantly reduce the need for labelling data by incremental semi-supervised learning. This approach not only enables AILL-IDS to detect unknown types of attacks and adapt its model dynamically with minimal labeled data but also ensures continuous detection during the model update process, enhancing both speed and accuracy in threat detection in vehicular networks or Internet of Thing (IoT) systems. Experimental results demonstrate that AILL-IDS can achieve a high detection rate of 0.97 and an average F1 score of 0.90, labelling only 5.5% of the total traning data, thereby offering an efficient and scalable solution for securing IoT against emerging cyber threats. |
| Prof. Adul RaufNanjing University of Information Science & Technology Professor Abdul Rauf obtained his Ph.D. in Finance (Applied Economics) from Southeast University, Nanjing, China. He is currently working as a Full Professor at the School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing China. Meanwhile, He is engaged as Module Lead/ Instructor Reading Academy-NUIST affiliated (University of Reading, United Kingdom (UK). Prof. Rauf has been published over 60 research papers in renowned leading SSCI/SCI，ABS-3 and ABDC journals, including the Journal of Environmental Management, Finance Research Letters, World Development, Journal of Cleaner Production and Energy etc. His research interests span sustainable development, green finance, energy economics, technological innovation, and macroeconomic policy. He has received research funding from the National Natural Science Foundation of China (NSFC) and NUIST. He has also delivered keynote speeches at several very prestigious international conferences. Additionally, he actively serves as an editor and peer reviewer for numerous high-impact SSCI and SCI journals. Research Area: Economics, Finance, Green Finance, Energy Economics, Low Carbonization, Digital Finance, Technological Innovation and Investment, Belt & Road Initiative, and adoption of Artificial Intelligence (AI) in Finance and Economics. Speech Title: AI adoption and ethical capitalistic orientation: investigating the moderating effect of human–AI synergy in China SME Abstract: This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making. |
| Prof. Thippa Reddy GadekalluZhejiang Agriculture and Forestry University Thippa Reddy Gadekallu is currently working as a Professor at Zhejiang A&F University and as a visiting professor at the Division of Research and Development, Lovely Professional University, Phagwara, India. He has obtained his Bachelors in Computer Science and Engineering from Nagarjuna University, India, in the year 2003, Masters in Computer Science and Engineering from Anna University, Chennai, Tamil Nadu, India in the year 2011 and his Ph.D in Vellore Institute of Technology, Vellore, Tamil Nadu, India in the year 2017. He has more than 15 years of experience in teaching and research. He has more than 400 international/national publications in reputed journals and conferences. Currently, his areas of research include Machine Learning, Internet of Things, Deep Neural Networks, Blockchain, Computer Vision. He has acted as a guest editor in several reputed publishers like IEEE, Elsevier, Springer, MDPI. He is recently recognized as one among the top 2% scientists in the world as per the survey conducted by Elsevier in the years 2021, 2022, 2023, 2024, 2025. He is also recognized as a highly cited researcher, young scientist category, by Clarivate (web of Science) for the period 2017-2022 in the year 2023. Speech Title: Federated Learning for Big Data- Opportunities, Applications, and Future Directions Abstract：Federated Learning (FL) offers a decentralized approach to machine learning that keeps data local while training global models, making it ideal for privacy-sensitive Big Data applications. This presentation surveys how FL enhances data acquisition, storage, analytics, and privacy across domains like smart cities, healthcare, and transportation. We discuss real-world implementations, popular FL platforms, and key challenges—including communication efficiency, data heterogeneity, and security threats. The talk concludes with future research directions aimed at scaling FL for real-world Big Data ecosystems, positioning FL as a critical enabler for secure, efficient, and collaborative AI. |
| Assoc. Prof. Hongbo LiShanghai University Hongbo Li is currently Associate Professor in the School of Management at Shanghai University, Shanghai, China. He obtained his PhD degree in Management Science in July 2014 from Beihang University, 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 project scheduling, data science, and artificial intelligence. He has published in a variety of refereed journals, such as Computers & Operations Research, IEEE Transactions on Engineering Management, Annals of Operations Research, International Journal of Production Research, and Decision Support Systems. Speech Title: Integrating Artificial Intelligence and Optimization for Project Portfolio Selection Problems Abstract: This presentation explores the integration of Artificial Intelligence (AI) and mathematical optimization to address complex Project Portfolio Selection Problems (PPSPs). Taking public institutions and enterprises as research subjects, we investigate their project portfolio selection problems from static and dynamic perspectives respectively. To tackle these problems, we construct targeted models including stochastic programming, integer programming and Markov decision processes, and develop a series of solution approaches such as approximate dynamic programming and intelligent optimization algorithms. |
```