ABSTRACT: We have passed the period of Digital-Follow-up, and now we are in Digital Twin, and trying to enter Digital-lead-off of Industrial Internet of Things. I will share lessons learned from our recent implementations of AIOT systems in oil refinery and glass factories in Middle East, United States, and China.

BIO:Yunhao Liu, ACM Fellow , IEEE Fellow, Professor at the Department of Automation in Tsinghua University, Beijing, China. He served as the Dean of School of Software in Tsinghua, and the MSU Foundation Professor and the Chairperson of Department of Computer Science and Engineering in Michigan State University. Yunhao received his B.S. degree in the Department of Automation at Tsinghua University, and an M.A. degree at Beijing Foreign Studies University, China. He received an M.S. and a Ph.D. degree in Computer Science and Engineering at Michigan State University, USA. Yunhao received Hong Kong ICT Best Innovation and Research Award Grand Prize 2007, China Ministry of Education First Class Natural Science Award 2010, Second Class National Natural Science Award 2011, ACM Presidential Award 2013, CCF Wang Xuan Award 2022, as well as many best paper awards including ACM MobiCom 2014 best paper award, ACM SenSys 2021 Best Paper Award, and SIGCOMM 2021 Best Student Paper Award.

ABSTRACT:This report addresses the global challenge of network attack assessment and innovatively proposes the Multi-dimensional Data Association and inTelligent Analysis model (MDATA) based on the exploration of the temporal, spatial, and semantic relationships inherent in human cognition. It includes the model's components, operating principles, and the methods and technologies for representing, acquiring, and utilizing cybersecurity knowledge. Utilizing this model, comprehensive, accurate, and real-time detection of network attacks has been achieved, along with associated tactical analysis and threat assessment methods. Finally, the successful application of this model at the Pengcheng Federal Cyber Range will be presented.

BIO:Dr. Yan Jia, Professor, currently serves as the head of a major project at the National Laboratory and as the chief at the National Engineering Research Center for Industrial Control System Information Security Technology. She is also the vice president of the Chinese Information Processing Society. Her main research directions include the application of artificial intelligence and big data analysis technologies in the field of cybersecurity. As a project leader, she has undertaken and led over 20 national-level major and key projects. She has received five Second-Class National Science and Technology Progress Awards (ranked 1, 1, 1, 2, 3) and has published more than 320 papers indexed by SCI and EI, authored eight monographs, and obtained over 100 invention patents. She is the principal initiator and committee chair of international forums such as FFD and international conferences including IEEE DSC and CSE.

ABSTRACT:The evolution of generative artificial intelligence (GenAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE's efficiencies, GenAI still faces challenges in resource utilization when deployed on local user devices. Therefore, we first propose mobile edge networks supported MoE-based GenAI. Rigorously, we review the MoE from traditional AI and GenAI perspectives, scrutinizing its structure, principles, and applications. Next, we present a new framework for using MoE for GenAI services in Metaverse. Moreover, we propose a framework that transfers subtasks to devices in mobile edge networks, aiding GenAI model operation on user devices. Moreover, we introduce a novel approach utilizing MoE, augmented with Large Language Models (LLMs), to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts, and weights each decision from the participating experts. In this process, the LLM acts as the gate network to oversee the expert models, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore, it can also leverage LLM's advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly, we insightfully identify research opportunities of MoE and mobile edge networks.

BIO:Dusit Niyato is currently a President's Chair Professor in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. Dusit's research interests are in the areas of mobile generative AI, edge intelligence, quantum computing and networking, and incentive mechanism design. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023) and will serve as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE) from 2025. He is also an area editor of IEEE Transactions on Vehicular Technology (TVT), topical editor of IEEE Internet of Things Journal (IoTJ), lead series editor of IEEE Communications Magazine, and associate editor of IEEE Transactions on Wireless Communications (TWC), IEEE Transactions on Mobile Computing (TMC), IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Cognitive Communications and Networking (TCCN), IEEE Data Descriptions, IEEE Transactions on Services Computing (TSC), and ACM Computing Surveys. He was also a guest editor of IEEE Journal on Selected Areas on Communications. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.

ABSTRACT:AI Cybersecurity is a hot research area. In this talk, I will present a detailed research topic about adversarial attacks prevention methods. Our group proposed an advanced gradient-based approach for mitigation of adversarial attacks in Deep Neural Networks (DNN).  The proposed approach adopted a random distortion transformation defense method called RDG (Random Distortion over Grids) and we combined it with non-linear defenses to thwart adversarial attacks. Extensive evaluation demonstrated the efficiency of this state-of-art defense approach.

BIO:Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received a Ph.D. degree in Computer Science from University of Texas at Dallas. Currently, He is a full professor at Augusta University. He is an ACM Distinguished Member. He received the Life-Achevement Award from IEEE Bio-Inspired Computing STC in 2023. He is also a Highly Cited Researcher in 2021 from Web of Science and IEEE Distinguished Visitor in 2021-2023. He is currently the Chair of IEEE Cyber Systems and Engineering Technical Committee and was the Chair of IEEE Smart Computing Special Technical Committee.  Till now his Google scholar citation is 26600+ and H-index 108.  His research interests include Cyber Security, AI, ML, Big Data, Smarting Computing, Embedded systems, etc. He has published extensively in top conferences such as ACM CCS, ICML, IJCAI, ECCV, DAC, and many IEEE/ACM Transactions. His paper on Tele-health system has won IEEE Systems Journal 2018 Best Paper Award.  His paper about data allocation for hybrid memory has been published in IEEE Transactions on Computers has been selected as IEEE TCSC 2016 Best Journal Paper and hot paper (1 in 1000 papers by Web of Science) in 2017. His paper published in IEEE Transactions on Computers about privacy protection for smart phones has been selected as a Highly Cited Paper in 2017-2020. He also won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. He has won another 10+ Conference Best Paper Awards (such as KSEM 2024 Best Paper Award) in recent years. 

ABSTRACT:The most kind of traditional DP (Differential Privacy) mechanisms (e.g. Laplace, Gaussian, etc.) have unlimited output range. In real scenarios, most datasets have bounded output range. Users would then need to use post-processing or truncated mechanisms to forcibly bound output distribution. However, these mechanisms would incur bias problem which has been a long-known DP challenge, resulting in various unfairness issues in subsequent applications. A tremendous amount of research has been done on analyzing this bias problem and its consequences, but no solutions can solve it fully.
As the world first solution to solve this long-known DP bias problem, this talk will present a new innovative DP mechanism named Composite DP-unbias. It will first illustrate this long-known bias problem, and then detail the rational of the new mechanism and its example noise functions as well as their implementation algorithms. All source codes are publicly available on Github for any deployment or verification.

BIO:Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include data privacy and security, cloud computing, scalable data processing, data systems and related various research topics. His research results have been published in more than 300 papers in international journals and conferences. He received various awards such as IEEE TCSC Award for Excellence in Scalable Computing and Australia’s Top Researchers. He has served as an Associate Editor for various journals such as ACM Computing Surveys, IEEE TC, TCC and TSUSC. He is a MAE (Academia Europea) and IEEE Fellow (IEEE Computer Society). He is Chair for IEEE TCSC (Technical Community for Scalable Computing).

ABSTRACT:Digital twins are revolutionizing industries by providing real-time simulation, monitoring, and predictive analytics capabilities. However, their success hinges on overcoming significant data and resource management challenges. This keynote will explore four key issues critical to the advancement and scalability of digital twins. First, we will discuss the complexities of real-time data processing within the edge-cloud-IoT continuum, emphasizing the need for seamless integration and efficient resource allocation across distributed systems. Second, we will explore the use of Large Language Models (LLMs) for dynamic verification of the resilience of digital twins, highlighting their potential to enhance adaptability and real-time decision-making. Third, we will examine end-to-end monitoring strategies to ensure data integrity, transparency, and reliability, enabling trust in automated decision processes. Finally, we will address the integration of emerging computational technologies, such as quantum accelerators (e.g., Quantum Brilliance) and neuromorphic chips (like Intel Loihi and BrainChip Akida), at the edge network to accelerate data processing and improve the responsiveness of digital twins. This talk will provide insights into how these advancements can be leveraged to develop robust, scalable, and intelligent digital twin ecosystems, driving innovation and efficiency in real-world applications.

BIO:Professor Rajiv Ranjan is an Australian-British computer scientist, of Indian origin, known for his research in Distributed Systems (Cloud Computing, Big Data, and the Internet of Things). He is University Chair Professor for the Internet of Things research in the School of Computing of Newcastle University, United Kingdom. He is an internationally established scientist in the area of Distributed Systems (having published about 350 scientific papers). He is a fellow of IEEE (2024), Academia Europaea (2022) and the Asia-Pacific Artificial Intelligence Association (2023). He is also the Founding Director of the International Centre (UK-Australia) on the EV Security and National Edge Artificial Intelligence Hub, both funded by EPSRC. He has secured more than $64 Million AUD (£32 Million+ GBP) in the form of competitive research grants from both public and private agencies. He is an innovator with strong and sustained academic and industrial impact and a globally recognized R&D leader with a proven track record. He serves on the editorial boards of top quality international journals including IEEE Transactions on Computers (2014-2016), IEEE Transactions on Cloud Computing, ACM Transactions on the Internet of Things, The Computer (Oxford University), and The Computing (Springer) and Future Generation Computer Systems. He led the Blue Skies section (department, 2014-2019) of IEEE Cloud Computing, where his principal role was to identify and write about the most important, cutting-edge research issues at the intersection of multiple, inter-dependent research disciplines within distributed systems research area including Internet of Things, Big Data Analytics, Cloud Computing, and Edge Computing. He is one of the highly cited authors in computer science and software engineering worldwide (h-index=80+, g-index=250+, and 31000+ Google Scholar citations, h-index=60+ and 16000+ Scopus citations, and h-index=50+ and 10000+ Web of Science Citations).