2023 2nd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC 2023)




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Prof. Wanyang Dai

Nanjing University, China

Bio: Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum, President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Quantum Information Processing, Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications.

Title: IoT & big model as a service via cloud computing and federated learning

Abstract: Based on cloud computing and blockchained federated learning, we establish an interactive technical and business model for internet of things (IoT) supported with big model. The purpose to study this model is to conduct interactive decision policy computing dynamically and to meet the targets in terms of both technical quality and financial performance. We will focus on the design and analysis of an optimal policy computing algorithm for smart contracts within the blockchain. Inside the system, each order associated with a demand may simultaneously require multiple service items from different suppliers and the corresponding arrival rate may depend on blockchain history data represented by a long-range dependent stochastic process. The optimality of the computed dynamic policy on maximizing the expected infinite-horizon discounted profit is proved concerning both demand and supply rate controls with dynamic pricing and sequential packaging scheduling in an integrated fashion. Our policy is a pathwise oriented one and can be easily implemented online. The effectiveness of our optimal policy is supported by simulation comparisons.


Prof. Weishan Zhang

China University of Petroleum 

(East China)

Bio: Weishan Zhang, professor of China University of Petroleum (East China). His main research directions are big data intelligent processing, artificial intelligence, etc. He is the director of Credible Intelligent Lab of Shandong Province. He has published more than 100 papers. Currently H index is 27, i10 index is 77. He is the PI/Co-PI of a number of projects such as the National Natural Science Foundation of China and the National Key R&D Program. For his research on federated intelligence, he won the first prize of Science and Technology Progress Award of Qingdao city, the second prize of Shandong Province Science and Technology Progress Award, the third prize of Wu Wenjun Artificial Intelligence Science and Technology Progress Award.

Title: Credible Federated Intelligence with Self-Learning

Abstract: Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, Non-IID characteristics of data affect the effectiveness of federated learning. And also, security attacks may course serious problems. Therefore, credible federated intelligence is necessary. In this talk, a reinforcement learning based federated learning, and a hypernetwork based Federated Self-Learning (CFSL) will be discussed, where credible, personalized federated self-learning for Non-IID environment will be evaluted to show the effectiveness of credible federated intelligence.


Researcher Yanjiao Chen

Zhejiang University, China

Bio: Dr Chen Yanjiao is currently a researcher and Ph.D. supervisor of Zhejiang University. She obtained the Ph.D. degree from the Department of Computer Science and Engineering, Hong Kong University of Science and Technology in 2015. She worked as a postdoctoral researcher at the University of Toronto and a researcher at Wuhan University. She has published more than 100 papers in international journals and conferences. She serves as a TPC member of ACM CCS, NDSS, USENIX Security and so on. She serves on the editorial board of IEEE Wireless Communications Letters and so on. She received the "Young Elite Scientists Sponsorship Program" by the China Association for Science and Technology. She was awarded honorary mention of the Young Scientist of Hong Kong Society.

Title: Security and Privacy of Voice Recognition Systems

Abstract: Voice recognition has been widely used in human-computer interaction and identity recognition in recent years. Threats to voice recognition systems may threaten financial interests and privacy of users. In this talk, we will introduce various attacks against voice recognition systems and corresponding countermeasures. We will discuss the root cause of failures of voice recognition systems. We will further explore the potential defenses against these attacks, and discuss future research directions on strengthening voice recognition systems.

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Assoc. Prof. Aaqif Afzaal 


Foundation University, Pakistan

Bio: Aaqif Afzaal Abbasi received Ph.D. degree in computer engineering from the School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. He is currently serving as an Associate Professor with the Department of Software Engineering, Foundation University, Islamabad, Pakistan. He was a guest researcher at the University of Essex, UK and and guest Professor at University of Pristina, Kosovo.  He also served as Assistant Professor at Wuhan University, China. His research interests focus on vast areas of performance tuning, optimization, and analysis of data and systems. Abbasi published numerous research papers in well-reputed impact factor journals and hosted several special issues on topics of his research interests. He is a member of the ACM.

Title: Resource Management in Internet of Things (IoT)

Abstract: With the recent growth in the Internet of things (IoT) paradigm, there is a lot research going on about managing smart devices for efficient data processing. However, the issue arises in organizing and enabling these devices to work in a coherent manner. This makes the task of resource management extremely important in such a paradigm. Improper utilization of the IoT resources degrades the IoT services quality. Therefore, we use cloud computing techniques for the rescue. This helps in managing the resources effectively. With the emergence of relatively newer cloud-based technologies such as edge and fog computing, resource management in the IoT has become far more effective.