Prof. Xiangjie Kong,Zhejiang University of Technology, China（IEEE enior Member）
Dr. Xiangjie Kong is currently a Full Professor in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), China. Previously, he was an Associate Professor in School of Software, Dalian University of Technology (DUT), China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://www.cssclab.cn). He is/was on the Editorial Boards of 6 International journals. He has served as the General Co-Chair, Workshop Chair, Publicity Chair or Program Committee Member of over 30 conferences. Dr. Kong has authored/co-authored over 140 scientific papers in international journals and conferences including IEEE TKDE, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 16 papers are ESI-Highly Cited Papers (Top 1%). His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 20 filed patents. He has an h-index of 39 and i10-index of 97, and a total of more than 5200 citations to his work according to Google Scholar. He is named in the 2019 and 2020 world’s top 2% of Scientists List published by Stanford University. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at 2 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide. His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.
Mobile Trajectory Generation and Anomaly Analytics: A Knowledge and Data Driven Perspective
Mobility trajectory data is of great significance for mobility pattern study, urban computing, and city science. Self-driving, traffic prediction, environment estimation, and many other applications require large-scale mobility trajectory datasets. However, mobility trajectory data acquisition is challenging due to privacy concerns, commercial considerations, missing values, and expensive deployment costs. Nowadays, mobility trajectory data generation has become an emerging trend in reducing the difficulty of mobility trajectory data acquisition by generating principled data. Despite the popularity of mobility trajectory data generation and anomaly analytics, literature surveys on this topic are rare. In this talk, we will introduce and discuss recent advances on mobility trajectory generation and anomaly analytics by artificial intelligence from knowledge-driven and data-driven views.
Prof. Hongzhi Wang, Harbin Institute of Technology, China（IEEE enior Member）
Hongzhi Wang, Professor, PHD supervisor, the head of massive data computing center, the secretary general of ACM SIGMOD China, outstanding CCF member, IEEE Senior member, a standing committee member CCF databases and a member of CCF big data committee. Research Fields include big data management and analysis, database systems, knowledge engineering and data quality. He was “starring track” visiting professor at MSRA and postdoctoral fellow at University of California, Irvine. Prof. Wang has been PI for more than 10 national or international projects including NSFC key project, NSFC projects and National Technical support project, and co-PI for more than 10 national projects include 973 project, 863 project and NSFC key projects. He also serves as a member of ACM Data Science Task Force. He has won First natural science prize of Heilongjiang Province, MOE technological First award, Microsoft Fellowship, IBM PHD Fellowship and Chinese excellent database engineer. His publications include over 300 papers in the journals and conferences such as VLDB Journal, IEEE TKDE, VLDB, SIGMOD, ICDE and SIGIR, 6 books and 6 book chapters. His PHD thesis was elected to be outstanding PHD dissertation of CCF and Harbin Institute of Technology. He severs as the reviewer of more than 20 international journal including VLDB Journal, IEEE TKDE, and PC members of over 50 international conferences including SIGMOD, VLDB, KDD, ICML, NeurpIS, ICDE, etc. His papers were cited more than 4000 times. His personal website ishttp://homepage.hit.edu.cn/wang.
Database Meets AI
Currently, databases are required to handle large-volume and variable modals data for complex applications such as in-database machine learning, which brings the difficulty in database tuning and design for DBA. Thus, it is necessary to bring AI into databases to achieve automation. However, database with AI brings new challenges to both database and AI techniques. This talk will introduce the background and challenges of database enhanced by AI. Then, this talk will introduce the exploration of the state-of-art techniques in database enhance by AI and prospect the further research direction.
Assoc. Prof. Wei Wei, Xi’an university of Technology, China (IEEE/ACM/CCF Senior Member)
Wei Wei (SM’17) received the M.S. and Ph.D. degrees from Xi’an Jiaotong University, Xi’an, China, in 2005 and 2011, respectively. He is currently an Associate Professor with the School of Computer Science and Engineering, Xi’an University of Technology, Xi’an. In 2022 Wei Wei was presented among "TOP 2% Scientists in the World" by Stanford University for his career achievements. He has an h-index of 60 and was also selected in to 2022 Elseiver top Hihg-cited scientist. He ran many funded research projects as principal investigator and technical members. His current research interests include the area of wireless networks, wireless sensor networks application, image processing, mobile computing, distributed computing, and pervasive computing, Internet of Things, and sensor data clouds. He has published around 400 research papers in international conferences and journals. Dr. Wei is a Senior Member of ACM and IEEE and the China Computer Federation. He is an Editorial Board Member of the Future Generation Computer System, the IEEE Access, Ad Hoc & Sensor Wireless Sensor Network, the Institute of Electronics, Information and Communication Engineers, and KSII Transactions on Internet and Information Systems. Including top journals special issues, ACM ToSN&TOIT, IEEE Trans on ITS&TII&JBHI,etc. He is a TPC member of many conferences and a regular Reviewer of the IEEE Transactions on Parallel and Distributed Systems, the IEEE Transactions on Image Processing, the IEEE Transactions on Mobile Computing, the IEEE Transactions on Wireless Communications, the Journal of Network and Computer Applications, and many other Elsevier journals.
Gradient-Driven Parking Navigation with Continuous Information Potential Field Based on WSNs
As Internet of Things(Iots ) are increasingly being deployed in some important applications, it becomes imperative that we consider application requirements in in-network processes. We intend to use a WSN to aid information querying and navigation within a dynamic and real-time environment. We propose a novel method that relies on the heat diffusion equation to finish the navigation process conveniently and easily. From the perspective of theoretical analysis, our proposed work holds the lower constraint condition. We use multiple scales to reach the goal of accurate navigation. We present a multi-scale gradient descent method to satisfy users’ requirements in WSNs. Formula derivations and simulations show that the method is accurately and efficiently able to solve typical sensor network configuration information navigation problems. Simultaneously, the structure of heat diffusion equation allows more flexibility and adaptability in searching algorithm designs.