Keynote Speakers / 主讲嘉宾


Prof. Qing Li 李青教授, 香港理工大学
IEEE Fellow, Head of the Department of Computing
IEEE会士, 香港理工大学电子计算系主任
Hong Kong Polytechnic University, China

Biography: Qing Li is a Chair Professor and Head of the Department of Computing, the Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media, Web services, and e-learning systems. He has authored/co-authored over 500 publications in these areas, with over 28100 total citations according to Google Scholars. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), and World Wide Web (WWW), in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits/sat on the Steering Committees of DASFAA, ACM RecSys, IEEE U-MEDIA, ER, and ICWL. Prof. Li is a Fellow of IEEE.

Speech Title: GSA: Facilitating Intra-Subject Study and Inter-Subject Development with Course Knowledge Graphs

Abtract:Knowledge graphs (KGs) have been actively studied for pedagogical purposes. To depict the rich but latent relations among different concepts in the course textbook, increasing efforts have been proposed to construct course KGs for university students. However, the application of course KGs for real study scenarios and career development remains unexplored and nontrivial. First, it is hard to enable personalized viewing and advising. Within the intricate university curricula, instructors aim to assist students in developing a personalized course selection pathway, which cannot be fulfilled by isolated course KGs. Second, locating concepts that are important to individuals poses challenges to students. Real-world course KGs may contain hundreds of concepts connected by hierarchical relations, e.g., contain subtopic, making it challenging to capture the key points. To tackle these challenges, in this talk, we present GSA, a novel system based on course knowledge graphs, to facilitate both intra-course study and inter-course development for students significantly. More specifically, we establish an interactive web system for both instructors to construct and manipulate course KGs, and students to view and interact. To visualize the centrality of a course KG based on various metrics, concept-level advising is designed; we also propose a tailored algorithm to suggest the learning path based on what concepts students have learned. Finally, course-level advising is instantiated with a course network, which indicates the prerequisite relations among different levels of courses, corresponding to the annually increasing curricular design and forming different major streams. Examples will be given to illustrate the effectiveness of GSA.


Prof. Yanfu Li 李彦夫教授, 清华大学
Director of Institute for Quality & Reliability
Tsinghua University, China

Biography: Dr. Yan-Fu Li is currently a full professor at the Department of Industrial Engineering (IE), Tsinghua University. He is the Director of the Institute for Quality & Reliability of Tsinghua University .He received his Ph.D in Industrial Engineering from National University of Singapore in 2010. He was a faculty member at Laboratory of Industrial Engineering at CentraleSupélec, France, from 2011 to 2016. His research areas include RAMS (reliability, availability, maintainability, safety) assessment and optimization with the applications onto telecom systems, energy systems, transport systems, etc. He is the Principal Investigator (PI) of several government projects including the key project funded by National Natural Science Foundation of China, the project in National Key R&D Program of China. He is also experienced in industrial research, the partners include Huawei, Volkswagen, Mitsubishi Heavy Industries, EDF, ALSTOM, etc. Dr. Li has published more than 120 research papers, including more than 70 peer-reviewed international journal papers with H-index 33. He is the Elsevier Highly Cited Chinese Researcher from 2019-2021. He is currently an Associate Editor of IEEE Transactions on Reliability, Guest editor of IEEE Transactions on Industrial Informatics and Reliability Engineering & Systems Safety, a senior member of IEEE and IISE. He is a vice president of the System Reliability Chapter of System Engineering Society of China.


Prof. Dazhao Cheng 程大钊教授, 武汉大学
IEEE Senior Member, Vice Dean of the School of Computer Science
IEEE高级会员, 武汉大学计算机科学学院副院长副院长
Wuhan University, China

Biography: Dr. Dazhao Cheng received his Ph.D. in Computer Science from University of Colorado, Colorado Springs in 2016, the M.S. degree in Electronic and Computer Engineering from the University of Science and Technology of China (USTC) in 2009, and the B.E. degree in Electronic and Computer Engineering from Hefei University of Technology in 2006. He is currently an Assistant Professor in the Computer Science Department at the University of North Carolina at Charlotte. His research interests include Big Data Analytics, Cloud Computing, Sustainable Datacenter and Distributed Systems. He has published prolifically in refereed journals and conference proceedings, such as ACM/IFIP/USENIX Middleware, IEEE IPDPS, IEEE ICDCS, IEEE INFOCOM, ACM/IEEE MASCOTS, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and ACM Transactions on Autonomous and Adaptive Systems.

Speech Title: QoE-Aware Power Management Via Scheduling and Governing Co-Optimization on Mobile Devices

Abstract: Scheduling and governing are two key technologies to trade off the Quality of Experience (QoE) against the power consumption on mobile devices with heterogeneous cores. However, there are still defects in the use of them, among which two of the decoupling issues are critical and need to be resolved. First, both the scheduling and governing decouple from QoE, one of the most important metrics of user experience on mobile platforms. Second, scheduling and governing also decouple from each other in mobile systems and they might weaken each other when being effective at the same time. To address the above issues, we propose Orthrus, a comprehensive QoE-aware power management approach that involves a governing approach based on deep reinforcement learning, a scheduling algorithm based on finite state machine, and coordination mechanism between the two to manage the impact between scheduling and governing. We implement Orthrus on Google Pixel 3 and the evaluation demonstrates that it reduces the average power consumption by up to 35.7% compared to three state-of-the-art techniques while ensuring the QoE on mobile platforms.

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