Speakers
| Prof. Yang Chen (H-index: 26)Fudan University, ChinaResearch Area: Social Computing, Computer Networks and Data Mining Introduction: Yang Chen is a Professor within the School of Computer Science at Fudan University, China. He leads the Big Data and Networking (DataNET) group since 2014. Before joining Fudan, he was a postdoctoral associate at the Department of Computer Science, Duke University, USA. He received his BSc and Ph.D. degrees from Department of Electronic Engineering, Tsinghua University in 2004 and 2009, respectively. His research interests include social computing, Internet architecture and mobile computing. He served as an OC / TPC Member for many international conferences, including SOSP, SIGCOMM, WWW, MobiSys, ICDCS, IJCAI, AAAI, IWQoS, ICCCN, GLOBECOM and ICC. He is a Senior Member of ACM, IEEE and CCF. Speech Title: EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis Abstract: Networks are powerful tools for representing the relationships and interactions between entities in various disciplines. However, existing network analysis tools and packages either lack powerful functionality or are not scalable for large networks. In this work, we present EasyGraph, an open-source network analysis library that supports several network data formats and powerful network mining algorithms. EasyGraph provides excellent operating efficiency through a hybrid Python/C++ implementation and multiprocessing optimization. It is applicable to various disciplines and can handle large-scale networks. We demonstrate the effectiveness and efficiency of EasyGraph by applying crucial metrics and algorithms to random and real-world networks in domains such as physics, chemistry, and biology. The results demonstrate that EasyGraph improves the network analysis efficiency for users and reduces the difficulty of conducting large-scale network analysis. |
Prof. Hua WangZhejiang University, ChinaResearch Area: Finance, FinTech,BlockChain，Economics Introduction: Professor, Ph.D. (University of Tokyo), CFA, Director of Financial Innovation and FinTech Research Center. Dr Wang has more than 10 years of professional and research experiences in international capital markets, financial products, FinTech, etc.,and 5 years as a senior Research Fellow at China Institute of Finance and Capital Markets. Dr. Wang has excellent proficiency in English and Japanese, with long-term international experience in Tokyo, London and Hong Kong, etc. He also participated in a number of research projects focusing on financial markets and government policies, and published tens of research articles and papers. Speech Title: Brief Introduction of Solutions in Transportation to Address a Low-carbon Economy |
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| Prof. Xinxing DuanChina University of Mining and Technology, ChinaResearch Area: Public management, educational economic management, ideological and political education Brief: Professor Duan Xinxing, PhD supervisor, PhD in Psychology, Beijing Normal University, visiting scholar of Brigham Young University, former Dean of School of Public Administration, China University of Mining and Technology, and director of Women's Committee, China University of Mining and Technology. Speech Title: Understanding Chinese College Students’ Emotions and Attributions during the COVID-19 Epidemic: an analysis based on Sina microblog Abstract: The outbreak of COVID-19 resulted in various restrictive measures imposed by the Chinese government and colleges, including home quarantine and online teaching. In this context, the Internet has emerged as a crucial medium for college students to express their emotions. Measuring and evaluating emotions from online texts can provide a precise representation of the emotions of college students. To this end, this study selected 18,300 texts posted by college students on Sina microblog in 2020 and used a text-mining method to evaluate their emotions during the COVID-19 epidemic. First, an emotion attribution system was constructed using a content analysis approach combined with emotion attribution theory. The system encompasses eight dimensions: epidemic, institutional, environmental, college, interpersonal, physical and mental, input, and ability attributions. Then, a four-level Bayesian classifier was developed to evaluate and analyze the online emotions of college students along two dimensions: emotion validity and emotion attribution. The study indicated that the overall validity of college students’ emotion was negative, with low levels of arousal. As for attribution, emotion varies greatly across several attribution dimensions. In terms of emotion validity, external attributions were associated with much more positive emotions than internal attributions, although unstable attributions were associated with greater emotional arousal. The results of the study contribute to a deeper understanding of the psychological reaction mechanisms of college students during sudden public crisis events and can aid colleges and universities in improving mental health education and psychological intervention. |
