报告一：
报告题目：从跨域多媒体内容分发到多媒体边缘计算
报告人：王智，清华大学深圳国际研究生院，副教授
报告时间：2021年12月3日 2:30-3:30 pm
报告地点：深圳大学沧海校区计算机与软件学院938会议室
报告摘要：
多媒体内容分发中内容生产和消费的社交化、流行度扁平化、以及基础设施的边缘化，给传统内容分发方案带来一系列挑战。本报告介绍一种将用户域、媒体域智能引入多媒体网络的跨域的内容分发框架，并展示这一思路在内容部署、行为预测和自适应流化中的高效性。同时，针对近年来边缘计算发展给多媒体网络优化带来的机会，探讨多媒体边缘计算新趋势，并介绍团队在面向边侧的分布式机器学习与在线决策等方面的进展。
报告人简介:
王智，清华大学深圳国际研究生院副教授，从事多媒体网络和系统方向研究，包括多媒体边缘计算与分布式机器学习等。先后获得教育部自然科学奖一等奖，国家自然科学二等奖，深圳市青年科技奖，中国电子学会技术发明一等奖，入选国家级青年人才计划。研究成果三次获得ACM Multimedia等国际学术会议最佳论文奖。担任IEEE TMM副编辑和ACM TIST客座编辑。关键技术获得腾讯移动互联网创业大赛第一名，研究成果被知名技术媒体MIT Technology Review等报道。
报告二：
报告题目：面向区块链体系结构的性能优化研究
报告人：黄华威，中山大学，副教授
报告时间：2021年12月3日3:30-4:30 pm
报告地点：深圳大学沧海校区计算机与软件学院938会议室
报告摘要：
Blockchain architecture has become a significant research topic. Especially, the blockchain sharding technique is believed as a promising solution to improving the scalability of blockchains. However, there are some typical challenges behind the sharding technique, such as large transaction latency and the management of committees. In this talk, I am going to first discuss about the preliminaries of blockchain sharding, then present our recent studies on the performance optimization towards the sharding-based blockchain.
报告人简介:
黄华威，中山大学百人计划副教授，博士生导师。曾于2016年取得日本会津大学计算机科学与工程博士学位；曾先后担任日本学术振兴会特别研究员、香港理工大学访问学者、日本京都大学助理教授。研究方向包括区块链、分布式系统与协议。近年来在区块链、网络资源分配和任务调度等方面深入研究，成果发表在 CCF-A 类期刊 IEEE JSAC, TPDS, TMC, TC，及其他期刊IEEE TCC，IEEE ComMag, IEEE Wireless Communications, IEEE Network, 以及国际会议ICDCS, IWQoS等，曾获得IEEE TrustCom 2016 最佳论文奖，曾担任多个国际学术会议的技术委员会成员，担任期刊 IEEE JSAC与 IEEE OJ-CS 的客座编辑，担任 IEEE International Symposium on Blockchain 2021组织主席, IEEE GLOBECOM 2021与 ICC 2022 Workshop on Scalable, Secure and Intelligent Blockchain程序主席。主持国家重点研发计划课题、广东省重点研发课题、国自然青年科学基金项目，以及多项广东省广州市科技计划项目。
报告三：
报告题目：Robust Online Learning with Application to Network Traffic Classification
报告人：李钰鹏，香港浸会大学，助理教授
报告时间：2021年12月3日4:30-5:30 pm
报告地点：深圳大学沧海校区计算机与软件学院938会议室
报告摘要：
Malicious data manipulation reduces the effectiveness of machine learning techniques, which rely on accurate knowledge of the input data. Motivated by the real-world needs for network traffic classification, we address the problem of robust online learning in the presence of malicious data generators that attempt to gain favourable classification outcome by manipulating the data features. In this talk, we will introduce proposed online classification algorithms for the cases where the malicious generators are non-clairvoyant and clairvoyant. For each of these cases of the malicious generators, we consider both static and dynamic feedback delay over time. The proposed algorithms have theoretical performance guarantees. Our experimental results using real-world data traces demonstrate that the proposed algorithms can approach the performance of an optimal static offline classifier that is not manipulated, while outperforming the same offline classifier when tested with a mixture of normal and manipulated data. We believe our outcomes will not only inspire future research in online classification, but also have practical significance that will be conducive to entities for network operators and Internet users.
报告人简介:
Yupeng Li is an Assistant Professor at Hong Kong Baptist University. He was a post-doctoral researcher at University of Toronto. His research interests are in general areas of network science and, in particular, algorithmic decision making and machine learning problems, which arise in networked systems such as information networks, social networks, the edge-clouds, and transportation networks. He is also excited about interdisciplinary research that applies algorithmic techniques to edging problems. He has served as TPC member and reviewer in some top-level international conferences and journals, and he has published papers in prestigious venues such as ACM MobiHoc, IEEE INFOCOM, IEEE Journal on Selected Areas in Communications, and IEEE/ACM Transactions on Networking.
