Prof. Yulin Wang

Wuhan University, China

Yulin Wang is a full professor in the School of Computer Science, Wuhan University, China. His research interests include image and video processing, digital rights management, information security, intelligent system, e-commerce, IoT, code clone and so on.He got his PhD degree from University of London, UK. He got his master and bachelor degree from Huazhong University of Science and Technology（HUST）and Xi-Dian University respectively, both in China. Before joining the Wuhan University, he has worked in Hi-tech IT industry, including HUAWEI© and national research institute, for more than ten years. He has involved more than 15 national and international research projects. In recently 10 years, Prof. Wang has published 1 book, and 50+ journal and conference papers, including in IEEE TIP. He holds 10 authorized patents.

Prof. Wang served as EiC of 2 international journals and reviewer of top IEEE and ACM journals. He also served as reviewer of Innovative talents projects and national  research funds, including National High Technology Research and Development Program of China. Prof. Wang was the external PhD advisor of Dublin City University, Ireland during 2008-2010. In recently 10 years, Prof. Wang served as chairman of more than 10 international conferences, and keynote speakers in more than 20 international conferences. Besides UK, he visited US, France,Italy, Portugal,Croatia, Australia, Germany, korea, Ireland,Singapore, Malaysia, Japan, and Hong Kong. In addition, Prof. Wang has been appointed as the deputy director of Hubei provincial science and technology commission (CAPD) since 2014.

Speech Title: Intelligent Multimedia Data Hiding: Techniques and Applications

Absrtact: Digital music, podcasts, live and recorded webinars, video calls, and streaming video have changed the way in which we communicate, and have become ubiquitous in virtually every organization. We employ these methods to convey ideas, train our employees, engage our customers, and of course entertain. The question is, does digital multimedia pose a threat? Could these channels be used to communicate information covertly, ex-filtrate intellectual property, share insider information, be used to convey command and control information, or provide the needed enabling technology for advanced persistent threats? Additionally, since the size of multimedia files are typically much larger than a single digital photo, does this mean that larger payloads of hidden information could be exchanged or leaked by exploiting weaknesses inherent in multimedia carriers? Or, on the contrary, is the human auditory system sensitive to even small changes in multimedia information such that we could detect anomalies caused by embedding hidden information in such streams? In this talk, we present the intelligent multimedia data hiding techniques and their possible application. We will cover some of the earliest and simplest forms of data hiding in digital multimedia and then move to some of the lasted innovations in order to provide insight into these questions. Some of the research branches, called reversible data hiding, is also depicted.

Prof. JIanfeng Lu

Wuhan University of Science and Technology, China

湖北省“楚天学者”特聘教授、浙江省杰出青年基金获得者、武汉科技大学三级教授、博导、CCF物联网/普适计算专委会执行委员、国家重点研发计划“物联网与智慧城市”重点专项答辩评审专家、湖北省/浙江省/上海市/广东省/江西省/海南省/黑龙江省科技计划项目评审专家等。

主要从事边缘智能、联邦学习、群智计算等方向研究，借鉴博弈论策略思维，采用最优化理论、李雅普诺夫优化、近似算法、机器学习等多学科交叉知识融会贯通，联合解决联邦学习与群智计算中比例公平激励、服务交换、众包竞争困境等难题。

近年来以第一作者/通讯作者在IEEE JSAC、IEEE TMC、IEEE TSC、IEEE TIFSIJCAI、 AAAI、 ACM TOIT、 IEEE TH、 IEEE TVT、 IEEE TCSS、 IEEE TCE.IEEE TETCI等有影响力的学术期刊及会议上发表论文50余篇，授权国家发明专利8件。先后主持国家自然科学基金4项、省部级课题7项

指导研究生获省/校优秀毕业生称号、优秀硕士学位论文、研究生国家奖学金、校长特别奖多人次，多人毕业后赴上海交通大学、东南大学、浙江师范大学、武汉科技大学等高校读博深造。

Speech Title: 联邦边缘学习激励设计与性能优化

Abstract: 联邦学习为边缘智能打破数据孤岛壁垒，联合构建群智感知模型提供了新颖的解决方案，领跑人工智能的最后一公里。然而异构联邦系统中多元群体难约束、任务在线匹配下社会公平难衡量、资源受限条件下系统高效难持续、复杂边缘环境中公平效益难平衡等问题严重阻碍了联邦边缘学习的公平协作增强与优化。本报告将介绍如何从寻求系统效益和社会公平最优平衡的视角出发，充分借鉴博弈论策略思维，采用最优化理论、李雅普诺夫优化、近似算法、机器学习等多学科交叉知识融会贯通，以博弈建模与均衡分析为特色，以新机制、新协议、新方法为创新，以原型系统开发与真实数据验证为保障，形成完整的研究体系联合解决联邦边缘学习激励设计与性能优化问题，突破制约联邦学习可持续性健康发展的关键技术瓶颈。

Prof. Qingxi Peng

Wuhan College, China

彭庆喜，工学博士，武汉学院信息工程学院教授，硕士生导师。2005年毕业于湖北大学系统分析与集成专业，获理学硕士学位。2018年毕业于武汉大学计算机软件与理论专业，获工学博士学位。CCF会员，国际工程师协会（IAENG）会员，SCI期刊Knowledge-based Systems(CCF B)、Future Generation Computer Systems(CCF C)、Applied Intelligence (CCF C)审稿人。主持和参与研发多个企业级软件系统，有半年海外工作经验。近年来聚焦大规模Web数据获取与智能推荐相关研究及其应用。

研究方向：数据挖掘、机器学习和基于位置的服务（LBS）

Speech Title: Enhancing Open Source Expertise Discovery: A Collaborative Network-Based Retrieval Model with OSC2vec

Abstract: This talk introduces OSDERM, an innovative framework for expert retrieval in open source ecosystems, addressing GitHub’s limitations in identifying domain expertise. By integrating collaborative network analysis and the OSC2vec algorithm, the model mainly consists of two core parts: Expert Profiling and Expert Finding. Expert Profiling aims to enrich the expertise information in the search results by labeling the expertise of developers; while Expert Finding achieves rapid location of the most suitable domain experts through keyword matching, which greatly saves the time and effort of searching for experts in the open source community. Experiments using the GitHub ecological dataset show that the model outperforms existing comparative algorithms in discovering open source domain experts, and can provide an effective reference for enterprise recruitment.