Keynote Speakers of ICBDC 2021
Prof. Changsheng Xu, Chinese Academy of Sciences, China
ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow
Changsheng Xu is a professor of Institute of Automation, Chinese Academy of Sciences. His research interests include multimedia content analysis/indexing/retrieval, pattern recognition and computer vision. He has hold 50+ granted/pending patents and published over 400 refereed research papers including 100+ IEEE/ACM Trans. papers in these areas. Prof. Xu serves as Editor-in-Chief of Multimedia Systems Journal and Associate Editor of ACM Trans. on Multimedia Computing, Communications and Applications. He received the Best Paper Awards of ACM Multimedia 2016, 2016 ACM Trans. on Multimedia Computing, Communications and Applications and 2017 IEEE Multimedia. He served as Associate Editor of IEEE Transactions on Multimedia and Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.
Speech Title: Connecting Isolated Social Multimedia Big Data
Abstract: The explosion of social media has led to various Online Social Networking (OSN) services. Today's typical netizens are using a multitude of OSN services. Exploring the user-contributed cross-OSN heterogeneous data is critical to connect between the separated data islands and facilitate value mining from big social multimedia. From the perspective of data fusion, understanding the association among cross-OSN data is fundamental to advanced social media analysis and applications. From the perspective of user modeling, exploiting the available user data on different OSNs contributes to an integrated online user profile and thus improved customized social media services. This talk will introduce a user-centric research paradigm for cross-OSN mining and applications and some pilot works along two basic tasks: (1) From users: cross-OSN association mining and (2) For users: cross-OSN user modeling.
Prof. Jianfei Cai
IEEE Fellow, Monash University, Australia
Jianfei is a Professor at Faculty of IT, Monash University, where he currently serves as the Head for the Data Science & AI Department. Before that, he was a full professor, a cluster deputy director of Data Science & AI Research center (DSAIR), Head of Visual and Interactive Computing Division and Head of Computer Communications Division in Nanyang Technological University (NTU). His major research interests include computer vision and multimedia. He has published more than 250+ technical papers in international conferences and journals, and has successfully trained 20+ PhD students. Many of them joined leading IT companies such as Facebook, Apple, Amazon, NVIDIA and Adobe or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP. He has served as an Associate Editor for IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for ICCV, ECCV, CVPR, IJCAI, ACM Multimedia, ICME and ICIP. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had also served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He is a Fellow of IEEE.
Speech Title: Bridging Vision and Language for Image Captioning
Abstract: As human beings, we can use our vision capabilities and language to perceive the world around us and to communicate with each other. While it seems to be easy for human beings to accomplish a wide variety of tasks that combine the two modalities, it is quite challenging for machines because it requires the model to understand both images and language, especially how they relate to each other. In this talk, I will discuss a few of my group’s recent works to bridge images and natural language via leveraging language inductive bias for the application of image captioning. I will also touch the future directions along this line.
Prof. Hong Shen (沈鸿教授)
China National Endowed Expert, Sun Yat-sen University, China
Dr. Hong Shen is a specially-appointed Professor in Sun Yat-sen University where he was the foundation Director of Institute for Advanced Computing. With main research interests in parallel and distributed computing, privacy preserving computing, optimization algorithms, wireless and optical networks, data mining, he has led numerous research centers and projects in different countries. He has published 400+ papers including over 100 papers in major international journals such as a variety of IEEE and ACM transactions. Prof. Shen has received many honours and awards including China National Endowed Expert , Ministry of Education Science and Technology Progress Award, and Chinese Academy of Sciences Natural Sciences Award. He has served on the editorial boards of numerous journals and chaired many conferences.
Speech Title: The Power of Differential Privacy for Secure Data Sharing
Abstract: In the era of cloud computing with the evolving demand of big data processing, privacy-preserving computing (PPC) has arisen as an effective way to achieve secure distributed computing and information sharing which serves as the base for realization of widespread Smart City and e-Society. PPC requires to develop a computation paradigm for solving a given problem that takes privacy-protected data as input and produces an output that is utilizable to the public yet secure against privacy attacks. There is a rich literature on the topic and numerous advancements have appeared in the past decade with the focus on improved security against various privacy attacks in the cloud computing environment. In these PPC paradigms, the demand of security assurance against emerging privacy attacks makes the task of maintaining output's utility to public become ever more challenging. In the first part of this talk, I will first introduce the problem of privacy-preserving computing, its research challenges in cloud big data computing, then give a taxonomy on data protection techniques categorized on the security levels of data publishing, with the focus on differential privacy as an effective method to combat inference attacks, and provide an overview on our contributions in privacy-preserving computing. In the second part, to show the power of differential privacy for secure data sharing, I will give two examples of our work of applying differential privacy to achieve privacy-preserving recommendation and data clustering against inference attacks. As concluding remarks, I will further illustrate the application of differential privacy in obtaining privacy-preserving solutions for some statistical and combinatorial optimization problems.