History of ICBDC

 

ICBDC 2021

2021 6th International Conference on Big Data and Computing
May 22-24, 2021| Shenzhen, China

全球疫情下各国参会人员旅行受到限制, 会务组继续保持在线会议的形式—— 2021年第六届大数据与计算国际会议于2021年5月22日至5月24日成功在云端会议平台顺利举办。ICBDC 2021大会录用文章被出版至 ICBDC 2021 论文集, 由ACM 出版, ICBDC 2021论文集出版刊号是 978-1-4503-8980-8,  已被ACM 数据库收录 , 稍后会被 EI 核心检索 和被 Scopus检索


                                               

 

Opening Remarks by Prof. Jianqiang Li ,
Shenzhen University, China
Keynote Session Chair: Prof. Yulin Wang,
Wuhan University, China
Prof. Hong Shen,  
Sun Yat-sen University, China
 
 
         
Prof. Jianfei Cai ,
Monash University, Australia
 
Prof. Changsheng Xu,
Chinese Academy of Sciences, China
 
   

 

 

Parallel Sessions

   
         

     

 

Excellent Oral Presentation Winners of ICBDC 2021

A Semi-Supervised Learning System for Classification

Presenter:  Eric Jiang, University of San Diego, USA

Automated Segmentation Based on Deep Learning of the MR Vessel Wall Imaging

Presenter: Wenjing Xu, Beijing University of Technology, China

A Utility-Optimized Mechanism for Private Data Aggregation

Presenter: Hang Fu, University of Science and Technology of China, China

Quantum Private Query of Blocks Based on D-dimensional Bell State

Presenter: Siwen Hu, Chongqing University of Posts and Telecommunications, China

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.