通知公告

为迎接哈尔滨工业大学百年校庆，加快“中国特色、世界一流、哈工大规格”百年强校建设步伐，2019年6月29日–6月30日，第十二届移动多媒体通信学术会议(MOBIMEDIA 2019)将在哈尔滨工业大学（威海）召开。

第十二届移动多媒体通信学术会议(MOBIMEDIA 2019)由哈尔滨工业大学（威海）计算机科学与技术学院承办，由欧盟创新联盟EAI(European Alliance for Innovation)认证，是国际通信领域的一次盛会。来自多媒体移动通信领域的国内外权威专家学者们将齐聚威海，围绕移动多媒体与网络技术前沿课题和最新研究成果进行了深入探讨与交流。

MOBIMEDIA国际会议由欧洲创新联盟(EAI)技术委员会创办，MOBIMEDIA首届会议于2006年在意大利举办，历经多年发展演进，该会议已成为国际多媒体移动通信领域的重要年度盛会。MOBIMEDIA 2019会议旨在采用跨学科的方式推动移动环境中的多媒体服务与应用，使多媒体、网络和物理层问题可以共同得到解决。本次会议接受的论文来自世界各地，分为6个不同的技术分会进行研讨，研究内容涵盖图像处理、行为分析、意图分析、协同云计算、深度强化学习、任务调度、无线网络安全等领域。

本次会议将于2019年6月29日–6月30日在山东省威海市哈尔滨工业大学（威海）天雅轩隆重召开，将为国内外移动多媒体通信研究者、开发者及用户提供一个开放、前瞻的交流平台，欢迎感兴趣的教师、研究生、本科生参加。

会议安排如下：

主题报告1：Opportunities and Challenges in Using the Power of the Crowd: Incentive Mechanisms, Truth Discovery, and Robustness

报告人：Guoliang Xue (Arizona State University)

摘要：With the proliferation of smart mobile devices and the popularity of social networks, crowdsourcing has emerged as a new computing and sensing paradigm, which uses collective power/intelligence of the crowd to accomplish computing or sensing tasks. For crowdsourcing to be useful, we need good incentive mechanisms to attract more users to participate in the activity, we need reliable methods to find the truth from the crowdsourced data. The system also needs to be robust against various attacks, and be able to resolve dispute between the task owners and the work providers. In this talk, we will discuss the crowdsourcing computing paradigms, truthful incentive mechanisms, Sybil attacks and counter-measures, truth discovery via supervised learning and unsupervised learning, and dispute resolution, along with open research issues.

报告人简介：Guoliang Xue is a Professor of Computer Science and Engineering at Arizona State University. He earned a PhD degree in Computer Science in 1991 from the University of Minnesota, an MS degree in Operations Research in 1984, and a BS degree in Mathematics in 1981, both from Qufu Normal University. His research interests include resource allocation in computer networks, security and survivability issues in networks, and big data enabled machine learning. He is a recipient of Best Paper Award at IEEE ICC’2012 and IEEE MASS’2011, as well as a Best Paper Runner-up at IEEE ICNP’2010. He is an Area Editor of IEEE Transactions on Wireless Communications for the Wireless Networking Area overseeing 12 editors. He is a past editor of IEEE/ACM Transactions on Networking, and Computer Networks. He was a TPC co-chair of IEEE INFOCOM’2010 and a co-General Chair of IEEE CNS’2014. He was a Keynote Speaker at IEEE LCN’2011, IEEE ICNC’2014, and IFIP WWIC’2018. He is an IEEE Fellow and served as the VP[1]

Conferences of the IEEE Communications Society from January 2016 to December

2017.

主题报告2：Distributed Learning for Big Data Analytics: From Cloud to Edge

报告人：Song Guo (The Hong Kong Polytechnic University)

摘要：When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. In this talk, we will analyze the methodology and limitations of popular approaches for supporting AI services on geo-distributed systems along the evolution from cloud computing to edge computing. In particular, we shall discuss how to deal with different sets of challenges in training and inference, the two phases of machine learning based applications, over heterogeneous geo-distributed systems. We shall also present our recent studies on data driven resource management among networked collaborative edges.

报告人简介：Song Guo is a Full Professor at Department of Computing, The Hong Kong Polytechnic University. His research interests are mainly in the areas of big data, cloud computing and networking, and distributed systems. His work was recognized by the 2016 Annual Best of Computing in ACM Computing Reviews. He is the recipient of the 2017 IEEE Systems Journal Annual Best Paper Award and other five Best Paper Awards from IEEE/ACM conferences. Prof. Guo was an Associate Editor of IEEE TPDS and an IEEE ComSoc Distinguished Lecturer. He is now on the editorial board of IEEE TCC, IEEE TETC, IEEE TSUSC, IEEE TGCN, IEEE Network, etc. Prof. Guo also served as General and TPC Chair for numerous IEEE conferences. He currently serves as a Director and Member of the Board of Governors of ComSoc.

特邀报告1：Bi-Directional Sensing of Mobile Service Ecosystem Evolution

报告人：Zhongjie Wang (Harbin Institute of Technology)

摘要：Nowadays, mobile Apps have become a dominate channel of service delivery. Service providers publicize their Apps into App Store, and users use Apps to access services in terms of their personalized demands. This is a typical BIRIS service pattern in which massive service providers and massive users are aggregated and App store plays the intermediary role between them. For users, it is difficult to get to know timely the latest updates of massive services/Apps; and for service providers, it is difficult to get to know precisely the personalized and dynamically-changing preferences/interests of users, too. In our research, we put forward (1) a method to monitor the changes of App Store to get the latest updates on the features of Apps; (3) a method to recovery personal Apps ecosystem and discover changing personal preferences of users; (3) a method to extract useful feature requests from user reviews and give feedback to App developers to help them make decisions on their next release, including release time, features to be updated, and users’ possible reactions on the release. Such bi-directional sensing of mobile service ecosystem evolution would help users make decisions on “what mobile Apps I would use” and help App developers make decisions on “how to improve my Apps to better adapt to user preferences and feedbacks”.

报告人简介：Zhongjie Wang is an associate dean，professor and doctoral supervisor of School of Computer Science and Technology, Harbin Institute of Technology, China. He got his PhD in Computer Science and Engineering in 2016. Currently his research interests in[1]clude services computing, software engineering, software architecture, software and service evolution, mining software repositories, mobile and social computing. He has published more than 60 papers on academic journals and conferences such as IEEE TSC, IEEE ICWS, IEEE SCC and ICSOC. He played roles of program committee chair, publication chair, symposium chair and organization chair on several conferences such as CCF NCSC, CCF ICSS, IEEE SOSE and IEEE SERVICES. He is now associate secretary of Technical Committee of Services Computing, CCF.

特邀报告2：Personalized Video Streaming Strategy

报告人：Liang Zhou (Nanjing University of Posts and Telecommunications)

摘要：Personalized service has become the trend of network-based multimedia applications. Generally, there are two primary and essential technical challenges: i) different users usually require diverse user experiences, and ii) the network environments may vary with the time and place as well. To resolve this dilemma, this work proposes a personalized multimedia streaming strategy by intelligently categorizing the user via the data mining and adaptively predicting the transmission fashion in real time via the reinforcement learning. Specifically, on one hand, a class[1]level joint user classification and data cleaning scheme is proposed by frequently updating the training processes. On the other hand, a neural network model is constructed by making use of the reinforcement learning. As such, the video rate in the future can react quickly through the neural network model even if in a dynamic environment. Moreover, the objective representation of user experience is modelled from the user class instead of user himself, and it is used as the incentive information to train and improve the above neural network model. Extensive results validate the efficiency of the proposed scheme.

报告人简介：Liang Zhou received his Ph.D. degree major at Electronic Engineering both from Ecole Normale Superieure (E.N.S.), Cachan, France and Shanghai Jiao Tong University, Shanghai, China in March 2009. Now, he is a professor in Nanjing University of Posts and Telecommunications, China. His research interests are in the area of multimedia communications and networks, in particular, resource allocation and scheduling, cognitive and cooperative communications, cross-layer design, multimedia security, multimedia signal processing. He currently serves as an editor for IEEE Wireless Communications (2018-), IEEE Network (2018-), IEEE Transactions on Circuits and Systems for Video Technology (2013-), IEEE Transactions on Multimedia (2014-), and guest editor for IEEE Systems Journal (2011), EURASIP Journal of Wireless Communications and Networking (2011), ACM/Springer Multimedia Systems Journal (2010), and International Journal of Communications System (2010). He also serves as Co-Chair and Technical Program Committee (TPC) member for a number of international conferences and workshops (e.g., IEEE Globecom’10-12, IEEE ICC’10-12 etc.). He is a senior member of IEEE, IEEE MMTC, and IEEE MMSP.

特邀报告3：Message Coverage Maximization in Infrastructure-based Urban Vehicular Networks

报告人：Min Song (Stevens Institute of Technology)

摘要：The success of vehicular networks is highly dependent on the coverage of messages, which refers to the trajectory of messages over time. Many of the existing works primarily performed in 1-D environments and merely focused on vehicle-to-vehicle communications to enhance the coverage in a given road network. Consequently, there still lacks a clear comprehension of using road infrastructures to improve message coverage in 2-D environments. In this talk, I will present a message coverage maximization algorithm (MCMA) that carefully deploys the roadside units to achieve the maximum message coverage in a 2-D environment. We first derive the analytical lower bounds of message dissemination distance for areas with different vehicle densities. The MCMA then utilizes the derived lower bounds to estimate the minimum spacing allowed between neighbor roadside units based on the prevailing traffic stream and delay constraint of applications. Also, we propose a disseminator selection algorithm for infrastructure-based urban vehicular networks to further improve message coverage. By selecting the desired types of applications, i.e., safety and non-safety, we obtain two different roadside unit deployment sets. Extensive simulation studies show that MCMA outperforms the alternative algorithms in terms of the message coverage and message dissemination speed.

报告人简介：Min Song joined Stevens Institute of Technology in July 2018 as Professor and Chair of the Department of Electrical and Computer Engineering. Before joining Stevens, he was the David House Professor, Chair of the Computer Science Department and Professor of Electrical and Computer Engineering at Michigan Tech from 2014 to 2018. He was also the founding director of the Michigan Tech Institute of Computing and Cybersystems. Prior to joining Michigan Tech, Min served as a program director with the National Science Foundation (NSF) from 2010 to 2014. Min’s professional career comprises 28 years in academia, government, and industry. Throughout his career, Min has published more than 165 technical papers and held various leadership positions. He served as TPC Co-Chair for many IEEE conferences including ICC and GLOBECOM. He has been serving as a member of the IEEE INFOCOM Steering Committee. He is the recipient of NSF CAREER Award in 2007 and NSF Director’s Award in 2012. Min is an IEEE Fellow.