A large number of papers are being
published every year, which makes it difficult for researchers to grasp the
relationship among the scientific literatures and the big picture of academic
fields. The new challenges have thus been raised, such as analyzing the
complicated citation and author network, mining valuable scientific knowledge,
and visualizing big scholarly data. The existing academic systems, such as
Google Scholar and DBLP have mainly adopted text-based methods, while some
other systems make attempts to better navigate the literatures, for example, AMiner and Science Navigation Map. Although these systems
show improvements, they fail to present the academic data in a holistic way,
and also have limited functions. Therefore, we need to develop new tools which
can realize more modules and further explore the academic literatures.
In this talk, we conceptualize and design a novel academic
system, AceMap, to analyze the big scholarly data and
present the results through a “map” approach. AceMap
integrates several algorithms in the field of network analysis and data mining,
and then displays the information in a clear and intuitive way, aiming to help
the researchers facilitate their work. After describing the big picture, we
present achieved results and our work in progress. By far, AceMap
has implemented the following functions: dynamic citation network display,
paper clustering, academic genealogy, author and conference homepage, etc. We
have also designed and performed distributed network analysis algorithms in a
cutting-edge Spark system and utilized modern visualization tools to present
the results. Finally, we conclude our paper by
proposing the future outlooks.
讲者简介：
Xinbing Wang received the B.S. degree (with hons.) in automation
from Shanghai Jiao Tong University, Shanghai, China, in 1998, the M.S. degree
in computer science and technology from Tsinghua University, Beijing, China, in
2001, and the Ph.D. degree with a major in electrical and computer engineering
and minor in mathematics from North Carolina State University, Raleigh, in
2006. Currently, he is a distinguished professor and deputy dean of School of
Electronic Information and Electrical Engineering, Shanghai Jiao Tong
University. He also serves as the vice president of ACM China Council.
Dr. Wang has been an associate editor for IEEE/ACM Transactions on networking,
IEEE Transactions on Mobile Computing, and ACM Transactions on Sensor Networks.
He had published more than 100 IEEE/ACM journal and conference papers with
4000+ Google Scholar Citations. He has also been the Technical Program
Committees member of several conferences including ACM MobiCom
2012,2014, ACM MobiHoc 2012-2017, IEEE INFOCOM
2009-2017. Dr Wang won the IEEE ComSoc
Asia-Pacific Outstanding Paper Award in 2014 and the IEEE ComSoc
Asia-Pacific Outstanding Young Researcher Award in 2009. He also won the IEEE ComSoc Outstanding Service Award in 2010.
特邀报告2：From
Data Analysis to Parallel Computing of Safety Route Design in A Smart City
特邀讲者：
Dr. Bin Xiao, Associate
Professor（The Hong Kong Polytechnic
University）
报告摘要：
Information about urban safety, e.g.,
the safety index of a position, is of great importance to protect humans and
support safe walking route planning. The problem of analyzing urban safety to
predict safety index throughout a city has not been sufficiently studied and
remains open. In this talk, we will first propose U-Safety, an urban safety
analysis system to infer safety index by leveraging multiple cross-domain urban
data. Then, based on the constructed safety index map of a city, we propose a
reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to
route planning in a smart city. The approach can achieve both safety
requirement and short distance in the route design for a smart city. Finally,
we show extensive experiments based on real data sources obtained in New York
City. The evaluation results demonstrate the advantages of U-Safety over other
methods. The MOHH approach can obtain more than 80% Pareto optimal solutions in
a large-scale road network.
讲者简介：
Dr. Bin Xiao is an associate
professor at Department of Computing, the Hong Kong Polytechnic University,
Hong Kong. Dr. Xiao received the B.Sc
and M.Sc degrees in Electronics Engineering from Fudan University, China, and Ph.D. degree in computer
science from University of Texas at Dallas, USA. After his Ph.D. graduation, he
joined the Department of Computing of the Hong Kong Polytechnic University as
an Assistant Professor. His research interests include distributed systems,
mobile computing and network security.Dr. Xiao has published more than 100 technical papers in international
top journals and conferences. Currently, he is the associate editor of the
Journal of Parallel and Distributed Computing (JPDC) and Security and
Communication Networks (SCN). He is the symposium chair of IEEE Globecom-CISS 2017 and IEEE ICC-AHSN 2018, and the general
chair of IEEE SECON 2018. He is the IEEE Senior member, CCF and ACM
member.
专题报告2：Adaptive Distributed Optimization for
Uploading Data with Redundancy in Cooperative Mobile Cloud
特邀讲者：
朱晓敏
副教授（国防科技大学）
报告摘要：
With the development of information
technology and the ubiquity of mobile devices, increasing amounts of data are generated,
processed and transmitted by mobile devices. In order to alleviate the tension
between the energy poverty of mobile devices and the increasing demand for
transmitting data, the energy-efficient data transmission problem attracts
considerable research interests. Nonetheless, how to upload data with
redundancy efficiently still lacks a thorough study in spite of the wide
existence of this problem in many situations like data storage among mobile
devices and mobile crowd sensing. Since uploading redundant data brings little
value while still consuming precious energy, it is important to design an
efficient approach for mobile devices to upload data with redundancy
cooperatively. In this talk, we formulate the uploading data with redundancy in
cooperative mobile cloud as an energy-constrained utility maximization problem.
To solve this problem, we propose an adaptive distributed optimization approach
consisting of the correlated upload decision and the online distributed
scheduling algorithm. By the correlated upload decision, each mobile device can
make adaptive decisions on how much data to upload and which data to upload
according to its own observations independently. The online distributed
scheduling algorithm enables mobile devices to optimally upload data while
requiring no future information.
担任国际期刊FGCS编辑，国际AIMS会刊Big
Data and Information Analytics (BDIA)编辑，FGCS、JCSS和JOCS专刊策划人和编辑，IEEE SC2 '15 国际会议程序委员会主席、LPDC
'16国际会议共同主席，ICDCS等十余个国际会议程序委员会委员。2009年获上海市优秀毕业生，是国防科技大学优秀引进人才。2014年荣立三等功一次。
专题报告3：Gradient-Driven
Parking Navigation Using a Continuous Information Potential Field Based on
Wireless Sensor Network
魏嵬副教授，2011年获得西安交通大学计算机软件与理论专业工学博士学位，2017年于美国德州大学达拉斯分校计算机系完成博士后研究工作。一直从事物联网及大数据等相关方面研究工作，发表研究论文60余篇，主持完成3项省部级基金，作为骨干获得5项省市级科技进步奖，作为骨干参与完成国家基金项目6项。其中第一作者发表SCI期刊收录的高水平论文13篇，包括中科JCR-1区、CCF B类国际期刊论文2篇（分别为IEEE Trans on Service Computing 和Information Sciences），以通讯作者身份发表中科院1区、CCF A 类期刊论文2篇（分别为IEEE Communication Magazine和IEEE Trans on Information INDUSTRIAL INFORMATICS）。所发表文章引用次数为：单篇google最高引用79次、SCI他引48次；累计google引用1054次，累计SCI他引160次。
目前作为IEEE高级会员，担任多个高水平期刊的正式编委和审稿人，如: 中科院2区期刊Future generation computer system及 Journal of Network and Computer Applications和 Adhoc-Sensor wirless networks， IEEE TRANS on Image Processing审稿人和多个国际会议TPC Member及共同主席，如：IEEE CCWMC2011 Co-Chair，TPC of IEEE Globecom 2014-2017, IEEE ICC2012~2017。
特邀报告1：Acemap: Design and Implementation
特邀讲者： 王新兵 教授、博士生导师（上海交通大学）
报告摘要：
A large number of papers are being published every year, which makes it difficult for researchers to grasp the relationship among the scientific literatures and the big picture of academic fields. The new challenges have thus been raised, such as analyzing the complicated citation and author network, mining valuable scientific knowledge, and visualizing big scholarly data. The existing academic systems, such as Google Scholar and DBLP have mainly adopted text-based methods, while some other systems make attempts to better navigate the literatures, for example, AMiner and Science Navigation Map. Although these systems show improvements, they fail to present the academic data in a holistic way, and also have limited functions. Therefore, we need to develop new tools which can realize more modules and further explore the academic literatures.
In this talk, we conceptualize and design a novel academic system, AceMap, to analyze the big scholarly data and present the results through a “map” approach. AceMap integrates several algorithms in the field of network analysis and data mining, and then displays the information in a clear and intuitive way, aiming to help the researchers facilitate their work. After describing the big picture, we present achieved results and our work in progress. By far, AceMap has implemented the following functions: dynamic citation network display, paper clustering, academic genealogy, author and conference homepage, etc. We have also designed and performed distributed network analysis algorithms in a cutting-edge Spark system and utilized modern visualization tools to present the results. Finally, we conclude our paper by proposing the future outlooks.
讲者简介：
特邀报告2：From Data Analysis to Parallel Computing of Safety Route Design in A Smart City
特邀讲者： Dr. Bin Xiao, Associate Professor（The Hong Kong Polytechnic University）
报告摘要：
Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this talk, we will first propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban data. Then, based on the constructed safety index map of a city, we propose a reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to route planning in a smart city. The approach can achieve both safety requirement and short distance in the route design for a smart city. Finally, we show extensive experiments based on real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods. The MOHH approach can obtain more than 80% Pareto optimal solutions in a large-scale road network.
讲者简介：
专题报告1：移动内容分发的云计算方法
特邀讲者：李振华 助理教授、博士生导师（清华大学）
报告摘要：
互联网存在的最基础意义就是内容分发，即将数字内容从一个节点分发到另一个或多个节点。由于互联网固有的动态性与异构性，尽管已经发展了几十年，内容分发的实际性能依然不尽如人意——回忆你最近一次打Skype电话或者看网络视频时的抓狂。伴随全球互联网步入移动时代，内容分发面临更多方面的挑战，比如信号不稳定、流量超预算、基站不安全等。基于和百度手机卫士、腾讯QQ旋风、小米移动等大规模工业系统的长期合作探索，我们清晰地认识到：移动内容分发的问题很难依靠几十MB的手机APP得到根治，而应依托稳定、弹性、集成化与智能化的云计算方法。为此，我们设计并实现了一系列显著提升移动内容分发性能的云计算方法，包括离线下载的自适应重定向算法、云端融合的跨应用手机节流算法、伪基站电信诈骗的识别与定位算法等。多项研究成果属于国际首创，并被工业系统实际采用，受益用户过亿。
李振华，清华大学软件学院助理教授、博导，研究领域包括云计算、大数据及移动互联网。于南京大学计算机系获得学士及硕士学位、北京大学计算机系获得博士学位。发表学术论文50余篇，三次登上重要期刊封面：IEEE云计算汇刊、中国计算机学会通讯、清华学报英文版（SCI索引），出版中英文专著各1部。主持国家自然科学基金面上项目1项、CCF-腾讯犀牛鸟科研和创意基金各1项。曾获得2009年中国大学出版社图书奖首届优秀学术著作一等奖、2015年中国人工智能学会优秀博士论文奖、2015年教育部自然科学一等奖、2017年ACM多媒体系统年会最佳学生论文奖。更多信息见个人主页http://www.greenorbs.org/people/lzh。
专题报告2：Adaptive Distributed Optimization for Uploading Data with Redundancy in Cooperative Mobile Cloud
特邀讲者： 朱晓敏 副教授（国防科技大学）
报告摘要：
With the development of information technology and the ubiquity of mobile devices, increasing amounts of data are generated, processed and transmitted by mobile devices. In order to alleviate the tension between the energy poverty of mobile devices and the increasing demand for transmitting data, the energy-efficient data transmission problem attracts considerable research interests. Nonetheless, how to upload data with redundancy efficiently still lacks a thorough study in spite of the wide existence of this problem in many situations like data storage among mobile devices and mobile crowd sensing. Since uploading redundant data brings little value while still consuming precious energy, it is important to design an efficient approach for mobile devices to upload data with redundancy cooperatively. In this talk, we formulate the uploading data with redundancy in cooperative mobile cloud as an energy-constrained utility maximization problem. To solve this problem, we propose an adaptive distributed optimization approach consisting of the correlated upload decision and the online distributed scheduling algorithm. By the correlated upload decision, each mobile device can make adaptive decisions on how much data to upload and which data to upload according to its own observations independently. The online distributed scheduling algorithm enables mobile devices to optimally upload data while requiring no future information.
讲者简介：
在IEEE TC、IEEE TPDS、IEEE TSC、JPDC、ICDCS、CLOUD、ICPP等著名学术期刊和会议上发表90多篇论文，包括中国计算机学会推荐的A类论文12篇、B类论文21篇。本领域权威的IEEE Transactions/Journal论文 14 篇(第一作者6篇，通信作者6篇)。分布式系统领域权威会议ICDCS论文3篇。SCI论文32篇，EI论文83篇。计算机学报、软件学报、电子学报等国内一级学报论文18篇。论文累计被引用1000余次。2篇论文进入学科ESI全球排名前10%（第一作者）。获IEEE HPCC '16大会最佳论文奖，IEEE EDGE '17大会最佳论文奖，AIAA SpaceOps '12 大会最佳主题论文奖。申请国家发明专利24项，其中授权2项，进入实审阶段18项。登记软件著作权2项。参与出版英文专著1部(Springer 2016年出版)。 在云资源管理组织方面的研究成果已物化成相应的软件系统，并已在战略支援部队某部部署应用，同时在国家自然科学基金重大计划——重大集成项目“基于平行应急管理的非常规突发事件动态仿真与计算实验集成升华平台”系统中进行了集成应用。
担任国际期刊FGCS编辑，国际AIMS会刊Big Data and Information Analytics (BDIA)编辑，FGCS、JCSS和 JOCS专刊策划人和编辑，IEEE SC2 '15 国际会议程序委员会主席、LPDC '16国际会议共同主席，ICDCS等十余个国际会议程序委员会委员。2009年获上海市优秀毕业生，是国防科技大学优秀引进人才。2014年荣立三等功一次。
专题报告3：Gradient-Driven Parking Navigation Using a Continuous Information Potential Field Based on Wireless Sensor Network
特邀讲者： 魏嵬 副教授（西安理工大学）
报告摘要：
汇报人提出了一种利用信息场导航的方法。本方法首次将信息扩散思想融入导航过程，利用了一种偏微分方程（Partial Differential Equation）—扩散方程，以全新的理念应用了物理学中的信息传播思想、电势场理论以及扩散方程的相关知识支持信息查询和导航系统，并使用变分模型构造平滑信息场，同时将梯度下降法融入其中来完成信息导航，大大降低了竞争冲突，克服了原有调和函数方法的强约束缺点，取得很好的降维效果。使得用户可以在高峰时间，更便捷有效地完成导航，到达空车位。
魏嵬副教授，2011年获得西安交通大学计算机软件与理论专业工学博士学位，2017年于美国德州大学达拉斯分校计算机系完成博士后研究工作。一直从事物联网及大数据等相关方面研究工作，发表研究论文60余篇，主持完成3项省部级基金，作为骨干获得5项省市级科技进步奖，作为骨干参与完成国家基金项目6项。其中第一作者发表SCI期刊收录的高水平论文13篇，包括中科JCR-1区、CCF B类国际期刊论文2篇（分别为IEEE Trans on Service Computing 和Information Sciences），以通讯作者身份发表中科院1区、CCF A 类期刊论文2篇（分别为IEEE Communication Magazine和IEEE Trans on Information INDUSTRIAL INFORMATICS）。所发表文章引用次数为：单篇google最高引用79次、SCI他引48次；累计google引用1054次，累计SCI他引160次。 目前作为IEEE高级会员，担任多个高水平期刊的正式编委和审稿人，如: 中科院2区期刊Future generation computer system及 Journal of Network and Computer Applications和 Adhoc-Sensor wirless networks， IEEE TRANS on Image Processing审稿人和多个国际会议TPC Member及共同主席，如：IEEE CCWMC2011 Co-Chair，TPC of IEEE Globecom 2014-2017, IEEE ICC2012~2017。
专题报告4：复杂生物分子网络比对研究
特邀讲者： 谢江 副教授（上海大学）
报告摘要：
复杂生物分子网络作为疾病表型与基因调控及表达之间的桥梁，一直是生物信息学中的研究热点。近年来，随着网络医学和精准医疗的发展，复杂生物分子网络的比对研究更是引起了越来越多的关注。本报告介绍复杂生物分子网络比对的算法、在大规模全基因组网络上的并行比对方法，以及我们的研究在物种进化和复杂疾病研究中的应用。
谢江，博士，副教授。中国计算机学会高级会员，中国计算机学会分布式计算与系统专业委员会委员（2011）， 上海市计算机生物信息学专业委员会委员（2016）。2008年于上海大学计算机工程与科学学院博士毕业，获工学博士学位，研究方向为计算机应用技术。2008年至今为上海大学计算机工程与科学学院副教授。2011年9月至2012年12月作为访问副研究员在加州大学尔湾分校数学与计算生物学中心（CMCB）和数学系访问。自2004年以来，谢江一直致力于高性能计算和生物信息学方面的研究，在《Scientific Reports》、《Biomaterials》、《IEEE Transactions on Computational Biology and Bioinformatics》等期刊和IEEE E-Science、IEEE/WIC/ACM等重要国际会议上发表相关论文60余篇。是教育部博士点基金和上海市自然科学基金的主持人，并作为主要责任人参与国家自然科学基金重大研究计划项目“高性能科学计算的基础算法与可计算建模”培育项目“基于格子Boltzmann方法的大规模可扩展并行计算研究”（2014-2016）和重点项目“商用客机气动噪声大规模并行计算建模、算法与软件”（2017-2019）、 科技部国家重点研发计划“海洋环境安全保障”重点专项“海洋大数据分析预报技术研发”项目“海洋大数据挖掘分析与预测技术”课题（2016-2020）、科技部国家重点研发计划“材料基因工程关键技术与支撑平台”重点专项“高通量并发式材料计算算法与软件”项目“高通量并发式计算算法主体与相关软件”课题（2017-2021）和上海市科委重点资助项目“一种高效能分布式计算系统及其应用研究”（2014-2015）。另获得发明专利3项，软件著作权3项。