CCF第11届服务计算学术会议与第13届服务科学国际会议

报告摘要：

We live in an era in which our physical and personal environments are becoming increasingly intertwined and smarter due to the advent of pervasive sensing, wireless communications, computing, control and actuation technologies. Indeed our daily lives in smart cities and connected communities depend on a wide variety of cyber-physical infrastructures, such as smart city, smart energy, smart transportation, smart healthcare, smart manufacturing, etc. Alongside, the availability of wireless sensors, Internet of Things (IoTs) and rich mobile devices are empowering humans with fine-grained information and opinion collection through crowdsensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to cyber-physical-social (CPS) convergence with human in the loop that exhibits complex interactions, inter-dependence and adaptations between the engineered/natural systems and human users with a goal to improve human quality of life and experience in smart living environments. However, huge challenges are posed by the scale, heterogeneity, big data, social dynamics, and resource limitations in sensors, IoTs and CPS networks. This talk will highlight unique research challenges in smart living , followed by novel frameworks and models for efficient mobility management, data gathering and fusion, security and trustworthiness, and trade-off between energy and information quality in multi-modal context recognition. Case studies and experimental results from smart energy and smart healthcare applications will be presented. The talk will be concluded with directions of future research.

报告人简介：

Dr. Sajal K. Das, whose academic genealogy includes Thomas Alva Edison, is a Professor of Computer Science and the Daniel St. Clair Endowed Chair at the Missouri University of Science and Technology, USA, where he was the Chair of Computer Science Department during 2013-2017. He is also the co-founder of Smart Health Beacons, LLC. Prior to 2013, he was a University Distinguished Scholar Professor of Computer Science and Engineering, and founding director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington. During 2008-2011, Dr. Das served the US National Science Foundation (NSF) as a Program Director in the Computer and Network Systems Division. His research interests include wireless sensor networks, mobile and pervasive computing, smart environments, cyber-physical systems, IoTs, crowdsensing, data analytics, cloud computing, cyber security, social and biological networks, and applied graph theory and game theory. He has contributed significantly to these areas and published 300+ papers in high quality journals, 400+ conference papers, and 53 book chapters. A holder of 5 US patents, Dr. Das has directed numerous funded projects over $20M and coauthored four books – Smart Environments: Technology, Protocols, and Applications (John Wiley, 2005); Handbook on Securing Cyber-Physical Critical Infrastructure: Foundations and Challenges (Morgan Kaufman, 2012); Mobile Agents in Distributed Computing and Networking (Wiley, 2012); and Principles of Cyber-Physical Systems: An Interdisciplinary Approach (Cambridge University Press, 2020). According to DBLP, Dr. Das is one of the most prolific authors in computer science. His h-index is 86 with more than 33,350 citations according to Google Scholar. He is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal, and serves as Associate Editor of several journals including IEEE Transactions on Mobile Computing, IEEE Transactions on Dependable and Secure Computing, and ACM Transactions on Sensor Networks. A founder of IEEE PerCom, WoWMoM, SMARTCOMP, and ACM ICDCN conferences, Dr. Das has served as General and Program Chair of numerous conferences. He is a recipient of 10 Best Paper Awards in prestigious conferences like ACM MobiCom and IEEE PerCom, and received numerous awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to sensor networks, and University of Missouri System President’s Award for Sustained Career Excellence. He has graduated 43 PhD students, 32 MS thesis students, and 9 postdoctoral researchers. Dr. Das is an IEEE Fellow.

报告摘要：

当今人类社会与经济正在步入由新一代信息技术支撑着的服务型社会形态。该报告从服务计算的本质与发展生态出发，尤其是今年的疫情对于服务业的影响、挑战和机遇，阐述了关于服务计算的发展源泉及驱动力的洞察，从技术层面探寻服务及服务系统的规律及特性，涉及计算服务与商务服务的连接与转换、服务发展生态、服务使能技术等；并从微观、中观和宏观层面介绍了服务计算与服务学涵盖的三个层面。进而，探究了从服务计算到服务科学与工程（服务学）的发展路线、软件服务工程学科与知识体系、现代服务业发展的理论基础、服务科学与工程专业学科及人才培养等，并提出了关于服务科学与工程一级学科建设与发展前景的展望与建议。

报告人简介：

徐晓飞博士，教授、博士生导师，哈尔滨工业大学副校长兼哈工大威海校区校长、中国计算机学会会士和理事，2016-2019年期间为CCF服务计算专委会主任。他兼任教育部高校软件工程专业教学指导委员会副主任、教育部高校教学信息化与教学方法创新指导委员会副主任、教育部产学合作协同育人项目专家组组长、全国高校在线开放课程联盟联席会执委会主任、中国高校计算机教育MOOC联盟常务副理事长、信息技术新工科产学研联盟副理事长、中国服务型制造联盟专家委员会副主任等；其主要研究方向为：服务计算与软件服务工程、大服务与务联网、企业智能计算、企业资源计划ERP与供应链管理SCM技术、数据挖掘与商务智能、软件工程等，是国家重点研发计划 “服务互联网理论与关键技术研究”项目首席科学家，曾担任国际学术会议大会主席或程序委员会共同主席20多次，获得IEEE服务计算技术委员会“杰出领导力奖”(IEEE TCSVC Outstanding Leadership Award)，还获得2018年国家教学成果一等奖。

报告摘要：

Advances in big data, IoT and AI coupled with software as a service paradigm enable tremendous automation opportunities (e.g., robotic process automation). New productivity and usability challenges have also emerged. More specifically,  commonly overlooked limitation of current systems is that they do not provide effective integration of AI enablement and end user processes. We discuss critical challenges in the effective integration of information, task, and collaboration services with natural language based conversational services.   

In this talk, we will discuss synergies between machine reading, data curation, digital augmentation and assistance services as step forward to empower end users to effectively leverage data and AI advances, while share and collaborate on the fly, in order to semi-automate processes, generate and evolve insights. We will also discuss integration of cognitive services and conversational AI to augment and improve productivity and effectiveness of customer, worker and stake-holder processes, automate business processes, deliver data-driven insights. We will discuss how integration of cognitive services and APIs are unlocking application, data and device silos through superimposition of natural language conversations over standardized interaction protocols and access interfaces. To leverage the opportunities that APIs bring, we need cognitive service development to 'scale' in terms of how efficiently and effectively they can integrate with potentially large number of evolving APIs. First, a core challenge is the lack of latent and rich intent and APIs knowledge to effectively and efficiently support dynamic mapping of complex and context-specific user intents to API calls. Second, user intents may be complex and their realization require composition of multiple APIs (e.g., triggering multiple APIs to control IoT devices using one user utterance). We will discuss challenges in API aware training of cognitive services. We will discuss novel latent knowledge-powered middleware techniques and services to accelerate bot development pipelines by: (i) devising novel intent and API element embeddings and matching techniques, (ii) declaratively specifying reusable and configurable conversation models to support complex user intent provisioning; and (iii) dynamically synthesizing API calls instead of ad hoc, rule-based and costly development of intent-to-executable-code mappings

报告人简介：

Prof. Boualem Benatallah is a scientia professor and research group leader at the School of Computer Science (CSE), University of New South Wales (UNSW, Sydney, Australia). His main research interests are developing fundamental concepts and techniques in service composition, cloud services orchestration, quality control in crowdsourcing services, cognitive services and case management, hybrid computation for software vulnerability discovery, legal investigations and systematic literature reviews. He has published more than 270 publications (including 240 refereed papers). A large number of his papers appeared in very selective and reputable conferences and journals. He has been frequently invited to give keynote talks, lectures and tutorials in international conferences and summer schools. His work on service composition, in collaboration with colleagues and students, made pioneering contributions, notably on integrating mathematical optimization techniques and service composition along with declarative composition. Boualem has been PC co-chair of number of international conferences including BPM'2005, ICSOC'2005, WISE'2007, ICWE'2010, IEEE/ACM WI'2011, IEEE SOCA'2011. He was research track co-chair for the WWW'2011 conference. He was the general chair of ICSOC'2008 and BPM’2018. He acted as a key official (tutorial chair, workshops chair, publication chair, area chair, PhD symposium chair) for several international conferences. He has been guest editor of number of special issues for reputable international journals including ACM TOIT and Parallel and Distributed Databases. He has been a PC member of all the reputable international conferences in his areas of research including VLDB, ICDE, WWW, EDBT, MDM, ICSOC, ICWS and ER. He is member of the steering committee of BPM and ICSOC conferences. He is member of the editorial board of numerous international journals and series including ACM Transactions on Web, IEEE transactions on Cloud Computing, Parallel and Distributed Databases, and Computing. He is guest researcher at Lyon University (France). He was a visiting Professor at INRIA-LORIA, CNRS, Claude Bernard University (France), University of Blaise Pascal (Clermont Ferrand, France), University of Trento (Italy), University of Paris Dauphine (France). As the chair of the CSE research committee, he was member of the team (comprising multiple university, government and industry partners) that constructed the successful bid for the Smart Services CRC (Cooperative Research Centre).  He is a leader of the data curation research stream at the Data to Decisions CRC. He was the leader of major collaborative and cross-institutional projects at the Smart Services and D2D CRCs, involving academic, government and industry partners. He is member of Executive Committee of IEEE Computer Society's Technical Committee on Business Informatics and Systems.''

报告摘要：

本演讲从计算与网络的融合发展介绍边缘计算的驱动力及应用场景。分析了5G边缘计算的架构与关键技术，从不同的维度对5G边缘计算的架构进行阐述。对边缘计算关键技术，包括边缘计算的分流、平台、管理、应用、开放等几个方面挑战和发展情况进行了分析。简要介绍中国移动近年来5G边缘计算的实践。进而对算力网络及其在6G的应用给出初步的思考。

报告人简介：

段晓东，现任中国移动研究院网络与IT研究所所长，中国移动技术咨询委员会网络专家组副组长，教授级高级工程师，享受国务院特殊津贴专家。长期从事网络技术攻关与创新工作，是中国移动网络技术的主要带头人之一。曾先后负责中国移动互联网、下一代互联网及IPv6、第四代移动通信技术体制、第五代移动通信技术架构创新等。目前主要从事5G独立组网、6G网络技术、下一代网络、边缘计算等技术攻关。曾在国内外的多个组织就任主席等职位。获得国家级奖励3项和省部级奖励20项，其中参加“第四代移动通信系统(TD-LTE)关键技术与应用”项目获得国家科技进步特等奖。入选国家“百千万人才工程”，获得国家“有突出贡献中青年专家”荣誉称号，并被授予中央企业劳动模范称号。 

报告摘要： 

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 distributed machine learning over heterogeneous geo-distributed systems. We shall also present our recent studies on parameter-server based framework among networked collaborative edges.

报告人简介：

Song Guo is a Full Professor and Associate Head at Department of Computing, The Hong Kong Polytechnic University. His research interests are mainly in the areas of big data, cloud computing, mobile computing, and distributed systems. He is the recipient of the 2019 IEEE TCBD Best Conference Paper Award, 2018 IEEE TCGCC Best Magazine Paper Award, 2017 IEEE Systems Journal Annual Best Paper Award, and other 6 Best Paper Awards from IEEE/ACM conferences. His work was also recognized by the 2016 Annual Best of Computing: Notable Books and Articles in Computing in ACM Computing Reviews. Prof. Guo was an IEEE ComSoc Distinguished Lecturer (2017-2018) and served in IEEE ComSoc Board of Governors (2018-2019). He has also served as General and Program Chair for numerous IEEE conferences.  Prof. Guo is an IEEE Fellow, the Editor-in-Chief of IEEE Open Journal of the Computer Society, and Associate Editor of IEEE Transactions on Cloud Computing, IEEE Transactions on Sustainable Computing, and IEEE Transactions on Green Communications and Networking.

报告摘要：

城市计算是计算机科学以城市为背景，跟城市规划、交通、能源、环境和经济等学科融合的新兴领域，通过不断获取、整合和挖掘城市中不同领域的大数据来解决城市痛点，是当今城市通向智能城市的途径。本报告将介绍城市计算框架和多种时空AI算法，分享基于人工智能的商业选址、城市流量预测等案例。

报告人简介：

张钧波，博士，京东城市时空AI产品部负责人、京东智能城市研究院人工智能实验室主任，主管面向城市时空大数据的AI平台、算法模型和技术研发。他担任人工智能领域国际权威期刊ACM Transactions on Intelligent Systems and Technology (ACM TIST)的Associate Editor，及IEEE TKDE, ACM TKDD等国内外知名期刊审稿人，及KDD、IJCAI、AAAI等CCF-A类会议在内的国内外会议程序委员。在加入京东之前，张钧波博士曾任微软亚洲研究院研究员，联想香港大数据研发中心研究员，在香港中文大学、华为香港诺亚方舟实验室、美国乔治亚州立大学、比利时核研究中心等学习工作多年，具备近十年的人工智能和时空数据挖掘经验，在Artificial Intelligence，IEEE TKDE等国际期刊和软件学报等国内期刊及KDD、AAAI、IJCAI、ACL、CIKM等国际会议上发表论文50余篇（引用2,700次，H-Index：24 [Google Scholar数据]，被SCI检索20余篇），申请发明专利10余项，在科学出版社出版编著1部。他是ACM、IEEE、CCF和CAAI会员，CCF人工智能与模式识别专委会委员。曾获得中国人工智能学会优秀博士论文提名奖，ACM分会优秀博士论文奖，获西南交通大学学生个人最高荣誉奖“竢实扬华奖章”。

报告摘要：

Virtual Reality (VR) has registered applications in multiple domains such as entertainment, medical, and education. In this talk, I will describe how AI and machine learning can be leveraged to improve the performance and resource efficiency of VR. Specifically, I will describe two research thrusts that I work on. In the first thrust, we employ deep learning to predict users' motion to allow VR systems to make judicious decisions on content fetching. In the second thrust, we apply Deep Super Resolution to improve the visual quality of 3D volumetric video streaming. I will also briefly talk about some future research directions on AI-powered VR.

报告人简介：

Feng Qian is an assistant professor in the Computer Science and Engineering Department at University of Minnesota. His research interests cover the broad areas of mobile systems, AR/VR, mobile networking, wearable computing, and system security. He obtained his Ph.D. from University of Michigan, and his bachelor's degree from the ACM Honors Class at SJTU. He has received several awards including the AT&T Key Contributor Award (KCA) (2014), NSF CRII Award (2016), Google Faculty Award (2016), ACM CoNEXT Best Paper Award (2016,2018), AT&T VURI Award (2017), NSF CAREER Award (2018), and DASH-IF Excellence Award (2019).

报告摘要： 

1、云原生与容器技术的发展

2、云原生在边缘的挑战

3、阿里云边缘计算概要

4、阿里云边缘原生应用实践

5、边缘原生演进与展望…

报告人简介： 

熊鹰，阿里云智能-边缘计算高级技术专家，8年互联网从业经验，先后就职于优酷、阿里云，长期从事分布式系统架构、分布式节点管理、分布式任务处理、分布式应用网络等方向的设计与研发，对互联网分布式应用有深刻的理解，阿里云智能工作期间，负责边缘节点服务和边缘安全网关的架构设计，在边缘计算领域拥有多项发明专利。

目前聚焦阿里云边缘容器服务、边缘云原生以及边缘开发者生态等领域，负责边缘容器服务产品的整体规划、架构设计及研发。

报告摘要：

作为诞生于云计算时代的新技术理念，云原生已经成为新的技术浪潮，其背后是以容器化、微服务等为代表的新技术体系。本报告简要分析了云原生的产生、内涵、优势以及对相关服务计算技术的问题挑战，然后结合项目研发实践对云原生下的服务弹性调度、自动伸缩等技术进行了重点介绍，最后总结归纳了未来的发展趋势和研究方向。

报告人简介：

丁志军，男，博士，教授，博士生导师，同济大学计算机科学与技术系主任。中国计算机学会服务计算专委会委员、中国自动化学会网络信息服务专委会秘书长、上海市计算机学会理事、协同与信息服务专委会主任、上海市人工智能学会理事。目前主要从事服务计算与系统、智能软件与交易风控、形式化方法等领域的研究工作。入选教育部新世纪优秀人才计划、上海市曙光学者计划等。出版专著1部，在ACM/IEEE系列汇刊、《计算机学报》和《软件学报》等国内外期刊和学术会议上发表论文100余篇。作为主要完成人获得30余项国家授权发明专利、1项国家技术发明二等奖、2项国家科技进步二等奖与4项省部级科研一等奖励等。

报告摘要：

滴滴服务于5.5亿用户，年运送乘客达到100亿次。如何利用人工智能+大数据技术提升用户出行服务效率和体验，是滴滴致力于解决的问题。本报告讲述滴滴的人工智能三层结构，分享滴滴共享智能出行服务的智能化应用实践。

报告人简介：

脱立恒博士 ，现任滴滴出行高级技术专家 ，原中科院声学所副研究员，2013年毕业于中国科学院大学，获博士学位；2015年到2017年任中科院声学研究所副研究员。主要从事人工智能、大数据技术和云计算等方向研究。曾参与、主持多个国家重点研发计划、863项目课题、中科院战略性先导科技专项、重大工程等项目课题。在EI期刊与顶级会议发表论文多篇，申请专利20多项。 

报告简介：

报告的主旨是探讨在云边端纵向融合架构的基础上，如何进一步实现跨多个边缘计算环境的横向协同计算架构。重点介绍边缘计算联盟架构、公有私有混合边缘计算架构、以及移动和固定边缘节点的协同架构。期望通过上述三个维度的设计，拓展边缘计算模式的内涵和外延，进而探索更广泛的边缘计算应用。

报告人简介：

郭得科，国防科技大学教授、博士生导师。先后于 2001年和2008 年从北京航空航天大学和国防科技大学获得工学学士和博士学位。目前担任科技部科技创新2030-“网络空间安全”重大项目专家、中国计算机学会分布式计算与系统专委会副主任。主要研究方向包括网络计算与系统、分布式计算与系统、移动计算与系统、网络空间安全等。以第一完成人获得湖南省自然科学一等奖一项，出版学术专著2部，并获得中国和美国授权发明专利35项和2项。在ACM/IEEE TON、IEEE JSAC、IEEE TC、IEEE TPDS、IEEE TKDE、IEEE TMC、IEEE TDSC、INFOCOM、ICNP、ICDCS等著名学术期刊和会议上发表150多篇论文，包括ACM/IEEE Transactions文章45篇、IEEE ICNP 2019最佳论文奖、中文三大学报论文15篇等。入选NSFC优青、国家万人青拔尖、军队高层次科技创新人才工程、湖南省杰青、教育部新世纪人才计划。

北京邮电大学，计算机学院教授、博士生导师、网络与交换技术国家重点实验室副主任、国家优秀青年科学基金获得者；先后主持/完成国家及省部级项目十余项，发表学术论文百余篇；担任IEEE CLOUD 2020/EDGE 2020/EDGE 2018/ICFCE 2017等多个国际会议的程序委员会主席或大会主席；目前担任中国计算学会服务计算专委会秘书长、IEEE服务计算技术委员会秘书长、IEEE云计算计算委员会副主席。

报告摘要：

Currently, Cloud/Edge computing adopts centralized frameworks for data processing. In the era of big data, the rapid increase of real-time streaming data processing with high concurrency is making centralized control problems more serious, including the single point of failure, performance bottleneck, and high maintenance cost. On the other hand, the blockchain technology is very promising to solve these problems because of its decentralized characteristics and aggregated enormous computing resources, but it often consumes these recourses for purely reaching decentralized consensus, instead of solving practical problems. The recently proposed proof of useful work (PoUW) consensus mechanism has therefore been proposed stepping forward to utilize these potential enormous recourses for meaningful data processing. However, in order to use the blockchain for data processing, two fundamental issues remain unknown: 1). how to remold the transaction-recording blockchain for decentralized data processing, 2). how to support data-processing fairness under decentralized control. This talk is devoted to solving these two issues.

报告人简介：

Qinglin Zhao (赵庆林) is currently a professor at the Faulty of Information Technology (FIT), Macau University of Science and Technology (MUST). He is a senior member of IEEE and a member of CCF Blockchain. He received his Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Sciences in 2005, and conducted his post-doctoral academic research first at the Chinese University of Hong Kong and then the Hong Kong University of Science and Technology from May 2005 to August 2009.  In September 2009, he joined the FIT, MUST, and has worked there so far. His research interests include blockchain, machine learning, Internet of Things, cloud/fog computing, wireless communication and networks, software-defined wireless networks, etc. He holds 8 US patents and has published more than 30 SCI papers, of which more than 10 academic papers were published in top-tier international journals, such as ACM/IEEE Transactions on Networking, IEEE Transactions on Mobile Computing/Wireless Communications/Vehicular Technology, etc.

报告摘要：

Implementing Deep Neural Network (DNN) applications usually requires powerful computing resources to process a large amount of data. In the mobile edge computing environment, edge devices have limited capacity and the DNN application further suffers from issues of the wireless connection, e.g., handovers, service outage, etc. Without properly addressing these issues, the wider application of DNN in practice will seriously be hindered. In this talk, I will address several key challenging problems in effective deployment and efficient execution of DNN models in the mobile edge computing environment.

个人简介：

Dong Yuan is a senior lecturer from the School of Electrical and Information Engineering, the University of Sydney. He received the B. Eng. degree and M. Eng. degree from Shandong University, Jinan, China, in 2005 and 2008, the PhD degree from Swinburne University of Technology, Melbourne, Australia, in 2012, all in computer science. His research interests include cloud and edge computing, parallel and distributed systems, scheduling and resource management, Internet of things, service computing and artificial intelligence. He has published over 80 papers in top journals and conferences including TPDS, TC, ToSEM, TIFS, ICDCS, IPDPS, MobiHoc, AAAI, CIKM, etc.

报告摘要：

The current cloud market is dominated by a few providers, which offer cloud services in a take-it-or-leave-it manner. The existing service-level agreement (SLA) management solutions cannot easily guarantee a trustworthy, distributed SLA adaptation due to the centralized authority of the cloud provider who may misbehave to pursue individual goals. To address the above issues, we propose an SLA management framework based on a decentralized cloud model, which facilitates the specification and enforcement of dynamic SLAs that enable one to describe how, and under which conditions, the offered service level can change over time. The work is partially supported by the EU H2020 ARTICONF and ENVRI-FAIR projects.

报告人简介：

Dr Zhiming Zhao is an assistant professor at University of Amsterdam (UvA). He leads the “Quality Critical Distributed Computing” research team in the group of Multiscale Networked Systems (MNS) at the System and Networking Lab (SNE). His research interests include big data management, cloud and edge computing, software engineering, and blockchain. He leads VRE development in the LifeWatch-ERIC Dutch Virtual Laboratory Innovation Center. He is also the UvA PI in several EU research and innovation projects such as SWITCH, ENVRIplus, ENVRI-FAIR, ARTICONF, and CLARIFY. https://staff.fnwi.uva.nl/z.zhao/

报告摘要：

A lot of studies in the field of edge computing has focused on mobile network operators and mobile users' needs, e.g., computation offloading and service placement. App vendors, like Uber and YouTube, are key players in edge computing, who hire computing resources at the network edge to serve their users. In this seminar, from app vendors' perspective, let us take a look at two new research topics that have attracted lots of attention in very recent years in the field of edge computing, i.e., edge user allocation and edge data caching.

报告人简介：

Qiang HE is currently an associate professor at Swinburne University of Technology, Australia. His major research interests include edge computing, service-oriented computing, cloud computing and software engineering. He has published 110+ papers at top venues, e.g., TPDS (3), TSE (5), TSC (8), TCC (4), TBD (5), ICWS (12), ICSOC (11), CLOUD (6), ICDCS, ICSE, WWW, ICDE, IJCAI. He is the recipient of the Best Student Paper Award at ICWS2019, Best Student Paper Awards at SCC2018 and ICWS2017 and the Best Paper Award at ICSOC2018.

邓水光，浙江大学计算机科学与技术学院教授，IET Fellow, EAI Felllow，麻省理工学院、斯坦福大学访问学者，国家万人计划青年拔尖人才，获中国青年五四奖章、IEEE TCSVC Rising Star、微软学者等荣誉称号。担任国际期刊Springer Computing、IEEE Access、IET CPS副主编。目前是IEEE  Senior  Member、CCF服务计算专委会常委、CCF软件工程委员会委员。主要研究方向为服务计算、边缘计算和流程管理等，在国际权威期刊和会议上发表论文100余篇，获ICSOC2017最佳论文，指导学生获IEEE NetSof 2020、IEEE ICWS 2019、CollaborateCOM 2019和IEEE SCC 2012四次最佳学生论文奖。授权国家发明专利100余项，出版4部学术专著，获国家科技进步二等奖1次，省部科技进步一等奖4次。

报告摘要： 

移动应用生态系统近年来呈现爆发式增长，海量移动服务已经融入用户日常生活。传统的网络欺诈行为已经迁移到移动互联网，面向移动服务的新型欺诈行为层出不穷，不仅给移动服务生态系统造成巨大的破坏，也给应用市场和监管部门带来严峻挑战。本次报告将从移动应用服务生命周期出发，提出一套核心技术检测新型欺诈行为，并对其背后的黑色产业链进行溯源和刻画。具体包括基于 UI 状态转移图的新型移动广告服务欺诈检测，基于域名误植生成的新型移动服务标识欺诈检测，以及面向移动服务的欺诈黑色产业链溯源。最后，本次报告将对服务计算与安全隐私领域的结合展望。

报告人简介：

王浩宇，北京邮电大学计算机学院副教授，博士生导师，“1551人才计划“托举人才。2016年博士毕业于北京大学，美国卡内基梅隆大学联合培养博士生。研究方向为软件系统、安全隐私与AI领域的结合，目前已在系统安全、软件分析、万维网等方向国际顶级会议和期刊发表学术论文50余篇，包括CCF A类顶级论文20余篇。曾获万维网顶会WWW 2020最佳学生论文奖，为中国研究机构首次获得该奖励。担任多个顶级会议和期刊的程序委员会委员和审稿人。

报告摘要： 

面向健康中国2030国家重大战略任务，围绕老有所养、老有所医、老有所乐的社会民生重大需求，研究提出了基于服务互联网的医养结合、物务结合、线上线下结合的新型养老服务模式，突破了基于知识图谱的智能服务匹配、基于网格的机构/居家社区/医联体混合管理服务技术、面向大规模个性化需求的服务定制等关键技术，研发了“百善”系列基于服务互联网的健康养老智能服务与大数据平台，在中国老龄健康促进工程（国家卫健委主导实施）和沈阳、威海、烟台、济南等地区的百余家养老服务企业得到应用。

报告人简介：

涂志莹，博士、哈尔滨工业大学副教授，硕士生导师。软件工程系副主任，CCF专业会员，CCF服务计算专委会委员。主要研究方向：服务计算、软件工程、认知型服务、边缘计算与微服务等。承担和参与了国家自然科学基金青年项目1项，国家自然科学基金重点项目1项，国家重点研发计划项目2项。在国内外学术期刊和会议上发表学术论文20余篇，申请发明专利6项，软件著作权10余项。出版教材1部，建设MOOC课程4门，其中2门获批国家精品在线开放课程。出版英文译著1部。

报告摘要： 

当今互联网所承载的巨量数据中，视频内容传输占比已经超过80%。在中国大陆，长视频、短视频用户均已超过6亿人。然而，高速发展的移动接入网、拥挤的核心网、以及正在建设的边缘网络之间还存在一些矛盾。因此，提高视频传输质量、增强视频观看体验，仍然面临着诸多挑战。为了解决以上问题，视频网络需要通过人工智能技术，来预测视频内容在时间和空间上的流行度，刻画网络动态变化，分析用户观看行为，从而协调网络边缘节点的存储资源和计算能力，通过缓存、动态码率分配、超分辨率重建、内容标注、分布式机器学习、等关键技术更好地为用户推荐视频内容，增强用户的观看体验。同时，为未来5G网络中的各种视频应用发展提供技术支持。

报告人简介：

边凯归，北京大学副教授, 长期从事计算机网络方面的研究工作，近年来专注于智能视频网络内容分发与边缘计算的研究，获相关领域5个学术会议最佳论文奖，发表30余篇CCF A类会议与期刊论文、1篇ESI热点论文、出版Springer专著一部，谷歌学术引用3000余次。获2018年教育部高等学校自然科学一等奖，2018年IEEE ComSoc亚太地区青年研究者奖，2019年JSPS国际研究奖，2020-2021年IEEE ComSoc杰出演讲者等。

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