the 6th ieee international conference on smart cloud (ieee

The 6th IEEE International Conference on Smart Cloud (IEEE SmartCloud 2021)

November 6-8, Virtual

Conference Program and Information Booklet

Organized By IEEE SmartCloud 2021

Committee

Sponsored By IEEE

IEEE Computer Society, IEEE TCSC

IEEE STC Smart Computing North America Chinese Talents Association

Longxiang High Tech

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Welcome to IEEE Computer Society Smart Computing Special Technical Community (SCSTC) IEEE SCSTC is built up for changing people’s future work and life; attracting intelligent computing talents in smart computing field; producing high quality research work and services in human-centric technologies to change the world; leading the research of smart computing by solving challenging problems; and expanding the smart computing community in a self-sustainable financial way. Two main layers are involved in the concept of smart: one is the traditional optimization; the other one is the intelligent living.

Vision: IEEE Computer Society Smart Computing STC is to enable smart life with smart data, smart cloud, and smart security and become a community leader in these technical fields.

We will create a smart computing society for changing people’s future work and life; attract intelligent computing talents in smart computing field; produce high quality research work and services in human-centric technologies to change the world; lead the research of smart computing by solving challenging problems; and expand the smart computing community in a self-sustainable financial way. Two main layers are involved in the concept of smart: one is the traditional optimization; the other one is the intelligent living.

Mission: IEEE Computer Society Smart Computing STC is to utilize smarting computing technologies to increase humans’ life by integrating smart data, smart cloud, and smart security in both optimizations and intelligences. We will build up the largest professional and academic community in smart computing and aim to enhance humans’ life by utilizing smart computing technologies. This expected community will be providing an integrative research platform for global researchers who are interested in smart computing that covers both optimizations and intelligent living. The target area is a convergence of three novel dimensions at the collaborative application layer, namely smart data, smart cloud, and smart security. This is a social network-based community that is planned to be a long-term self-sustaining organization.

Purpose: The main purpose of this proposed STC is to serve the smart computing research community and advance the research by covering three dimensions, including smart data, smart cloud, and smart security. Current existing STCs cannot satisfy the demands of research interests in convergences of multiple disciplines, which include data, cloud computing, and security. Most existing STCs only have isolative focus in one specific field. However, data, cloud computing, and security are becoming strongly tied techniques, which are hard to separately considered for many contemporary researches or future technical development. Therefore, building up a STC in Smart Computing has an urgent demand for both smart computing research and professional practices.

Scope: the scope of Smart Computing STC is a technical group within the Computer Society. Term Smart in “Smart Computing” mainly covers two aspects, including optimizations and intelligence, by which smart concept will be adopted for new networking-oriented technologies. We are looking for intelligent approaches gaining optimal performances by high-speed data mining and data analysis throughout all aspects in distributed computing and integrated systems. Both aspects are strongly relevant to the performance of the system at the application layer during the process of data transmissions within the distributed environment. This concentration emphasizes the optimizations and intelligences of networking performances and empowers the capabilities of the connected computing devices in distributed systems, which distinguishes from other societies or communities.

Activities: IEEE Computer Society Smart Computing STC organizes a bunch of research community-oriented activities. We aim to unionize scholars or students who have similar or relevant research interests in smart computing and grow the research community globally. Our memberships owners will have a great opportunity to build up an active social network and strengthen the knowledge scope throughout the following activities:

• Improve communications and interconnections between peers. • Explore the theory, applications, implementations, and research of smart computing. • Publish whitepapers, reports, technical manual, and handbooks on research, policies, standards,

products, services, and applications. • Organize conferences and workshops that are related to smart computing. • Release newsletters with updated news regularly. • Host academic publications focusing on smart computing. • Develop smart computing standards. • Standardize the mechanisms, operating principles, and industrial manual guidelines.

Official Permanent Site: https://stc.computer.org/smart-stc/

About IEEE SCSTC

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IEEE SMARTCLOUD 2021 PROGRAM AT A GLANCE ............................................................................... - 4 -

IEEE SMARTCLOUD 2021 KEYNOTES ..................................................................................................... - 5 -

TECHNICAL PROGRAM ......................................................................................................................... - 8 -

TECHNICAL PROGRAM ......................................................................................................................... - 8 -

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Table of Contents

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Saturday, November 6, 2021

Room A

8:00-8:45 Conference Preparing and Online Facility Tuning

8:45 - 9:00 Opening

9:00 – 9:55 Keynote by Prof. Bhavani Thuraisingham

9:55-10:00 Break

10:00-10:55 Keynote by Prof. Xue Liu

11:00-14:00 Break

14:00-15:00 SmartCloud 1

Sunday, November 7, 2021

Room A



11:00-14:00 Break



Registration: Online Registration System (http://www.cloud-conf.net/smartcloud/2021/registration.html) Presentation Online Rooms: Zoom (https://zoom.us/) Virtual Conference Link: https://zoom.us/j/83412036356?pwd=ZWxVUGUybDhWNVEzaHFUVmpaQmh1Zz09 Password to join Zoom will be sent via email. Important Notice: Due to the outbreak of COVID-19, this year the IEEE SmartCloud 2021 will be a virtual conference online. For all participants, please do notice all the time mentioned in this booklet is based on the time zone of east USA which is Eastern Daylight Time (EDT), UTC -4.

IEEE SmartCloud 2021 Program at a Glance

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November 6, 2021, 9:00, Room A

Title: SecAI: Integrating Cyber Security and Artificial Intelligence

Prof. Bhavani Thuraisingham Fellow of IEEE, ACM, AAAS

The University of Texas at Dallas, USA Bio: Dr. Bhavani Thuraisingham is the Founders Chaired Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas. She is also a visiting Senior Research Fellow at Kings College, University of London and a Fellow of the ACM, IEEE, the AAAS, the NAI and the BCS. She has received several awards including the IEEE CS 1997 Technical Achievement Award, ACM SIGSAC 2010 Outstanding Contributions Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and2019. She co-chaired the Women in Cyber Security Conference (WiCyS) in 2016 and delivered the featured address at the 2018 Women in Data Science (WiDS) at Stanford University and has chaired several conferences for ACM and IEEE. Her 39 years career included industry (Honeywell), federal laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 130+ journal articles, 300+ conference papers, 140+ keynote and featured addresses, six US patents, fifteen books as well as technology transfer of the research to commercial and operational systems. She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK. Abstract: Artificial Intelligence (AI) emerged as a field of study in Computer Science in the late 1950s. Researchers were interested in designing and developing systems that could behave like humans. This interest resulted in substantial developments in areas such as expert systems, machine learning, planning systems, reasoning systems and robotics. However, it

IEEE SmartCloud 2021 Keynotes

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is only recently that these AI systems are being used in practical applications in various fields such as medicine, finance, marketing, defense, and manufacturing. The main reason behind the success of these AI systems is due to the developments in data science and high-performance computing. For example, it is now possible collect, store, manipulate, analyze and retain massive amounts of data and therefore the AI systems are now able to learn patterns from this data and make useful predictions. While AI has been evolving as a field during the past sixty years, the developments in computing systems and data management systems have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be identified. Furthermore, the computing systems are being attacked by malware resulting in disastrous consequences. In order words, as progress is being made with technology, the security of these technologies is in serious question due to the malicious attacks. Over the decade. AI and Security are being integrated. For example, machine learning techniques are being applied to solve security problems such as malware analysis, intrusion detection and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine leading techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. Furthermore, while collecting massive amounts of data causes security and privacy concerns, big data analytics applications in cyber security is exploding. For example, an organization can outsource activities such as identity management, intrusion detection and malware analysis to the cloud. While AI techniques are being applied to solve cyber security problems, the AI systems have to be protected. For example, how can the machine learning systems be protected from the attacks? What are the threats to the planning systems? How can expert system carry out their functions in the midst of malware attacks? What are the appropriate access control models for AI systems? How can we develop appropriate security policies for AI systems? These are questions that researchers are beginning to provide answers to. To assess the developments on the integration of AI and Security over the past decade and to determine future directions, the presentation will focus on two major questions: (i) how can the developments in AI techniques be used to solve security problems and (ii) how can we ensure that the AI systems are secure and(iii) what are the security and privacy considerations for AI systems. Second, it will describe the application of AI including machine learning for cyber security applications such as insider threat detection. Third, it will discuss the trends in areas such as adversarial machine learning that take into consideration the attacker’s behavior in developing machine learning techniques. Fourth, it will discuss some emerging trends in carrying out trustworthy AI so that the AI techniques can be secured against malicious attacks. Fifth, it will focus on the privacy threats due to the collection of massive amounts of data and potential solutions. Finally, it will discuss the next steps.

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November 6, 2021, 10:00, Room A

Title: AI for Systems, Systems for AI

Prof. Xue Liu

Fellow of the IEEE, Fellow of the Canadian Academy of Engineering

The McGill University, Canada Bio: Dr. Steve Liu is a VP R&D, Chief Scientist, and Co-

Director of the Samsung AI Center Montreal. He is also a Professor and William Dawson Scholar at McGill University. He received his Ph.D. with multiple honors from the University of Illinois at Urbana-Champaign. He has also worked as the Samuel R. Thompson Chaired Associate Professor in the University of Nebraska-Lincoln, as a visiting scientist at HP Labs in Palo Alto, California, and as the Chief Scientist of Tinder Inc. Dr. Liu is a Fellow of the IEEE and a Fellow of the Canadian Academy of Engineering. He has received many awards and recognitions, including the Mitacs Award for Exceptional Leadership – Professor, Outstanding Young Canadian Computer Science Researcher Prize, the Tomlinson Scientist Award, and several IEEE or ACM Best Paper Awards. He serves/has served as an Editor/Associate Editor for several prestigious ACM and IEEE Transactions and has served on the organizing committees of many IEEE or ACM conferences, including INFOCOM, IWQos, CPS-IoT Week, ICCPS, e-Energy, RTSS, RTAS, SenSys, etc. Abstract: Recent years witnessed the successful resurgence of artificial intelligence. Deep learning and other machine learning methods have brought amazing breakthroughs in many areas, including computer vision, speech recognition, and natural language processing. Artificial intelligence has become a disruptive technology in the information era. In this talk, I will discuss the interaction of AI and system research (including cloud computing). I will use a few recent example developments to demonstrate that they are crucial in advancing each other: On the one hand, AI techniques are particularly useful to solve challenging problems in systems and network management; On the other hand, we need to improve the current system solutions and even design and build new systems to make AI systems and services more effective and efficient. Finally, I argue that the positive cycle of advancing AI and the underlying systems that support it will lead to highly autonomous and intelligence systems for the future.

IEEE SmartCloud 2021 Keynotes

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Technical Program

The 6th IEEE International Conference on Smart Cloud (IEEE SmartCloud 2021)

SmartCloud 1: November 6, 2021, 14:00, Room A

• Yeting Huang and Hancui Zhang. A Safety Vehicle Detection Mechanism Based on YOLOv5.

• Jing Long, Fei Fang and Haibo Luo. A Survey of Machine Learning-based IoT Intrusion Detection Techniques

• Ke Ren, Peng Jiang, Keke Gai, Liehuang Zhu and Jingjing Huang. SM9-based Traceable and Accountable Access Control for Secure Multi-user Cloud Storage.

• Liangqun Huang, Lei Xu, Liehuang Zhu and Keke Gai. A Blockchain-Assisted Privacy-Preserving Cloud Computing Method with Multiple Keys.

• Jiapeng Li. Research Study on Edge Computing.


• Jatin Patel and Talal Halabi. Optimizing the Performance of Web Applications in Mobile Cloud.

• Zisang Xu, Jianbo Xu and Jixin Zhang. A Token-based Authentication and Key Agreement Protocol for Cloud Computing.

• Tong Zhu, Ziyang Zhou, Chenyang Liao, Xinyu Li and Qingfu Zhang. Unsupervised Learning Based Detection Method for The Life Cycle of Virtual Cryptocurrency.

• Jiawei Ye, Jiaqian Li, Erpanjiang Ai and Chengrong Wu. PF_RING-TA: A Traffic-adaptive Method of Packet Capturing by Enhancing PF_Ring.

• Cheng Zhang. Time-To-Live Based Caching Mechanism with Nonlinear Pricing for Information-centric Networks.


• Chunyan Diao, Dafang Zhang, Juan Chen and Yuting Tang. Research on the Application of Time and Space Prediction Method in Traffic Flow Prediction.

• Lars Nielsen and Daniel E. Lucani. Hekate a tool for gauging Data Deduplication Performance.

• Yingjie Liu, Wenhui Hu, Xueyang Liu and Minghui Zhang. FBLE: A Feature Based Lable Enhancement Method for Corporate Bond Credit Rating Prediction.

• Yujie Wang, Xiaoli Wan, Xin Du, Xuzhao Chen and Zhihui Lu. A Resource Allocation Strategy for Edge Services Based on Intelligent Prediction.

• Boyao Zhang, Yonghua Zhao, Chao Yang and Jingqi Sun. Similarity Analysis of Knowledge Graph-based Company Embedding for Stocks Portfolio.

Technical Program

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• Alexandra Samsonova, Barry J. Devereux, Georgios Karakonstantis and Lev Mukhanov. A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers.

• Art Sedighi, Doug Jacobson and Thomas Daniels. T-PKI for Anonymous Attestation in TPM.

• Yu Huang, Wenhui Hu and Xueyang Liu. Study of SG-CIM Mapping Entity Relationship Inference and Verification Techniques Based on Graph Representation Learning Technology.

• Jun Wang, Chu Wang, Xusheng Shi and Tong Zhang. Optimizing the Uncertainty of PPM on Small Batch of Quality Data.

• Chunsheng Wu, Honghao Wu, Lei Song, Hui Tong. Deep Learning in Automatic Fingerprint Identification


• Fengtao Hu, Huang Hua and Zhihan Guo. Review on Service Composition. • Arun Sojan, Ranjit Rajan and Pasi Kuvaja. Monitoring solution for cloud-native DevSecOps. • Wenjing Wu and Wu-Chang Feng. Game to Dethrone: A Least Privilege CTF. • Tian Hui, Xiaoqing Chen, Ronggang Zhu, Yuelei Xu and Zhaoxiang Zhang. Cross-view

Matching Neural Network for Remote Sensing Images. • Jialong Guo, Zhiwei Liu, Zongguo Wang, Jue Wang and Yangang Wang. A Genetic

Algorithm-Based Artificial Network Method for Material Feature Recombination

the 6th ieee international conference on smart cloud (ieee

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