The 19th IEEE International Symposium on Parallel and Distributed Processing with

Applications (IEEE ISPA 2021)

The 11th IEEE International Conference on Big Data and Cloud Computing

(BDCloud 2021)

The 14th IEEE International Conference on Social Computing and Networking (IEEE

SocialCom 2021)

The 11th IEEE International Conference on Sustainable Computing and Communications

(IEEE SustainCom 2021)

September 30-October 3, New York, USA

Conference Program and Information Booklet

Organized By IEEE ISPA/BDCloud/ SocialCom/SustainCom 2021

Committee

Sponsored By IEEE

IEEE Computer Society, IEEE TCSC

IEEE STC Smart Computing North America Chinese Talents Association

Longxiang High Tech

!

!

!

!

!

!

!

!

!

!

!

!

!

!

IEEE ISPA/BDCLOUD/ SOCIALCOM/SUSTAINCOM 2021 PROGRAM AT A GLANCE ................................. - 1 -

ISPA 2021 KEYNOTES ........................................................................................................................... - 2 -

ISPA 2021 KEYNOTES ........................................................................................................................... - 3 -

ISPA 2021 KEYNOTES ........................................................................................................................... - 4 -

TECHNICAL PROGRAM ......................................................................................................................... - 5 -

TECHNICAL PROGRAM ......................................................................................................................... - 5 - !

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

Table of Contents

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

!

!

!

Friday, October 1, 2021

Room A Room B Room C

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

8:45 - 9:00 Opening

9:00 – 10:00 Keynote by Prof. Sun-Yuan Kung

10:00-11:00 Keynote by Prof. Witold Pedrycz

11:00 –12:00 Keynote by Prof. Robert Kozma

12:00 –13:30 Break

13:30 –14:30 ISPA 1 ISPA 2 BDCloud 1 & SustainCom 1

14:30 –15:30 ISPA 3 ISPA 4 SocialCom 1

15:30-16:30 ISPA 5 ISPA 6 SpaCCS 1

Saturday, October 2, 2021

Room A Room B Room C

9:00-10:00 ISPA 7 ISPA 8 ISPA 9

10:00-11:00 ISPA 10 ISPA 11 ISPA 12

11:00-12:00 ISPA 13 ISPA 14 ISPA 15

12:00-13:30 break

13:30-14:30 ISPA 16 ISPA 17 SpaCCS 2

14:30-15:30 ISPA 18 ISPA 19 AINets 2021

15:30-16:30 ISPA 20 ISPA 21 CAI 2021

16:30-17:30 ISPA 22 ISPA 23 TrustData 2021

Sunday, October 3, 2021

Room A Room B Room C

9:00-10:00 ISPA 24 ISPA 25 ISPA 26

10:00-11:00 ISPA 27 ISPA 28 ISPA 29

11:00-12:00 ISPA 30 ISPA 31 ISPA 32

12:00-13:30 break

13:30-14:30 ISPA 33 ISPA 34 SCS 2021

14:30-15:30 ISPA 35 ISPA 36 TSP 2021

15:30-16:30 ISPA 37 SPIoT 2021

Registration: Online Registration System (http://www.cloud-conf.net/ispa2021/registration.html) Presentation Online Rooms:Zoom (https://zoom.us/) Virtual Conference Link: https://us02web.zoom.us/j/5911036727?pwd=NTJnRjA1ZWdKVDRhdEFZcGJhM0FhUT09 Important Notice: Due to the outbreak of COVID-19, this year the ISPA 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 ISPA/BDCloud/ SocialCom/SustainCom 2021 Program at a Glance

- 2 -

Oct. 1st, 2021, 9:00, Room A

Title: On Regressive Neural Architectural Search (RNAS)

Prof. Sun-Yuan Kung Princeton University, USA

Bio: S.Y. Kung, Life Fellow of IEEE, is a Professor at Department of Electrical Engineering in Princeton University. His research areas include machine learning, data mining, systematic design of (deep-learning) neural networks, statistical estimation, VLSI array processors, signal and multimedia information processing, and most recently compressive privacy. He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society. He was elected to Fellow in 1988 and served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a recipient of IEEE Signal Processing Society's Technical Achievement Award for the contributions on "parallel processing and neural network algorithms for signal processing" (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994); a recipient of IEEE Signal Processing Society's Best Paper Award for his publication on principal component neural networks (1996); and a recipient of the IEEE Third Millennium Medal (2000). Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. He served as the first Associate Editor in VLSI Area (1984) and the first Associate Editor in Neural Network (1991) for the IEEE Transactions on Signal Processing. He has authored and co-authored more than 500 technical publications and numerous textbooks including "VLSI Array Processors", Prentice-Hall (1988); "Digital Neural Networks", Prentice-Hall (1993) ; "Principal Component Neural Networks", John-Wiley (1996); "Biometric Authentication: A Machine Learning Approach", Prentice-Hall (2004); and "Kernel Methods and Machine Learning”, Cambridge University Press (2014). Abstract: We have recently witnessed unprecedented proliferation of deep learning architectures with impressive performances superseding the state-of-the-arts. The task of optimizing the network parameters is usually handled by BP learning, often with great success. In contrast, the task of optimizing such network structure is usually left to trial-and-error. Moreover, it could be costly if we must start from scratch a brand new round of architectural search for new applications. Needless to say, it is highly desirable that if we could automate such an architecture learning task just like we learn the network parameters. This leads us to "Neural Architecture Search" (NAS), the process of automating architecture engineering. This talk starts by noting the vital roles of dimensionality in NAS. We shall review the curse/blessing of dimensionality (both depth and width) in deep learning networks. We shall then compare the (very different) design principles behind PNAS (Progressive NAS) and RNAS (Regressive NAS) and explain why do we favor RNAS over PNAS. This is especially so when we harness the vital roles RNAS to mitigate the aforementioned curse of dimensionality. In this talk, we shall address two technical areas related to the RNAS research: 1. Deep Compression: It is natural to augment the BP-Learning with a structural learning paradigm, leading to a X-Learning strategy to jointly learn the structure and parameters of the learning models. Based on LASSO-type (i.e. L0-norm) regression we derive a notion of Deleterious Neurons (DNs). This will ultimately lead to the proposed X-learning paradigm where deleterious links (DLs) will be gradually trimmed so as to reach an improved network structure. As to be demonstrated during the talk, X-learn is applicable to both types of application scenarios: Classification-type, e.g. CIFAR or ImageNet. and Regression-type, e.g. super-resolution (SR) hetero-encoder. This talk will also highlight our on-going development on XNAS: an reinforcement-learning autonomous NAS. 2. Data Compression: For data compression sectors, we have developed a dimension-reduction method called Regression Component Analysis (RCA), with closed-form error analysis. RCA is intimately related to PCA and RCA. For example, while PCA has already proven adequate for auto-encoder, we will need to resort to RCA in order to handle the (more general) hetero-encoder applications, e.g. SR imaging. As another example, we note that DCA and RCA are respectively the only closed-form mathematical tools for subspace analysis, the former for classification and the latter for regression. Note further that DCA has already enjoyed great success in visualization of data from different class labels, an emergent important research field in big data analysis. By the same token, RCA's promising roles for big data analyses can be naturally anticipated as well.

ISPA 2021 Keynotes

- 3 -

Oct. 1st, 2021, 10:00, Room B

Title: Rule-Based Modeling in Interpretable

Environment of Federated Learning

Prof. Witold Pedrycz University of Alberta, Canada

Bio: Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others. Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer). Abstract: With the evident omnipresence of mobile devices, massive distributed data, limited communication bandwidth, and security and privacy requirements, federated learning becomes a suitable and practically viable design alternative supporting model construction. As a learning paradigm, it constitutes a radical departure from the well-known development schemes commonly encountered in machine learning. Rule-based models coming in the form of conditional if-then statements exhibit highly desirable modularity feature, which is relevant in the context of interpretability of their architectures. In this talk, we revisit the existing schemes of federated learning and develop different averaging and gradient-based learning mechanisms by elaborating on ways of their application to the condition and conclusion parts of the rules and the main strategies of communication of the parameters of the models and gradients among clients and a server. A role of information granules and Granular Computing is discussed. We demonstrate that the quality of the rules at the level of the individual client can be characterized by introducing their granular counterparts (viz. the rules with granular rather than numeric parameters). It is also shown that a quality of the global model being formed at the level of the server can be conveniently quantified by endowing it with a granular generalization. Furthermore, a three-tier layered topology of federated learning involving a layer of edge computing is investigated. To make the talk self-contained, all required prerequisites are covered.

ISPA 2021 Keynotes

- 4 -

Oct. 1st, 2021, 11:00, Room C

Title: Sustainable Artificial Intelligence

Prof. Robert Kozma University of Memphis, USA

Bio: Dr. Robert Kozma (Fellow IEEE; Fellow INNS) holds a Ph.D. in Applied Physics (Delft, The Netherlands), two M.Sc. degrees (Mathematics, Hungary; and Power Engineering, Moscow MEI, Russia). He is Professor of Mathematics, funding Director of Center for Large-Scale Intelligent Optimization and Networks (CLION), FedEx Institute of Technology, University of Memphis, TN. He has been Visiting Professor of Computer Science, University of Massachusetts Amherst since 2016. Previous assignments include US Air Force Research Laboratory, Sensors Directorate; NASA Jet Propulsion Laboratory, Robotics Division; University of California at Berkeley, EECS and Division of Neurobiology; Otago University, Information Sciences, New Zealand; Tohoku University, Quantum Science and Engineering, Japan. He has over 30 years of experience in intelligent signal processing, large-scale networks and graph theory, distributed sensor systems, biomedical domains, including brain dynamics. He has 7 book volumes, over 300 articles in journals and proceedings, and 3 patent disclosures. Research funding by agencies NASA, DARPA, AFRL, AFOSR, NSF, and others. He is Editor-In-Chief of IEEE Transactions on Systems, Man, and Cybernetics - Systems, and serves on the Editorial Boards on several journals. He serves on the Governing Board of IEEE Systems, Man, and Cybernetics Society, and previously served on the AdCom of the IEEE Computational Intelligence Society. He is past President of the International Neural Networks Society (INNS), recipient of the Denis Gabor Award of INNS. He has been General Chair of the International Joint Conference on Neural Networks, IJCNN2009, and has been Program Chair/Co-Chair and TC member of several dozens of international conferences. Abstract: The development of increasingly powerful computing devices has been dominated by Moore's law for over half a century, which may reach an end soon demanding a drastic reformulation of existing approaches to computing. A key issue is the massive energy utilization by the electronics components and the consequent dissipation of the energy in the form of heat. Energy constraints are often ignored or have just secondary role in typical cutting-edge AI approaches. For example, Deep Learning Neural Networks often require huge amount of data/ time/ parameters/ energy/ computational power, which may not be readily available in various scenarios, such as edge computing and on-board applications. Human brains are very efficient devices using 20W power, which is many orders of magnitudes less than the power consumption of today’s supercomputers requiring MWs to innovatively solve a given machine learning task. Brain waves and spatio-temporal oscillations implement pattern-based computing principles. Analyzing brain oscillations and metabolism can help to develop computational and hardware implementations, which are energy-efficient and provide a path towards sustainable AI. Among the various potential solutions for sustainable AI, neuromorphic computing and chip designs gained prominence in recent years. Popular crossbar architectures are especially well suited for pattern-based computing, with the potential of complementing the sequential symbol manipulation paradigm of traditional Turing machines. Applications include autonomous on-board signal processing and control, distributed sensor systems, autonomous robot navigation and control, and rapid response to emergencies.

ISPA 2021 Keynotes

- 5 -

Technical Program The 19th IEEE International Symposium on Parallel and Distributed

Processing with Applications (IEEE ISPA 2021)

ISPA 1: Oct. 1st, 2021, 13:30, Room A

• Siqing Fu, Tiejun Li, Jianmin Zhang, Sheng Ma and Sheng Liu. MiniMCTAD: Minimalist Monte Carlo Transport Architecture Design.

• Xuan Li, Liqiong Chang and Xue Liu. CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication.

• Aijuan Qian, Chenlu Li, Xiaoju Dong, Shengtao Chen and Yanling Zhang. SLAMVis: An Interactive Visualization Approach for Smart Labeling on Multidimensional Data.

• Ling Xiao, Beiji Zou, Chengzhang Zhu, Meng Zeng and Zhi Chen. HFBT: An Efficient Hierarchical Fault-tolerant Method for Cloud Storage System.

• Xin Lu and Zhijun Wu. ATMCC: Design of the Integration Architecture of Cloud Computing and Blockchain for Air Traffic Management.

ISPA 2: Oct. 1st, 2021, 13:30, Room B

• Xiao Huang, Yu Jiang, Hao Fan, Huayun Tang, Yiping Wang, Jin Jin, Hai Wan and Xibin Zhao. TATA: Throughput-Aware TAsk Placement in Heterogeneous Stream Processing with Deep Reinforcement Learning.

• Yi Xu, Zhenyi Chen, Binhong Huang, Ximeng Liu and Chen Dong. HTtext: A TextCNN-based pre-silicon detection for hardware Trojans.

• Chun Liu, Lin Yang, Linru Ma, Liucheng Shi, Xuexian Hu, Weipeng Cao and Jingjing Zhang. PEBIID: Privacy-preserving and Efficient Biometric Identification for IoV Dapp.

• Feiyang Wu, Zhuoran Song, Jing Ke, Li Jiang, Naifeng Jing and Xiaoyao Liang. PIPArch: Programmable Image Processing Architecture Using Sliding Array.

• Saqing Yang, Yi Ren, Jianfeng Zhang, Jianbo Guan and Bao Li. KubeHICE: Performance-aware Container Orchestration on Heterogeneous-ISA Architectures in Cloud-Edge Platforms.

ISPA 3: Oct. 1st, 2021, 14:30, Room A

• Xiaohui Wei, Changbao Zhou, Yong Sheng, Yan Wu, Lina Li and Shang Gao. RLConfig: Run-time Configuration of Cluster Schedulers via Deep Reinforcement Learning.

• Xiaohui Wei, Yong Sheng, Lina Li and Changbao Zhou. DRL-Deploy:Adaptive Service Function Chains Deployment with Deep Reinforcement Learning.

• Xiaorui Zhu, Lei Gong, Zongwei Zhu and Xuehai Zhou. Vapor: A GPU Sharing Scheduler with Communication and Computation Pipeline for Distributed Deep Learning.

• Kunli Lin, Bibo Tu, Haojun Xia and Kun Zhang. AddrArmor: An Address-based Runtime Code-reuse Attack Mitigation for Shared Objects at the Binary-level.

• Penghao Sun, Dongliang Xue, Litong You, Yan Yan and Linpeng Huang. HyperKV: A High Performance Concurrent Key-Value Store for Persistent Memory.

ISPA 4: Oct. 1st, 2021, 14:30, Room B

• Yihuan Qian, Songwen Pei, Jihong Yuan, Dianle Zhou, Tong Liu and Linghe Kong. DRAM: Dragonfly Routing Algorithm on Multi-objects by Optimal Thresholds.

• Ming Xu, Luoyi Zhang, Baoming Zhang, Meng Cao, Yuan Jinliang and Wang Chongjun. EPINE: Enhanced Proximity Information Network Embedding.

• Libo Chang, Shengbing Zhang, Huimin Du, Shiyu Wang, Meikang Qiu and Jihe Wang. Accuracy vs. Efficiency: Achieving both Through Hardware-Aware Quantization and Reconfigurable Architecture with Mixed Precision.

• Chendi Li, Haipeng Jia, Hang Cao, Jianyu Yao, Boqian Shi, Chunyang Xiang, Jinbo Sun, Pengqi Lu and Yunquan Zhang. AutoTSMM: An Auto-tuning Framework for Building High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on CPUs.

• Jia Ma, Xianqi Zheng, Yubo Liu and Zhiguang Chen. KBP: Mining Block Access Pattern for I/O Prediction with K-Truss.

Technical Program

- 6 -

ISPA 5: Oct. 1st, 2021, 15:30, Room A

• Yinglong Xiong, Jiang Zhou, Lin Su, Weiping Wang and Yong Chen. ECCH: Erasure Coded Consistent Hashing for Distributed Storage Systems.

• Zhihao Liu, Qiang Wang, Yongjian Li and Yongxin Zhao. CMSS: Collaborative Modeling of Safety and Security Requirements for Network Protocols.

• Baoke Li, Cong Cao, Yanbing Liu, Yuhai Lu, Gongxin Sun, Baohui Li and Binxing Fang. D2Graph: An Efficient and Unified Out-of-Core Graph Computing Model.

• Milan Shetti, Bingzhe Li and David H.C. Du. E-VM: An Elastic Virtual Machine Scheduling Algorithm to Minimize the Total Cost of Ownership in a Hybrid Cloud.

• Ke Ren, Peng Jiang, Liehuang Zhu and Keke Gai. SM9-based Anonymous Search over Encrypted Data.

ISPA 6: Oct. 1st, 2021, 15:30, Room B

• Huang Xin, Wang Guozheng and Lei Yongmei. GR-ADMM: A Communication Efficient Algorithm Based on ADMM.

• Moming Duan, Duo Liu, Xinyuan Ji, Renping Liu, Liang Liang, Xianzhang Chen and Yujuan Tan. FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure.

• Renjie Zhou, Dezun Dong, Shan Huang and Yang Bai. FastTune: Timely and Precise Congestion Control in Data Center Network.

• Chunhua Xiao and Dandan Xu. FGPA: Fine-Grained Pipelined Acceleration for Depthwise Separable CNN in Resource Constraint Scenarios.

• Junchao Ma, Dezun Dong, Cunlu Li, Ke Wu and Liquan Xiao. PAARD: Proximity-Aware All-Reduce Communication for Dragonfly Networks.

ISPA 7: Oct. 2nd, 2021, 9:00, Room A

• Lanju Kong, Yaming Dou, Qingqing Yin, Xinping Min and Qingzhong Li. WST+iMPT: A High-performance Incremental Verification World State Model for Massive Accounts.

• Penghao Zhang, Heng Pan, Gaogang Xie, Zhenyu Li and Penglai Cui. NetANNS: A High-Performance Distributed Search Framework Based On In-Network Computing.

• Lizhi Zhang, Zhiquan Lai and Dongsheng Li. PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching.

• Zhen Xu and Wenbo Zhang. QuickCDC: A Quick Content Defined Chunking Algorithm Based on Jumping and Dynamically Adjusting Mask Bits.

• Xinmeng Li, Nengguang Luo, Dan Tang, Zhiqing Zheng, Zheng Qin and Xinxiang Gao. BA-BNN: Detect LDoS Attacks in SDN Based on Bat Algorithm and BP Neural Network.

ISPA 8: Oct. 2nd, 2021, 9:00, Room B

• Ziyue Xu, Lixiao Cui, Gang Wang and Xiaoguang Liu. Alter: Towards Optimizing Persistent Indexes in Hybrid Memory Systems.

• Jiaming Huang, Chuming Xiao, Weigang Wu, Ye Yin and Hongli Chang. MADC: Multi-scale Attention-based Deep Clustering for Workload Prediction.

• Yuzhao Wang, Junqing Yu and Zhibin Yu. Treator: a Fast Centralized Cluster Scheduling at Scale Based on B+ Tree and BSP.

• Tao Zhong, Junsheng Chang, Peichang Shi, Linhui Li and Fei Gao. Dyacon: JointCloud Dynamic Access Control Model of Data Security Based on Verifiable Credentials.

• Li Lin, Shuang Li, Xuhui Lv and Bo Li. BTDetect: An Insider Threats Detection Approach based on Behavior Traceability for IaaS Environments.

ISPA 9: Oct. 2nd, 2021, 9:00, Room C

• Ding Shuai, Ge Jingguo, Yuan Xiaowei and Du Xinhui. Encrypt DNS Traffic: Automated Feature Learning Method for Detecting DNS Tunnels.

• Shuyue Ma, Shudian Song, Lingyu Yang, Jingmei Zhao, Feng Yang and Linbo Zhai. IMOPSOQ: Offloading Dependent Tasks in Multi-access Edge Computing.

• Xinxuan Huang, Jigang Wu, Jiaxing Li and Chengpeng Xia. BEADS: Blockchain-Empowered Auction in Decentralized Storage.

• Qizhen Xu, Zhijie Zhang, Liwei Chen and Gang Shi. Finding Runtime Usable Gadgets: On the Security of Return Address Authentication.

- 7 -

• Xiaobo Guo, Fali Wang, Neng Gao, Zeyi Liu and Kai Liu. ConvMB: Improving Convolution-Based Knowledge Graph Embeddings by Adopting Multi-Branch 3D Convolution Filters.

ISPA 10: Oct. 2nd, 2021, 10:00, Room A

• Xiu Ma, Guangli Li, Lei Liu, Huaxiao Liu, Lei Liu and Xiaobing Feng. Understanding the Run-time Overheads of Deep Learning Inference on Edge Devices.

• Jiaquan Yan, Yalan Wu, Long Chen and Jigang Wu. Parameter Servers Placement for Distributed Deep Learning in Edge computing.

• Haiyang Lin, Mingyu Yan, Duo Wang, Wenming Li, Xiaochun Ye and Dongrui Fan. Alleviating Imbalance in Synchronous Distributed Training of Deep Neural Network.

• Dai Bin, Ren Tao, Niu Jianwei, Hu Zheyuan, Hu Shucheng and Qiu Meikang. A Distributed Computation Offloading Scheduling Framework based on Deep Reinforcement Learning.

• Shiqin Liu, Shiyuan Feng, Jinxia Wu, Wei Ren, Weiqi Wang and Wenwen Zheng. How to Protect the Copyright yet Maintain the Utility for Images in Deep Learning.

ISPA 11: Oct. 2nd, 2021, 10:00, Room B

• Zhiyi Gui, Qi Qi, Jingyu Wang, Haifeng Sun, Xiang Yang and Jianxin Liao. Grouping Synchronous to Eliminate Stragglers with Edge Computing in Distributed Deep Learning.

• Degan Zhang, Hongrui Fan and Jie Zhang. Novel Resource Allocation Algorithm of Edge Computing Based on Deep Reinforcement Learning Mechanism.

• Zhijun Wu, Cheng Liang and Yuqi Li. Intrusion Detection Method based on Deep Learning. • Yang Liang, Zhigang Hu and Liu Yang. A Two-stage Replica Management Mechanism for

Latency-Aware Applications in Multi-Access Edge Computing. • Pengwei Wang, Yajun Zhao and Zhaohui Zhang. Joint Optimization of Data Caching and

Task Scheduling Based on Information Entropy for Mobile Edge Computing. ISPA 12: Oct. 2nd, 2021, 10:00, Room C

• Tian Dong, Han Qiu, Jialiang Lu, Meikang Qiu and Chun Fan. Towards Fast Network Intrusion Detection based on Communication-Efficient Federated Learning.

• Shili Yan, Bing Tang, Jincheng Luo, Xing Fu and Xiaoyuan Zhang. Unsupervised Anomaly Detection with Variational Auto-Encoder and Local Outliers Factor for KPIs.

• Yinjun Ye, Yongdong Zhang and Weicai Ye. User-level failure detection algorithms based on fail-lagging model for HPC.

• Chuanjia Hou, Tong Jia, Ying Li and Yifan Wu. Diagnosing Performance Issues in Microservices with Heterogeneous Data Source.

• Zhongyang Wang, Yijie Wang, Zhenyu Huang and Yongjun Wang. Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data.

ISPA 13: Oct. 2nd, 2021, 11:00, Room A

• Jingxuan Kang, Bin Zang and Weipeng Cao. Domain Adaptive Semantic Segmentation via Image Translation and Representation Alignment.

• Lingyao Chen, Yongxin Zhu and Xiaoying Zheng. Image Distillation based screening for X-ray crystallography diffraction images.

• Ziheng Zhou, Hong Guo, Yuwen Guo and Huanhuan Sheng. Synthesis and segmentation method of cross-staining style nuclei pathology image based on adversarial learning.

• Rui Miao, Hongxu Jiang, Xiaobin Li and Yonghua Zhang. Real-Time Ship detection from Infrared Images Through Multi-feature Fusion.

• Gang Qiu and Yanli Guo. Movie Big Data Intelligent Recommendation System Based on Knowledge Graph.

ISPA 14: Oct. 2nd, 2021, 11:00, Room B

• Jihe Wang, Hao Chen, Danghui Wang, Kuizhi Mei, Shengbing Zhang and Xiaoya Fan. A Heterogeneous Stochastic Computing Multiplier for Universally Accurate and Energy-Efficient DNNs.

• Han Yu, Hu Wei, Liu Jing and Gan Yu. Energy-Efficient Scheduling Algorithms with Reliability Goal on Heterogeneous Embedded Systems.

• Qiu Qinnan, Lei Yongmei, Wang Dongxia and Wang Guozheng. An efficient hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm.

- 8 -

• Suna He, Jigang Wu, Bing Wei and Jiaxin Wu. Task Tree Partition and Subtree Allocation for Heterogeneous Multiprocessors.

• Hao Shi, Yixiang Chen and Jinyi Xu. An Efficient Scheduling Algorithm for Distributed Heterogeneous systems with Task Duplication Allowed.

ISPA 15: Oct. 2nd, 2021, 11:00, Room C

• Hongjun Dai and Jiong Zhang. An Effective Outlier Detection Method for EAF Based on an Iterative Heterogeneous Ensemble.

• Qingyuan Jiang, Jinyi Xu and Yixiang Chen. A Genetic Algorithm for Scheduling in Heterogeneous Multicore System Integrated with FPGA.

• Weiyu Tan, Zhenyu Zeng, Tian Ling, Ying Tao and Yuxia Cheng. Learning Heterogeneous DAG Tasks Scheduling Policies With Efficient Neural Network Evolution.

• Hong Guo and Hanjing Cui. An Edge Computing Architecture and Application Oriented to Distributed Microgrid.

• Yifan Liang and Hongjun Dai. Application Virtualization: An Agent Encapsulation of Software in Virtual Machines to Archive the Execution Performance in Hosts.

ISPA 16: Oct. 2nd, 2021, 13:30, Room A

• Zhijun Wu, Junjun Guo and Weihang Cui. Analysis of BBR's Non-queuing Optimal Model. • Jianfeng An, Hong Yun You, Jinhua Sun and Jiawei Cao. Fault tolerant XY-YX routing

algorithm supporting backtracking strategy for NoC. • Hong Guo and Nan Guo. Research and Application of a multidimensional association rules

mining algorithm based on Hadoop. • Shuyu Wu, Linghe Kong, Qiao Xiang, Zhenzhe Zheng, Luoyi Fu and Guihai Chen. A

Lightweight, Privacy-Preserving Tensor Completion Framework for Internet of Things. • Jiang Zhou, Lin Su, Yong Chen, Weiping Wang and Yinglong Xiong. Consistent Hashing

in Deep Storage Architectures. ISPA 17: Oct. 2nd, 2021, 13:30, Room B

• Tanhong Chen, Zheyuan Hu, Tao Ren, Jianwei Niu and Qingfeng Li. An Novel Shape-based Robotic Sorting Approach based on Computer Vision.

• Yaqiong Ma, Xiangyu Bai, Zhaoran Wang. Trajectory Privacy Protection Method based on Shadow vehicles.

• Ying Huang, Xiaoying Zheng, Yongxin Zhu, Xiangcong Kong and Xinru Jing. CPU-GPU Collaborative Acceleration of Bulletproofs - A Zero-Knowledge Proof Algorithm.

• Jiayi Lai, Lin Gan and Lanning Wang. Mixed-precision Methods to Reconstruct Numerical Ocean Simulations.

• Zhenchao Ouyang, Xiaoyun Dong, Zeling Guo and Jianwei Niu. Polarmask-Tracker: Lightweight Multi-Object Tracking and Segmentation Model for Edge Device.

ISPA 18: Oct. 2nd, 2021, 14:30, Room A

• Jinyu Wang, Yifei Kang, Yiwen Li, Weiguo Wu, Song Liu and Longxiang Wang. Hexagonal Tiling based Multiple FPGAs Stencil Computation Acceleration and Optimization Methodology.

• Syeed Abrar Zaoad, Tauhid Tanjim, Mir Hasan, Md. Mamun-Or-Rashid, Ibrahem Abdullah Almansour and Md. Mosaddek Khan. Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing.

• Yuekai Zhao, Jianzhuang Lu and Xiaowen Chen. Vectorized Winograd's algorithm for Convolution Neural networks.

• Lihua Song, Yuzhu Jin, Pengyu Wang, Dongchao Ma, Wenlong Chen and Laizhong Cui. Multi-path Routing Deployment Method Based on SRv6.

• Shanshan Shi, Jun Li, Bin Han, Haibo Wu, Yuxiang Ma and Qian Dong. PI and Available Bandwidth based Multi-Path Forwarding Strategy for Named Data Networking.

ISPA 19: Oct. 2nd, 2021, 14:30, Room B

• Ying Zeng, Minghua Wang and Bo Fan. Cooperative Charging Algorithm Based on K-mean++ Clustering for WRSN.

• Dong Wentao, Li Zhuo and Chen Xin. Deep Reinforcement Learning-based Adaptive Clustering Approach in Short Video Sharing through D2D Communication.

- 9 -

• Xin Shen, Zhuo Li and Xin Chen. Optimization for Node Cooperation in Hierarchical Federated Learning.

• Tingting Zhang, Mingqi Zhang, Lintao Yang, Tao Dong, Jie Yin, Zhihui Liu, Jing Wu and Hao Jiang. Satellite network traffic scheduling algorithm based on multi-agent reinforcement learning.

• Luis Herrera-Huisa, Nicole Arias-Meza and Michael Cabanillas-Carbonell. Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature.

ISPA 20: Oct. 2nd, 2021, 15:30, Room A

• Shuyu Chen, Shiyong Sun, Haopeng Chen, Jinteng Ruan and Ziming Wang. A Game Theoretic Approach to Task Offloading for Multi-Data-Source Tasks in Mobile Edge Computing.

• Yanrong Lu, Meng Yue and Zhijun Wu. Content Security over ICN based Smart Grid: A Cryptographic Solution.

• Dawei Li, Yingxian Song, Lixin Zhang, Di Liu, Baoquan Ma and Zhenyu Guan. Unified Authentication Scheme for IoT Blockchain Based on PUF.

• Zhenyu Guan, Xiaoqing Wen, Dawei Li, Mai Xu and Haihua Li. A Blockchain-based verified locating scheme for IoT devices.

• Yongqing Zhu, Zhenyu Guan, Ziyi Wang, Dawei Li, Yawei Wang and Mai Xu. SRAM-PUF Based Lightweight Mutual Authentication Scheme for IoT.

ISPA 21: Oct. 2nd, 2021, 15:30, Room B

• Jiaofu Zhang, Lianzhong Liu, Zihang Huang, Tongge Xu, Jingyi Zhang, Yangyang Li, Yifeng Liu and Md Zakirul Alam Bhuiyan. Robust Social Event Detection via Deep Clustering.

• Linlin Liu, Minghua Wang and Yan Wang. An Energy-balanced Routing by Considering Neighbour Node Energy Based on Small World WSN.

• Liuhui Ding, Dachuan Li, Bowen Liu, Wenxing Lan, Bing Bai, Qi Hao and Weipeng Cao. Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles.

• Xingshuo Han, Kangjie Chen, Yuan Zhou, Tianwei Zhang, Meikang Qiu, Chun Fan and Yang Liu. A Unified Anomaly Detection Methodology for Lane-Following of Autonomous Driving Systems.

• Chen Zhao, Jiaqi Yin, Huibiao Zhu and Ran Li. Modeling and Verifying Ticket-Based Authentication Scheme for IoT Using CSP.

ISPA 22: Oct. 2nd, 2021, 16:30, Room A

• Pengfei Yue, Ru Li and Bin Pang. The Random Content Poisoning Attack in NDN. • Yang Guoliang, Xiong Mengqi, Feng Guangsheng and Liu Yuzheng. Distributed V2V

Computing Offloading Method Based on Delay and Fairness Awareness. • Feifei Liu, Di Liu, Yu Sun, Dawei Li, Jian Cui, Zhenyu Guan and Jianwei Liu. Secure vehicle

platooning protocol for 5G C-V2X. • Ya-Nan Cao, Yujue Wang, Yong Ding, Haibin Zheng, Zhenyu Guan and Huiyong Wang. A

PUF-based Lightweight Authenticated Metering Data Collection Scheme with Privacy Protection in Smart Grid.

• Lemei Da, Yujue Wang, Yong Ding, Wanjun Xiong, Huiyong Wang and Hai Liang. An Efficient Certificateless Signcryption Scheme for Secure Communication in UAV Cluster Network.

ISPA 23: Oct. 2nd, 2021, 16:30, Room B

• Tongshuai Wu, Liwei Chen, Gewangzi Du, Chenguang Zhu and Gang Shi. Self-Attention based Automated Vulnerability Detection with Effective Data Representation.

• Ke Yu, Hongwang Xiao, Ying Zhao, Jiao Tian and Jinjun Chen. A Framework for Deep Q-Learning Based Hybrid DVFS Algorithms for Real-Time Systems.

• Jinyu Cheng, Kai Zhao and Yuanchao Xu. Global-view based Task Migration for Deep Learning Processor.

• Jianyu Chen, Keke Gai and Liehuang Zhu. An Empirical Study on Heuristic Approaches for Solving Assignment Problems on Blockchain.

- 10 -

• Chongyou Xu, Guangxian Lv, Jian Du, Lei Chen, Yu Huang and Wang Zhou. Kubeflow-based automatic data processing service for Data Center of State Grid Scenario.

ISPA 24: Oct. 3rd, 2021, 9:00, Room A

• Da An, Rui Zhang, Shuang Li and Weipeng Cao. Target-oriented Self Training for Heterogeneous Domain Adaptation.

• Ningjing Liang, Xingjun Zhang, Xurui Wu, Heng Chen and Changjiang Zhang. An Endurance-aware RAID-6 Code with Low Computational Complexity and Write Overhead.

• Shikui Zhang, Ce Chi, Kaixuan Ji, Zhiyong Liu, Fa Zhang, Penglei Song, Huimei Yuan, Dehui Qiu and Xiaohua Wan. A new meta-heuristic task scheduling algorithm foroptimizing energy efficiency in data centers.

• Wenting Wang, Xianghua Fu and Xinze Lin. Edge-Based Sampling for Complex Network with Self-Similar Structure.

• Anping Song, Yi Dai, Ruyi Ji and Ziheng Song. Extend PBFT Protocol with L-DAG. ISPA 25: Oct. 3rd, 2021, 9:00, Room B

• Shintaro Narisada, Kazuhide Fukushima and Shinsaku Kiyomoto. Fast GPU Implementation of Dumer's Algorithm Solving the Syndrome Decoding Problem.

• Xiaobin Li, Hongxu Jiang and Yonghua Zhang. Optimal FPGA-oriented lightweight network architecture search under multi-objective constraints.

• Wuji Liu, Qianwen Ye, Chase Wu, Yangang Liu, Xin Zhou and Yunpeng Shan. Machine Learning-assisted Computational Steering of Large-scale Scientific Simulations.

• Han Huang and Hua Luan. Rethinking Insertions to B+-Trees on Coupled CPU-GPU Architectures.

• Jacek Gambrych. Influence of optimization techniques on software performance for subsequent generations of CUDA architecture.

ISPA 26: Oct. 3rd, 2021, 9:00, Room C

• Xiang Hou, Rui Xu, Sheng Ma, Wei Jiang and Hongyi Lu. Co-designing the Topology/Algorithm to Accelerate Distributed Training.

• Xiandong Huang, Qinglin Wang, Shuyu Lu, Ruochen Hao, Songzhu Mei and Jie Liu. NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs.

• Ji Li, Zewei Chen and Xin Liu. Deep reinforcement learning for partial offloading with reliability guarantees.

• Huiling Meng, Yaobin Wang, Ling Li, Manasah Musariri and Xinyi Wang. Parallel analysis of TACLeBench kernel benchmark's loop and procedure level speculation.

• Kangkang Li, Yitao Qiu, Kaiqiang Zhang, Congfeng Jiang and Jian Wan. Cost-Aware Virtual Cluster Placement in Software-Defined Cloudlet Networks.

ISPA 27: Oct. 3rd, 2021, 10:00, Room A

• Zhiwei Guo, Dian Meng, Huiyan Zhang, Heng Wang and Keping Yu. Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks.

• Lei Zhang, Xuefei Chen and Yuxiang Ma. A Similarity-Based Method for Base Station Selection in 5G Networks.

• Chunhua Xiao, Wei Ran, Fangzhu Lin and Lin Zhang. Dynamic Fine-Grained Workload Partitioning for Irregular Applications on Discrete CPU-GPU Systems.

• Lin Chen, Kang He, Hao Jiang, Roberto Barrio, Jie Liu, Tiejun Li. Quantization improvements for LU Decomposition to Solve Linear Equations.

• Zhipeng Li, Yan Ding, Xiaofan Chen, Pan Dong, Chenlin Huang, Liantao Song and Peng Wang. Agile Approach on the Performance Prediction of ARM TrustZone-based Mandatory Access Control Security Enhancement.

ISPA 28: Oct. 3rd, 2021, 10:00, Room B

• Edward Chuah, Arshad Jhumka, Samantha Alt, Richard Evans and Neeraj Suri. Failure Diagnosis for Cluster Systems using Partial Correlations.

• Caihong Zhao and Wenpeng Neng. A Sleep Stage Classification Method via Combination of Time and Frequency Domain Features based on Single-Channel EEG.

• Jie Zhang, Baoming Zhang, Kefan Li, Ming Xu and Chongjun Wang. QMIMC: Q-Learning Model Based on Imperfect-information under Multi-agent Crowdtesting.

- 11 -

• Hongjun Dai and Chengzhen Meng. An Obstacle Avoidance Method Based on Advanced Rapidly-exploring Random Tree for Autonomous Navigation.

• Luming Yang, Shaojing Fu, Yuchuan Luo and Lin Liu. Packet-Sequence and Byte-distribution is Enough for Real-Time Identification of Application Traffic.

ISPA 29: Oct. 3rd, 2021, 10:00, Room C

• Qixun Zhang, Tong Jia, Wensheng Xia, Ying Li and Zhonghai Wu. Mining Configuration Items From System Logs through Distant Supervision.

• Shiyu Wang, Shengbing Zhang, Jihe Wang, Libo Chang, Xiaoya Fan and Liangyou Feng. Hardware architecture design of HEVC entropy decoding.

• Junsung Yook and Bernhard Egger. Modeling Cache and Application Performance on Modern Shared Memory Multiprocessors.

• Jinhua Du, Xianming Gao, Jingchao Wang, Shaohua Liu, Weipeng Cao, Yanbo Song and Shanqing Jiang. Research on An Approach of ARP Flooding Suppression in Multi-Controller SDN Networks.

• Wei Gao, Meihong Yang, Wei Zhang, Yuhan Zhao, Yan Zhou and Kai Zhang. FFDNet-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems.

ISPA 30: Oct. 3rd, 2021, 11:00, Room A

• Ziheng Wang, Heng Chen and Weiling Cai. A hybrid CPU/GPU Scheme for Optimizing ChaCha20 Stream Cipher.

• Junwei Zhou, Qian Wei, Chao Wu and Guangzhong Sun. A High Performance Computing Method for Noise Cross-correlation Functions of Seismic Data.

• Jianmin Zhang, Tiejun Li and Yan Sun. A Two-level Concurrent Address Translation Cache of High Performance Interconnect Network.

• Hongbo Li, Tao Xie, Jin Xie. A Decentralized Trading Model Based on Public Blockchain with Traceable Two-Tier Identities.

• Xiaofeng Chen, Zhiguang Chen, Yutong Lu and Dan Huang. A Fine-grained Optimization to Winograd Convolution Based on Micro-architectural Features of CPU.

ISPA 31: Oct. 3rd, 2021, 11:00, Room B

• Yonghua Zhang, Hongxu Jiang, Xiaobin Li, Rui Miao, Jinyan Nie and Yu Du. Energy-Efficient CNNs Accelerator Implementation on FPGA with Optimized Storage and Dataflow.

• Wenwen Zheng, Weiqi Wang, Wei Ren, Shiyuan Feng, Shiqin Liu and Yi Ren. A Random Distribution Method by Adversarial Examples Generated CAPTCHAs Based on User Behaviors.

• Xingyi Yuan, Long Chen, Jiale Huang and Jigang Wu. Coalitional Double Auction for Ride-Sharing with Buyout Ask Price.

• Wei Jiang, Rui Xu, Sheng Ma, Xiang Hou and Lu. A Memory Saving Mechanism Based on Data Transferring for Pipeline Parallelism.

• Qingxian Wang, Renjian Zhang, Kangkang Ma, Bo Chen, Jiufang Chen and Xiaoyu Shi. Siamese Generative Adversarial Predicting Network for Extremely Sparse Data in Recommendation System。

ISPA 32: Oct. 3rd, 2021, 11:00, Room C

• Zihang Huang, Lianzhong Liu, Jiaofu Zhang, Tongge Xu, Yangyang Li, Daohong Shen, Yifeng Liu and Md Zakirul Alam Bhuiyan. Semantic Social Event Evolution Discovering.

• Mingqi Li, Hong Guo, Jing Liu, Yu Gan and Wei Hu. Decrease Iteration Time Deterministic Cyclic Scheduling for Real-time Periodic Tasks.

• Ruixue Li, Ning Li, Ruiliang Song and Jing Wu. Blind Zone Performance Enhancement with THz Communication.

• Jason High and Wuxu Peng. A Comparison Study of Issues of HPC Workflows Under Cloud Bandwidth Constraints.

• Siyuan Zheng, Changqing Yin and Bin Wu. Keys as Secret Messages: Provably Secure and Efficiency-balanced Steganography on Blockchain.

ISPA 33: Oct. 3rd, 2021, 13:30, Room A

• Fang Liu, Zimeng Fan, Yanxiang He and Min Peng. Software-Hardware Co-Optimization for CNNs Based on Reconfigurable Devices.

- 12 -

• Weiran Wang, Ning Li, Ruiliang Song and Jing Wu. Joint User Association and Resource Allocation for MmWave and THz Coexistence Networks.

• Zheyuan Hu, Jianwei Niu, Renluan Hou, Xiaolong Yu, Tao Ren and Qingfeng Li. Object Pose Estimation for Robotic Grasping based on Multi-view Keypoint.

• Huayan Yu, Xin Li, Ligang Yuan and Xiaolin Qin. Efficient Spatio-temporal-Data-Based Range Query Processing for Air Traffic Flow Statistics.

• Jingyuan Zhao, Xin Liu, Yao Liu, Penglong Jiao, Jinshuo Liu and Wei Xue. Profiling HPC Applications with Low Overhead and High Accuracy.

ISPA 34: Oct. 3rd, 2021, 13:30, Room B

• Chang Shu, Yan Wang, Jianxi Fan and Huanwen Zhang. Fault-Tolerant Routing of Generalized Hypercubes under 3-Component Connectivity.

• Xiangyu Gao, Meikang Qiu and Zhen He. Big Data Analysis with Momentum Strategy on Data-driven Trading.

• Dazheng Liu, Jianping Wu, Tao Jiang, Yingjie Wang, Xiaotian Pan and Penglun Li. Optimization of the parallel semi-Lagrangian scheme in the YHGSM based on the adaptive maximum wind speed.

• Yuantong Zhang, Liwei Chen, Xiaofan Nie, Zhijie Zhang, Haolai Wei and Gang Shi. Making Data-Flow Analysis More Efficient by Full-Partitioning.

• Ziqiang Weng, Weiyu Zhang and Xiazhong Xiu. Adaptive attention encoder for attribute graph embedding.

ISPA 35: Oct. 3rd, 2021, 14:30, Room A

• Weicai Ye, Hao Wu, Zhijun Tan and Qingsong Zou. An Adaptive Time stepping Algorithm and Its Application to Platelet Aggregation Simulation.

• Jia Wei, Xingjun Zhang, Zeyu Ji, Jingbo Li and Zheng Wei. PANDA: Population Automatic Neural Distributed Algorithm for Deep Leaning.

• Meng Fanchao and Ji Qingran. Modeling and Solution Algorithm of Virtual Machines Optimization Provision Problem for Application Deployment in Public Cloud.

• Junhang Wu, Ruimin Hu, Dengshi Li, Yilin Xiao, Lingfei Ren and Wenyi Hu. Multi-network Embedding for Missing POI Identification.

• Yuyang Liu, Yuxiang Ma, Junruo Gao, Zefang Zhao and Jun Li. Coupled Self-Exciting Process for Information Diffusion Prediction.

ISPA 36: Oct. 3rd, 2021, 14:30, Room B

• Yue Gao, Jinqiao Shi, Xuebin Wang and Yanyan Yang. Practical deanonymization attack in Ethereum based on P2P network analysis.

• Jinyu Wang, Yifei Kang, Yaqi Feng, Yiwen Li, Weiguo Wu and Guoliang Xing. Lossless Compression of Bitstream Configuration Files: Towards FPGA Cloud.

• Ting Xiong, Tao Xie, Jin Xie and Xiaochun Luo. ORIC: A Self-Adjusting Blockchain Protocol with High Throughput.

• Jianguo Sun, Yuqing Qiao, Zechao Liu, Yitao Chen and Yang Yang. Practical Multi-Authority Ciphertext Policy Attribute-Based Encryption from R-LWE.

• Zhang Jie, Li Kefan, Baoming Zhang, Ming Xu and Chongjun Wang. Parallel Counterfactual Regret Minimization in Crowdsourcing Imperfect-information Expanded Game.

ISPA 37 Oct. 3rd, 2021, 15:30, Room A

• Shang Wu and Yijie Wang. Attention-based Encoder-Decoder Recurrent Neural Networks for HTTP Payload Anomaly Detection.

• Xingguang Zhou, Ziyu Meng and Yi Liu. Fast Settlement Scheme of Aviation Business Privacy Preservation Based on Consortium Blockchain.

• Zhenyu Guan, Lixin Zhang, Yingpeng Zhang, Dawei Li, Yu Sun and Jian Cui. Off-chain Anonymous Payment Channel Scheme based on Mimblewimble.

• Yuxiang Ma, Xuefei Chen and Lei Zhang. Base Station handover Based on User Trajectory Prediction in 5G Networks.

• Mengqi Li, Liang Liu, Yanlin Wang, Jianfei Peng, Jie Xi and Lisong Wang. Efficient Wireless Static Chargers Deployment for UAV Networks.

- 13 -

The 11th IEEE International Conference on Big Data and Cloud Computing (BDCloud 2021)

BDCloud 1: Oct. 1st, 2021, 13:30, Room C

• Anantaa Kotal, Anupam Joshi and Karuna Pande Joshi. The Effect of Text Ambiguity on creating Policy Knowledge Graphs.

• Naghmeh Dezhabad, Sudhakar Ganti and Gholamali C Shoja. An Optimum Decision Making Framework for Allocating Spot Instances to Execute Batch Workload in Cloud.

The 14th IEEE International Conference on Social Computing and

Networking (IEEE SocialCom 2021) SocialCom 1:

Oct. 1st, 2021, 14:30, Room C • Bin Li and Teng Liu. An Analysis of Multi-Modal Deep Learning for Art Price Appraisal. • Regina Marin and Luciano Gallegos. A Survey on Privacy Approaches for Social Networks. • Sarthak Khanal, Rus Refati, Kyle Glandt, Doina Caragea, Sifan Xu and Chien-Fei Chen.

Using Content Analysis and Machine Learning to Identify COVID-19 Information Relevant to Low-income Households on Social Media.

• Leyu Gao, Sandeep Shah, Nasser Assery, Xiaohong Yuan, Xiuli Qu and Kaushik Roy. Semi-Supervised Self Training to Assess the Credibility of Tweets.

• Hieu Nguyen, Jihye Moon and Swapna Gokhale. Sarcasm Detection in Politically Motivated Social Media Content.

• Oladapo Oyebode and Rita Orji. MediNER: Understanding Diabetes Management Strategies Based on Social Media Discourse.

The 11th IEEE International Conference on Sustainable Computing and Communications (IEEE SustainCom 2021)

SustainCom 1: Oct. 1st, 2021, 13:30, Room C

• Danghui Wang, Zhaoqing Wang, Linfang Yu, Ying Wu, Jiaqi Yang, Ze Tian and Jihe Wang. A Survey of Stochastic Computing in Energy- Efficient DNNs on-Edge.

The 14th IEEE International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2021) SpaCCS 1:

Oct. 1st, 2021, 15:30, Room C • Pietro Tedeschi, Savio Sciancalepore and Roberto Di Pietro. Modelling a Communication

Channel under Jamming: Experimental Model and Applications. • Yingjie Wang and Matthew Wilchek. Synthetic Differential Privacy Data Generation for

Revealing Bias Modelling Risks. • Anshika Rawat, Mortaza S. Bargh, Marijn Janssen and Sunil Choenni. Designing a User

Interface for Improving the Usability of a Statistical Disclosure Control Tool. • Sana Imtiaz, Zannatun Tania, Hassan Nazeer Chaudhry, Muhammad Arsalan, Ramin

Sadre and Vladimir Vlassov. Machine Learning with Reconfigurable Privacy on Resource-Limited Computing Devices.

SpaCCS 2: Oct. 2nd, 2021, 13:30, Room C

• Parjanya Vyas, Rudrapatna Shyamasundar, Bhagyesh Patil, Snehal Borse and Satyaki Sen. SPLinux: An Information Flow Secure Linux.

• Anjie Chen and Zhibing Zhang. A comparative study of credentialed vulnerability scanning and non-credentialed vulnerability scanning.

• Ahmed Aleroud, Faten Masalha and Ahmad S'Efan. Identifying GDPR Privacy Violations Using an Augmented LSTM: Towards an AI-based Violation Alert Systems.

• Siyuan Zheng, Changqing Yin and Bin Wu. Malicious Conspiracy on Permissioned Blockchain to Mount Denial-of-endorsement Attacks.

- 14 -

Workshops Workshop: The International Workshop on Artificial Intelligence for Intelligent Network Management (AINets 2021) Oct. 2nd, 2021, 14:30, Room C

• Wojciech Wasko, Dotan Levi, Teferet Geula, Amit Mandelbaum. Artificial Accurate Time-stamp in Network Adapters.

• Nikumani Choudhury, Moustafa Nasralla, Prakhar Gupta, Ikram Ur Rehman. Centralized Graph based TSCH Scheduling for IoT Network Applications.

Workshop: The 13th International Workshop on Cyberspace Security and Artificial Intelligence (CAI 2021) Oct. 2nd, 2021, 15:30, Room C

• Yen-Hung Hu and Chung-Chu Hsieh. A Study of Classifying Advanced Persistent Threats With Multi-Layered Deep Learning Approaches.

• Giacomo Iadarola, Federico Gerardi, Fabio Martinelli, Antonella Santone and Francesco Mercaldo. Perturbation of Image-based Malware Detection with Smali level morphing techniques.

• Alexander Omara, Izzat Alsmadi and Ahmed Aleroud. Generative Adversarial Analysis of Phishing Attacks on Static and Dynamic Content of Webpages.

Workshop: The 12th International Workshop on Trust, Security and Privacy for Big Data (TrustData 2021) Oct. 2nd, 2021, 16:30, Room C

• Weiwei Miao, Zeng Zeng, Mingxuan Zhang, Siping Quan, Zhen Zhang, Shihao Li, Li Zhang and Qi Sun. Workload Prediction in Edge Computing based on Graph Neural Network.

• Ali Raheem, Rand Hussein, Thomas M Chen and Ahmed Alkhayyat. Estimation of Ransomware Payments in Bitcoin Ecosystem.

• Dingkun Yang, Nan Zhao, Hongshuang Ma and Jiasheng Yang. Provisioning with Fine-grained Affinity for Container-enabled Cloud-edge System.

• Zeng Zeng, Weiwei Miao, Shihao Li, Xiaoyun Liao, Mingxuan Zhang, Rui Zhang and Changzhi Teng. Adaptive Task Scheduling in Cloud-Edge System for Edge Intelligence Application.

Workshop: The 7th International Symposium on Sensor-Cloud Systems (SCS 2021) Oct. 3rd, 2021, 13:30, Room C

• Bo Ma, Kuncheng Zhuo, Zanyou Su, Donghai Huang and Yonglong Zou. The Design of Network Query System for Automobile Inspection Data.

• Feng Jing, Ying Jiang, Shengbin Chen, Xirong Lv and Jiayi He. Optimization of key and value storage in vector map rendering.

• Bo Zhang, Kuncheng Zhuo, Yifan Lan and Dayuan Chen. Intelligent Movement Method for Trajectory Map Matching.

• Bo Ma, Zanyou Su, Kuncheng Zhuo, Meng Li, Donghai Huang and Chuyi Yu. Research and Development of Enterprise Data Security Transmission Management System.

• Feng Jing, Donghai Huang, Yifei Wang, Xirong Lv and Qi Du. Remote Sensing Data Backup and Recovery System for Power Industry.

• Yanqi Xie, Xirong Lv, Ying Jiang, Zanyou Su and Nan Luo. A software defect detection algorithm based on asymmetric classification evaluation.

Workshop: The 11th International Symposium on Trust, Security and Privacy for Emerging Applications (TSP 2021) Oct. 3rd, 2021, 14:30, Room C

• Weiwei Miao, Zeng Zeng, Mingxuan Zhang, Siping Quan, Zhen Zhang, Shihao Li, Li Zhang and Qi Sun. Multi-Agent Reinforcement Learning for Edge Resource Management with Reconstructed Environment.

• Weiwei Miao, Zeng Zeng, Shihao Li, Lei Wei, Chengling Jiang, Siping Quan and Yong Li. Microservice Replacement Algorithm in Cloud-Edge System for Edge Intelligence.

Workshop: The 10th International Symposium on Security and Privacy on the Internet of Things (SPIoT 2021) Oct. 3rd, 2021, 15:30, Room B

• Christelle Nader, Elias Bou-Harb. Revisiting IoT Fingerprinting behind a NAT. • Gianpiero Costantino, Ilaria Matteucci, Davide Antonino Vincenzo Micale, Giuseppe

Patanè. Private Drivers Identification based on users’ routine. • Martin Vivian, Ramin Sadre. Reverse-Engineering the Physical Configuration of Smart

Homes. • Mohd Khan, Yu Chen. A Randomized Switched-Mode Voltage Regulation System for IoT

Edge Device to Defend Against Power Analysis based Side Channel Attack.