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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

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Deep Learning-based Digital Twin for Human Activity Recognition 29

Deep Learning-based Digital Twin for Human Activity Recognit...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Su, Jian Liao, Zhenlong Mao, Qiankun Sheng, Zhengguo Liu, Alex X. Nanjing University of Information Science and Technology The School of Software Nanjing China Zhejiang Wanli University The Hwamei College of Life and Health Sciences Ningbo China Nanjing University of Information Science and Technology The School of Computer Nanjing China University of Sussex The Department of Engineering and Design Brighton United Kingdom Michigan State University The Department of Computer Science and Engineering East Lansing United States

ISBN: (纸本)9798350330717

With the rapid development of the Internet of Things (IoT) related technologies, the application of digital twins (DT) in industry and healthcare becomes possible. Human activity recognition (HAR) is emerging as a hot research area with great potential in healthcare. Activity recognition systems combined with DT will make it easier to monitor human health conditions to improve the quality of life and happiness with individualized healthcare. In this paper, we design an effective HAR system, called HAR-Net, which uses WiFi time series data collected by sensors to train a deep learning network. Deep learning's great learning ability is utilized to extract features of various human activities for activity recognition. We built the DT system with Unity, which is combined with the HAR system. In the DT system, real-world physical activities are mapped onto human models. The results of activity prediction can be evaluated in real-time in DT, and warnings can be issued quickly when dangerous activities occur. To make our human activity recognition system more adaptive, we propose a one-shot recognition method based on meta-learning. Specifically, we design a Bi-path basic network that extracts features in the time-domain and frequency-domain, and a meta-learning framework with a classification module and a WiFi metric module. Using datasets from different environments, we conducted various experiments on HAR-Net, and the results proved that our presented method was superior to the baseline network. © 2023 IEEE.

关键词： Digital Twin Healthcare Human Activity Recognition Meta-Learning WiFi

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Keynote: engineering the New Fabric of the distributed Compute Continuum

Keynote: Engineering the New Fabric of the Distributed Compu...

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20th IEEE international Conference on Pervasive Computing and Communications (IEEE PerCom)

作者： Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781665416436

As humans, things, software and AI continue to become the entangled fabric of distributed systems, systems engineers and researchers are facing novel challenges. In this talk, we analyze the role of IoT, Edge, and Cloud, as well as AI in the co-evolution of distributed systems for the new decade. We identify challenges and discuss a roadmap that these new distributed systems have to address. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end distributed systems.

关键词： Pervasive computing Conferences Fabrics software Artificial intelligence

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A coarse-to-fine parallelizable surface defect detection approach for railway trackside equipment 29

A coarse-to-fine parallelizable surface defect detection app...

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29th IEEE international Conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Guanjia Xing, Weiwei Yang, Shuzhong Liu, Weibin Wei, Xiang Zhang, Jian Zhang, Shunli Beijing Jiaotong University School of Software Engineering Beijing China China Academy of Railway Sciences Corporation Limited Infrastructure Inspection Research Institute Beijing China Beijing Jiaotong University Institute of Information Science Beijing China

ISBN: (纸本)9798350330717

Surface defects of railway trackside equipment pose a serious risk on the safety of railway transportation systems. Image-based surface defect detection methods have made significant progress. However, the image background of trackside equipment is complex, and there is a large amount of noise, which makes existing methods inadequate in accurately detecting small surface defect regions. To tackle with this issue, we propose a coarse-to-fine parallelizable surface defect detection approach to hierarchically detect the defects of trackside equipment. Firstly, a detection network is designed to locate and extract trackside equipment, which aims at roughly focusing the detection field from the original image to the region of interest of individual trackside equipment. Then, a novel semantic segmentation network is proposed to segment the major components of trackside equipment, so as to further finely focus on the defect regions. We apply multiple segmentation networks to parallelly segment various trackside equipment. In the segmentation network, a dense feature enhancement method is introduced to strengthen the high-level semantic information, and a feature partitioning enhancement strategy is designed to improve the segmentation performance for small defect regions. Finally, according to the visual characteristics of the segmentation output, we propose a defect recognizer to discriminate the defects. Extensive experimental results demonstrate that the proposed surface defect detection approach achieves higher accuracy for trackside equipment. © 2023 IEEE.

关键词： coarse-to-fine defect detection railway trackside equipment semantic segmentation

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Ensemble-Based System Benchmarking for HPC

Ensemble-Based System Benchmarking for HPC

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international symposium on parallel and distributed Computing

作者： Anna Fuchs Jannek Squar Michael Kuhn Universität Hamburg Hamburg Germany Otto von Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

In HPC supercomputers, the CPU, memory, network and storage play a critical role in application performance. Established benchmarks measure the theoretical peak performance of these components, but storage benchmarks often focus solely on I/O and lack realism. To address this, we present numio, a benchmark that simulates and evaluates overlapping compute, communication and I/O phases. Furthermore, we present a novel ensemble-driven system benchmarking strategy. This approach involves running multiple benchmarks in parallel to analyse their interactions and assess the system’s ability to handle the workload. Using a real-world example, we demonstrate how this approach reveals performance issues in complex HPC systems that remain hidden when using traditional methods using isolated benchmarks on empty systems.

关键词： Economics Data centers Runtime File systems System performance Estimation Benchmark testing Supercomputers software distributed computing

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Hierarchical Data-driven Predictive Control Strategy for Energy Management of Electric Vehicles 21

Hierarchical Data-driven Predictive Control Strategy for Ene...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Bin He, Guo Zhou, Feng Huang, Hao Liu, Wei Du, Ronghua Changsha University of Science and Technology College of Automotive and Mechanical Engineering Changsha China Changsha University of Science and Technology College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350329223

For electric vehicles (EVs) equipped with hybrid energy storage systems (HESS), a challenge is reasonably distributing and optimizing load power. To optimize the performances of the vehicle power and battery capacity, This paper introduces an innovative data-driven Energy Management Strategy (EMS) designed for HESS, which combines a hierarchical strategy of fuzzy neural network (FNN) and data-enabled predictive control (DeePC). At the driving level, FNN predicts load power demand by collecting historical speed and acceleration information along with current data. At the energy management level, the EMS adopts a novel data-enabled model, which collects the load power predicted by an upper layer. The DeePC achieves power distribution by controlling the supercapacitor current while maintaining the state of charge (SoC) within the expected range. The simulation results verify the effectiveness of the developed EMS. Compared with long-short term memory (LSTM), artificial neural network (ANN), and support vector regression (SVR), FNN demonstrates the best-fitting performance, while the prediction results are closer to the actual load power. Compared with model predictive control (MPC), the proposed DeePC approximately reduces battery capacity loss by 0.15% and demonstrates well control performance. © 2023 IEEE.

关键词： Electric vehicles

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Preserving Privacy of Input Features Across All Stages of Collaborative Learning 21

Preserving Privacy of Input Features Across All Stages of Co...

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作者： Lu, Jianrong Xue, Lulu Wan, Wei Li, Minghui Zhang, Leo Yu Hu, Shengqing Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Software Engineering Wuhan China Griffith University School of Information and Communication Technology Brisbane Australia Huazhong University of Science and Technology Union Hospital Tongji Medical College Department of Nuclear Medicine Wuhan China

ISBN: (纸本)9798350329223

Collaborative learning is a widely used privacy-preserving distributed training framework where users participate in global training using gradients instead of disclosing their private data. However, gradient inversion attacks have challenged the privacy of this approach by reconstructing private inputs from gradients. While prior works have proposed various defenses against gradient inversion attacks, their privacy assessments have mainly focused on untrained models, lacking consideration for the trained model, which should be the primary focus in collaborative learning. In this context, we first conduct a comprehensive privacy evaluation across all stages of collaborative learning. We uncover the limitations of existing defenses in providing sufficient privacy protection for trained models. To address this challenge, we introduce GradPrivacy, a novel framework tailored to safeguard the privacy of trained models without compromising their performance. GradPrivacy comprises two key components: the amplitude perturbation module, which perturbs gradient parameters associated with critical features to thwart attackers from reconstructing essential input feature information, and the deviation correction module, which effectively maintains model performance by correcting deviations in model update directions from previous rounds. Extensive evaluations demonstrate that GradPrivacy successfully achieves effective privacy preservation, surpassing state-of-the-art methods in terms of the privacy-accuracy trade-off. © 2023 IEEE.

关键词： Economic and social effects

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Intro basics of modeling user rights management for the university diploma issuing process with the support of the Hyperledger Fabric 22

Intro basics of modeling user rights management for the univ...

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22nd international symposium INFOTEH-JAHORINA, INFOTEH 2023

作者： Čeke, Denis Buzadija, Nevzudin University of Zenica Polytechnic Faculty Department of Software Engineering Zenica Bosnia and Herzegovina University of Tuzla It Department Tuzla Bosnia and Herzegovina

ISBN: (纸本)9781665475464

The protection and preservation of the privacy of personal data are one of the main requirements when it comes to an application dealing with the processing of such data. It is no different when it comes to information systems that store and process data about students in higher education systems. The public presentation of such data represents a serious threat to the safety of students as well as their status within the higher education system. For this reason, it is necessary to use the possibility of advanced technologies in order to raise data security to the highest level. One such technology that is able to provide transparency, security and data protection at a high level is blockchain technology. In this work, the Hyperledger Fabric distributed ledger private blockchain network was analyzed and its usability in terms of user rights management in higher education system was evaluated. Experimental analysis showed that such a platform has the ability to take advantage of private blockchain technologies in terms of user rights management and to provide security, flexibility and scalability of the system. © 2023 IEEE.

关键词： Blockchain

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Application Research of Agricultural Digital Management System Based on Internet of Things and Cloud Computing 2

Application Research of Agricultural Digital Management Syst...

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2nd international symposium on Computer Applications and Information systems, ISCAIS 2023

作者： Zhang, Jing Wang, Jinlong Mu, Xuelian Li, Li Li, Jiaoyue Weifang Engineering Vocational College Shandong Province Qingzhou China

ISBN: (纸本)9781510666665

The popularization of the Internet of Things and cloud computing has provided powerful conditions for the construction of agricultural digital management systems. Among them, the Internet of Things has advantages such as efficient resource utilization, convenient data collection, and security;Cloud computing stands out for its secure, inexpensive, and stable computing and storage capabilities. Therefore, design agricultural digital management systems under the Internet of Things and cloud computing, build a network environment based on the Internet of Things, manage software computing processes through distributed processing means, and maximize the data attributes of agricultural digital management systems. The scientific design system framework includes a management system application layer, a cloud computing network layer, a big data access layer, and a big data awareness layer. This article focuses on designing three basic frameworks for the cloud computing network layer, and designs the logic and functional entity structure of the system’s functional modules based on the construction requirements of the Internet of Things and cloud computing for agricultural digital management systems. The research results show that the designed agricultural digital management system has a high technical level and a high utilization rate of system resources, which is optimistic. © 2023 SPIE.

关键词： Cloud computing

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A Message Passing Interface Library for High-Level Synthesis on Multi-FPGA systems 15

A Message Passing Interface Library for High-Level Synthesis...

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15th IEEE international symposium on Embedded Multicore/Many-Core systems-on-Chip, MCSoC 2022

作者： Hironaka, Kazuei Iizuka, Kensuke Amano, Hideharu Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (纸本)9781665464994

One obstacle to application development on multi-FPGA systems with high-level synthesis (HLS) is a lack of support for a programming interface. Implementing and debugging an application on multiple FPGA boards is difficult without a standard interface. Message Passing Interface (MPI) is a standard parallel programming interface commonly used in distributed memory systems. This paper presents a tool-independent MPI library called FiC-MPI that can be used in HLS for multi-FPGA systems in which each FPGA node is connected directly. By using FiC-MPI, various parallel software, including a general-purpose benchmark, can be easily implemented. FiC-MPI was implemented and evaluated on the M-KUBOS cluster consisting of Zynq MPSoC boards connected with a static time-division multiplexing network. By using the FiC-MPI simulator, parallel programs can be debugged before implementing on real machines. As a case study, the Himeno-BMT benchmark was implemented with FiC-MPI. It achieved 178.7 MFLOPS with a single node and scaled to 643.7 MFLOPS with four nodes, and 896.9 MFLOPS with six nodes of the M-KUBOS cluster. Through the implementation, the easiness of developing parallel programs with FiC-MPI on multi-FPGA systems was demonstrated. © 2022 IEEE.

关键词： parallel programming

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ProMD: A Proactive Intrusion Response System for Enterprise Network with Multi-Domain 21

ProMD: A Proactive Intrusion Response System for Enterprise ...

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作者： Leng, Siyuan Li, Fenghua Zhang, Lin Wen, Yitong Guo, Yunchuan Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

Enterprise networks are divided into sub-domain structures for resource aggregation and trust dependency. However, the complex domain structure results in delayed and incomplete defenses from Intrusion Response systems(IRS). This paper proposes a proactive intrusion response system specifically designed for multi-domain networks called ProMD. A Bayesian Risk Graph is designed to analyze and evaluate the risks dynamically. The paper formalizes the selection of countermeasures as a multi-objective optimization problem (MOOP) and proves it is NP-Hard. Therefore a genetic-based heuristic algorithm OPGA is designed to generate optimal response policy sets and an unique policy is selected based on the Entropy Weight method. Experimental results demonstrate that the OPGA outperforms benchmark algorithms. © 2023 IEEE.

关键词： Risk assessment

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