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STB-GraCapsNet: A Novel Capsule network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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On Adaptive Client/Miner Selection for Efficient Blockchain-based Decentralized Federated Learning 98

On Adaptive Client/Miner Selection for Efficient Blockchain-...

引用

98th IEEE Vehicular Technology conference (VTC-Fall)

作者： Tomimasu, Yuta Sato, Koya Univ Electrocommun Artificial Intelligence eXplorat Res Ctr Chofu Tokyo Japan

ISBN: (纸本)9798350329285

This study presents a fast and accurate blockchain-based decentralized federated learning (BC-DFL) based on an adaptive client/miner selection algorithm. BC-DFL is a learning method that manages the machine learning models on a blockchain. Although the blockchain can improve the security of model sharing and realize reward management, its mining process extensively increases computation and communication load. The training accuracy could also be degraded if each client's data distribution follows non-independent and identically distributed (Non-IID) conditions. In the proposed method, a client selection allows parallel processing of local training and mining on the network, reducing round time. In addition, using a client selection algorithm based on the estimation of the label distribution, the accuracy degradation caused by Non-IID is suppressed. Numerical results demonstrate that the proposed method can improve training time and accuracy performances compared to related frameworks.

关键词： Decentralized federated learning Blockchain Client selection

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A Lightweight Chinese Summarization Generation Method based on Knowledge Distillation 4

A Lightweight Chinese Summarization Generation Method Based ...

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4th International conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Liu, Shiyue Xiao, Yannan Wang, Xueyong Huang, Wenming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

ISBN: (纸本)9798350337181

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models. © 2023 IEEE.

关键词： Distillation

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MAS-DDPG: Multi-Sensor Self-Adaptive Scheduling for UAV Path Planning in network-Unavailable Environments 21

MAS-DDPG: Multi-Sensor Self-Adaptive Scheduling for UAV Path...

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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： Lin, Na Wang, Zhijiang Zhao, Liang Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798350329223

In network-unavailable environments, the path planning of unmanned aerial vehicles (UAVs) becomes constrained by limitations in their energy resources and computing capabilities within a predefined mission timeframe. Additional low-power flight control must be considered to effectively complete a flight mission. To support UAV path planning in network-unavailable environments, this paper proposes a multi-sensor self-adaptive scheduling and optimized depth deterministic strategy gradient (MAS-DDPG) algorithm. Specifically, unlike considering activating all sensors in varying scenarios, a multi-sensor self-adaptive scheduling (MAS) strategy based on Multi-nominal Logit (MNL) is proposed. This approach allows the UAV to choose an appropriate scheduling strategy according to different scenarios. Furthermore, instead of relying on randomly chosen experiences from a pool, a mechanism based on screening experience is proposed. This mechanism takes into account successful experiences along with their contextual counterparts to enhance the learning process. Our goal is to maximize the average flight speed of the UAV and minimize the number of flight decisions and flight energy consumption. Simulation results show that the proposed approach can achieve significantly hinter speed and lower energy consumption than the benchmark and other typical algorithms. © 2023 IEEE.

关键词： Multiobjective optimization

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A Genetic Algorithm based Adaptive Offloading Scheme for Domain Generalization in VEC 21

A Genetic Algorithm Based Adaptive Offloading Scheme for Dom...

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作者： Li, Tianyu Zhao, Liang Mao, Chaojin Lin, Na He, Qiang Wang, Xingwei Shenyang Aerospace University Shenyang China Northeastern University Shenyang China

ISBN: (纸本)9798350329223

Vehicular Edge Computing (VEC) is a computational paradigm which integrates Mobile Edge Computing (MEC) into vehicular environments. Task offloading in VEC enables tasks generated by vehicles to be offloaded to the nearby Roadside Units (RSU) or surrounding vehicles, thereby reducing computational latency and energy consumption. However, current task offloading schemes still face numerous challenges, such as varying scenarios, intricately highly dynamic network topologies, and demands for lower latency. In this paper, we introduce a vehicle-to-vehicle (V2V) offloading scheme, namely, a genetic algorithm based adaptivE offloadiNg schEme For domaIn generalizaTion (BENEFIT). First, to deal with the problem of varying scenarios in VEC, we consider the Domain Generalization (DG) problem in trajectory prediction and employ causal inference to construct a generalized prediction model. This serves as a pre-processing step and simultaneously reduces the overall decision space of the system to assist offloading. Second, we put forth an adaptive model targeting the cumulative offloading cost to handle highly dynamic network topologies, which dynamically adjusts delay and energy coefficients in various scenarios to facilitate adaptive offloading. Finally, we utilize the enhanced Non-Uniform Genetic Algorithm (NUGA) for making offloading decisions with considering the limited resources and collaboration processes among vehicles. According to simulation results, our scheme effectively decreases the total cost of the system. The BENEFIT achieves a 3 percent cost savings with regard to other schemes. © 2023 IEEE.

关键词： Genetic algorithms

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A Trusted Agent Strategy in Decentralized network Environments 28

A Trusted Agent Strategy in Decentralized Network Environmen...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Karishma Rao, Shrisha Int Inst Informat Technol Bangalore Bangalore Karnataka India

ISBN: (纸本)9781728165820

A problem currently faced by autonomous systems, learning agents, multi-agent systems and other cognitive systems is the extreme susceptibility to had information and malicious misdirection. In this paper, we propose a strategy to identify trusted agents in a multi-agent system, so that a system can eliminate the knowledge provided by malicious or unknown agents. The strategy is covered in three phases by each agent;firstly sharing information with other agents in the system, secondly calculating the conclusion based on the majority of information received from others and lastly identifying the trusted, malicious and unknown agents in the system. The theoretical analysis shows that the trusted agent strategy (TAS) is capable of identifying malicious or compromised agents if a system has less than 25% of malicious agents. This is further improved in the advanced:FAS which can handle up to 40% malicious agents. The experimental results show that trusted and malicious agents can be identified with high accuracy.

关键词： network security multi-agent system parallel computing

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Hercules: Scalable and network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications 29th

Hercules: Scalable and Network Portable In-Memory Ad-Hoc Fil...

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29th International conference on parallel and distributed Computing (Euro-Par)

作者： Garcia-Blas, Javier Sanchez-Gallegos, Genaro Petre, Cosmin Martinelli, Alberto Riccardo Aldinucci, Marco Carretero, Jesus Univ Carlos III Madrid Leganes Spain Univ Torino Turin Italy

ISBN: (纸本)9783031396977;9783031396984

The growing demands for data processing by new data-intensive applications are putting pressure on the performance and capacity of HPC storage systems. The advancement in storage technologies, such as NVMe and persistent memory, are aimed at meeting these demands. However, relying solely on ultra-fast storage devices is not cost-effective, leading to the need for multi-tier storage hierarchies to move data based on its usage. To address this issue, ad-hoc file systems have been proposed as a solution. They utilise the available storage of compute nodes, such as memory and persistent storage, to create a temporary file system that adapts to the application behaviour in the HPC environment. This work presents the design, implementation, and evaluation of a distributed ad-hoc in-memory storage system (Hercules), highlighting the new communication model included in Hercules. This communication model takes advantage of the Unified Communication X framework (UCX). This solution leverages the capabilities of RDMA protocols, including Infiniband, Onmipath, shared memory, and zero-copy transfers. The preliminary evaluation results show excellent network utilisation compared with other existing technologies.

关键词： HPC Data intensive In-memory storage

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Automatic Program Repair via Learning Edits on Sequence Code Property Graph 29

Automatic Program Repair via Learning Edits on Sequence Code...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Xiaoyu Wu, Jingzheng Ling, Xiang Luo, Tianyue Wu, Yanjun Institute of Software Chinese Academy of Sciences Intelligent Software Research Center China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350330717

In recent years, deep learning has been widely applied in the research of automatic program repair, namely, learning-based program repair, which treats the program repair task as either a neural machine translation problem or a transformation toward the abstract syntax tree of the program. However, all existing learning-based program repair solutions have not adequately modeled the deep semantic information of the programs. To be specific, those solutions based on neural machine translation do not involve any semantic information of the programs at all, while other solutions based on abstract syntax tree transformations also do not involve information such as control flow and data flow. Additionally, the neural networks they use are also unable to sufficiently learn the semantic information. As a result, they cannot fix some bugs that involve semantic *** this paper, we propose a new learning-based program repair approach called SGEPR. SGEPR uses a novel intermediate representation named sequence code property graph (SCPG) to model program semantic information, which can cover multiple types of semantic information in the program. SGEPR then utilizes a graph neural network based on the attention mechanism to learn the semantic information within SCPG. Finally, a well-designed prediction network is employed to generate the repair patches for the buggy program. To evaluate the performance of SGEPR, we conducted comparative experiments on a real-world dataset and the results show that SGEPR can achieve a top-3 accuracy of 56.85%, which is 15.69% higher than the state-of-the-art learning-based program repair method. © 2023 IEEE.

关键词： automatic program repair graph neural network program representation

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Comprehensive Competency Evaluation of Vocational Undergraduate Students based on multi-layer ER Rules 4

Comprehensive Competency Evaluation of Vocational Undergradu...

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4th International conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Jin, Shukun Mu, Xingyu Guangdong Vocational and Technical University of Business and Technology Guangdong Zhaoqing China Jilin Province Meteorological Information Network Center Jilin Changchun China

ISBN: (纸本)9798350337181

Aiming at the problems in the evaluation of the comprehensive ability of vocational undergraduates and analyze the requirements of vocational undergraduates for the comprehensive ability of college students, study the problems of the index system and existing methods for evaluation problems, and propose a model for assessing the comprehensive ability of vocational undergraduates based on multilayer evidential reasoning rules. Evidential reasoning rule is an intelligent information fusion algorithm developed and extended based on Dempster-Shafer evidence theory, which can deal with the problems of diversifying evaluation information, describing the uncertainty of assessment information, and considering the reliability of evaluation information. The model firstly analyzes diversified information using natural language processing methods and standardizes it using rule/utility methods to realize data analysis and mining of diversified information;secondly, it uses hierarchical analysis and mathematical statistics methods to solve the weight and reliability of indicator information;finally, it realizes the fusion of diversified information based on evidence-based inference rules to evaluate the comprehensive ability of vocational undergraduates. The model is multilevel and interpretable, and can realize the combination of stage evaluation and result evaluation. Validate the effectiveness of the method through case study. © 2023 IEEE.

关键词： Students

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The Adaptive Fault-tolerant Routing based on an Improved Local Security Information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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