Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第6页

Accelerate Multi-view Inference with End-edge Collaborative Computing 利用端缘协同计算加速多视图推理

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152842

Wangbing Cheng, MinFeng Zhang, Fang Dong, Shucun Fu

{"title":"Accelerate Multi-view Inference with End-edge Collaborative Computing","authors":"Wangbing Cheng, MinFeng Zhang, Fang Dong, Shucun Fu","doi":"10.1109/CSCWD57460.2023.10152842","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152842","url":null,"abstract":"Multi-view inference can utilize visual information from several views like a human being and significantly improve accuracy in some scenes, but it inevitably incurs more computing overhead than traditional DNN inference. To meet the requirement of low latency in typical scenarios, we consider utilizing model partition technique of edge computing to speed up multi-view inference, and design a multi-view end-edge co-inference execution framework (MV-IEF) which can make use of both end and edge resources for multi-view inference tasks. However, when employing the framework simply, the efficiency of multi-view inference will be constrained by network dynamics and heterogeneity of devices corresponding to multiple views. To break this constraint, we establish an optimization model based on the framework to minimize the multi-view inference time and solve it on the basis of game theory. And meanwhile, we propose a joint optimization algorithm for multi-view resource allocation and model partition (MV-JRAMP), which can make remarkable decisions of resource allocation and model partiton according to network status and computing capabilities of devices. Finally, we build a prototype and evaluate the performance of MV-JRAMP. Experiments show that MV-JRAMP can accelerate multi-view inference by up to 3.71×.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"42 1","pages":"1625-1631"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80875422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Manufacturing Collaboration Platform for 3D Curved Plates Based on Graph Matching 基于图匹配的三维曲面板智能制造协同平台

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152618

Yanjun Dong, Haoyuan Hu, Min Zhu, Pan Hu, Lihong Jiang, Hongming Cai

{"title":"Intelligent Manufacturing Collaboration Platform for 3D Curved Plates Based on Graph Matching","authors":"Yanjun Dong, Haoyuan Hu, Min Zhu, Pan Hu, Lihong Jiang, Hongming Cai","doi":"10.1109/CSCWD57460.2023.10152618","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152618","url":null,"abstract":"The three-dimensional (3D) curved plate manufacturing is performed by constructing surfaces corresponding to the shape of the curved plate for multi-point forming. However, in the manufacturing process, the rebound restricts the forming accuracy, and the currently adopted rebound control methods cannot predict the rebound amount accurately. Meanwhile, the process involves multi-role collaboration and multiple data conversions and comparisons. These problems lead to a high degree of manual dependence, which affects manufacturing efficiency and accuracy. To address the above problems, this paper proposes a collaborative platform for the intelligent manufacturing of curved plates based on graph matching. Firstly, this paper establishes information models covering the whole process of curved plate manufacturing and forms a unified topology graph model. Then, the intelligent generation method of processing parameters based on graph matching is proposed, which realizes similar case recommendation and case-based processing parameters generation. Finally, we design and develop a collaboration platform based on micro-service architecture to support efficient collaboration among various departments and roles. In this paper, we use sail-shaped curved plates as a case of processing parameters generation and verify that this intelligent method can improve the accuracy of rebound control by comparison with related work, which shows that our method can be effectively applied to curved plate manufacturing.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"52 4 1","pages":"1650-1655"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83551151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DSP-Based Industrial Defect Detection for Intelligent Manufacturing 基于dsp的智能制造工业缺陷检测

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152825

Han Yue, Rucen Wang, Yi Gao, Ailing Xia, Jianhua Zhang

引用次数: 0

SimulE: A novel convolution-based model for knowledge graph embedding SimulE:一种新的基于卷积的知识图嵌入模型

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152758

Chaoyi Yan, Xinli Huang, H. Gu, Siyuan Meng

{"title":"SimulE: A novel convolution-based model for knowledge graph embedding","authors":"Chaoyi Yan, Xinli Huang, H. Gu, Siyuan Meng","doi":"10.1109/CSCWD57460.2023.10152758","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152758","url":null,"abstract":"Knowledge graph embedding technique is one of the mainstream methods to handle the link prediction task, which learns embedding representations for each entity and relation to predict missing links in knowledge graphs. In general, previous convolution-based models apply convolution filters on the reshaped input feature maps to extract expressive features. However, existing convolution-based models cannot extract the interaction information of entities and relations among the same and different dimensional entries simultaneously. To overcome this problem, we propose a novel convolution-based model (SimulE), which utilizes two paths simultaneously to capture the rich interaction information of entities and relations. One path uses 1D convolution filters on 2D reshaped input maps, which maintains the translation properties of the triplets and has the ability to extract interaction information of entities and relations among the same dimensional entries. Another path employs 3D convolution filters on the 3D reshaped input maps, which is suitable for capturing the interaction information of entities and relations among the different dimensional entries. Experimental results show that SimulE can effectively model complex relation types and achieve state-of-the-art performance in almost all metrics on three benchmark datasets. In particular, compared with baseline ConvE, SimulE outperforms it in MRR by 2.9%, 9.8% and 2.8% on FB15k-237, YAGO3-10 and DB100K respectively.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"38 1","pages":"624-629"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84399534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measurement and Optimization of Repetition Scheme in NB-IoT Uplink NB-IoT上行链路重复方案的测量与优化

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152761

Kai Chen, Xiangmao Chang, Jun Zhan, Yanchao Zhao

引用次数: 0

A Blockchain-Based Privacy-Preserving Data Sharing Scheme with Security-Enhanced Access Control 基于区块链的安全增强访问控制的隐私保护数据共享方案

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152751

Benyu Li, Jing Yang, Yuxiang Wang, Xiaojun Huang, Junshuai Ren, Liming Wang

{"title":"A Blockchain-Based Privacy-Preserving Data Sharing Scheme with Security-Enhanced Access Control","authors":"Benyu Li, Jing Yang, Yuxiang Wang, Xiaojun Huang, Junshuai Ren, Liming Wang","doi":"10.1109/CSCWD57460.2023.10152751","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152751","url":null,"abstract":"In the data-driven economy, data sharing is a key approach to unleashing the true value of data. Blockchain, as a decentralized ledger, can provide a trusted data sharing platform in an untrusted environment. However, existing blockchain-based data sharing schemes suffer from inefficiency and inadequate protection of security and privacy. To address the above issues, we propose a blockchain-based privacy-preserving data sharing scheme with security-enhanced access control. In the scheme, a secure data sharing architecture using dual-blockchain and the interplanetary file system (IPFS) is presented to provide decentralized and scalable storage. Based on the architecture, a blockchain-assisted multi-authority attribute-based encryption (BA-MA-ABE) algorithm with efficient attribute revocation and computation is designed in our work. Our BA-MA-ABE lever-ages blockchain to securely manage partial decryption keys and provides fine-grained access control over encrypted data. We also devise smart contracts that can support traceable access control over the flow of data while protecting user identity privacy with verifiable attribute credentials. In comparison with some existing work, our scheme shows more comprehensive security features with lower user computation overhead.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"825-830"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86644059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FreezePipe: An Efficient Dynamic Pipeline Parallel Approach Based on Freezing Mechanism for Distributed DNN Training FreezePipe:一种基于冻结机制的高效动态管道并行分布式DNN训练方法

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152643

Caishan Weng, Zhiyang Shu, Zhengjia Xu, Jinghui Zhang, Junzhou Luo, Fang Dong, Peng Wang, Zhengang Wang

{"title":"FreezePipe: An Efficient Dynamic Pipeline Parallel Approach Based on Freezing Mechanism for Distributed DNN Training","authors":"Caishan Weng, Zhiyang Shu, Zhengjia Xu, Jinghui Zhang, Junzhou Luo, Fang Dong, Peng Wang, Zhengang Wang","doi":"10.1109/CSCWD57460.2023.10152643","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152643","url":null,"abstract":"Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"10 1","pages":"303-308"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82888753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile Application Traffic 基于多特征融合的移动应用加密流量分类方法

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152687

Qingya Yang, Peipei Fu, Junzheng Shi, Bingxu Wang, Zhuguo Li, G. Xiong

{"title":"Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile Application Traffic","authors":"Qingya Yang, Peipei Fu, Junzheng Shi, Bingxu Wang, Zhuguo Li, G. Xiong","doi":"10.1109/CSCWD57460.2023.10152687","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152687","url":null,"abstract":"With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. Therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. Therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. Then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"2004 1","pages":"1112-1117"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82963594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Targeted Online Password Guessing Algorithm Based on Old Password 一种新的基于旧密码的针对性在线猜密码算法

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152712

Xizhe Zhang, Xiong Zhang, Jiahao Hu, Yuesheng Zhu

引用次数: 0

HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method 基于隐私保护的二值低秩矩阵分解方法的异构联邦学习

IF 2.4 3区计算机科学

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI: 10.1109/CSCWD57460.2023.10152714

Jiaqi Liu, Kaiyu Huang, Lunchen Xie

{"title":"HeteFed: Heterogeneous Federated Learning with Privacy-Preserving Binary Low-Rank Matrix Decomposition Method","authors":"Jiaqi Liu, Kaiyu Huang, Lunchen Xie","doi":"10.1109/CSCWD57460.2023.10152714","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152714","url":null,"abstract":"Federated learning is a machine learning paradigm where many clients collaboratively train a machine learning model while ensuring the nondisclosure of local data sets. Existing federated learning methods conduct optimization over the same model structure, which ensures the convenience of parameter updates. However, the same structure among clients and the server may pose risks of privacy leakage as parameters from one’s model can fit in others’ models. In this paper, we propose a heterogeneous federated learning method to preserve privacy. Each client utilizes neural architecture search to determine distinct models via local data and update the server model via a federated learning framework with knowledge distillation. Besides, we develop a privacy-preserving binary low-rank matrix decomposition method (Blow), i.e., decomposing the output matrix into two low-rank binary matrices, to further ensure the secrecy of distilled information. A simple but efficient alternating optimization method is proposed to address a key subproblem arising from the binary low-rank matrix decomposition, which falls into the category of the Np-hard bipartite boolean quadratic programming. Based on extensive experiments over the image classification task, we show our algorithm provides satisfactory accuracy and outperforms baseline algorithms in both privacy protection and communication efficiency.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"1238-1244"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88722861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0