Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第7页

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

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/CSCWD57460.2023.10152566

Weihong Yang, Hua Dai, Haozhe Wu, Geng Yang, Meng Lu, Guineng Zheng

{"title":"A Human Pose Similarity Calculation Method Based on Partition Weighted OKS Model","authors":"Weihong Yang, Hua Dai, Haozhe Wu, Geng Yang, Meng Lu, Guineng Zheng","doi":"10.1109/CSCWD57460.2023.10152566","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152566","url":null,"abstract":"Image-based calculation of human pose similarity is one of the computer vision research fields. Most existing research uses the human skeleton joint to calculate the human pose similarity, but usually does not consider the influence of inaccurate recognition of skeleton joint on similarity calculation caused by the complex environment (such as the occlusion of body parts, etc.). We propose a human pose similarity calculation method based on partition weighted OKS model. Due to the influence of external factors such as occlusion, the skeleton joint extracted by the human pose estimation algorithm is inaccurate, which leads to the decrease of the accuracy of the human pose similarity calculation. We propose the partition rule of human skeleton joints and the dynamic strategy adjustment of partition weight. The partition weighted OKS model and a human pose similarity calculation method based on the partition weighted OKS model are given. The experimental results on datasets show that the proposed method for human pose similarity calculation is superior to the traditional one.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"222 1","pages":"1760-1765"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91164461","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

Dirty page prediction by machine learning methods based on temporal and spatial locality 基于时空局部性的机器学习脏页面预测方法

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/CSCWD57460.2023.10152768

Yahui Lu, Yuping Jiang

引用次数: 0

Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition 基于骨架的动作识别的长时间记忆图卷积网络

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/CSCWD57460.2023.10152568

Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu

{"title":"Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition","authors":"Yanpeng Qi, Chen Pang, Yiliang Liu, Hong Liu, Lei Lyu","doi":"10.1109/CSCWD57460.2023.10152568","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152568","url":null,"abstract":"Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"57 1","pages":"843-848"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77407709","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 Reinforcement Learning-driven Iterated Greedy Algorithm for Traveling Salesman Problem 旅行商问题的强化学习驱动迭代贪心算法

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/CSCWD57460.2023.10152696

Xi Song, Mingyang Li, Weidong Xie, Yuanyuan Mao

引用次数: 0

Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk 通过众包调查作者研究相关性:MTurk的复制研究

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/CSCWD57460.2023.10152707

António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca

{"title":"Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk","authors":"António Correia, Dennis Paulino, H. Paredes, D. Guimaraes, D. Schneider, Benjamim Fonseca","doi":"10.1109/CSCWD57460.2023.10152707","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152707","url":null,"abstract":"Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"26 3","pages":"77-82"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72394065","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

Unsupervised Graph Neural Network with Self-Expressive Attention for Community Detection 基于自表达注意的无监督图神经网络社区检测

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/CSCWD57460.2023.10152715

Xu Sun, Weiyu Zhang, Xinchao Guo, Wenpeng Lu

引用次数: 0