Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第2页

Understanding the Support of IoT and Persuasive Technology for Smart Bin Design: A Scoping Review 理解物联网和说服性技术对智能垃圾箱设计的支持:范围审查

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/CSCWD57460.2023.10152762

Emilly Marques Da Silva, António Correia, Claudio Miceli, D. Schneider

引用次数: 0

A Fault-tolerant and Cost-efficient Workflow Scheduling Approach Based on Deep Reinforcement Learning for IT Operation and Maintenance 基于深度强化学习的IT运维容错高效工作流调度方法

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/CSCWD57460.2023.10152783

Yunsong Xiang, Xuemei Yang, Y. Sun, Hong Luo

{"title":"A Fault-tolerant and Cost-efficient Workflow Scheduling Approach Based on Deep Reinforcement Learning for IT Operation and Maintenance","authors":"Yunsong Xiang, Xuemei Yang, Y. Sun, Hong Luo","doi":"10.1109/CSCWD57460.2023.10152783","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152783","url":null,"abstract":"With the promotion of cloud computing, a large number of hardware and software systems in the cloud bring massive and complex operation and maintenance (O&M) work. To ensure the O&M efficiency of IT infrastructures, it is necessary to implement automatic and reliable scheduling for the directed acyclic graph (DAG) workflow which is composed of multiple O&M tasks. Considering the changing status of networks and machines in the cloud and the position constraints that some tasks must be executed on the specified machines in some O&M scenarios, we propose a novel workflow scheduling approach based on Deep Reinforcement Learning (DRL) to minimize the workflow execution makespan and implement the fault tolerance with the position constraints of tasks execution. In our proposal, we first design a fault-tolerant mechanism according to the reliability requirement and the probability distributions of the machine failure parameters with consideration of different failure rates in the heterogeneous environment. Then, we employ proximal policy optimization (PPO) to optimize the task scheduling strategy and ensure the strategy to satisfy the position constraints of tasks execution by action masking in proximal policy optimization. The experimental results show that our proposal can effectively reduce the makespan of the fault-tolerant workflow on the premise of 99.9% reliability.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"6 1","pages":"411-416"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80352842","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

Lightweight Image Dehazing Algorithm Based on Detail Feature Enhancement 基于细节特征增强的轻量化图像去雾算法

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/CSCWD57460.2023.10152843

Chenxing Gao, Lingjun Chen, Caidan Zhao, Xiangyu Huang, Zhiqiang Wu

引用次数: 0

Research on Medical Data Storage and Secure Sharing Scheme Based on Blockchain 基于区块链的医疗数据存储与安全共享方案研究

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/CSCWD57460.2023.10152705

Wenxu Han, Qi Li, Meiju Yu, Ru Li

引用次数: 0

Blockchain-based Trust Management Mechanism in V-NDN 基于区块链的V-NDN信任管理机制

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/CSCWD57460.2023.10152633

Z. Liu, Meiju Yu, Ru Li

{"title":"Blockchain-based Trust Management Mechanism in V-NDN","authors":"Z. Liu, Meiju Yu, Ru Li","doi":"10.1109/CSCWD57460.2023.10152633","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152633","url":null,"abstract":"The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"4 1","pages":"1433-1438"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76341440","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

An Improved MOEA Based on Adaptive Adjustment Strategy for Optimizing Deep Model of RFID Indoor Positioning 基于自适应调整策略的改进MOEA优化RFID室内定位深度模型

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/CSCWD57460.2023.10152841

Jiahui Liu, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang

{"title":"An Improved MOEA Based on Adaptive Adjustment Strategy for Optimizing Deep Model of RFID Indoor Positioning","authors":"Jiahui Liu, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang","doi":"10.1109/CSCWD57460.2023.10152841","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152841","url":null,"abstract":"Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"36 1","pages":"357-362"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76837658","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

Dynamic Link Prediction Using Graph Representation Learning with Enhanced Structure and Temporal Information 基于增强结构和时间信息的图表示学习的动态链接预测

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/CSCWD57460.2023.10152711

Chaokai Wu, Yansong Wang, Tao Jia

{"title":"Dynamic Link Prediction Using Graph Representation Learning with Enhanced Structure and Temporal Information","authors":"Chaokai Wu, Yansong Wang, Tao Jia","doi":"10.1109/CSCWD57460.2023.10152711","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152711","url":null,"abstract":"The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future time. How to effectively learn the temporal and structural pattern of the network dynamics is the key. In this paper, we propose a graph representation learning model based on enhanced structure and temporal information (GRL_EnSAT). For structural information, we exploit a combination of a graph attention network (GAT) and a self-attention network to capture structural neighborhood. For temporal dynamics, we use a masked self-attention network to capture the dynamics in the link evolution. In this way, GRL_EnSAT not only learns low-dimensional embedding vectors but also preserves the nonlinear dynamic feature of the evolving network. GRL_EnSAT is evaluated on four real datasets, in which GRL_EnSAT outperforms most advanced baselines. Benefiting from the dynamic self-attention mechanism, GRL_EnSAT yields better performance than approaches based on recursive graph evolution modeling.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"5 1","pages":"279-284"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87066427","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

BERT-based Question Answering using Knowledge Graph Embeddings in Nuclear Power Domain 基于bert的核电领域知识图嵌入问答

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/CSCWD57460.2023.10152692

Zuyang Ma, Kaihong Yan, Hongwei Wang

引用次数: 1

Smart Cities in Focus: A Bicycle Transport Applications Analysis 智慧城市聚焦:自行车交通应用分析

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/CSCWD57460.2023.10152820

L. Silva, Marcos Calazans, L. Vasconcelos, Raissa Barcellos, D. Trevisan, J. Viterbo

引用次数: 0

Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems 异构分布式系统的能量约束任务调度

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/CSCWD57460.2023.10152593

Cheng Chen, Jie Zhu, Haiping Huang, Yingmeng Gao

引用次数: 0