Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第5页

A Privacy-Preserving Online Deep Learning Algorithm Based on Differential Privacy 一种基于差分隐私保护的在线深度学习算法

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/CSCWD57460.2023.10152847

Jun Li, Fengshi Zhang, Yonghe Guo, Siyuan Li, Guanjun Wu, Dahui Li, Hongsong Zhu

{"title":"A Privacy-Preserving Online Deep Learning Algorithm Based on Differential Privacy","authors":"Jun Li, Fengshi Zhang, Yonghe Guo, Siyuan Li, Guanjun Wu, Dahui Li, Hongsong Zhu","doi":"10.1109/CSCWD57460.2023.10152847","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152847","url":null,"abstract":"Deep Reinforcement Learning (DRL) combines the perceptual capabilities of deep learning with the decision-making capabilities of Reinforcement Learning RL, which can achieve enhanced decision-making. However, the environmental state data contains the privacy of the users. There exists consequently a potential risk of environmental state information being leaked during RL training. Some data desensitization and anonymization technologies are currently being used to protect data privacy. There may still be a risk of privacy disclosure with these desensitization techniques. Meanwhile, policymakers need the environmental state to make decisions, which will cause the disclosure of raw environmental data. To address the privacy issues in DRL, we propose a differential privacy-based online DRL algorithm. The algorithm will add Gaussian noise to the gradients of the deep network according to the privacy budget. More important, we prove tighter bounds for the privacy budget. Furthermore, we train an autocoder to protect the raw environmental state data. In this work, we prove the privacy budget formulation for differential privacy-based online deep RL. Experiments show that the proposed algorithm can improve privacy protection while still having relatively excellent decisionmaking performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"559-564"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81634318","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

Neural Network Model Pruning without Additional Computation and Structure Requirements 无需额外计算和结构要求的神经网络模型修剪

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/CSCWD57460.2023.10152777

Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang

{"title":"Neural Network Model Pruning without Additional Computation and Structure Requirements","authors":"Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang","doi":"10.1109/CSCWD57460.2023.10152777","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152777","url":null,"abstract":"In past work, deep learning researchers always designed hyperparameters such as model structure and learning rate first and then used the training set to train the weights in this model. While unrestricted model structure design leads to massive neuron redundancy in neural network models. By pruning these redundant neurons, not only can the storage be compressed effectively, but also the operation can be accelerated. In this paper, we propose a method to utilize the training set to prune the model structure during training: 1) train the initialized model and bring it to basic convergence; 2) feed the entire training set into the model and calculate the activations of neurons in each layer; 3) calculate the threshold for neuron pruning in each layer according to the pruning ratio, delete neurons whose activation value is lower than the threshold, and correspondingly delete the weights of the upper and lower layers; 4) further train the pruned model so that it eventually converges. This method of deleting redundant neurons not only greatly deletes the parameters in the model but also achieves model acceleration. We applied this method to some mainstream neural network models: VGGNet and ResNet, and achieved good results.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"17 1","pages":"1734-1740"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89679863","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 Cooperative Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning 基于多智能体深度强化学习的协同边缘缓存方法

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/CSCWD57460.2023.10152789

Xiang Cao, Ningjiang Chen, Xuemei Yuan, Yifei Liu

引用次数: 0

AHIP: An Adaptive IP Hopping Method for Moving Target Defense to Thwart Network Attacks 一种自适应IP跳变防御移动目标的网络攻击方法

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/CSCWD57460.2023.10152746

Fengyuan Shi, Zhou-yu Zhou, Wei Yang, Shu Li, Qingyun Liu, Xiuguo Bao

引用次数: 0

Few-shot Malicious Domain Detection on Heterogeneous Graph with Meta-learning 基于元学习的异构图少射恶意域检测

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/CSCWD57460.2023.10152708

Yi Gao, Fangfang Yuan, Cong Cao, Majing Su, Dakui Wang, Yanbing Liu

{"title":"Few-shot Malicious Domain Detection on Heterogeneous Graph with Meta-learning","authors":"Yi Gao, Fangfang Yuan, Cong Cao, Majing Su, Dakui Wang, Yanbing Liu","doi":"10.1109/CSCWD57460.2023.10152708","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152708","url":null,"abstract":"The Domain Name System (DNS), one of the essential basic services on the Internet, is often abused by attackers to launch various cyber attacks, such as phishing and spamming. Researchers have proposed many machine learning-based and deep learning-based methods to detect malicious domains. However, these methods rely on a large-scale dataset with labeled samples for model training. The fact is that the labeled domain samples are limited in the real-world DNS dataset. In this paper, we propose a few-shot malicious domain detection model named MetaDom, which employs a meta-learning algorithm for model optimization. Specifically, We first model the DNS scenario as a heterogeneous graph to capture richer information by analysing the complex relations among domains, IP addresses and clients. Then, we learn the domain representations with a heterogeneous graph neural network on the DNS HG. Finally, considering that only few labeled data are available in the real-world DNS scenario, a meta-learning algorithm with knowledge distillation is introduced to optimize the model. Extensive experiments on the real DNS dataset show that MetaDom outperforms other state-of-the-art methods.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"62 1","pages":"727-732"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76791528","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

Iterative Greedy Selection Hyper-heuristic with Linear Population Size Reduction 线性种群缩减的迭代贪心选择超启发式算法

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/CSCWD57460.2023.10152792

Fuqing Zhao, Yuebao Liu, Tianpeng Xu

引用次数: 0

Keynote 2 : Promoting the diversity of digital technologies 主题演讲2:促进数字技术的多样性

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/cscwd57460.2023.10152801

引用次数: 0

Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing 多跳拼车的两阶段车辆对调度

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/CSCWD57460.2023.10152680

Xiaobo Wei, Peng Li, Weiyi Huang, Zhiyuan Liu, Qin Liu

{"title":"Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing","authors":"Xiaobo Wei, Peng Li, Weiyi Huang, Zhiyuan Liu, Qin Liu","doi":"10.1109/CSCWD57460.2023.10152680","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152680","url":null,"abstract":"Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"67 1","pages":"255-260"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80437028","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

Bi-objective Optimization for UAV Swarm-enabled Relay Communications via Collaborative Beamforming 协同波束成形无人机群中继通信双目标优化

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/CSCWD57460.2023.10152645

Chuang Zhang, Geng Sun, Jiahui Li, Xiaoya Zheng

引用次数: 1

Lightweight Gesture Based Trigger-Action Programming for Home Internet-of-Things 基于轻量级手势的家庭物联网触发动作编程

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/CSCWD57460.2023.10152738

Lifu Wang, K. Dong, Xiaodan Gu, Zhen Ling, Ming Yang

引用次数: 0