Computer Supported Cooperative Work-The Journal of Collaborative Computing最新文献_第3页

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

Improving the Quality of Textual Adversarial Examples with Dynamic N-gram Based Attack 基于动态n图的攻击提高文本对抗示例的质量

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/CSCWD57460.2023.10152569

Xiaojiao Xie, Pengwei Zhan

{"title":"Improving the Quality of Textual Adversarial Examples with Dynamic N-gram Based Attack","authors":"Xiaojiao Xie, Pengwei Zhan","doi":"10.1109/CSCWD57460.2023.10152569","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152569","url":null,"abstract":"Natural language models have been widely used for their impressive performance in various tasks, while their poor robustness also puts critical applications at high risk. These models are vulnerable to adversarial examples, which contain imperceptible noise that leads the model to wrong predictions. To ensure such malicious examples are imperceptible to humans, various word-level attack methods have been proposed. Previous works on word-level attacks attempt to generate adversarial examples by substituting words in sentences. They utilize different candidate substitution selection methods and substitution strategies to improve attack effectiveness and the quality of generated examples. However, previous works are all unigram-based attack methods, which ignore the connection between words. The unigram nature of these methods downgrades fluency, increases grammatical errors, and biases the semantics of adversarial examples, making adversarial examples easier to be detected by humans. In this paper, to improve the quality of textual adversarial examples and makes the adversarial example more imperceptible to human, we propose a black-box word-level attack method called Dynamic N-Gram Based Attack (DyGram). DyGram tokenizes the entire sentence into multiple n-gram units, rather than individual words as in previous works, and substitutes words in a sentence in descending order of n-gram unit importance. Extensive experiments demonstrate that DyGram achieves higher attack success rates than previous attack methods and improves the quality of generated adversarial examples in terms of the number of perturbed words, perplexity, grammatical correctness, and semantic similarity.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"53 1","pages":"594-599"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81156230","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

QoS and Fuzzy Logic Based Communication Handover Strategy in 5G Cellular Internet of Vehicles 基于QoS和模糊逻辑的5G蜂窝车联网通信切换策略

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/CSCWD57460.2023.10152793

Bo Li, Zisu Na, Rongrong Qian, Hongwei Ding

引用次数: 0

A Subdomain Uncertainty-Guided Kriging Method with Subset Simulation for Reliability Estimation 基于子集仿真的子域不确定性引导Kriging可靠性估计方法

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/CSCWD57460.2023.10152591

Dapeng Wang, H. Qiu, L. Gao

{"title":"A Subdomain Uncertainty-Guided Kriging Method with Subset Simulation for Reliability Estimation","authors":"Dapeng Wang, H. Qiu, L. Gao","doi":"10.1109/CSCWD57460.2023.10152591","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152591","url":null,"abstract":"Direct Monte Carlo Simulation for reliability estimation of rare failure event is challenged by the complicated performance function evaluations and large candidate sample pool. To address these challenges, a subdomain uncertainty- guided Kriging method with subset simulation is proposed. With a concise uncertainty assessment function, efficient subdomain uncertainty-guided sampling strategy is first developed to refine the Kriging model that is used to replace real performance function approximately. Moreover, the number of candidate samples required by subset simulation is also significantly reduced. By sequentially exploiting within the candidate sample pools generated in the first intermediate failure event and other intermediate failure events, an accurate Kriging model can be constructed subsequently. The ingenious method of coupling Kriging and subset simulation can greatly improve the efficiency of reliability estimation. Finally, three classical examples are investigated as benchmark to explore the performance of the proposed method. The comparison results demonstrate the good capability and applicability of the proposed method.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"20 1","pages":"1526-1531"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85501965","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

Internet of Things in Power Systems: A Bibliometric Analysis 电力系统中的物联网:文献计量学分析

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/CSCWD57460.2023.10152802

Yanhao Feng, Xiaojie Lin, Zitao Yu

引用次数: 0

Traffic accident location study based on AD-DBSCAN Algorithm with Adaptive Parameters 基于自适应参数AD-DBSCAN算法的交通事故定位研究

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/CSCWD57460.2023.10152613

Xijun Zhang, Jin Su, Hong Zhang, Xianli Zhang, Xuanbing Chen, Yong Cui

{"title":"Traffic accident location study based on AD-DBSCAN Algorithm with Adaptive Parameters","authors":"Xijun Zhang, Jin Su, Hong Zhang, Xianli Zhang, Xuanbing Chen, Yong Cui","doi":"10.1109/CSCWD57460.2023.10152613","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152613","url":null,"abstract":"Aiming at the shortcomings of the traditional Density-Based Spatial Clustering of Applications with Noise -DBSCAN algorithm such as insignificant clustering effect and the choice of parameter combinations. This paper proposes an AD-DBSCAN algorithm with adaptive parameters, which makes the algorithm more difficult in the selection of the parameters. By establishing a DBSCAN algorithm model to adapt to finding the optimal distance threshold and the minimum number of neighbor points, the clustering is more accurate, and the noise point identified in the data is more accurate. Through the observation of the calculation model of the Calinski-Harabasz index, the evaluation index of the clustering algorithm, the selection of the optimal best distance threshold and the minimum number of neighborhood points, the accuracy of noise point recognition is improved by 5 times in the clustering algorithm, and the Calinski-Harabasz index improved by about 39.84%. The applicability of the algorithm in clustering the locations of urban road traffic accidents is verified.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"32 1","pages":"1160-1165"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84688528","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

The trip to WiFi indoor localization across a decade — A systematic review 十年来WiFi室内定位之旅——系统回顾

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/CSCWD57460.2023.10152700

Shuang Qiao, Chenhong Cao, Haoquan Zhou, Wei Gong

引用次数: 0

Multi-scale Convolutional Feature Approximation for Defocus Blur Detection 散焦模糊检测的多尺度卷积特征逼近

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/CSCWD57460.2023.10152667

Rui Huang, Huan Lu, Yan Xing, Wei Fan

{"title":"Multi-scale Convolutional Feature Approximation for Defocus Blur Detection","authors":"Rui Huang, Huan Lu, Yan Xing, Wei Fan","doi":"10.1109/CSCWD57460.2023.10152667","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152667","url":null,"abstract":"Deep learning technology has promoted the performance of defocus blur detection. However, blur detectors suffer from background clutter, scale ambiguity and blurred boundaries of the defocus blur regions. To conquer these issues, previous methods propose to use multi-scale image patches or images for blur detection, which costs much computation time. In this paper, we propose a deep neural network that takes a single-scale image as input to generate robust defocus blur detection. Specifically, we first extract multi-scale convolutional features by a feature extraction network. And then we resize the convolutional features of each layer by a fixed ratio to approximate convolutional features that extracted from a resized image with the same ratio. By approximation, it not only generates features extracted from a scaled image but also reduces the computation of feature extraction from multi-scale images. We concatenate the features extracted from the original image with the approximated features at the corresponding layers by convolutional layers to increase the blur distinguish ability. We gradually fuse the convolutional features from top-to-bottom by Conv-LSTMs to refine the blur predictions. We compare our method with nine state-of-the-art defocus blur detectors on two defocus blur detection benchmark datasets. Experiment results demonstrate the effectiveness of our proposed defocus blur detector.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"156 1","pages":"1172-1177"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79876407","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

Understanding Social Relations with Graph-Based and Global Attention 用基于图和全局关注的方法理解社会关系

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/CSCWD57460.2023.10152575

Hanqing Li, Niannian Chen

{"title":"Understanding Social Relations with Graph-Based and Global Attention","authors":"Hanqing Li, Niannian Chen","doi":"10.1109/CSCWD57460.2023.10152575","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152575","url":null,"abstract":"Social relations, as the basic relationships in our daily life, are a phenomenon unique to human society that shows how people interact in society. Social relations understanding is to infer the existing social relationships between individuals in a given scenario, which is crucial for us to analyze social behavior. Existing research methods are usually limited to extracting features of characters and related entities, which limits the scope of attention and may miss important clues such as interactions between characters. In this paper, we propose a global attention mechanism that adaptively grasps scenes, objects, and human interactions for reasoning about social relationships. We propose an end-to-end global attention network, which consists of three modules, namely, a convolutional attention module, a graph inference module, and an attentional inference module. The visual and location information is first extracted by the convolutional attention module as the feature information of the person pairs, then it is made to process the relationships between character nodes on the graph inference network, and finally, the attention is fully utilized to classify the social relationships. Extensive experiments on the PISC and PIPA datasets show that our proposed method outperforms the state-of-the-art methods in terms of accuracy.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"41-46"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80530826","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