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检索条件"任意字段=Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing"

共 1518 条记录，以下是331-340 订阅

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Good Systems: Ethical AI for CSCW 22

Good Systems: Ethical AI for CSCW

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22nd acm conference on computer-supported cooperative work and social computing (CSCW)

作者： Fleischmann, Kenneth R. Greenberg, Sherri R. Gurari, Danna Stang, Abigale Verma, Nitin Day, Jaxsen R. Simons, Rachel N. Yeh, Tom Univ Texas Austin Austin TX 78701 USA Texas Womans Univ Denton TX 76204 USA Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9781450366922

Artificial intelligence is revolutionizing work, including what it means for cooperative work to be supported by computers. The increased use of AIin CSCW can lead to many advantages, including increased productivity and efficiency, but it can also include several potential ethical trade-offs, such as invasions of privacy, loss of autonomy, and job displacement. This workshop will explore the ethical dimensions of Al in CSCW, building on Good Systems, a UT Grand Challenge. Specifically, the first half of the workshop will focus on the need to design Al to work for all users and to avoid bias through the use of universal design as well as the need for Al and CSCW researchers to interact with policy and legal experts to work together to ensure that Al will be developed in an ethical manner with sufficient consideration of its societal implications, and also that Al will be regulated and legislated in ways that will maximize its benefits to all people.

关键词： Artificial intelligence machine learning automation work universal design policy ethics human values

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Collaborative and GPS Sensing for Waiting Time Prediction on Service Queues through Smartphones 23

Collaborative and GPS Sensing for Waiting Time Prediction on...

引用

IEEE 23rd International conference on computer supported cooperative work in Design (CSCWD)

作者： Moraes, Adriano D. Freitas, Allan E. S. Marques Neto, Manoel C. Inst Fed Bahia PPGESP Salvador BA Brazil

ISBN: (纸本)9781728103501

Mobile computing may improve pervasively user experience through context perception. In this paper, we exercise a prediction service for waiting time on service queues using only GPS information and a little user collaboration ("are you on a queue?") - this combination may be used by the vast majority of current mobile phones, once it does not require sophisticated sensing (such as accelerometers and other stuff). In our proof of concept, we achieve a less than 1 minute predicting error on monitoring a minimarket queue service. There results suggest that our tool may be useful and can be extended for other scenarios based on different classes of service queues.

关键词： mobile computing collaborative and social computing sensors geofencing queues

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Catch You if You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems 20

Catch You if You Deceive Me: Verifiable and Privacy-Aware Tr...

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15th acm Asia conference on computer and Communications Security, ASIA CCS 2020

作者： Xu, Guowen Li, Hongwei Xu, Shengmin Ren, Hao Zhang, Yinghui Sun, Jianfei Deng, Robert H. University of Electronic Science and Technology of China Chengdu China Singapore University of Technology and Design Singapore Singapore Singapore Management University Singapore Singapore

ISBN: (纸本)9781450367509

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy"cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads. © 2020 acm.

关键词： Cloud computing

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Welcome to cscw 2016, the acm conference on computer-supported cooperative work and social computing

Proceedings of the ACM Conference on Computer Supported Coop...

引用

proceedings of the acm conference on computer supported cooperative work, CSCW 2016年 27卷 xi页

作者： Gergle, Darren Microsoft Research Northwestern University Meredith Ringel Morris United States

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Understanding How social Prompts Influence Expert's Sharing of How-to Knowledge 19

Understanding How Social Prompts Influence Expert's Sharing ...

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22nd acm conference on computer-supported cooperative work and social computing (CSCW)

作者： Yang, Chi-Lan Wang, Hao-Chuan Univ Tsukuba Empowerment Informat Tsukuba Ibaraki Japan Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

ISBN: (纸本)9781450366922

Numerous how-to tutorials and videos are pre-produced and archived for future learners' self-paced online learning. However, the co-presence of instructor and potential learners is missing when experts pre-produce how-to tutorials or videos offline. Hence, the content being shared by experts may not meet future learners' needs. To ensure the instructional value of archived tutorials for future learners, we explored ways to improve content production by analyzing interactive social prompts from a social partner during the time when experts share knowledge. We paired experts with partners of varying expertise and asked them to freely interact with the experts along the process. To simulate sharing knowledge via video-recording, a cooking task was deployed and we asked cooking experts to think-aloud while executing a cooking task with feedback from one of three types of paired partners, including another cooking expert, a novice, or no partner. This study identified six types of social prompts that can inform the design of computer-mediated knowledge sharing. Moreover, content quality was rated and evaluated, which shows that the best when knowledge was shared with the aid of expert's prompts. This study provides understanding of how various types of social prompts affect the externalization of how-to knowledge for asynchronous knowledge transfer between experts and future learners.

关键词： Knowledge sharing social prompts asynchronous knowledge transfer computer-mediated knowledge sharing expert novice

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BERT-Based Mental Model, a Better Fake News Detector 20

BERT-Based Mental Model, a Better Fake News Detector

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6th International conference on computing and Artificial Intelligence, ICCAI 2020

作者： Ding, Jia Hu, Yongjun Chang, Huiyou School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China School of Management Guangzhou University Guangzhou510006 China

ISBN: (纸本)9781450377089

Automatic fake news detection is a challenging problem which needs a number of verifiable facts support back. Wang et al. [16] introduced LIAR, a validated dataset, and presented a six classes classification task with several popular machine learning methods to detect fake news in linguistic level. However, empirical results have shown that the CNN and RNN based model can not perform very well especially when integrating all features with claim. In this paper, we are the first to present a method to build up a BERT-based [4] mental model to capture the mental feature in fake news detection. In details, we present a method to construct a patterned text in linguistic level to integrate the claim and features appropriately. Then we fine-tune the BERT model with all features integrated text. Empirical results show that our method provides significant improvement over the state-of-art model based on the LIAR dataset we have known by 16.71% in accuracy. © 2020 acm.

关键词： Classification (of information)

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Welcome to CSCW 2016, the acm conference on computer-supported cooperative work and social computing

Proceedings of the ACM Conference on Computer Supported Coop...

引用

proceedings of the acm conference on computer supported cooperative work, CSCW 2016年 26-February-2016卷 xi页

作者： Gergle, Darren Morris, Meredith Ringel Northwestern University United States Microsoft Research United States

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How to enhance collaboration in Latin America to be more competitive worldwide? A Multimodal Learning Analytics approach 9

How to enhance collaboration in Latin America to be more com...

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9th Latin American conference on Human computer Interaction (CLIHC)

作者： Riquelme, Fabian Munoz, Roberto Noel, Rene Mac Lean, Roberto Univ Valparaiso Escuela Ingn Civil Informat Valparaiso Chile

We propose the creation of an interdisciplinary research group related to Multimodal Learning Analytics and social Network Analysis for the study of collaborative work groups in Latin America. We believe that the adva... 详细信息

ISBN: (纸本)9781450376792

关键词： computer-supported cooperative work collaboration multimodal learning analytics social networks

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Anomaly Detection Using Spatio-Temporal Correlation and Information Entropy in Wireless Sensor Networks 13

Anomaly Detection Using Spatio-Temporal Correlation and Info...

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2020 IEEE Congress on Cybermatics: 13th IEEE International conferences on Internet of Things, iThings 2020, 16th IEEE International conference on Green computing and Communications, GreenCom 2020, 13th IEEE International conference on Cyber, Physical and social computing, CPSCom 2020 and 6th IEEE International conference on Smart Data, SmartData 2020

作者： Chen, Lingqiang Xu, Li Li, Guanghui School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9781728176475

Wireless Sensor Network (WSN) has already been deployed widely in industrial, agriculture and many other fields. Meaningful information extracted from the data collected by WSNs can assist people in making control and decisions. Anomaly detection of sensor data is critical to ensure the data quality and the reliability of WSNs. In recent years, distance-based anomaly detection methods have attracted the attention of many experts. Though these methods can achieve a good detection effect, the exploitation of the spatio-temporal correlation of sensor data is not sufficient. Therefore, we propose an anomaly detection method (STCIE) using the spatio-temporal correlation and information entropy. By exploiting the spatio-temporal relationship of sensor data, the maximum correlation and weighted variation coefficient are introduced. The maximum correlation and weighted variation coefficient are used to analyze the state of the sensor nodes, and the confidence interval method is adopted to realize the adaptive update of the threshold. To improve the detection accuracy, the abnormal sensor node is confirmed by the linear least square estimation method using information entropy (IE-LLSE). The information entropy is adopted to analyze the data fluctuation to achieve the best prediction accuracy. Experimental results showed that the false positive rate and false negative rate of the proposed method were significantly reduced, and the accuracy was stable at more than 97%. © 2020 IEEE.

关键词： Anomaly detection

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Volunteer work: Mapping the Future of Moderation Research 19

Volunteer Work: Mapping the Future of Moderation Research

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22nd acm conference on computer-supported cooperative work and social computing (CSCW)

作者： Kiene, Charles Shores, Kenny Chandrasekharan, Eshwar Jhaver, Shagun Jiang, Jialun Aaron Dym, Brianna Seering, Joseph Gilbert, Sarah Lo, Kat Wohn, Donghee Yvette Dosono, Bryan Univ Washington Seattle WA 98195 USA Riot Games Inc West Los Angeles CA USA Georgia Inst Technol Atlanta GA 30332 USA Univ Colorado Boulder CO 80309 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Maryland College Pk MD 20742 USA Univ Calif Irvine Irvine CA USA New Jersey Inst Technol Newark NJ 07102 USA Airbnb Inc San Francisco CA USA

ISBN: (纸本)9781450366922

Research on the governance of online communities often requires exchanges and interactions between researchers and moderators. While a growing body of work has studied commercial content moderation in the context of platform governance and policy enforcement, only a small number of studies have begun to explore the work of unpaid, volunteer community moderators who manage the millions of different subcommunities that exist on platforms. This workshop will create a pathway for future scholars to tackle the challenges and opportunities of research on volunteer community moderators and establish best practices for engaging with volunteer moderators without disrupting their work. Through lightning talks, collaborative brainstorming exercises, and small-group activities applying principles to research practice, workshop participants will bring together their diverse experiences and perspectives to map the future of moderation research. Both industry and academic researchers as well as experienced moderators will lead this one-day workshop that may accommodate up to 20 participants.

关键词： Content moderation online communities governance volunteer work platforms social media

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