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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"

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Two computer scientists and a cultural scientist get hit by a driver-less car: a method for situating knowledge in the cross-disciplinary study of F-A-T in machine learning: translation tutorial 20

Two computer scientists and a cultural scientist get hit by ...

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proceedings of the 2020 conference on Fairness, Accountability, and Transparency

作者： Maya Indira Ganesh Francien Dechesne Zeerak Waseem Leuphana University Lueneburg Germany Leiden University Leiden Netherlands University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781450369367

In a workshop organized in December 2017 in Leiden, the Netherlands, a group of lawyers, computer scientists, artists, activists and social and cultural scientists collectively read a computer science paper about 'improving fairness'. This session was perceived by many participants as eye-opening on how different epistemologies shape approaches to the problem, method and solutions, thus enabling further cross-disciplinary discussions during the rest of the workshop. For many participants it was both refreshing and challenging, in equal measure, to understand how another discipline approached the problem of fairness. Now, as a follow-up we propose a translation tutorial that will engage participants at the FAT* conference in a similar exercise. We will invite participants to work in small groups reading excerpts of academic papers from different disciplinary perspectives on the same theme. We argue that most of us do not read outside our disciplines and thus are not familiar with how the same issues might be framed and addressed by our peers. Thus the purpose will be to have participants reflect on the different genealogies of knowledge in research, and how they erect walls, or generate opportunities for more productive inter-disciplinary work. We argue that addressing, through technical measures or otherwise, matters of ethics, bias and discrimination in AI/ML technologies in society is complicated by the different constructions of knowledge about what ethics (or bias or discrimination) means to different groups of practitioners. In the current academic structure, there are scarce resources to test, build on-or even discard-methods to talk across disciplinary lines. This tutorial is thus proposed to see if this particular method might work.

关键词： bias

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Ransomware triage using deep learning: Twitter as a case study

Ransomware triage using deep learning: Twitter as a case stu...

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2019 Cybersecurity and Cyberforensics conference, CCC 2019

作者： Vinayakumar, V. Alazab, Mamoun Jolfaei, Alireza Kp, Soman Poornachandran, Prabaharan Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Charles Darwin University Australia Department of Computing Macquarie University SydneyNSW2109 Australia Center for Cyber Security Systems and Networks Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9781728126005

The increasing number of cyberattacks in recent years has expedited development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of online social networks to discover the rising of ransomware attacks. Twitter is a popular micro-blogging platform which allows millions of users to share their opinions on what happens all over the world. The subscribers can tweet messages of maximum 280 characters to share general information with URLs and hash tags. In this paper, we analysed 25 families of ransomware over a period of 7 years, from 2010 to 2017. We proposed a deep learning architecture to categorize ransomware tweets to their corresponding family. The proposed method can continuously monitor the online posts in social media data and thus is able to provide early warnings about ransomware spreads. This helps the incident management to better prioritize resources and procedures to mitigate the malicious activities. Tests have been performed to evaluate the performance of the proposed method and results show the effectiveness of our implementation. © 2019 IEEE.

关键词： Cybersecurity

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Co-Creating the workplace: Participatory Efforts to Enable Individual work at the Hoffice 16

Co-Creating the Workplace: Participatory Efforts to Enable I...

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16th European conference on computer-supported cooperative work - The International Venue on Practice-Centred computing and the Design of Cooperation Technologies (ECSCW)

作者： Rossitto, Chiara Lampinen, Airi Stockholm Univ Dept Comp & Syst Sci DSV Stockholm Sweden

This paper analyzes the self-organizing network Hoffice - a merger between the words home and office - that brings together people who wish to co-create temporary workplaces. The Hoffice concept entails a co-working methodology, and a set of practices inherent in opening up one's home as a temporary, shared workplace, with the help of existing social media platforms, particularly Facebook. We discuss both the practices of co-creating temporary workplaces, particularly for workers who lack a stable office and orchestrate flexible work arrangements, and the values and rhetoric enshrined in Hoffice. We collected our research materials through interviews, participant observation, and workshops. Our findings draw attention to i) the practical arrangement of Hoffice events, ii) the participatory efforts to get individual work done, and 3) the co-creation of an alternative social model that encourages trust, self-actualization, and openness. To conclude, we discuss how Hoffice is already making change for its members, and how this is indicative of a politics of care. We contribute to research on computer-supported collaborative work (CSCW) by highlighting grassroots efforts to create alternative ways of organizing nomadic work and navigating non-traditional employment arrangements.

关键词： Ethnography workplace Mobile work Flexibility Network hospitality politics of care

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Data work in Healthcare: Challenges for Patients, Clinicians and Administrators 18

Data Work in Healthcare: Challenges for Patients, Clinicians...

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

作者： Pine, Kathleen H. Bossen, Claus Chen, Yunan Ellingsen, Gunnar Grisot, Miria Mazmanian, Melissa Moller, Naja Holten Arizona State Univ Tempe AZ 85287 USA Aarhus Univ Informat Studies Aarhus Denmark Univ Calif Irvine Informat Irvine CA USA Arctic Univ Norway Tromso Norway Kristiania Univ Coll Dept Technol Oslo Norway Univ Calif Irvine Sch Informat & Comp Sci Informat Irvine CA USA Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9781450360180

With the wide adoption of information infrastructures in healthcare (IIH), citizens and healthcare professionals now carry out intensive 'data work' as part of their healthcare and self-management practices. For example, citizens use mobile apps to track personal health data for both formal and informal usages, clinicians rely on electronic health records (EHR) to document patient information, and hospital administrators use EHRs to generate data to monitor quality and efficiency of healthcare services. The creation, accumulation, management, and communication of data is increasingly central to patient work, clinical work, and management and governance of healthcare providers. To better understand data work in healthcare, this workshop aims to convene researchers from different disciplines to discuss data work performed among the entire ecosystem of clinical care delivery from patients to clinicians to administrators. Critical topics include but are not limited to: patient data work (both voluntary and compelled by clinicians as part of treatment);algorithmic authority of information infrastructure and effects on the exercise of expertise and discretion of healthcare professions;clinician demands for documentation (and balancing these demands with patient-centered care);new forms of healthcare data work, including new occupations and tasks;and datadriven accountability and management in healthcare.

关键词： Accountability coordination data work data-driven management electronic health records information infrastructures for healthcare healthcare information infrastructures patients PROM

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Optimizing information sharing in crowd networks based on reinforcement learning 19

Optimizing information sharing in crowd networks based on re...

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4th International conference on Crowd Science and Engineering, ICCSE 2019

作者： Wang, Xiaoni Pan, Zhiwen Li, Zhouxia Du, Yifan Zhu, Yuanlin Ji, Wen Yang, Feng School of Computer and Information Technology Beijing Jiaotong University No. 3 Shangyuancun Haidian District Beijing100044 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuannanlu Haidian District Beijing100080 China Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuannanlu Haidian District Beijing100080 China

ISBN: (纸本)9781450376402

With the progress of human society and the development of machine learning technology in recent years, crowd intelligence has become more and more widespread. Specifically, to optimize the finishing quality of complicated tasks, various homogeneous and heterogeneous intelligent agents (including both human and machines) are interconnected to form more and more complex and diverse intelligent networks named crowd networks, where agents can interact and collaborate with each other. In the process of optimizing and evaluating crowd network reasonably, the complexity and heterogeneity of agent in crowd network is a major concern. It is particularly important to discuss the influence of information exchange and sharing between homogeneous and heterogeneous agents based on the intelligence level of intelligent subject, so as to establish efficient cooperative operation mechanism. In order to discuss the above problems, we describe the patterns of shared information in crowd network, and combine it with Q-learning in reinforcement learning. Combined with the Q learning in reinforcement learning, the information interaction mode in the intelligence network is optimized. Each time which neighbor agent is selected for interaction needs to be determined according to the Q learning model, and the optimal interaction is obtained by traversing the whole network. It is expected that the cumulative performance of the learning process will be evaluated and the intelligence level of the network will be analyzed. Combined with examples, the paper proposes a model named Optimized Information Sharing (OIS) Model based on shared information and Q-learning. © 2019 Association for computing Machinery.

关键词： Reinforcement learning

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MomentSA: A Fast and Accurate Method for Stochastic Kronecker Graph Parameter computing 22

MomentSA: A Fast and Accurate Method for Stochastic Kronecke...

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22nd IEEE International conference on computer supported cooperative work in Design (CSCWD)

作者： Li, Yongqiang Shao, Zhiyuan Huang, Hong Li, Yinuo Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538614822

Stochastic Kronecker Graph model is widely used to generate synthetic graphs that simulate real-world graphs. In this model, the initiator matrix decides the degree to which the synthetic graph approximates the real-world graph. The computing of initiator matrix, however, requires that the number of the nodes of input graph is the power of the dimension of the initiator matrix. In order to fulfill such requirement, some methods (e.g., KronFit and Moment) add isolated nodes to the input graph, which damages the input graph's properties. Other method (e.g., KronEM) predicts the links between the nodes after adding isolated nodes. Unfortunately, the prediction dramatically increases the complexity of computing. In this paper, we propose a method named as MomentSA to solve the problems. Our method completes the input graphs by leveraging the law of property changes of graphs, which does not need to predict the links as method KronEM. Simultaneously, our method uses the moment-based estimation and ADAM (Adaptive Moment Estimation) method to compute the initiator matrix, which can compute the initiator matrix quickly. Experiment results on our prototype implementation suggest that the initiator matrix computed from our method is more accurate than existing state-of-art systems, and the speed of computing is about three to four orders of magnitude faster than state-of-art systems.

关键词： Stochastic Kronecker Graph model Initiator Matrix Moment-based Estimation

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Conversations in the Eye of the Storm: At-Scale Features of Conversational Structure in a High-Tempo, High-Stakes Microblogging Environment

Conversations in the Eye of the Storm: At-Scale Features of ...

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CHI conference on Human Factors in computing Systems (CHI)

作者： Kogan, Marina Palen, Leysia Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Univ Colorado Informat Sci & Comp Sci Boulder CO 80309 USA

ISBN: (纸本)9781450356206

This work propels social media research beyond the single post as the unit of analysis toward fuller treatment of interaction by making the construct of the conversation analytically available. We offer a method for constructing @reply conversations in Twitter to apprehend social media conversational features at scale. We apply this method to the high-tempo, high-stakes environment of 2012's Hurricane Sandy, with its high volume of online talk by affected locals and distinct disaster-stage phasing by which to consider interactional difference. We investigate the temporality of conversations;the relationality of who speaks to whom;the number and kind of conversationalists;and how content affects temporal features. The analysis reveals that, during the height of the emergency, people expand conversations both in number and kind of conversational partners just as their information search intensifies. This expansion contributes to longer, slower-paced conversations in the high emergency period, suggesting reliance on online relationships during times of greatest uncertainty.

关键词： Crisis Informatics computer supported cooperative work social computing social Media

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Energy-efficient Inference Service of Transformer-based Deep Learning Models on GPUs 13

Energy-efficient Inference Service of Transformer-based Deep...

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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： Wang, Yuxin Wang, Qiang Chu, Xiaowen Hong Kong Baptist University Dept. Computer Science Hong Kong Hong Kong

ISBN: (纸本)9781728176475

Inference-as-a-service (IAAS) has been recently launched by cloud service providers to support on-demand AI applications. Many natural language processing (NLP) services are based on the Transformer Sequence Transduction model. However, the inference process of the Transformer model consumes a significant amount of energy due to the large model size (e.g., billions of parameters) and tremendous computations. How to reduce the energy consumption of IAAS without violating the service-level agreement (SLA) becomes a practical challenge for service providers. In this work, we conduct a comprehensive study on the inference performance and energy efficiency of a Transformer model trained for the language translation service. First, we empirically characterize some essential performance metrics, including latency, throughput, and energy consumption on three different GPUs with diversified workload configurations. The detailed workload separation facilitates a thorough and deep understanding of the inference process of the Transformer model. Second, we provide an energy consumption model for the Transformer based on the observed data. Finally, we propose the Aligned scheduling scheme that optimizes throughput and energy efficiency with up to 2.86× and 2.73× improvement at the cost of 40% average latency loss. Our findings provide a full scope of Transformer inference, and suggest that the workload balancing and scheduling have great potentials to offer energy-efficient Transformer inference services. © 2020 IEEE.

关键词： Energy efficiency

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Poster: Lightcert - Designing smaller certificates for the internet of things devices 16

Poster: Lightcert - Designing smaller certificates for the i...

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16th acm International conference on Mobile Systems, Applications, and Services,MobiSys 2018

作者： Kwon, Hyuk Sang Ko, Jeong Gil Department of Computer Engineering Ajou University Korea Republic of

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In Search for the Perfect Pathway: Supporting Knowledge work of Welfare workers 16

In Search for the Perfect Pathway: Supporting Knowledge Work...

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16th European conference on computer-supported cooperative work - The International Venue on Practice-Centred computing and the Design of Cooperation Technologies (ECSCW)

作者： Boulus-Rodje, Nina Roskilde Univ Dept People & Technol Informat & User Driven IT Innovat Univ Vej 108-2 DK-4000 Roskilde Denmark

This paper investigates the collaborative practices and computational artifacts that welfare workers use in a public welfare agency. Specifically, the paper focuses on caseworkers' knowledge practices related to assessing unemployed citizens and identifying 'perfect' pathways. I draw upon an ongoing ethnographic study, carried out in one of the largest municipal jobcentres in Denmark. Findings from this research point out that existing computational artifacts support compliance with welfare policy, while limited support is provided to caseworkers in helping citizens obtain an employment. The contribution of the paper is three-folded: 1) identifying fundamental characteristics of the caseworkers' knowledge work entailed in assessing unemployed citizens and identifying appropriate pathways, 2) examining the conditions surrounding these knowledge practices, and 3) discussing implications for the design of computational artifacts that better support local knowledge practices. While maintaining support to policy compliance, I argue that computational artifacts can also support 'data-driven knowledge', meaning the creation of knowledge that is based on data collected from the wide range of cases of unemployed.

关键词： Ethnography Expertise sharing Knowledge management Knowledge repositories Knowledge sharing Public employment agency Jobcentre social service Welfare workers

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