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You Got Yourself A Whole New Life, and All I've Got is Half This Old One: Breaking Up and Moving On in the social Media Age 21

You Got Yourself A Whole New Life, and All I've Got is Half ...

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24th acm conference on computer-supported cooperative work and social computing (CSCW)

作者： Pinter, Anthony T. Univ Colorado Boulder Boulder CO 80309 USA

ISBN: (纸本)9781450384797

Going through a break-up can be difficult. Break-ups are emotionally fraught;two people have to untangle their lives. While prior research has paid attention to the implications of a break-up on the involved parties' mental and emotional well-being, and identified best practices for healing and growing from a break-up, these findings and practices are not well suited to how people's lives and experiences are represented in social media. People know how to break-up offline;however, they - and by extension, social media algorithms that recommend or remind - lack the ability to accurately represent the experience of breaking up online. Features that encourage connection or reminiscence are ill-suited for handling break-ups, making suggestions at a time when they may not be perceived as appropriate or desired. In my dissertation, I use three interpretivist, interview-based studies to investigate people's expectations of what is or is not appropriate behavior online after a break-up, and who to remain connected with on social media sites. My prior work established that algorithms are not always attuned to the nuances of human experiences like break-ups. Building on that work, my dissertation explores what people want when it comes to their online audiences and sharing behavior after going through a break-up, and leverages that understanding to offer design suggestions to improve both user experience and algorithmic systems in social media. My dissertation will result in empirical and design contributions that improve people's online experiences when going through a break-up.

关键词： relationship dissolution life transitions social media digital identity empirical work

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Beyond the Blackout: Qualitative Insights and Sentiment Analysis on the Impact, Adaptation, and Design Solutions During the Internet Shutdown in Bangladesh

Beyond the Blackout: Qualitative Insights and Sentiment Anal...

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2025 CHI conference on Human Factors in computing Systems, CHI EA 2025

作者： Karim, Farhan Tahmidul Saffat Zabin, Sardar Md. Tarin, Ishika Nurain, Novia Al Islam, A.B.M. Alim Bangladesh University of Engineering and Technology Dhaka Bangladesh School of Information University of Michigan Ann Arbor United States

ISBN: (纸本)9798400713958

The recent period of Internet restriction in Bangladesh, which occurred from July 14 to August 5 during the anti-discrimination student movement, had a significant impact on individuals’ lives. This study aims to know how their communication, information access, and daily activities were affected and how they used to cope with that, again what were their expectations from social media and communication tools. Through interviews with 15 individuals from diverse demographics, supported by qualitative analysis and sentiment analysis, the findings provide valuable insights into the challenges faced, experiences shared, and recommendations offered by participants. Based on these findings, we propose design implications to develop a robust digital infrastructure capable of addressing similar challenges in future scenarios. Our work contributes to CHI by serving as a foundation for policymakers and designers to create effective solutions that mitigate the adverse effects of Internet shutdowns. © 2025 Copyright held by the owner/author(s).

关键词： Human-computer Interaction Qualitative Studies

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Graph Contrastive Learning Reimagined: Exploring Universality 24

Graph Contrastive Learning Reimagined: Exploring Universalit...

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33rd acm Web conference, WWW 2024

作者： Zhuo, Jiaming Cui, Can Fu, Kun Niu, Bingxin He, Dongxiao Wang, Chuan Guo, Yuanfang Wang, Zhen Cao, Xiaochun Yang, Liang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus Sun Yat-Sen University Shenzhen China

ISBN: (纸本)9798400701719

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks. © 2024 acm.

关键词： Graph neural networks

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Fine-Grained Diagnosis Method for Microservice Faults Based on Hierarchical Correlation Analysis 16th

Fine-Grained Diagnosis Method for Microservice Faults Based ...

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16th CCF conference on computer supported cooperative work and social computing (ChineseCSCW)

作者： Tang, Yusi Chen, Ningjiang Zhang, Yu Yao, Xuyi Yu, Siyu Guangxi Univ Sch Comp & Elect Informat Nanning 530004 Peoples R China Guangxi Key Lab Multimedia Communicat & Network T Nanning 530004 Peoples R China

ISBN: (纸本)9789811945465;9789811945458

With the expansion of application scale, the dependence of microservices becomes more and more complex, which makes it difficult to accurately locate the root cause of failure. At present, many microservices fault diagnosis methods have the problems of high cost of fine-grained location and difficult to predict the fault propagation path, which leads to more false positives and low diagnosis efficiency. Therefore, this paper proposes a microservice fault diagnosis method based on correlation analysis. Firstly, the historical exception and fault data of microservices is collected and processed, and the exception event is defined. Secondly, the machine learning algorithm is used to mine the correlation between microservices and abnormal events, and the hierarchical correlation graph is constructed. Finally, a microservice fault diagnosis mechanism based on a hierarchical correlation graph is designed to infer the root cause of the fault and provide effective diagnosis information for administrators. Experimental results show that the mechanism can quickly locate the fault location and infer the root cause of microservice failure in a more fine-grained way.

关键词： Microservice Correlation analysis Fault diagnosis Cloud computing

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GAUSS: GrAph-customized Universal Self-Supervised Learning 24

GAUSS: GrAph-customized Universal Self-Supervised Learning

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33rd acm Web conference, WWW 2024

作者： Yang, Liang Hu, Weixiao Xu, Jizhong Shi, Runjie He, Dongxiao Wang, Chuan Cao, Xiaochun Wang, Zhen Niu, Bingxin Guo, Yuanfang School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400701719

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises. © 2024 acm.

关键词： Graph neural networks

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social Robot Assistant for Group Interactions with Secondary School Students: A Participatory Design Study

Social Robot Assistant for Group Interactions with Secondary...

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Joint of the acm workshops at the International conference on Intelligent User Interfaces 2025, IUI-WS 2025

作者： Adikari, Manith O'brien, Annie Hamilton, Suzanne Hennessey, Alexandra Vinanzi, Samuele Cangelosi, Angelo Department of Computer Science University of Manchester United Kingdom Manchester Centre for Robotics and AI United Kingdom Manchester Institute of Education University of Manchester United Kingdom School of Computing & Digital Technologies Sheffield Hallam University United Kingdom

The application of social robots in group settings is an emerging area of research with the potential to transform numerous fields, particularly education. This paper explores the potential of social robots as assistants in collaborative group interactions among secondary school students through a Participatory Design (PD) study. This was achieved by conducting a focus group (10 participants, ages 11 to 15 years) that included discussions, robot interactions, and co-design activities. The findings reveal the students' challenges in group interactions, and their perceptions of how robots could assist them. The first part of the focus group was a exploration and co-design stage to encourage participants to discuss about and interact with social robots for group collaboration. This stage highlighted some of the challenges students face during group work and how they believe a social robot could assist them. The second part of the focus group involved getting the participants to discuss and co-design robot behaviours for a specific group collaborative task. This revealed the participants emphasis on the robot behaviours being clear, specific and relevant for the task. These insights contribute to the design of effective social robots for group collaborative settings for this user group. © 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： social robots

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14th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2019

14th CCF Conference on Computer Supported Cooperative Work a...

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14th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2019

ISBN: (纸本)9789811513763

The proceedings contain 60 papers. The special focus in this conference is on computer supported cooperative work and social computing. The topics include: A Dynamic Evolutionary Scheduling Algorithm for Cloud Tasks with High Efficiency and High QoS Satisfactions;improved Bat Algorithm for Multiple Knapsack Problems;customer Value Analysis Method Based on Automotive Multi-value-Chain Collaboration;research on Power Distribution Strategy for Bi-directional Energy Cooperation Diamond Channel with Energy Harvesting Nodes;research on Cloudlet Placement in Wireless Metropolitan Area Network;research on Customer Segmentation Method for Multi-value-Chain Collaboration;a Data-Driven Agent-Based Simulator for Air Ticket Sales;a New Information Exposure Situation Awareness Model Based on Cubic Exponential Smoothing and Its Prediction Method;budget Constraint Bag-of-Task Based workflow Scheduling in Public Clouds;function-Structure Collaborative Mapping Induced by Universal Triple I Systems;Solving the Signal Relay Problem of UAV in Disaster Relief via Group Role Assignment;a Feature Selection Based on Network Structure for Credit Card Default Prediction;trust-Based Agent Evaluation in Collaborative Systems;adaptive Graph Planning Protocol: An Adaption Approach to Collaboration in Open Multi-agent Systems;Dynamic Adaptive Bit-Rate Selection Algorithm Based on DASH Technology;KBCBP: A Knowledge-Based Collaborative Business Process Model Supporting Dynamic Procuratorial Activities and Roles;An Analysis Method for Subliminal Affective Priming Effect Based on CEEMDAN and MPE;cultivating Online: Understanding Expert and Farmer Participation in an Agricultural Q&A Community;characterizing Urban Youth Based on Express Delivery Data;a Local Dynamic Community Detection Algorithm Based on Node Contribution;a Recommendation Method of Barrier-Free Facilities Construction Based on Geographic Information.

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Inter-brain Synchrony and Eye Gaze Direction During Collaboration in VR

Inter-brain Synchrony and Eye Gaze Direction During Collabor...

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CHI conference on Human Factors in computing Systems (CHI)

作者： Gumilar, Ihshan Barde, Amit Sasikumar, Prasanth Billinghurst, Mark Hayati, Ashkan F. Lee, Gun Munarko, Yuda Singh, Sanjit Momin, Abdul Univ Auckland Auckland Bioengn Inst Auckland New Zealand Univ South Australia Adelaide SA Australia Univ Muhammadiyah Malang Malang Indonesia Rutgers State Univ Newark NJ 07102 USA Indian Inst Informat Technol Gwalior India

ISBN: (纸本)9781450391566

Brain activity sometimes synchronises when people collaborate together on real world tasks. Understanding this process could to lead to improvements in face to face and remote collaboration. In this paper we report on an experiment exploring the relationship between eye gaze and inter-brain synchrony in Virtual Reality (VR). The experiment recruited pairs who were asked to perform finger-tracking exercises in VR with three different gaze conditions: averted, direct, and natural, while their brain activity was recorded. We found that gaze direction has a significant effect on inter-brain synchrony during collaboration for this task in VR. This shows that representing natural gaze could influence inter-brain synchrony in VR, which may have implications for avatar design for social VR. We discuss implications of our research and possible directions for future work.

关键词： Hyperscanning Eye gaze Remote collaboration Brain synchronization Inter-brain synchrony Virtual Reality (VR) Brain social neuroscience computer supported cooperative work (CSCW)

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KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks 23

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge En...

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2023 World Wide Web conference, WWW 2023

作者： Yu, Zhizhi Jin, Di Huo, Cuiying Wang, Zhiqiang Liu, Xiulong Qi, Heng Wu, Jia Wu, Lingfei College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Dalian University of Technology Liaoning China School of Computing Macquarie University Sydney Australia Content and Knowledge Graph Pinterest New York United States

ISBN: (纸本)9781450394161

social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods. © 2023 acm.

关键词： Internet of things

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Third Wave or Winter? The Past and Future of Smell in HCI

Third Wave or Winter? The Past and Future of Smell in HCI

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Extended Abstracts of the 2023 CHI conference on Human Factors in computing Systems, CHI EA 2023

作者： Brooks, Jas Lopes, Pedro Obrist, Marianna Amores Fernandez, Judith Kaye, Jofish University of Chicago United States University College London United Kingdom Microsoft Research Elevance Health

ISBN: (纸本)9781450394222

Over the last hundred years, the integration of scent in technology could roughly be seen as having two public waves, analogous to virtual reality's three or four. These waves include the cinematic technologies of the 1960s and the rise of olfactory desktop peripherals and home fragrance technologies for the internet in the 1990s and early 2000s. In the last few decades, an impressive, multi-disciplinary effort in Human-computer Interaction has been made to incorporate smell into interactive systems. This panel asks whether exciting recent developments in smell for Human-computer Interaction mark the cusp of a third wave and whether the field is here to stay or should prepare for another winter. © 2023 Owner/Author.

关键词： Human computer interaction

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