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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Yatsenko, Olena Sushchenko, Oleksandra Dégallier Rochat, Sarah Institute for Human-Centered Engineering School of Engineering and Computer Science Bern University of Applied Sciences Biel Switzerland Department of Art and Media School of ARTS Aalto University Espoo Finland Human Digital Transformation Strategic Thematic Field Bern University of Applied Sciences Bern Switzerland

ISBN: (纸本)9781643685670

The authors of the article examine the reasons for the pessimistic perspective, that envisions the possibility of catastrophe for humanity caused by the "singularity". We argue that the interaction between humans and machines is neither a matter of competition nor collaboration. Instead, a more realistic outcome of human-machine interactions is the erosion of the genuine foundations of human identity nature. This perspective is based on an analysis of the essential elements that constitute human identity, as well as the most significant ways in which robotics and artificial intelligence (AI) affect individual, social, and cultural life. Through this analysis, we aim to identify the threats and opportunities, as well as propose viable solutions for the preservation and sustainable development of humanity.. © 2025 IOS Press. All rights reserved.

关键词： Microrobots

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To Reply or to Quote: Comparing Conversational Framing Strategies on Twitter

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ACM Journal on Computing and Sustainable Societies 2024年第1期2卷 1-27页

作者： Himanshu Zade Spencer Williams Theresa T. Tran Christina Smith Sukrit Venkatagiri Gary Hsieh Kate Starbird Human Centered Design & Engineering University of Washington Department of Computer Science Swarthmore College Human Centered Design & Engineering and Center for an Informed Public University of Washington

Social media platform affordances allow users to interact with content and with each other in diverse ways. For example, on Twitter,1 users can like, reply, retweet, or quote another tweet. Though it’s clear that these different features allow various types of interactions, open questions remain about how these different affordances shape the conversations. We examine how two similar, but distinct conversational features on Twitter — specifically reply vs. quote — are used differently. Focusing on the polarized discourse around Robert Mueller’s congressional testimony in July 2019, we look at how these features are employed in conversations between politically aligned and opposed accounts. We use a mixed methods approach, employing grounded qualitative analysis to identify the different conversational and framing strategies salient in that discourse and then quantitatively analyzing how those techniques differed across the different features and political alignments. Our research (1) demonstrates that the quote feature is more often used to broadcast and reply is more often used to reframe the conversation; (2) identifies the different framing strategies that emerge through the use of these features when engaging with politically aligned vs. opposed accounts; (3) discusses how reply and quote features may be re-designed to reduce the adversarial tone of polarized conversations on Twitter-like platforms.

关键词： Twitter social network analysis microblogging platforms social media platforms quote tweets reply tweets conversational framing

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Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation

arXiv

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arXiv 2025年

作者： Li, Xiangju Yang, Dong Zhu, Xiaogang Huang, Faliang Zhang, Peng Zhao, Zhongying College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266500 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China

Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method’s effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://***/zxgnlp/InstruDa-LLM. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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Working in Extended Reality in the Wild: Worker and Bystander Experiences of XR Virtual Displays in Real-World Settings

arXiv

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arXiv 2024年

作者： Pavanatto, Leonardo Biener, Verena Chandran, Jennifer Kalamkar, Snehanjali Lu, Feiyu Dudley, John J. Hu, Jinghui Nikki Ramirez-Saffy, G. Kristensson, Per Ola Giovannelli, Alexander Schlueter, Luke Müller, Jörg Grubert, Jens Bowman, Doug A. The Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States The Visualization Research Center University of Stuttgart Germany Coburg University of Applied Sciences and Arts Germany The University of Bayreuth Germany The Department of Engineering University of Cambridge United Kingdom

Although access to sufficient screen space is crucial to knowledge work, workers often find themselves with limited access to display infrastructure in remote or public settings. While virtual displays can be used to extend the available screen space through extended reality (XR) head-worn displays (HWD), we must better understand the implications of working with them in public settings from both users’ and bystanders’ viewpoints. To this end, we conducted two user studies. We first explored the usage of a hybrid AR display across real-world settings and tasks. We focused on how users take advantage of virtual displays and what social and environmental factors impact their usage of the system. A second study investigated the differences between working with a laptop, an AR system, or a VR system in public. We focused on a single location and participants performed a predefined task to enable direct comparisons between the conditions while also gathering data from bystanders. The combined results suggest a positive acceptance of XR technology in public settings and show that virtual displays can be used to accompany existing devices. We highlighted some environmental and social factors. We saw that previous XR experience and personality can influence how people perceive the use of XR in public. In addition, we confirmed that using XR in public still makes users stand out and that bystanders are curious about the devices, yet have no clear understanding of how they can be used. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting 29

DP-Rewrite: Towards Reproducibility and Transparency in Diff...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Transparency

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Prediction of Budd-Chiari syndrome based on attention mechanisms of high-risk factors in multi-hop graph learning

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science China(Information sciences) 2025年

作者： Lei WANG Shengli LI Xiaorui SU Zhengwei LI Mengmeng WEI Bowei ZHAO Maoheng ZU Qingqiao ZHANG Zhuhong YOU School of Computer Science and Technology China University of Mining and Technology Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Guangxi Academy of Sciences Clinical Research Institute The Affiliated Hospital of Xuzhou Medical University University of Chinese Academy of Sciences Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Department of Interventional Radiology The Affiliated Hospital of Xuzhou Medical University

Budd-Chiari syndrome (BCS) is a series of clinical syndromes caused by obstruction of the hepatic vein and/or hepatic segment inferior vena cava,which can seriously damage the health of the body and lead to patient death[1].Over recent years,with the improvement of diagnostic technology,especially the introduction of multidisciplinary intervention methods,more and more potential cases have been discovered and treated[2].However,conventional clinical diagnosis is usually based on traditional indicators[3],and diagnostic criteria are largely dependent on the results of statistical analyses,often ignoring the importance of the association between different factors[4],especially high-risk factors,and the disease[5].Effectively capturing the critical factors that contribute to disease occurrence is the challenge for improving the predictive performance of BCS.

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Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

arXiv

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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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Finding Our Joy: Queer Perspectives on HCI Research

Finding Our Joy: Queer Perspectives on HCI Research

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： O’Reilly-Kime, Molly Feng, Yifan Cipelli, Francesca Osipova, Ekat Kender, Kay Steeds, Madeleine Clinch, Sarah Rubegni, Elisa Rode, Jennifer School of Computing & Communications Lancaster University Lancaster United Kingdom Knowledge Lab. University College London London United Kingdom Creative Computing Institute University of the Arts London London United Kingdom Catholic University of the Sacred Heart Milan Italy Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Smurfit Business School University College Dublin Dublin Ireland Department of Computer Science The University of Manchester Manchester United Kingdom School of Computing and Communication Lancaster University Lancaster United Kingdom

ISBN: (纸本)9798400713958

How do we make space for joy in Queer HCI? This work draws on our authors’ experiences as Queer researchers and allies, navigating a field that is ostensibly receptive to our work, yet provides a narrow framework for acceptable research. We relate personal accounts of contending with identity, practice, and publication;we discuss what joy means to us, and the gap we see in Queer HCI where joy should exist. We believe that Queer HCI and research can itself be subversive, and assert that it needs to be in order for our research community to flourish. Our work emerges from a need for Queer joy in our research practice, and we approach Queerness as a multifaceted experience that by nature resists the categorisation that we often see in HCI. Finally, we present reflective guidelines for future work, that will allow us achieve inclusive, intentional, and joyful research. © 2025 Copyright held by the owner/author(s).

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Communication Sequences Indicate Team Cohesion: A Mixed-Methods Study of Ad Hoc League of Legends Teams

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Proceedings of the ACM on human-computer interaction 2022年第CHIPLAY期6卷 1–27页

作者： Tan, Evelyn T S Rogers, Katja Nacke, Lennart E. Drachen, Anders Wade, Alex Department of Computer Science University of York United Kingdom Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo Canada Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo WaterlooON Canada Maersk McKinney Moeller Institute University of Southern Denmark Denmark Department of Psychology University of York United Kingdom

Team cohesion is a widely known predictor of performance and collaborative satisfaction. However, how it develops and can be assessed, especially in fast-paced ad hoc dynamic teams, remains unclear. An unobtrusive and objective behavioural measure of cohesion would help identify determinants of cohesion in these teams. We investigated team communication as a potential measure in a mixed-methods study with 48 teams (n=135) in the digital game League of Legends. We first established that cohesion shows similar performance and satisfaction in League of Legends. teams as in non-game teams and confirmed a positive relationship between communication word frequency and cohesion. Further, we conducted an in-depth exploratory qualitative analysis of the communication sequences in a high-cohesion and a low-cohesion team. High cohesion is associated with sequences of apology-→encouragement, suggestion-→agree/acknowledge, answer-→answer, and answer-→question, while low-cohesion is associated with sequences of opinion/analysis-→opinion/analysis, disagree-→disagree, command-→disagree, and frustration-→frustration. Our findings also show that cohesion is important to team satisfaction independently of the match outcomes. We highlight that communication sequences are more useful than frequencies to determine team cohesion via player interactions. © 2022 ACM.

关键词： computer programming

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Enhancing DDoS Attack Detection in SDN with a Stacked Model Framework Utilizing Deep Neural Networks

Enhancing DDoS Attack Detection in SDN with a Stacked Model ...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Sharma, Aishita Singh, Sunil K. Kumar, Sudhakar Pan, Shin Hung Gupta, Brij B. Chui, Kwok Tai Department of CSE Chandigarh College of Engineering and Technology Chandigarh India Department of Information Management Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Sciences Warsaw Poland Hong Kong Metropolitan University Hong Kong

ISBN: (纸本)9798331521165

Software Defined Networking enhances network management and security through centralized control, vital for the growth of consumer electronics. With the rise of smart devices and IoT systems, the demand for scalable network management has intensified. While SDN streamlines network operations and supports essential technologies, it remains vulnerable to DDoS attacks that can disrupt these services. However, SDN environments are vulnerable to Distributed Denial of Service (DDoS) attacks, posing significant risks to operational integrity. This paper analyzes the performance of various machine learning models at the individual level, providing insights into their effectiveness in detecting DDoS attacks. Based on these findings, a hybrid model is introduced, utilizing stacking techniques with a Deep Neural Network (DNN) as the meta-learner. The hybrid approach achieves a remarkable accuracy of 99.09%. The results underscore the crucial role of machine learning, particularly deep learning methodologies, in safeguarding SDN networks against cyber threats. As reliance on complex networked systems in consumer electronics increases, enhancing the security of SDN environments becomes essential for maintaining seamless operations. © 2025 IEEE.

关键词： Machine learning

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