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

作者： Cheon, EunJeong Zaga, Cristina Lee, Hee Rin Lupetti, Maria Luce Dombrowski, Lynn Jung, Malte F. Syracuse Univ Sch Informat Studies Syracuse NY USA Univ Twente Human Ctr Design Grp DesignLab Enschede Netherlands Michigan State Univ Media & Informat E Lansing MI USA Delft Univ Technol Fac Ind Design Engn Delft Netherlands IUPUI Sch Informat & Comp Indianapolis IN USA Cornell Univ Dept Informat Sci Ithaca NY USA

ISBN: (纸本)9781450384797

Technologies in the workplace have been a major focus of CSCW, including studies that investigate technologies for collaborative work, explore new work environments, and address the importance of political and organizational aspects of technologies in work-places. Emerging technologies, such as AI and robotics, have been deployed in various workplaces, and their proliferation is rapidly expanding. These technologies have not only changed the nature of work but also reinforced power and social dynamics within work-places, requiring us to rethink the legitimate relationship between emerging technologies and human workers. It will be critical to the development of equitable future work arrangements to identify how these emerging technologies will develop relationships with human workers who have limited power and voice in their workplaces. How can these emerging technologies develop mutually beneficial partnerships with human workers? In this one-day workshop, we seek to illustrate the meaning of human-machine partnerships (HMP) by highlighting that how we define HMP may shape the design of future robots at work. By incorporating interdisciplinary perspectives, we aim to develop a taxonomy of HMP by which we can broaden our relationship with embodied agents but also evaluate and reconsider existing theoretical, methodological, and epistemological challenges in HMP research.

关键词： the future of work partnerships embodied AI agent agency automation

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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Flow simulation

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Future of Money and HCI

Future of Money and HCI

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

作者： Blair, Johnna Brozena, Jeff Vines, John Kaye, Jofish Matthews, Mark Abdullah, Saeed Pennsylvania State University University ParkPA United States College of Information Sciences and Technology Pennsylvania State University University ParkPA United States University of Edinburgh Edinburgh United Kingdom Wells Fargo Menlo ParkCA United States School of Computer Science University College Dublin Dublin 4 Co. Dublin Ireland

ISBN: (纸本)9798400713958

Money and financial activities reflect social connections and societal norms. Collaborative financial activities and decision-making are highly common in our day-to-day activities. However, existing financial technologies (fintech) are often limited to individual-centric approaches and goals. Recent HCI work has repeatedly noted the need for creating new interaction strategies and design paradigms to better support our financial behaviors, habits, and goals. However, there has not been much concrete work yet, specifically when it comes to supporting collaborative behaviors and social norms that underpin much of our daily financial activities. In this in-person workshop, we will bring together an interdisciplinary group of researchers interested in reshaping the current landscape of digital money and fintech with a focus on social and collaborative interactions. Specifically, we will identify limitations of existing fintech approaches and potential strategies to address these limitations. We will also discuss key challenges for fintech design and development, including collaboration, privacy, agency, trust, and accessibility. The workshop will lead to identifying novel HCI research and implementation directions focusing on the future of financial technologies. © 2025 Copyright held by the owner/author(s).

关键词： Decision making

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An Autoencoder-Driven FDIA Detection Method for Virtual Coupling Systems of Heavy-Haul Trains 21

An Autoencoder-Driven FDIA Detection Method for Virtual Coup...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International conference on Big Data and Cloud computing, 16th IEEE International conference on social computing and Networking and 13th International conference on Sustainable computing and Communications, ISPA/BDCloud/socialCom/SustainCom 2023

作者： Ren, Wanwan Peng, Jun Zhang, Rui Rong, Jieqi Shu, Boyu Liu, Yongting Yang, Yingze Central South University School of Computer Science and Engineering Changsha China Central South University School of Electronic Information Changsha China Changsha University School of Electronic Information and Electrical Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

The virtual coupling system provides a solution for improving the transport capacity of heavy-haul trains, and its implementation relies on reliable train-to-train communication. However, communications are vulnerable to cyberattacks. Among them, false data injection attacks (FDIAs) are a common and highly concealed form of attack. They interfere with train information interaction by destroying the authenticity of data, disrupting the cooperative control objectives of the virtual coupling system, and thus affecting the stable operation of the train. In order to ensure the safe, reliable, and efficient operation of heavy-haul trains, an autoencoder-driven FDIA detection method for virtual coupling systems of heavy-haul trains is proposed. First, the virtual coupling system of heavy-haul trains is modeled. Next, the collaborative control law of the virtual coupling system is designed, and the potential impact of false data injection on the system is analyzed. Afterward, a stacked autoencoder model is developed. Using the unsupervised idea, the model is trained to learn to reconstruct normal operating data, and the reconstruction error of the trained model is used as an attack detection indicator. Finally, the effectiveness of the proposed scheme is evaluated against several attack scenarios. © 2023 IEEE.

关键词： Learning systems

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Demonstration of VRSight: AI-Driven Real-Time Descriptions to Enhance VR Accessibility for Blind People

Demonstration of VRSight: AI-Driven Real-Time Descriptions t...

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

作者： Killough, Daniel Feng, Justin Dyava, Rithvik Ching, Zheng Xue Wang, Daniel Tian, Yapeng Zhao, Yuhang Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Department of Computer Science The University of Texas at Dallas RichardsonTX United States

ISBN: (纸本)9798400713958

Virtual Reality (VR) technologies are becoming increasingly mainstream. However, VR headsets largely rely on visuals to convey information, making them inaccessible to blind and low vision people (BLV). Therefore, we present VRSight, an end-to-end system using state-of-the-art machine learning models to empower BLV to more effectively interact with VR environments. VRSight applies to any VR application post-hoc, not needing developer implementation. VRSight uses our fine-tuned object detection model trained on VR scenes to recognize virtual elements in each frame, like interactable objects, signs, and seating areas. VRSight automatically sonifies important objects like safety information while including a keyboard-based menu system for users to query other objects. All objects are sonified corresponding to their spatial locations in VR with sound effects and text-to-speech in an application-relevant tone. VRSight increases VR accessibility for BLV, serving as a foundation for more inclusive VR design and encourages broader participation in virtual social spaces. © 2025 Copyright held by the owner/author(s).

关键词： Virtual reality

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Horizontal Federated Traffic Speed Prediction Base on Secure Node Attribute Aggregation 17th

Horizontal Federated Traffic Speed Prediction Base on Secur...

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17th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2022

作者： Ye, Enjie Guo, Kun Guo, Wenzhong Chen, Dangrun Zhang, Zihan Li, Fuan Zheng, JiaChen Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923847

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves

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Information Processing in Agriculture 2025年第2期12卷 221-231页

作者： Polimena, Stefano Pio, Gianvito Cefola, Maria Palumbo, Michela Ceci, Michelangelo Attolico, Giovanni Department of Computer Science University of Bari Aldo Moro Via E. Orabona 4 Bari70125 Italy Via Ariosto 25 Rome00185 Italy Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy via Amendola 122/D Bari70126 Italy Department of Knowledge Technologies Jožef Stefan Institute Jamova cesta 39 1000 Ljubljana Slovenia Institute of Sciences of Food Production National Research Council of Italy c/o CS-DAT Via Michele Protano Foggia71121 Italy Department of Science of Agriculture Food and Environment University of Foggia Via Napoli 25 Foggia71122 Italy

The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications. © 2024 The Author(s)

关键词： Ammonia

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Tip of the Finger or Tip of the Tongue? The Effects of Verbal Communication on Online Multi-Player Team Performance 21

Tip of the Finger or Tip of the Tongue? The Effects of Verba...

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24th acm conference on computer-supported cooperative work and social computing, CSCW 2021

作者： Simpson, James Nalepka, Patrick Crone, Cassandra L. Kallen, Rachel W. Dras, Mark Reichle, Erik D. Hosking, Simon G. Best, Christopher J. Richards, Deborah Richardson, Michael J. Department of Psychology Macquarie University Sydney Australia Department of Computing Macquarie University Sydney Australia Aerospace Division Defence Science and Technology Group Melbourne Australia

ISBN: (纸本)9781450384797

Virtual environments are increasingly being used to support team training. However, despite their growing ubiquity, less is known on how task difficulty in such environments mitigates verbal communication patterns. We report on a study investigating this research question, finding that although manipulations of task difficulty did appear to impact broad team level communication measures (e.g. how much they talked), it largely had minimal impact on team communication dynamics (e.g. the flow of communication between various roles in the teams). The implications of this study for the design of future team training environments along with the possibility of artificial agents to support team training as a novel use case are then discussed in turn. © 2021 acm.

关键词： E-learning

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AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems 25

AEGIS: Human Attention-based Explainable Guidance for Intell...

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

作者： Zhuang, Zhuoli Lu, Cheng-You Chang, Yu-Cheng Fred Wang, Yu-Kai Do, Thomas Lin, Chin-Teng School of Computer Science Feit University of Technology Sydney SydneyNSW Australia University of Technology Sydney SydneyNSW Australia

ISBN: (纸本)9798400713941

Improving decision-making capabilities in Autonomous Intelligent Vehicles (AIVs) has been a heated topic in recent years. Despite advancements, training machine to capture regions of interest for comprehensive scene understanding, like human perception and reasoning, remains a significant challenge. This study introduces a novel framework, Human Attention-based Explainable Guidance for Intelligent Vehicle Systems (AEGIS1). AEGIS uses a pre-trained human attention model to guide reinforcement learning (RL) models to identify critical regions of interest for decision-making. By collecting 1.2 million frames from 20 participants across six scenarios, AEGIS pre-trains a model to predict human attention patterns. The learned human attention2 guides the RL agent's focus on task-relevant objects, prioritizes critical instances, enhances robustness in unseen environments, and leads to faster learning convergence. This approach enhances interpretability by making machine attention more comparable to human attention and thus enhancing the RL agent's performance in diverse driving scenarios. The code is available in The code and data processing script is available in https://***/ALEX95GOGO/AEGIS. © 2025 Copyright held by the owner/author(s).

关键词： Human computer interaction

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