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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Garrett Souza Nina Lutz Katlyn M Turner Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Human Centered Design and Engineering University of Washington Seattle Washington USA Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

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What Is So Deep About Deepfakes? A Multi-Disciplinary Thematic Analysis of Academic Narratives About Deepfake Technology

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2025年第1期6卷 64-79页

作者： Twomey, John Ching, Didier Aylett, Matthew Peter Quayle, Michael Linehan, Conor Murphy, Gillian University College Cork School of Applied Psychology CorkT12 CY82 Ireland Lero—The Science Foundation Ireland Research Centre for Software LimerickV94 NYD3 Ireland Heriot Watt University Mathematics and Computer Science Department EdinburghEH14 4AS United Kingdom CereProc Ltd. EdinburghEH3 9DR United Kingdom University of Limerick Centre for Social Issues Research Department of Psychology LimerickV94 T9PX Ireland University of KwaZulu-Natal School of Applied Human Sciences Department of Psychology Pietermaritzburg3201 South Africa

Deepfakes are a form of synthetic media that uses deep-learning technology to create fake images, video, and audio. The emergence of this technology has inspired much commentary and speculation from academics across a range of disciplines, who have contributed expert opinions regarding the implications of deepfake proliferation on fields such as law, politics, and entertainment. A systematic scoping review was carried out to identify, assemble, and critically analyze those academic narratives. The aim is to build on and critique previous attempts at defining the technology and categorizing the harms and benefits of deepfake technology. A range of databases were searched for relevant articles from 2017 to 2023, resulting in a large multi-disciplinary dataset of 102 papers, 181,659 words long, which were analyzed qualitatively through thematic analysis. Implications for future research include questioning the lack of research evidence for the supposed positives of deepfakes, recognizing the role that identity plays in deepfake technology, challenging the perceived accessibility/ believability of deepfakes, and proposing a more nuanced approach to the dichotomous "positive and negatives" of deepfakes. Furthermore, we show how definitional issues around what a deepfake is versus other forms of fake media feeds confusion around the novelty and impacts of deepfakes. © 2024 The Authors.

关键词： Deepfakes Reviews Generative AI Software Psychology Social networking (online) Organizations Lenses Law Internet

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Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff 25

Coach, Data Analyst, and Protector: Exploring Data Practices...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Mollie Brewer Kevin Childs Spencer Thomas Celeste Wilkins Kristy Elizabeth Boyer Jennifer A. Nichols Kevin R.B. Butler Garrett F. Beatty Daniel P. Ferris Department Of Computer & Information Science & Engineering University of Florida Gainesville FL USA University Athletic Association Gainesville FL USA J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida Gainesville FL USA Department of Computer & Information Science & Engineering University of Florida Gainesville FL USA College of Health & Human Performance University of Florida Gainesville FL USA

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Applied intelligence with deep learning assisted automated sarcasm recognition in twitter data

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Alexandria Engineering Journal 2025年 128卷 79-91页

作者： Alzaidi, Muhammad Swaileh A. Alsudais, Taghreed Ali Eltahir, Majdy M. Ebad, Shouki A. Alshuhail, Asma Alshahrani, Mohammed Alanazi, Sultan Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Teaching and Learning College of Education and Human Development Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

Sarcasm is essential in human interaction, particularly on social networking, where individuals share their feelings through criticism, humour, and satire. Sarcasm detection is vital in understanding the context of communication and the sentiment on platforms such as Twitter. This presents sarcasm detection as a complex problem in natural language processing (NLP). The challenges and importance upsurge, particularly in languages such as Arabic and Urdu, where resources for NLP are inadequate. The classical rule-based techniques need to improve performance owing to sarcasm's context-based and subtle nature. On the other hand, the current developments in deep learning (DL) and NLP offer efficient solutions. Therefore, this study presents an Applied Intelligence with Deep Learning Assisted Automated Sarcasm Recognition Approach (AIDL-ASRA) on Twitter Data. The presented AIDL-ASRA technique concentrates on identifying sarcastic and non-sarcastic contexts in tweets. In the AIDL-ASRA technique, the initial pre-processing stage is executed with different subprocesses. Next, Global Vectors for Word Representation (GloVe)-based word embedding process occurs. The AIDL-ASRA technique utilizes the Discriminative Deep Belief Networks (DDBN) method for sarcasm recognition. The DBN model's effectiveness is improved using the cat swarm optimization (CSO) model. A set of experiments were involved in evaluating the performance of the AIDL-ASRA method under the Arabic sarcastic dataset comprising 10,000 tweets. The experimental validation of the AIDL-ASRA method attained a superior accuracy value of 91.25 % over existing techniques under distinct measures. © 2025 The Authors

关键词： Sentiment analysis

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Dynamic Localisation of Spatial-Temporal Graph Neural Network 25

Dynamic Localisation of Spatial-Temporal Graph Neural Networ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Wenying Duan Shujun Guo Zimu Zhou Wei Huang Hong Rao Xiaoxi He Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau Macao Macao Macao

ISBN: (纸本)9798400712456

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings.

关键词： graph sparsification

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Neoteric Threat Intelligence Ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems

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Procedia computer science 2025年 254卷 39-47页

作者： Sudhakar Kumar Sunil K. Singh Rakesh Kumar Chandra Kumari Subba Kwok Tai Chui Brij B. Gupta Department of CSE Chandigarh College of Engineering and Technology Chandigarh India GL Bajaj Institute of Tech. & Mgmt Greater Noida UP India Sikkim University India Hong Kong Metropolitan University (HKMU) Hong Kong Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan University of Economics and Human Science Warsaw Poland

In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems. As Sixth Generation and Beyond (6G and B) wireless networks undergo rapid evolution, our framework is designed to proactively anticipate and counter security incidents by utilizing advanced machine learning algorithms. This approach effectively addresses the shortcomings of conventional models, ensuring that digital assets and communications remain secure, trustworthy, and under rightful control. The study delves into the theoretical integration of this paradigm within the NextG network architecture, reinforcing digital sovereignty through a dynamic and adaptable defense mechanism. In-depth technical examinations include advanced machine learning algorithms, adaptive defenses, and scalability considerations. By critically analyzing and comparing existing security approaches, this study significantly advances technical knowledge and practical applications for wireless network security, supporting defenses against the evolving and complex threats characteristic of the 6G and Beyond era.

关键词： Neoteric Threat Intelligence Proactive Defense Analytics 6GandB Wireless Networks Machine Learning Algorithms NextG Network Architecture Digital Sovereignty and Trust

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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Automated Facial Expression Recognition and Age Estimation Using Deep Learning

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computers, Materials & Continua 2022年第6期71卷 5235-5252页

作者： Syeda Amna Rizwan Yazeed Yasin Ghadi Ahmad Jalal Kibum Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi122612UAE Department of Human-Computer Interaction Hanyang UniversityAnsan15588Korea

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor *** proposed system first takes an input image pre-process it and then detects faces in the entire *** that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age *** proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups *** system achieved remarkable results over these benchmark datasets about recognition accuracy and computational *** proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

关键词： Feature extraction face expression model local transform features and recurrent neural network(RNN)

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Educational playgrounds: How context-aware systems enable playful coached learning

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interactions 2017年第1期24卷 54-57页

作者： Korn, Oliver Dix, Alan Department of Human-computer Interaction Offenburg University Germany Human-Computer Interaction Centre University of Birmingham United Kingdom Talis United Kingdom

PCL is a good example of a combinatory innovation: Most components are already in place, but they have not yet been combined and tailored to fit the field of education. We think a system that directly assists users in practical learning tasks will help increase the overall quality of education. Additionally, it will reduce the stress for trainers and educators who must teach large groups with limited time resources. A motivating learning experience that incorporates the emotional cues of the student will help to raise motivation for selflearning and contribute to practice and skill acquisition. On the path toward PCL, there already is a first large-scale research project on the way: KoBeLU (contextaware learning environment). We are happy to collaborate with researchers from the areas of education, affective computing, pattern recognition, and machine learning. Therefore, if playful coached learning is something that might interest you or your students, do not hesitate to contact us.

关键词： Students

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CounselAR: Exploring How AR Filters Facilitate Online Psychotherapy In the Wild With South Korean Young Adults 25

CounselAR: Exploring How AR Filters Facilitate Online Psycho...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hayoun Noh Salman Muin Kayser Chishti Hyunah Jo Nikki Newhouse Songi Kim Max Van Kleek Younah Kang Department of Computer Science University of Oxford Oxford United Kingdom Yonsei University Seoul Republic of Korea IRIHS Nuffield Department of Primary Care Health Sciences University of Oxford Oxford United Kingdom Graduate School of Logistics Inha University Incheon Republic of Korea Information and Interaction Design Yonsei University Seoul Republic of Korea

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