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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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Body Politics: Unpacking Tensions and Future Perspectives for Body-Centric Design Research in HCI

Body Politics: Unpacking Tensions and Future Perspectives fo...

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

作者： Tsaknaki, Vasiliki Alaoui, Sarah Fdili Homewood, Sarah Fritsch, Jonas Brynskov, Anna Núñez-Pacheco, Claudia Carlson, Kristin Spiel, Katta Gillies, Marco Fyfe Pietro Harrington, Christina IT University of Copenhagen Copenhagen Denmark Creative Computing Institute University of the Arts London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Computer Science and Media Technology Malmö University Malmö Sweden Creative Technologies Program Illinois State University NormalIL United States Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Computing Goldsmiths University of London London United Kingdom HCI Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713958

human bodies are deeply political as they carry historical and social meanings, including race, gender, sexuality, ethnicity, class, and abilities. The expanding body-centric research in HCI can be traced in the plurality of methods, theories and domains that take bodies as a central point of departure, when designing or studying interaction with technologies. This one-day workshop will bring together researchers and practitioners within the CHI community to discuss, map, and unpack emerging tensions and challenges on the topic of body politics for HCI. Interested participants are invited to submit examples from their own research, which, in the workshop, will be used as a point of departure to critically reflect on and expand body-centric methods, theories and domains through the lens of body politics. Workshop outcomes will include charting future directions for body-centric research to address challenges and opportunities of acknowledging that bodies are always political in design research. © 2025 Copyright held by the owner/author(s).

关键词： Bodies design research politics

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Digital Shadows for Robotic Assembly in the World Wide Lab 56

Digital Shadows for Robotic Assembly in the World Wide Lab

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Behery, Mohamed Brauner, Philipp Kluge-Wilkes, Aline Baier, Ralph Mertens, Alexander Schmitt, Robert H. Ziefle, Martina Lakemeyer, Gerhard Knowledge-Based Systems Group Ahornstraße 55 Aachen52074 Germany Human-Computer Interaction Centre Campus-Boulevard 57 Aachen52074 Germany Laboratory of Machine Tools and Production Engineering Campus-Boulevard 30 Aachen52074 Germany Institute of Industrial Engineering and Ergonomics Eilfschornsteinstraße 18 Aachen52062 Germany

Due to the lacking standardisation of Digital Shadow (DS) of changeable assembly processes, these processes have not yet been fully integrated into the World Wide Lab (WWL). We propose generalising, standardising, and using Behaviour Trees (BTs) as standard DS for assembly in the WWL. A BT models processes in a modular, human-readable tree structure that has shown great success in many applications. BT monitor task execution giving real-time feedback to their operators. We show that BTs cross several developmental milestones of DSs and give future recommendations for further development and integration into the WWL. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

关键词： Behaviour Trees Digital Shadows Industrial Internet of Things Industry 4.0 Robotic Assembly

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Towards Unified Robustness Against Both Backdoor and Adversarial Attacks

arXiv

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arXiv 2024年

作者： Niu, Zhenxing Sun, Yuyao Miao, Qiguang Jin, Rong Hua, Gang The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xi-dian University China DAMO Academy Alibaba Group Beijing China Wormpex AI Research BellevueWA98004 United States

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples;(2) for an infected model, its adversarial examples have similar features as the triggered images. Based on these observations, a novel Progressive Unified Defense (PUD) algorithm is proposed to defend against backdoor and adversarial attacks simultaneously. Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness. At the early stage, the adversarial examples of infected models are utilized to erase backdoors. With the backdoor gradually erased, our model purification can naturally turn into a stage to boost the model's robustness against adversarial attacks. Besides, our PUD algorithm can effectively identify poisoned images, which allows the initial extra dataset not to be completely clean. Extensive experimental results show that, our discovered connection between backdoor and adversarial attacks is ubiquitous, no matter what type of backdoor attack. The proposed PUD outperforms the state-of-the-art backdoor defense, including the model repairing-based and data filtering-based methods. Besides, it also has the ability to compete with the most advanced adversarial defense methods. The code is available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Purification

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Biogas Fermentation Monitoring Research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

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Information and Communication Technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

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An Automatic Tooth Position and Dental Disease Detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms 10

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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10th International Conference on computer and Communications, ICCC 2024

作者： Ding, Lihui Wang, Hongliang Fu, Lijun Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Beijing China

A detailed analysis of seizure semiology, the symptoms and signs that occur during a seizure, is crucial for the management of epilepsy patients. The inter-rater reliability of qualitative visual analysis is often low, highlighting the need for automatic and quantitative analysis of video-recorded seizures for objective assessment. To enhance evaluation accuracy and consistency, computer-aided video analysis of seizures has emerged as a natural direction of development. In the field of medical applications, deep learning and computer vision methods have driven significant advancements. However, analyzing video data presents challenges due to high dimensionality, variations in lighting, posture, and video quality. Additionally, the duration of seizures can range from seconds to minutes. To effectively extract features from seizures, we propose a novel deep learning model, STSE-xLSTM, designed to learn deep representations of arbitrarily long seizure videos. This model integrates a Spatio-Temporal Convolutional Network (STCNN) for feature extraction, a Squeeze-and-Excitation (SE) attention mechanism to enhance relevant features, and an extended Long Short-Term Memory network (xLSTM) to process long video sequences. The SE mechanism adaptively recalibrates channel weights, improving recognition of minority classes and reducing information redundancy. The xLSTM introduces scalar and matrix memory units, optimizing memory capacity and multi-scale feature extraction, thereby enhancing detection accuracy. When evaluated on the GESTURES dataset, our model outperformed state-of-the-art epilepsy detection methods in classification performance. Key contributions include the integration of STCNN, SE attention mechanism, and xLSTM to address data imbalance issues and improve the handling of complex sequential data in epilepsy detection. © 2024 IEEE.

关键词： Video analysis

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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International Conference on computer and Communications (ICCC)

作者： Lihui Ding Hongliang Wang Lijun Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

关键词： Deep learning Adaptation models Visualization Attention mechanisms Accuracy Video sequences Epilepsy Feature extraction Reliability Long short term memory

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Digital Shadows for Robotic Assembly in the World Wide Lab

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Procedia CIRP 2023年 120卷 165-170页

作者： Mohamed Behery Philipp Brauner Aline Kluge-Wilkes Ralph Baier Alexander Mertens Robert H. Schmitt Martina Ziefle Gerhard Lakemeyer Knowledge-Based Systems Group Ahornstraße 55 52074 Aachen Germany Human-Computer Interaction Centre Campus-Boulevard 57 52074 Aachen Germany Laboratory of Machine Tools and Production Engineering Campus-Boulevard 30 52074 Aachen Germany Institute of Industrial Engineering and Ergonomics Eilfschornsteinstraße 18 52062 Aachen Germany

关键词： Digital Shadows Behaviour Trees Robotic Assembly Industry 4.0 Industrial Internet of Things

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SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

arXiv

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arXiv 2023年

作者： Huang, Zeyuan He, Qiang Maher, Kevin Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Qin, Sheng-Feng Liu, Yong-Jin Wang, Hongan The Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences and also with the School of Computer Science and Technology University of Chinese Academy of Sciences China Diatom Design Limited Liability Company United States The School of Computer Science and Informatics Cardiff University United Kingdom The School of Design Northumbria University United Kingdom The Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement. Copyright © 2023, The Authors. All rights reserved.

关键词： Modal analysis

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