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18th International Conference on Tangible, Embedded, and Embodied interaction, TEI 2024

作者： Vega-Cebrian, Jose Manuel Segura, Elena Marquez Tajadura-Jimenez, Ana Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain Ucl Interaction Centre University College London London United Kingdom

ISBN: (纸本)9798400704024

Inspired by the strong concept of Intercorporeal Biofeedback, here we present the design and development of a minimalist embodied sketching toolkit for designing wearables for motor learning. The toolkit includes technology probes featuring minimalist wearable digital units that support hands-on explorations and the design of potential future interactions driven by movement with multisensory feedback. These units are self-contained and generate audiovisual or vibrotactile patterns in response to body inputs such as movement, spatial orientation, and touch. Here, we present and characterise the toolkit, together with its theoretical and empirical grounding and the design values driving the design process. The toolkit can be useful for those interested in an embodied design approach to designing wearables in general, and specifically for those targeting movement, such as in motor learning application domains. It can complement and be used with other assorted non-digital objects during Embodied Sketching sessions, like bodystorming. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Probes

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Perceiving and Countering Hate: The Role of Identity in Online Responses

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Proceedings of the ACM on human-computer interaction 2025年第2期9卷 1-28页

作者： Ping, Kaike Hawdon, James Rho, Eugenia H Department of Computer Science Virginia Tech Blacksburg United States College of Liberal Arts and Human Sciences Virginia Tech Blacksburg United States

This study investigates how online counterspeech, defined as direct responses to harmful online content with the intention of dissuading the perpetrator from further engaging in such behavior, is influenced by the match between a target of the hate speech and a counterspeech writer's identity. Using a sample of 458 English-speaking adults who responded to online hate speech posts covering race, gender, religion, sexual orientation, and disability status, our research reveals that the match between a hate post's topic and a counter-speaker's identity (topic-identity match, or TIM) shapes perceptions of hatefulness and experiences with counterspeech writing. Specifically, TIM significantly increases the perceived hatefulness of posts related to race and sexual orientation. TIM generally boosts counter-speakers' satisfaction and perceived effectiveness of their responses, and reduces the difficulty of crafting them, with an exception of gender-focused hate speech. In addition, counterspeech that displayed more empathy, was longer, had a more positive tone, and was associated with higher ratings of effectiveness and perceptions of hatefulness. Prior experience with, and openness to AI writing assistance tools like ChatGPT, correlate negatively with perceived difficulty in writing online counterspeech. Overall, this study contributes insights into linguistic and identity-related factors shaping counterspeech on social media. The findings inform the development of supportive technologies and moderation strategies for promoting effective responses to online hate. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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Histo-Genomic Knowledge Association for Cancer Prognosis from Histopathology Whole Slide Images

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2170-2181页

作者： Wang, Zhikang Zhang, Yumeng Xu, Yingxue Imoto, Seiya Chen, Hao Song, Jiangning Monash Biomedicine Discovery Institute Australia Monash University Department of Biochemistry and Molecular Biology Australia The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The University of Tokyo Human Genome Center Institute of Medical Science Japan

Histo-genomic multi-modal methods have emerged as a powerful paradigm, demonstrating significant potential for cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this, we propose a novel Genome-informed Hyper-Attention Network, termed G-HANet, which is capable of effectively learning the histo-genomic associations during training to elevate uni-modal whole slide image (WSI)-based inference for the first time. Compared with the potential knowledge distillation strategy for this setting (i.e., distilling a multi-modal network to a uni-modal network), our end-to-end model is superior in training efficiency and learning cross-modal interactions. Specifically, the network comprises cross-modal associating branch (CAB) and hyper-attention survival branch (HSB). Through the genomic data reconstruction from WSIs, CAB effectively distills the associations between functional geno-types and morphological phenotypes and offers insights into the gene expression profiles in the feature space. Subsequently, HSB leverages the distilled histo-genomic associations as well as the generated morphology-based weights to achieve the hyper-attention modeling of the patients from both histopathology and genomic perspectives to improve cancer prognosis. Extensive experiments are conducted on five TCGA benchmarking datasets and the results demonstrate that G-HANet significantly outperforms the state-of-the-art WSI-based methods and achieves competitive performance with genome-based and multi-modal methods. G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology. The code is available at https://***/ZacharyWang-007/G-HANet. © 1982-2012 IEEE.

关键词： Gene expression

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Critical Tools for Machine Learning: Working with Intersectional Critical Concepts in Machine Learning Systems Design 22

Critical Tools for Machine Learning: Working with Intersecti...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Klumbyte, Goda Draude, Claude Taylor, Alex S. Faculty of Electrical Engineering and Computer Science University of Kassel Germany Centre for Human-Computer Interaction Design City University of London United Kingdom

ISBN: (纸本)9781450393522

This paper investigates how intersectional critical theoretical concepts from social sciences and humanities research can be worked with in machine learning systems design. It does so by presenting a case study of a series of speculative design workshops, conducted in 2021. These workshops drew on intersectional feminist methodologies to construct interdisciplinary interventions in the design of machine learning systems, towards more inclusive, accountable, and contextualized systems design. The concepts of "situating/situated knowledges", "figuration", "diffraction", and "critical fabulation/speculation"were taken up as theoretical and methodological tools for concept-led design workshops. This paper presents the design framework of the workshops and highlights tensions and possibilities with regards to interdisciplinary machine learning systems design towards more inclusive, contextualized, and accountable systems. It discusses the role that critical theoretical concepts can play in a design process and shows how such concepts can work as methodological tools that nonetheless require an open-ended experimental space to function. It presents insights and discussion points regarding what it means to work with critical intersectional knowledge that is inextricably connected to its historical and socio-political roots, and how this reframes what it might mean to design fair and accountable systems. © 2022 Owner/Author.

关键词： Systems analysis

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A Robot Jumping the Qeue: Expectations About Politeness and Power During Conflicts in Everyday human-Robot Encounters

A Robot Jumping the Qeue: Expectations About Politeness and ...

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2024 CHI Conference on human Factors in Computing Sytems, CHI 2024

作者： Babel, Franziska Welsch, Robin Miller, Linda Hock, Philipp Thellman, Sam Ziemke, Tom Dept. of Computer & Information Science Linköping University Linköping Sweden Department of Computer Science Aalto University Espoo Finland Human Factors Ulm University Ulm Germany

ISBN: (纸本)9798400703300

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations. © 2024 Copyright held by the owner/author(s)

关键词： human robot interaction

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Robust intrusion detection for network communication on the Internet of Things: a hybrid machine learning approach

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Cluster Computing 2024年第7期27卷 9975-9991页

作者： Soltani, Nasim Rahmani, Amir Masoud Bohlouli, Mahdi Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu Taiwan Research and Innovation Department Petanux GmbH Bonn Germany Institute of Research and Development Duy Tan University Da Nang Viet Nam Department of Computer Science University of Human Development Sulaymaniyah Iraq

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Spatial grids assisted multi-task fusing framework for incremental point cloud mapping of large scenes

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Luo, Nan Hu, Daoru Liang, Jie Wang, Yifeng Zhao, Hui Wang, Quan Xidian University Key Laboratory of Smart Human Computer Interaction and Wearable Technology of Shannxi Province School of Computer Science and Technology Xi'an710126 China

Recovering the point cloud maps of large-scale scenes from multi-sensor data is the fundamental building block of numerous 3D related applications. Available SLAM (Simultaneous Localization and Mapping) frameworks require rigorous constraints in data capturing and suffer from heavy workload when facing large-scale scenes. To push the research to further extent, this work proposes an incremental multi-task mapping framework for large-scale scenes under the guidance of spatial grids. (1) This framework first encodes the target scene into spatial grids according to the GNSS (The Global Navigation Satellite System) coordinates, then the mapping task is accomplished via multiple sub-scene SLAM tasks that can be executed independently, and finally the mapped point clouds are incrementally aligned with the assistance of encoded spatial grids to form the whole picture of the large-scale scene. (2) In each sub-scene SLAM task, we develop a new fast loop detection method based on spatial grids and perceptual hashing, which replaces the memory-expensive BoW (bag-of-words) model for image similarity determination, saves the unnecessary computational cost and lifts the localization accuracy. (3) In the aligning part, a new grid-wise merging method is designed for sub-scene point clouds, which improves the robustness and efficiency of point cloud registration and the applicability for large-scale scenes, supporting incremental multi-task mapping. We extensively evaluate the localization accuracy, loop detection efficiency on indoor EuRoC dataset, and multi-machine point cloud mapping on outdoor KITTI dataset. Experimental results reveal that the proposed framework provides a feasible multi-task fusing solution for incremental mapping of large scenes. © 1963-2012 IEEE.

关键词： Global positioning system

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Unifying Reality and Virtuality: Constructing a Cohesive Metaverse Using Complex Numbers 4th

Unifying Reality and Virtuality: Constructing a Cohesive M...

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4th International Conference on Spatial Data and Intelligence, SpatialDI 2023

作者： Li, Haibo Department of Media Technology and Interaction Design School of Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

ISBN: (纸本)9783031329098

In this position paper we present a novel mathematical framework for building metaverses, which is a potential way to unify reality and virtuality to create a cohesive whole universe. We argue that the nature of metaverses is inherently mathematical, and propose that the system of complex numbers could play a key role in constructing them. Specifically, we provide context for our argument and offer a supporting example, the analytic signal, to demonstrate how to construct its imaginary counterpart with the Hilbert transform to a given real signal and how to unify them to form a cohesive complex signal that facilitates the analysis of local dynamic behaviors of the signal. This framework has significant potential for building a metaverse. By leveraging the power of complex numbers, one can create a unified mathematical system that merges the physical and virtual worlds. We believe that this proposal will inspire further research and development of metaverses in this field and that our framework will contribute to the construction of a metaverse that offers unprecedented levels of interactivity and immersion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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Daily Pain Prediction in Workplace Using Gaussian Processes

Daily Pain Prediction in Workplace Using Gaussian Processes

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16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Puri, Chetanya Keyaerts, Stijn Szymanski, Maxwell Godderis, Lode Verbert, Katrien Luca, Stijn Vanrumste, Bart KU Leuven Belgium Leuven Belgium Department ofPublic Health and Primary Care KU Leuven Leuven Belgium Department of Computer Science KU Leuven Belgium Human-computer Interaction research group Department of Computer Science KU Leuven Belgium Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium

Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe, Long term impacts of MSDs include "Pain chronification'' which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person's future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Prediction models

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