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

作者： Sabnis, Nihar Wittchen, Dennis Otaran, Ata Badmos, Fatima Liu, Siyi Saini, Aryan Regal, Georg van Rheden, Vincent Mueller, Florian Nanayakkara, Suranga Matthies, Denys J.C. Nachtigall, Troy Strohmeier, Paul Vidal, Laia Turmo Elvitigala, Don Samitha Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Faculty of Informatics / Mathematics Dresden University of Applied Sciences Saxony Dresden Germany Saarland University Saarland Informatics Campus Saarland Saarbrücken Germany Technological University Dublin Dublin Ireland Faculty of Information Technology Monash University Melbourne Australia Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne VIC Australia Center for Technology Experience AIT Austrian Institute of Technology GmbH Vienna Austria Human Computer Interaction Division University of Salzburg Salzburg Austria Augmented Human Lab. School of Computing National University of Singapore Singapore Singapore Augmented Human Lab. Auckland Bioengineering Institute Auckland New Zealand Eindhoven University of Technology Eindhoven Netherlands KTH Royal Institute of Technology Stockholm Sweden Department of Human Centred Computing Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9798400713958

Over the past decade, a noticeable increase in literature can be seen in wearable foot interfaces, which have evolved from activity tracking to enhancing human capabilities. Our legs, being the largest body limbs, play an essential role in various functions such as locomotion, maintaining balance, supporting proper posture and providing ground-contact using our feet. Hence, foot augmentations offer the opportunity to augment our entire body. However, most prior research focuses on specific application areas, thus affording a research agenda to further understand the full potential of feet in designing augmentations and to contextualize it in the broader human augmentation space. To achieve this, in this workshop, we invite researchers, designers, and practitioners, novice and expert, interested in designing human and foot augmentations. We will discuss how early foot interfaces helped in augmenting humans, and based on current work and trends in foot augmentation, we will formulate strategies for the next steps and discuss the applicability of such strategies in the broader space of human augmentation. © 2025 Copyright held by the owner/author(s).

关键词： foot augmentation human augmentation interaction techniques

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Commissioning Development: Grantmaking, Community Voices, and their Implications for ICTD 2022

Commissioning Development: Grantmaking, Community Voices, an...

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International Conference on Information & Communication Technologies and Development 2022

作者： Manika Saha Tom Bartindale Sharifa Sultana Gillian Oliver Dan Richardson Shakuntala Haraksingh Thilsted Syed Ishtiaque Ahmed Patrick Olivier Action Lab Human-centric computing Faculty of IT Monash University Australia Department of Information Science Cornell University USA Faculty of IT Monash University Australia Global Public Health Nutrition WorldFish Malaysia Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781450397872

While information and communication technology for development (ICTD) researchers have prioritized advocating for community voices in innovation design and development, we have limited insights into how community voices are incorporated by the high-level decision-makers who fund and initiate development projects and programs in the Global South. Indeed, understanding local communities’ voices (expressions of needs, challenges, and priorities) in tailoring effective development projects for sustainable development is widely considered an unmet goal. Using a qualitative survey of eight decision-makers (including grantmaking donors, central governments and INGOs) we explored a number of key factors, including national and global political climates, insider-outsider interactions, and evidence-based approaches that influence the high-level decision making process, workflows, and perceptions of community voice in project commissioning within Bangladesh’s public health nutrition development arena. Our findings reveal the tensions that arise among high-level decision-makers, and highlight the challenges associated with connecting with communities during development project design and implementation. We suggest broader implications and design opportunities for inventive project commissioning approaches to bridge the gap between communities and decision-makers. Our findings are of potential value for ICTD and HCI4D researchers interested in sustainable innovation and understanding and participating in the complex workflows of the project commissioning process in sustainable global development.

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Discovering Chunks in Neural Embeddings for Interpretability

arXiv

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arXiv 2025年

作者： Wu, Shuchen Alaniz, Stephan Schulz, Eric Akata, Zeynep Explainable Machine Learning Lab Helmholtz Munich Germany Max Planck Institute for Biological Cybernetics Germany Munich Center for Machine Learning Germany Department of Computer Science Technical University of Munich Germany Institute for Human-Centered AI Helmholtz Munich Germany

Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process. © 2025, CC BY.

关键词： Network embeddings

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GFG - Gender-Fair Generation: A CALAMITA Challenge

arXiv

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arXiv 2024年

作者： Frenda, Simona Piergentili, Andrea Savoldi, Beatrice Madeddu, Marco Rosola, Martina Casola, Silvia Ferrando, Chiara Patti, Viviana Negri, Matteo Bentivogli, Luisa Interaction Lab Heriot-Watt University Edinburgh United Kingdom aequa-tech Turin Italy Fondazione Bruno Kessler Trento Italy University of Trento Trento Italy Computer Science Department University of Turin Turin Italy Universitat de Barcelona Barcelona Spain MaiNLP MCML LMU Munich Germany

Gender-fair language aims at promoting gender equality by using terms and expressions that include all identities and avoid reinforcing gender stereotypes. Implementing gender-fair strategies is particularly challenging in heavily gender-marked languages, such as Italian. To address this, the Gender-Fair Generation challenge intends to help shift toward gender-fair language in written communication. The challenge, designed to assess and monitor the recognition and generation of gender-fair language in both mono- and cross-lingual scenarios, includes three tasks: (1) the detection of gendered expressions in Italian sentences, (2) the reformulation of gendered expressions into gender-fair alternatives, and (3) the generation of gender-fair language in automatic translation from English to Italian. The challenge relies on three different annotated datasets: the GFL-it corpus, which contains Italian texts extracted from administrative documents provided by the University of Brescia;GeNTE, a bilingual test set for gender-neutral rewriting and translation built upon a subset of the Europarl dataset;and Neo-GATE, a bilingual test set designed to assess the use of non-binary neomorphemes in Italian for both fair formulation and translation tasks. Finally, each task is evaluated with specific metrics: average of F1-score obtained by means of BERTScore computed on each entry of the datasets for task 1, an accuracy measured with a gender-neutral classifier, and a coverage-weighted accuracy for tasks 2 and 3. © 2024, CC BY.

关键词： Linguistics

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Semantic Visual Simultaneous Localization and Mapping: A Survey

arXiv

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arXiv 2022年

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Institute of Computer Vision College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy National University of Defense Technology China Università di Genova Genoa Italy School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Institute of Computer Vision School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

arXiv

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arXiv 2024年

作者： Zhu, Lizhen Wang, James Z. Lee, Wonseuk Wyble, Brad Data Science and Artificial Intelligence Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Human-Computer Interaction Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Communication and Media School of Social Sciences and Humanities Loughborough University Leicestershire Loughborough United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Department of Psychology The Pennsylvania State University University ParkPA United States

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： computer vision

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Predicting RNA-RBP interactions by Using a Pseudo-Siamese Network

Predicting RNA-RBP Interactions by Using a Pseudo-Siamese Ne...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Fu Yuan, Liangliang Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665401265

Large scale RNA-protein binding data made computational identification of RNA-protein interactions possible, which provides great convenience for revealing the interplay between non-coding RNAs and RNA-binding proteins (RBPs). Various machine learning methods have been applied to the prediction of RNA-protein interactions (RPIs). However, most of the RPI predictors were designed for linear RNAs, while methods for circular RNAs (circRNAs) are few. Besides, most of the predictors use only RNA sequence or only protein sequences as input. Automatic feature learning from RNA and RBP sequences using deep neural networks has great potential to improve RPI predictors. In this paper, we propose a new method, PSi-bind, to predict the binding relationship between RNAs and RBPs, which is featured by a pseudo-Siamese neural network. We feed both RNA and RBP sequence embedding features to the network and perform an end-to-end learning to discover the latent association between RNAs and RBPs. Especially, we train our model on a large-scale circRNA dataset. The experimental results show that the model achieves very high accuracy on independent test set, and outperforms the state-of-the-art RPI predictors by a large margin. © 2021 IEEE.

关键词： RNA

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EmoBridge: Bridging the Communication Gap between Students with Disabilities and Peer Note-Takers Utilizing Emojis and Real-Time Sharing

arXiv

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arXiv 2024年

作者： Song, Hyungwoo Shin, Minjeong Chu, Hyehyun Hong, Jiin Lee, Jaechan Eun, Jinsu Lim, Hajin Human Centered Computing Lab Seoul National University Seoul Korea Republic of Information Science and Culture Studies Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of HCI+D Lab. Seoul National University Seoul Korea Republic of

Students with disabilities (SWDs) often struggle with note-taking during lectures. Therefore, many higher education institutions have implemented peer note-taking programs (PNTPs), where peer note-takers (PNTs) assist SWDs in taking lecture notes. To better understand the experiences of SWDs and PNTs, we conducted semi-structured interviews with eight SWDs and eight PNTs. We found that the interaction between SWDs and PNTs was predominantly unidirectional, highlighting specific needs and challenges. In response, we developed EmoBridge, a collaborative note-taking platform that facilitates real-time collaboration and communication between PNT-SWD pairs using emojis. We evaluated EmoBridge through an in-the-wild study with seven PNT-SWD pairs. The results showed improved class participation for SWDs and a reduced sense of sole responsibility for PNTs. Based on these insights, we discuss design implications for collaborative note-taking systems aimed at enhancing PNTPs and fostering more effective and inclusive educational experiences for SWDs. © 2024, CC BY.

关键词： Students

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Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

arXiv

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arXiv 2023年

作者： Chitnis, Soham Rohit Liu, Sidong Dash, Tirtharaj Verlekar, Tanmay Tulsidas Ieva, Antonio Di Berkovsky, Shlomo Vig, Lovekesh Srinivasan, Ashwin Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani India Faculty of Medicine Health and Human Sciences Macquarie University Australia Boolean Lab University of California San Diego United States TCS Research India

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models’ confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://***/ soham-chitnis10/WSI-domain-specific. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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A human-human Physical interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

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