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Challenging Futures: Using Chatbots to Reflect on Aging and Dementia 25

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Challenging Futures: Using Chatbots to Reflect on Aging and ...

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

作者： Rucha Khot Teis Arets Joel Wester Franziska Burger Niels van Berkel Rens Brankaert Wijnand IJsselsteijn Minha Lee Industrial Design Eindhoven University of Technology Eindhoven Netherlands Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Department of Computer Science Aalborg University Aalborg Denmark Industrial Design Engineering University of Ghent Ghent Belgium Industrial Design Eindhoven University of Technology Eindhoven Netherlands Tranzo Tilburg University Tilburg Netherlands and Fontys School of Applied Sciences Eindhoven Netherlands Human Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Industrial Design Eindhoven University of Technology Eindhoven Netherlands and Centrum Wiskunde & Informatica Amsterdam Netherlands

ISBN: (纸本)9798400713941

Intertemporal reflection, flexibly thinking forward and backward in time, is vital for one's future planning. Yet, cultivating intertemporal reflection about encountering difficult futures, e.g., developing a progressive cognitive condition like dementia, can be challenging. We assessed people's attitudes towards dementia following conversing with a chatbot presented as either neurotypical or simulating dementia symptoms. While neither the chatbot's presentation nor the framing of participants' future selves impacted attitudes toward dementia, it influenced participants' experiences. When framed as future selves, the chatbot evoked a strong emotional connection, leading to reflection on aging, particularly with the chatbot simulating dementia symptoms. Participants interacting with the chatbot framed as a stranger with simulated symptoms often felt frustrated, especially when they had a task-oriented *** can be promising tools for prompting reflections on challenging futures, such as dementia, although their effectiveness varies due to the tensions between simulated cognitive decline and expectations for effective communication. © 2025 Copyright held by the owner/author(s).

关键词： Dementia

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

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

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

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

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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

GFG - Gender-Fair Generation: A CALAMITA Challenge

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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

Semantic Visual Simultaneous Localization and Mapping: A Survey

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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

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

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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

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

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

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

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

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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