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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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Enhancing the De-identification of Personally Identifiable Information in Educational Data

arXiv

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arXiv 2025年

作者： Shen, Yuntian Ji, Zilyu Lin, Jionghao Koedinger, Kenneth R. Carnegie Mellon University PittsburghPA15217 United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Faculty of Information Technology Monash University ClaytonVIC3800 Australia Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University PittsburghPA15217 United States

Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4omini’s performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data’s utility for research and pedagogical analysis. Our code is available on GitHub: https://***/AnonJD/PrivacyAI © 2025, CC BY.

关键词： Differential privacy

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The BookBot Project: Conceptual Design of a Social Robot Facilitating Reading Motivation 8th

The BookBot Project: Conceptual Design of a Social Robot F...

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8th International Workshop on Chatbots and human-Centered AI, CONVERSATIONS 2024

作者： Chilufya, Emma Mainza Arvola, Mattias Severinsson, Susanne Bylund, Anna Martín Stenliden, Linnéa Mortazavi, Arezou Ziemke, Tom Human-Centered Systems Department of Computer and Information Science Linköping University Linköping Sweden Education Teaching and Learning Department of Behavioural Sciences and Learning Linköping University Linköping Sweden Learning Aesthetics Natural Science Department of Behavioural Sciences and Learning Linköping University Linköping Sweden

ISBN: (纸本)9783031880445

The motivation to read among children in Nordic countries has seen a noticeable decline in recent years. This study explores the design of social robots for stimulating interest in reading among fourth-grade students (age 10–11). We used a combination of conceptual design methods to engage teachers and students from four classes in two Swedish schools in co-creation workshops. Ideas on functions, qualities, and robot designs were generated, and based on this a set of ten distinct design concepts were created: the Facilitator, the Librarian, the Coach, the Buddy, the Assistant, the Narrator, the Creator, the Apprentice, the Portable, and the Gamer. The strengths and weaknesses of different designs were evaluated, resulting in a final design named ‘The BookBot,’ aiming to inspire and engage students in book reading through discussions of book content, character portrayal, and personalised book recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Social robots

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Shared Autonomy for Proximal Teaching

arXiv

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arXiv 2025年

作者： Srivastava, Megha Iranmanesh, Reihaneh Cui, Yuchen Gopinath, Deepak Sumner, Emily Silva, Andrew Dees, Laporsha Rosman, Guy Sadigh, Dorsa Computer Science Department Stanford University Stanford United States Computer Science Department Amherst College Amherst United States Computer Science Department University of California Los Angeles Los Angeles United States Human Interactive Driving Toyota Research Institute Cambridge United States

Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have leveraged AI-assistance to improve instruction of tasks ranging from rehabilitation to surgical robot tele-operation. However, these works often make simplifying assumptions on the student learning process, and fail to model how a teacher’s assistance interacts with different individuals’ abilities when determining optimal teaching strategies. Inspired by the idea of scaffolding from educational psychology, we leverage shared autonomy, a framework for combining user inputs with robot autonomy, to aid with curriculum design. Our key insight is that the way a student’s behavior improves in the presence of assistance from an autonomous agent can highlight which sub-skills might be most "learnable" for the student, or within their Zone of Proximal Development. We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills. In a user study (n=50), where we teach high performance racing in a simulated environment of the Thunderhill Raceway Park with the CARLA Autonomous Driving simulator, we show that Z-COACH helps identify which skills each student should first practice, leading to an overall improvement in driving time, behavior, and smoothness. Our work shows that increasingly available semi-autonomous capabilities (e.g. in vehicles, robots) can not only assist human users, but also help teach them. © 2025, CC BY.

关键词： Students

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Correction: Prompt4LJP: prompt learning for legal judgment prediction

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The Journal of Supercomputing 2025年第5期81卷 1-2页

作者： Huang, Qiongyan Xia, Yuhan Long, Yunfei Fang, Hui Liang, Ruiwei Guan, Yin Xu, Ge College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China School of Computer Science and Electronic Engineering University of Essex Colchester UK School of Computer and Big Data Minjiang University Fuzhou China Fujian Mental Health Human-Computer Interaction Technology Research Center Minjiang University Fuzhou China Fuzhou Institute of Oceanography Fuzhou China

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Discrepancies in online postoperative care protocols for Mohs surgery

JAAD Reviews

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JAAD Reviews 2025年 3卷 128-130页

作者： Afua Ofori-Darko Ashley M. Perez Devin Barzallo Jill Lehman Bryan T. Carroll Case Western Reserve University School of Medicine Cleveland Ohio Carnegie Mellon University Human-Computer Interaction Institute Pittsburgh Pennsylvania Department of Dermatology University Hospitals Cleveland Medical Center Cleveland Ohio

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Application of Explainable Artificial Intelligence in Autism Spectrum Disorder Detection

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Cognitive Computation 2025年第3期17卷 1-26页

作者： Vimbi, Viswan Shaffi, Noushath Sadiq, Mohamed A. K. Sirasanagandla, Srinivasa Rao Aradhya, V. N. Manjunath Kaiser, M. Shamim Wang, Shuqiang Mahmud, Mufti College of Computing and Information Science University of Technology and Applied Sciences Sohar Oman Department of Computer Science College of Science Sultan Qaboos University Muscat Oman Department of Human and Clinical Anatomy College of Medicine and Health Sciences Sultan Qaboos University Muscat Oman Department of Computer Applications J.S.S Science and Technology University Mysuru 570006 India Institute of Information Technology Jahangirnagar University Savar 1342 Bangladesh Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Information & Computer Science Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Autism spectrum disorder (ASD) is a developmental disorder typically diagnosed in early childhood. With the advent of machine learning (ML) and deep learning (DL) models, accurate diagnosis of ASD has been enhanced. However, the widespread adoption of these AI models in real-life scenarios has been limited due to their “black box” nature, which lacks transparency and interpretability. To address this, eXplainable Artificial Intelligence (XAI) models have gained popularity, offering more transparent and interpretable detection methods. This review systematically explores XAI frameworks and underlying AI models by addressing four critical research questions (RQs). Relevant research outputs were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach from five major databases: IEEE, PubMed, Springer, scienceDirect and ACM. From an initial pool of 1551 articles, 38 studies were identified that focused on learning models and XAI in ASD prediction. These studies were critically analysed across six modalities, twenty classifiers, and five XAI frameworks. The selected studies demonstrate the application of various XAI frameworks in enhancing the transparency and interpretability of AI models used for ASD prediction. The review highlights the benefits of XAI in improving model trustworthiness and adoption, while identifying challenges, such as the trade-off between interpretability and model performance. This review provides a comprehensive overview of the current state of the art of XAI in ASD prediction, identifying key benefits, challenges, and future research avenues. The insights gained from this review could guide researchers in further developing XAI frameworks that balance interpretability and predictive accuracy, thereby facilitating broader adoption in clinical practice. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Artificial intelligence Autism spectrum disorder Deep learning Explainable artificial intelligence Machine learning Multimodal data

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Exploring Assumptions about Sustainability: Towards a Constructive Framework for Action in Sustainable HCI 25

Exploring Assumptions about Sustainability: Towards a Constr...

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

作者： Minna Laurell Thorslund Ola Leifler Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Information Science Linköping University Linköping Sweden

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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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IEEE Access 2025年 13卷 85507-85525页

作者： Kočić, Veljka Lukač, Niko Rožajac, Dž Schweng, Stefan Gollob, Christoph Nothdurft, Arne Stampfer, Karl Ser, Javier Del Holzinger, Andreas University of Natural Resources and Life Sciences Vienna Human-Centered AI Lab Institute of Forest Engineering Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria TU Graz Institute of Human-Centered Computing Graz8010 Austria University of Maribor Faculty of Electrical Engineering and Computer Science Maribor2000 Slovenia Institute of Forest Growth Department of Ecosystem Management Climate and Biodiversity University of Natural Resources and Life Sciences Vienna Vienna1190 Austria TECNALIA Derio48160 Spain Department of Mathematics Leioa48940 Spain

Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount. © 2013 IEEE.

关键词： k-nearest neighbors

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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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