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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhang, Junyao Shimonishi, Kei Ueda, Hirotada Kondo, Kazuaki Nakamura, Yuichi Graduate School of Informatics Kyoto University Kyoto Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

ISBN: (纸本)9789819601219

Predicting the internal conditions of people who have difficulties explaining their internal states because of dementia or other problems has gained increasing attention. Monitoring internal states requires precise evaluation of weak (less intense) facial expressions, which is challenging due to the significant influence of individual characteristics on facial expressions. For instance, a person may look more like they are smiling or frowning than others even when they are in a neutral state. In addition, ambiguous changes may occur. For example, subtle changes from neutral to positive facial expressions could be similar to those from neutral to negative expressions, which further complicates the evaluation of facial expressions. Conventional FER systems designed for various people with various personal differences often have difficulties in detecting and indexing such subtle changes caused by motion artifacts that exceed the displacement of feature points. In this study, we developed a personalized and customized facial expression recognition (FER) model that can monitor changes in a person’s internal state by recognizing the transitions in their daily facial expressions. The model employs image comparison to monitor a specific person. It is tailored to each individual by training it with the individual’s actual facial expressions. This model is not universal but can be adjusted to individual emotional nuances. To account for arbitrary transitions along positive–negative facial expressions, we developed a two-stage FER framework comprising three deep neural networks (DNNs). The proposed framework uses images without detecting facial feature points. The first stage performs binary classification on the input image pairs and categorizes each image as positive or negative. The second stage compares the images to predict the direction of changes, i.e., whether the expression shifts toward positive or negative. This stage is trained on transitional image pairs, focusing

关键词： Deep neural networks

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SmartDeco: AR for Soft Furnishing Design 1st

SmartDeco: AR for Soft Furnishing Design

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1st International Conference on Extended Reality, ICXR 2024

作者： Wang, Jingyuan Zhang, Yichun Liu, Ruilong Chen, Shiyi Hu, Yang Yao, Junfeng Beijing University of Posts and Telecommunications Haidian District Beijing China Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

Augmented reality (AR) is increasingly being integrated into the home renovation sector, yet current AR solutions often fail to address the challenges of soft decoration design. This study introduces SmartDeco, an AR-based application for the Chinese market, enhanced by large language models (LLMs) to improve user experiences in home decoration. Serving merchants, interior designers, and users, SmartDeco enables AR simulations of soft furnishing combinations, supports collaborative design, and utilizes LLMs to offer real-time design recommendations, addressing potential issues and improving decision-making. User testing demonstrates SmartDeco’s effectiveness in enhancing usability, simplifying design processes, reducing errors, and boosting satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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Science China Technological Sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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MatrikalinDiabetes: User-centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-Computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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Computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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GIGA: Generalizable Sparse Image-driven Gaussian Avatars

arXiv

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arXiv 2025年

作者： Zubekhin, Anton Zhu, Heming Gotardo, Paulo Beeler, Thabo Habermann, Marc Theobalt, Christian Max Planck Institute for Informatics Saarland Informatics Campus Germany Saarbrücken Research Center for Visual Computing Interaction and AI Germany Google Switzerland

Driving a high-quality and photorealistic full-body human avatar, from only a few RGB cameras, is a challenging problem that has become increasingly relevant with emerging virtual reality technologies. To democratize such technology, a promising solution may be a generalizable method that takes sparse multi-view images of an unseen person and then generates photoreal free-view renderings of such identity. However, the current state of the art is not scalable to very large datasets and, thus, lacks in diversity and photorealism. To address this problem, we propose a novel, generalizable full-body model for rendering photoreal humans in free viewpoint, as driven by sparse multi-view video. For the first time in literature, our model can scale up training to thousands of subjects while maintaining high photorealism. At the core, we introduce a MultiHeadUNet architecture, which takes sparse multi-view images in texture space as input and predicts Gaussian primitives represented as 2D texels on top of a human body mesh. Importantly, we represent sparse-view image information, body shape, and the Gaussian parameters in 2D so that we can design a deep and scalable architecture entirely based on 2D convolutions and attention mechanisms. At test time, our method synthesizes an articulated 3D Gaussian-based avatar from as few as four input views and a tracked body template for unseen identities. Our method excels over prior works by a significant margin in terms of cross-subject generalization capability as well as photorealism. The project page is available at ***/projects/GIGA. © 2025, CC BY-NC-ND.

关键词： Gaussian distribution

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FedCSpc: A Cross-Silo Federated Learning System with Error-Bounded Lossy Parameter Compression

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1372-1386页

作者： Zhang, Zhaorui Di, Sheng Zhao, Kai Jin, Sian Tao, Dingwen Ji, Zhuoran Liu, Benben Alharthi, Khalid Ayed Cao, Jiannong Cappello, Franck The Hong Kong Polytechnic University Department of Computing Hong Kong Florida State University Department of Computer Science United States Temple University Department of Computer and Information Sciences United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering United States Shandong University School of Cyber Science and Technology China The University of Hong Kong LSCM R&D Center Hong Kong University of Bisha Department of Computer Science College Of Computing And Information Technology Saudi Arabia Argonne National Laboratory United States

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. To bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality). © 1990-2012 IEEE.

关键词： Bandwidth compression

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PaintingScapeVR: Exploring Gamified Interaction in Virtual Galleries 1st

PaintingScapeVR: Exploring Gamified Interaction in Virtual ...

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1st International Conference on Extended Reality, ICXR 2024

作者： Cai, Dongning Ren, Le Chang, Yaqi Li, Xiaodan Lin, Yuxin Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism New Taipei City Taiwan School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

In reality, galleries usually display paintings in a protective manner, partly to delay the damage to the original artwork and ensure the long-term preservation of the artwork. On the other hand, it also hinders visitors’ deep appreciation and learning of the artwork. In the face of this challenge, our research aims to provide users with immersive viewing and entertainment, as well as interactive gaming experiences with artwork, through the use of virtual reality technology. So the paper proposes PaintingScapeVR, which is based on a virtual gallery and reproduces the three-dimensional scenes corresponding to different paintings. Visitors can freely explore in this virtual environment and gain a deeper understanding of the meaning and artistic value of artworks through interaction with them. Our results indicate that the reproduction of 3D scenes and gamified interactive design can not only significantly enhance visitors’ appreciation of painting art, but also leave a deeper learning impression. This immersive experience stimulates visitors’ desire to appreciate and explore, prompting them to actively delve into the background and techniques of art works, thereby enhancing their artistic appreciation abilities. At the same time, this work also demonstrates its potential in enhancing visitors’ understanding of painting works, allowing more people to explore the joy of painting and art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interactive gamification Three-dimensional displays Virtual reality

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