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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Shindo, Arisa Kawasaki, Yosuke Takahashi, Masaki School of Science for Open and Environmental Systems Graduate School of Science and Technology Keio University Kanagawa Japan Department of System Design Engineering Faculty of Science and Technology Keio University Kanagawa Japan

ISBN: (纸本)9789819635214

Recent studies have explored the integration of large language models (LLMs) into caregiving robots. The use of LLMs facilitates the generation of human-like natural dialogues and diverse, varied expressions. However, in case of a lack of direct and specific instructions from a user, gaining an accurate understanding of the user's intent is challenging, which may result in miscommunication. Thus, this study proposed a method to achieve robust communication even in the absence of direct instructions by considering the uncertainty in destination prediction probabilities. The proposed system leveraged GPT-4 to generate various Japanese speech content candidates and employed neighborhood components analysis to facilitate dimensionality reduction. Consequently, notable features of the rich linguistic embeddings were preserved while transforming them into manageable data. The system selected appropriate utterances based on the destination prediction probabilities. Furthermore, this study investigated changes in the speech content according to the variations in these probabilities, and the incorporation of uncertainty in communication was validated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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NeurTV: Total Variation on the Neural Domain*

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SIAM Journal on Imaging Sciences 2025年第2期18卷 1101-1140页

作者： Luo, Yisi Zhao, Xile Ye, Kai Meng, Deyu School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi Xi'an China Pengcheng Laboratory Shenzhen China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa China School of Mathematical Sciences University of Electronic Science and Technology of China Sichuan Chengdu China

Recently, we have witnessed the success of total variation (TV) for many imaging applications. However, traditional TV is defined on the original pixel domain, which limits its potential. In this work, we suggest a new TV regularization defined on the neural domain. Concretely, the discrete data is implicitly and continuously represented by a deep neural network (DNN), and we use the derivatives of DNN outputs with respect to (w.r.t.) input coordinates to capture local correlations of data. As compared with classical TV on the original domain, the proposed TV on the neural domain (termed NeurTV) enjoys the following advantages. First, NeurTV is free of discretization error induced by the discrete difference operator. Second, NeurTV is not limited to meshgrid but is suitable for both meshgrid and non-meshgrid data. Third, NeurTV can more exactly capture local correlations across data for any direction and any order of derivatives attributed to the implicit and continuous nature of neural domain. We theoretically reinterpret NeurTV under the variational approximation framework, which allows us to build the connection between NeurTV and classical TV and inspires us to develop variants (e.g., space-variant NeurTV). Extensive numerical experiments with meshgrid data (e.g., color and hyperspectral images) and non-meshgrid data (e.g., point clouds and spatial transcriptomics) showcase the effectiveness of the proposed methods. © 2025 Society for Industrial and Applied Mathematics.

关键词： Deep neural networks

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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Computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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A Combinatorial Optimization Method for Large Scale Problems Based on an Inverse Self-Convolution Algorithm

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第2期145卷 145-155页

作者： Shigehiro, Yuji Yamamura, Mayuko Faculty of Engineering Osaka Institute of Technology 5-16-1 Ohmiya Asahi-ku Osaka535-8585 Japan NTT Data Sekisui Systems Corporation 1-11-8 Shibuya Shibuya-ku Tokyo150-0002 Japan

In this paper, we consider an effective search method for large scale combinatorial optimization problems, only by means of neighborhood operations, not by means of such operation as crossover in genetic algorithm. The fundamental ideas on which our method is based are the following: 1) Many different neighborhood operations, which consist of the iterations of unit neighborhood operations, are applied to solutions. 2) The probability distribution of the objective function values of the neighborhood solutions is estimated, from the data obtained in the search process. 3) The neighborhood operation, which maximizes the expected value of the amount of the improvement of the current solution, is selected to be applied. From these ideas, a new method for searching for solutions is constructed, on the basis of the self-convolution and the inverse self-convolution. We have applied the local search method, the previously proposed method, and the newly proposed method to traveling salesman problems and maximum satisfiability problems. The effectiveness of the newly proposed method is shown by the computational experiments. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Combinatorial optimization

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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A Computer Vision-Based Physical Activity Application for Children with Autism

A Computer Vision-Based Physical Activity Application for Ch...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eda Obuz Kaan Toraman Serdar Solak Information Systems Engineering Faculty of Technology Kocaeli University

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Autism Spectrum Disorder (ASD) is a neurological condition characterized by challenges in social interaction, communication, repetitive behaviors, and restricted interests. While the exact cause remains unknown, ASD results from genetic and environmental factors and persists throughout life. Early diagnosis and intervention enhance quality of life and potential. Games have proven effective in nurturing socialization, skill acquisition, and adaptability in children with ASD, leveraging their structured and visually appealing *** study adopted a game-based approach to support motor skills, socialization, and learning in children with ASD. Using computer vision techniques, children’s movements were captured via cameras and transformed into interactive virtual characters in real time. Activities included movement mimicry games to improve attention and motor skills, dance games for physical coordination, and turn-based games to enhance social skills. The system, developed in this study using Google MediaPipe Pose and Unity, is cost-effective and suitable for preschools and home use. Feedback from educators at the Autism Sports and Life Center guided system refinement. The work presented in this article offers a robust foundation for fostering independence and social participation in children with ASD.

关键词： Autism Humanities Robot kinematics Games Variable speed drives Motors Real-time systems Internet Sports Testing

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Vibration control via finite and semi-infinite vibration attenuation regions in the chain of mass-in-mass units: from theory to experiments

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Nonlinear Dynamics 2025年 1-17页

作者： Kovacic, Ivana Teofanov, Ljiljana Zhu, Rui Wang, Xiao Zhao, Jianlei Faculty of Technical Sciences Centre of Excellence for Vibro-Acoustic Systems and Signal Processing CEVAS University of Novi Sad Novi Sad Serbia Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia School of Aerospace Engineering Beijing Institute of Technology University of Novi Sad Beijing100081 China

This study deals with a longitudinally excited chain of external linear oscillators, enhanced with internal linear oscillators. The undamped case is considered first as a benchmark, and then a viscously damped chain is examined. Qualitatively different vibration attenuation regions are identified, including the cases of no attenuation region, a finite and semi-infinite attenuation region, which represents a new result for this type of chain of interest for vibration control. The influence of the number of unit cells, the mass and the damping ratio on their appearance is examined thoroughly and illustrated in the form of novel and original 2D and 3D behavioural maps, which can be used as a design criterion for metastructures, but also for other systems modelled as this type of chains. The nonlinear boundary between the finite and semi-infinite vibration region involving the non-dimensional damping coefficient and the number of units is determined for the first time. Besides, the nonlinear change of the critical non-dimensional damping ratio, which assures the appearance of the semi-infinite vibration attenuation region in the system response, with the non-dimensional total mass, is also innovatively obtained. The theoretical identification of three distinct regions of vibration attenuation is experimentally validated through an original damping control method utilizing an air track and an air pump with a variable power output capability. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Damping

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Machine learning-enhanced soft robotic system inspired by rectal functions to investigate fecal incontinence

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Bio-Design and Manufacturing 2025年第3期8卷 482-494页

作者： Mao, Zebing Suzuki, Sota Nabae, Hiroyuki Miyagawa, Shoko Suzumori, Koichi Maeda, Shingo Faculty of Engineering Yamaguchi University Yamaguchi 755-0039 Japan School of Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Faculty of Nursing and Medical Care Keio University Kanagawa 252-0883 Japan Research Center for Autonomous Systems Materialogy (ASMat) Institute of Innovative Research Tokyo Institute of Technology Kanagawa 226-8501 Japan

Fecal incontinence (FI), which can arise from various pathogenic mechanisms, has attracted considerable attention worldwide. Despite its importance, the reproduction of the defecatory system to study the mechanisms of FI remains limited, largely because of social stigma and being considered inappropriate. Inspired by the rectum’s functionalities, we developed a soft robotic system that includes a power supply, pressure sensors, data acquisition systems, a flushing mechanism, stages, and a rectal module. Specifically, the innovative soft rectal module includes actuators inspired by sphincter muscles, both soft and rigid covers, and a soft rectum mold. The rectal mold, which was fabricated from materials that mimic human rectal tissue, was produced using a mold replication fabrication method. Both the soft and rigid components of the mold were created using three-dimensional (3D) printing technology. In addition, the sphincter muscle-inspired actuators featured double-layer pouch structures that were modeled and optimized based on multilayer perceptron methods to obtain a high contraction ratio (100%), generate high pressure (9.8 kPa), and have a short recovery time (3 s). Upon assembly, this defecation robot could smoothly expel liquid feces, perform controlled solid fecal cutting, and defecate extremely solid long feces, thus closely replicating the functions of the human rectum and anal canal. This defecation robot has the potential to facilitate human understanding of the complex defecation system and contribute to the development of improved quality-of-life devices related to defecation. (Figure presented.) © Zhejiang University Press 2025.

关键词： Defecation Fecal incontinence Machine learning Pneumatic Soft robot

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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