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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems

arXiv

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arXiv 2025年

作者： Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Alsheikh, Mohammad Abu Kuhn, Carlos C.N. Alem, Yibeltal F. Radwan, Ibrahim School of Electrical Data Engineering University of Technology SydneyNSW2007 Australia School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia Faculty of Science & Technology University of Canberra CanberraACT2617 Australia

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and quantization effects. Existing approaches often assume error-free transmission in indoor settings, limiting their applicability to real-world scenarios. To address these challenges, we propose a novel deep learning-based framework for human pose reconstruction over orthogonal frequency-division multiplexing (OFDM) systems. The framework introduces a two-stage deep learning receiver: the first stage jointly estimates the wireless channel and decodes OFDM symbols, and the second stage maps the received sensor signals to full 3D body poses. Simulation results demonstrate that the proposed neural receiver reduces bit error rate (BER), thus gaining a 5 dB gap at 10−4 BER, compared to the baseline method that employs separate signal detection steps, i.e., least squares channel estimation and linear minimum mean square error equalization. Additionally, our empirical findings show that 8-bit quantization is sufficient for accurate pose reconstruction, achieving a mean squared error of 5×10−4 for reconstructed sensor signals, and reducing joint angular error by 37% for the reconstructed human poses compared to the baseline. © 2025, CC BY.

关键词： Mean square error

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Comparison of Electronic Prescription Systems in the European Union: Benchmarking Development, Use, and Future Trends

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3712-3722页

作者： Bruthans, Jan Duftschmid, Georg Hammar, Tora Kardas, Przemyslaw Bertalan, Lorant Hug, Martin J. Bullow, Cille Muscat, Hugo Agius Lillevali, Anett Jormanainen, Vesa Fernandez-Llimos, Fernando Fidalgo, Luz Karanikas, Haralampos Nyssen, Marc Popescu, Nirvana Cudejkova, Maria Tachkov, Konstantin Hadziabdic, Maja Ortner Neofytou, Marios Dauksiene, Jurgita Huisman, Anneke Ryan, Brid Kasparans, Aris Napoleoni, Matteo Perfili, Mirko Smet, Daisy Calafiore, Matthieu Stanimirovic, Dalibor Czech Technical University in Prague Department of Biomedical Technology Faculty of Biomedical Engineering Prague Czech Republic Medical University of Vienna Section for Medical Information Management Center for Medical Data Science Vienna Austria Linnaeus University eHealth Institute Kalmar Sweden Medical University of Lodz Medication Adherence Research Centre Department of Family Medicine Lodz Poland Semmelweis University Faculty of Health Sciences Budapest Hungary Medical Center-University of Freiburg Pharmacy Freiburg Germany Bispebjerg and Frederiksberg University Hospital Department of Clinical Pharmacology Copenhagen Denmark Ministry for Health and Active Ageing Directorate for Health Information & Research Valletta Malta Estonian Health Insurance Fund Tallinn Estonia University of Helsinki Department of Public Health Faculty of Medicine Helsinki Finland University of Porto Applied Molecular Biosciences Unit Laboratory of Pharmacology Faculty of Pharmacy Porto Portugal Ministry of Health Madrid Spain University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Vrije Universiteit Brussel Department of Public Health Brussel Belgium National University of Science and Technology POLITEHNICA Bucharest Computer Science Department Bucharest Romania National Health Information Centre Bratislava Slovakia Medical University of Sofia Faculty of Pharmacy Sofia Bulgaria University of Zagreb Faculty of Pharmacy and Biochemistry Zagreb Croatia University of Cyprus Electronic Health Laboratory Computer Science Department Nicosia Cyprus Lithuanian University of Health Sciences Drug technology and Social Pharmacy Department Vilnius Lithuania Royal Dutch Pharmacists Association The Hague Netherlands Health Service Executive ePharmacy Programme Dublin Ireland National Health Service of Latvia Riga Latvia GE Healthcare Milan Italy Bambino Gesu' Children's Research Hospital IRCCS Rome Italy Agence eSanté Luxembourg Luxe

While Electronic Prescription Systems (EPS) adoption varies across EU Member States, there's a lack of comprehensive comparative analysis. Existing studies focus on single EPSs, employ diverse methodologies, and lack up-to-date data. This study fills this gap by providing a comprehensive overview of EPS development, functionalities, and usage statistics in each EU Member State. Most EU Member States widely adopted EPS by 2022, with exceptions including Germany, France, and Luxembourg, where pilot projects or just plans existed at that time. Out of the 27 EPSs, 25 employ a similar design featuring a central server and end-user software or web-based applications. Among these, 22 are structured as single national systems. The fundamental technical solution is remarkably similar across the EU. Despite these similarities, functionalities, authentication methods, prescription validity, and medication coverage differ significantly among EPSs. A multinational team, including co-authors from each EU Member State, collected data using a structured questionnaire. The study underscores the need for standardized methodologies in EPS research and emphasizes the importance of comprehensive comparative analysis to inform healthcare policies and digitalization efforts. © 2013 IEEE.

关键词： Electronic health record

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Thai voice language encryption and decryption using 3DES and AES algorithm for communication

Thai voice language encryption and decryption using 3DES and...

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International Electrical Engineering Congress (iEECON)

作者： Naruemon Dobay Mongkhon Thakong Krit Somkantha Kritsanapong Somsuk Department of Data Science and Information Technology Faculty of Science Udon Thani Rajabhat University Thailand Graduate School Udon Thani Rajabhat University Udon Thani Thailnad General Education Udon Thani Rajabhat University Thailand Faculty of Science Udon Thani Rajabhat University Thailand Faculty of Technology and Engineering Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The aim of this paper is to compare the performance of the encryption and decryption of Thai voice language by using Triple data Encryption Standard (3DES) and Advanced Encryption Standard (AES) algorithms for secure communication. The research methodology involves collecting Thai voice samples, converting speech to text by using Google’s API to be applied with cryptography algorithms. Performance comparison between 3DES and AES focuses on accuracy and processing time. Experimental results demonstrate 100% accuracy in preserving original text content for both algorithms. However, AES consistently outperforms 3DES in processing speed, particularly for larger data sizes. The study reveals a linear relationship between input size and processing time for both algorithms. Furthermore, the findings imply that AES offers superior performance in terms of speed and security for encrypting Thai voice data, making it more suitable for secure communication applications. Therefore, this research contributes to improve data security in Thai voice communications by providing empirical evidence on the efficacy of modern encryption standards.

关键词： Electrical engineering Three-dimensional displays Accuracy Encryption Text to speech Standards Speech to text Automatic speech recognition

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Optimizing hyperparameters for dual-attention network in lung segmentation

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Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

arXiv

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arXiv 2025年

作者： Lei, Yuchen Xiang, Yuexin Wang, Qin Dowsley, Rafael Yuen, Tsz Hon Yu, Jiangshan School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Information Technology Monash University Melbourne Australia CSIRO Data61 Sydney Australia School of Computer Science University of Sydney Sydney Australia

Cryptocurrencies are widely used, yet current methods for analyzing transactions heavily rely on opaque, black-box models. These lack interpretability and adaptability, failing to effectively capture behavioral patterns. Many researchers, including us, believe that Large Language Models (LLMs) could bridge this gap due to their robust reasoning abilities for complex tasks. In this paper, we test this hypothesis by applying LLMs to real-world cryptocurrency transaction graphs, specifically within the Bitcoin network. We introduce a three-tiered framework to assess LLM capabilities: foundational metrics, characteristic overview, and contextual interpretation. This includes a new, human-readable graph representation format, LLM4TG, and a connectivity-enhanced sampling algorithm, CETraS, which simplifies larger transaction graphs. Experimental results show that LLMs excel at foundational metrics and offer detailed characteristic overviews. Their effectiveness in contextual interpretation suggests they can provide useful explanations of transaction behaviors, even with limited labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Bitcoin

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A Multi-tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Haiyang Xiao, Bo Zhou, Xiaokang Zhang, Yonghong Liu, Xiaodong the School of Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China the School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing210044 China the Faculty of Business Data Science Kansai University Osaka565-0823 Japan the School of Automation Nanjing University of Information Science and Technology Nanjing210044 China the School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

—In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications. © 2025 IEEE.

关键词： Computation offloading

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