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SSRN 2025年

作者： Shen, Linjie Wang, Yanbin Li, Zhao Ma, Wenrui School of Computer Science and Technology Zhejiang Gongshang University Zhejiang Hangzhou China Department of Engineering Shenzhen MSU-BIT University Guangdong Shenzhen518172 China Zhejiang Lab Zhejiang Hangzhou China Department of Big Data and Future E-Commerce Technology Zhejiang Key Laboratory Zhejiang Hangzhou China

The surge in smartphone usage has significantly increased SMS traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches. Traditional rule-based and blacklist methods fail against evolving spamming techniques, prompting the adoption of machine learning and deep learning approaches. However, models like CNNs and RNNs struggle to capture global co-occurrence patterns and complex semantics, while transformer-based models like BERT lack explicit syntactic and co-occurrence modeling. To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Cooccurrence, Heterogeneous, and Integrated Syntactic Graphs. This multi-graph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks (GCNs). Experiments on two benchmark datasets demonstrate that BERT-G3CN achieves superior accuracy of 99.28% and 93.78%. © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Deterministic and Dynamic Joint Placement and Scheduling of VNF-FGs for Remote Robotic Surgery

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1841-1858页

作者： Hentati, Amina Ebrahimzadeh, Amin Glitho, Roch H. Belqasmi, Fatna Mizouni, Rabeb The Computer Science Programme University of Western Cape Capetown7535 South Africa The College of Innovative Technology Zayed University Abu Dhabi United Arab Emirates The Department of ECE Khalifa University Abu Dhabi127788 United Arab Emirates The Telecommunication Service Engineering Research Lab Concordia University MontréalQCH3G 1M8 Canada CIISE Concordia University MontréalQC Canada University of Western Cape Bellville South Africa Zayed University Dubai United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates

During a Remote Robotic Surgery (RRS) session, multimodal data traffic with different requirements is initiated. In order to achieve a cost-effective deployment of such a system, it is crucial to tailor resource allocation policies based on the different quality of service (QoS) requirements of each data traffic. In this paper, we focus on resource allocation in a 5G-enabled tactile Internet RRS system using network function virtualization (NFV). In particular, we investigate the joint placement and scheduling of Virtualized Network Functions (VNFs) in a RRS system under both deterministic and dynamic settings. An integer linear program (ILP) is used to formulate the problem. Due to its high computational complexity, we first propose an efficient greedy algorithm to solve the ILP under deterministic settings. Simulation results show that our proposed algorithm achieves near-optimal performance and outperforms the benchmark solutions in terms of cost and admission rate. It can reduce cost by up to 37% and improve admission rate by up to 34% while satisfying both latency and reliability constraints. Furthermore, our results show that modeling the multimodal data traffic by multiple VNF Forwarding Graphs (VNF-FGs) with different QoS requirements achieves a significant gain in terms of cost and acceptance rate compared to modeling it by a single VNF-FG with the most stringent requirements. We then considered a dynamic environment where latency variations and traffic arrivals may occur over time. Using the principles of optimal stopping theory, we propose an adaptive dynamic scheduler that is capable of triggering recalculations of the existing optimal solution based on the observed cumulative number of traffic arrivals and latency violations without the need for predictions. Our proposed optimal scheduler minimizes the migration cost compared to other schedulers. © 2004-2012 IEEE.

关键词： Resource allocation

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Fail-safety techniques and their extensions to concurrent systems

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computer LANGUAGES 1996年第4期22卷 193-203页

作者： Drew, SJ Programming Languages and Systems Group Computer Science Department Queensland University of Technology Sydney Queensland Australia 4000

Fail-safety is a system attribute which ensures that a program either completes its execution satisfying its post-conditions in the normal manner or signals its failure to do so to its operating environment. Such an attribute is desirable of any system as it ensures the correctness of results which are produced. A very few modern sequential programming languages offer program fail-safety through the judicious use of a well designed exception handling mechanism. In this paper the exception handling techniques used in those sequential systems are developed to provide the guidelines for fail-safe concurrent system design. (C) 1997 Elsevier science Ltd.

关键词： computer system recovery

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Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices 1st

Environmental Considerations in the Ethics of AI Adoption in...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Lautre, Hitendra Kumar Gowrishankar, R. Prathibha, R.S. Pradeep, T. Parveen Akhter, A. Ravisankar, A. Sundramurthy, Venkatesa Prabhu Department of Chemistry BYOS SCIENTIFIC LAB Aman Nagar Chhattisgarh Mowa Raipur492007 India Coimbatore641402 India Department of Chemistry Amrita College of Engineering and Technology Erachakulam Nagercoil629004 India Department of Civil Engineering Kongu Engineering College Erode Tamilnadu Perundurai638060 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Erode Tamil Nadu Perineuria638057 India Center of Excellence for Bioprocess and Biotechnology Department of Chemical Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031843969

Medical treatment may be expanded to disadvantaged areas, and gaps in healthcare provider availability can be closed using artificial intelligence (AI). Artificial intelligence (AI) can potentially improve healthcare, but it also poses risks to patients. Ethical concerns about AI in healthcare have been raised, but the essential thing to consider is how AI will affect the natural world, not software flaws. In many nations, the healthcare sector significantly contributes to greenhouse gas emissions. Ethical considerations must consider the potential for AI to harm the environment as it becomes increasingly integrated into medical practice. Therefore, this paper will begin with a general summary of healthcare's carbon footprint. Second, it will discuss five issues with using carbon estimates to tackle healthcare sustainability. By extracting normative conceptions from green bioethics, the objective of medicines and the ethics of biomedicines and this study will offer healthiness, justice, and conservation of resources as the criterion for sustainability Artificial Intelligence in fitness care. Finally, the essay concludes with a discussion of how health, justice, and resource conservation might shed light on the offered case studies of sustainable and unsustainable growth and the application of AI in health care. Ethical considerations surrounding the application of AI in healthcare should include the requirement for environmental sustainability to prevent the release of further damaging carbon emissions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Greenhouse gas emissions

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ReMirrorFugue: Examining the Emotional Experience of Presence and (Illusory) Communications Across Time 25

ReMirrorFugue: Examining the Emotional Experience of Presenc...

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

作者： Xiao Xiao Hayoun Noh Adrien Lefevre Lucy Li Holly McKee Alaa Algargoosh Hiroshi Ishii De Vinci Research Center De Vinci Higher Education Paris France and MIT Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Computer Science University of Oxford Oxford United Kingdom and MIT Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA MIT Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA Hasso Plattner Institute University of Potsdam Potsdam Germany and MIT Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

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Comparative Study of Embedded Energy Management Methods based on Machine Learning for Dual-Source Electric Vehicles

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IEEE Transactions on Transportation Electrification 2025年

作者： Adnane, Marouane Nguyen, Bao-Huy Khoumsi, Ahmed Trovao, Joao Pedro F. Université de Sherbrooke Department of Electrical Engineering and Computer Engineering SherbrookeQCJ1K 2R1 Canada Hanoi University of Science and Technology CTI Lab4EV School of Electrical and Electronic Engineering Hanoi Viet Nam

In the actual context of dual-source electric vehicles (DSEVs), efficient energy management strategies (EMSs) are essential to optimize energy distribution between batteries and supercapacitors. However, achieving real-time decision-making and adaptability in resource-constrained environments remains a significant challenge. This study addresses these challenges by comparing two advanced machine learning (ML)-based EMSs: Driving Mode Prediction-based EMS (DMP-EMS) and Multi-Ensemble Learning-based EMS (MEL-EMS). Using Signal Hardware-In-the-Loop simulation and a Raspberry Pi 4 platform, the two EMSs are evaluated based on various criteria, including performance accuracy, model complexity, training and prediction times, required computing resources, scalability, and energy consumption. The findings highlight the trade-offs between these EMSs, offering insights into designing tailored EMSs for DSEVs under varying operational demands. While MEL-EMS excels in environments where prediction accuracy is paramount since it offers superior predictive performance suitable for applications demanding high accuracy and scalability, DMP-EMS provides a more efficient and practical solution for real-time energy management in resource-constrained settings. © 2015 IEEE.

关键词： Hardware-in-the-loop simulation

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Spin-wave eigenmodes in nanoscale magnetic tunnel junctions with perpendicular magnetic anisotropy

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Physical Review Applied 2025年第3期23卷 034086-034086页

作者： Andrea Meo Chengcen Sha Emily Darwin Riccardo Tomasello Mario Carpentieri Ilya N. Krivorotov Giovanni Finocchio Department of Electrical and Information Engineering Politecnico di Bari I-70125 Bari Italy Department of Physics and Astronomy University of California Irvine California 92697 USA Magnetic & Functional Thin Films Lab Empa Swiss Federal Laboratories for Materials Science and Technology CH-8600 Dübendorf Switzerland Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina I-98166 Messina Italy

Magnetic tunnel junctions (MTJs) are key enablers of spintronic technologies used in a variety of applications, including information storage, microwave generation and detection, and unconventional computing. Here, we present experimental and theoretical studies of the quantized spin wave eigenmodes in perpendicular MTJs focusing on the coupled magnetization dynamics in the free (FL) and reference (RL) layers of the MTJ, where the RL is a synthetic antiferromagnet (SAF). Spin-torque ferromagnetic resonance (ST-FMR) measurements reveal the excitation of two spin-wave eigenmodes in response to an applied microwave current. These modes show opposite frequency shifts as a function of an out-of-plane magnetic field. Our micromagnetic simulations accurately reproduce the dependence of the mode frequencies on the magnetic field and reveal the spatial profiles of the excitations in the FL and RL. The FL and RL modes generate rectified voltage signals of opposite polarity, which makes this device a promising candidate for a tunable dual-frequency microwave signal detector. The simulations show that a weak interlayer exchange coupling within the SAF enhances the mode amplitudes. We also calculate the response of the detector as a function of an in-plane magnetic field bias and find that its sensitivity significantly grows with increasing field strength. We experimentally confirm this prediction via ST-FMR measurements as a function of the in-plane magnetic field. Our results provide a deeper understanding of the quantized spin-wave eigenmodes in nanoscale MTJs with perpendicular magnetic anisotropy and demonstrate the potential of these devices for frequency-selective dual-channel microwave signal detectors.

关键词： Spin waves

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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