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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Leveraging Unlabeled Corpus for Arabic Dialect Identification

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computers, Materials & Continua 2025年第5期83卷 3471-3491页

作者： Mohammed Abdelmajeed Jiangbin Zheng Ahmed Murtadha Youcef Nafa Mohammed Abaker Muhammad Pervez Akhter School of Computer Science Northwestern Polytechnical UniversityXi'an710072China Department of Computer Science Applied CollegeKing Khalid UniversityMuhayil6311Saudi Arabia Computer Science Department National University of Modern LanguagesFaisalabad38000Pakistan

Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in *** state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given *** the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world *** alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID ***,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal *** key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the ***,we evaluate the proposed solution on benchmark datasets for *** extensive experiments show that it performs signifcantly better,especially,with sparse labeled *** comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID *** code will be available on GitHub upon the paper's acceptance.

关键词： Arabic dialect identification natural language processing bidirectional encoder representations from transformers pre-trained language models gradient reversal layer

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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DP-ARTS: Dynamic Prioritization for Adaptive Resource Allocation and Task Scheduling in Cloud Computing

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 793-807页

作者： BEN ALLA, Hicham BEN ALLA, Said EZZATI, Abdellah Computer Science Mathematics and Computer Science Department Science and Technical Faculty Hassan 1 University Settat Morocco Embedded Systems and Computer Science National School of Applied Sciences of Berrechid Hassan 1 University Settat Morocco

Efficient task scheduling and resource allocation are essential for optimizing performance in cloud computing environments. The presence of priority constraints necessitates advanced solutions capable of addressing the increased complexity in the task scheduling process and resource allocation management. In this paper, we propose a novel approach, Dynamic Prioritization and Adaptive Resource Allocation and Task Scheduling (DP-ARTS), to tackle these challenges by integrating dynamic task prioritization with adaptive resource allocation and scheduling strategies. Our approach introduces a scoring mechanism to prioritize tasks based on deadlines, resource requirements, and other critical characteristics. Furthermore, we utilize K-means clustering to group Virtual Machines (VMs) according to their capabilities, facilitating more efficient task-to-VM mapping. Through simulations conducted with real-world workload traces in the CloudSim toolkit, DP-ARTS demonstrates superior performance in reducing makespan and energy consumption, while also improving resource utilization and load balancing compared to existing solutions. © (2025), (International Association of Engineers). All rights reserved.

关键词： K-means clustering

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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A High-Order Hybrid-Spectral Incompressible Navier–Stokes Model for Non-Linear Water Waves

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International Journal for Numerical Methods in Fluids 2025年第6期97卷 1009-1021页

作者： Melander, Anders Ebstrup Bitsch, Max Chen, Dong Peter Engsig-Karup, Allan Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

We present a new high-order accurate computational fluid dynamics model based on the incompressible Navier–Stokes equations with a free surface for the accurate simulation of non-linear and dispersive water waves in the time domain. The spatial discretization is based on Chebyshev polynomials in the vertical direction and a Fourier basis in the horizontal direction, allowing for the use of the fast Chebyshev and Fourier transforms for the efficient computation of spatial derivatives. The temporal discretization is done through a generalized low-storage explicit fourth-order Runge–Kutta, and for the scheme to conserve mass and achieve high-order accuracy, a velocity-pressure coupling needs to be satisfied at all Runge–Kutta stages. This results in the emergence of a Poisson pressure problem that constitutes a geometric conservation law for mass conservation. The occurring Poisson problem is proposed to be solved efficiently via an accelerated iterative solver based on a geometric (Formula presented.) -multigrid scheme, which takes advantage of the high-order polynomial basis in the spatial discretization and hence distinguishes itself from conventional low-order numerical schemes. We present numerical experiments for validation of the scheme in the context of numerical wave tanks demonstrating that the (Formula presented.) -multigrid accelerated numerical scheme can effectively solve the Poisson problem that constitute the computational bottleneck, that the model can achieve the desired spectral convergence, and is capable of simulating wave-propagation over non-flat bottoms with excellent agreement in comparison to experimental results. © 2025 The Author(s). International Journal for Numerical Methods in Fluids published by John Wiley & Sons Ltd.

关键词： Time domain analysis

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Performance analysis of naive vehicular named data networks

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International Journal of Wireless and Mobile Computing 2025年第3期28卷 234-244页

作者： Nithin Rao, R. Sharma, Rinki Department of Computer Science and Engineering Ramaiah University of Applied Sciences Karnataka Bangalore India

Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challenges such as interest/data flooding, routing, naming schemes, channel constraints and security, among others. The proposed work aims to analyse the behaviour of the naive VNDN under diverse scenarios, which is crucial for devising effective forwarding, caching and security strategies to enhance VNDN’s performance. In this paper, the naive VNDN architecture is implemented and its performance is evaluated by considering the metrics such as Throughput, Packet Delivery Ratio (PDR), Interest Satisfaction Delay (ISD), Interest Satisfaction Ratio (ISR), Copies of Data Packets Processed (CDPP) and Hop Count Difference (HCD). These metrics are tested against varying vehicle speed, network size and interests generated by each vehicle in the network. The simulation results provide insights that should be considered while developing the forwarding strategies and caching mechanisms that will improve the performance of VNDN. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： caching content forwarding data packets delay naming timestamp

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Teaching Coordination to Selfish Learning Agents in Resource-Constrained Partially Observable Markov Games

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IEEE Transactions on Automatic Control 2025年第5期70卷 3449-3455页

作者： Tsaousoglou, Georgios Technical University of Denmark Department of Applied Mathematics and Computer Science Lyngby2800 Denmark

Of increasing relevance to engineering systems are problems that include online resource allocation to agents that feature adaptation and learning capabilities. This article considers the case where a coordinator gets to design a resource allocation mechanism (i.e., a bidding-allocation-rewards protocol) to efficiently allocate a resource to selfish agents that try to gain access by learning to communicate strategically. Toward aligning the agents' incentives with the social objective, a critical-value-based mechanism is proposed. Analytic results are presented for a simple, stylized setting, whereas simulation results for a use case with reinforcement learning agents controlling flexible loads in the smart grid demonstrate the mechanism's ability to teach coordinated behavior to the distributed learners. © 1963-2012 IEEE.

关键词： Adversarial machine learning

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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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computer Modeling in Engineering & sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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