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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

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Towards Drought Modeling in South Asia: Machine Learning Approaches, Challenges and Opportunities

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IEEE Access 2025年

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, M.A. Rajab, Khairan Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering Istanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state- of-the-art in ML and DL (Complex Machine learning and deep learning models) for drought modeling, highlighting current challenges such as incomplete and inconsistent datasets, Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.). © 2013 IEEE.

关键词： Federated learning

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A review on machine learning techniques for network security

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Journal of Cyber Security Technology 2025年

作者： Nazir, Rashid Laghari, Asif Ali Dahri, Fida Hussain Shoulin, Yin Alhakeem, Zaineb M. Hakim, Heba Mughal, Zeeshan Ahmed Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Software college Shenyang Normal University Shenyang China School of Computer Science and Engineering Southeast University Nanjing China Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq

Network security encompasses the strategies and techniques to protect networks from unauthorized access and potential threats. Network security is essential to protect layers of networks and data transferring on them. Data integrity and the underlying network infrastructure are necessary in today’s digital landscape. This paper explores the role of machine learning (ML) in enhancing network security by developing intelligent tools and methodologies. We critically analyze various ML techniques utilized in this field, their practical applications, and the advancements achieved in fortifying networks against cyber threats. We address ML’s challenges and limitations in network security, concluding with a discussion of open research issues for future investigation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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Optimizing Existing SHM Systems: Retasking as a Self-Healing Solution for Improved Fault Tolerance

Optimizing Existing SHM Systems: Retasking as a Self-Healing...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Aaron Mano Cherian D. Ajitha Avanish Gouraha Dipanshu Mandal School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (clustering, Fault Tolerant Multiple Redundancy (FTMR) and reinforcement learning) are analyzed against the choice of creating a novel retasking algorithm designed for dynamic resource redistribution and optimal monitoring coverage. Unlike conventional methods, retasking will allow adapting the coverage in real time, whereby system down time will be reduced, with less computational load achieved through task redistribution through functional sensors. Findings showed that retasking improved reliability and scalability of the SHM systems drastically, providing a simple yet powerful resolution towards modern infrastructure monitoring. This study stresses the retasking capability to redefine self-healing in the SHM systems for future directions in infrastructure safety.

关键词： Training Heuristic algorithms Fault tolerant systems Redundancy Clustering algorithms Reinforcement learning Solids Real-time systems Monitoring Stress

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Autonomous Computing Emerging Networks Mobile devices Smart network Trusted Data Management

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EU-Nets: Enhanced, Explainable and Parsimonious U-Nets

arXiv

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arXiv 2025年

作者： Sun, Bohang Liò, Pietro School of Information and Software Engineering University of Electronic Science and Technology of China China Department of Computer Science and Technology University of Cambridge United Kingdom

In this study, we propose MHEX+, a framework adaptable to any U-Net architecture. Built upon MHEX+, we introduce novel U-Net variants, EU-Nets, which enhance explainability and uncertainty estimation, addressing the limitations of traditional U-Net models while improving performance and stability. A key innovation is the Equivalent Convolutional Kernel, which unifies consecutive convolutional layers, boosting interpretability. For uncertainty estimation, we propose the collaboration gradient approach, measuring gradient consistency across decoder layers. Notably, EU-Nets achieve an average accuracy improvement of 1.389% and a variance reduction of 0.83% across all networks and datasets in our experiments, requiring fewer than 0.1M parameters. © 2025, CC BY.

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Evaluating Different Hybrid Learning Algorithms Based Grid Search Algorithm for Epileptic Seizure Zone Detection

SSRN

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SSRN 2025年

作者： Özer, Ezgi Kinataş, Ahmet Furkan YıĞit, Emine Demir, Hamza Bedirhan Birinci, Alper Piri Reis University Engineering Faculty Computer Engineering Department İstanbul34940 Turkey İstinye University Engineering and Natural Science Faculty Software Engineering Department İstanbul34396 Turkey

Background and objective: Epilepsy is among the most prevalent illnesses of the central nervous system. This condition results in frequent, uncontrolled seizures that happen suddenly and are caused by a variety of trigger factors, including brain injury, physiological, genetic, etc. Involuntary spasms or distraction during seizures can cause severe bodily harm or even death for epileptics. In this paper, an effective method for accurately classifying Electroencephalogram (EEG) data for the early identification of epileptic seizures is ***: The suggested process essentially hybridizes several statistical data, discrete wavelet transformations (DWT), machine learning algorithms, and feature selection techniques independently. Through the use of DWT, the automated multi-resolution signal processing approach decomposes EEG signals into detail and approximation coefficients after splitting them into detailed parts with varying window sizes to guarantee an accurate classification performance. Statistical latent features are extracted from these coefficients that describe the nonlinear and dynamical patterns in the signals. Feature selection techniques were used to reduce the dimension of the feature matrix while highlighting the important elements. Different classifier structures were developed to classify input matrices. For all classifiers, the optimal hyperparameters were found using grid search techniques. Performance metrics for classification were calculated to assess the model's ***: In the analysis, to compare the proposed procedure with the other approaches in terms of detecting the epileptic behaviors correctly, a benchmark data set from the University of Bonn database was used. The results showed that the proposed approach can estimate more robust models concerning performance metrics and information criteria in classifying EEG signals. Also, the most important frequency bands were detected to distinguish EEG ***: Th

关键词： Discrete wavelet transforms

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Runtime Enforcement of CPS against Signal Temporal Logic

arXiv

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arXiv 2025年

作者： Su, Han Shankar, Saumya Pinisetty, Srinivas Roop, Partha S. Zhan, Naijun Institute of Software CAS & University of CAS Beijing China Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Indian Institute of Technology School of Electrical Sciences Bhubaneswar India The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Peking University School of Computer Science Institute of Software CAS Beijing China

Cyber-Physical Systems (CPSs), especially those involving autonomy, need guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to formally ensure that some specified properties are satisfied over the executions of the system. Hence, there is recent interest in the RE of CPS. However, existing methods are not designed to tackle specifications suitable for the hybrid dynamics of CPS. With this in mind, we develop runtime enforcement of CPS using properties defined in Signal Temporal Logic (STL). In this work, we aim to construct a runtime enforcer for a given STL formula to minimally modify a signal to satisfy the formula. To achieve this, the STL formula to be enforced is first translated into a timed transducer, while the signal to be corrected is encoded as timed words. We provide timed transducers for the temporal operators until and release noting that other temporal operators can be expressed using these two. Our approach enables effective enforcement of STL properties for CPS. A case study is provided to illustrate the approach and generate empirical evidence of its suitability for CPS. © 2025, CC BY-NC-ND.

关键词： Transducers

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Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?

arXiv

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arXiv 2025年

作者： Liang, Qingyuan Zhang, Zhao Sun, Zeyu Lin, Zheng Luo, Qi Xiao, Yueyi Chen, Yizhou Zhang, Yuqun Zhang, Haotian Zhang, Lu Chen, Bin Xiong, Yingfei School of Computer Science Peking University China Kuaishou Technology China Institute of Software Chinese Academy of Sciences China Department of Computer Science and Engineering Southern University of Science and Technology China

Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. However, as language models scale to the billion level or beyond, syntax-level errors become rare, making it unclear whether grammar information still provides performance benefits. To explore this, we develop a series of billion-scale GrammarCoder models, incorporating grammar rules in the code generation process. Experiments on HumanEval (+) and MBPP (+) demonstrate a notable improvement in code generation accuracy. Further analysis shows that grammar-based representations enhance LLMs’ ability to discern subtle code differences, reducing semantic errors caused by minor variations. These findings suggest that grammar-based code representations remain valuable even in billion-scale models, not only by maintaining syntax correctness but also by improving semantic differentiation.1 © 2025, CC BY.

关键词： Semantics

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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