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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Celik, Yasar Bilgili, Bilge Al-Dubai, Ahmed Canberk, Berk Department of AI and Data Engineering Istanbul Technical University Turkey BTS Group Turkey The Ohio State University United States School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance’s location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle’s next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN’s key role in significantly reducing the witnessed gap within the HITS’s DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%. © 2025, CC BY.

关键词： Deep neural networks

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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ProgMate: An Intelligent Programming Assistant Based on LLM

ProgMate: An Intelligent Programming Assistant Based on LLM

引用

Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang Xiantao Zhang Peng Shi Da Yang Xuefei Huang School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like “omnipresence”, “adaptive guidance” and “customization”. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education.

关键词： Adaptive systems Navigation Large language models Digital transformation Education Educational technology Programming profession Monitoring

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

引用

2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control

Over-the-Air Federated Edge Learning with Error-Feedback One...

引用

International Conference on Computer and Communications (ICCC)

作者： Yuding Liu Dongzhu Liu Richeng Jin Guangxu Zhu Qingjiang Shi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Computing Science University of Glasgow Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. The existing one-bit digital over-the-air aggregation scheme features one-bit gradient quantization at edge devices and majority voting-based decoding at edge server, and therefore offers enhanced communication efficiency. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation, while the aggregation errors caused by channel fading in Air-FEEL further impair the performance. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) with an optimized power control policy. To this end, we first derive the impact of aggregation errors on the convergence of FEEL with EFOBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The analytical results and optimized power control policy are validated by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the vanilla one-bit quantization scheme without error feedback or optimized power control policy.

关键词： Performance evaluation Fading channels Training Quantization (signal) Wireless networks Atmospheric modeling Power control Channel estimation Training data Convergence

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM

Visualizing Program Behavior: A Study of Enhanced Program Di...

引用

Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang ShiJie Gui Peng Shi Xuefei Huang Da Yang Xiaozhou Zhang Yiming Gai School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning.

关键词： Location awareness Analytical models Codes Accuracy Statistical analysis Large language models Education data visualization Optimization methods Programming profession

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Joint Sensing, Computation, and DAI Resource Management for Integrated Non-Terrestrial and Terrestrial Networks

引用

IEEE Network 2024年

作者： Gong, Yongkang Liu, Xiaonan Yao, Haipeng Xiong, Zehui Cheng, Xiuzhen Niyato, Dusit Shandong University School of Computer Science and Technology China University of Aberdeen School of Natural and Computing Science United Kingdom Beijing University of Posts and Telecommunications School of Information and Communication Engineering China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Nanyang Technological University College of Computing and Data Science China

Integrated Non-Terrestrial and Terrestrial Networks (NTN-TN) have been emerging as a promising architecture to facilitate state sensing, computation migration, and privacy protection for terrestrial users, where both low, medium and geostationary orbit satellites collaboratively support various tasks and allocate resources effectively. Nevertheless, an integration of sensing, computation, and artificial intelligence (AI) in dynamic NTN-TNs has certain challenges in achieving high-efficient task offloading and guaranteeing robust privacy protection. To address these issues, we introduce an intelligent NTN-TN framework, focusing on sensing, computation migration, and privacy protection. Furthermore, we develop a versatile and scalable experimental platform for NTN-TNs to validate related network functions. Simulation results show that the proposed framework reduces network latency compared with advanced benchmarks. Finally, we present potential research directions and innovative solutions in future NTN-TNs. © 2024 IEEE.

关键词： Differential privacy

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LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

arXiv

引用

arXiv 2024年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Cao, Zhiqiang Wei, Dawei Liu, Jie Li, Zhijun AIoT Lab. Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed;if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT’s prowess: it remarkably decreases Deit-S’s FLOPs by 63% and concurrently amplifies throughput twofold. Code of this project is at https://***/edgeai1/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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