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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Environment Descriptions for Usability and Generalisation in Reinforcement Learning

arXiv

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arXiv 2024年

作者： Soemers, Dennis J.N.J. Samothrakis, Spyridon Driessens, Kurt Winands, Mark H.M. Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands Institute for Analytics and Data Science University of Essex Colchester United Kingdom

The majority of current reinforcement learning (RL) research involves training and deploying agents in environments that are implemented by engineers in general-purpose programming languages and more advanced frameworks such as CUDA or JAX. This makes the application of RL to novel problems of interest inaccessible to small organisations or private individuals with insufficient engineering expertise. This position paper argues that, to enable more widespread adoption of RL, it is important for the research community to shift focus towards methodologies where environments are described in user-friendly domain-specific or natural languages. Aside from improving the usability of RL, such language-based environment descriptions may also provide valuable context and boost the ability of trained agents to generalise to unseen environments within the set of all environments that can be described in any language of choice. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural environment

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FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xiao, Bingnan Zhang, Jingjing Ni, Wei Wang, Xin Fudan University School of Information Science and Technology Department of Communication Science and Engineering Shanghai200433 China CSIRO Data61 MarsfieldNSW2122 Australia School of Computing Science and Engineering KenningtonNSW2052 Australia

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper bound of FLARE is established rigorously under a general setting with non-convex models in the presence of non-i.i.d. datasets and imbalanced computing powers. By minimizing the upper bound, we further optimize the scheduling of FLARE to exploit the channel heterogeneity. A nested problem structure is revealed to facilitate iteratively allocating the bandwidth with binary search and selecting devices with a new greedy method. A linear problem structure is also identified and a low-complexity linear programming scheduling policy is designed when training models have large Lipschitz constants. Experiments demonstrate that FLARE consistently outperforms the baselines in test accuracy, and converges much faster with the proposed scheduling policy. © 2002-2012 IEEE.

关键词： Resource allocation

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C-ADAPTER: ADAPTING DEEP CLASSIFIERS FOR EFFICIENT CONFORMAL PREDICTION SETS

arXiv

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arXiv 2024年

作者： Liu, Kangdao Zeng, Hao Huang, Jianguo Zhuang, Huiping Vong, Chi-Man Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology China

Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors. To address this issue, we introduce Conformal Adapter (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy. In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs. Using C-Adapter, the model tends to produce extremely high non-conformity scores for incorrect labels, thereby enhancing the efficiency of prediction sets across different coverage rates. Extensive experiments demonstrate that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method. Copyright © 2024, The Authors. All rights reserved.

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS

arXiv

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arXiv 2024年

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Regression analysis

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Intrusion Detection System in Industrial Cyber-Physical System Using Clustered Federated Learning

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SN Computer science 2023年第5期4卷 452页

作者： Jayagopal, Vellingiri Elangovan, Mohanraj Singaram, Saranya Sathasivam Shanmugam, Kavitha Bharathi Subramaniam, Balu Bhukya, Srinivasarao School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Artificial Intelligence and Data Science K.S.Rangasamy College of Technology Tamil Nadu Tiruchengode 637215 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Chennai 603203 India Department of Computer Technology Kongu Engineering College Tamil Nadu Perundurai Erode 638060 India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamil Nadu Tiruchengode Namakkal 637205 India Department of Computer Science and Engineering Koneru Lakshmayya Education Foundation Andhra Pradesh Vaddeswaram 520002 India

The quick convergence in the industrial infrastructure with computing and networking has proliferated the attacks of industry-based Cyber-Physical Systems (CPS). Protecting such massive, sophisticated, and varied industrial CPSs from cyber-attacks is difficult. This research proposed a novel Clustered Federated Learning (CFL) technique that identifies cyber-attacks against industrial CPSs. A CFL architecture enables many industrial CPS to create an extensive model for maintaining privacy. The contribution of the research is to identify the intrusion in the CPS using CFL that is highly effective in terms of accuracy, error rate, and MCC. CFL is a Federated Multi-Task Learning (FMTL) paradigm that groups the attacks into clusters with simultaneously trainable data distributions by taking advantage of geometric features of the Federated Learning (FL) loss surface. Extensive tests on an industrial CPS dataset show their proposed CFL technique's high efficacy in identifying cyber-attacks on industrial CPSs and its advantages over cutting-edge solutions. Experiments are conducted on gas pipelining system-based datasets. In this research study, the data resource is split into two main categories: training is 80%, and testing is 20%. The proposed CFL method is effective by attaining the highest accuracy of 99.3%, the minimal error rate of 86%, and the highest Mathews Coefficient Correlation (MCC) of 0.7994 for communication round 10. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry 4 Learning Privacy Security Smart objects

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A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy

A Real Time approach for detecting and counting Vehicles usi...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Suneetha, Irala Prathima, Ch. Sirisala, Srinivasulu Mahalakshmi, S.S.D.K. Balaji Nanda Kumar Reddy, K. Sambana, Bosubabu Annamacharya Institute of Technology and Sciences Ece Department Tirupati India Mohan Babu University Dept. of Ds Tirupati India Cvr College of Engineering Department of Cse Telagana India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Annamacharya Institute of Technology and Sciences Eee Department Tirupati India Mohan Babu University School of Computing Department of Data Science Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331508685

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes. © 2025 IEEE.

关键词： Noise abatement

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning

Network link weight optimization based on antisymmetric deep...

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Next Generation data-driven Networks (NGDN), International Conference on

作者： Yuhan Chen Yingya Guo Department of Computer and Data Science Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios.

关键词： Measurement Network topology Optimization methods Telecommunication traffic Feature extraction Deep reinforcement learning Routing Load management Routing protocols Next generation networking

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image database Consortium and Image database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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ReviewRanker: A Semi-supervised Learning-Based Approach for Code Review Quality Estimation 6th

ReviewRanker: A Semi-supervised Learning-Based Approach for ...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Mahbub, Saifullah Easin Arafat, Md. Rahman, Chowdhury Rafeed Ferdows, Zannatul Hasan, Masum Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh School of Computing National University of Singapore Singapore Singapore Department of Computer Science University of Rochester RochesterNY United States

ISBN: (纸本)9789819734412

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning-based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and well-defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection by reducing human bias and the effort required for such a task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://***/saifarnab/code_review. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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