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arXiv 2024年

作者： Li, Lin Gou, Jianping Yu, Baosheng Du, Lan Yi, Zhang Tao, Dacheng The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The College of Computer and Information Science College of Software Southwest University Chongqing400715 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Faculty of Information Technology Monash University ClaytonVIC Australia The School of Computer Science Sichuan University Chengdu610065 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practical applications of FL. To address these limitations, the integration of knowledge distillation (KD) into FL has been proposed, forming what is known as Federated Distillation (FD). FD enables more flexible knowledge transfer between clients and the server, surpassing the mere sharing of model parameters. By eliminating the need for identical model architectures across clients and the server, FD mitigates the communication costs associated with training large-scale models. This paper aims to offer a comprehensive overview of FD, highlighting its latest advancements. It delves into the fundamental principles underlying the design of FD frameworks, delineates FD approaches for tackling various challenges, and provides insights into the diverse applications of FD across different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Game-Theoretic Dependent Task Offloading and Resource Pricing in Vehicular Edge Computing

Game-Theoretic Dependent Task Offloading and Resource Pricin...

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International Workshop on Quality of Service

作者： Liang Zhao Shuai Huang Huan Zhou Zilong Bai Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

This paper proposes a Stacklberg game-based Dependent task Offloading and resource Pricing framework (SDOP), where vehicles partially offload their dependent substaks to the SDN controller and pays corresponding fees. Firstly, we model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG outperforms other baseline schemes under various scenarios.

关键词： Simulation Computational modeling Pricing Games Quality of service Benchmark testing Optimization

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Multi-scale Attention-Based Feature Pyramid Networks for Object Detection 11th

Multi-scale Attention-Based Feature Pyramid Networks for Obj...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zhao, Xiaodong Chen, Junliang Liu, Minmin Ye, Kai Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783030873547

Feature pyramid network (FPN) is widely used for multi-scale object detection. While lots of FPN based methods have been proposed to improve detection performance, there exists semantic difference between cross-scale features. Therefore, simple connections bring spatial or channel information loss, while excessive connections bring extra parameters and inference cost. Besides, the fusion of too many features may lead to the information decay and feature aliasing. To deal with the above problems, we propose the Multi-scale Attention-based Feature Pyramid Networks (MAFPN), to fully exploit spatial and channel information and generate a better feature representation for each level from multi-scale features. Taking scale, spatial and channel information into consideration at the same time, MAFPN can process multi-scale input more comprehensively than most conventional methods. The experimental results show that our MAFPN can improve the detection performance of both two-stage and one-stage detectors with an acceptable increase of inference cost. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge Computing: A Stackelberg Game Approach

Dependency-aware Task Offloading and Resource Pricing in Veh...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Liang Zhao Shuai Huang Yuxiang Cao Huan Zhou Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

关键词： Energy consumption Computational modeling Road side unit Process control Games Pricing Quality of service Delays Edge computing Genetic algorithms

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

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computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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