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Intelligent Task Offloading and Collaborative Computation over D2D Communication

China Communications 2021年第3期18卷 251-263页

作者： Cuili Jiang Tengfei Cao Jianfeng Guan Department of Computer Technology and Applications Qinghai UniversityXining 810016China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and *** the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation ***,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high ***,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,***,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

关键词： utility maximization lyapunov optimization task offloading mobile edge computing

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Inconsistency of evaluation metrics in link prediction

arXiv

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arXiv 2024年

作者： Bi, Yilin Jiao, Xinshan Lee, Yan-Li Zhou, Tao CompleX Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Software Engineering Xihua University Chengdu610039 China

Link prediction is a paradigmatic and challenging problem in network science, which predicts missing links, future links and temporal links based on known topology. Along with the increasing number of link prediction algorithms, a critical yet previously ignored risk is that the evaluation metrics for algorithm performance are usually chosen at will. This paper implements extensive experiments on hundreds of real networks and 25 well-known algorithms, revealing statistically significant inconsistency of evaluation metrics, namely different metrics probably produce remarkably different rankings of algorithms. Therefore, we conclude that any single metric cannot comprehensively or credibly evaluate algorithm performance. Further analysis suggests the usage of at least two metrics: one is the area under the receiver operating characteristic curve (AUC), and the other is one of the following three candidates metrics, say the area under the precision-recall curve (AUPR), the area under the precision curve (AUC-Precision), and the normalized discounted cumulative gain (NDCG). In addition, as we have proved the essential equivalence of threshold-dependent metrics, if in a link prediction task, some specific thresholds are meaningful, we can consider any one threshold-dependent metric with those thresholds. This work completes a missing part in the landscape of link prediction, and provides a starting point toward a well-accepted criterion or standard to select proper evaluation metrics for link prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth

arXiv

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arXiv 2024年

作者： Yuan, Xinyu Qiao, Yan Li, Meng Wei, Zhenchun Feng, Cuiying School of Computer Science and Information Engineering Hefei University of Technology China School of Information and Software Engineering University of Electronic Science and Technology of China China

Estimating the frequency of items on the high-volume, fast data stream has been extensively studied in many areas, such as database and network measurement. Traditional sketch algorithms only allow to give very rough estimates with limited memory cost, whereas some learning-augmented algorithms have been proposed recently, their offline framework requires actual frequencies that are challenging to access in general for training, and speed is too slow for real-time processing, despite the still coarse-grained accuracy. To this end, we propose a more practical learning-based estimation framework namely UCL-sketch, by following the line of equation-based sketch to estimate per-key frequencies. In a nutshell, there are two key techniques: online training via equivalent learning without ground truth, and highly scalable architecture with logical estimation buckets. We implemented experiments on both real-world and synthetic datasets. The results demonstrate that our method greatly outperforms existing state-of-the-art sketches regarding per-key accuracy and distribution, while preserving resource efficiency. Our code is attached in the supplementary material, and will be made publicly available at https://***/Y-debug-sys/UCL-sketch. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning 38

Parameter Disparities Dissection for Backdoor Defense in Het...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Wenke Ye, Mang Shi, Zekun Wan, Guancheng Li, He Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Backdoor attacks pose a serious threat to federated systems, where malicious clients optimize on the triggered distribution to mislead the global model towards a predefined target. Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts. In our work, we observe that benign heterogeneous distributions and malicious triggered distributions exhibit distinct parameter importance degrees. We introduce the Fisher Discrepancy Cluster and Rescale (FDCR) method, which utilizes Fisher Information to calculate the degree of parameter importance for local distributions. This allows us to reweight client parameter updates and identify those with large discrepancies as backdoor attackers. Furthermore, we prioritize rescaling important parameters to expedite adaptation to the target distribution, encouraging significant elements to contribute more while diminishing the influence of trivial ones. This approach enables FDCR to handle backdoor attacks in heterogeneous federated learning environments. Empirical results on various heterogeneous federated scenarios under backdoor attacks demonstrate the effectiveness of our method. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

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Journal of Advanced Research 2023年 48卷 191-211页

作者： Ukwuoma, Chiagoziem C. Qin, Zhiguang Belal Bin Heyat, Md Akhtar, Faijan Bamisile, Olusola Muaad, Abdullah Y. Addo, Daniel Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China IoT Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen 518060 China Centre for VLSI and Embedded System Technologies International Institute of Information Technology Telangana Hyderabad 500032 India Department of Science and Engineering Novel Global Community Educational Foundation Hebersham 2770 NSW Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center Chengdu University of Technology Chengdu China Department of Studies in Computer Science University of Mysore Manasagangothri Mysore India Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul 05006 South Korea

Introduction: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. Objectives: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. Methods: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. Results: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. Conclusion: The proposed hybrid deep learning framewo

关键词： Chest X-ray imaging Explainable artificial intelligence (XAI) Pneumonia identification Self-attention network Transfer ensemble learning Transformer encoder (TE)

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Discrete Zhang Neural Dynamics Algorithms for Time-Varying Matrix Generalized Sinkhorn Scaling 12

Discrete Zhang Neural Dynamics Algorithms for Time-Varying M...

引用

12th International Conference on Information science and Technology, ICIST 2022

作者： Lu, Ji Xiao, Jianzhen Chen, Canhui Mao, Mingzhi Zhang, Yunong School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Software Engineering Sun Yat-sen University Zhuhai519082 China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

In this paper, we first introduce a continuous model for time-varying matrix generalized Sinkhorn scaling (TVMGSS) on the basis of the continuous Zhang neural dynamics (ZND) model. Subsequently, a high-precision 10-instant Zhang time discretization (ZTD) formula with theoretical analysis is presented. Further, we utilize the 10-instant ZTD formula to discretize the continuous ZND model, resulting in a discrete ZND algorithm named 10-instant discrete ZND (10IDZND) algorithm for TVMGSS. For comparison, two other time discretization formulas are also considered, and the corresponding discrete algorithms for TVMGSS are derived. The comparative numerical experiments are performed, and the results substantiate the effectiveness and superior accuracy of the 10IDZND algorithm. In addition, we verify the effectiveness of the 10IDZND algorithm for higher-dimensional TVMGSS through numerical experiments. Finally, we experimentally investigate the effects of the design parameters and the sampling period on the convergence of the 10IDZND algorithm. © 2022 IEEE.

关键词： Matrix algebra

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Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

引用

computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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Model-Driven Intelligent Management: Constructing a New Framework for Efficient engineering Safety Monitoring 4

Model-Driven Intelligent Management: Constructing a New Fram...

引用

4th International Symposium on computer Technology and Information science, ISCTIS 2024

作者： Li, Hong Wang, Fei Deng, Shaoxun Guizhou Bit Software Co. Ltd. Research and Development Department Guizhou Guiyang China Institute of Engineering Safety Science Power China Guiyang Engineering Co. Ltd. Guizhou Guiyang China Guizhou University School of Computer Science and Technology Guizhou Guiyang China

ISBN: (纸本)9798350354560

In the context of ensuring operational safety for reservoir dams and other vital infrastructures, engineering safety monitoring systems play an essential role, functioning as a core component in assessing and guaranteeing structural health. Traditional monitoring systems, however, have been constrained by their reliance on data architectures tailored to specific manufacturers' devices, limiting system scalability and maintainability. This paper introduces an innovative strategy aimed at eliminating differences among instruments, achieving an integrated process from data acquisition, processing, analysis to report *** study adopts JSON format to standardize instrument models, incorporating abstract design principles that define not only fundamental parameters and data fields but also specific computational logic to accommodate diverse monitoring needs. particularly for complex instruments like multipoint displacement meters, the system introduces the concept of virtual instrument types, realizing advanced functionality through combinations of basic instrument models. Furthermore, by employing predefined templates for data visualization, graphics, and analysis, the system can flexibly adapt to various monitoring *** approach has been successfully implemented in the engineering safety monitoring projects of China Power Construction Group Guiyang Institute, covering over 82 dams, foundation pits, and other structures, processing more than 20 million monitoring records, significantly enhancing monitoring efficiency and data analysis quality. While the current system operates effectively with relational databases, future research aims to explore its potential within a Bson and NoSQL environment, further boosting scalability and flexibility. © 2024 IEEE.

关键词： Structural health monitoring

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