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A Learning-Based Stochastic Game for Energy Efficient Optimization of UAV Trajectory and Task Offloading in Space/Aerial Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Jialiuyuan Shi, You Dai, Chen Yi, Changyan Yang, Yuxiao Zhai, Xiangping Zhu, Kun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing211106 China

In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) and low earth orbital (LEO) satellite assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs). The key challenge involves how to efficiently execute tasks offloaded from IoT devices, given limited on-board computation capabilities of UAVs and delay constraints of tasks. To address it, UAVs are first allowed to adjust their trajectories to accommodate as many tasks as possible from IoT devices. Subsequently, UAVs compute a portion of these tasks while delegating the rest to LEO satellites for further execution. Accordingly, we formulate a joint optimization problem including UAV trajectory planning, task offloading, and bandwidth allocations, aiming to maximize the long-term energy efficiency of UAVs and LEO satellites (i.e., the total data size in computation offloading divided by energy consumption of UAVs and LEO satellites). Considering the interactions among intelligent UAVs, this problem is reformulated as a set of interconnected multi-agent stochastic games, and the existence of corresponding Nash Equilibrium (NE) is theoretically proved. To obtain the NE, a multi-agent reinforcement learning-based algorithm, namely UTPTR, is developed concerning system dynamics and uncertainties. Simulations evaluate the performance of the UTPTR and demonstrate its superiority compared to existing approaches. © 1967-2012 IEEE.

关键词： Stochastic systems

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Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation

arXiv

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arXiv 2024年

作者： Dong, Wei Sun, Yuan Yang, Yiting Zhang, Xing Lin, Zhijun Yan, Qingsen Zhang, Haokui Wang, Peng Yang, Yang Shen, Hengtao College of Information and Control Engineering Xi’an University of Architecture and Technology China School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China

A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix. This matrix is decomposed into a product of down-projection and up-projection matrices, with the bottleneck dimensionality being crucial for reducing the number of learnable parameters, as exemplified by prevalent methods like LoRA and Adapter. However, these low-rank strategies typically employ a fixed bottleneck dimensionality, which limits their flexibility in handling layer-wise variations. To address this limitation, we propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix. We utilize Householder transformations to construct orthogonal matrices that efficiently mimic the unitary matrices, requiring only a vector. The diagonal values are learned in a layer-wise manner, allowing them to flexibly capture the unique properties of each layer. This approach enables the generation of adaptation matrices with varying ranks across different layers, providing greater flexibility in adapting pre-trained models. Experiments on standard downstream vision tasks demonstrate that our method achieves promising fine-tuning performance. © 2024, CC BY.

关键词： Singular value decomposition

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Unsupervised Time Series Anomaly Detection under Data Contamination 12

Unsupervised Time Series Anomaly Detection under Data Contam...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

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Generic,efficient,and effective deobfuscation and semantic-aware attack detection for Power Shell scripts

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Frontiers of Information Technology & Electronic engineering 2022年第3期23卷 361-381页

作者： Chunlin XIONG Zhenyuan LI Yan CHEN Tiantian ZHU Jian WANG Hai YANG Wei RUAN College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Electrical Engineering and Computer Science Northwestern UniversityEvanstonIL 60208USA College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Magic Shield Co. Ltd.Hangzhou 310027China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

In recent years,Power Shell has increasingly been reported as appearing in a variety of cyber ***,because the PowerShell language is dynamic by design and can construct script fragments at different levels,state-of-the-art static analysis based Power Shell attack detection approaches are inherently vulnerable to *** this paper,we design the first generic,effective,and lightweight deobfuscation approach for PowerShell *** precisely identify the obfuscated script fragments,we define obfuscation based on the differences in the impacts on the abstract syntax trees of PowerShell scripts and propose a novel emulation-based recovery ***,we design the first semantic-aware PowerShell attack detection system that leverages the classic objective-oriented association mining algorithm and newly identifies 31 semantic *** experimental results on 2342 benign samples and 4141 malicious samples show that our deobfuscation method takes less than 0.5 s on average and increases the similarity between the obfuscated and original scripts from 0.5%to 93.2%.By deploying our deobfuscation method,the attack detection rates for Windows Defender and VirusTotal increase substantially from 0.33%and 2.65%to 78.9%and 94.0%,***,our detection system outperforms both existing tools with a 96.7%true positive rate and a 0%false positive rate on average.

关键词： PowerShell Abstract syntax tree Obfuscation and deobfuscation Malicious script detection

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SIMSE: a contrastive learning method combining sample importance metric and semantic enhancement

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Expert Systems with Applications 2025年 286卷

作者： Gao, Yangyang Zheng, Zhi Huang, Wenjun Lin, Xiaomin College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

In complex application scenarios, the lack of accurate labels for training samples has positioned contrastive learning a key focus in self-supervised learning. This approach effectively extracts meaningful representations from unlabeled data. A significant challenge lies in identifying high-value samples and exploring their semantic features to improve performance. Most existing self-supervised learning methods do not focus much on mining or augmenting valuable samples, relying mainly on contrastive learning between original samples. As a result, performance suffers in cases with limited or sparse data. To address this, we propose a contrastive learning method that combines sample importance measurement and semantic enhancement, overcoming the limitations of traditional methods that only use original samples. First, we generate additional valuable samples using interpolation and apply a similarity-based strategy to better distinguish positive and negative samples, refining the sample partitioning process. Second, we design a semantic enhancement mechanism to better capture and strengthen shared high-level semantic features between samples. Third, we introduce a new metric to evaluate sample value by measuring oscillations in the learning model caused by gradients, determining the importance of each sample. We also use confidence learning to identify and correct mislabeled samples. Extensive evaluations conduct on multiple benchmark datasets demonstrate that our method improves linear classification accuracy by 2.23 %, 4.3 %, 1.6 %, 3.73 %, and 4.69 % on ImageNet-100, ImageNet-10, CIFAR-10, CIFAR-100, and STL-10, respectively. Additionally, accelerates convergence speed by 1.5x and effectively detects mislabeled samples. © 2025 Elsevier Ltd

关键词： Self-supervised learning

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Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4 15

Detection of Self-explosive Insulators in Aerial Images Base...

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2022 15th International Conference on computer and Electrical engineering, ICCEE 2022

作者： Yi, Li Jinguo, Li Yongjie, Zhai Ping, Meng College of Computer Science and Technology Shanghai University of Electric Power Shanghai200000 China School of Control and Computer Engineering North China Electric Power University Baoding Hebei071000 China

In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm. In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators. The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K. The experimental results show that the proposed algorithm has a mAP of 92.0 % on the self-explosive insulator dataset and has an F1-score of 78.70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Explosives

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Geometric Reachability for Attitude control Systems via Contraction Theory

arXiv

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arXiv 2025年

作者： Xu, Chencheng Jafarpour, Saber Zhao, Chengcheng Shi, Zhiguo Chen, Jiming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States College of Control Science and Engineering Zhejiang University Hangzhou China

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold SO(3) × 3 and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Optimal Strategies Trajectory with Multi-Local-Worlds Graph

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computers, Materials & Continua 2023年第4期75卷 2079-2099页

作者： Xiang Yu Chonghua Wang Xiaojing Zheng Chaoyu Zeng Brij BGupta School of Electronics and Information Engineering Taizhou UniversityTaizhouZhejiang318000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China School of Economics and Management Weifang University of Science and TechnologyShouguangShandong262700China National Computer System Engineering Research Institute of China Beijing102699China Department of Computer Science and Information Engineering Asia UniversityTaiwanTaichung413China Research and Innovation Department Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates Staffordshire University Stoke-on-TrentST42DEUK

This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.

关键词： Multi-Local-world graph stochastic differential game optimal strategy attractor

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ELOFS: An Extensible Low-Overhead Flash File System for Resource-Scarce Embedded Devices

ELOFS: An Extensible Low-Overhead Flash File System for Reso...

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作者： Zhang, Runyu Liu, Duo Chen, Xianzhang She, Xiongxiong Yang, Chaoshu Tan, Yujuan Shen, Zhaoyan Shao, Zili Qiao, Lei Chongqing400044 China Chongqing University College of Computer Science Chongqing400044 China Shandong University School of Computer Science and Technology Qingdao266237 China The Chinese University of Hong Kong Department of Computer Science and Engineering NT 999077 Hong Kong Beijing Institute of Control Engineering Beijing100190 China

Emerging applications like machine learning in embedded devices (e.g., satellites and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-scale flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for two reasons. First, prior arts store data structures of file systems either in flash or in main memory, which severely magnifies the scarcity of computing and memory resources. Moreover, the fine-grained metadata management in the existing embedded file systems induces significant energy consumption for large-capacity storage. In this paper, we propose a novel embedded file system, ELOFS, to tackle the above issues and manage large-capacity NAND flash on resource-scarce devices. ELOFS is made efficient through three novel techniques. First, we redefine the space management granularity and streamline the metadata to speed up the mounting performance. In addition, we design hybrid file structures to adapt dissimilar access patterns of embedded devices. Furthermore, ELOFS provides opportunities for in-depth cooperation with application-specific systems. We implement ELOFS with Memory Technology Device (MTD) interfaces, and the experimental results show that ELOFS outperforms YAFFS and UBIFS in terms of write, read, and deletions with orders of magnitude reductions on memory footprint and mounting time. © 1968-2012 IEEE.

关键词： Metadata

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CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

CAMInterHand: Cooperative Attention for Multi-View Interacti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guwen Han Qi Ye Anjun Chen Jiming Chen College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineeringthe State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

关键词： Fuses Semantics Feature extraction Probabilistic logic Data mining Robotics and automation Image reconstruction

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