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A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

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computer Modeling in engineering & sciences 2024年第9期140卷 3001-3016页

作者： Weijian Song Xi Li Peng Chen Juan Chen Jianhua Ren Yunni Xia School of Computer and Software Engineering Xihua UniversityChengdu610039China West China Second University Hospital Sichuan UniversityChengdu610065China School of Computer Science Chongqing UniversityChongqing400044China

With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other ***,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly ***,it is crucial to detect anomalies in the collected IoT time series from various ***,deep learning models have been leveraged for IoT anomaly ***,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning ***,the absence of accurate abnormal information in unsupervised learning methods limits their *** address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly *** performs better than unsupervised methods using only a small amount of labeled *** Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the ***,the dependencies between data are often unknown in time series *** solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series *** not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key *** have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.

关键词： IoT multivariate time series anomaly detection graph learning semi-supervised mean teachers

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On UAV Serving Node Deployment for Temporary Coverage in Forest Environment:A Hierarchical Deep Reinforcement Learning Approach

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Chinese Journal of Electronics 2023年第4期32卷 760-772页

作者： WANG Li WU Xuewei WANG Yanhui XIAO Zhe LI Liang FEI Aiguo School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing University of Posts and Telecommunications

Unmanned aerial vehicles(UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station or an available base station. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning(DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL(HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.

关键词： Deep learning Base stations Simulation Clustering algorithms Forestry Reinforcement learning Autonomous aerial vehicles

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Committed-programming reductions: formalizations,implications and relations

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science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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swSpAMM:optimizing large-scale sparse approximate matrix multiplication on Sunway Taihulight

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Frontiers of computer science 2023年第4期17卷 29-41页

作者： Xiaoyan LIU Yi LIU Bohong YIN Hailong YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

Although matrix multiplication plays an essential role in a wide range of applications,previous works only focus on optimizing dense or sparse matrix *** Sparse Approximate Matrix Multiply(SpAMM)is an algorithm to accelerate the multiplication of decay matrices,the sparsity of which is between dense and sparse *** addition,large-scale decay matrix multiplication is performed in scientific applications to solve cutting-edge *** optimize large-scale decay matrix multiplication using SpAMM on supercomputers such as Sunway Taihulight,we present swSpAMM,an optimized SpAMM algorithm by adapting the computation characteristics to the architecture features of Sunway ***,we propose both intra-node and inter-node optimizations to accelerate swSpAMM for large-scale *** intra-node optimizations,we explore algorithm parallelization and block-major data layout that are tailored to better utilize the architecture advantage of Sunway *** inter-node optimizations,we propose a matrix organization strategy for better distributing sub-matrices across nodes and a dynamic scheduling strategy for improving load balance across *** compare swSpAMM with the existing GEMM library on a single node as well as large-scale matrix multiplication methods on multiple *** experiment results show that swSpAMM achieves a speedup up to 14.5×and 2.2×when compared to xMath library on a single node and 2D GEMM method on multiple nodes,respectively.

关键词： approximate calculation sunway processor performance optimization

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2025年第16期84卷 16249-16280页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

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Frontiers of computer science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix

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Reinforcement learning based energy efficient robot relay for unmanned aerial vehicles against smart jamming

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science China(Information sciences) 2022年第1期65卷 234-246页

作者： Xiaozhen LU Jingfang JIE Zihan LIN Liang XIAO Jin LI Yanyong ZHANG Department of Information and Communication Engineering Xiamen University School of Computer Science and Educational Software Guangzhou University School of Computer Science and Technology University of Science and Technology of China

Unmanned aerial vehicles(UAVs) with limited energy resources, severe path loss, and shadowing to the ground base stations are vulnerable to smart jammers that aim to degrade the UAV communication performance and exhaust the UAV energy. The UAV anti-jamming communication performance, such as the outage probability, degrades if the robot relay is not aware of the jamming policies and the UAV network topology. In this paper, we propose a robot relay scheme for UAVs against smart jamming, which combines reinforcement learning with a function approximation approach named tile coding, to jointly optimize the robot moving distance and relay power with the unknown jamming channel states and locations. The robot mobility and relay policy are chosen based on the received jamming power, the robot received signal quality,location and energy consumption, and the bit error rate of the UAV messages. We also present a deep reinforcement learning version for the robot with sufficient computing resources. It uses three deep neural networks to choose the robot mobility and relay policy with reduced sample complexity, so as to avoid exploring dangerous policies that lead to the high outage probability of the UAV messages. The network architecture of the three networks is designed with fully connected layers instead of convolutional layers to reduce the computational complexity, which is analyzed by theoretical analyses. We provide the performance bound of the proposed schemes in terms of the bit error rate, robot energy consumption and utility based on a game-theoretic study. Simulation results show that the performance of our proposed relay schemes,including the bit error rate, the outage probability, and the robot energy consumption outperforms the existing schemes.

关键词： unmanned aerial vehicles relay jamming game theory reinforcement learning

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Fault diagnosis of chemical process based on CNN-BiGRU 24

Fault diagnosis of chemical process based on CNN-BiGRU

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Sun, Chengfu Ni, Yang Du, Rui Huaiyin Institute of Technology School of Computer and Software Engineering Huaian China

ISBN: (纸本)9798400718199

targeting issues where the fault features in the chemical production process are hard to discern and the fault diagnosis method's accuracy is low. This paper proposes an improved CNN-BiGRU (Convolutional Neural Networks and Bidirectional Gated Recurrent Unit) model, which is different from the usual serial structure, and adopts a parallel structure to combine the improved CNN and Bidirectional Gated Recurrent Unit. First, CNN is used to extract local features adaptively, and a batch normalization (BN) layer is used in CNN to avoid internal covariate shift and accelerate network training. In order to prevent overfitting and minimize network calculations, the global pooling layer is introduced. Second, BiGRU is used to extract global features from the data, and by incorporating a linear layer into the conventional BiGRU network, the model's accuracy is increased. Eventually, the fusion layer fuses the features that were extracted by the two networks. The experiment's outcomes demonstrate how well the model can raise fault detection accuracy. Taking the Eastman Process of Tennessee process (TE) as the research object, experiments show that the proposed method has excellent performance. © 2024 ACM.

关键词： Failure analysis

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Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems

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computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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