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DRLLog: Deep Reinforcement Learning for Online Log Anomaly Detection

IEEE Transactions on Network and Service Management 2025年第3期22卷 2382-2395页

作者： Zhou, Junwei Gao, Yuyang Zhu, Ying Yu, Xiangtian Yang, Yanchao Tan, Cheng Xiang, Jianwen Wuhan University of Technology Sanya Science and Education Innovation Park School of Computer Science and Artificial Intelligence Wuhan430070 China

System logs record the system's status and application behavior, providing support for various system management and diagnostic tasks. However, existing methods for log anomaly detection face several challenges, including limitations in recognizing current types of anomalous logs and difficulties in performing online incremental updates to the anomaly detection models. To address these challenges, this paper introduces DRLLog, which applies Deep Reinforcement Learning (DRL) networks to detect anomalous events. DRLLog uses Deep Q Network (DQN) as the agent, with log entries serving as reward signals. By interacting with the environment generated from log data and adopting various action behaviors, it aims to maximize the reward value obtained as feedback. Through this approach, DRLLog achieves learning from historical log data and perception of the current environment, enabling continuous learning and adaptation to different log sequence patterns. Additionally, DRLLog introduces low-rank adaptation by using two low-rank parameter matrices in the fully connected layer of the DQN to represent changes in its weight matrix. During online model learning, only low-rank parameter matrices of the model are updated, effectively reducing the model's overhead. Furthermore, DRLLog introduces focal loss to focus more on learning the features of anomalous logs, effectively addressing the issue of imbalanced quantities between normal and anomalous logs. We evaluated the performance on widely used log datasets, including HDFS, BGL and ThunderBird, showing an average improvement of 3% in F1-Score compared to baseline methods. During online model learning, DRLLog achieves an average reduction of 90% in parameter count and a significant decrease in training and testing time as well. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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Latent low-rank representation guided dual linear regression with different regression matrices for subspace learning

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Multimedia Tools and Applications 2025年 1-35页

作者： Zhang, Wentao Chen, Xiuhong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China

Linear regression model is one of the important learning models for classification tasks. However, the data from practical application inevitably contains some noise or is corrupted, which may lead to the decline of the learning performance of the regression model. To this end, this paper proposes two dual linear regression models based on latent low-rank representation for supervised and unsupervised cases respectively. Both models use the "clean" data to replace the original data and establish corresponding regression models, where the "clean" data learned from latent low rank representation consists of two parts, namely, the linear combination of all training samples and the linear combination of data features. It is noted that the two items in the "clean" data have different functions in reconstructing the original data matrix, so the two items in the models should have different regression coefficient matrices. Therefore, the proposed models make full use of the global structural and intrinsic feature information of the data samples, that is, the information in the row and column directions of the data matrix X, and are more robust to outliers or/and noise due to the regression of "clean" data. The iterative algorithm for solving these two models is designed by using the augmented Lagrange multiplier method based on alternating iteration method, and their convergence and computational complexity are analyzed. The comprehensive experimental results on multiple data sets also verify the effectiveness of the proposed methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Multiple linear regression

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Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells

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computers, Materials & Continua 2025年第4期83卷 559-574页

作者： Chuanyun Xu Die Hu Yang Zhang Shuaiye Huang Yisha Sun Gang Li School of Computer and Information Science Chongqing Normal UniversityChongqing401331China School of Artificial Intelligence Chongqing University of TechnologyChongqing401331China

Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical ***,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell *** often refer to surrounding cells to identify *** emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal ***-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall ***,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different *** handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such *** results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.

关键词： Cervical abnormal cells image detection multi-scale feature fusion contextual information

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Bi-directional Projection Framework for Fast Single Image Super Resolution

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Recent Patents on Engineering 2024年第9期18卷 152-167页

作者： Zhou, Ying Zheng, Zhichao Sun, Quansen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing China

Background: Collaborative Representation (CR) has been widely used in Single Image Super Resolution (SISR) with the assumption that Low-resolution (LR) and high-resolution (HR) features can be linearly represented by neighborhoods and share consistent CR coefficients. Numerous patents and journal papers have been published. However, this CR consistency does not hold in the reconstruction phase, which leads to degraded performance. Methods: To fulfill this gap, we propose a novel bi-directional projection model (BDPM) to establish a bi-directional mapping between LR and HR features without any consistency constraint. The multiple projection matrices are offline computed to reduce reconstruction time greatly. We further develop several strategies to extract features and group neighborhoods such that local structures can be preserved better. Results: Compared to the learning-based methods, BDPM is about 2 to 10 times faster and compared to the reconstruction-based methods, it is about 500 to 2,000 times faster. Conclusion: The empirical studies verify the effectiveness of BDPM and extensive experimental results demonstrate that BDPM achieves better SISR performance than many state-of-the-arts. © 2024 Bentham science Publishers.

关键词： Mapping

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Constrained Multiobjective Optimization via Relaxations on Both Constraints and Objectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第12期5卷 6709-6722页

作者： Ming, Fei Xue, Bing Zhang, Mengjie Gong, Wenyin Zhen, Huixiang China University of Geosciences School of Computer Science Wuhan430074 China Victoria University of Wellington Center of Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand

Since most multiobjective optimization problems in real-world applications contain constraints, constraint-handling techniques (CHTs) are necessary for a multiobjective optimizer. However, existing CHTs give no relaxation to objectives, resulting in the elimination of infeasible dominated solutions that are promising (potentially useful but inferior) for detecting feasible regions and the constrained Pareto front (CPF). To overcome this drawback, in this work, we propose an objective relaxation technique that can preserve promising by relaxing objective function values, i.e., convergence, through an adaptively adjusted relaxation factor. Further, we develop a new constrained multiobjective optimization evolutionary algorithm (CMOEA) based on relaxations on both constraints and objectives. The proposed algorithm evolves one population by the constraint relaxation technique to preserve promising infeasible solutions and the other population by both objective and constraint relaxation techniques to preserve promising infeasible dominated solutions. In this way, our method can overcome the drawback of existing CHTs. Besides, an archive update strategy is designed to maintain encountered feasible solutions by the two populations to approximate the CPF. Experiments on challenging benchmark problems and real-world problems have demonstrated the superiority or at least competitiveness of our proposed CMOEA. Moreover, to verify the generality of the objective relaxation technique, we embed it into two existing CMOEA frameworks and the results show that it can significantly improve the performance in handling challenging problems. © 2020 IEEE.

关键词： Constrained optimization

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Modifying Lyapunov exponent of chaotic map by self-cascading

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science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun School of Electronic and Information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Impact of asymptomatic infected individuals on epidemic transmission dynamics in multiplex networks with partial coupling

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Chinese Physics B 2024年第10期33卷 80-87页

作者： Xin Hu Jiaxing Chen Chengyi Xia School of Control Science and Engineering Tiangong UniversityTianjin 300387China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China School of Artificial Intelligence Tiangong UniversityTianjin 300387China

The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.

关键词： asymptomatic infected individuals multi-layer networks partial interdependence

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(Information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Robust Regularization Design of Graph Neural Networks Against Adversarial Attacks Based on Lyapunov Theory

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Chinese Journal of Electronics 2024年第3期33卷 732-741页

作者： Wenjie YAN Ziqi LI Yongjun QI School of Artificial Intelligence Hebei University of Technology School of Computer Science and Engineering of North China Institute of Aerospace Engineering

The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical analysis on the principle of *** order to tackle the weakness of current robustness designing methods,this paper gives new insights into how to guarantee the robustness of GNNs.A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov *** results give new insights into how regularization can mitigate the various adversarial effects on different graph *** experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-theart methods such as L1-norm,L2-norm,L2-norm,Pro-GNN,PA-GNN and GARNET against various types of graph adversarial attacks.

关键词： Deep learning Graph neural network Robustness Lyapunov Regularization

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Gria:an efficient deterministic concurrency control protocol

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Frontiers of computer science 2024年第4期18卷 25-37页

作者： Xinyuan WANG Yun PENG Hejiao HUANG School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou 510006China

Deterministic databases are able to reduce coordination costs in a *** property has fostered a significant interest in the design of efficient deterministic concurrency control ***,the state-of-the-art deterministic concurrency control protocol Aria has three ***,it is impractical to configure a suitable batch size when the read-write set is ***,Aria running in low-concurrency scenarios,e.g.,a single-thread scenario,suffers from the same conflicts as running in high-concurrency ***,the single-version schema brings write-after-write *** address these issues,we propose Gria,an efficient deterministic concurrency control *** has the following ***,the batch size of Gria is ***,Gria’s conflict probability in low-concurrency scenarios is lower than that in high-concurrency ***,Gria has no write-after-write conflicts by adopting a multi-version *** further reduce conflicts,we propose two optimizations:a reordering mechanism as well as a rechecking *** evaluation result on two popular benchmarks shows that Gria outperforms Aria by 13x.

关键词： deterministic concurrency control transaction processing

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