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arXiv 2024年

作者： Sun, Haili Huang, Yan Han, Lansheng Fu, Cai Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Accurate detection and diagnosis of abnormal behaviors such as network attacks from multivariate time series (MTS) are crucial for ensuring the stable and effective operation of industrial cyber-physical systems (CPS). However, existing researches pay little attention to the logical dependencies among system working states, and have difficulties in explaining the evolution mechanisms of abnormal signals. To reveal the spatio-temporal association relationships and evolution mechanisms of the working states of industrial CPS, this paper proposes a fine-grained adaptive anomaly diagnosis method (i.e. MAD-Transformer) to identify and diagnose anomalies in MTS. MAD-Transformer first constructs a temporal state matrix to characterize and estimate the change patterns of the system states in the temporal dimension. Then, to better locate the anomalies, a spatial state matrix is also constructed to capture the inter-sensor state correlation relationships within the system. Subsequently, based on these two types of state matrices, a three-branch structure of series-temporal-spatial attention module is designed to simultaneously capture the series, temporal, and space dependencies among MTS. Afterwards, three associated alignment loss functions and a reconstruction loss are constructed to jointly optimize the model. Finally, anomalies are determined and diagnosed by comparing the residual matrices with the original matrices. We conducted comparative experiments on five publicly datasets spanning three application domains (service monitoring, spatial and earth exploration, and water treatment), along with a petroleum refining simulation dataset collected by ourselves. The results demonstrate that MAD-Transformer can adaptively detect fine-grained anomalies with short duration, and outperforms the state-of-the-art baselines in terms of noise robustness and localization performance. © 2024, CC BY.

关键词： Cyber Physical System

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MPFFNet:Semantic Segmentation of Remote Sensing Image Buildings Based on Multipath Feature Fusion

MPFFNet:Semantic Segmentation of Remote Sensing Image Buildi...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Jie Dong Jingkun Mao Kun Liu Liangyong Cheng School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Interplanetary Space Technology Development Co. LTD Tianjin China

The buildings with variable scales and complex background information in remote sensing images often result in missed and misclassified segmentation of buildings. To address this issue, this paper designs an multipath feature fusion network (MPFFNet). MPFFNet has an overall encoding and decoding structure, embedded with an residual depth Inception block(RDI) module to extract multi-scale features. At the same time, MPFFNet uses the multipath residual channel attention (MPRCA) module to expand the Receptive field of the feature map to obtain rich multi-scale context information. The validity of the proposed algorithm is proved on the WHU Buildings data set, with F1 reaching 94.70%, IOU reaching 89.93%, and Recall reaching 94.98%.

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MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning

arXiv

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arXiv 2024年

作者： Chen, Jiaxin Ding, Jinliang Tan, Kay Chen Qian, Jiancheng Li, Ke State Key Laboratory of Sythetical Automation for Process Industries Northeastern University of China Shenyang110819 China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom

Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances robustness and efficiency in optimizing ML pipelines, while the lower-level problem is an expensive optimization problem that focuses on tuning their optimal hyperparameters. Due to the high-dimensional search space characterized by feature redundancy and inconsistent data distributions, the upper-level problem combines feature selection, transfer learning, and classification to leverage limited and heterogeneous historical data. Meanwhile, an ensemble learning method is proposed to capture differences in cross-project distribution and generalize across diverse datasets. Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively. To evaluate the performance of MBL-CPDP, we compare it with five automated ML tools and 50 CPDP techniques across 20 projects. Extensive empirical results show that MBL-CPDP outperforms the comparison methods, demonstrating its superior adaptability and comprehensive performance evaluation capability. © 2024, CC BY.

关键词： Adversarial machine learning

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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Computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression

arXiv

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arXiv 2023年

作者： Pan, Zhiyu Chen, Yinpeng Zhang, Jiale Lu, Hao Cao, Zhiguo Zhong, Weicai Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Huawei CBG Consumer Cloud Service Search Product & Big Data Platform Department China

Automatic image cropping algorithms aim to recompose images like human-being photographers by generating the cropping boxes with improved composition quality. Cropping box regression approaches learn the beauty of composition from annotated cropping boxes. However, the bias of annotations leads to quasi-trivial recomposing results, which has an obvious tendency to the average location of training samples. The crux of this predicament is that the task is naively treated as a box regression problem, where rare samples might be dominated by normal samples, and the composition patterns of rare samples are not well exploited. Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, i.e., similar composition patterns. We propose a novel Contrastive Composition Clustering (C2C) to regularize the composition features by contrasting dynamically established similar and dissimilar pairs. In this way, common composition patterns of multiple images can be better summarized, which especially benefits the rare samples and endows our model with better generalizability to render nontrivial results. Extensive experimental results show the superiority of our model compared with prior arts. We also illustrate the philosophy of our design with an interesting analytical visualization. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting

arXiv

引用

arXiv 2022年

作者： Wang, Fusen Liu, Kai Long, Fei Sang, Nong Xia, Xiaofeng Sang, Jun School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Currently, for crowd counting, the fully supervised methods via density map estimation are the mainstream research directions. However, such methods need location-level annotation of persons in an image, which is time-consuming and laborious. Therefore, the weakly supervised method just relying upon the count-level annotation is urgently needed. Since CNN is not suitable for modeling the global context and the interactions between image patches, crowd counting with weakly supervised learning via CNN generally can not show good performance. The weakly supervised model via Transformer was sequentially proposed to model the global context and learn contrast features. However, the transformer directly partitions the crowd images into a series of tokens, which may not be a good choice due to each pedestrian being an independent individual, and the parameter number of the network is very large. Hence, we propose a Joint CNN and Transformer Network (JCTNet) via weakly supervised learning for crowd counting in this paper. JCTNet consists of three parts: CNN feature extraction module (CFM), Transformer feature extraction module (TFM), and counting regression module (CRM). In particular, the CFM extracts crowd semantic information features, then sends their patch partitions to TRM for modeling global context, and CRM is used to predict the number of people. Extensive experiments and visualizations demonstrate that JCTNet can effectively focus on the crowd regions and obtain superior weakly supervised counting performance on five mainstream datasets. The number of parameters of the model can be reduced by about 67%∼73% compared with the pure Transformer works. We also tried to explain the phenomenon that a model constrained only by count-level annotations can still focus on the crowd regions. We believe our work can promote further research in this field. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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DNGNN: Efficient Deep Noisy Graph Neural Network for Spatio-Temporal Series Forecasting

DNGNN: Efficient Deep Noisy Graph Neural Network for Spatio-...

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2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS)

作者： Songbo Wang Yifan Chen Haizhi Xu Jianqiao Hu Peiyu Zeng Ziming Hu School of Computing and Data Science The University of Hong Kong Hong Kong China School of Microelectronics Science and Technology Sun Yat-sen University Zhuhai China School of Software Engineering South China University of Technology Guangzhou China School of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Spatio-temporal series forecasting plays a vital role in the domain of multivariate time series prediction. By leveraging accurate and comprehensive spatio-temporal sequence prediction models, it becomes possible to extract higher-order spatio-temporal interactions between real-world connections, thereby contributing to smart city construction. Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a highly effective approach for addressing spatio-temporal series prediction challenges. Nevertheless, most existing methods predominantly rely on traditional graph convolutional networks (GCNs) and their variants for spatial feature extraction. Due to inherent limitations such as over-smoothing, these models often constrain spatial feature encoding to shallow architectures, typically limited to 2-3 layers. Additionally, the presence of noisy data in real-world scenarios significantly impacts the prediction accuracy of existing STGNN models. In this paper, building upon the classic STGNN framework, we propose a graph neural network framework based on deep residual connections and noise injection mechanism to address the aforementioned issues. Specifically, inspired by the principles of initial residual connection and identity mapping, we proposed a deep spatio-temporal graph neural network framework of 12 layers to capture the high-dimensional interactions, while using noise injection mechanism to mitigate the adverse effects of noisy data. Sufficient experimental results on typical time-series datasets demonstrate the effectiveness of our proposed framework.

关键词： Accuracy Noise Time series analysis Predictive models Feature extraction Transformers Graph neural networks Spatiotemporal phenomena Noise measurement Forecasting

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Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

arXiv

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arXiv 2024年

作者： Yuwono, Steve Hussain, Ahmar Kamal Schwung, Dorothea Schwung, Andreas Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Lübecker Ring 2 Soest59494 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Artificial Intelligence and Data Science in Automation Technology Hochschule Düsseldorf University of Applied Sciences Münsterstraße 156 Düsseldorf40476 Germany

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Research and application of artificial intelligence based webshell detection model: A literature review

arXiv

引用

arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of artificial intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Classification of Ship Navigation Behavior in Inland Crossing Channels

Classification of Ship Navigation Behavior in Inland Crossin...

引用

2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Huang, Ziyi Zuo, Yi Li, Tieshan Wang, Haocheng Navigation College Dalian Maritime University Dalian China Collaborative Innovation Center of Maritime Big Data and Shipping Artificial General Intelligence Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665443227

With the development of inland river transportation in China and the popularization of automatic identification systems (AIS), it has become more and more important to collect ship movement information and use it for maritime monitoring and management. However, ship movement information is complicated, and AIS data is often missing. It is very difficult to classify ship navigation behavior. This paper proposes an improved SRC model to solve it. In order to verify the performance of the model, the preprocessed data set of Huai'an section of Beijing-Hangzhou Canal is applied to the model. After comparing with other traditional classification models, the results show that the SRC model can effectively classify the navigation behavior of ships, and in most cases is better than other traditional classification models. © 2021 IEEE.

关键词： Ships

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