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Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel Special Feature on Future Network-Research Article

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1195-1208 doi: 10.1631/FITEE.1800436

Abstract: We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model.The feature-pattern graph model presented employs network patterns as nodes and similarity as weightedThe proposed method can discover DDoS attacks using a graph-based neighborhood classification method;it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graphupdate tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially

Keywords： Software-defined network     Distributed denial-of-service (DDoS)     Behavior discovery     Distance metric learning     Feature-patterngraph    

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph featureAnd the edge connections in the graph depend on the relationship between signals.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced trainingeffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graphfeature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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The Realistic Pattern and Path Choice of the Development of Agricultural Software Industry

Ma Chen, Li Jin, Zhang Qian, Feng Xian, Jie Xiaojing

Strategic Study of CAE 2021, Volume 23, Issue 4,   Pages 19-29 doi: 10.15302/J-SSCAE-2021.04.003

Abstract:

As the integration of information technology and agricultural development accelerates, the agricultural software industry has emerged to support the development of smart agriculture. In this article, we first analyze the development status of and challenges faced by China’s agricultural software industry by analyzing the development strategies of the industry in China and abroad and using literature review and survey data. Subsequently, we propose the strategic goals, major engineering projects, and policy measures for the development of China’s agricultural software industry. China’s agricultural software industry has a large gap with other countries in terms of technology development and promotion, enterprise operation, and user accumulation. The major challenges include difficulty in development, weak innovation capabilities, low return on investment, and insufficient protection of intellectual property rights, restricting the growth of China’s agricultural software industry. China should regard the software development of agricultural technologies as the main line and focus on strengthening the innovative capabilities of its agricultural software industry by 2035. The major engineering projects we proposed involve agricultural enabling software and platform development, precision agriculture management software application promotion, agricultural software industry cluster establishment, and agricultural software enterprise cultivation. Furthermore, China should improve its policy support system, strengthen the overall coordination mechanism, optimize the discipline system, and strengthen talent training for the agricultural software industry.

Keywords： agricultural software industry     feature classification     current pattern     path choice     suggestions for    

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Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-sourceFT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a featureThe feature template is used to extract local context features, and a neural network model is used to

Keywords： Network security entity     Security knowledge graph (SKG)     Entity recognition     Feature template     Neural network    

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Large-scale graph processing systems: a survey Review

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 384-404 doi: 10.1631/FITEE.1900127

Abstract: Graph is a significant data structure that describes the relationship between entries.Many application domains in the real world are heavily dependent on graph data.However, graph applications are vastly different from traditional applications.of specific graph processing platforms.In this survey, we systematically categorize the graph workloads and applications, and provide a detailed

Keywords： Graph workloads     Graph applications     Graph processing systems    

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A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Strategic Study of CAE 2004, Volume 6, Issue 2,   Pages 44-47

Abstract: The proposed method suits for all the problems of algebraic feature extraction.

Keywords： pattern recognition     feature extraction     disciminant analysis     generalized optimal set of discriminant    

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Frontiers in Energy 2020, Volume 14, Issue 4,   Pages 817-835 doi: 10.1007/s11708-020-0709-9

Abstract: dynamic operating data set with steep slope signals is created based on physics equations and then a featuresimilarity-based learning model with an encoder and a decoder is built and trained to achieve feature

Keywords： gas turbine     dynamic simulation     data-driven     transfer learning     feature similarity    

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Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0737-8

Abstract: This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPsThe weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchyThe measured average sampling surface roughness of the most crucial feature of the bracket in the original

Keywords： selective laser melting (SLM)     build orientation determination     multi-feature mechanical part (MFMP)    

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Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 38-42

Abstract: It can extract nonlinear feature components of samples.However, feature extraction for one sample requires that kernel functions between training samples andSo, the size of training sample set affects the efficiency of feature extraction.It is supposed that in feature space the eigenvectors may be linearly expressed by a part of trainingIKPCA extracts feature components of one sample efficiently, only based on kernel functions between nodes

Keywords： KPCA(Kernel PCA)     IKPCA(Improved KPCA)     feature extraction     feature space    

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composition differences between processed protein from different animal species by self-organizing feature

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

Frontiers of Agricultural Science and Engineering 2016, Volume 3, Issue 2,   Pages 171-179 doi: 10.15302/J-FASE-2016095

Abstract: In this study, self-organizing feature maps (SOFM) were used to visualize amino acid composition of fish

Keywords： self-organizing feature maps     visualization     processed animal proteins (PAPs)     amino acid    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutional    

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Distributed coordination inmulti-agent systems: a graph Laplacian perspective

Zhi-min HAN,Zhi-yun LIN,Min-yue FU,Zhi-yong CHEN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 429-448 doi: 10.1631/FITEE.1500118

Abstract: This paper reviews some main results and progress in distributed multi-agent coordination from a graphsurvey of existing literature in distributed multi-agent coordination and a new perspective in terms of graphFor different types of graph Laplacians, we summarize their inherent coordination features and specific

Keywords： Multi-agent systems     Distributed coordination     Graph Laplacian    

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Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 498-505 doi: 10.1007/s11684-020-0791-8

Abstract: On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis

Keywords： disorders of sex development (DSD)     intelligent diagnosis     dynamic uncertain causality graph    

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A Practical Approach to Constructing a Knowledge Graph for Cybersecurity Article

Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li

Engineering 2018, Volume 4, Issue 1,   Pages 53-60 doi: 10.1016/j.eng.2018.01.004

Abstract: At present, it is very significant that certain scholars have combined the concept of the knowledge graph

Keywords： Cybersecurity     Knowledge graph     Knowledge deduction    

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Title Author Date Type Operation

Discoverymethod for distributed denial-of-service attack behavior inSDNs using a feature-pattern graphmodel

Ya XIAO, Zhi-jie FAN, Amiya NAYAK, Cheng-xiang TAN

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

The Realistic Pattern and Path Choice of the Development of Agricultural Software Industry

Ma Chen, Li Jin, Zhang Qian, Feng Xian, Jie Xiaojing

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Journal Article

Large-scale graph processing systems: a survey

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Journal Article

A Study on the Essence of Optimal Statistical Uncorrelated Discriminant Vectors

Wu Xiaojun,Yang Jingyu,Wang Shitong,Liu Tongming,Josef Kittler

Journal Article

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Journal Article

Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

Journal Article

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Journal Article

composition differences between processed protein from different animal species by self-organizing feature

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Journal Article

Distributed coordination inmulti-agent systems: a graph Laplacian perspective

Zhi-min HAN,Zhi-yun LIN,Min-yue FU,Zhi-yong CHEN

Journal Article

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

Journal Article

A Practical Approach to Constructing a Knowledge Graph for Cybersecurity

Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li

Journal Article