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Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

neural COMPUTING & APPLICATIONS 2022年第24期34卷 22241-22255页

作者： Bi, Jinying Wang, Fei Yan, Xin Ping, Jingyu Wen, Yongzhao Northeastern Univ Coll Informat Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China

Electroencephalogram (EEG)-based emotion recognition has become a hot research field, with the most attention given to decoding three basic types of emotional states (i.e., positive, negative, and neutral) by EEG. Traditional EEG emotion recognition is a single feature input mode that cannot cover multiple feature information. For brain function networks, the feature extraction process is very cumbersome. There will also be individual differences in the characteristics of different subjects. Therefore, the theory of graph convolution and brain function connection is introduced into this research, and the multi-domain fusion features input deep graph convolution neural network (MdGCNN) is proposed in this paper. Pearson correlation is used to determine the adjacency matrix. The Sortpooling layer is employed as a bridge between the graph convolution neural layer and the normal neural network layer and sorts the node features in a consistent order. Based on analyzing the characteristics of a single electrode, the brain topology structure features are automatically extracted. Taking MdGCNN as the basic model and considering the method of minimizing the feature distance between the source and the target domain, we propose a transfer learning (TL) emotion recognition model for cross-subject called MdGCNN-TL. Meanwhile, MdGCNN-TL is extended to traverse the target domain of a single subject in a two-to-one domain form. According to the idea of principal component analysis, the transfer model with a high recognition effect is determined with the degree of subject correlation (SC), and MdGCNN-TL is upgraded to MdGCNN-TL-SC. Experimental analysis on the SEED dataset is performed to evaluate the proposed models. Further validation of the model is implemented on the DEAP dataset. The results show that the proposed model has achieved better performance in EEG emotion recognition.

关键词： Emotion recognition Multi-domain fusion features graph convolution neural network Transfer learning

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A graph convolution neural network Based Method for Insider Threat Detection 20

A Graph Convolution Neural Network Based Method for Insider ...

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20th IEEE Int Symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Fei, Kexiong Zhou, Jiang Su, Lin Wang, Weiping Chen, Yong Zhang, Fan Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Texas Tech Univ Dept Comp Lubbock TX USA China Mobile Ctr Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665464970

In this research, we propose Log2graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Insider threat detection cluster security graph construction graph convolution neural network

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Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network

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neural COMPUTING & APPLICATIONS 2021年第24期33卷 17081-17101页

作者： Zhao, Guyu He, Hongdou Huang, Yifang Ren, Jiadong Yanshan Univ Sch Software Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China

Massive studies focus on the prediction of main pollutants, to improve air quality by revealing the evolution of pollutants. However, existing prediction methods mostly emphasize the fitting analysis of time series, but ignore the spatial propagation effect among nearby places, resulting in a low prediction accuracy. To address this issue, this paper proposes a novel synthesis prediction method to simultaneously excavate the time series changing law and the spatial propagation effect. This method combines a characterization model named air quality spatial-temporal network (AQSTN) and a neural network model called graph convolution neural network (GCN). Firstly, by calculating three correlation coefficients, the time series of most related meteorological factors and aerosol data are gained for feature construction. The geographic distances between locations are computed to evaluate the spatial propagation cost. After that, AQSTN with locations as nodes and propagation relations as edges is constructed, compositing the temporal and spatial relationships. The network is regarded as graph data and input into GCN in chronological order. Secondly, GCN processing graph-structured data fits the optimal parameters in the training stage, simultaneously analyzes the spatial and temporal dimensions of the target site and its adjacent sites. And, the predicted PM2.5 concentration is gained in the test stage. The near-surface monitoring data of Beijing-Tianjin-Hebei area are adopted for experiment. Compared with the second-best model, the RMSE value of AQSTN-GCN is 6.85% lower, MAE value is 13.79% lower, MSE value is 13.23% lower, and MAPE value is 21.53% lower.

关键词： Air pollution graph theory Deep learning graph convolution neural network PM2.5 Forecasting

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Multi-scale hybrid attention graph convolution neural network for remote sensing images super-resolution

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SIGNAL PROCESSING 2023年第1期207卷

作者： Liang, Guojun Kintak, U. Yin, Haichang Liu, Jin Luo, Huibin Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Guangdong Commun Polytech Sch Transportat & Econ Management Guangzhou 510000 Guangdong Peoples R China Beijing Inst Technol Sch Comp Zhuhai 519000 Guangdong Peoples R China

The super-resolution (SR) method of remote sensing image based on deep learning has achieved satis-factory performance. However, when faced with remote sensing images with large size difference and high complexity, the image SR algorithm based deep learning cannot restore more accurate information, resulting in the reconstructed images with blur problem. To solve this problem, we propose a multi-scale hybrid attention graph convolution neural network for remote sensing image SR (MAGSR). Specifically, we construct a chain module that is composed of a multi-scale hybrid attention module and graph con-volution neural network to extract more multi-scale deep features and multi-scale high-frequency detail information. Moreover, we design a hybrid attention mechanism, which combines the advantages of spa-tial attention and channel attention to learn the spatial dependence on each channel, obtaining more abundant critical high-frequency information. Our proposed MAGSR algorithm obtains the highest PSNR and SSIM values on the Google dataset of SIRI-WHU_earth, UCMerced and NWPU-RESISC45. Compared with the suboptimal algorithm DRCAN, our proposed MAGSR achieve 0.03 dB improvements for PSNR. All experimental results show that our proposed MAGSR is superior to other mainstream SR methods and can reconstruct clear SR remote sensing image with better visual effect.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Super -resolution Remote sensing image Deep learning graph convolution neural network Hybrid attention mechanism

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TFG-Net:Tropical Cyclone Intensity Estimation from a Fine-grained perspective with the graph convolution neural network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 118卷

作者： Xu, Guangning Li, Yan Ma, Chi Li, Xutao Ye, Yunming Lin, Qingquan Huang, Zhichao Chen, Shidong Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen Peoples R China Shenzhen Polytech Sch Artificial Intelligence Shenzhen Peoples R China CGN New Energy Holdings Co Ltd Beijing Peoples R China

Tropical Cyclone Intensity Estimation (TIE) is a fundamental study subject for tropical cyclone development, flood or landslide avoidance, etc. Despite considerable efforts, two main challenges remain unresolved in this critical endeavor. The first challenge is that the TIE task is frequently conducted as a coarse-grained recognition problem rather than a fine-grained one. The second challenge is that the prediction fails to consider general wind speed information. To conquer these two challenges, we offer a novel model, namely Tropical cyclone intensity estimation from a Fine-grained perspective with the graph convolution neural network (TFG-Net). It is composed of three key components, viz., the Backbone, the Fine-grained Tropical cyclone Features Extractor (FTFE), and the Wind Scale Transition Rule Generator (WTRG), which aim at extracting general spatial features, subtle spatial features, and general wind speed information, respectively. To validate the proposed method, extensive experiments on a well-known real-world tropical dataset named GridSat were carried out. Following the standard benchmark task setting that the model estimates the wind speed from a given satellite image, the proposed TFG-Net reaches 11.12 knots in the RMSE metric, which outperforms 33.33%, 2.54% to the traditional method and the state-of-the-art deep learning method, respectively. The code is available on GitHub: https://***/xuguangning1218/TI_Estimation and its reproductive result is available on Code Ocean: https://***/10.24433/CO.6606867.v1.

关键词： Intensity estimation Tropical cyclone intensity estimation Tropical cyclone Fine-grained graph convolution neural network

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Automatic Recognition of Teachers’ Nonverbal Behaviors Based on graph convolution neural network 22

Automatic Recognition of Teachers’ Nonverbal Behaviors Base...

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Proceedings of the 14th International Conference on Education Technology and Computers

作者： Shiyan Pang Anran Zhang Shuhui Lai Yuqin Yang Faculty of Artificial Intelligence in Education Central China Normal University China

ISBN: (纸本)9781450397766

Teachers' nonverbal behavior recognition is one of the most important ways to evaluate teachers' teaching behavior. Traditional methods of teachers' nonverbal behavior recognition are mostly from manual judgment which is time-consuming and labor-intensive. In recent years, with the continuous progress of artificial intelligence technology, automatic recognition of teachers' nonverbal behaviors has gradually attracted researchers' attention. In this paper, an automatic recognition of teachers’ nonverbal behaviors based on graph convolution neural network is proposed. Firstly, we define five types of teacher nonverbal behaviors based on the existing relevant literature, and two datasets are made for experimental verification. Secondly, based on the extracted teacher's skeleton points by target detection algorithm and human pose estimation algorithm, a graph convolutional neural network is constructed to automatically identify teacher nonverbal behaviors. Experimental results show that the proposed method can effectively realize teacher's nonverbal behavior recognition. The research in this paper is helpful to solve the automatic recognition of teachers' nonverbal behaviors, and help teachers to optimize teaching strategies and improve teaching efficiency.

关键词： Skeleton Point Extraction graph convolution neural network Target Detection Teachers’ Nonverbal Behavior

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Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2022年第36期9卷

作者： Geng, Yubo Anhui Int Studies Univ Sch English Language Hefei 230000 Peoples R China

Implicit discourse relation recognition is a sub-task of discourse relation recognition, which is challenging because it is difficult to learn the argument representation with rich semantic information and interactive information. To solve this problem, this paper proposes a self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition. The method adopts the preliminary training language model BERT (Bidirectional Encoder Representation from Transformers) coding argument for argument. A classification model based on self-organizing incremental and graph convolutional neural network is constructed to obtain the argument representation which is helpful for English implicit discourse relation recognition. The experimental results show that the proposed method is superior to the benchmark model in terms of contingency relations and expansion relations.

关键词： English discourse relation recognition self-organizing incremental graph convolution neural network BERT

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Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds

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NEUROCOMPUTING 2020年 413卷 487-498页

作者： Zhang, Wenjing Su, Songzhi Wang, Beizhan Hong, Qingqi Sun, Li Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

Effective representation of objects in irregular and unordered point clouds is one of the core challenges in 3D vision. Transforming point cloud into regular structures, such as 2D images and 3D voxels, are not ideal. It either obscures the inherent geometry information of 3D data or results in high computational complexity. Learning permutation invariance feature directly from raw 3D point clouds using deep neural network is a trend, such as PointNet and its variants, which are effective and computationally efficient. However, these methods are weak to reveal the spatial structure of 3D point clouds. Our method is delicately designed to capture both global and local spatial layout of point cloud by proposing a Local k-NNs Pattern in Omni-Direction graph convolution neural network architecture, called LKPO-GNN. Our method converts the unordered 3D point cloud into an ordered 1D sequence, to facilitate feeding the raw data into neural networks and simultaneously reducing the computational complexity. LKPO-GNN selects multi-directional k-NNs to form the local topological structure of a centroid, which describes local shapes in the point cloud. Afterwards, GNN is used to combine the local spatial structures and represent the unordered point clouds as a global graph. Experiments on ModelNet40, ShapeNetPart, ScanNet, and S3DIS datasets demonstrate that our proposed method outperforms most existing methods, which verifies the effectiveness and advantage of our work. Additionally, a deep analysis towards illustrating the rationality of our approach, in terms of the learned the topological structure feature, is provided. Source code is available at https://***/zwj12377/***. (C) 2020 Elsevier B.V. All rights reserved.

关键词： 3D point cloud Spatial layout Omni-Directional k-NNs pattern graph convolution neural network

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Extracting Drug-drug Interactions with a Dependency-based graph convolution neural network

Extracting Drug-drug Interactions with a Dependency-based Gr...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiong, Wuti Li, Fei Yu, Hong Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Univ Massachusetts Dept Comp Sci Lowell MA USA

ISBN: (纸本)9781728118673

Drug-drug interactions (DDIs) play a key role in various applications of biomedicine such as pharmacovigilance. DDIs are frequently reported in biomedical publications, making them an effective source for DDI extraction. Although neural networks have achieved competitive performances in DDI extraction, previous work depended on dependency paths to remove noise from the sentences of biomedical publications. However, such method may ignore crucial information about DDIs. Effectively exploiting much dependency information can improve DDI extraction. In this article, we proposed a model that combines the graph convolution neural network (GCNN) and bidirectional long short-term memory (BiLSTM) to extract DDI interactions from entire dependency graphs of sentences. We evaluated our model in the benchmark corpus for this domain, namely the DDIExtraction 2013 corpus. Our model achieved the state-of-the-art result (77.0% in F1), which are superior to the results reported in previous work.

关键词： drug-drug interaction dependency graph convolution neural network relation extraction

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Extracting Drug-drug Interactions with a Dependency-based graph convolution neural network

Extracting Drug-drug Interactions with a Dependency-based Gr...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Wuti Xiong Fei Li Hong Yu Donghong Ji School of Cyber Science and Engineering Wuhan University Department of Computer Science University of Massachusetts Lowell

ISBN: (纸本)9781728118680

关键词： drug-drug interaction dependency graph convolution neural network relation extraction Drug Interactions DEPENDENCE neural network convolution biomedicine Adjudication Line graph

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