检索结果-内蒙古大学图书馆

A multi-behavior recommendation method exploring the preference differences among various behaviors

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Gan, Mingxin Xu, Gangxin Ma, Yingxue Univ Sci & Technol Beijing Sch Econ & Management Dept Management Sci & Engn Beijing 100083 Peoples R China Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China

User behavior data has been widely used in recent research of recommendation systems. Existing work usually utilize only single behavior instead of multi-behavior. However, there are various typed of user behaviors in practical scenarios. For example, there are view, purchase, add-to-cart and add-to-favorite behaviors in a realworld e-commerce platform. For recommendation systems, using more user information is better for exploiting intrinsic user characteristics, as the single behavior records are usually too sparse to conduct preference mining. Therefore, multi-behavior based recommendation methods are increasingly emphasized by researchers. In this paper, we propose a novel framework (FPD) for utilizing multi-behavior data: generating embedding and building training losses. We compute an additional supplementary score by capturing user's preference difference among behaviors, instead of merely using initial embedding to obtain a ordinary prediction score. For better optimization model parameters, we use various behaviors to build multiple training losses. We optimize the loss function of the non-sampling strategy and set personal positive weights for each user. Experimental results on two real-world datasets demonstrates that our model outperformed the state-of-the-art methods in terms of various evaluation measurements. Furthermore, extensive ablation experiments and visualization analysis are conducted to verify the effectiveness of the proposed idea and to further explain the principle of the proposed method proposed.

关键词： Multi -behavior recommendation User preference graph convolution neural network Recommendation system

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CariesFG: A fine-grained RGB image classification framework with attention mechanism for dental caries

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Jiang, Hao Zhang, Peiliang Che, Chao Jin, Bo Zhu, Yongjun Dalian Univ Minist Educ Key Lab Adv Design & Intelligent Comp Dalian 116622 Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Yonsei Univ Dept Lib & Informat Sci Seoul 26493 South Korea

Dental caries is one of the most prevalent oral diseases, and deep learning methods have been used for caries diagnosis in large populations by leveraging RGB images. The existing attention-based fine-grained image classification methods have the problem of underutilization of features and easy interference by background and irrelevant information. To tackle these issues, we propose a fine-grained RGB image classification framework with attention mechanism for dental caries (CariesFG). Specifically, it consists of 4 components: (1) Multi-Spectral channel Attention Module (MSAM), which can retain the useful frequency components in the feature map. (2) Position Attention Module (PAM), which captures feature dependencies in the spatial dimension. (3) Discriminative Point Selection strategy (DPS), which can find the most discriminative feature points. (4) graph convolution and Aggregation module (GCA), which aims to aggregate discriminative feature points at different scales of feature maps. To enhance the ability to extract the discriminative features, PAM and MSAM are integrated into the backbone network to consist of feature extraction networks incorporating attention mechanism. Discriminative feature points at different scales of feature maps are extracted by DPS and aggregated as global discriminative features by GCA. By testing on a caries fine-grained classification dataset, CariesFG achieved an accuracy of 68.36%, an f1-score of 66.77% and a specificity of 84.17%, respectively, significantly outperforming state-of-the-art methods. Moreover, visualization results on attention parts show that CariesFG can effectively learn discriminative features and discriminative parts.

关键词： Fine-grained classification of dental caries Caries recognition Attention mechanism graph convolution neural network Medical assistant decision making

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A Flight Trajectory Prediction Method Based on Internal Relationships between Attributes

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计算机科学与技术汇刊（中英文版） 2023年第1期10卷 1-10页

作者： Liwei Wu Yuqi Fan School of Computer Science and Information Engineering/Hefei University of Technology HefeiAnhui230000China

The rapid development of the aviation industry urgently requires airspace traffic management,and flight trajectory prediction is a core component of airspace traffic *** trajectory is a multidimensional time series with rich spatio-temporal characteristics,and existing flight trajectory prediction methods only target the trajectory point temporal relationships,but not the implicit interrelationships among the trajectory point *** this paper,a graph convolutional network(AR-GCN)based on the intra-attribute relationships is proposed for solving the flight track prediction ***,the network extracts the temporal features of each attribute and fuses them with the original features of the attribute to obtain the enhanced attribute features,then extracts the implicit relationships between attributes as inter-attribute relationship ***,the enhanced attribute features are used as nodes and the inter-attribute relationship features are used as edges to construct the inter-attribute relationship ***,the graph convolutional network is used to aggregate the attribute *** on the full fusion of the above features,we achieved high accuracy prediction of the *** this paper,experiments are conducted on ADS-B historical track *** compare our method with the classical method and the proposed *** results show that our method achieves significant improvement in prediction accuracy.

关键词： Deep Learning graph convolution neural network Flight Trajectory Prediction

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Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold network 24th

Cardiac Transmembrane Potential Imaging with GCN Based Itera...

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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Mu, Lide Liu, Huafeng Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

ISBN: (纸本)9783030872311;9783030872304

Accurate reconstruction and imaging of cardiac transmembrane potential through body surface ECG signals can provide great help for the diagnosis of heart disease. In this paper, a cardiac transmembrane potential reconstruction method (GISTA-Net) based on graph convolutional neural network and iterative soft threshold algorithm is proposed. It fully combines the rigor of mathematical derivation of traditional iterative threshold shrinkage algorithm and the powerful expression ability of deep learning method, as well as the characterization ability of graph convolutional neural network for non-Euclidean space data. We used this algorithm to simulate ectopic pacing data and simulated myocardial infarction data. The experimental results show that this algorithm can not only accurately locate the ectopic pacing point, but also accurately reconstruct the edge details of myocardial infarction scar while graph convolution makes full use of the connection information between the nodes on the heart surface.

关键词： Cardiac electrophysiological reconstruction ISTA graph convolution neural network

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Semantic Relation graph Reasoning network for Visual Question Answering 12

Semantic Relation Graph Reasoning Network for Visual Questio...

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12th International Conference on Signal Processing Systems

作者： Lan, Hong Zhang, Pufen Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China

ISBN: (纸本)9781510642768;9781510642751

In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the dynamic interaction between different objects. This task inherently requires reasoning the visual relationships among the objects of image. Meanwhile, the visual reasoning process should be guided by the information of the question. In this paper, we proposed a semantic relation graph reasoning network, the process of semantic relation reasoning is guided by the cross-modal attention mechanism. In addition, a Gated graph convolutional network (GGCN) constructed based on cross-modal attention weights that novelly injects the semantic interaction information between objects into their visual features, and the features with relational awareness are produced. In particular, we trained a semantic relationship detector to extract the semantic relationship between objects for constructing the semantic relation graph. Experiments demonstrate that proposed model outperforms most state-of-the-art methods on the VQA v2.0 benchmark datasets.

关键词： visual question answering graph convolution neural network attention mechanism sematic relationship relational reasoning

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Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption

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MICROPOROUS AND MESOPOROUS MATERIALS 2022年第0期331卷 111666-111666页

作者： Wang, Ruihan Zou, Yurong Zhang, Chunchun Wang, Xin Yang, Mingli Xu, Dingguo Sichuan Univ Coll Chem MOE Key Lab Green Chem & Technol Chengdu 610064 Sichuan Peoples R China Sichuan Univ Analyt & Testing Ctr Chengdu 610064 Sichuan Peoples R China Sichuan Univ Res Ctr Mat Genome Engn Chengdu 610065 Sichuan Peoples R China

A novel method capable of efficiently predicting the methane adsorption uptakes by metal-organic frameworks (MOFs) is proposed, which is built on the crystal graph convolutional neural network algorithm. Our method considers certain key physical features of MOFs along with information on secondary building units. Because the general force field fails to reproduce methane-MOF interactions involving the open metal site (OMS) adequately, the new force field parameters for methane are refined specifically for the OMS MOFs using a generic algorithm, which is particularly useful in constructing a more accurate dataset via the high-throughput grand canonical Monte Carlo. Extensive analyses demonstrate that the Pearson correlation coefficient and mean average error can reach as high as 0.973 and 9.98 cm(3)/cm(3) for the newly developed algorithm, respectively. Furthermore, this model acquires information from the adsorption volume as embedding representations. This allows for the application of transfer learning by fine-tuning a pretrained graph network for the different properties. Finally, we predicted the methane adsorption volumes by all the MOFs in a 137953 hypothetical database at 298 K and 65 bar within hours. It is believed that this new method could be a useful tool in the early stages of high-throughput virtual screening of novel porous materials for gas adsorption or separation.

关键词： Metal-organic frameworks Methane adsorption graph convolution neural network Domain knowledge Transfer learning Virtual screening

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graph Sample and Aggregate-Attention network for Hyperspectral Image Classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Ding, Yao Zhao, Xiaofeng Zhang, Zhili Cai, Wei Yang, Nengjun Xian Res Inst High Technol Xian 710000 Peoples R China

graph convolutional network (GCN) has shown potential in hyperspectral image (HSI) classification. However, GCN is a transductive learning method, which is difficult to aggregate the new node. The available GCN-based methods fail to understand the global and contextual information of the graph. To address this deficiency, a novel semisupervised network based on graph sample and aggregate-attention (SAGE-A) for HSIs' classification is proposed. Different from the GCN-based method, SAGE-A adopts a multilevel graph sample and aggregate (graphSAGE) network, as it can flexibly aggregate the new neighbor node among arbitrarily structured non-Euclidean data and capture long-range contextual relations. Inspired by the convolution neural network (CNN) self-attention mechanism, the proposed network uses the graph attention mechanism to characterize the importance among spatially neighboring regions, so the deep contextual and global information of the graph can be learned automatically by focusing on important spatial targets. Extensive experimental results on different real hyperspectral data sets demonstrate the performances of our proposed method compared with the state-of-the-art methods.

关键词： convolution Feature extraction neural networks Hyperspectral imaging Aggregates Training Mathematical model Global and contextual information graph convolution neural network hyperspectral image (HSI) classification

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graph Clustering via Variational graph Emb e dding

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PATTERN RECOGNITION 2022年 122卷 108334-108334页

作者： Guo, Lin Dai, Qun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

graph clustering based on embedding aims to divide nodes with higher similarity into several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph structure and node attributes into the low dimensional feature space. Furthermore, most of the current advanced methods of graph nodes clustering adopt the strategy of separating graph embedding technology and clustering algorithm, and ignore the potential relationship between them. Therefore, we propose an innovative end to-end graph clustering framework with joint strategy to handle the complex problem in a non-Euclidean space. In terms of learning the graph embedding, we propose a new variational graph auto-encoder algorithm based on the graph convolution network (GCN), which takes into account the boosting influence of joint generative model of graph structure and node attributes on the embedding output. On the basis of embedding representation, we implement a self-training mechanism through the construction of auxiliary distribution to further enhance the prediction of node categories, thereby realizing the unsupervised clustering mode. In addition, the loss contribution of each cluster is normalized to prevent large clusters from distorting the embedding space. Extensive experiments on real-world graph datasets validate our design and demonstrate that our algorithm has highly competitive in graph clustering over state-of-theart methods. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： graph convolution neural network Variational graph embedding graph clustering Variational graph auto-encoder

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Service Recommendation for Composition Creation based on Collaborative Attention convolutional network

Service Recommendation for Composition Creation based on Col...

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IEEE International Conference on Web Services (ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Yan, Ruyu Fan, Yushun Zhang, Jia Zhang, Junqi Lin, Haozhe Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Southern Methodist Univ Dept Comp Sci Dallas TX 75205 USA

ISBN: (纸本)9781665416818

Service recommendation for composition creation is a widely applied technique, which expedites mashup development by reusing existing services. The core of service recommendations is to simultaneously understand user needs as well as the functions of available services. However, the descriptions provided by users and service providers may not always be accurate or up to date, which poses significant challenges to composition creating. To tackle this problem, in this paper we propose a deep learning-based service recommendation framework named coACN, short for Collaborative Attention convolutional network, which can effectively learn the bilateral information toward service recommendation. On the one hand, a domain-level attention module is constructed to refine user needs embeddings by drawing messages from related service domains. On the other hand, a graph convolutional network is established to excavate the service-composition graph and fuse structured information into service embeddings. For a service node in the graph, the information of its compositions as its first-order neighbor nodes is used to supplement the latest functions and features of the service;and the information of the services as its second-order neighbor nodes may bring collaborative relationships into the service. Extensive experiments on the real-world ProgrammableWeb dataset show the significant improvement of our proposed coACN framework over state-of-the-art methods.

关键词： Service Composition graph convolution neural network Attention Mechanism Service Recommendation Collaborative Filtering

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Semi-Supervised Locality Preserving Dense graph neural network With ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Ding, Yao Zhao, Xiaofeng Zhang, Zhili Cai, Wei Yang, Nengjun Zhan, Ying Xian Res Inst High Technol Xian 710000 Peoples R China

The application of graph convolutional networks (GCNs) to hyperspectral image (HSI) classification is a heavily researched topic. However, GCNs are based on spectral filters, which are computationally costly and fail to suppress noise effectively. In addition, the current GCN-based methods are prone to oversmoothing (the representation of each node tends to be congruent) problems. To circumvent these problems, a novel semi-supervised locality-preserving dense graph neural network (GNN) with autoregressive moving average (ARMA) filters and context-aware learning (DARMA-CAL) is proposed for HSI classification. In this work, we introduce the ARMA filter instead of a spectral filter to apply to GNNs. The ARMA filter can better capture the global graph structure and is more robust to noise. More importantly, the ARMA filter can simplify calculations compared with the spectral filter. In addition, we show that the ARMA filter can be approximated by a recursive method. Furthermore, we propose a dense structure, which not only implements the ARMA filter in the structure, but is also locality-preserving. Finally, we design a layerwise context-aware learning mechanism to extract the useful local information generated by each layer of the dense ARMA network. The experimental results on three real HSI datasets show that DARMA-CAL outperforms the compared state-of-the-art methods.

关键词： Feature extraction convolution Hyperspectral imaging Data mining Support vector machines Deep learning Recurrent neural networks Autoregressive moving average (ARMA) filter graph convolution neural network hyperspectral image (HSI) classification

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