Expert Systems with Applications最新文献

{"title":"MOHGCN: A trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis","authors":"Wenhao Wu ,&nbsp;Shudong Wang ,&nbsp;Yuanyuan Zhang ,&nbsp;Kuijie Zhang ,&nbsp;Wenjing Yin ,&nbsp;Shanchen Pang","doi":"10.1016/j.eswa.2024.125772","DOIUrl":"10.1016/j.eswa.2024.125772","url":null,"abstract":"<div><div>With the advancement of cutting-edge sequencing methodologies, the integration of multi-omics data provides invaluable opportunities for researchers to study complex diseases from a molecular perspective while at the same time being challenged by the deployment of safety-critical applications such as computer-aided diagnostics. However, existing methods in multi-omics data integration primarily explore interactions between omics or samples, neglecting high-order interaction information among biomolecules specific to certain diseases. In this work, we propose MOHGCN, a trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis, aiming to maximize the utilization of biomolecular interactions in patients with specific diseases for precise diagnosis to enhance the model’s credibility. In the approach, we constructed a heterogeneous graph of samples and genes and devised the HGCN graph convolution model specifically tailored to the sample–gene heterogeneous graph. Concurrently, techniques such as trustworthy attention weights and self-attention mechanisms were incorporated to unveil relationships between different omics, facilitating the efficient integration of multi-omics data. Through comprehensive experimentation on four publicly available multi-omics medical datasets, our proposed framework consistently demonstrates superior performance across various classification tasks. Simultaneously, the experimental results substantiate the model’s effectiveness in extracting features from multi-omics data and unveiling latent associations among different omics.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125772"},"PeriodicalIF":7.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wastewater treatment monitoring: Fault detection in sensors using transductive learning and improved reinforcement learning","authors":"Jing Yang ,&nbsp;Ke Tian ,&nbsp;Huayu Zhao ,&nbsp;Zheng Feng ,&nbsp;Sami Bourouis ,&nbsp;Sami Dhahbi ,&nbsp;Abdullah Ayub Khan ,&nbsp;Mouhebeddine Berrima ,&nbsp;Lip Yee Por","doi":"10.1016/j.eswa.2024.125805","DOIUrl":"10.1016/j.eswa.2024.125805","url":null,"abstract":"<div><div>Wastewater treatment plants (WWTPs) increasingly utilize sensors to optimize operations and ensure treated water quality. These sensors’ rich datasets are well-suited for automated monitoring and fault detection. This study introduces a deep learning method for fault detection in sensors designed to tackle significant challenges, including a class imbalance in datasets where normal operational data significantly outnumber anomalies and sensitivity to hyperparameters. We employ a novel spatial attention-based transductive long short-term memory (TLSTM) network designed to detect subtle temporal variations in time-series data, facilitating the binary classification of faults in key processes like oxidation and nitrification. To address the challenge of data imbalance prevalent in WWTP monitoring, our model integrates the off-policy proximal policy optimization (Off-Policy PPO) framework. This adaptation enhances the traditional PPO algorithm for off-policy learning environments, improving data utilization and algorithm stability. In this system, data points are treated as a sequence of decisions, with the neural network functioning as an intelligent agent. The Off-Policy PPO approach employs a reward mechanism that prioritizes the correct prediction of minority-class instances over majority-class ones by assigning higher rewards. Moreover, the model incorporates the differential evolution (DE) algorithm for autonomous hyperparameter optimization, thereby minimizing reliance on manual tuning. Our rigorous testing on the Valdobbiadene dataset shows that our approach outperforms existing methods. Additionally, we apply transfer learning (TL) to the BSM1 dataset to further validate the model’s effectiveness. Achieving F-measures of 87.24% on the Valdobbiadene dataset and 82.48% on the BSM1 dataset demonstrates the model’s capability to promptly identify faults, significantly enhancing the reliability and efficiency of WWTP monitoring systems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"264 ","pages":"Article 125805"},"PeriodicalIF":7.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-order attributes information fusion via hypergraph matching for popular fashion compatibility analysis","authors":"Kexin Sun ,&nbsp;Zhiheng Zhao ,&nbsp;Ming Li ,&nbsp;George Q. Huang","doi":"10.1016/j.eswa.2024.125758","DOIUrl":"10.1016/j.eswa.2024.125758","url":null,"abstract":"<div><div>Popular fashion compatibility modeling aims to quantitatively assess the compatibility of a set of wearable items for everyday pairings and clothing purchases to assist human decision-making, which has garnered extensive academic attention. Earlier approaches that studied garments as a whole could only discern loose relationships between items, resulting in low accuracy and poor interpretability. Although existing state-of-the-art methods attempt to reveal the mechanism of garment compatibility at a fine-grained level by quantifying pairwise attribute compatibility, they overlook the fact that multi-attribute combinations between apparel items tend to play a more salient role in compatibility. Considering the complex and high-order characteristics of compatibility data, we propose a network named MAIF to deeply mine and reveal the intricate compatibility mechanisms of clothing by fusing multi-order attributes compatibility information through hypergraph matching. Specifically, we use the compatibility modeling of top-item and bottom-item as an example. First, we construct an adaptive hypergraph representation module to model the multi-attribute association combinations of individual clothing items and fuse single-attribute variable information to form multi-order attribute association information. Second, we learn the multi-order compatibility information of attributes between clothing items through spatial similarity matching. Considering the varying compatibility impacts caused by different attribute combinations, we construct a dynamic cross-plot matching mechanism to model the impact weights of multi-order attribute compatibility information. Finally, personalized ranking loss is designed to optimize the model parameters using fashion context information. Experimental and user survey studies conducted on the FashionVC and Polyvore-Maryland datasets verified the validity and superiority of MAIF in accurately assessing apparel compatibility, demonstrating its ability to interpret multi-order attribute compatibility information.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125758"},"PeriodicalIF":7.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge enhanced edge-driven graph neural ranking for biomedical information retrieval","authors":"Xiaofeng Liu,&nbsp;Jiajie Tan,&nbsp;Shoubin Dong","doi":"10.1016/j.eswa.2024.125787","DOIUrl":"10.1016/j.eswa.2024.125787","url":null,"abstract":"<div><div>Neural networks used for information retrieval tend to capture textual matching signals between a query and a document. However, neural ranking models for biomedical information retrieval often struggle to semantically well match the query to the documents. The main reasons are that biomedical terms have many different representations and the fact description related to the query is non-consecutive and non-local in the documents. In this paper, we present an edge-driven graph neural ranking method for biomedical information retrieval by incorporating knowledge from medical databases. First, we propose to form an edge-driven graph by connecting some biomedical terms in the query and the document through different types of edges. Then, we design a novel way of knowledge integration to introduce knowledge related to biomedical terms into the graph and construct a knowledge-query-doc graph. Finally, a graph neural ranking model is applied to capture non-local and non-contiguous match signals between the query and the document. Experimental results show on the biomedical datasets that our method outperforms the advanced neural models. And further analysis shows that the knowledge integration method can well reduce the semantic gap between the query and the document, and our graph model can provide interpretation for matching between the query and the document.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125787"},"PeriodicalIF":7.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pixel-level assessment method of the aging status of silicone rubber insulators based on hyperspectral imaging technology and IPCA-SVM model","authors":"Yihan Fan ,&nbsp;Yujun Guo,&nbsp;Yang Liu ,&nbsp;Song Xiao ,&nbsp;Junbo Zhou,&nbsp;Guoqiang Gao ,&nbsp;Xueqin Zhang ,&nbsp;Guangning Wu","doi":"10.1016/j.eswa.2024.125788","DOIUrl":"10.1016/j.eswa.2024.125788","url":null,"abstract":"<div><div>Acidic environments are a significant factor in the aging and failure of silicone rubber insulators. Addressing the effective assessment of insulators’ aging state to prevent power transmission accidents has been a critical and urgent issue for the power grid. Therefore, hyperspectral imaging (HSI) technology was employed in this paper, capturing spectral line data of silicone rubber in six aging states in both visible and near-infrared regions, respectively. To reduce data redundancy, genetic algorithm (GA) and band weighting were introduced to improve traditional principal component analysis (PCA), with performance compared using overall accuracy (OA) and Kappa, against 12 other feature extraction or dimensionality reduction methods. The improved principal component analysis − support vector machine (IPCA-SVM) model proposed effectively minimizes irrelevant information in hyperspectral original data, exceeding 93% accuracy and improving OA by 8.26% compared to all bands data. Finally, the IPCA-SVM model was used for pixel-level assessment of the surface aging state of silicone rubber insulators, demonstrating its reliability. This method effectively characterizes the aging state of composite insulators, providing a solid foundation for the safe and stable operation of power grids.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125788"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unbalanced graph isomorphism network for fracture identification by well logs","authors":"Ning Ma ,&nbsp;Shaoqun Dong ,&nbsp;Lexiu Wang ,&nbsp;Leting Wang ,&nbsp;Xu Yang ,&nbsp;Shuo Liu","doi":"10.1016/j.eswa.2024.125794","DOIUrl":"10.1016/j.eswa.2024.125794","url":null,"abstract":"<div><div>Fracture identification and prediction are of great significance for the production of tight oil and gas reservoirs. The high angles of fractures limit their traceability and reduce drilling intersection, leading to significant data imbalance and making fracture identification an imbalanced classification problem. Lithology and fluid properties can create similar features in fracture samples, often resulting in nonlinear relationships and a non-Euclidean structure. This complexity makes fracture identification a nonlinear process. To address this issue, the unbalanced graph isomorphism network (UGIN) algorithm is introduced. This approach leverages the GIN and incorporates a binary cross-entropy loss function specifically designed for unbalanced samples during fracture identification, aiming to adjust the model’s focus toward minority classes by assigning higher penalties to misclassified fracture samples, thereby improving detection accuracy in imbalanced datasets. The identification process is divided into three stages: First, the sample logging similarity information is integrated into the graph structure using the sequence edge method. Second, node-level information is embedded via the GIN algorithm, and nodes are clustered using K-means to derive the local graph’s embedding representation. Finally, nodes are classified using the model. To test the validation of the UGIN algorithm, a dataset of fractured carbonate reservoirs in A Oilfield, of the Zagros Mountain fold belt is used. The results demonstrate robust generalization on both training and test datasets through the use of cross-validation, achieving an AUC score of 0.938, higher than the baseline model. The classification accuracy on test data reaches 96.7%, with particularly strong performance in identifying fracture samples. To evaluate the impact of different graph construction methods on UGIN’s performance, we compare the K-means clustering method, hierarchical clustering method, the comprehensive connectivity method, the enhanced linkage strategy and the sequence edge method. Results indicate that the sequence edge method performs best, maximizing the retention of depth-related information in logging features and enhancing sample embedding.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125794"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper real-time flame detection: Dynamic insights from event cameras and FlaDE dataset","authors":"Saizhe Ding ,&nbsp;Haorui Zhang ,&nbsp;Yuxin Zhang ,&nbsp;Xinyan Huang ,&nbsp;Weiguo Song","doi":"10.1016/j.eswa.2024.125746","DOIUrl":"10.1016/j.eswa.2024.125746","url":null,"abstract":"<div><div>Bio-inspired sensors known as event cameras offer significant advantages over traditional frame-based RGB cameras, particularly in overcoming challenges like static backgrounds, overexposure, and data redundancy. In this paper, we explore the potential of event cameras in flame detection. Firstly, we establish an open-access Flame Detection dataset based on Event Cameras (FlaDE). To mitigate noise in extreme conditions with event cameras, we then propose a denoising preprocessing module termed Recursive Event Denoiser (RED). By leveraging distinctive probability distributions between signals and noise, RED achieves 0.974 (MESR) on the E-MLB benchmark, outperforming than other statistical denoising methods. Furthermore, we delve into the physical meaning behind the event rates, enabling statistical extraction of flame amidst static background and other dynamic sources. Based on this insight, we develop the hardware-efficient BEC-SVM flame detection algorithm. Benchmarked against other prominent detection modules using the FlaDE dataset, our approach highlights the feasibility of leveraging event data for robust flame detection, achieving a detection accuracy of 96.6% (AP.50) with a processing speed of 505.7 FPS on CPU. This research contributes valuable insights for future advancements in flame detection methodologies. The implementation of the code is available at <span><span>https://github.com/KugaMaxx/cocoa-flade</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125746"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pulse transfer learning: Multi-area river ammonia nitrogen prediction with limited data","authors":"Zichen Song ,&nbsp;Boying Nie ,&nbsp;Sitan Huang","doi":"10.1016/j.eswa.2024.125730","DOIUrl":"10.1016/j.eswa.2024.125730","url":null,"abstract":"<div><div>Accurately predicting ammonia nitrogen content in irrigation water is essential for evaluating water quality, especially in ensuring the safety of agricultural water. However, most existing studies rely on traditional models such as Back Propagation (BP) neural networks, Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models within Artificial Neural Networks (ANN), along with newly proposed deep learning models like DeepTCN-GRU and GRU-N-Beats. These models, however, often focus on single monitoring points, limiting their practical applicability.</div><div>To better accommodate temporal dynamic data, we combine the existing Long Short-Term Memory (LSTM) model, which has demonstrated strong performance in handling time series datasets, with the third-generation neural network model, Spiking Neural Network (SNN). This integration leads to the development of a more temporally-driven model: SNN-LSTM (Spiking Neural Network-Long Short-Term Memory). Additionally, the multi-head attention mechanism enhances the model’s ability to process multivariate time series data. Models like BP, SVR, RNN, LSTM, CNN-LSTM, DeepTCN-GRU, and GRU-N-Beats were trained and tested on water quality data from Huangshui River Bridge, Xigu District, Lanzhou. Comparing the MAE, R-Squared, and RMSE of predictions shows that the SNN-LSTM with multi-head attention significantly outperforms other models.</div><div>The Elastic Weight Consolidation (EWC) method was used to integrate spatial and temporal features, enhancing model stability across regions. Using data from eight monitoring sites in Gansu Province, the EWC-enhanced SNN-LSTM-MT model was evaluated. Results based on MAE and RMSE showed improved generalization and reliability in predictions across different regions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125730"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-level discrimination index for intuitionistic fuzzy coverings and its applications in feature selection","authors":"Zihang Jia ,&nbsp;Junsheng Qiao ,&nbsp;Minghao Chen","doi":"10.1016/j.eswa.2024.125735","DOIUrl":"10.1016/j.eswa.2024.125735","url":null,"abstract":"<div><div>Intuitionistic fuzzy (<strong>IF</strong>) covering is a generalization of covering through replacing crisp sets with <strong>IF</strong> sets. Recently, <strong>IF</strong> covering has been widely considered in multi-attribute decision-making. However, there is a paucity of research on the uncertainty measure of <strong>IF</strong> coverings. Meanwhile, the uncertainty measure has close relationship with feature selection. The main purpose of this article is to investigate the uncertainty measure of <strong>IF</strong> coverings and develop a corresponding feature selection method. To begin with, for multiple <strong>IF</strong> coverings, we introduce four novel types of <strong>IF</strong> neighborhood operators and corresponding discrimination indices to measure their discrimination ability. Then, to analyze data from a fine granularity, we introduce the multi-level discrimination index (<strong>MLDI</strong>) for <strong>IF</strong> coverings based on <span><math><mrow><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>)</mo></mrow></math></span>-aggregation functions. After that, we design a novel feature selection framework, which includes a fuzzy <span><math><mi>c</mi></math></span>-means clustering based generation method of <strong>IF</strong> coverings and a heuristic algorithm with conditional <strong>MLDI</strong> to find a relative reduction. Finally, we conduct a series of numerical experiments. The experimental results show that the proposed method can select better features than some existing methods for classification tasks. The obtained results bridge the gap in uncertainty measure of <strong>IF</strong> coverings and offer an effective feature selection approach for high-dimensional data classification.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125735"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making transformer hear better: Adaptive feature enhancement based multi-level supervised acoustic signal fault diagnosis","authors":"Shuchen Wang ,&nbsp;Qizhi Xu ,&nbsp;Shunpeng Zhu ,&nbsp;Biao Wang","doi":"10.1016/j.eswa.2024.125736","DOIUrl":"10.1016/j.eswa.2024.125736","url":null,"abstract":"<div><div>Acoustic signal fault diagnosis has been receiving increasing attention in the field of engine health management due to its effectiveness and non-invasiveness. Despite the progress made in fault diagnosis models, challenges still exist due to the complexity of acoustic signals and environmental factors. (1) End-to-end deep networks for fault diagnosis are at risk of underperformance or overfitting due to complex models and imbalanced data. (2) The complex acoustic environment within the vehicle power compartment poses obstacles to extracting subtle fault features. (3) Time–Frequency (TF) analysis has been proven to be an effective tool for characterizing the nonlinear features of fault signals, but it falls short in achieving ideal fidelity and resolution. To address these issues, an engine acoustic signal fault diagnosis method based on multi-level supervised learning and time–frequency transformation was proposed. First, adopting a multi-level supervised learning paradigm decomposes the fault diagnosis task into three stages: feature enhancement, fault detection, and fault identification, thereby incorporating additional experiential knowledge to mitigate overfitting. Second, an adaptive fault feature band extraction algorithm based on the fusion of multiple time–frequency analyses is proposed, specifically for extracting unique features from different vehicle datasets. Finally, a frequency band attention module was designed to focus on the frequency range most relevant to the characteristics of engine fault. The proposed method was validated on various audio signal fault datasets, and the results indicated its superior performance compared to other state-of-art fault detection and identification methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"264 ","pages":"Article 125736"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}