Expert Systems最新文献

{"title":"Enhancing depression detection: A multimodal approach with text extension and content fusion","authors":"Jinyan Chen,&nbsp;Shuxian Liu,&nbsp;Meijia Xu,&nbsp;Peicheng Wang","doi":"10.1111/exsy.13616","DOIUrl":"10.1111/exsy.13616","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>With ubiquitous social media platforms, people express their thoughts and emotions, making social media data valuable for studying and detecting depression symptoms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>First, we detect depression by leveraging textual, visual, and auxiliary features from the Weibo social media platform. Second, we aim to comprehend the reasons behind the model's results, particularly in medicine, where trust is crucial.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address challenges such as varying text lengths and abundant social media data, we employ a text extension technique to standardize text length, enhancing model robustness and semantic feature learning accuracy. We utilize tree-long short-term memory and bidirectional gate recurrent unit models to capture long-term and short-term dependencies in text data, respectively. To extract emotional features from images, the integration of optical character recognition (OCR) technology with an emotion lexicon is employed, addressing the limitations of OCR technology in accuracy when dealing with complex or blurred text. In addition, auxiliary features based on social behaviour are introduced. These modalities’ output features are fed into an attention fusion network for effective depression indicators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Extensive experiments validate our methodology, showing a precision of 0.987 and recall rate of 0.97 in depression detection tasks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By leveraging text, images, and auxiliary features from Weibo, we develop text picture sentiment auxiliary (TPSA), a novel depression detection model. we ascertained that the emotional features extracted from images and text play a pivotal role in depression detection, providing valuable insights for the detection and assessment of the psychological disorder.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Generalized hop-based approaches for identifying influential nodes in social networks","authors":"Tarun Kumer Biswas,&nbsp;Alireza Abbasi,&nbsp;Ripon Kumar Chakrabortty","doi":"10.1111/exsy.13649","DOIUrl":"10.1111/exsy.13649","url":null,"abstract":"&lt;p&gt;Locating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation-based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path-based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop-distance and provide no guidance on capturing spread sizes beyond two-hops. In this paper, we introduce Hop-based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop-distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mfrac&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;/mfrac&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; under a limited hop-measures, whereas a multiplicative &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfrac&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;σ&lt;/mi&gt;\u0000 &lt;mi&gt;h&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mfrac&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;e&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;σ&lt;/mi&gt;\u0000 &lt;mi&gt;h&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;mi&gt;α&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Data science methods for response, incremental response and rate sensitivity to response modelling in banking","authors":"Jorge M. Arevalillo","doi":"10.1111/exsy.13644","DOIUrl":"10.1111/exsy.13644","url":null,"abstract":"<p>This work provides a review of data science methods that can be used to address a wide variety of business problems in the banking sector. The paper examines three modelling paradigms: the response, incremental response and the rate sensitivity to response approaches, emphasising the role they play to address these problems. These paradigms and the methods they involve are presented in combination with real cases to illustrate their potential in extracting valuable business insights from data. It is enhanced their usefulness to help business experts like risk managers, commercial managers, financial directors and chief executive officers to plan their strategies and guide decision making on the basis of the insights given by their outcomes. The scope of the work is twofold: it presents a unified view of the methods and how the fit the aforementioned paradigms while, at the same time, it examines some business cases for their application. Both issues will be of interest for technical and managerial teams involved in running data science projects in banking.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-label logo recognition and retrieval based on weighted fusion of neural features","authors":"Marisa Bernabeu,&nbsp;Antonio Javier Gallego,&nbsp;Antonio Pertusa","doi":"10.1111/exsy.13627","DOIUrl":"10.1111/exsy.13627","url":null,"abstract":"<p>Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colours. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, colour, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi-label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analysed, such as the incomplete labelling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (seven times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state-of-the-art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labelling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient integration of perceptual variational autoencoder into dynamic latent scale generative adversarial network","authors":"Jeongik Cho,&nbsp;Adam Krzyzak","doi":"10.1111/exsy.13618","DOIUrl":"10.1111/exsy.13618","url":null,"abstract":"<p>Dynamic latent scale GAN is an architecture-agnostic encoder-based generative model inversion method. This paper introduces a method to efficiently integrate perceptual VAE into dynamic latent scale GAN to improve the performance of dynamic latent scale GAN. When dynamic latent scale GAN is trained with a normal i.i.d. latent random variable and the latent encoder is integrated into the discriminator, a sum of a predicted latent random variable of real data and a scaled normal noise follows the normal i.i.d. random variable. Since this random variable is paired with real data and follows the latent random variable, it can be used for both VAE and GAN training. Furthermore, by considering the intermediate layer output of the discriminator as the feature encoder output, the VAE can be trained to minimise the perceptual reconstruction loss. The forward propagation &amp; backpropagation for minimising this perceptual reconstruction loss can be integrated with those of GAN training. Therefore, the proposed method does not require additional computations compared to typical GAN or dynamic latent scale GAN. Integrating perceptual VAE to dynamic latent scale GAN improved the generative and inversion performance of the model.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soutcom: Real‐time sentiment analysis of Arabic text for football fan satisfaction using a bidirectional LSTM","authors":"Sultan Alfarhood","doi":"10.1111/exsy.13641","DOIUrl":"https://doi.org/10.1111/exsy.13641","url":null,"abstract":"In the last few years, various topics, including sports, have seen social media platforms emerge as significant sources of information and viewpoints. Football fans use social media to express their opinions and sentiments about their favourite teams and players. Analysing these opinions can provide valuable information on the satisfaction of football fans with their teams. In this article, we present Soutcom, a scalable real‐time system that estimates the satisfaction of football fans with their teams. Our approach leverages the power of social media platforms to gather real‐time opinions and emotions of football fans and applies state‐of‐the‐art machine learning‐based sentiment analysis techniques to accurately predict the sentiment of Arabic posts. Soutcom is designed as a cloud‐based scalable system integrated with the X (formerly known as Twitter) API and a football data service to retrieve live posts and match data. The Arabic posts are analysed using our proposed bidirectional LSTM (biLSTM) model, which we trained on a custom dataset specifically tailored for the sports domain. Our evaluation shows that the proposed model outperforms other machine learning models such as Random Forest, XGBoost and Convolutional Neural Networks (CNNs) in terms of accuracy and <jats:italic>F</jats:italic>1‐score with values of 0.83 and 0.82, respectively. Furthermore, we analyse the inference time of our proposed model and suggest that there is a trade‐off between performance and efficiency when selecting a model for sentiment analysis on Arabic posts.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"54 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 compact artificial bee colony metaheuristic for global optimization problems","authors":"Palvinder Singh Mann,&nbsp;Shailesh D. Panchal,&nbsp;Satvir Singh,&nbsp;Simran Kaur","doi":"10.1111/exsy.13621","DOIUrl":"10.1111/exsy.13621","url":null,"abstract":"<p>Computationally efficient and time-memory saving compact algorithms become a keystone for solving global optimization problems, particularly the real world problems; which involve devices with limited memory or restricted use of battery power. Compact optimization algorithms represent a probabilistic view of the population to simulate the population behaviour as they broadly explores the decision space at the beginning of the optimization process and keep focus on to search the most promising solution, therefore narrows the search space, moreover few number of parameters need be stored in the memory thus require less space and time to compute efficiently. Role of population-based algorithms remain inevitable as compact algorithms make use of the efficient search ability of these population based algorithms for optimization but only through a probabilistic representation of the population space in order to optimize the real world problems. Artificial bee colony (ABC) algorithm has shown to be competitive over other population-based algorithms for solving optimization problems, however its solution search equation contributes to its insufficiency due to poor exploitation phase coupled with low convergence rate. This paper, presents a compact Artificial bee colony (cABC) algorithm with an improved solution search equation, which will be able to search an optimal solution to improve its exploitation capabilities, moreover in order to increase the global convergence of the proposed algorithm, an improved approach for population sampling is introduced through a compact <span></span><math>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>Student</mtext>\u0000 <mo>'</mo>\u0000 </msup>\u0000 <mi>s</mi>\u0000 <mo>−</mo>\u0000 <mi>t</mi>\u0000 </mrow></math> distribution which helps in maintaining a good balance between exploration and exploitation search abilities of the proposed compact algorithm with least memory requirements, thus became suitable for limited hardware access devices. The proposed algorithm is evaluated extensively on a standard set of benchmark functions proposed at IEEE CEC'13 for large-scale global optimization (LSGO) problems. Numerical results prove that the proposed compact algorithm outperforms other standard optimization algorithms.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 10","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Robust anomaly detection in industrial images by blending global–local features","authors":"Mingjing Pei,&nbsp;Ningzhong Liu,&nbsp;Shifeng Xia","doi":"10.1111/exsy.13624","DOIUrl":"10.1111/exsy.13624","url":null,"abstract":"<p>Industrial image anomaly detection achieves automated detection and localization of defects or abnormal regions in images through image processing and deep learning techniques. Currently, utilizing the approach of reverse knowledge distillation has yielded favourable outcomes. However, it is still a challenge in terms of the feature extraction capability of the image and the robustness of the decoding of the student network. This study first addresses the issue that the teacher network has not been able to extract global information more effectively. To acquire more global information, a vision transformer network is introduced to enhance the model's global information extraction capability, obtaining better features to further assist the student network in decoding. Second, for anomalous samples, to address the low similarity between features extracted by the teacher network and features restored by the student network, Gaussian noise is introduced. This further increases the probability that the features decoded by the student model match normal sample features, enhancing the robustness of the student model. Extensive experiments were conducted on industrial image datasets AeBAD, MvtecAD, and BTAD. In the AeBAD dataset, under the PRO performance metric, the result is 89.83%, achieving state-of-the-art performance. Under the AUROC performance metric, it reaches 83.35%. Similarly, good results were achieved on the MvtecAD and BTAD datasets. The proposed method's effectiveness and performance advantages were validated across multiple industrial datasets, providing a valuable reference for the application of industrial image anomaly detection methods.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"ABANet: Attention boundary-aware network for image segmentation","authors":"Sadjad Rezvani,&nbsp;Mansoor Fateh,&nbsp;Hossein Khosravi","doi":"10.1111/exsy.13625","DOIUrl":"10.1111/exsy.13625","url":null,"abstract":"<p>Deep learning techniques have attained substantial progress in various face-related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID-19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary-aware attention network combined with a new hybrid loss to provide a map, patch, and pixel-level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting-edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state-of-the-art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% <i>F</i>1-score rate. Our dataset of masked faces with mask region labels and source code will be available online.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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-armed bandit based online model selection for concept-drift adaptation","authors":"Jobin Wilson,&nbsp;Santanu Chaudhury,&nbsp;Brejesh Lall","doi":"10.1111/exsy.13626","DOIUrl":"10.1111/exsy.13626","url":null,"abstract":"<p>Ensemble methods are among the most effective concept-drift adaptation techniques due to their high learning performance and flexibility. However, they are computationally expensive and pose a challenge in applications involving high-speed data streams. In this paper, we present a computationally efficient heterogeneous classifier ensemble entitled OMS-MAB which uses online model selection for concept-drift adaptation by posing it as a non-stationary multi-armed bandit (MAB) problem. We use a MAB to select a single <i>adaptive learner</i> within the ensemble for learning and prediction while systematically exploring promising alternatives. Each ensemble member is made drift resistant using explicit drift detection and is represented as an arm of the MAB. An exploration factor <span></span><math>\u0000 <mrow>\u0000 <mi>ϵ</mi>\u0000 </mrow></math> controls the trade-off between predictive performance and computational resource requirements, eliminating the need to continuously train and evaluate all the ensemble members. A rigorous evaluation on 20 benchmark datasets and 9 algorithms indicates that the accuracy of OMS-MAB is statistically at par with state-of-the-art (SOTA) ensembles. Moreover, it offers a significant reduction in execution time and model size in comparison to several SOTA ensemble methods, making it a promising ensemble for resource constrained stream-mining problems.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}