Expert Systems最新文献_第2页

Multi-Objective Manifold Representation for Opinion Mining 意见挖掘的多目标流形表示

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-07-05 DOI: 10.1111/exsy.70092

Pshtiwan Rahman, Fatemeh Daneshfar, Hashem Parvin

{"title":"Multi-Objective Manifold Representation for Opinion Mining","authors":"Pshtiwan Rahman, Fatemeh Daneshfar, Hashem Parvin","doi":"10.1111/exsy.70092","DOIUrl":"https://doi.org/10.1111/exsy.70092","url":null,"abstract":"<div>\u0000 \u0000 <p>Sentiment analysis plays a crucial role across various domains, requiring advanced methods for effective dimensionality reduction and feature extraction. This study introduces a novel framework, multi-objective manifold representation (MOMR) for opinion mining, which uniquely integrates deep global features with local manifold representations to capture comprehensive data patterns efficiently. Unlike existing methods, MOMR employs advanced dimensionality reduction techniques combined with a self-attention mechanism, enabling the model to focus on contextually relevant textual elements. This dual approach not only enhances interpretability but also improves the performance of sentiment analysis. The proposed method was rigorously evaluated against both classical techniques such as long short-term memory (LSTM), naive Bayes (NB) and support vector machines (SVMs), and modern state-of-the-art models including recurrent neural networks (RNN) and convolutional neural networks (CNN). Experiments on diverse datasets: IMDB, Fake News, Twitter and Yelp demonstrated the superior accuracy and robustness of MOMR. By outperforming competing methods in terms of generalizability and effectiveness, MOMR establishes itself as a significant advancement in sentiment analysis, with broad applicability in real-world opinion mining tasks (https://github.com/pshtirahman/Sentiment-Analysis.git).</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558003","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}

引用次数: 0

Security in Metaverse Markets: Challenges and Solutions—A Comprehensive Review 元宇宙市场的安全：挑战与解决方案-全面回顾

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-07-03 DOI: 10.1111/exsy.70094

Mohammad Z. Aloudat, Mahmoud Barhamgi, Elias Yaacoub, Dani Aoun

{"title":"Security in Metaverse Markets: Challenges and Solutions—A Comprehensive Review","authors":"Mohammad Z. Aloudat, Mahmoud Barhamgi, Elias Yaacoub, Dani Aoun","doi":"10.1111/exsy.70094","DOIUrl":"https://doi.org/10.1111/exsy.70094","url":null,"abstract":"<p>This review paper provides a systematic overview of the metaverse markets security problems and solutions. The metaverse is an emerging digital space, bridging virtual, augmented and mixed reality environments. As the metaverse evolves, issues related to customer security have emerged, which include breaches of privacy, thefts of identity and cybercrimes, all of this compounded by insecurities in the decentralised structures. Through this systematic literature review, we analyse these challenges and assess current approaches to mitigate them, including encryption, decentralised identity and related regulatory frameworks, but highlight their limited capacity for dealing with immersive virtual spaces' unique risks. The review also explores some advanced solutions adopting artificial intelligence, blockchain and privacy enhancing technologies (PETs) for securing the metaverse and enhancing its privacy. Furthermore, the review also points out the gaps in the current literature, particularly a lack of customised customer protection structures and a poor analysis of the psychological effects. It also proposes future solutions such as quantum-resistant security and zero-trust architecture to fortify the security. This review highlights the necessity of partnership between the industries, as well as the setting of security protocols so as to safeguard customer trust and engagement in the growing digital space.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550943","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}

引用次数: 0

CasText: Fusion of Text Information Flow and Global Perspective for Predicting the Size of Information Dissemination 文本信息流融合与信息传播规模预测的全局视角

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-26 DOI: 10.1111/exsy.70089

Hao Luo, Guixiang Cheng, Zhongying Deng, Haiyang Chi, Xin Yan

{"title":"CasText: Fusion of Text Information Flow and Global Perspective for Predicting the Size of Information Dissemination","authors":"Hao Luo, Guixiang Cheng, Zhongying Deng, Haiyang Chi, Xin Yan","doi":"10.1111/exsy.70089","DOIUrl":"https://doi.org/10.1111/exsy.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately predicting the size of information dissemination has important theoretical and practical significance for formulating content distribution strategies, optimising network resource allocation and conducting effective public opinion management. Current research on the cascade growth size of social media information dissemination mostly focuses on network structure and user behaviour analysis. Still, it neglects the crucial role of textual information in driving information dissemination. We propose a deep learning framework called CasText, which integrates multisource features such as text information, global propagation graphs and local propagation structures to more accurately predict the size of information propagation. Using Sentence-BERT to extract deep semantic features of text and combining it with GNN, precise capture of the interaction between text information and cascading structures has been achieved; using DeepWalk to view the entire social network as a complex graphic structure, high-dimensional feature representations of each social media user can be automatically learned. This global perspective helps to reveal broader dissemination patterns and potential influence paths, thereby improving the accuracy of predicting the size of future information dissemination. In multiple comparative experiments based on a real Weibo cascaded text retweeting dataset, the CasText model improved the MSLE index by 3.1% compared to the baseline model, significantly demonstrating the effectiveness of multisource feature fusion in predicting information dissemination size. We further confirmed the importance of text information, global propagation graphs and local propagation embeddings in improving model performance through ablation experiments.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492594","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}

引用次数: 0

The Performance of Distances Between Time Series: An In-Depth Comparison 时间序列间距离的性能：深度比较

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-24 DOI: 10.1111/exsy.70093

Margarida G. M. S. Cardoso, Ana A. Martins

{"title":"The Performance of Distances Between Time Series: An In-Depth Comparison","authors":"Margarida G. M. S. Cardoso, Ana A. Martins","doi":"10.1111/exsy.70093","DOIUrl":"https://doi.org/10.1111/exsy.70093","url":null,"abstract":"<div>\u0000 \u0000 <p>The performance of distance measures between time series has been discussed in diverse studies. Most identified performance as the accuracy resulting from the use of a specific distance in 1-Nearest Neighbour. Few studies have addressed the related computation time, and no systematic analyses of the associations between the distances' performance (1-NN-based accuracy and computation time) and the time series' characteristics have been presented yet. We propose to fill this research gap by analysing these relationships considering the following features: the training and test sets' dimensions, the time series' length, the number of classes, and the classes' separability as measured by the Average Silhouette index. This last characteristic was not mentioned in previous studies. A methodological approach is devised to compare nine distance measures, including three recently proposed combined distances (COMB and two variants). We resort to a stepwise method for multiple comparisons and deal with the experiment-wise error rate to obtain homogeneous groups of distances with indistinct performances. The CART algorithm is used to explore the relationships between accuracy values corresponding to each distance measure under study (target) and the time series characteristics (predictors). Our analyses are based on datasets from the UCR time series classification archive. We concluded that the combined distance (COMB), dynamic time warping distance (DTW), and complexity invariance distance (CID) are consistently included in the subset of best-performing distances in all experimental scenarios. The latter (CID) has a significantly lower computational cost. We determined that the classes' separability is the time series' attribute most associated with the distances' performance.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473041","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}

引用次数: 0

Machine Learning-Based Prediction in HTTP Request–Response Cycles: Impacts on Webpage Quality Metrics 基于机器学习的HTTP请求-响应周期预测：对网页质量指标的影响

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-24 DOI: 10.1111/exsy.70085

Ala Alarood

{"title":"Machine Learning-Based Prediction in HTTP Request–Response Cycles: Impacts on Webpage Quality Metrics","authors":"Ala Alarood","doi":"10.1111/exsy.70085","DOIUrl":"https://doi.org/10.1111/exsy.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>The hypertext transfer protocol (HTTP) request–response cycles during webpage access and content posting exhibit recognisable patterns; however, no unified standard currently streamlines both activities, despite the existence of independent specifications for each. Previous research has leveraged cycles of client–server requests and responses to predict outcomes such as user behaviour (UB) analysis, anomaly detection (AD), performance optimisation (PE), predictive maintenance (PM) and user authentication and security (UA), often without explicitly associating these activities. Addressing this gap, the present study focuses on the combined modelling of HTTP request–response cycles for both webpage access and personal information submission. An experimental study was conducted, where HTTP sessions were generated and analysed for both access and posting activities. Six machine learning models—Decision Tree, Random Forest, Gradient Boosting, k-Nearest Neighbours (kNNs), Logistic Regression and Support Vector Machine—were applied to both the CSIC 2010 HTTP dataset and lab-generated HTTP transmission datasets across the UB, AD, PE-PM and UA tasks. Results indicate that the Random Forest classifier achieved the highest accuracy of 97.53% in predicting AD-based HTTP request–response cycles during webpage access, and 85.93% accuracy in predicting PE-PM tasks during content posting. Gradient Boosting, kNNs and Support Vector Machine models also demonstrated strong versatility and robustness across different HTTP cycle prediction tasks. Furthermore, the analysis concluded that HTTP request–response cycles for webpage access exhibit greater structural consistency compared to those associated with content posting activities.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144473040","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}

引用次数: 0

FLAMES—Federated Learning for Advanced MEdical Segmentation 火焰-联邦学习用于高级医学分割

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-24 DOI: 10.1111/exsy.70090

Martina Savoia, Edoardo Prezioso, Francesco Piccialli

{"title":"FLAMES—Federated Learning for Advanced MEdical Segmentation","authors":"Martina Savoia, Edoardo Prezioso, Francesco Piccialli","doi":"10.1111/exsy.70090","DOIUrl":"https://doi.org/10.1111/exsy.70090","url":null,"abstract":"<p>Federated learning (FL) is gaining traction across numerous fields for its ability to foster collaboration among multiple participants while preserving data privacy. In the medical domain, FL enables institutions to share knowledge while maintaining control over their data, which often vary in modality, source, and quantity. Institutions are often specialised in treating one or a few types of tumours, typically focusing on a specific organ. Hence, different institutions may contribute with distinct types of medical imaging data of various organs, originating from diverse machines. Collaboration among these institutions enhances performance on shared tasks across different areas of the body. The framework employs modality-specific models hosted on the server, each designed for a particular imaging modality and designed to predict the presence of tumours in scans from its respective modality, regardless of the organ being imaged. Clients focus on their specific imaging modality, utilising knowledge derived from images contributed by institutions employing the same modality. This approach facilitates broader collaboration, extending beyond institutions specialising in the same organ to include those working within the same imaging modality. This approach also helps avoid the introduction of potential noise from clients with images of different modalities, which might hinder the model's ability to effectively specialise and adapt to the data specific to each institution. Experiments showed that FLAMES achieves strong performance on server data, even when tested across different organs, demonstrating its ability to generalise effectively across diverse medical imaging datasets. Our code is available at https://github.com/MODAL-UNINA/FLAMES.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472904","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}

引用次数: 0

Federated Cross-Domain Recommendation Framework With Graph Neural Network 基于图神经网络的联邦跨域推荐框架

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-23 DOI: 10.1111/exsy.70087

Deling Huang, Qilong Feng

{"title":"Federated Cross-Domain Recommendation Framework With Graph Neural Network","authors":"Deling Huang, Qilong Feng","doi":"10.1111/exsy.70087","DOIUrl":"https://doi.org/10.1111/exsy.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Cross-domain recommendation (CDR) leverages more abundant source-domain information to improve target-domain recommendation accuracy. However, traditional centralized CDR approaches face two critical limitations: (1) centralized data storage causes privacy vulnerabilities against malicious servers, and (2) gradient leakage during uploading enables recovery of source data. To address these challenges, in this work, we propose FedGraphCDR, a federated learning-based cross-domain recommendation framework that integrates local differential privacy (LDP) with pseudo item injection during gradient aggregation to prevent gradient leakage attacks, while utilizing graph neural networks to identify comparable users and mitigate cold-start problems. Evaluation on a real-life Douban dataset spanning three domains demonstrates that our framework successfully combines LDP with pseudo items to enhance privacy protection while achieving superior recommendation accuracy over benchmark methods. The results confirm that FedGraphCDR effectively resolves privacy concerns and improves recommendation quality, particularly for cold-start users, and establishes a practical solution for privacy-preserving cross-domain recommendation.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339607","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}

引用次数: 0

A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series 多元时间序列异常检测的混合尖峰模型

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-19 DOI: 10.1111/exsy.70086

Wei Zhang, Ping He, Shengrui Wang, Fan Yang, Ying Liu

{"title":"A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series","authors":"Wei Zhang, Ping He, Shengrui Wang, Fan Yang, Ying Liu","doi":"10.1111/exsy.70086","DOIUrl":"https://doi.org/10.1111/exsy.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep neural networks have exhibited preeminent performance in anomaly detection, but they struggle to effectively capture changes over time in multivariate time-series data and suffer from resource consumption issues. Spiking neural networks address these limitations by capturing the change in time-varying signals and decreasing resource consumption, but they sacrifice performance. This paper develops a novel spiking-based hybrid model incorporated a temporal prediction network and a reconstruction network. It integrates a unique first-spike frequency encoding scheme and a firing rate based anomaly score method. The encoding scheme enhances the event representation ability, while the anomaly score enables efficient anomaly identification. Our proposed model not only maintains low resource consumption but also improves the ability of anomaly detection. Experiments on publicly real-world datasets confirmed that the proposed model acquires state-of-the-art performance superior to existing approaches. Remarkably, it costs 5.04× lower energy consumption compared with the artificial neural network version.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323439","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}

引用次数: 0

Enabling the Application of Graph Neural Networks on Graphs With Unknown Connectivity 图神经网络在未知连通性图上的应用

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-18 DOI: 10.1111/exsy.70088

Jorge García-Carrasco, Alejandro Maté, Juan Trujillo

{"title":"Enabling the Application of Graph Neural Networks on Graphs With Unknown Connectivity","authors":"Jorge García-Carrasco, Alejandro Maté, Juan Trujillo","doi":"10.1111/exsy.70088","DOIUrl":"https://doi.org/10.1111/exsy.70088","url":null,"abstract":"<p>Graph Neural Networks (GNNs) have proven to be reliable methods for working with graph-structured data. However, it is common to find graphs with partially or fully inaccessible connectivity patterns, hindering the direct application of GNNs to the task at hand. To tackle this problem, several Graph Structure Learning (GSL) methods have been proposed, with the objective of jointly optimizing both the graph structure and the GNN model by adding loss terms that enforce desired graph properties. These properties, such as sparseness and connectivity of similar nodes, can have a drastic impact on the performance of a GNN. However, current methods offer little control on the desired degree of sparseness, which may lead to non-optimal connectivity and reduced efficiency. In this paper, we propose a new method called Adaptative Sparsification Graph Learning (ASGL), which enables fine-grained, linear control over the total number of edges in the resulting learned graph via a novel perturbation-based loss term. ASGL not only provides flexibility in sparsity control but also improves both accuracy and computational efficiency, outperforming state-of-the-art methods in most benchmarks. We demonstrate its robustness through extensive experiments and highlight how adjusting sparsity enables optimizing the trade-off between accuracy, complexity, and interpretability.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315183","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}

引用次数: 0

Toxic Discourse in the Digital Battlefield: Analysing Telegram Channels During the Russia–Ukraine ‘Conflict’ 数字战场上的有毒话语：分析俄乌“冲突”期间的电报频道

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-06-16 DOI: 10.1111/exsy.70081

Arsenii Tretiakov, Sergio D'Antonio-Maceiras, Áurea Anguera de Sojo Hernández, Alejandro Martín

{"title":"Toxic Discourse in the Digital Battlefield: Analysing Telegram Channels During the Russia–Ukraine ‘Conflict’","authors":"Arsenii Tretiakov, Sergio D'Antonio-Maceiras, Áurea Anguera de Sojo Hernández, Alejandro Martín","doi":"10.1111/exsy.70081","DOIUrl":"https://doi.org/10.1111/exsy.70081","url":null,"abstract":"<p>Instant messenger Telegram has emerged as a favoured platform for far-right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre-trained language models to detect and measure toxicity in political content on Telegram channels. The proposed techniques have shown notable advancements in identifying toxic information using a fine-tuned RoBERTa model. Through the combination of data analysis, time-series analysis, and BERTopic modelling, the research demonstrates how toxicity varies by topic, country, and time period, using metadata. The study identified key topics in the dataset, which includes 23.6 million messages from 1491 Telegram channels, including the Russian–Ukrainian conflict and political tensions in Europe and the United States from 2016 to 1 July 2023. Despite these achievements, challenges such as the dominance of Russian language content and a focus on specific topics were highlighted. This research advances the understanding of how toxic language and propaganda are disseminated across different languages and political narratives, contributing to the study of digital communication and information warfare.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292778","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}

引用次数: 0