International Journal of Approximate Reasoning最新文献

{"title":"FDACNet: Enhancing time-series classification with fuzzy feature and integrated self-attention and temporal convolution","authors":"Xiuwei Chen,&nbsp;Li Lai,&nbsp;Maokang Luo","doi":"10.1016/j.ijar.2025.109521","DOIUrl":"10.1016/j.ijar.2025.109521","url":null,"abstract":"<div><div>Time-series classification is crucial in time series analysis and holds significant importance in real-world scenarios. Applying self-attention and temporal convolution techniques is paramount when dealing with time series data. The self-attention mechanism enables the capture of correlations between different time steps in a sequence, thereby facilitating the handling of long-term dependencies. Meanwhile, temporal convolution is designed explicitly for processing time series data, effectively capturing temporal dependencies through convolutional layers. The integration of the two technologies plays a pivotal role in time series analysis, enabling accurate temporal classification. This paper proposes a novel net with fuzzy features and integrated self-attention and temporal convolution, denoted as FDACNet. The proposed net introduces two key components: FD-FE for fuzzy dominated feature extraction, and ATCmix for integrating self-attention and temporal convolution. FD-FE captures trend information by defining gradient relationship between time points within a time series sample. On the other hand, ATCmix combines convolution and self-attention to reduce parameters and enhance efficiency in handling time-series data. Finally, the proposed method is evaluated on twenty datasets and compared against twelve other state-of-the-art approaches. Experimental results demonstrate the superior classification accuracy of the proposed model, showcasing a 5.2% and 7.1% enhancement in average accuracy compared to the state-of-the-art convolution-based and transformer-based methods ModernTCN and iTransformer.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109521"},"PeriodicalIF":3.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"PAC learning of concept inclusions for ontology-mediated query answering","authors":"Sergei Obiedkov ,&nbsp;Barış Sertkaya","doi":"10.1016/j.ijar.2025.109523","DOIUrl":"10.1016/j.ijar.2025.109523","url":null,"abstract":"<div><div>We present a probably approximately correct algorithm for learning the terminological part of a description-logic knowledge base via subsumption queries. The axioms we learn are concept inclusions between conjunctions of concepts from a specified set of concept descriptions. By varying the distribution of queries posed to the oracle, we adapt the algorithm to improve the recall when using the resulting TBox for ontology-mediated query answering. Experimental evaluation on OWL 2 EL ontologies suggests that our approach helps significantly improve recall while maintaining a high precision of query answering.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109523"},"PeriodicalIF":3.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Double Boolean algebras: Constructions, sub-structures and morphisms","authors":"Gael Tenkeu Kembang ,&nbsp;Yannick Léa Tenkeu Jeufack ,&nbsp;Etienne Romuald Temgoua Alomo ,&nbsp;Leonard Kwuida","doi":"10.1016/j.ijar.2025.109519","DOIUrl":"10.1016/j.ijar.2025.109519","url":null,"abstract":"<div><div>Double Boolean algebras are algebras <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder><mo>:</mo><mo>=</mo><mo>(</mo><mi>D</mi><mo>;</mo><mo>⊓</mo><mo>,</mo><mo>⊔</mo><mo>,</mo><mo>¬</mo><mo>,</mo><mo>⌟</mo><mo>,</mo><mo>⊥</mo><mo>,</mo><mo>⊤</mo><mo>)</mo></math></span> of type <span><math><mo>(</mo><mn>2</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>0</mn><mo>,</mo><mn>0</mn><mo>)</mo></math></span> introduced by Rudolf Wille to capture the equational theory of the algebra of protoconcepts. Every double Boolean algebra <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> contains two Boolean algebras: <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span>. Three main goals are achieved in this paper. First we characterize sub-algebras of a double Boolean algebra <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> as join sets of sub-algebras of the Boolean algebras <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and a subset of <span><math><mi>D</mi><mo>﹨</mo><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> (where <span><math><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>=</mo><msub><mrow><mi>D</mi></mrow><mrow><mo>⊓</mo></mrow></msub><mo>∪</mo><msub><mrow><mi>D</mi></mrow><mrow><mo>⊔</mo></mrow></msub></math></span>) satisfying certain conditions. Second, we characterize homomorphisms between two double Boolean algebras <span><math><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></math></span> and <span><math><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></math></span> by homomorphisms between the Boolean algebras <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊓</mo></mrow></msub></math></span>, <span><math><msub><mrow><munder><mrow><mi>D</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and <span><math><msub><mrow><munder><mrow><mi>E</mi></mrow><mo>_</mo></munder></mrow><mrow><mo>⊔</mo></mrow></msub></math></span> and maps between <span><math><mi>D</mi><mo>﹨</mo><msub><mrow><mi>D</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> and <em>E</em> satisfying certain conditions. Third, we give some tools to construct some classes of pure double Boolean algebras.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109519"},"PeriodicalIF":3.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Joining copulas of extreme implicit dependence copulas","authors":"Noppawit Yanpaisan,&nbsp;Tippawan Santiwipanont,&nbsp;Songkiat Sumetkijakan","doi":"10.1016/j.ijar.2025.109518","DOIUrl":"10.1016/j.ijar.2025.109518","url":null,"abstract":"<div><div>Copulas of uniform-<span><math><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></math></span> random variables <em>U</em> and <em>V</em> satisfying <span><math><mi>α</mi><mo>(</mo><mi>U</mi><mo>)</mo><mo>=</mo><mi>β</mi><mo>(</mo><mi>V</mi><mo>)</mo></math></span> almost surely for some measure-preserving transformations <em>α</em> and <em>β</em> are called <em>implicit dependence copulas</em>. They were recently shown to coincide with the generalized Markov products of <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span> with respect to a class of joining copulas <span><math><msub><mrow><mo>(</mo><msub><mrow><mi>A</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>)</mo></mrow><mrow><mi>t</mi><mo>∈</mo><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></msub></math></span>. If <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span> are not two-sided invertible, then most implicit dependence copulas, especially when <span><math><msub><mrow><mi>A</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>≡</mo><mi>Π</mi></math></span>, are not extreme points in the class of copulas. For a given pair of left and right invertible copulas <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>e</mi><mo>,</mo><mi>α</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>β</mi><mo>,</mo><mi>e</mi></mrow></msub></math></span>, we characterize extreme implicit dependence copulas in terms of the extremality of the joining copulas in the class of subcopulas on a domain involving the invertible copulas. This result is then extended to the multivariate case.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109518"},"PeriodicalIF":3.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Cauchy Graph Convolutional Networks","authors":"Taurai Muvunza ,&nbsp;Yang Li ,&nbsp;Ercan Engin Kuruoglu","doi":"10.1016/j.ijar.2025.109517","DOIUrl":"10.1016/j.ijar.2025.109517","url":null,"abstract":"<div><div>A common approach to learning Bayesian networks involves specifying an appropriately chosen family of parameterized probability density such as Gaussian. However, the distribution of most real-life data is leptokurtic and may not necessarily be best described by a Gaussian process. In this work we introduce Cauchy Graphical Models (CGM), a class of multivariate Cauchy densities that can be represented as directed acyclic graphs with arbitrary network topologies, the edges of which encode linear dependencies between random variables. We develop CGLearn, the resultant algorithm for learning the structure and Cauchy parameters based on Minimum Dispersion Criterion (MDC). Experiments using simulated datasets on benchmark network topologies demonstrate the efficacy of our approach when compared to Gaussian Graphical Models (GGM). Most Graph Convolutional Neural Networks (GCN) process input graphs as ground-truth representations of node relationships, yet these graphs are constructed based on modeling assumptions and noisy data and their use may lead to suboptimal performance on downstream prediction tasks. We propose Cauchy GCN which leverages CGM to infer graph topology that depicts latent relationships between nodes. We evaluate the effectiveness and quality of the structural graphs learned by CGM, and demonstrate that Cauchy-GCN achieves superior performance compared to widely used graph construction methods.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109517"},"PeriodicalIF":3.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Dynamic concept reduction methods based on local information","authors":"Mei-Zheng Li ,&nbsp;Lei-Jun Li ,&nbsp;Ju-Sheng Mi ,&nbsp;Qian Hu","doi":"10.1016/j.ijar.2025.109514","DOIUrl":"10.1016/j.ijar.2025.109514","url":null,"abstract":"<div><div>Knowledge reduction is one of the core research issues in formal concept analysis. As a new technique of knowledge reduction, concept reduction has received increasing attention recently. One typical method of calculating concept reducts is based on representative concept matrix (RC-matrix, for short), which can obtain all concept reducts. However, it is confronted with the following challenges: (1) before the construction of the RC-matrix, all concepts of the formal context need to be calculated, which is both time and space consuming; (2) there is a lot of redundant information in the constructed RC-matrix, which is not helpful to calculate the concept reducts; (3) when the data changes dynamically, the concept reducts need to be calculated for scratch. To address these issues, dynamic concept reduction methods based on local information are proposed in this paper. Firstly, the characteristics of the minimal elements (with respect to set inclusion) in the RC-matrix are analyzed, and all the minimal elements are directly labeled from the formal context; secondly, the advantage of local information is taken to construct each minimal elements of the RC-matrix, from which all the concept reducts can be obtained; besides, a new simplified version of RC-matrix, named as Type-I minimal RC-matrix, is further constructed to compute one concept reduct; and finally, when data dynamically changes, the connections between concept reducts of the original formal context and those of the new one are analyzed, consequently, two dynamic concept reduction algorithms are proposed.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109514"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Structural reliability analysis for parameterized probability box based on efficient global optimization and dimension-reduction method","authors":"Haibo Liu ,&nbsp;Wen Lai ,&nbsp;Weifeng Luo ,&nbsp;Shufeng Zhang ,&nbsp;Huichao Xie ,&nbsp;Qiong Wang","doi":"10.1016/j.ijar.2025.109513","DOIUrl":"10.1016/j.ijar.2025.109513","url":null,"abstract":"<div><div>In practical engineering, structural reliability analysis plays an important role in the safe operation of mechanical systems. The parameterized probability-box (p-box) model can effectively capture aleatory and epistemic uncertainties with flexibility and tunability to adapt to different conditions. This paper proposes a structural reliability analysis method for the problem with parameterized p-box based on efficient global optimization (EGO) and the univariate dimension reduction method (UDRM) to efficiently solve the upper and lower bounds of the failure probability of structures. First, the UDRM is used to calculate the origin moments of the performance function. Second, based on the results of the first four moments, the probability density function (PDF) of the performance function is constructed by the maximum entropy method (MEM) to compute the failure probability. Third, the EGO is utilized to obtain the upper and lower bounds of the failure probability of structures. Finally, the effectiveness of the proposed method is demonstrated through five numerical examples.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109513"},"PeriodicalIF":3.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Feasible strategies in three-way conflict analysis with three-valued ratings","authors":"Jing Liu ,&nbsp;Mengjun Hu ,&nbsp;Guangming Lang","doi":"10.1016/j.ijar.2025.109516","DOIUrl":"10.1016/j.ijar.2025.109516","url":null,"abstract":"<div><div>Most existing work on three-way conflict analysis has focused on trisecting agent pairs, agents, or issues. While these trisections lay the groundwork for understanding the nature of conflicts, further actions need to be formulated to address conflict resolution. One of the widely studied approaches is to construct feasible strategies. This paper aims to investigate feasible strategies from two perspectives of consistency and non-consistency. Particularly, we begin with computing the overall rating of a clique of agents based on positive and negative similarity degrees. Afterwards, considering the weights of both agents and issues, we propose weighted consistency and non-consistency measures, which are respectively used to identify the feasible strategies for a clique of agents. Algorithms are developed to identify feasible strategies, <em>L</em>-order feasible strategies, and the corresponding optimal ones. Finally, to demonstrate the practicality, effectiveness, and superiority of the proposed models, we apply them to two commonly used case studies on NBA labor negotiations and development plans for Gansu Province and conduct a sensitivity analysis on parameters and a comparative analysis with existing state-of-the-art conflict analysis approaches. The comparison results demonstrate that our conflict resolution models outperform the conventional approaches by unifying weighted agent-issue evaluation with consistency and non-consistency measures to enable the systematic identification of not only feasible strategies but also optimal solutions.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109516"},"PeriodicalIF":3.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Computationally efficient variational-like approximations of possibilistic inferential models","authors":"Leonardo Cella ,&nbsp;Ryan Martin","doi":"10.1016/j.ijar.2025.109506","DOIUrl":"10.1016/j.ijar.2025.109506","url":null,"abstract":"<div><div>Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficient computation is a challenge. This paper presents a simple yet powerful numerical strategy for approximating the IM's possibility contour, or at least its <em>α</em>-cut for a specified <span><math><mi>α</mi><mo>∈</mo><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></math></span>. Our proposal starts with the specification of a parametric family that, in a certain sense, approximately covers the credal set associated with the IM's possibility measure. Akin to variational inference, we then propose to tune the parameters of that parametric family so that its <span><math><mn>100</mn><mo>(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo>)</mo><mtext>%</mtext></math></span> credible set roughly matches the IM contour's <em>α</em>-cut. This parametric <em>α</em>-cut matching strategy implies a full approximation to the IM's possibility contour at a fraction of the computational cost associated with previous strategies.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109506"},"PeriodicalIF":3.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Conflict management in a distance to prototype-based evidential neural network","authors":"Dănuţ-Vasile Giurgi ,&nbsp;Mihreteab Negash Geletu ,&nbsp;Thomas Josso-Laurain ,&nbsp;Maxime Devanne ,&nbsp;Jean-Philippe Lauffenburger ,&nbsp;Jean Dezert","doi":"10.1016/j.ijar.2025.109508","DOIUrl":"10.1016/j.ijar.2025.109508","url":null,"abstract":"<div><div>Despite advances in integrating reasoning based on belief functions to generalise probabilistic representations, distance-to-prototype-based evidential deep neural networks are still emerging and require further consolidation. Existing studies in segmentation or classification tasks typically perform prior initialisation and do not address or mitigate the potential conflicts that may arise during fusion. This work investigates high-conflict scenarios within an evidential neural network for segmentation in autonomous driving, focusing on the distance-to-prototypes component, where prototypes, derived from feature maps, serve as sources of evidence and may yield contradictory information. Conflict is mitigated through parameter adjustments within the evidential reasoning, enhancing consistency before fusion. This enables more reliable data integration and a valid application of fusion rules and decision-making processes. The proposed rectification is validated on two prototype configurations of a deep evidential lidar-camera cross-fusion architecture, using two distance-based decision strategies and adapted metrics. The impact on the network's predictions is demonstrated through qualitative and quantitative results on road detection tasks with the KITTI dataset.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"186 ","pages":"Article 109508"},"PeriodicalIF":3.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}