Concurrency and Computation-Practice & Experience最新文献

{"title":"Enhanced Gradient-Based Optimizer Algorithm With Multi-Strategy for Feature Selection","authors":"Tianbao Liu,&nbsp;Yang Li,&nbsp;Xiwen Qin","doi":"10.1002/cpe.70034","DOIUrl":"https://doi.org/10.1002/cpe.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>Feature selection is an effective tool for processing data. It is employed to eliminate redundant or irrelevant features and select optimal feature subsets to improve the performance of learning models. The gradient-based optimizer (GBO) received extensive attention in solving different optimization problems, which have the gradient search rule (GSR) and the local escaping operation (LEO). However, when addressing complex optimization and feature selection problems, GBO exhibits deficiencies in balancing global exploration and exploitation, and tends to converge to local optima. This article presents a modified version of GBO, named FWZGBO, for solving feature selection problems. Firstly, inspired by the iterative method and its theory, we propose an enhanced strategy for significantly accelerating the search capability in GSR. This strategy utilizes an optimal fourth-order iterative method to perform the corresponding function of the second-order Newton's method. Secondly, we suggest an enhanced refraction learning approach with Gaussian distribution to help the algorithm escape from local optima and enhance population diversity. Thirdly, this work devises a new adaptive weight based on the cosine strategy in both GSR and LEO to attain a harmonious balance between exploration and exploitation. To validate the performance of the FWZGBO algorithm, 28 benchmark functions and 20 well-known datasets are tested and compared with 14 optimization algorithms. The experimental results show that FWZGBO is significantly superior in solving global optimization and feature selection problems. Meanwhile, the effectiveness of the FWZGBO algorithm is validated using the Friedman test with the corresponding post-hoc test.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602780","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":"Dynamic Algorithms for Approximate Steiner Trees","authors":"Hemraj Raikwar,&nbsp;Harshil Sadharakiya,&nbsp;Sushanta Karmakar","doi":"10.1002/cpe.70040","DOIUrl":"https://doi.org/10.1002/cpe.70040","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;This study investigates the dynamic Steiner tree problem. The objective of the Steiner tree problem is to compute a minimum-weight tree connecting a set of designated vertices called terminals in a connected weighted graph with positive real edge weights. A dynamic graph is one in which the set of edges, the set of vertices, or both can change over time. Here, we focus on dynamic graphs where edges can change over time. The work begins by establishing a lower bound on the update time required to maintain an MST heuristic based &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mi&gt;ϵ&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(2-epsilon right) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-approximate Steiner tree (where &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ϵ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ epsilon $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; is a small fraction) in a general graph undergoing edge insertions or deletions. Subsequently, we propose two dynamic algorithms: A fully dynamic algorithm to maintain an approximate Steiner tree in planar graphs and an incremental algorithm to maintain an approximate Steiner tree in general graphs. We focus on edge-weighted connected graphs. The graph undergoes dynamic updates where edges with specific weights can be either inserted or deleted. The goal is to efficiently compute a Steiner tree of the updated graph, guaranteeing a solution quality (Steiner tree cost) within a good factor of the optimal Steiner tree. In the fully dynamic case, our analysis demonstrates that the presented algorithm maintains an approximation factor of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;+&lt;/mo&gt;\u0000 &lt;mi&gt;ϵ&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ left(2+epsilon right) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. The worst case update time for processing a series of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ k $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; number of updates is &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Õ&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mo&gt;|&lt;/mo&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;|&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;msqrt&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 ","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602702","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":"Adaptive Dynamic Service Placement Approach for Edge-Enabled Vehicular Networks Based on SAC and RF","authors":"Yuan Zeng,&nbsp;Hengzhou Ye,&nbsp;Gaoxing Li","doi":"10.1002/cpe.70041","DOIUrl":"https://doi.org/10.1002/cpe.70041","url":null,"abstract":"<div>\u0000 \u0000 <p>Edge computing offers crucial computational and storage support to vehicles by providing various services within the framework of the Internet of Vehicles in intelligent transportation systems. Service placement (SP) becomes particularly challenging when edge resources are limited and vehicles exhibit high-mobility. Many current dynamic placement methods rely on real-time placement, often leading to increased costs, instability, and frequent changes. This paper proposes SACRF-SP, an adaptive dynamic service placement algorithm based on Soft Actor-Critic (SAC) and Random Forest (RF), for dynamic urban traffic scenarios. This algorithm utilizes the SAC method to identify optimal placement nodes and integrates an RF model to predict service request trends. A decision network is constructed to assess the necessity of redeployment. Extensive simulation experiments demonstrate that SACRF-SP significantly reduces latency, resource usage, and the frequency of redeployment.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602720","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 Robust Lattice-Based Post-Quantum Three-Party Key Exchange Scheme for Mobile Devices","authors":"Akanksha Singh,&nbsp;Harish Chandra,&nbsp;Saurabh Rana","doi":"10.1002/cpe.70036","DOIUrl":"https://doi.org/10.1002/cpe.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we introduce a lattice-based authenticated three-party key agreement scheme for mobile devices with the aim of achieving both post-quantum security and efficiency. Our scheme is inspired by the authenticated key exchange protocol developed. We revisit the recently suggested system, which is a communication-efficient three-party password-authenticated key exchange, in which we found that the scheme is not fully correct and also demonstrate that the scheme is not safe from user's anonymity and impersonation assaults. We provide an enhanced scheme that is both effective and resistant to the mentioned assault. We also demonstrate its security in a ROM (Random Oracle Model). A comparison analysis that includes performance, security evaluations, energy consumption, and packet loss rate is also provided, proving the suitability of the suggested design.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602781","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":"Stereo Calibration of Non-Overlapping FOV Cameras With Phase-Shifting Wedge Grating","authors":"Zhaolu Zuo,&nbsp;Jie Wang,&nbsp;Jing Wang","doi":"10.1002/cpe.70033","DOIUrl":"https://doi.org/10.1002/cpe.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Stereo cameras have been widely used in three-dimensional (3D) reconstruction, target ranging and environment perception. The calibration of stereo cameras is the key technology of vision system. Most of the existing methods are not flexible due to limitations of the size of the cameras' overlapping fields of view (FOV) and the degree of defocus. This paper presents a stereo calibration method in which the feature points on the target are globally encoded by the phase-coding method. Each feature point has a specific code, and the mapping relationship between image coordinates and world coordinates can be uniquely determined. So, the stereo cameras whether or not capture the same area of the target, both the camera coordinate systems can be unified under one global coordinate system. In addition, calibration can be accomplished when cameras are out of focus seriously, for the coding and extraction of feature points are phase-based. Simulations and experiments were carried out to prove flexibility and robustness of the proposed method.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602779","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":"Machine Learning-Based Modeling of Clinical Diagnosis and Treatment of Patients With Hemorrhagic Stroke","authors":"Qingqing Wu,&nbsp;Tinghong Gao","doi":"10.1002/cpe.70042","DOIUrl":"https://doi.org/10.1002/cpe.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>Stroke includes both hemorrhagic and ischaemic stroke, and with the rising incidence of stroke, the mortality rate of hemorrhagic stroke is higher than that of ischaemic stroke, accounting for 15% of the stroke mortality rate. In this area, clinically intelligent diagnosis and treatment plays an important role. By integrating imaging features, patient clinical information, treatment plans and diagnosis, accurate personalized efficacy assessment and prognosis prediction can be achieved. In this study, machine learning models (Random Forest, XGBoost, logistic regression, LGBoost, and AdaBoost) for the exploration of factors associated with the risk of haematoma expansion (HE) were developed based on patients' diagnostic data. mRS scores were used to assess the prognostic status of the patients, Principal Component Analysis was used for data dimensionality reduction, and Spearman Correlation Analysis was used to analyze the features' direct correlation. Five machine learning models were applied to predict the probability of HE and the prognosis of hemorrhagic stroke in patients. The models were tuned using grid search and ten-fold cross-validation methods to obtain more accurate predictions. The results of the study showed that the mRS index and factors such as history of diabetes, history of coronary heart disease, haematoma volume and age were closely related to the prognosis of patients. Among them, RF and XGBoost performed well in predicting the probability of HE, with the area under the ROC curve reaching 0.98, while LGBoost performed best in predicting the prognostic status of hemorrhagic stroke patients.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602721","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":"Personalized Self-Directed Learning Recommendation System Based on Social Knowledge in Distributed Web","authors":"Baoqing Tai,&nbsp;Xianxian Yang,&nbsp;Ju Chong,&nbsp;Lei Chen","doi":"10.1002/cpe.70030","DOIUrl":"https://doi.org/10.1002/cpe.70030","url":null,"abstract":"<div>\u0000 \u0000 <p>Personalized self-directed learning recommender systems help users manage their learning paths more effectively. This paper proposed a personalized self-directed learning recommendation system based on social knowledge in cloud-supported web databases. The system leverages Long Short-Term Memory (LSTM) neural networks and Graph Attention Networks (GAT) to enhance the accuracy and effectiveness of recommendations. The LSTM neural network is used for modeling the temporal sequences of learning activities, while the Graph Attention Network is employed to extract knowledge from social interactions and relationships among users. By combining these two models, the system can provide precise and personalized recommendations to users. Experimental results demonstrate that this system can improve learning efficiency by delivering appropriate and timely content, thereby enhancing the users learning experience. The use of cloud databases also ensures easy access and high scalability of the system over distributed web.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602778","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":"Hybrid Deep Learning Models for Tennis Action Recognition: Enhancing Professional Training Through CNN-BiLSTM Integration","authors":"Zhaokun Chen,&nbsp;Qin Xie,&nbsp;Wei Jiang","doi":"10.1002/cpe.70029","DOIUrl":"https://doi.org/10.1002/cpe.70029","url":null,"abstract":"<div>\u0000 \u0000 <p>Classifying tennis movements from video data presents significant challenges, including overfitting, limited datasets, low accuracy, and difficulty in capturing dynamic, real-world conditions such as variable lighting, camera angles, and complex player movements. Existing approaches lack robustness and practicality for real-time applications, which are crucial for sports analysts and coaches. To address these challenges, this paper proposes an advanced architecture that strategically integrates the Bidirectional Long Short-Term Memory Network (BiLSTM) and transfer learning from the lightweight Convolutional Neural Network (CNN) MobileNetV2. The motivation behind this work lies in enabling coaches to objectively analyze player performance and tailor training strategies based on precise movement recognition. The model is designed to enhance video representation capture, improve action classification accuracy, and operate efficiently in real-world conditions. Validation with the THETIS dataset demonstrates state-of-the-art results, achieving 96.72% accuracy and 96.97% recall, significantly outperforming existing methods. Additionally, the integration of cloud and edge computing capabilities facilitates real-time detection of tennis actions, providing immediate, actionable insights for practitioners. A motivating case study showcases how this method can effectively identify and analyze complex movements such as smashes and slices, addressing long-standing challenges in video-based tennis training. This research offers a robust and adaptable solution for classifying tennis actions, with promising implications for trainers and sports analysts seeking efficient and scalable tools for video analysis.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602777","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":"Analysis and Performance Measure of Dynamic Cluster Based Hierarchical Real Time Scheduling for Distributed Systems","authors":"Girish Talmale,&nbsp;Urmila Shrawankar","doi":"10.1002/cpe.70038","DOIUrl":"https://doi.org/10.1002/cpe.70038","url":null,"abstract":"<div>\u0000 \u0000 <p>Efficient scheduling is critical for real-time systems, which are ubiquitous in daily life, as they demand high computational performance while minimizing power consumption and thermal inefficiencies. Multi-core platforms address these requirements but present challenges in task scheduling. Existing scheduling strategies include partitioned scheduling, which statically assigns tasks to processors to eliminate migration costs but suffers from NP-hard task allocation and low CPU utilization, and global scheduling, which allows task migration across processors to improve system utilization but incurs significant migration and preemption overheads. Neither strategy alone is sufficient to handle all real-time task sets effectively, highlighting the need for a hybrid solution for multi-core platforms. To address these challenges, this manuscript proposes a dynamic, cluster-based hybrid real-time scheduling algorithm that employs a hierarchical approach. By grouping cores into clusters, this method balances the trade-offs between partitioned and global scheduling. It minimizes migration and preemption overheads while improving resource utilization and system reliability. Dynamic cluster resizing and task assignment strategies further enhance efficiency by tailoring the scheduling process to workload demands. Simulation results demonstrate the proposed scheduler's superiority over partitioned and global scheduling approaches. It achieves higher resource utilization, better job acceptance rates, and reduced response times while lowering migration, preemption, and scheduling overheads. This work introduces an innovative scheduling framework that combines task assignment and scheduling in a two-step process: (1) Task Assignment: Allocates tasks to cores with controlled migration based on workload. (2) Task Scheduling: Sequences the execution of allocated tasks within clusters to ensure efficiency. The proposed approach offers a scalable and reliable solution for managing real-time tasks on multi-core systems, addressing limitations of traditional scheduling methods.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602719","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":"Quantitative Risk Assessment for Cloud-Based Software Migration Processes","authors":"Wen Zeng,&nbsp;Wenjing Yan,&nbsp;Emily Simpson,&nbsp;Carlos Molina–Jimenez","doi":"10.1002/cpe.70009","DOIUrl":"https://doi.org/10.1002/cpe.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud migration is the process of moving data, files, and applications to a cloud computing environment. With the success of cloud computing, cloud migration is becoming fashionable. However, different cloud service providers have their own migration processes as currently, no standard cloud-based software migration procedure exists to guide how to move digital resources to the cloud. Moreover, there are serious security risks associated with cloud migration processes that threaten business processes that have not been systematically analyzed. In addition, no formal models and security metrics exist to evaluate and analyze these risks. Therefore, it is necessary to develop a generic approach with real customer use cases. In this study, firstly, we will develop a general cloud-based software migration procedure to help organizations migrate their digital resources to cloud platforms. Secondly, we will develop a risk assessment model for analyzing migration processes. Thirdly, on the basis of this risk assessment model, we use stochastic colored Petri nets to describe the dynamic behavior of the model, thus the concurrency, synchronization, mutual exclusion, and conflicts can be analyzed to assess the risks of migration automatically. Fourthly, security metrics will be defined to quantitatively evaluate the risks and vulnerabilities of the organizations. We believe that this study can help chief information officers identify which risk will have more opportunity to occur during the migration processes and make informed decisions about software migration and security.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 6-8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602812","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}