Concurrency and Computation-Practice & Experience最新文献_第3页

Improved deep network-based load predictor and optimal load balancing in cloud-fog services 基于深度网络的改进型负载预测器和云雾服务中的优化负载平衡

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-09-04 DOI: 10.1002/cpe.8275

Shubham Singh, Amit Kumar Mishra, Siddhartha Kumar Arjaria, Chinmay Bhatt, Daya Shankar Pandey, Ritesh Kumar Yadav

{"title":"Improved deep network-based load predictor and optimal load balancing in cloud-fog services","authors":"Shubham Singh, Amit Kumar Mishra, Siddhartha Kumar Arjaria, Chinmay Bhatt, Daya Shankar Pandey, Ritesh Kumar Yadav","doi":"10.1002/cpe.8275","DOIUrl":"10.1002/cpe.8275","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing is commonly utilized in remote contexts to handle user demands for resources and services. Each assignment has unique processing needs that are determined by the time it takes to complete. However, if load balancing is not properly managed, the effectiveness of resources may suffer dramatically. Consequently, cloud service providers have to emphasize rapid and precise load balancing as well as proper resource supply. This paper proposes a novel enhanced deep network-based load predictor and load balancing in cloud-fog services. In prior, the workload is predicted using a deep network called Multiple Layers Assisted in LSTM (MLA-LSTM) model that considers the capacity of virtual machine (VM) and task as input and predicts the target label as underload, overload and equally balanced. According to this prediction, the optimal load balancing is performed through a hybrid optimization named Osprey Assisted Pelican Optimization Algorithm (OAPOA) while taking into account several parameters such as makespan, execution cost, resource consumption, and server load. Additionally, a process known as load migration is carried out, in which machines with overload tasks are assigned to machines with underload tasks. This migration is applied optimally via the OAPOA strategy under the consideration of constraints including migration cost and migration efficiency.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196615","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

Large language model evaluation for high-performance computing software development 高性能计算软件开发的大型语言模型评估

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-09-04 DOI: 10.1002/cpe.8269

William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter

{"title":"Large language model evaluation for high-performance computing software development","authors":"William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter","doi":"10.1002/cpe.8269","DOIUrl":"10.1002/cpe.8269","url":null,"abstract":"<p>We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C <span>++</span>, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C<span>++</span> (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple <span><kernel> + <programming model> + <optional hints></span> prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C<span>++</span> correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. We expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196614","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

Detection and mitigation of few control plane attacks in software defined network environments using deep learning algorithm 利用深度学习算法检测和缓解软件定义网络环境中的少量控制平面攻击

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-09-03 DOI: 10.1002/cpe.8256

M. Anand Kumar, Edeh Michael Onyema, B. Sundaravadivazhagan, Manish Gupta, Achyut Shankar, Venkataramaiah Gude, Nagendar Yamsani

{"title":"Detection and mitigation of few control plane attacks in software defined network environments using deep learning algorithm","authors":"M. Anand Kumar, Edeh Michael Onyema, B. Sundaravadivazhagan, Manish Gupta, Achyut Shankar, Venkataramaiah Gude, Nagendar Yamsani","doi":"10.1002/cpe.8256","DOIUrl":"10.1002/cpe.8256","url":null,"abstract":"<div>\u0000 \u0000 <p>In order to make networks more adaptable and flexible, software-defined networking (SDN) is an architecture that abstracts the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man-in-the-middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep-learning-based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196616","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

Novel hawk swarm-optimized deep learning classification with K-nearest neighbor based decision making for autonomous vehicle movement controller 基于 K 最近邻决策的新型鹰群优化深度学习分类法，用于自动驾驶汽车运动控制器

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-31 DOI: 10.1002/cpe.8241

Zhang Qingmiao, Zhang Dinghua

{"title":"Novel hawk swarm-optimized deep learning classification with K-nearest neighbor based decision making for autonomous vehicle movement controller","authors":"Zhang Qingmiao, Zhang Dinghua","doi":"10.1002/cpe.8241","DOIUrl":"10.1002/cpe.8241","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM-GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line-based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K-nearest neighbor (KNN)-based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal-to-noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196619","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

To secure an e-commerce system using epidemic mathematical modeling with neural network 利用神经网络流行病数学模型确保电子商务系统的安全

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-29 DOI: 10.1002/cpe.8270

Kumar Sachin Yadav, Ajit Kumar Keshri

{"title":"To secure an e-commerce system using epidemic mathematical modeling with neural network","authors":"Kumar Sachin Yadav, Ajit Kumar Keshri","doi":"10.1002/cpe.8270","DOIUrl":"10.1002/cpe.8270","url":null,"abstract":"<div>\u0000 \u0000 <p>Securing an e-commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e-commerce systems is the complexity of the approach. The epidemic-based recurrent long short-term memory (E-RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e-commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E-RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e-commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL-KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber-attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196617","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

Secure access technology for industrial internet of things 工业物联网安全接入技术

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-29 DOI: 10.1002/cpe.8231

Bingquan Wang, Jin Peng, Meili Cui

引用次数: 0

A distributed memory parallel randomized Kaczmarz for sparse system of equations 稀疏方程组的分布式内存并行随机化卡兹马兹算法

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-27 DOI: 10.1002/cpe.8274

Ercan Selçuk Bölükbaşı, Fahreddin Şükrü Torun, Murat Manguoğlu

{"title":"A distributed memory parallel randomized Kaczmarz for sparse system of equations","authors":"Ercan Selçuk Bölükbaşı, Fahreddin Şükrü Torun, Murat Manguoğlu","doi":"10.1002/cpe.8274","DOIUrl":"10.1002/cpe.8274","url":null,"abstract":"<p>Kaczmarz algorithm is an iterative projection method for solving system of linear equations that arise in science and engineering problems in various application domains. In addition to classical Kaczmarz, there are randomized and parallel variants. The main challenge of the parallel implementation is the dependency of each Kaczmarz iteration on its predecessor. Because of this dependency, frequent communication is required which results in a substantial overhead. In this study, a new distributed parallel method that reduces the communication overhead is proposed. The proposed method partitions the problem so that the Kaczmarz iterations on different blocks are less dependent. A frequency parameter is introduced to see the effect of communication frequency on the performance. The communication overhead is also decreased by allowing communication between processes only if they have shared non-zero columns. The experiments are performed using problems from various domains to compare the effects of different partitioning methods on the communication overhead and performance. Finally, parallel speedups of the proposed method on larger problems are presented.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.8274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225197","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

Noise-robust neural networks for medical image segmentation by dual-strategy sample selection 通过双策略样本选择实现用于医学图像分割的降噪神经网络

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-26 DOI: 10.1002/cpe.8271

Jialin Shi, Youquan Yang, Kailai Zhang

{"title":"Noise-robust neural networks for medical image segmentation by dual-strategy sample selection","authors":"Jialin Shi, Youquan Yang, Kailai Zhang","doi":"10.1002/cpe.8271","DOIUrl":"10.1002/cpe.8271","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep neural networks for medical image segmentation often face the problem of insufficient clean labeled data. Although non-expert annotations are more readily accessible, these low-quality annotations lead to significant performance degradation of existing neural network methods. In this paper, we focus on robust learning of medical image segmentation with noisy annotations and propose a novel noise-tolerant framework based on dual-strategy sample selection, which selects the informative samples to provide effective supervision information. First, we propose the first round of sample selection by designing a novel joint loss, which includes conventional supervised loss and regularization loss. To further select information-rich samples, we propose confidence-based pseudo-label sample selection from a novel perspective as the complement. The dual strategies are used in a collaborative manner and the network is optimized with mined informative samples. We conducted extensive experiments on datasets with both simulated noisy labels and real-world noisy labels. For instance, on a simulated dataset with 25% noise ratio, our method achieves segmentation Dice value with 90.56% <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation>$$ pm $$</annotation>\u0000 </semantics></math> 0.03%. Furthermore, increasing the noise ratio to 95%, our method still maintains a high Dice value of 73.85% <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation>$$ pm $$</annotation>\u0000 </semantics></math> 0.28% compared to other baselines. Extensive results have demonstrated that our method can weaken the effects of noisy labels on medical image segmentation.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196623","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

Fair XIDS: Ensuring fairness and transparency in intrusion detection models 公平的 XIDS：确保入侵检测模型的公平性和透明度

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-26 DOI: 10.1002/cpe.8268

Chinu, Urvashi Bansal

{"title":"Fair XIDS: Ensuring fairness and transparency in intrusion detection models","authors":"Chinu, Urvashi Bansal","doi":"10.1002/cpe.8268","DOIUrl":"https://doi.org/10.1002/cpe.8268","url":null,"abstract":"<div>\u0000 \u0000 <p>An intrusion detection system (IDS) is valuable for detecting anomalies and unauthorized access to a system or network. Due to the black-box nature of these IDS models, network experts need more trust in systems to act on alerts and transparency to understand the model's inner logic. Moreover, biased models' decisions affect the model performance and increase the false positive rates, directly affecting the model's accuracy. So, maintaining Transparency and Fairness simultaneously in IDS models is essential for accurate decision-making. Existing methods face challenges of the tradeoff between fairness and accuracy, which also affects the reliability and robustness of the model. Motivated by these research gaps, we developed the Fair-XIDS model. This model clarifies its internal logic with visual explanations and promotes fairness across its entire lifecycle. The Fair-XIDS model successfully integrates complex transparency and fairness algorithms to address issues like Imbalanced datasets, algorithmic bias, and postprocessing bias with an average 85% reduction in false positive rate. To ensure reliability, the proposed model effectively mitigates the tradeoff between accuracy and fairness with an average of 90% accuracy and more than 85% fairness. The assessment results of the proposed model over diverse datasets and classifiers mark its model-agnostic nature. Overall, the model achieves more than 85% consistency among diverse classifiers.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525409","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

Building an intrusion detection system on UNSW-NB15: Reducing the margin of error to deal with data overlap and imbalance 在 UNSW-NB15 上构建入侵检测系统：降低误差幅度以处理数据重叠和不平衡问题

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2024-08-22 DOI: 10.1002/cpe.8242

Zeinab Zoghi, Gursel Serpen

{"title":"Building an intrusion detection system on UNSW-NB15: Reducing the margin of error to deal with data overlap and imbalance","authors":"Zeinab Zoghi, Gursel Serpen","doi":"10.1002/cpe.8242","DOIUrl":"10.1002/cpe.8242","url":null,"abstract":"<p>This study addresses the challenge of data imbalance and class overlap in machine learning for intrusion detection, proposing that targeted algorithmic adjustments can significantly enhance model performance. Our hypothesis contends that an ensemble framework, adeptly integrating novel threshold-adjustment algorithms, can improve classification sensitivity and specificity. To test this, we developed an ensemble model comprising Balanced Bagging (BB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF), fine-tuned using grid search for BB and XGBoost, and augmented with the Hellinger metric for RF to tackle data imbalance. The innovation lies in our algorithms, which adeptly adjust the discrimination threshold to rectify the class overlap problem, enhancing the model's ability to discern between negative and positive classes. Utilizing the UNSW-NB15 dataset, we conducted a comparative analysis for binary and multi-category classification. Our ensemble model achieved a binary classification accuracy of 97.80%, with a sensitivity rate of 98.26% for detecting attacks, and a multi-category classification accuracy and sensitivity that reached up to 99.73% and 97.24% for certain attack types. These results substantially surpass those of existing models on the same dataset, affirming our model's superiority in dealing with complex data distributions prevalent in network security domains.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"36 25","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.8242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225198","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