IEEE Transactions on Sustainable Computing最新文献_第10页

PTCC: A Privacy-Preserving and Trajectory Clustering-Based Approach for Cooperative Caching Optimization in Vehicular Networks PTCC：基于隐私保护和轨迹聚类的车载网络合作缓存优化方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-01-05 DOI: 10.1109/TSUSC.2024.3350386

Tengfei Cao;Zizhen Zhang;Xiaoying Wang;Han Xiao;Changqiao Xu

{"title":"PTCC: A Privacy-Preserving and Trajectory Clustering-Based Approach for Cooperative Caching Optimization in Vehicular Networks","authors":"Tengfei Cao;Zizhen Zhang;Xiaoying Wang;Han Xiao;Changqiao Xu","doi":"10.1109/TSUSC.2024.3350386","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350386","url":null,"abstract":"5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to 9.8% and 12.8% with different file numbers and caching capacities, compared with other state-of-the-art solutions.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"615-630"},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965831","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}

引用次数: 0

Heterogeneous Ensemble Federated Learning With GAN-Based Privacy Preservation 基于 GAN 隐私保护的异构集合联盟学习

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-01-05 DOI: 10.1109/TSUSC.2024.3350040

Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong

{"title":"Heterogeneous Ensemble Federated Learning With GAN-Based Privacy Preservation","authors":"Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong","doi":"10.1109/TSUSC.2024.3350040","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350040","url":null,"abstract":"Multi-party collaborative learning has become a paradigm for large-scale knowledge discovery in the era of Big Data. As a typical form of collaborative learning, federated learning (FL) has received widespread research attention in recent years. In practice, however, FL faces a range of challenges such as objective inconsistency, communication and synchronization issues, due to the heterogeneity in the clients’ local datasets and devices. In this paper, we propose EnsembleFed, a novel ensemble framework for heterogeneous FL. The proposed framework first allows each client to train a local model with full autonomy and without having to consider the heterogeneity of local datasets. The confidence scores of training samples output by each local model are then perturbed to defend against membership inference attacks, after which they are submitted to the server for use in constructing the global model. We apply a GAN-based method to generate calibrated noise for confidence perturbation. Benefiting from the ensemble framework, EnsembleFed disengages from the restriction of real-time synchronization and achieves collaborative learning with lower communication costs than traditional FL. Experiments on real-world datasets demonstrate that the proposed EnsembleFed can significantly improve the performance of the global model while also effectively defending against membership inference attacks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"591-601"},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965767","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}

引用次数: 0

Apict:Air Pollution Epidemiology Using Green AQI Prediction During Winter Seasons in India Apict：利用绿色空气质量指数预测印度冬季空气污染流行病学

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-01-01 DOI: 10.1109/TSUSC.2023.3343922

Sweta Dey;Kalyan Chatterjee;Ramagiri Praveen Kumar;Anjan Bandyopadhyay;Sujata Swain;Neeraj Kumar

{"title":"Apict:Air Pollution Epidemiology Using Green AQI Prediction During Winter Seasons in India","authors":"Sweta Dey;Kalyan Chatterjee;Ramagiri Praveen Kumar;Anjan Bandyopadhyay;Sujata Swain;Neeraj Kumar","doi":"10.1109/TSUSC.2023.3343922","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3343922","url":null,"abstract":"During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for \u0000<inline-formula><tex-math>$boldsymbol {PM_{2.5}}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$boldsymbol {NO_{2}}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$boldsymbol {CO}$</tex-math></inline-formula>\u0000 pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the \u0000<inline-formula><tex-math>$boldsymbol {PM_{2.5}}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$boldsymbol {NO_{2}}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$boldsymbol {CO}$</tex-math></inline-formula>\u0000 concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"559-570"},"PeriodicalIF":3.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264521","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}

引用次数: 0

ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments ATOM：面向无服务器边缘计算环境的人工智能可持续资源管理

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-12-29 DOI: 10.1109/TSUSC.2023.3348157

Muhammed Golec;Sukhpal Singh Gill;Felix Cuadrado;Ajith Kumar Parlikad;Minxian Xu;Huaming Wu;Steve Uhlig

{"title":"ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments","authors":"Muhammed Golec;Sukhpal Singh Gill;Felix Cuadrado;Ajith Kumar Parlikad;Minxian Xu;Huaming Wu;Steve Uhlig","doi":"10.1109/TSUSC.2023.3348157","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3348157","url":null,"abstract":"Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and \u0000<inline-formula><tex-math>$CO_{2}$</tex-math></inline-formula>\u0000 emission amount of these models are evaluated and compared for the training and prediction phases.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"817-829"},"PeriodicalIF":3.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810516","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}

引用次数: 0

Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A Review 云数据中心的热建模和热感知节能方法：综述

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-12-25 DOI: 10.1109/TSUSC.2023.3346332

Jianpeng Lin;Weiwei Lin;Huikang Huang;Wenjun Lin;Keqin Li

{"title":"Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A Review","authors":"Jianpeng Lin;Weiwei Lin;Huikang Huang;Wenjun Lin;Keqin Li","doi":"10.1109/TSUSC.2023.3346332","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3346332","url":null,"abstract":"Constructing energy-efficient cloud data centers (CDCs) is an essential path for the further expansion of cloud computing. As one of the core subsystems of a data center, the cooling system provides a reliable thermal environment for the safe operation of IT equipment while posing a huge energy consumption and carbon emission problem. Thus, it is evident that optimizing energy management of cooling systems with considerable energy-saving potential will be essential to realize the green and low-carbon development of CDCs. Therefore, to track the research progress of data center thermal management technologies, this review focuses on two research efforts: thermal modeling and thermal-aware energy saving methods. First, various thermal modeling approaches are reviewed for air-cooled and liquid-cooled data centers. Secondly, a comprehensive review of existing advanced thermal management approaches is conducted from three perspectives: thermal-aware IT load scheduling, cooling system control optimization, and joint optimization of the IT and cooling systems. Finally, we put forward some open issues and future research directions for thermal management that have not been completely solved. This review aims to provide reasonable suggestions to enhance cooling energy efficiency and further promote the transformation of CDCs to lower energy consumption and sustainable direction.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"571-590"},"PeriodicalIF":3.9,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264413","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}

引用次数: 0

Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning 针对联合学习的振幅对齐个性化和稳健聚合

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-12-12 DOI: 10.1109/TSUSC.2023.3341836

Yongqi Jiang;Siguang Chen;Xiangwen Bao

{"title":"Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning","authors":"Yongqi Jiang;Siguang Chen;Xiangwen Bao","doi":"10.1109/TSUSC.2023.3341836","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3341836","url":null,"abstract":"In practical applications, federated learning (FL) suffers from slow convergence rate and inferior performance resulting from the statistical heterogeneity of distributed data. Personalized FL (pFL) has been proposed to overcome this problem. However, existing pFL approaches mainly focus on measuring differences between entire model dimensions across clients, ignore the layer-wise differences in convolutional neural networks (CNNs), which may lead to inaccurate personalization. Additionally, two potential threats in FL are that malicious clients may attempt to poison the entire federation by tampering with local labels, and the model information uploaded by clients makes them vulnerable to inference attacks. To tackle these issues, 1) we propose a novel pFL approach in which clients minimize local classification errors and align the local and global prototypes for data from the class that is shared with other clients. This method adopts layer-wise collaborative training to achieve more granular personalization and converts local prototypes to the frequency domain to prevent source data leakage; 2) To prevent the FL model from misclassifying certain test samples as expected by poisoners, we design a robust aggregation method to ensure that benign clients who provide trustworthy model predictions for its local data are weighted far more heavily in the aggregation process than malicious clients. Experiments show that our scheme, especially in the data heterogeneity situation, can produce robust performance and more stable convergence while preserving privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"535-547"},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264311","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}

引用次数: 0

BitFT: An Understandable, Performant and Resource-Efficient Blockchain Consensus BitFT：可理解、高性能、高资源效率的区块链共识

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-12-12 DOI: 10.1109/TSUSC.2023.3341440

Rui Hao;Xiaohai Dai;Weiqi Dai

{"title":"BitFT: An Understandable, Performant and Resource-Efficient Blockchain Consensus","authors":"Rui Hao;Xiaohai Dai;Weiqi Dai","doi":"10.1109/TSUSC.2023.3341440","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3341440","url":null,"abstract":"Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while the \u0000<small>Rbc</small>\u0000 protocol and \u0000<inline-formula><tex-math>$QC$</tex-math></inline-formula>\u0000 mechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"522-534"},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264515","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}

引用次数: 0

Editorial Sustainable Defence and Security Systems 编辑可持续防卫与安全系统

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-12-08 DOI: 10.1109/TSUSC.2023.3308471

Paul D. Yoo;Zahir Tari

引用次数: 0

REPFS: Reliability-Ensured Personalized Function Scheduling in Sustainable Serverless Edge Computing REPFS：可持续无服务器边缘计算中的可靠性有保障的个性化功能调度

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-11-29 DOI: 10.1109/TSUSC.2023.3336691

Kun Cao;Jian Weng

{"title":"REPFS: Reliability-Ensured Personalized Function Scheduling in Sustainable Serverless Edge Computing","authors":"Kun Cao;Jian Weng","doi":"10.1109/TSUSC.2023.3336691","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3336691","url":null,"abstract":"In recent years, serverless edge computing has been widely employed in the deployments of Internet-of-things (IoT) applications. Despite considerable research efforts in this field, existing works fail to jointly consider essential factors such as energy, reliability, personalized user requirements, and stochastic application executions. This oversight results in an inefficient utilization of computation and communication resources within serverless edge computing networks, subsequently diminishing the profit of service providers and degrading the quality-of-experience (QoE) of end users. In this paper, we explore the problem of reliability-ensured personalized function scheduling (REPFS) to jointly optimize the profit of service providers and the holistic QoE of end users in sustainable serverless edge computing. A personality-driven user QoE prediction method is first designed to accurately estimate the QoE of individual end users with differentiated personality types. Afterward, a deterministic function scheduling policy is developed on the problem-specific augmented non-dominated sorting genetic algorithm II (PSA-NSGA-II). Given the inherent uncertainty of application executions, a stochastic function scheduling strategy that can be easily parallelized for modern multicore scheduler platforms is also devised to accelerate solution generation for stochastic applications. Experimental results show that our deterministic function scheduling policy achieves 15% performance enhancement compared with representative multiobjective evolutionary algorithms. Furthermore, our stochastic function scheduling strategy promotes the service profit by 78% and the holistic user QoE by 118% on average compared with the developed deterministic scheduling policy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"494-511"},"PeriodicalIF":3.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Is Hot IT a False Economy? An Analysis of Server and Data Center Energy Efficiency as Temperatures Rise 热门 IT 是虚假经济吗？温度升高时服务器和数据中心能效分析

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-11-27 DOI: 10.1109/TSUSC.2023.3336801

Stephen Clement;Kat Burdett;Nour Rteil;Astrid Wynne;Rich Kenny

{"title":"Is Hot IT a False Economy? An Analysis of Server and Data Center Energy Efficiency as Temperatures Rise","authors":"Stephen Clement;Kat Burdett;Nour Rteil;Astrid Wynne;Rich Kenny","doi":"10.1109/TSUSC.2023.3336801","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3336801","url":null,"abstract":"As demand for digital services grows, there is need to improve efficiency and reduce the environmental impact of data centers. The largest energy consumer in any data center is the IT, followed by the systems dedicated to cooling. Aiming to improve efficiency, and driven by metrics like PUE, there is a trend towards running data centers hotter to reduce the cooling energy. There is little research investigating the effect this will have on the IT beyond failure rates. To ensure overall efficiency is improving, we must view the data center as a system of systems, taking a holistic view rather than focusing on individual sub-systems. In this paper we use industry standard benchmarks and a wind-tunnel to profile typical enterprise IT. We analyze the effect of environmental conditions on IT efficiency, showing minor increases in temperature or pressure impact the efficiency of servers. Using an idealized, simulated data center case study we show that the interaction between cooling systems, server behavior and local climate are non-trivial and increasing temperatures has potential to worsen efficiency.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"482-493"},"PeriodicalIF":3.9,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264312","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}

引用次数: 0