IEEE Transactions on Emerging Topics in Computing最新文献_第2页

SIMMS: Securing the IoT Through a Multi-Protocol Multi-Gateway System SIMMS：通过多协议多网关系统保护物联网

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-04-13 DOI: 10.1109/TETC.2026.3681542

Özlem Şeker;Umut Can Çabuk;Gökhan Dalkılıç

{"title":"SIMMS: Securing the IoT Through a Multi-Protocol Multi-Gateway System","authors":"Özlem Şeker;Umut Can Çabuk;Gökhan Dalkılıç","doi":"10.1109/TETC.2026.3681542","DOIUrl":"https://doi.org/10.1109/TETC.2026.3681542","url":null,"abstract":"Securing the Internet of Things (IoT) is a big challenge since the emergence of the IoT. Lack of a global security standard is mainly caused by the availability of multiple modes of connectivity and heavily customized network architectures. This research introduces the Secure Multi-protocol Multi-gateway Scheme (SIMMS), a robust framework designed to enhance security in IoT setups by integrating multi-factor authentication, role-based access authorization, and data encryption across Wi-Fi, Bluetooth Low Energy (BLE), and ZigBee. The scheme proposes a two-layered gateway lineup: one (or more) relay gateway(s) handling communication over various protocols in the lower layer, and a main gateway organizing the dataflow on the higher level. Utilizing MQTT, SIMMS supports dynamic token changes and incorporates QR code-based secret transmissions alongside HMAC-based one-time passwords (HOTP) to ensure privacy and access authorization. Our prototype, with ESP32 modules, Raspberry Pi, and a PC-based identity server, demonstrates the framework’s practicality and effectiveness. Performance evaluations confirm that SIMMS maintains message reliability and network performance, as throughputs remain higher than 100 kbps for BLE and 1 kbps for ZigBee. Overall, SIMMS offers a flexible, scalable security solution that greatly enhances the security capabilities of IoT systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"777-795"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

HENM: A High-Performance CKKS Key Switching Accelerator via Near-Memory Computing 基于近内存计算的高性能CKKS键交换加速器

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-07-08 DOI: 10.1109/TETC.2026.3708841

Zewen Ye;Tianshun Huang;Tianyu Wang;Yudan Bai;Yonggen Li;Chengxuan Wang;Ray C.C. Cheung;Kejie Huang

{"title":"HENM: A High-Performance CKKS Key Switching Accelerator via Near-Memory Computing","authors":"Zewen Ye;Tianshun Huang;Tianyu Wang;Yudan Bai;Yonggen Li;Chengxuan Wang;Ray C.C. Cheung;Kejie Huang","doi":"10.1109/TETC.2026.3708841","DOIUrl":"https://doi.org/10.1109/TETC.2026.3708841","url":null,"abstract":"With the development of artificial intelligence and Big Data technology, privacy data protection has become increasingly important. Cheon-Kim-Kim-Song (CKKS) is an approximate Homomorphic Encryption (HE) scheme that supports real numbers, playing a crucial role in privacy-preserving cloud computing. Several CKKS hardware accelerators have been proposed to address the significant computation overhead and low performance associated with encrypted data computing. Nevertheless, a challenge remains in balancing performance, power consumption, and area, as many existing solutions exhibit good performance at the cost of large area and power consumption. In this paper, we propose a near-memory hardware accelerator for the critical bottleneck operation - Key Switching (KS) in CKKS. A novel near-memory hardware architecture is proposed to accelerate the Number Theoretic Transform (NTT) and KS. The near-memory circuits are designed using our proposed modular reduction algorithm without multipliers and CKKS moduli choosing scheme. Besides, to address the data dependency issue and minimize twiddle factor memory updates, we propose a conflict-free NTT dataflow scheme that allows for reusing twiddle factors. This scheme is tailored to efficiently manage diverse data dependency issues and various levels of parallel processing units. Leveraging this optimized NTT design, we introduce a novel KS hardware design and an associated operation scheduling scheme to enhance performance. Our design achieves a relatively fast speed and better area-time product when evaluated on the XCU250 FPGA platform. The ASIC synthesis results with 40 nm CMOS technology show an area consumption of 51.2 mm<sup>2</sup> and power consumption of 1.8 to 2.2 W, which achieves a significant supremacy of energy efficiency compared with state-of-the-art works.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"931-942"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

PPSplitFed: Privacy-Preserving Splitfed Learning via Functional Encryption PPSplitFed：通过功能加密保护隐私的分离学习

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-04-24 DOI: 10.1109/TETC.2026.3683420

Jing Ma;Xixiang Lyu;Yong Yu;Stephan Sigg

{"title":"PPSplitFed: Privacy-Preserving Splitfed Learning via Functional Encryption","authors":"Jing Ma;Xixiang Lyu;Yong Yu;Stephan Sigg","doi":"10.1109/TETC.2026.3683420","DOIUrl":"https://doi.org/10.1109/TETC.2026.3683420","url":null,"abstract":"Federated learning (FL) and split learning (SL) are two emerging distributed learning mechanisms that enable model training without sharing the local data. The integration of FL and SL, called splitfed learning (SFL), reduces the training burden on the client side and parallelizes the model training to achieve efficient distributed model optimization without sharing the local data and the whole global model. Currently, concerns about the privacy leakage risks of splitfed learning have garnered much attention, but effective solutions have not yet been fully explored. We propose a novel privacy-preserving splitfed learning framework, named PPSplitFed, that guarantees the confidentiality of the intermediate parameters to protect data and model privacy. By designing a secure model training algorithm based on single-input functional encryption and a secure model aggregation algorithm based on multi-input functional encryption, PPSplitFed achieves secure model optimization. We describe the implementations of PPSplitFed on Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN) models and compare the performance with centralized learning (CL), FL, SL, SFL, and the state-of-the-art privacy-preserving SFL scheme, as well as privacy-preserving model training scheme on multi-sourced datasets. The experimental results show that PPSplitFed is effective and efficient with reduced computation and communication costs while maintaining the model accuracy.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"749-764"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

ES-CSFL: Compressed Sensing-Based Efficient Privacy-Preserving Federated Learning With Enhanced Security ES-CSFL：基于压缩感知的高效隐私保护联邦学习与增强安全性

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-04-16 DOI: 10.1109/TETC.2026.3682893

Lvjun Chen;Di Xiao;Maolan Zhang;Yu Ren;Xinchun Fan

{"title":"ES-CSFL: Compressed Sensing-Based Efficient Privacy-Preserving Federated Learning With Enhanced Security","authors":"Lvjun Chen;Di Xiao;Maolan Zhang;Yu Ren;Xinchun Fan","doi":"10.1109/TETC.2026.3682893","DOIUrl":"https://doi.org/10.1109/TETC.2026.3682893","url":null,"abstract":"Local gradient leakage and high communication cost present significant challenges in federated learning (FL).Compressed sensing (CS)-based FL emerges as a promising solution to address these issues simultaneously. However, existing CS-based FL solutions still face potential security issues by assuming all clients use an identical measurement matrix (local encryption key) for gradient encryption, thus leaving room for local gradient leakage. To strengthen security, we introduce a CS-based FL algorithm with enhanced security (ES-CSFL). This algorithm allows each client to generate a unique local encryption key for encrypting and compressing its gradient, mitigating security vulnerabilities associated with shared keys. Moreover, we propose a method for computing the global measurement matrix (global decryption key). This solves the issue of reconstructing the global gradient when local gradients are encrypted with distinct keys. Additionally, we present an efficient two-server functional encryption algorithm (2SFE) without a trusted third party. This algorithm ensures that the parameter server only possesses the global key rather than the local keys of individual clients. Theoretical analyses confirm the secure and efficient aggregation of local gradients achieved by the proposed algorithms. Furthermore, extensive experimental results show the practical performance of our ES-CSFL.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"796-811"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Ensemble Method for Detecting Energy Theft in Smart Grids 一种新的智能电网能量盗窃检测集成方法

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-07-15 DOI: 10.1109/TETC.2026.3711716

Xun Yuan;Yang Yang;Asif Iqbal;Prosanta Gope;Biplab Sikdar

{"title":"A Novel Ensemble Method for Detecting Energy Theft in Smart Grids","authors":"Xun Yuan;Yang Yang;Asif Iqbal;Prosanta Gope;Biplab Sikdar","doi":"10.1109/TETC.2026.3711716","DOIUrl":"https://doi.org/10.1109/TETC.2026.3711716","url":null,"abstract":"Energy theft, characterized by manipulating energy consumption readings to reduce payments, poses a dual threat—causing financial losses for grid operators and undermining the performance of smart grids. Effective Energy Theft Detection (ETD) methods become crucial in mitigating these risks by identifying such fraudulent activities in their early stages. However, the majority of current ETD methods rely on supervised learning, which is hindered by the difficulty of labelling data and the risk of overfitting known attacks. Several unsupervised ETD methods have been proposed to address these challenges, focusing on learning the normal patterns from honest users, specifically the reconstruction of input. However, our investigation reveals a limitation in current unsupervised ETD methods, as they can only detect anomalous behaviours in users exhibiting regular patterns. Users with high-variance behaviours pose a challenge to these methods. In response, this paper introduces a Denoising Diffusion Probabilistic Model (DDPM)-based ETD approach. This innovative approach demonstrates impressive ETD performance on high-variance smart grid data by incorporating additional attributes correlated with energy consumption. The proposed methods improve the average ETD performance on high-variance smart grid data from below 0.5 to over 0.9 w.r.t. AUC.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1093-1107"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Bit-ViP: Leveraging Bit-Planes to Preserve Visual Privacy in Images Through Obfuscation 位- vip：利用位平面通过混淆来保护图像中的视觉隐私

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-07-20 DOI: 10.1109/TETC.2026.3713256

Vishesh Kumar Tanwar;Ashish Gupta;Sanjay Madria;Sajal K. Das

{"title":"Bit-ViP: Leveraging Bit-Planes to Preserve Visual Privacy in Images Through Obfuscation","authors":"Vishesh Kumar Tanwar;Ashish Gupta;Sanjay Madria;Sajal K. Das","doi":"10.1109/TETC.2026.3713256","DOIUrl":"https://doi.org/10.1109/TETC.2026.3713256","url":null,"abstract":"The unprecedented growth of computer vision applications, such as surveillance systems and social media, raises security and visual privacy concerns, especially when data is stored on cloud servers. Image obfuscation offers a way to preserve visual privacy while maintaining an adequate level of usability; thus, it has been a topic of great interest in recent years. However, prior obfuscation schemes are either vulnerable to malicious attacks, such as model inversion to reconstruct original images from obfuscated images, or generate non-trainable obfuscated images, making them unusable for achieving reasonable accuracy. This paper proposes a novel bit-plane-based image obfuscation scheme, <italic>Bit-ViP</i>, to preserve visual privacy for image-based recognition tasks. The Bit-ViP scheme produces secure, usable images by incorporating an innovative end-to-end obfuscation function. While doing so, the obfuscated image would contain non-invertible noise (generated by Lorenz’s chaotic system and differential privacy), making it hard for an adversary to reconstruct the original image. We conduct extensive experiments on two popular activity recognition datasets, namely UCF101 and HMDB51, to validate the effectiveness of Bit-ViP. In the face of attacks on reconstruction, pixel frequency, information entropy, and pixel inter-correlation, we present a rigorous security analysis demonstrating tangible improvements over existing schemes.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1226-1240"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Delegated Quantum Approximate Optimization Algorithm With Quantum One-Time Pad for MAX-CUT Problem 最大切割问题的量子一次性衬垫安全委托量子近似优化算法

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2025-11-04 DOI: 10.1109/TETC.2025.3626768

Juyoung Kim;Doyoung Chung

引用次数: 0

Systematic Analysis of Interconnect and Contact Defects in RRAMs 随机存取存储器互连和接触缺陷的系统分析

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-16 DOI: 10.1109/TETC.2026.3701213

Hanzhi Xun;Moritz Fieback;Sicong Yuan;Mottaqiallah Taouil;Said Hamdioui

{"title":"Systematic Analysis of Interconnect and Contact Defects in RRAMs","authors":"Hanzhi Xun;Moritz Fieback;Sicong Yuan;Mottaqiallah Taouil;Said Hamdioui","doi":"10.1109/TETC.2026.3701213","DOIUrl":"https://doi.org/10.1109/TETC.2026.3701213","url":null,"abstract":"Resistive Random Access Memory (RRAM) has emerged as a promising candidate to succeed conventional memory technologies, offering advantages such as low energy operation and scalable storage density. As different manufacturers continue to advance RRAM toward large-scale production and commercial deployment, the demand for accurate and efficient testing methodologies becomes increasingly critical. This paper investigates interconnect and contact defects in RRAM arrays, with particular attention to the influence of the Data Background (DB), and proposes corresponding test strategies. A comprehensive defect space covering interconnect and contact defects is formulated for a layout-independent RRAM design. Systematic defect injection and circuit-level simulations are conducted to derive fault models, encompassing not only single-cell and two-cell coupling faults but also multi-cell faults strongly affected by DBs. The analysis identifies previously unreported 3-cell and 4-cell faults, which originate from defect-related sneak paths in the array. Conventional RRAM test schemes are unable to detect such faults. To address this limitation, a DB-aware test generation scheme is presented, capable of efficiently identifying all the aforementioned fault types and thereby enhancing the overall fault and defect coverage in RRAM devices.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1019-1033"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fairness in Reliability: Equitable Use of Reliability Enhancement in Caches 可靠性的公平性：在缓存中公平使用可靠性增强

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-08-18 DOI: 10.1109/TETC.2026.3723060

Zuhal Ozturk;Haluk Rahmi Topcuoglu;Mahmut Taylan Kandemir

{"title":"Fairness in Reliability: Equitable Use of Reliability Enhancement in Caches","authors":"Zuhal Ozturk;Haluk Rahmi Topcuoglu;Mahmut Taylan Kandemir","doi":"10.1109/TETC.2026.3723060","DOIUrl":"https://doi.org/10.1109/TETC.2026.3723060","url":null,"abstract":"Continuous technology scaling has made modern computer systems increasingly vulnerable to soft errors, making fault tolerance approaches increasingly important. Although such approaches generally provide substantial reliability enhancements, their performance and energy overheads can be prohibitive. Selective fault tolerance methods address this problem by providing sufficient reliability instead of conservative (worst case) reliability. However, when multiple applications use the same architecture with limited reliability enhancement capability, allocating this capability across competing applications in a fair manner remains an open research problem. Our paper addresses fairness problem in the reliability domain by focusing on cache memory. The proposed framework dynamically allocates ECC-protected cache blocks to concurrently running applications based on their error rates. To achieve this, errors are monitored at three levels-physical, data access, and application-each employing a distinct reliability metric. The effectiveness of the framework is evaluated in various scenarios as part of our empirical study. Our framework successfully enhances fairness in reliability, particularly when applications exhibit comparable error resilience. In heterogeneous workloads, fairness in reliability (<inline-formula><tex-math>$FiR$</tex-math></inline-formula>), calculated as the error rate difference across applications, is improved from 0.48 to 0.29; on the other hand, for homogeneous workloads, the <inline-formula><tex-math>$FiR$</tex-math></inline-formula> value is improved from 0.67 to 0.30. Experimental results indicate that error rates obtained from lower levels allow us to adjust the error rates at the application level, resulting in fairer usage of protected hardware resources.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1122-1134"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Adaptive Object Exploration and Mapping Through Meta-Reinforcement Learning 基于元强化学习的自适应对象探索和映射

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-03-02 DOI: 10.1109/TETC.2026.3667418

Ruyu Liu;Junzhe Xu;Zhenhua Wang;Jianhua Zhang

{"title":"Adaptive Object Exploration and Mapping Through Meta-Reinforcement Learning","authors":"Ruyu Liu;Junzhe Xu;Zhenhua Wang;Jianhua Zhang","doi":"10.1109/TETC.2026.3667418","DOIUrl":"https://doi.org/10.1109/TETC.2026.3667418","url":null,"abstract":"Object Exploration and Mapping (OEM) is a novel embodiedAI task that requires an agent to autonomously explore unknown environments, simultaneously detect and localize multiple objects, and construct a semantic object-level map. To solve this challenge, we propose a two-stage framework. First, a deep reinforcement learning-based OEM framework (DRL-OEM) is proposed to leverage auxiliary perception tasks and memory-augmented policy learning to guide efficient exploration. Second, we extend the DRL-OEM to Meta-OEM, where a meta-reinforcement learning framework is employed to enhance adaptation to unseen environments with a novel key-behavior-aware prioritized sampling strategy by reusing high-reward transitions. Extensive experiments conducted on customized AI2Thor scenes across diverse layouts and object configurations demonstrate that Meta-OEM significantly outperforms strong baselines, including DRL-OEM, PEARL, and MQL, achieving up to 2× higher success rates and faster convergence during adaptation. Visual and quantitative results confirm its superior object discovery coverage, pose estimation accuracy, and generalization capability in open-world scenarios.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"879-890"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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