IEEE Transactions on Services Computing最新文献_第9页

HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model 基于超图神经网络和扩散模型的Next POI推荐

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562352

Yinchen Pan;Jun Zeng;Ziwei Wang;Haoran Tang;Junhao Wen;Min Gao

{"title":"HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model","authors":"Yinchen Pan;Jun Zeng;Ziwei Wang;Haoran Tang;Junhao Wen;Min Gao","doi":"10.1109/TSC.2025.3562352","DOIUrl":"10.1109/TSC.2025.3562352","url":null,"abstract":"In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1445-1458"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849651","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

Battery Swapping Tour Optimization Problem in Dockless Electric Bike Sharing Service Systems With Distance-Aware User Incentives 具有距离感知用户激励的无桩电动自行车共享服务系统换电池行程优化问题

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/tsc.2025.3562337

Chun-An Yang, Shih-Chieh Chen, Jian-Jhih Kuo, Yi-Hsuan Peng, Yu-Wen Chen, Ming-Jer Tsai

引用次数: 0

Towards Hybrid Architectures for Big Data Analytics: Insights From Spark-MPI Integration 迈向大数据分析的混合架构：来自Spark-MPI集成的见解

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562342

Mengbing Zhou;Qiuyan Li;Mingyuan Cai;Chengzhong Xu;Yang Wang

{"title":"Towards Hybrid Architectures for Big Data Analytics: Insights From Spark-MPI Integration","authors":"Mengbing Zhou;Qiuyan Li;Mingyuan Cai;Chengzhong Xu;Yang Wang","doi":"10.1109/TSC.2025.3562342","DOIUrl":"10.1109/TSC.2025.3562342","url":null,"abstract":"High-Performance Data Analytics (HPDA) combines high-performance computing (HPC) with data analytics to uncover patterns and insights in dual-intensive applications that are both data-intensive and compute-intensive. Traditional Big Data frameworks and HPC technologies often struggle to address these demands independently, prompting researchers to explore their integration. Spark, known for its efficient in-memory computing with RDDs, and MPI, a foundational standard in HPC, are prominent candidates for such integration. This survey explores the integration of Spark and MPI for HPDA, highlighting their potential for unified data processing and computation. We first classify application workloads and review the characteristics and limitations of traditional frameworks. Then, we analyze the challenges and requirements of integrated architectures, focusing on the specific implementations of typical middleware-level architectures. Through comparative analysis, we highlight their advantages and limitations. Finally, we present application examples, outline key challenges and future research directions, and briefly discuss progress in integration approaches for other technology combinations.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1852-1868"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849769","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

Integrating Deep Spiking Q-Network Into Hypergame-Theoretic Deceptive Defense for Mitigating Malware Propagation in Edge Intelligence-Enabled IoT Systems 将深度尖峰 Q 网络集成到超游戏理论欺骗性防御中，缓解边缘智能物联网系统中的恶意软件传播

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562355

Yizhou Shen;Carlton Shepherd;Chuadhry Mujeeb Ahmed;Shigen Shen;Shui Yu

{"title":"Integrating Deep Spiking Q-Network Into Hypergame-Theoretic Deceptive Defense for Mitigating Malware Propagation in Edge Intelligence-Enabled IoT Systems","authors":"Yizhou Shen;Carlton Shepherd;Chuadhry Mujeeb Ahmed;Shigen Shen;Shui Yu","doi":"10.1109/TSC.2025.3562355","DOIUrl":"10.1109/TSC.2025.3562355","url":null,"abstract":"Internet of Things (IoT) systems are susceptible to compromise due to malware propagation, leading to the data breach and information theft. In this paper, we propose a proactive deception-oriented hypergame-theoretic malware propagation-mitigation (DHMPM) model between IoT nodes and edge devices under asymmetric information in edge intelligence (EI)-enabled IoT systems. We then explore malware-propagated deceptive defense strategies based on deep reinforcement learning. Specifically, IoT nodes and edge devices continually adjust their strategies based on obtained utilities under beliefs perceived by uncertainties from the game environment and system dynamics. Built upon the proposed game DHMPM, we next apply spiking neural networks (SNNs) into deep Q-network to form hypergame-theoretic deep spiking Q-network (HGDSQN), practically converging to the optimal malware-propagated deceptive defense strategy in EI-enabled IoT systems. Such SNNs can simulate biological brains with the pulse communication mechanism and break through the bottleneck of temporal processing in traditional models with deep neural networks, realizing intelligent decision-making and real-time malware defense. We eventually perform experimental simulations that assess the effect of attack arrival probability and learning rate on the optimal learning strategy selection, demonstrating the effectiveness of the proposed HGDSQN algorithm.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1487-1499"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849649","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

Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation 用于第三方图书馆推荐的隐式监督辅助图协同过滤技术

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562349

Lianrong Chen;Mingdong Tang;Naidan Mei;Fenfang Xie;Guo Zhong;Qiang He

{"title":"Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation","authors":"Lianrong Chen;Mingdong Tang;Naidan Mei;Fenfang Xie;Guo Zhong;Qiang He","doi":"10.1109/TSC.2025.3562349","DOIUrl":"10.1109/TSC.2025.3562349","url":null,"abstract":"Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1459-1471"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849754","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

LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts LEAGAN：用于可升级智能合约的去中心化版本控制框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562323

Gulshan Kumar;Rahul Saha;Mauro Conti;William Johnston Buchanan

{"title":"LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts","authors":"Gulshan Kumar;Rahul Saha;Mauro Conti;William Johnston Buchanan","doi":"10.1109/TSC.2025.3562323","DOIUrl":"10.1109/TSC.2025.3562323","url":null,"abstract":"Smart contracts are integral to decentralized systems like blockchains and enable the automation of processes through programmable conditions. However, their immutability, once deployed, poses challenges when addressing errors or bugs. Existing solutions, such as proxy contracts, facilitate upgrades while preserving application integrity. Yet, proxy contracts bring issues such as storage constraints and proxy selector clashes - along with complex inheritance management. This article introduces a novel upgradeable smart contract framework with version control, named ”decentraLized vErsion control and updAte manaGement in upgrAdeable smart coNtracts (LEAGAN).” LEAGAN is the first decentralized updatable smart contract framework that employs data separation with Incremental Hash (IH) and Revision Control System (RCS). It updates multiple contract versions without starting anew for each update, and reduces time complexity, and where RCS optimizes space utilization through differentiated version control. LEAGAN also introduces the first status contract in upgradeable smart contracts, and which reduces overhead while maintaining immutability. In Ethereum Virtual Machine (EVM) experiments, LEAGAN shows 40% better space utilization, 30% improved time complexity, and 25% lower gas consumption compared to state-of-the-art models. It thus stands as a promising solution for enhancing blockchain system efficiency.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1529-1542"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849647","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 Cross-Chain Hierarchical Federated Learning Framework for Enhancing Service Security and Communication Efficiency 一种提高服务安全性和通信效率的跨链分层联邦学习框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562329

Li Duan;He Huang;Chao Li;Wei Ni;Bo Cheng

{"title":"A Novel Cross-Chain Hierarchical Federated Learning Framework for Enhancing Service Security and Communication Efficiency","authors":"Li Duan;He Huang;Chao Li;Wei Ni;Bo Cheng","doi":"10.1109/TSC.2025.3562329","DOIUrl":"10.1109/TSC.2025.3562329","url":null,"abstract":"Traditional federated learning (FL) uploads local models to a central server for model aggregation and suffers from server centralization. While blockchain-based FL addresses the issue of centralization, new challenges arise, including limited scalability of a single chain, expensive overhead of blockchain consensus, and inconsistent quality of uploaded models. This article proposes a new cross-chain-based FL (CBFL) framework. Specifically, we propose a three-layer cross-chain FL architecture consisting of a task-releasing chain, a relay chain, and local model uploading chains. The task-releasing chain is used for task issuers to release FL tasks and global model aggregation. The local model uploading chain manages local devices, stores local models and aggregates these local models. To verify the quality of local models, we propose a dual-criteria model quality inspection method based on cross entropy and cosine similarity to exclude substandard local models. We also propose hierarchical FL before global model aggregation to further reduce the communication overhead. Moreover, multi-signature is used to ensure the consistent transmission of models in the cross-chain process. Experiments corroborate that the proposed CBFL improves performance by about 50% compared to the existing BFL framework. Moreover, the proposed dual-criteria model quality inspection method has better robustness than Krum and Trimmed Mean.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1199-1212"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849650","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

Puncturable Signature and Applications in Privacy-Aware Data Reporting for VDTNs 可标点符号及其在面向 VDTN 的隐私意识数据报告中的应用

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562318

Chenhao Wang;Yang Ming;Hang Liu;Songnian Zhang;Rongxing Lu

{"title":"Puncturable Signature and Applications in Privacy-Aware Data Reporting for VDTNs","authors":"Chenhao Wang;Yang Ming;Hang Liu;Songnian Zhang;Rongxing Lu","doi":"10.1109/TSC.2025.3562318","DOIUrl":"10.1109/TSC.2025.3562318","url":null,"abstract":"In vehicular digital twin networks (VDTNs), digital twin (DT) can assist the vehicle in data handling and report traffic data to the management server, thereby providing enhanced and scalable services for intelligent transport systems. However, the reported data may suffer from forgery and eavesdropping attacks due to the transmission on the open channel. In addition, a critical threat in VDTNs is the physical vehicle capture attack, namely, an adversary is capable of compromising the vehicle to obtain the current secret key, which can break the reliability of historical reported data and make the services provided by DT unavailable. Puncturable signature (PS) is a promising solution to eliminate these concerns, despite that the existing PS constructions have non-negligible false-positive errors and impose a significant cost on practical deployments. In this article, we design a novel PS and apply it to privacy-aware data reporting protocol (PA-DRP) for VDTNs. Specifically, the designed PS adopts a derivation-based way to achieve puncturing functionality, which is free from false-positive errors while extremely reducing the storage overhead of the secret keys. Meanwhile, we employ the designed PS to construct PA-DRP that enjoys authentication and forward security. Additionally, PA-DRP not only allows DT to remove privacy-sensitive information from the signed data but also provides fuzzy identity for protecting the real identity of the vehicle. Furthermore, the security analysis and performance evaluation demonstrate that the designed PS and PA-DRP not only can withstand various security and privacy assaults for VDTNs but also are efficient and practical.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1669-1682"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849752","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

Enhancing LLM QoS Through Cloud-Edge Collaboration: A Diffusion-Based Multi-Agent Reinforcement Learning Approach 通过云边缘协作增强LLM QoS：一种基于扩散的多智能体强化学习方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562362

Zhi Yao;Zhiqing Tang;Wenmian Yang;Weijia Jia

{"title":"Enhancing LLM QoS Through Cloud-Edge Collaboration: A Diffusion-Based Multi-Agent Reinforcement Learning Approach","authors":"Zhi Yao;Zhiqing Tang;Wenmian Yang;Weijia Jia","doi":"10.1109/TSC.2025.3562362","DOIUrl":"10.1109/TSC.2025.3562362","url":null,"abstract":"Large Language Models (LLMs) are widely used across various domains, but deploying them in cloud data centers often leads to significant response delays and high costs, undermining Quality of Service (QoS) at the network edge. Although caching LLM request results at the edge using vector databases can greatly reduce response times and costs for similar requests, this approach has been overlooked in prior research. To address this, we propose a novel <underline>Vector database-assisted cloud-<underline>Edge collaborative <underline>LLM QoS <underline>Optimization (VELO) framework that caches LLM request results at the edge using vector databases, thereby reducing response times for subsequent similar requests. Unlike methods that modify LLMs directly, VELO leaves the LLM's internal structure intact and is applicable to various LLMs. Building on VELO, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and design an algorithm based on Multi-Agent Reinforcement Learning (MARL). Our algorithm employs a diffusion-based policy network to extract the LLM request features, determining whether to request the LLM in the cloud or retrieve results from the edge's vector database. Implemented in a real edge system, our experimental results demonstrate that VELO significantly enhances user satisfaction by simultaneously reducing delays and resource consumption for edge users of LLMs. Our DLRS algorithm improves performance by 15.0% on average for similar requests and by 14.6% for new requests compared to the baselines.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1412-1427"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849652","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

Seamless Graph Task Scheduling Over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions 动态车辆云上的无缝图任务调度：一种集成驾驶员和瞬时决策的混合方法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-04-18 DOI: 10.1109/TSC.2025.3562340

Bingshuo Guo;Minghui Liwang;Xiaoyu Xia;Li Li;Zhenzhen Jiao;Seyyedali Hosseinalipour;Xianbin Wang

{"title":"Seamless Graph Task Scheduling Over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions","authors":"Bingshuo Guo;Minghui Liwang;Xiaoyu Xia;Li Li;Zhenzhen Jiao;Seyyedali Hosseinalipour;Xianbin Wang","doi":"10.1109/TSC.2025.3562340","DOIUrl":"10.1109/TSC.2025.3562340","url":null,"abstract":"Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called <italic>templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we <bold>propose a <bold>hybrid graph <bold>task <bold>scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes inapplicable due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1753-1768"},"PeriodicalIF":5.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849723","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