IEEE Transactions on Services Computing最新文献

{"title":"AgenticOTA: An Agentic AI Pipeline for Context-Aware Over-the-Air Update Deployments in Connected Autonomous Vehicles","authors":"Shikhar Vashistha;Neetesh Kumar","doi":"10.1109/TSC.2026.3700604","DOIUrl":"10.1109/TSC.2026.3700604","url":null,"abstract":"Connected Autonomous Vehicles (CAVs) rely on software code for functioning, which requires continuous maintenance and real-time over-the-air (OTA) updates to ensure optimal performance in dynamically changing environments. However, current OTA update systems operate on pre-defined schedules or reactive diagnostics, failing to adapt deployment strategies based on contextual insights such as subsystem performance degradation and user demands. Furthermore, existing approaches lack support for on-demand decision-making across heterogeneous CAV stacks, treating vehicle software as a monolith and limiting fine-grained interventions at individual functional modules. To address these limitations, this work presents AgenticOTA, a novel agentic AI pipeline for context-aware OTA update deployments in CAVs. To the best of the authors’ knowledge, this is the first method that enables context-aware, fine-grained, and autonomous OTA deployments in CAVs. AgenticOTA utilizes LLM-driven agent integration to autonomously select validated container images and determine deployment viability based on real-time contextual information. The pipeline implements a modular, fine-grained software management layer that enables targeted updates and the selective replacement of individual functional modules in the event of failures, through containerized microservices, without impacting the entire software system. The effectiveness of AgenticOTA is demonstrated through comprehensive experiments against the state-of-the-art baselines. Experimental results demonstrate significant improvements, including a higher update success rate with fewer failures and rollbacks, as well as an orchestration initiation latency of 0.008 seconds, with a full end-to-end latency of 2.34 s, across baseline methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2688-2701"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288547","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}

{"title":"SecDiv: Privacy-Preserving Diversity-Constrained Top-$k$k Query Processing in the Cloud","authors":"Yinxing Zhang;Guang Tang;Qingwang Wang;Songlei Wang;Zhiquan Liu;Zhongyun Hua","doi":"10.1109/TSC.2026.3700282","DOIUrl":"10.1109/TSC.2026.3700282","url":null,"abstract":"With the proliferation of cloud computing, outsourcing databases has become a common strategy for reducing on premise storage and computation costs. However, this approach raises serious privacy concerns, as sensitive data and query information may be exposed to the cloud. While existing top-<inline-formula><tex-math>$k$</tex-math></inline-formula> query methods have made progress in performance and privacy protection, they offer limited support for the more advanced requirement of diversity-constrained queries. In light of this, we present SecDiv, the first privacy-preserving query system that supports diversity constraints over ciphertext in the cloud. SecDiv is built on a two-server distributed trust model and lightweight additive secret sharing, and hides data contents under an honest-but-curious, non-colluding adversary model to ensure that cloud servers learn no sensitive information. SecDiv comprises three customized secure components: SecDMap maps the structured database of the data owner into two secret-shared tables; SecQMap translates each SQL statement and its diversity constraints into vectors whose lengths match the database attributes, thereby hiding targeted attributes and literal values; and SecCQ performs secure filtering, ordering, and top-<inline-formula><tex-math>$k$</tex-math></inline-formula> selection in the cloud, centered on a secure most significant bit comparison implemented by a parallel-prefix adder. A formal security analysis is conducted to provide theoretical guarantees for the security of SecDiv. SecDiv is evaluated on three real datasets, with diversity constraints configured using top-<inline-formula><tex-math>$k$</tex-math></inline-formula> and category count conditions to emulate practical ranking scenarios. Compared with a plaintext baseline, SecDiv achieves identical results with 100% accuracy. Query latency remains practical, with second-level response times in typical settings, and communication overhead increases as expected. Overall, experimental results demonstrate that SecDiv attains a balanced trade-off among privacy, accuracy, and efficiency in real-world cloud service environments.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3029-3041"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288549","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}

{"title":"Online Multi-Task Business Process Prediction Using Dynamic Representation","authors":"Xiwei Zhang;Xianwen Fang;Wei Bao;Cong Liu","doi":"10.1109/TSC.2026.3706819","DOIUrl":"10.1109/TSC.2026.3706819","url":null,"abstract":"Existing Predictive Process Monitoring (PPM) methods typically rely on static offline methods, limiting their ability to adapt to dynamic process evolution driven by emerging activities and shifting behavioral patterns. This constraint is particularly critical for multi-task prediction, such as the simultaneous forecasting of the next activity and the remaining process time. To address this challenge, we propose an online multi-task prediction framework based on dynamic graph representations. The framework enables a Graph Neural Network (GNN) to incrementally learn newly emerging activities by leveraging dynamic graph snapshots and an architecture expansion strategy. For efficient online adaptation, the framework incorporates two update strategies, a standard periodic update and a drift-aware adaptive update triggered by the Maximum Mean Discrepancy (MMD<bold><inline-formula><tex-math>${^{2}}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula></b>) between subgraph embeddings. Both strategies are integrated with a Prioritized Experience Replay (PER) mechanism, augmented with a rarity-aware bonus, to ensure rapid and robust model adjustments in non-stationary environments. Comprehensive experiments on multiple real-world event logs demonstrate that our framework, when combined with different GNN backbones such as GCN, GAT, and GIN, significantly outperforms state-of-the-art baselines in both next-activity and remaining-time prediction. Notably, under concept drift, the proposed drift-aware strategy exhibits strong adaptability, highlighting the framework’s effectiveness and potential for addressing complex online process prediction challenges.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2956-2969"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314112","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}

{"title":"SpaceMeet: Bringing Conferencing Closer Through In-Orbit Conferencing Services","authors":"Songshi Dou;Feihu Jin;Jinxian Wu;A-Long Jin;Kwan L. Yeung","doi":"10.1109/TSC.2026.3707012","DOIUrl":"10.1109/TSC.2026.3707012","url":null,"abstract":"Real-time conferencing services have significantly reshaped global communication. These services typically require low-latency network conditions, which have traditionally been supported by high-speed terrestrial networks. However, emerging Low Earth Orbit (LEO) satellite constellations offer opportunities to provide Internet access to remote or underdeveloped regions that lack terrestrial connectivity. Despite this potential, users in these areas may still experience high latency issues due to rigid routing patterns and insufficient terrestrial facility deployment. Existing works either optimize terrestrial-based media server allocation without being tailored for these satellite-based Internet users, or rely on satellite-cloud cooperative relays but still depend on terrestrial facilities. Consequently, neither fundamentally resolves the inherent high-latency issue, especially for users without reliable terrestrial connectivity. To fill the research gap, we propose <sc>SpaceMeet</small> to leverage LEO satellites as direct hosts for conferencing sessions, thereby minimizing latency by avoiding terrestrial detours. Considering the dynamic energy availability caused by varying satellite idle periods and illumination conditions, we formulate a satellite-based conferencing service assignment problem to strategically allocate satellites for session hosting. Since solving this problem for real-time decision-making is infeasible due to its high complexity, we introduce a heuristic algorithm enhanced through iterative refinement using large language models. Extensive evaluations utilizing realistic LEO satellite constellation configurations demonstrate that <sc>SpaceMeet</small> significantly reduces latency by up to 7.78% compared to the baseline while efficiently utilizing idle satellite energy resources.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3085-3098"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148314113","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}

{"title":"Integrating Multi-Agent Reinforcement Learning and XGBoost for Efficient Large-Scale Dynamic Workflow Scheduling in Cloud Environments","authors":"Fan Ding;Shuzhan Zhang;Yaqian Yuan","doi":"10.1109/TSC.2026.3707320","DOIUrl":"10.1109/TSC.2026.3707320","url":null,"abstract":"Workflow scheduling in heterogeneous cloud environments must operate under dynamic and heterogeneous conditions. Heuristic algorithms cannot adapt online to changing conditions, while single-agent deep reinforcement learning (DRL) faces issues such as exponential growth in the action space, excessive training time, and limited interpretability. This paper proposes MAR-XGB, a hybrid scheduling framework that combines multi-agent reinforcement learning (MARL) with an XGBoost predictor. The tree ensemble estimates task runtime and execution cost, from which an urgency score is derived. Each agent uses lightweight Monte Carlo Tree Search (MCTS) to select VM locations and make online placement decisions over the Top-<italic>k</i> tasks, ensuring real-time adaptability while reducing action-space complexity and improving prediction transparency and decision auditability. Experiments on representative WfCommons scientific workflows and controlled high-load DAG scenarios show that our approach outperforms traditional and state-of-the-art schedulers in terms of makespan, resource utilization, and cost efficiency. As the complexity and scale of workflows increase, our approach demonstrates improved scalability, indicating its potential to handle dynamic cloud workloads with fluctuating demand.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2829-2842"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148342280","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}

{"title":"MS$^{2}$2-Diff: Multi-View Guided Diffusion With Active Retrieval for Service Generative Recommendation","authors":"Zhen Chen;Heng Wang;Jinyu Chen;Mengmeng Sun;Dianlong You;Yueshen Xu","doi":"10.1109/TSC.2026.3702081","DOIUrl":"10.1109/TSC.2026.3702081","url":null,"abstract":"Efficient discovery and composition of services from large-scale cloud API repositories are fundamental to Mashup development and the rapid evolution of service-oriented software. However, extreme interaction sparsity severely limits existing recommendation methods, hindering their ability to infer unobserved relationships and capture complex service dependencies. Most prior approaches rely on deterministic point estimation in latent spaces, which leads to several critical issues under sparse supervision: difficulty in modeling preference uncertainty, high susceptibility to overfitting, and static and passive incorporation of multi-source auxiliary information. To address these challenges, we propose MS<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-Diff, a conditional diffusion-based generative framework for service recommendation that reformulates recommendation as a conditional probabilistic denoising process. MS<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-Diff has a three-fold main idea: 1) it characterizes two levels and four types of priors that drive Mashup development, including structural homophily and collaborative reachability at the structural level, as well as functional alignment and intent alignment at the semantic level; 2) it introduces a guidance-driven active retrieval mechanism via inverted attention within the denoising process, which actively retrieves and reconstructs latent relational patterns from noisy representations under the control of multi-view priors; and 3) it adopts a step-aware adaptive fusion strategy that dynamically coordinates structural and semantic priors across different denoising stages, enabling complementary and collaborative effects throughout the generation process. Extensive experiments on two real-world benchmark datasets demonstrate that MS<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>-Diff achieves competitive and overall superior performance across multiple evaluation metrics, while improving robustness in sparse and cold-start scenarios.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2943-2955"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288511","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}

{"title":"LogMUSE: Log Anomaly Detection via Multi-Scale Semantic Representation","authors":"Mengyao Liu;Tianbo Wang;Yuan Zhao;Chunhe Xia;Yingming Zeng;Yuan Tao","doi":"10.1109/TSC.2026.3700403","DOIUrl":"10.1109/TSC.2026.3700403","url":null,"abstract":"Logs serve as an effective data source for recording and judging system states and abnormal events in complex systems. Current deep learning-based methods have proven effective in detecting anomalies in these system logs. However, existing anomaly detection methods, which predominantly rely on template-based and global window-based approaches, still face challenges in terms of flexibility and practicality. Template-based methods, while widely adopted for their simplicity and efficiency, overlook parameter information and fail to capture the true execution semantics. And global window-based methods, despite their effectiveness in modeling global dependencies, cannot simultaneously capture both global and local dependencies, leading to the obscuration of important local features. To address these issues, we propose a <bold>Log</b> anomaly detection method based on <bold>MU</b>lti-scale <bold>SE</b>mantic representation, <bold>LogMUSE</b>. Specifically, LogMUSE obtains template and parameter information through log parsing, employs a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for template semantic embedding, and enhances log entry representations via cross-attention mechanisms to effectively capture different parameter features under the same template. Additionally, we design the multi-scale Transformer model to capture global and local anomaly patterns, which enable fixed-length log sequences to focus on features at different scales. Extensive experiments on real-world benchmark datasets, including BGL, Thunderbird and Spirit, show that LogMUSE outperforms existing methods in log anomaly detection, achieving F1-scores of 98.62%, 94.32%, and 99.20% respectively. These results surpass the performance of current state-of-the-art methods and demonstrate the strong generalization across different system scenarios.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2903-2916"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288546","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}

{"title":"Collaborative Service of On-Device Unimodal Model and Cloud-Based Multimodal Model for Mobile Livestreaming Content Understanding","authors":"Chaoyue Niu;Yufei Zhu;Yutong Dai;Yikai Yan;Zhijie Cao;Hao Jiang;Chengfei Lv;Shaojie Tang;Fan Wu;Guihai Chen","doi":"10.1109/TSC.2026.3697495","DOIUrl":"10.1109/TSC.2026.3697495","url":null,"abstract":"To guide consumers to mobile livestreams that involve their interested products, it is necessary to have a good understanding of livestreaming contents, and the key task is to accurately recognize the products being promoted by streamers. The mainstream cloud-based service framework is challenged by the high concurrency of requests, the high overhead of multimodal recognition, and the requirement of low latency. To break these bottlenecks, we propose a new device-cloud collaborative service framework, where each streamer’s mobile device holds unimodal model that can process most of video frames and also uploads the extracted unimodal features to facilitate the cloud-side multimodal recognition of the remaining few frames. On-device unimodal model is further incrementally trained over the samples constructed by leveraging the streamers’ manual labeling behaviors, leading to personalized version that can adapt to the heterogeneous and dynamic livestreaming contents of different streamers. Nevertheless, the device-side personalized unimodal features are misaligned in feature space and cannot be directly fused into the cloud-side multimodal model. We thus design a pluggable prompt generation module to transform the personalized unimodal features into prompt embeddings and prepend them to the original inputs of the multimodal backbone network, instructing feature fusion in a streamer-specific manner. We extensively evaluate on the public Fashion-Gen dataset and an industrial dataset collected from Taobao Live. We also perform online testing and practical overhead testing in Taobao Live. Evaluation results reveal the effectiveness and efficiency of our design as well as the consistent advantage over existing baselines.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2746-2759"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288581","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}

{"title":"Skewness-Aware Stream Partitioning: A Key Splitting Suppression Method for Distributed Stream Processing","authors":"Dawei Sun;Weilong Lv;Shang Gao;Keqin Li;Rajkumar Buyya","doi":"10.1109/TSC.2026.3707515","DOIUrl":"10.1109/TSC.2026.3707515","url":null,"abstract":"Partitioning algorithms are crucial in distributed stream computing, as they directly affect load balancing across downstream task instances, cluster performance utilization, and data stream processing efficiency. However, existing algorithms suffer from key limitations: they fail to effectively balance load imbalance and key-splitting overhead as excessive splitting increases aggregation costs and memory usage, while insufficient splitting fails to reduce skew. In addition, they cannot timely identify newly emerging high-frequency keys, because historical frequency records dominate detection, which worsens load imbalance over time. To address these limitations, we propose Sa-Stream, a skewness-aware partitioning framework that rapidly captures changes in stream skewness and dynamically adjusts partitioning decisions, thereby balancing load imbalance and key-splitting overhead under evolving stream distributions. (1) A key frequency decay mechanism progressively reduces the influence of historical frequency statistics to improve the prediction of evolving key frequencies. (2) An adaptive threshold modulation method dynamically adjusts the hot-key threshold based on the current key frequency distribution, balancing load imbalance and aggregation costs. (3) A multi-tiered key segregation mechanism classifies keys into cold, warm, and hot categories: cold keys use deterministic hash routing to minimize aggregation costs, hot keys use degree-adjusted key splitting to reduce load imbalance, and warm keys use one-time key splitting to bridge the two extremes. Experiments conducted on Apache Storm demonstrate that Sa-Stream outperforms existing stream partitioning approaches, increasing throughput by up to 16% and reducing latency by up to 50%. Additionally, key splitting is reduced by up to 18%, and load imbalance is decreased by up to 98%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"3059-3071"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148342276","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}

{"title":"Matching and Exchange-Based Utility Maximization for Fog Computing-Enabled Smart Healthcare","authors":"Moirangthem Biken Singh;Ajay Pratap;Mihir Kumar Badkur;Dhruv Mishra","doi":"10.1109/TSC.2026.3708126","DOIUrl":"10.1109/TSC.2026.3708126","url":null,"abstract":"The rapid growth of Wireless Body Area Network (WBAN) and the increasing demand for real-time healthcare services have emerged fog computing as a promising paradigm to reduce latency and energy consumption in health monitoring. However, the growing need for computation-intensive services increases computational load on Fog Servers (FSs) and WBANs, leading to high latency and energy costs. Nevertheless, existing approaches often optimize either latency or energy while neglecting Healthcare Service Providers’ (HSPs) profit and the diverse computational needs of patients. Thus, this paper presents a fog computing-enabled WBAN framework that minimizes patients’ latency and energy costs while maximizing HSPs’ profit by formulating utility maximization as an NP-hard problem, where utility is a weighted combination of latency, energy costs, and profit, while prioritizing critical patients. To solve this, we design a matching and exchange-based suboptimal algorithm with polynomial-time complexity <inline-formula><tex-math>$O(PFlog PF, P^{2})$</tex-math></inline-formula>, where <inline-formula><tex-math>$P$</tex-math></inline-formula> and <inline-formula><tex-math>$F$</tex-math></inline-formula> denote the number of patients and FSs, respectively, leveraging the concept of preferences. Furthermore, we introduce a dynamic pricing scheme that allows patients to pay in proportion to their computational needs. Extensive simulations and analyses using real-world health data demonstrate that the proposed approach achieves an average utility of 99.01% of the optimal solution.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 4","pages":"2917-2928"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371843","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}