IEEE Transactions on Emerging Topics in Computing最新文献

CARAF: Root Cause Analysis Through Granger Causal Graphs With Ambiguous Residual Factors in Microservice Systems 基于残余因素模糊的格兰杰因果图的微服务系统根本原因分析

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-07-31 DOI: 10.1109/TETC.2026.3713344

Xinming Yu;Xinhong Liu;Tianxiang Sun;Yangfan Li;Fengxiao Tang;Ming Zhao;Nei Kato

{"title":"CARAF: Root Cause Analysis Through Granger Causal Graphs With Ambiguous Residual Factors in Microservice Systems","authors":"Xinming Yu;Xinhong Liu;Tianxiang Sun;Yangfan Li;Fengxiao Tang;Ming Zhao;Nei Kato","doi":"10.1109/TETC.2026.3713344","DOIUrl":"https://doi.org/10.1109/TETC.2026.3713344","url":null,"abstract":"Root Cause Analysis (RCA) is critical for fault diagnosis in microservice systems, where timely identification of failure origins is essential for maintaining system reliability. Existing RCA methods primarily rely on constructing causal graphs based on explicit periodicity and trend in time series data. However, in this paper, we reveal that ambiguous residual features—components remaining after subtracting periodicity and trend—are crucial for constructing accurate causal graphs. We propose a novel residual compensation network that exploits these ambiguous residual features to construct Granger causal graphs and improve root cause tracing accuracy. Additionally, we propose a trace-constrained formulation with a dynamic directed edge scoring function to effectively mitigate the influence of confounding factors in Granger causal graphs, ensuring reliable fault tracing in dynamic and complex fault propagation scenarios. Our approach was evaluated on three publicly available online service system datasets, namely Sock Shop, Online Boutique, and Train Ticket, and achieved state-of-the-art (SOTA) performance in root cause tracing.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1153-1163"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902767","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

Inter-Set Correlation-Aware Replacement Policy for Energy Efficient RTM-Based Caches 高效rtm缓存的集间关联感知替换策略

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-04 DOI: 10.1109/TETC.2026.3696641

Elham Cheshmikhani;Hamed Farbeh

{"title":"Inter-Set Correlation-Aware Replacement Policy for Energy Efficient RTM-Based Caches","authors":"Elham Cheshmikhani;Hamed Farbeh","doi":"10.1109/TETC.2026.3696641","DOIUrl":"https://doi.org/10.1109/TETC.2026.3696641","url":null,"abstract":"Today’s data-centric applications demand cache architectures that can scale with growing workloads while maintaining high performance and energy efficiency.Fundamental issues such as excessive area consumption and leakage power are increasingly challenging traditional SRAM-based caches, thereby motivating the exploration of non-volatile alternatives. Among these, <italic>racetrack memory</i> (RTM) stands out due to its remarkable storage density, achieved through nanowires hosting sequential magnetic domains that can be manipulated via domain wall or skyrmion techniques. Despite its advantages, racetrack memory’s inherent serialized access introduces considerable shift overhead, leading to elevated energy consumption and latency. In this paper, we analyze the placement strategies in conventional RTM-based last-level caches and identify that current methods trigger redundant shift operations as a shift intended to read one block fails to preposition other blocks subsequently accessed. To resolve this, we introduce an innovative data placement and replacement scheme that intelligently groups correlated blocks, ensuring that a single shift not only retrieves the target block but also aligns subsequent blocks closer to the access port. Our simulation results using the gem5 simulator and the SPEC CPU2017 benchmarks reveal that our scheme reduces shift overhead by 49.0% and cache energy consumption by 35.9% with negligible performance impact. In addition, this scheme exhibits robust scalability to longer nanowire tracks for higher cache density.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1075-1092"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902816","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

VARADE++: An Edge-Friendly Framework for Real-Time Anomaly Detection in Production Plants 用于生产工厂实时异常检测的边缘友好框架

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-08-27 DOI: 10.1109/TETC.2026.3726298

Alessio Mascolini;Sebastiano Gaiardelli;Francesco Ponzio;Nicola Dall’Ora;Stefano Quer;Franco Fummi;Sara Vinco;Santa Di Cataldo

{"title":"VARADE++: An Edge-Friendly Framework for Real-Time Anomaly Detection in Production Plants","authors":"Alessio Mascolini;Sebastiano Gaiardelli;Francesco Ponzio;Nicola Dall’Ora;Stefano Quer;Franco Fummi;Sara Vinco;Santa Di Cataldo","doi":"10.1109/TETC.2026.3726298","DOIUrl":"https://doi.org/10.1109/TETC.2026.3726298","url":null,"abstract":"Real-time anomaly detection is pivotal to the success of smart robotics, particularly in production plants, where even minor system failures can result in significant machine downtime and costly process disruptions. To address this, a specialized Machine Learning (ML) model must be seamlessly integrated into a network of interconnected machinery, sensors, and actuators, all processing vast streams of multidimensional sensor data with minimal latency that cloud-based solutions often struggle to achieve. In this work, we introduce VARADE++, an edge-optimized anomaly detection framework designed to navigate the complex trade-offs between anomaly detection accuracy, inference speed, and computational efficiency. By leveraging a lightweight auto-regressive architecture rooted in attentionless transformers, paired with a variational training paradigm, we achieve real-time processing capabilities. Our model is integrated into an advanced IoT infrastructure, enabling low-latency handling of intricate data streams. The effectiveness of VARADE++ is demonstrated across two public benchmarks and validated through a real-world case study within a sensorized industrial pilot production line, with an industrial robot as the primary focus. Our results not only highlight the superior anomaly detection capabilities of VARADE++ but also showcase its operational efficiency in a real-time edge computing environment, outperforming state-of-the-art solutions in the balance between detection performance and practical deployability.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"891-906"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11668997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

sGprAle: Self-Supervised Gaussian Process Regression for Average Localisation Error Estimation in Wireless Networks 基于自监督高斯过程回归的无线网络平均定位误差估计

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/TETC.2026.3699235

Abhilash Singh;Jaiprakash Nagar;J. Amutha;Sandeep Sharma

{"title":"sGprAle: Self-Supervised Gaussian Process Regression for Average Localisation Error Estimation in Wireless Networks","authors":"Abhilash Singh;Jaiprakash Nagar;J. Amutha;Sandeep Sharma","doi":"10.1109/TETC.2026.3699235","DOIUrl":"https://doi.org/10.1109/TETC.2026.3699235","url":null,"abstract":"This paper presents sGprAle, a self-supervised framework for predicting Average Localisation Error (ALE) in wireless sensor networks under limited labeled data conditions. The proposed approach integrates Gaussian Process Regression (GPR) with an autoencoder-based feature learning strategy to enhance robustness, generalisation, and predictive reliability. By learning task-agnostic latent representations from unlabeled data through a reconstruction objective, sGprAle enables the GPR model to operate in a compact and informative latent space using only a small labeled subset. Bayesian optimisation is employed to adapt kernel hyperparameters, ensuring calibrated uncertainty estimates and stable performance when labeled data are scarce. The framework is systematically evaluated across multiple labeled-data scenarios using ablation studies, uncertainty analysis, and statistical comparisons. Results demonstrate that the self-supervised latent representation substantially improves model robustness and label efficiency, allowing sGprAle to achieve competitive accuracy relative to existing methods while requiring significantly fewer labeled samples. These findings highlight the effectiveness of combining self-supervised representation learning with probabilistic regression for addressing the node localisation problem. The proposed model offers a reliable and energy-efficient solution for ALE prediction, contributing to improved localisation performance and operational efficiency in wireless sensor networks.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"969-981"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902699","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

Optimized Multi-Scale Convolutional Neural Network-Wavelet Feature Fusion With Reinforcement-Based Explainable Selection 基于可解释选择的优化多尺度卷积神经网络小波特征融合

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-07-13 DOI: 10.1109/TETC.2026.3710978

Akansha Singh;Soumendu Chakraborty;Mainak Adhikari

{"title":"Optimized Multi-Scale Convolutional Neural Network-Wavelet Feature Fusion With Reinforcement-Based Explainable Selection","authors":"Akansha Singh;Soumendu Chakraborty;Mainak Adhikari","doi":"10.1109/TETC.2026.3710978","DOIUrl":"https://doi.org/10.1109/TETC.2026.3710978","url":null,"abstract":"The escalating complexity of modern datasets demands sophisticated techniques for effective feature extraction, representation, and selection, as treating distinct data sources equally often hinders optimal performance. This study introduces a framework for Multi-Scale Feature Fusion and Adaptive Selection, transforming limited, fragmented information into meaningful insights, namely, AttenFusion-DualRL. The proposed strategy integrates a multi-stage pipeline: starting with data preprocessing for MNIST and Fashion-MNIST, followed by VGG16 feature extraction to leverage pre-trained visual knowledge. To enrich these with localized frequency information, we apply the Discrete Wavelet Transform (DWT). Subsequently, variance-based feature selection reduces dimensionality by retaining discriminative features. The core innovation lies in Cross-Domain Attention Fusion, which dynamically aligns and combines features from heterogeneous datasets. Finally, a Reinforcement Learning (RL) agent with a dual reward function optimizes feature selection by simultaneously maximizing classification accuracy, promoting sparsity, and enhancing interpretability through SHAP-guided rewards. Experiments on benchmark datasets demonstrate that the proposed strategy achieves higher classification accuracy with fewer features, showcasing improved model robustness. This approach promises significant future applications in image recognition, federated learning, and other domains that require efficient, explainable processing of diverse information sources.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"993-1004"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903141","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

SAPPhire: Practical Tiered Memory Systems With Memory Availability Prioritization 蓝宝石：具有内存可用性优先级的实用分层内存系统

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-24 DOI: 10.1109/TETC.2026.3690019

Minjae Kim;Kyeonghoon Lim;Jiwon Lee;Won Hur;Jiwan Kim;Jaewon Kwon;Won Woo Ro

{"title":"SAPPhire: Practical Tiered Memory Systems With Memory Availability Prioritization","authors":"Minjae Kim;Kyeonghoon Lim;Jiwon Lee;Won Hur;Jiwan Kim;Jaewon Kwon;Won Woo Ro","doi":"10.1109/TETC.2026.3690019","DOIUrl":"https://doi.org/10.1109/TETC.2026.3690019","url":null,"abstract":"Increasing application memory demands have led to the emergence of tiered memory systems, which place significant pressure on local memory nodes. The fundamentals of such systems involve selecting promotion candidates based on page access behaviors (e.g., frequency and recency) and migrating these candidates between memory tiers to leverage both low access latency and large capacity. In this paper, we examine the practical challenges of tiered memory systems, particularly under severe memory pressure conditions. Specifically, this study makes the following key observation: Tiered memory systems can suffer slowdowns from existing on-demand synchronous promotion methods, as they continue to perform promotion candidate selection processes even when local memory is unavailable. Furthermore, such excessive candidates have an equal chance of being promoted, and relatively more frequently accessed pages lose opportunities for successful promotion. To address these issues, we propose <italic>SAPPhire</i>, a system that prioritizes local memory availability when selecting promotion candidates and conditionally ensures the successful promotion of hot pages. The complementary application of these two policies effectively enhances performance in both kernel and user spaces. As a result, experimental results demonstrate that <italic>SAPPhire</i> outperforms six prior page migration methods by an average of 1.2×.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1213-1225"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903153","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

MEV Detection for Ethereum Security: An Empirical Analysis on Agreement and Scalability 以太坊安全的MEV检测：协议和可扩展性的实证分析

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-10 DOI: 10.1109/TETC.2026.3700162

Shraddha M. Naik;Huned Materwala;Davor Svetinovic

{"title":"MEV Detection for Ethereum Security: An Empirical Analysis on Agreement and Scalability","authors":"Shraddha M. Naik;Huned Materwala;Davor Svetinovic","doi":"10.1109/TETC.2026.3700162","DOIUrl":"https://doi.org/10.1109/TETC.2026.3700162","url":null,"abstract":"Maximal Extractable Value (MEV) poses significant threats to the security and fairness of Ethereum’s decentralized finance ecosystem by enabling participants to exploit transaction ordering to extract profits at the expense of others. Heuristic-based detection methods have been widely adopted to identify MEV transactions such as sandwich, arbitrage, and liquidation. However, a lack of standardized evaluation across heuristics limits the ability to compare their detection behavior and computational characteristics. This paper presents a unified experimental framework to systematically evaluate the detection capabilities, agreement rates, and resource efficiency of existing heuristic approaches. An agreement metric is introduced to quantify consistency across detection methods. Additionally, we assessed resource utilization and execution time to evaluate computational scalability. Our empirical findings reveal that while these approaches exhibit scalability, their reliability, measured in terms of detection consistency, varies significantly across different MEV types. Agreement rates averaged 0.53 for sandwich detection, 0.40 for arbitrage, and 0.81 for liquidation, highlighting substantial differences in detection capabilities and heuristic formulations. These findings offer valuable insights into the practical challenges of achieving consistent MEV detection and highlight the need for developing more robust security countermeasures.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1005-1018"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902700","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

Bi-Layer Deep Learning With Neural Networks and Large Language Models for Cognitive Biometrics 基于神经网络和大型语言模型的认知生物识别双层深度学习

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2025-01-17 DOI: 10.1109/TETC.2025.3528874

Matthew Swann;Stavros Shiaeles;Nicholas Kolokotronis

引用次数: 0

AI4FIDS: Multimodal Federated Intrusion Detection AI4FIDS：多模态联邦入侵检测

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2025-04-24 DOI: 10.1109/TETC.2025.3562346

Panagiotis Radoglou-Grammatikis;Pavlos S. Bouzinis;Ioannis Makris;Thomas Lagkas;Vasileios Argyriou;Georgios Th. Papadopoulos;Panagiotis Fouliras;George Seritan;Panagiotis Sarigiannidis

{"title":"AI4FIDS: Multimodal Federated Intrusion Detection","authors":"Panagiotis Radoglou-Grammatikis;Pavlos S. Bouzinis;Ioannis Makris;Thomas Lagkas;Vasileios Argyriou;Georgios Th. Papadopoulos;Panagiotis Fouliras;George Seritan;Panagiotis Sarigiannidis","doi":"10.1109/TETC.2025.3562346","DOIUrl":"https://doi.org/10.1109/TETC.2025.3562346","url":null,"abstract":"The rapid progression of smart technologies creates several advantages like enhanced connectivity, personalisation solutions and environmental sustainability. However, this revolution creates also several cyber risks. In particular, the attackers have the ability to synthesise and automate advanced attack scenarios over time, while it is evident that Artificial Intelligence (AI) allows the composition of intelligent attack vectors that can adapt in real-time to conventional countermeasures. Despite the fact that AI can also benefit defensive mechanisms, there are still functional and privacy issues that need to be resolved. First, AI requires appropriate datasets that can differ from environment to environment. In addition, these datasets usually are not available due to privacy issues. Finally, adversarial attacks have the ability to target and affect the AI-based decision-making process. Therefore, in light of the previous remarks, we provide AI4FIDS, a multimodal Intrusion Detection System (IDS) for critical infrastructures. AI4FIDS leverages Federated learning (FL) and combines multiple data sources, thus allowing cooperative intelligence across multiple domains in a private manner and minimising the impact of potential adversarial attacks. In this paper, we present in detail the architectural design and specifications of AI4FIDS, while the evaluation results demonstrate their detection performance, taking into account several datasets and aggregation strategies. Finally, based on the evaluation results, we discuss how the overall reliability and detection capabilities (in terms of detecting multi-step attack scenarios) of AI4FIDS can be improved by combining the detection outcomes of the components behind AI4FIDS.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"812-826"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Outcome Uncertainty Analysis and Quorum Redesign in Probabilistic Consensus 概率共识下的结果不确定性分析与群体重新设计

IF 4.8 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2026-07-01 Epub Date: 2026-06-05 DOI: 10.1109/TETC.2026.3698441

Yixuan Fan;Kairong Ma;Lei Zhang;Xianbin Wang

{"title":"Outcome Uncertainty Analysis and Quorum Redesign in Probabilistic Consensus","authors":"Yixuan Fan;Kairong Ma;Lei Zhang;Xianbin Wang","doi":"10.1109/TETC.2026.3698441","DOIUrl":"https://doi.org/10.1109/TETC.2026.3698441","url":null,"abstract":"Distributed consensus (DC) protocols typically assume a deterministic fault tolerance model, such as requiring fewer than one third of the nodes to be faulty in Byzantine Fault Tolerance (BFT). However, this assumption is often impractical in real-world scenarios, where node reliability is probabilistic, making fixed thresholds rigid and inflexible. Probabilistic Consensus (PC) offers an alternative by replacing these fixed limits with probability-based non-Byzantine behavior models. Yet, existing studies primarily focus on system performance while overlooking the implications of this assumption on DC mechanisms. In this paper, we address two critical issues. First, we systematically analyze the non-deterministic outcomes of PC caused by probabilistic node reliability, focusing on two key dimensions: consistency, which ensures agreement among nodes, and correctness, which ensures that consensus decisions are unaffected by faulty nodes. We propose both a rapid estimation method and a detailed probabilistic assessment. Second, we redesign quorum selection in PC by introducing the reliability quorum, the minimal subset of nodes required to achieve a specified system reliability level. Finally, we validate our theoretical findings through simulations.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"14 3","pages":"1034-1045"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902731","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