Internet of Things最新文献

{"title":"WMPA-ConvBERT-BM: A hybrid deep learning model optimized by whale-marine predator algorithm for IoT-enabled malicious URL detection","authors":"Zihan Zhao ,&nbsp;Yulin Zhu ,&nbsp;Shilong Zhang ,&nbsp;Amr Tolba ,&nbsp;Osama Alfarraj ,&nbsp;Keping Yu","doi":"10.1016/j.iot.2025.101683","DOIUrl":"10.1016/j.iot.2025.101683","url":null,"abstract":"<div><div>The increasing prevalence of malicious Uniform Resource Locators (URLs) in Internet of Things (IoT) environments poses a significant threat to network security. To address this challenge, we propose WMPA-ConvBERT-BM, a novel hybrid deep learning architecture that integrates ConvBERT and a multi-head attention-enhanced bidirectional Gated Recurrent Unit to effectively capture both semantic and structural features from URLs. The model utilizes a dual embedding strategy combining word-level and character-level representations for enriched multi-granular input. Furthermore, we introduce a Whale-Marine Predator Algorithm (WMPA), a hybrid intelligent optimization algorithm designed to adaptively search for optimal learning rates during training, enhancing convergence and generalization. Comprehensive experiments conducted on a large-scale multi-class malicious URL dataset demonstrate that our model achieves state-of-the-art performance, attaining an accuracy of 97.6%, precision of 96.769%, recall of 97.344% and F1-score of 97.032%, outperforming existing baselines and optimization methods. Ablation studies validate the critical contributions of each component, and further comparisons show the superiority of WMPA over conventional intelligent optimization algorithms. Additionally, SHAP-based interpretability analysis reveals how integrated embeddings contribute to prediction decisions, offering transparency and insights into model behavior. Results show that our WMPA-ConvBERT-BM provides an effective and interpretable solution for robust malicious URL detection in IoT scenarios.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101683"},"PeriodicalIF":6.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RadSpecFusion: Dynamic attention weighting for multi-radar human activity recognition","authors":"Ayesha Ibrahim ,&nbsp;Muhammad Zakir Khan ,&nbsp;Muhammad Imran ,&nbsp;Hadi Larijani ,&nbsp;Qammer H. Abbasi ,&nbsp;Muhammad Usman","doi":"10.1016/j.iot.2025.101682","DOIUrl":"10.1016/j.iot.2025.101682","url":null,"abstract":"<div><div>This paper presents RadSpecFusion, a novel dynamic attention-based fusion architecture for multi-radar human activity recognition (HAR). Our method learns activity-specific importance weights for each radar modality (24 GHz, 77 GHz, and Xethru sensors). Unlike existing concatenation or averaging approaches, our method dynamically adapts radar contributions based on motion characteristics. This addresses cross-frequency generalization challenges, where transfer learning methods achieve only 11%–34% accuracy. Using the CI4R dataset with spectrograms from 11 activities, our approach achieves 99.21% accuracy, representing a 15.8% improvement over existing fusion methods (83.4%). This demonstrates that different radar frequencies capture complementary information about human motion. Ablation studies show that while the three-radar system optimizes performance, dual-radar combinations achieve comparable accuracy (24GHz+77GHz: 96.1%, 24GHz+Xethru: 95.8%, 77GHz+Xethru: 97.2%), enabling flexible deployment for resource-constrained applications. The attention mechanism reveals interpretable patterns: 77 GHz radar receives higher weights for fine movements (superior Doppler resolution), while 24 GHz dominates gross body movements (better range resolution). The system maintains 71.4% accuracy at 10 dB SNR, demonstrating environmental robustness. This research establishes a new paradigm for multimodal radar fusion, moving from cross-frequency transfer learning to adaptive fusion with implications for healthcare monitoring, smart environments, and security applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101682"},"PeriodicalIF":6.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient Q-learning-based path planning for UAV-Aided data collection in agricultural WSNs","authors":"Khedidja Medani ,&nbsp;Chirihane Gherbi ,&nbsp;Hakim Mabed ,&nbsp;Zibouda Aliouat","doi":"10.1016/j.iot.2025.101698","DOIUrl":"10.1016/j.iot.2025.101698","url":null,"abstract":"<div><div>Recently, unmanned aerial vehicles (UAVs) have emerged as a promising solution for efficient data collection in agricultural Wireless Sensor Networks (WSNs). However, rely on a single UAV faces significant challenges due to vast coverage areas, energy constraints, and connectivity limitations, leading to scalability and energy inefficiency issues. This paper proposes a multi-UAV-based data collection approach, leveraging Q-learning (QL)-based UAV path planning to optimize trajectory selection dynamically while addressing the challenges of minimizing energy consumption and travel distance. We model the UAV path planning as a variant of the Traveling Salesman Problem (TSP), where UAVs start and end their routes at a ground base station (GBS) after visiting designated data collection points (CPs). Unlike traditional heuristic-based methods, the QL model adapts in real-time to environmental and operational conditions, ensuring fault tolerance and operational robustness. Through extensive simulations using NS3, our results demonstrate that deploying multiple UAVs instead of a single UAV improves mission completion time by approximately 67% and increases residual energy levels by more than 80% when managing up to 500 sensor nodes with four UAVs. Furthermore, the proposed QL-based path planning significantly reduces total travel distance and energy consumption, enabling balanced workload distribution among UAVs and ensuring higher data collection reliability in large-scale agricultural WSNs. These findings confirm the effectiveness of a multi-UAV, QL-based approach for scalable, fault-tolerant, and energy-efficient UAV-aided data collection systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101698"},"PeriodicalIF":6.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fire risk assessment system for food and sustainable farming using ai and IoT technologies: Benefits and challenges","authors":"Abdennabi Morchid ,&nbsp;Zahra Oughannou ,&nbsp;Haris M. Khalid ,&nbsp;Hassan Qjidaa ,&nbsp;Rachid El Alami ,&nbsp;Pierluigi Siano","doi":"10.1016/j.iot.2025.101704","DOIUrl":"10.1016/j.iot.2025.101704","url":null,"abstract":"<div><div>Fire detection and prevention in agriculture and forestry are major challenges for food security, ecosystem preservation, and natural resource management. Forest and agricultural fires have devastating impacts on the environment, the economy, and biodiversity. Faced with this problem, the adoption of innovative technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) offers new opportunities for proactive fire risk management. This review explores the joint use of AI and IoT for fire detection, monitoring, and prevention in the agricultural and forestry sectors. The foundations of AI in agriculture are examined first, with a particular focus on machine learning and massive data processing techniques for predicting fire risk. AI applications in fire detection are then analyzed, notably through predictive models and intelligent sensor systems. At the same time, the study highlights the principles of IoT for environmental monitoring, emphasizing the role of sensors, communication networks, and cloud platforms to collect, analyze, and transmit data in real-time. Finally, the combination of AI and IoT is explored as an integrated solution for preventing fires through continuous monitoring and rapid response capability. The benefits, as well as the challenges associated with implementing these technologies, are also discussed. This review aims to provide a detailed analysis of current research, identify existing gaps, and propose perspectives for the future of agricultural and forest fire management while highlighting the importance of a technological and sustainable approach to tackling the global challenges of climate change and food security.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101704"},"PeriodicalIF":6.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint trajectory and incentive optimization for privacy-preserving UAV crowdsensing via multi-agent federated reinforcement learning","authors":"Chaoyang Zhu ,&nbsp;Xiao Zhu ,&nbsp;Tuanfa Qin","doi":"10.1016/j.iot.2025.101689","DOIUrl":"10.1016/j.iot.2025.101689","url":null,"abstract":"<div><div>UAV-assisted mobile crowdsensing (MCS) presents a promising paradigm for enhancing data collection in smart city environments, but faces a critical systems challenge: the intricate coupling between spatial trajectory planning, economic incentive mechanisms, and information-theoretic privacy guarantees. Traditional approaches addressing these dimensions in isolation often lead to suboptimal performance. In this paper, we propose <strong>PRISM</strong>, a <em>unified framework</em> designed around a holistic optimization approach with three components. First, PRISM models the strategic interactions among Service Providers, UAVs, and ground devices through a multi-level Stackelberg game, capturing the hierarchical economic dynamics that influence participation decisions. Second, it incorporates a privacy-aware incentive mechanism that explicitly links UAV navigation decisions to privacy constraints, dynamically managing the privacy-utility trade-off based on data quality. Third, to address the resulting multi-objective optimization across distinct decision timescales, we introduce <strong>TMFR</strong>, a Two-timescale Multi-agent Federated Reinforcement Learning algorithm that enables UAVs to collaboratively learn policies for both spatial navigation and incentive allocation. Experimental evaluations demonstrate that TMFR achieves 30% faster convergence and 20% higher data quality compared to baselines that optimize only subsets of the problem. These results highlight its suitability for next-generation smart city applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101689"},"PeriodicalIF":6.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent and secure internet of robotic things: A review and conceptual framework","authors":"Md. Abdur Rahim ,&nbsp;Mohammad Rokonuzzaman ,&nbsp;Ahmad Abu Alqumsan ,&nbsp;Adetokunbo Arogbonlo ,&nbsp;Md. Zohirul Islam ,&nbsp;Hieu Trinh ,&nbsp;Md Shofiqul Islam","doi":"10.1016/j.iot.2025.101684","DOIUrl":"10.1016/j.iot.2025.101684","url":null,"abstract":"<div><div>Robots are increasingly being deployed across various fields and applications, including agriculture, manufacturing, transportation, surveillance, and rescue missions. The integration of technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) with robotics significantly enhances their capabilities and expands their range of applications. Rapid advancements in these technologies have driven notable progress in robotics research, particularly in the domain of AI-enabled Internet of Robotic Things (IoRT). However, our extensive literature study finds that there is a growing need for the development of big data–enabled secure IoRT systems capable of handling vast amounts of data generated by large-scale heterogeneous, homogeneous robots and multiple stakeholders. Addressing this need is essential to meet modern industrial and societal demands. Therefore, this paper presents a conceptual framework for AI-enabled, big data analytics–driven secure and reliable IoRT, based on a comprehensive review of the literature. The framework emphasizes key enabling technologies, including connectivity, cloud and edge computing, big data analytics, and cybersecurity systems, with the ultimate aim of supporting the transition to Industry 5.0. Furthermore, we explore several promising research directions that could enhance the intelligence and security of future IoRT systems. Before concluding, we provide an in-depth discussion of the benefits, limitations, and challenges associated with IoRT. We hope this work will serve as a valuable resource for researchers and practitioners in advancing next-generation intelligent robotic systems for the betterment of society.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101684"},"PeriodicalIF":6.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring","authors":"Kateryna Voitiuk ,&nbsp;Spencer T. Seiler ,&nbsp;Mirella Pessoa de Melo ,&nbsp;Jinghui Geng ,&nbsp;Tjitse van der Molen ,&nbsp;Sebastian Hernandez ,&nbsp;Hunter E. Schweiger ,&nbsp;Jess L. Sevetson ,&nbsp;David F. Parks ,&nbsp;Ash Robbins ,&nbsp;Sebastian Torres-Montoya ,&nbsp;Drew Ehrlich ,&nbsp;Matthew A.T. Elliott ,&nbsp;Tal Sharf ,&nbsp;David Haussler ,&nbsp;Mohammed A. Mostajo-Radji ,&nbsp;Sofie R. Salama ,&nbsp;Mircea Teodorescu","doi":"10.1016/j.iot.2025.101671","DOIUrl":"10.1016/j.iot.2025.101671","url":null,"abstract":"<div><div>The analysis of tissue cultures requires a sophisticated integration and coordination of multiple technologies for monitoring and measuring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Our approach enables continuous, communicative, non-invasive interactions within an Internet of Things (IoT) architecture among various sensing and actuation devices, achieving precisely timed control of <em>in vitro</em> biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids while measuring their neuronal activity. The organoids are cultured in custom, 3D-printed chambers affixed to commercial microelectrode arrays. Periodic feeding is achieved using programmable microfluidic pumps. We developed a computer vision fluid volume estimator used as feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a set of 7-day studies of mouse cerebral cortex organoids, comparing manual and automated protocols. It was shown that the automated protocols maintained robust neural activity throughout the experiment while enabling hourly electrophysiology recordings during the experiments. The median firing rates of neural units increased for each sample, and dynamic patterns of organoid firing rates were revealed by high-frequency recordings. Surprisingly, feeding did not affect the firing rate. Furthermore, media exchange during a recording did not show acute effects on firing rate, enabling the use of this automated platform for reagent screening studies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101671"},"PeriodicalIF":6.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twins in Additive Manufacturing: A systematic review","authors":"Md Manjurul Ahsan ,&nbsp;Yingtao Liu ,&nbsp;Shivakumar Raman ,&nbsp;Zahed Siddique","doi":"10.1016/j.iot.2025.101692","DOIUrl":"10.1016/j.iot.2025.101692","url":null,"abstract":"<div><div>Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) due to their ability to create virtual replicas of physical components of AM machines, which helps in real-time production monitoring. Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing intelligent and adaptable DTs in manufacturing processes. However, questions remain regarding scalability, the integration of high-quality data, and the computational power required for real-time applications in developing DTs. Understanding the current state of DTs in AM is essential to address these challenges and fully utilize their potential in advancing AM processes. Considering this opportunity, this work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: (1) What are the key types of DTs used in AM and their specific applications? (2) What are the recent developments and implementations of DTs? (3) How are DTs employed in process improvement and hybrid manufacturing? (4) How are DTs integrated with Industry 4.0 technologies? By discussing current applications and techniques, we aim to offer a better understanding and potential future research directions for researchers and practitioners in AM and DTs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101692"},"PeriodicalIF":6.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of an innovative solution to integrate and orchestrate IoT technologies with chatbots for smart home automation","authors":"Muhammad Ans ,&nbsp;Teodoro Montanaro ,&nbsp;Ilaria Sergi ,&nbsp;Giovanni Troisi ,&nbsp;Andrea Sponziello ,&nbsp;Miriam Pezzuto ,&nbsp;Luigi Patrono","doi":"10.1016/j.iot.2025.101693","DOIUrl":"10.1016/j.iot.2025.101693","url":null,"abstract":"<div><div>Nowadays, smart homes are rapidly gaining popularity but significant challenges still affect the sector. For instance, optimizing energy usage is essential to fully harness their potentiality. Many existing solutions rely on conventional control methods that require user interaction or need experts to configure complex automatic rules. This paper presents an innovative framework that exploits multiple chatbots to autonomously manage operations in smart homes. The framework acts at all the levels of an IoT system by autonomously: collect real-time data from sensors, interpret data, make decisions based on revealed situations, actuate strategies through actuators, and contact users in case of criticalities. Such an automation is performed through three different types of chatbots, i.e., AutomationBot, SensorBot, and ActuatorBot, each performing dedicated roles in real-time system monitoring, decision-making, and operation management. They autonomously manage and coordinate operations, only escalating issues to the user in critical scenarios, ensuring efficient system functioning with minimal user involvement 24 h a day, 7 days a week. It can be programmed by every kind of user through the provided no-code platform. The system’s effectiveness has been assessed through a series of experiments conducted in a simulated smart home environment developed through various technologies (i.e., MQTT, RabbitMQ, Raspberry Pi, Tiledesk-Chat21) focusing on heat pump management and indoor environmental condition regulation. Our results highlight that the chatbot system could independently monitor, control, and optimize operation of critical devices, maintaining operational reliability and user comfort with manual intervention. The framework represents a significant step toward realizing fully autonomous chatbot-driven smart homes.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101693"},"PeriodicalIF":6.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pow-MUCB: A new client selection method based on Pow-d and modified UCB for federated learning in IoT","authors":"Naghmeh Khajehali ,&nbsp;Jun Yan ,&nbsp;Yang-Wai Chow ,&nbsp;Mahdi Fahmideh","doi":"10.1016/j.iot.2025.101687","DOIUrl":"10.1016/j.iot.2025.101687","url":null,"abstract":"<div><div>Federated learning (FL) is a collaborative machine learning (ML) approach that enables distributed training between multiple clients, achieving collective intelligence while preserving privacy. In FL, client selection (CS) plays an important role in choosing a portion of clients for training. The more efficient CS is, the more FL’s performance will be improved. A balanced CS is required to reduce the effects of discrepancies between clients’ local data and clients’ performance. For this problem, using historical data can be an effective solution. Due to a lack of reasonable and balanced use of historical data, existing CS methods in the literature suffer from serious drawbacks, like over/under-representation of clients, and data model skewness. This can adversely affect FL performance, particularly in terms of convergence rate and model accuracy. To help solve these challenges, this paper proposes a new CS method (Pow-MUCB) which is based on the Power of choice-(Pow-d-) and is equipped with a new, modified Upper Confidence Bound (UCB) approach to evaluate the clients’ contribution and performance. This method selects clients whose participation results in more balanced, representative selection and informative global updates, avoids over/ underrepresentation of clients, and model skewness, improving overall FL performance. To validate the method’s performance in both static and dynamic client sets, comprehensive comparisons and experimental results are provided. The results demonstrated that Pow-MUCB enhances the overall performance and significantly outperforms the existing baselines in terms of global model accuracy (up to 7%), convergence rate, resulting from a reduced communication rounds needed to reach the convergence.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101687"},"PeriodicalIF":6.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}