Information Fusion最新文献

{"title":"Multi-view support vector machine classifier via L0/1 soft-margin loss with structural information","authors":"Chen Chen ,&nbsp;Qianfei Liu ,&nbsp;Renpeng Xu ,&nbsp;Ying Zhang ,&nbsp;Huiru Wang ,&nbsp;Qingmin Yu","doi":"10.1016/j.inffus.2024.102733","DOIUrl":"10.1016/j.inffus.2024.102733","url":null,"abstract":"<div><div>Multi-view learning seeks to leverage the advantages of various views to complement each other and make full use of the latent information in the data. Nevertheless, effectively exploring and utilizing common and complementary information across diverse views remains challenging. In this paper, we propose two multi-view classifiers: multi-view support vector machine via <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></math></span> soft-margin loss (Mv<span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></math></span>-SVM) and structural Mv<span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></math></span>-SVM (Mv<span><math><mrow><mi>S</mi><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></mrow></math></span>-SVM). The key difference between them is that Mv<span><math><mrow><mi>S</mi><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></mrow></math></span>-SVM additionally fuses structural information, which simultaneously satisfies the consensus and complementarity principles. Despite the discrete nature inherent in the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></math></span> soft-margin loss, we successfully establish the optimality theory for Mv<span><math><mrow><mi>S</mi><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn><mo>/</mo><mn>1</mn></mrow></msub></mrow></math></span>-SVM. This includes demonstrating the existence of optimal solutions and elucidating their relationships with P-stationary points. Drawing inspiration from the P-stationary point optimality condition, we design and integrate a working set strategy into the proximal alternating direction method of multipliers. This integration significantly enhances the overall computational speed and diminishes the number of support vectors. Last but not least, numerical experiments show that our suggested models perform exceptionally well and have faster computational speed, affirming the rationality and effectiveness of our methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102733"},"PeriodicalIF":14.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Open knowledge graph completion with negative-aware representation learning and multi-source reliability inference","authors":"Huang Peng,&nbsp;Weixin Zeng,&nbsp;Jiuyang Tang,&nbsp;Mao Wang,&nbsp;Hongbin Huang,&nbsp;Xiang Zhao","doi":"10.1016/j.inffus.2024.102729","DOIUrl":"10.1016/j.inffus.2024.102729","url":null,"abstract":"<div><div>Multi-source data fusion is essential for building smart cities by providing a comprehensive and holistic understanding of urban environments. Specifically, smart city-oriented knowledge graphs (KGs) require supplementary information from other open sources to increase their completeness, thus better supporting downstream tasks for smart cities. Nevertheless, existing open knowledge graph completion (KGC) approaches often overlook source quality assessment and fail to fully utilize prior knowledge, which tend to yield less satisfying results. To fill in these gaps, in this work, we propose a new open KGC method with negative-aware representation learning and multi-source reliability inference, i.e., <span>Nari</span>, which can effectively integrate the multi-source data concerning sustainable cities, providing reliable knowledge for downstream tasks. Specifically, we first train a graph neural network based encoder with a novel negative sampling strategy to better characterize prior knowledge in KG, and then identify new facts based on the learned prior knowledge and source reliability. The experiments on both general benchmark and waterlogging benchmark pertaining to sustainable cities demonstrate the effectiveness and wide applicability of <span>Nari</span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102729"},"PeriodicalIF":14.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Blockchain-based privacy-preserving incentive scheme for internet of electric vehicle","authors":"Qian Mei ,&nbsp;Wenxia Guo ,&nbsp;Yanan Zhao ,&nbsp;Liming Nie ,&nbsp;Deepak Adhikari","doi":"10.1016/j.inffus.2024.102732","DOIUrl":"10.1016/j.inffus.2024.102732","url":null,"abstract":"<div><div>The emerging proportion of renewable energy resources penetration and the rapid popularity of Electric Vehicles (EVs) have promoted the development of the Internet of Electric Vehicles (IoEV), which enables seamless EV’ information collection and energy delivery by leveraging wireless power transfer. However, vulnerabilities in internet infrastructure and the self-interested behavior of EVs pose significant security and privacy risks during energy delivery in IoEV. In addition, EVs often lack the incentive to cooperate for regional energy balance. To tackle these questions, this paper proposes a blockchain-based privacy-preserving incentive mechanism for energy delivery in IoEV. Based on cryptographic technology, this paper introduces a group signature scheme with self-controlled and sequential linkability, which safeguards the privacy of EV users and ensures transaction records maintain exact sequence during energy delivery. Furthermore, an incentive mechanism based on co-utile reputation management is presented to encourage EV users to participate honestly and cooperatively in energy delivery. Moreover, a comprehensive security analysis of the proposed group signature scheme and incentive mechanism is given. Finally, extensive experimental results demonstrate the feasibility and efficiency of the proposed approach compared to existing schemes.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102732"},"PeriodicalIF":14.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 efficient cross-view image fusion method based on selected state space and hashing for promoting urban perception","authors":"Peng Han ,&nbsp;Chao Chen","doi":"10.1016/j.inffus.2024.102737","DOIUrl":"10.1016/j.inffus.2024.102737","url":null,"abstract":"<div><div>In the field of cross-view image geolocation, traditional convolutional neural network (CNN)-based learning models generate unsatisfactory fusion performance due to their inability to model global correlations. The Transformer-based fusion methods can well compensate for the above problems, however, the Transformer has quadratic computational complexity and huge GPU memory consumption. The recent Mamba model based on the selection state space has a strong ability to model long sequences, lower GPU memory occupancy, and fewer GFLOPs. It is thus attractive and worth studying to apply Mamba to the cross-view image geolocation task. In addition, in the image-matching process (i.e., fusion of satellite/aerial and street view data.), we found that the storage occupancy of similarity measures based on floating-point features is high. Efficiently converting floating-point features into hash codes is a possible solution. In this study, we propose a cross-view image geolocation method (S6HG) based purely on Vision Mamba and hashing. S6HG fully utilizes the advantages of Vision Mamba in global information modeling and explicit location information encoding and the low storage occupancy of hash codes. Our method consists of two stages. In the first stage, we use a Siamese network based purely on vision Mamba to embed features for street view images and satellite images respectively. Our first-stage model is called S6G. In the second stage, we construct a cross-view autoencoder to further refine and compress the embedded features, and then simply map the refined features to hash codes. Comprehensive experiments show that S6G has achieved superior results on the CVACT dataset and comparable results to the most advanced methods on the CVUSA dataset. It is worth noting that other floating-point feature-based methods (4096-dimension) are 170.59 times faster than S6HG (768-bit) in storing 90,618 retrieval gallery data. Furthermore, the inference efficiency of S6G is higher than ViT-based computational methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102737"},"PeriodicalIF":14.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Dynamic collaborative learning with heterogeneous knowledge transfer for long-tailed visual recognition","authors":"Hao Zhou ,&nbsp;Tingjin Luo ,&nbsp;Yongming He","doi":"10.1016/j.inffus.2024.102734","DOIUrl":"10.1016/j.inffus.2024.102734","url":null,"abstract":"<div><div>Solving the long-tailed visual recognition with deep convolutional neural networks is still a challenging task. As a mainstream method, multi-experts models achieve SOTA accuracy for tackling this problem, but the uncertainty in network learning and the complexity in fusion inference constrain the performance and practicality of the multi-experts models. To remedy this, we propose a novel dynamic collaborative learning with heterogeneous knowledge transfer model (DCHKT) in this paper, in which experts with different expertise collaborate to make predictions. DCHKT consists of two core components: dynamic adaptive weight adjustment and heterogeneous knowledge transfer learning. First, the dynamic adaptive weight adjustment is designed to shift the focus of model training between the global expert and domain experts via dynamic adaptive weight. By modulating the trade-off between the learning of features and classifier, the dynamic adaptive weight adjustment can enhance the discriminative ability of each expert and alleviate the uncertainty of model learning. Then, heterogeneous knowledge transfer learning, which measures the distribution differences between the fusion logits of multiple experts and the predicted logits of each expert with different specialties, can achieve message passing between experts and enhance the consistency of ensemble prediction in model training and inference to promote their collaborations. Finally, extensive experimental results on public long-tailed datasets: CIFAR-LT, ImageNet-LT, Place-LT and iNaturalist2018, demonstrate the effectiveness and superiority of our DCHKT.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102734"},"PeriodicalIF":14.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city","authors":"Changdong Wang ,&nbsp;Huamin Jie ,&nbsp;Jingli Yang ,&nbsp;Tianyu Gao ,&nbsp;Zhenyu Zhao ,&nbsp;Yongqi Chang ,&nbsp;Kye Yak See","doi":"10.1016/j.inffus.2024.102739","DOIUrl":"10.1016/j.inffus.2024.102739","url":null,"abstract":"<div><div>Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102739"},"PeriodicalIF":14.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Adversarial robust image processing in medical digital twin","authors":"Samaneh Shamshiri ,&nbsp;Huaping Liu ,&nbsp;Insoo Sohn","doi":"10.1016/j.inffus.2024.102728","DOIUrl":"10.1016/j.inffus.2024.102728","url":null,"abstract":"<div><div>Recent advancements in state-of-the-art technologies, including Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing, have led to the emergence of an innovative technology known as digital twins (DTs). A digital twin is a virtual replica of the physical entity, with data connections in between. This technology has proven highly effective in several industries by improving decision-making and operational efficiency. In critical areas like healthcare, digital twins are increasingly being used to address the limitations of conventional approaches by creating virtual simulations of hospitals, medical equipment, patients, or even individual organs. These medical digital twins (MDT) revolutionize the healthcare industry by offering advanced solutions to enhance treatment outcomes and overall patient care. However, these systems are challenging because of the security and critical issues involved. Therefore, despite their achievements, the numerous security threats make it crucial to address the security challenges of digital twin technology. Given the lack of research on attacks targeting MDT functionalities, we concentrated on a specific cyber threat called adversarial attacks. Adversarial attacks exploit the model’s performance by introducing small, carefully crafted perturbations to manipulate the input data. To assess the vulnerability of medical digital twins to such attacks, we carried out a proof-of-concept study. Using image processing techniques and an artificial neural network model, we created a digital twin to diagnose breast cancer through thermography images. Then, we employed this digital twin to initiate an adversarial attack. For this purpose, we inserted adversarial perturbation as input to the trained model. Our results demonstrated the vulnerability of the digital twin model to adversarial attacks. To tackle this problem, we implemented an innovative modification to the digital twin’s architecture to enhance its robustness against various attacks. We proposed a novel defense method that fuses wavelet denoising and adversarial training, substantially strengthening the model’s resilience to adversarial attacks. Furthermore, the proposed digital twin is evaluated using a dataset of diabetic foot ulcers. To the best of our knowledge, it is the first defense method that makes the medical digital twin significantly robust against adversarial attacks.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102728"},"PeriodicalIF":14.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Human activity recognition using binary sensors: A systematic review","authors":"Muhammad Toaha Raza Khan,&nbsp;Enver Ever,&nbsp;Sukru Eraslan,&nbsp;Yeliz Yesilada","doi":"10.1016/j.inffus.2024.102731","DOIUrl":"10.1016/j.inffus.2024.102731","url":null,"abstract":"<div><div>Human activity recognition (HAR) is an emerging area of study and research field that explores the development of automated systems to identify and categorize human activities using data collected from various sensors. In the field of Human Activity Recognition (HAR), binary sensors offer a distinct approach by providing simpler on/off readings to indicate the presence of events such as door openings or light switch activations. Compared to other sensors used for HAR, binary sensors have several advantages, including lower cost, low power consumption, ease of installation, and privacy preservation. For instance, they can be effectively used in smart homes to detect when someone enters or leaves a room without user input. This study presents a systematic review of the state-of-the-art methods and techniques for HAR using binary sensors. We comprehensively consider five crucial aspects: data collection methods, preprocessing techniques, feature extraction and fusion strategies, classification algorithms, and evaluation metrics. Furthermore, we identify the gaps and limitations of the existing studies and provide directions for future research. This comprehensive and up-to-date review can serve as a valuable reference for researchers and practitioners in the field of HAR using binary sensors.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102731"},"PeriodicalIF":14.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Explainable natural language processing for corporate sustainability analysis","authors":"Keane Ong ,&nbsp;Rui Mao ,&nbsp;Ranjan Satapathy ,&nbsp;Ricardo Shirota Filho ,&nbsp;Erik Cambria ,&nbsp;Johan Sulaeman ,&nbsp;Gianmarco Mengaldo","doi":"10.1016/j.inffus.2024.102726","DOIUrl":"10.1016/j.inffus.2024.102726","url":null,"abstract":"<div><div>Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and the economy. With sustainability becoming a synonym of acceptable and legitimate behaviour, it is being increasingly demanded and regulated. Several frameworks and standards have been proposed to measure the sustainability impact of corporations, including United Nations’ sustainable development goals and the recently introduced global sustainability reporting framework, amongst others. However, the concept of corporate sustainability is complex due to the diverse and intricate nature of firm operations (<em>i.e.</em> geography, size, business activities, interlinks with other stakeholders). As a result, corporate sustainability assessments are plagued by subjectivity both within data that reflect corporate sustainability efforts (<em>i.e.</em> corporate sustainability disclosures) and the analysts evaluating them. This subjectivity can be distilled into distinct challenges, such as incompleteness, ambiguity, unreliability and sophistication on the data dimension, as well as limited resources and potential bias on the analyst dimension. Put together, subjectivity hinders effective cost attribution to entities non-compliant with prevailing sustainability expectations, potentially rendering sustainability efforts and its associated regulations futile. To this end, we argue that Explainable Natural Language Processing (XNLP) can significantly enhance corporate sustainability analysis. Specifically, linguistic understanding algorithms (lexical, semantic, syntactic), integrated with XAI capabilities (interpretability, explainability, faithfulness), can bridge gaps in analyst resources and mitigate subjectivity problems within data.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102726"},"PeriodicalIF":14.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Enhancing few-shot lifelong learning through fusion of cross-domain knowledge","authors":"Yaoyue Zheng ,&nbsp;Xuetao Zhang ,&nbsp;Zhiqiang Tian ,&nbsp;Shaoyi Du","doi":"10.1016/j.inffus.2024.102730","DOIUrl":"10.1016/j.inffus.2024.102730","url":null,"abstract":"<div><div>Humans can continually solve new problems with a few examples and enhance their learned knowledge by incorporating new ones. Few-shot lifelong learning (FSLL) has been presented to mimic human learning ability. However, they overlook the significance of cross-domain knowledge and little effort has been made to investigate it. In this paper, we explore the effects of cross-domain knowledge in FSLL and propose a new framework to enhance the model’s ability by fusing cross-domain knowledge into the learning process. Moreover, we investigate the impact of both debiased and non-debiased models in the FSLL context for the first time. Compared with previous works, our setting presents a unique challenge: the model should continually learn new knowledge from cross-domain few-shot data and update its existing knowledge by fusing new knowledge throughout its lifelong learning process. To address this challenge, the proposed framework focuses on learning and updating while migrating the well-known issues of forgetting and overfitting. The framework comprises three key components designed for learning cross-domain knowledge: the Debiased Base Learning strategy, Knowledge Acquisition, and Knowledge Update. The superiority of the framework is validated on mini-ImageNet, CIFAR-100, OfficeHome, and Meta-Dataset. Experiments show that the proposed framework exhibits the capability to perform in cross-domain situations and also achieves state-of-the-art performance in the non-cross-domain situation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102730"},"PeriodicalIF":14.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}