Information Fusion最新文献

{"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}

{"title":"Towards facial micro-expression detection and classification using modified multimodal ensemble learning approach","authors":"Fuli Zhang ,&nbsp;Yu Liu ,&nbsp;Xiaoling Yu ,&nbsp;Zhichen Wang ,&nbsp;Qi Zhang ,&nbsp;Jing Wang ,&nbsp;Qionghua Zhang","doi":"10.1016/j.inffus.2024.102735","DOIUrl":"10.1016/j.inffus.2024.102735","url":null,"abstract":"<div><div>A micro-expression is a fleeting, delicate and localized facial gesture. It can expose the true feelings that someone is trying to hide and is seen to be a crucial indicator for spotting lies. Because of its possible applications in a variety of sectors, micro-expression research has garnered a lot of attention. The accuracy of micro-expression recognition still needs to be improved, though, because of the brief and weak motions that make up micro-expressions. In recent years, Deep convolution neural methods have depicted a higher degree of efficiency for complex challenge of face detection. Although several attempts were made for micro-expression recognition (MER), the problem is far from being resolved problem which is portrayed by the lowest accuracy rate depicted by the other models. In this study, present a Facial Micro-Expression Detection and Classification using Modified Multimodal Ensemble Learning (FMEDC-MMEL) approach. The major intention of the FMEDC-MMEL technique lies in the proficient identification of MEs that exist in the facial images. As a pre-processing phase, the FMEDC-MMEL technique exploits histogram equalization (HE) approach to improve the contrast level of the image. In the FMEDC-MMEL technique, improved densely connected networks (DenseNet) model is used for learning feature patterns from the pre-processed images. To enhance the proficiency of the improved DenseNet model, stochastic gradient descent (SGD) approach is used for hyperparameter selection process. For facial ME detection, the FMEDC-MMEL technique follows an ensemble of three classifiers namely bi-directional gated recurrent unit (Bi-GRU), long short-term memory (LSTM) and extreme learning machine (ELM). A tailored ensemble learning approach is shown, which combines many machine learning models to improve classification performance and detection accuracy. Sophisticated feature extraction methods are utilized to extract the subtle aspects of micro-expressions, and precision is maintained by optimizations that minimize computing cost. Empirical findings reveal that this methodology notably surpasses conventional techniques, providing enhanced precision and resilience on a variety of complex and demanding datasets. In addition to pushing the boundaries of micro-expression analysis research, the proposed strategy has potential uses in the real world in fields including security, psychology testing, and human-computer interaction.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102735"},"PeriodicalIF":14.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446474","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 survey of evidential clustering: Definitions, methods, and applications","authors":"Zuowei Zhang ,&nbsp;Yiru Zhang ,&nbsp;Hongpeng Tian ,&nbsp;Arnaud Martin ,&nbsp;Zhunga Liu ,&nbsp;Weiping Ding","doi":"10.1016/j.inffus.2024.102736","DOIUrl":"10.1016/j.inffus.2024.102736","url":null,"abstract":"<div><div>In the realm of information fusion, clustering stands out as a common subject and is extensively applied across various fields. Evidential clustering, an increasingly popular method in the soft clustering family, derives its strength from the theory of belief functions, which enables it to effectively characterize the uncertainty and imprecision of data distributions. This survey provides a comprehensive overview of evidential clustering, detailing its theoretical foundations, methodologies, and applications. Specifically, we start by briefly recalling the theory of belief functions with its transformations into other uncertainty reasoning theories. Then, we introduce the concepts of soft data, partitions, and methods with an emphasis on data and partitioning within the theory of belief functions. Subsequently, we summarize the advancements and quantitative evaluations of existing evidential clustering methods and provide a roadmap to help in selecting an appropriate method based on specific application needs. Finally, we identify the major challenges faced in the development and application of evidential clustering, pointing out promising avenues for future research, including theoretical limitations, applicable datasets, and application domains. The survey offers a structured understanding of existing evidential clustering methods, highlighting their theoretical underpinnings, practical implementations, and future research directions. It serves as a valuable resource for researchers seeking to deepen their understanding of evidential clustering.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102736"},"PeriodicalIF":14.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532036","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":"Attention-guided hierarchical fusion U-Net for uncertainty-driven medical image segmentation","authors":"Afsana Ahmed Munia ,&nbsp;Moloud Abdar ,&nbsp;Mehedi Hasan ,&nbsp;Mohammad S. Jalali ,&nbsp;Biplab Banerjee ,&nbsp;Abbas Khosravi ,&nbsp;Ibrahim Hossain ,&nbsp;Huazhu Fu ,&nbsp;Alejandro F. Frangi","doi":"10.1016/j.inffus.2024.102719","DOIUrl":"10.1016/j.inffus.2024.102719","url":null,"abstract":"<div><div>Small inaccuracies in the system components or artificial intelligence (AI) models for medical imaging could have significant consequences leading to life hazards. To mitigate those risks, one must consider the precision of the image analysis outcomes (e.g., image segmentation), along with the confidence in the underlying model predictions. U-shaped architectures, based on the convolutional encoder–decoder, have established themselves as a critical component of many AI-enabled diagnostic imaging systems. However, most of the existing methods focus on producing accurate diagnostic predictions without assessing the uncertainty associated with such predictions or the introduced techniques. Uncertainty maps highlight areas in the predicted segmented results, where the model is uncertain or less confident. This could lead radiologists to pay more attention to ensuring patient safety and pave the way for trustworthy AI applications. In this paper, we therefore propose the Attention-guided Hierarchical Fusion U-Net (named AHF-U-Net) for medical image segmentation. We then introduce the uncertainty-aware version of it called UA-AHF-U-Net which provides the uncertainty map alongside the predicted segmentation map. The network is designed by integrating the Encoder Attention Fusion module (EAF) and the Decoder Attention Fusion module (DAF) on the encoder and decoder sides of the U-Net architecture, respectively. The EAF and DAF modules utilize spatial and channel attention to capture relevant spatial information and indicate which channels are appropriate for a given image. Furthermore, an enhanced skip connection is introduced and named the Hierarchical Attention-Enhanced (HAE) skip connection. We evaluated the efficiency of our model by comparing it with eleven well-established methods for three popular medical image segmentation datasets consisting of coarse-grained images with unclear boundaries. Based on the quantitative and qualitative results, the proposed method ranks first in two datasets and second in a third. The code can be accessed at: <span><span>https://github.com/AfsanaAhmedMunia/AHF-Fusion-U-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102719"},"PeriodicalIF":14.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"title":"Interpretability research of deep learning: A literature survey","authors":"Biao Xu,&nbsp;Guanci Yang","doi":"10.1016/j.inffus.2024.102721","DOIUrl":"10.1016/j.inffus.2024.102721","url":null,"abstract":"<div><div>Deep learning (DL) has been widely used in various fields. However, its black-box nature limits people's understanding and trust in its decision-making process. Therefore, it becomes crucial to research the DL interpretability, which can elucidate the model's decision-making processes and behaviors. This review provides an overview of the current status of interpretability research. First, the DL's typical models, principles, and applications are introduced. Then, the definition and significance of interpretability are clarified. Subsequently, some typical interpretability algorithms are introduced into four groups: active, passive, supplementary, and integrated explanations. After that, several evaluation indicators for interpretability are briefly described, and the relationship between interpretability and model performance is explored. Next, the specific applications of some interpretability methods/models in actual scenarios are introduced. Finally, the interpretability research challenges and future development directions are discussed.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"115 ","pages":"Article 102721"},"PeriodicalIF":14.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532033","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}