IET Biometrics最新文献_第8页

Transferability analysis of adversarial attacks on gender classification to face recognition: Fixed and variable attack perturbation 性别分类对抗性攻击对人脸识别的可转移性分析:固定和可变攻击扰动

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-27 DOI: 10.1049/bme2.12082

Zohra Rezgui, Amina Bassit, Raymond Veldhuis

{"title":"Transferability analysis of adversarial attacks on gender classification to face recognition: Fixed and variable attack perturbation","authors":"Zohra Rezgui, Amina Bassit, Raymond Veldhuis","doi":"10.1049/bme2.12082","DOIUrl":"10.1049/bme2.12082","url":null,"abstract":"Most deep learning-based image classification models are vulnerable to adversarial attacks that introduce imperceptible changes to the input images for the purpose of model misclassification. It has been demonstrated that these attacks, targeting a specific model, are transferable among models performing the same task. However, models performing different tasks but sharing the same input space and model architecture were never considered in the transferability scenarios presented in the literature. In this paper, this phenomenon was analysed in the context of VGG16-based and ResNet50-based biometric classifiers. The authors investigate the impact of two white-box attacks on a gender classifier and contrast a defence method as a countermeasure. Then, using adversarial images generated by the attacks, a pre-trained face recognition classifier is attacked in a black-box fashion. Two verification comparison settings are employed, in which images perturbed with the same and different magnitude of the perturbation are compared. The authors’ results indicate transferability in the fixed perturbation setting for a Fast Gradient Sign Method attack and non-transferability in a pixel-guided denoiser attack setting. The interpretation of this non-transferability can support the use of fast and train-free adversarial attacks targeting soft biometric classifiers as means to achieve soft biometric privacy protection while maintaining facial identity as utility.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"407-419"},"PeriodicalIF":2.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88686270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

An empirical analysis of keystroke dynamics in passwords: A longitudinal study 密码击键动力学的实证分析：一项纵向研究

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-27 DOI: 10.1049/bme2.12087

Simon Parkinson, Saad Khan, Alexandru-Mihai Badea, Andrew Crampton, Na Liu, Qing Xu

{"title":"An empirical analysis of keystroke dynamics in passwords: A longitudinal study","authors":"Simon Parkinson, Saad Khan, Alexandru-Mihai Badea, Andrew Crampton, Na Liu, Qing Xu","doi":"10.1049/bme2.12087","DOIUrl":"https://doi.org/10.1049/bme2.12087","url":null,"abstract":"The use of keystroke timings as a behavioural biometric in fixed-text authentication mechanisms has been extensively studied. Previous research has investigated in isolation the effect of password length, character substitution, and participant repetition. These studies have used publicly available datasets, containing a small number of passwords with timings acquired from different experiments. Multiple experiments have also used the participant's first and last name as the password; however, this is not realistic of a password system. Not only is the user's name considered a weak password, but their familiarity with typing the phrase minimises variation in acquired samples as they become more familiar with the new password. Furthermore, no study has considered the combined impact of length, substitution, and repetition using the same participant pool. This is explored in this work, where the authors collected timings for 65 participants, when typing 40 passwords with varying characteristics, 4 times per week for 8 weeks. A total of 81,920 timing samples were processed using an instance-based distance and threshold matching approach. Results of this study provide empirical insight into how a password policy should be created to maximise the accuracy of the biometric system when considering substitution type and longitudinal effects.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 1","pages":"25-37"},"PeriodicalIF":2.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50145548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption 混合生物识别模板保护:解决在布隆过滤器和同态加密之间选择的痛苦

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-16 DOI: 10.1049/bme2.12075

Amina Bassit, Florian Hahn, Raymond Veldhuis, Andreas Peter

{"title":"Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption","authors":"Amina Bassit, Florian Hahn, Raymond Veldhuis, Andreas Peter","doi":"10.1049/bme2.12075","DOIUrl":"10.1049/bme2.12075","url":null,"abstract":"Bloom filters (BFs) and homomorphic encryption (HE) are prominent techniques used to design biometric template protection (BTP) schemes that aim to protect sensitive biometric information during storage and biometric comparison. However, the pros and cons of BF- and HE-based BTPs are not well studied in literature. We investigate the strengths and weaknesses of these two approaches since both seem promising from a theoretical viewpoint. Our key insight is to extend our theoretical investigation to cover the practical case of iris recognition on the ground that iris (1) benefits from the alignment-free property of BFs and (2) induces huge computational burdens when implemented in the HE-encrypted domain. BF-based BTPs can be implemented to be either fast with high recognition accuracy while missing the important privacy property of ‘unlinkability’, or to be fast with unlinkability-property while missing the high accuracy. HE-based BTPs, on the other hand, are highly secure, achieve good accuracy, and meet the unlinkability-property, but they are much slower than BF-based approaches. As a synthesis, we propose a hybrid BTP scheme that combines the good properties of BFs and HE, ensuring unlinkability and high recognition accuracy, while being about seven times faster than the traditional HE-based approach.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"430-444"},"PeriodicalIF":2.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90056623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 3

Point-convolution-based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple-input multiple-output radar 基于点卷积的毫米波调频连续波多输入多输出雷达人体骨骼位姿估计

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-13 DOI: 10.1049/bme2.12081

Jinxiao Zhong, Liangnian Jin, Ran Wang

{"title":"Point-convolution-based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple-input multiple-output radar","authors":"Jinxiao Zhong, Liangnian Jin, Ran Wang","doi":"10.1049/bme2.12081","DOIUrl":"10.1049/bme2.12081","url":null,"abstract":"Compared with traditional approaches that used vision sensors which can provide a high-resolution representation of targets, millimetre-wave radar is robust to scene lighting and weather conditions, and has more applications. Current methods of human skeletal pose estimation can reconstruct targets, but they lose the spatial information or don't take the density of point cloud into consideration. We propose a skeletal pose estimation method that combines point convolution to extract features from the point cloud. By extracting the local information and density of each point in the point cloud of the target, the spatial location and structure information of the target can be obtained, and the accuracy of the pose estimation is increased. The extraction of point cloud features is based on point-by-point convolution, that is, different weights are applied to different features of each point, which also increases the nonlinear expression ability of the model. Experiments show that the proposed approach is effective. We offer more distinct skeletal joints and a lower mean absolute error, average localisation errors of 6.1 cm in X, 3.5 cm in Y and 3.3 cm in Z, respectively.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 4","pages":"333-342"},"PeriodicalIF":2.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91101921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms 合成虹膜图像的鲁棒呈现攻击检测算法分析

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-07 DOI: 10.1049/bme2.12084

Jose Maureira, Juan E. Tapia, Claudia Arellano, Christoph Busch

{"title":"Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms","authors":"Jose Maureira, Juan E. Tapia, Claudia Arellano, Christoph Busch","doi":"10.1049/bme2.12084","DOIUrl":"10.1049/bme2.12084","url":null,"abstract":"The LivDet-2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genuine) images used to train such algorithms. Unfortunately, the capture and creation of PAI and even the capture of Bona fide images are sometimes complex to achieve. The generation of synthetic images with Generative Adversarial Networks (GAN) algorithms may help and has shown significant improvements in recent years. This paper presents a benchmark of GAN methods to achieve a novel synthetic PAI from a small set of periocular near-infrared images. The best PAI was obtained using StyleGAN2, and it was tested using the best PAD algorithm from the LivDet-2020. The synthetic PAI was able to fool such an algorithm. As a result, all images were classified as Bona fide. A MobileNetV2 was trained using the synthetic PAI as a new class to achieve a more robust PAD. The resulting PAD was able to classify 96.7% of synthetic images as attacks. BPCER10 was 0.24%. Such results demonstrated the need for PAD algorithms to be constantly updated and trained with synthetic images.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 4","pages":"343-354"},"PeriodicalIF":2.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82133166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Multiresolution synthetic fingerprint generation 多分辨率合成指纹生成

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-06-03 DOI: 10.1049/bme2.12083

Andre Brasil Vieira Wyzykowski, Mauricio Pamplona Segundo, Rubisley de Paula Lemes

{"title":"Multiresolution synthetic fingerprint generation","authors":"Andre Brasil Vieira Wyzykowski, Mauricio Pamplona Segundo, Rubisley de Paula Lemes","doi":"10.1049/bme2.12083","DOIUrl":"10.1049/bme2.12083","url":null,"abstract":"Public access to existing high-resolution databases was discontinued. Besides, a hybrid database that contains fingerprints of different sensors with high and medium resolutions does not exist. A novel hybrid approach to synthesise realistic, multiresolution, and multisensor fingerprints to address these issues is presented. The first step was to improve Anguli, a handcrafted fingerprint generator, to create pores, scratches, and dynamic ridge maps. Using CycleGAN, then the maps are converted into realistic fingerprints, adding textures to images. Unlike other neural network-based methods, the authors’ method generates multiple images with different resolutions and styles for the same identity. With the authors’ approach, a synthetic database with 14,800 fingerprints is built. Besides that, fingerprint recognition experiments with pore- and minutiae-based matching techniques and different fingerprint quality analyses are conducted to confirm the similarity between real and synthetic databases. Finally, a human classification analysis is performed, where volunteers could not distinguish between authentic and synthetic fingerprints. These experiments demonstrate that the authors’ approach is suitable for supporting further fingerprint recognition studies in the absence of real databases.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 4","pages":"314-332"},"PeriodicalIF":2.0,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77469700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 4

Forearm multimodal recognition based on IAHP-entropy weight combination 基于IAHP熵权组合的前臂多模态识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-05-27 DOI: 10.1049/bme2.12080

Chaoying Tang, Mengen Qian, Ru Jia, Haodong Liu, Biao Wang

{"title":"Forearm multimodal recognition based on IAHP-entropy weight combination","authors":"Chaoying Tang, Mengen Qian, Ru Jia, Haodong Liu, Biao Wang","doi":"10.1049/bme2.12080","DOIUrl":"https://doi.org/10.1049/bme2.12080","url":null,"abstract":"Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID-19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near-Infrared (Near-Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process-entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"12 1","pages":"52-63"},"PeriodicalIF":2.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50146449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Towards pen-holding hand pose recognition: A new benchmark and a coarse-to-fine PHHP recognition network 面向握笔姿势识别:一个新的基准和粗到精的php识别网络

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-05-17 DOI: 10.1049/bme2.12079

Pingping Wu, Lunke Fei, Shuyi Li, Shuping Zhao, Xiaozhao Fang, Shaohua Teng

{"title":"Towards pen-holding hand pose recognition: A new benchmark and a coarse-to-fine PHHP recognition network","authors":"Pingping Wu, Lunke Fei, Shuyi Li, Shuping Zhao, Xiaozhao Fang, Shaohua Teng","doi":"10.1049/bme2.12079","DOIUrl":"10.1049/bme2.12079","url":null,"abstract":"Hand pose recognition has been one of the most fundamental tasks in computer vision and pattern recognition, and substantial effort has been devoted to this field. However, owing to lack of public large-scale benchmark dataset, there is little literature to specially study pen-holding hand pose (PHHP) recognition. As an attempt to fill this gap, in this paper, a PHHP image dataset, consisting of 18,000 PHHP samples is established. To the best of the authors’ knowledge, this is the largest vision-based PHHP dataset ever collected. Furthermore, the authors design a coarse-to-fine PHHP recognition network consisting of a coarse multi-feature learning network and a fine pen-grasping-specific feature learning network, where the coarse learning network aims to extensively exploit the multiple discriminative features by sharing a hand-shape-based spatial attention information, and the fine learning network further learns the pen-grasping-specific features by embedding a couple of convolutional block attention modules into three convolution blocks models. Experimental results show that the authors’ proposed method can achieve a very competitive PHHP recognition performance when compared with the baseline recognition models.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 6","pages":"581-587"},"PeriodicalIF":2.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75725455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1

Recognition of human Iris for biometric identification using Daugman’s method 用道格曼方法识别人体虹膜进行生物特征识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-05-14 DOI: 10.1049/bme2.12074

Reend Tawfik Mohammed, Harleen Kaur, Bhavya Alankar, Ritu Chauhan

{"title":"Recognition of human Iris for biometric identification using Daugman’s method","authors":"Reend Tawfik Mohammed, Harleen Kaur, Bhavya Alankar, Ritu Chauhan","doi":"10.1049/bme2.12074","DOIUrl":"10.1049/bme2.12074","url":null,"abstract":"Iris identification is a well-known technology used to detect striking biometric identification procedures for recognizing human beings based on physical behaviour. The texture of the iris is unique and its anatomy varies from individual to individual. As we know, the physical features of human beings are unique, and they never change; this has led to a significant development in the field of iris recognition. Iris recognition tends to be a reliable domain of technology as it inherits the random variation of the data. In the proposed study of approach, we have designed and implemented a framework using various subsystems, where each phase relates to the other iris recognition system, and these stages are discussed as segmentation, normalisation, and feature encoding. The study is implemented using MATLAB where the results are outcast using the rapid application development (RAD) approach. We have applied the RAD domain, as it has an excellent computing power to generate expeditious results using complex coding, image processing toolbox, and high-level programing methodology. Further, the performance of the technology is tested on two informational groups of eye images MMU Iris database, CASIA V1, CASIA V2, MICHE I, MICHE II iris database, and images captured by iPhone camera and Android phone. The emphasis on the current study of approach is to apply the proposed algorithm to achieve high performance with less ideal conditions.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 4","pages":"304-313"},"PeriodicalIF":2.0,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89044719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Breast mass classification based on supervised contrastive learning and multi-view consistency penalty on mammography 基于监督对比学习和多视点一致性惩罚的乳腺肿块分类

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-05-12 DOI: 10.1049/bme2.12076

Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

{"title":"Breast mass classification based on supervised contrastive learning and multi-view consistency penalty on mammography","authors":"Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu","doi":"10.1049/bme2.12076","DOIUrl":"10.1049/bme2.12076","url":null,"abstract":"Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5-year survival rate. However, the lack of public available breast mammography databases in the field of Computer-aided Diagnosis and the insufficient feature extraction ability from breast mammography limit the diagnostic performance of breast cancer. In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. A multi-view network is designed to extract the complementary information between the Craniocaudal (CC) and Mediolateral Oblique (MLO) mammographic views of a breast mass. For the different predictions of the features extracted from the CC view and MLO view of the same breast mass, the proposed algorithm forces the network to extract the consistent features from the two views by the cross-entropy function with an added consistent penalty term. To exploit the discriminative features from the insufficient mammographic images, the authors learnt an encoder in the classification model to learn the invariable representations from the mammographic breast mass by Supervised Contrastive Learning (SCL) to weaken the side effect of colour jitter and illumination of mammographic breast mass on image quality degradation. The experimental results of all the classification algorithms mentioned in this paper on Digital Database for Screening Mammography (DDSM) illustrate that the proposed algorithm greatly improves the classification performance and diagnostic speed of mammographic breast mass, which is of great significance for breast cancer diagnosis.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 6","pages":"588-600"},"PeriodicalIF":2.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89203516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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