Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00010
Xin Lu, Junying Jia, Zhiwei Pei, Daolin Wang, Jialin Wang, Bo Sun
The surface defect detection technology of irregular object based on machine vision has been widely used in various industrial scenarios in recent years. In this paper, we take Bluetooth headsets as an example, propose a Bluetooth headset surface defect detection method. Based on the analysis of the surface characteristics and defects types of Bluetooth headset, the scratch and glue-overflowed problem on the surface of the headset are accurately detected. The experimental results shows that the detection algorithm can quickly and effectively detect the surface defects of Bluetooth headset, and the accuracy of defect recognition reaches 98%. Therefore, the detection algorithm has a certain practical application value in industry.
{"title":"A Method of Surface Defect Detection of Bluetooth Headset Based on Machine Vision*","authors":"Xin Lu, Junying Jia, Zhiwei Pei, Daolin Wang, Jialin Wang, Bo Sun","doi":"10.1109/CSE53436.2021.00010","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00010","url":null,"abstract":"The surface defect detection technology of irregular object based on machine vision has been widely used in various industrial scenarios in recent years. In this paper, we take Bluetooth headsets as an example, propose a Bluetooth headset surface defect detection method. Based on the analysis of the surface characteristics and defects types of Bluetooth headset, the scratch and glue-overflowed problem on the surface of the headset are accurately detected. The experimental results shows that the detection algorithm can quickly and effectively detect the surface defects of Bluetooth headset, and the accuracy of defect recognition reaches 98%. Therefore, the detection algorithm has a certain practical application value in industry.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"13 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88755959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00029
Lu Zhang, R. Cushing, C. D. Laat, P. Grosso
A Digital Data Marketplace (DDM) is a digital infrastructure to facilitate policy-governed data sharing in a secure and trustworthy manner with container-based virtualization technologies. An intrusion detection systems (IDS) is essential to enforce the policies. We propose a real-time intrusion detection system that monitors and analyzes the Linux-kernel system calls of a running container. We adopt the One-Class Support Vector Machine (OC-SVM) to detect anomalies. The training data of the OC-SVM algorithm is collected and sanitized in a secure environment. We evaluate the detection capability of our proposed system against modern attacks, e.g. Machine Learning (ML) adversarial attacks, with a customized attack dataset. In addition, we investigate the influence of various feature extraction methods, kernel functions and segmentation length with four metrics. Our experimental results show that we can achieve a low FPR, with a worst case of 0.12, and a TPR of 1 for most attacks, when we adopt the term-frequency feature extraction method and we choose segmentation length of 30000. Furthermore, the optimal kernel functions depend on the concrete application being examined.
数字数据市场(Digital Data Marketplace, DDM)是一种数字基础设施，它使用基于容器的虚拟化技术，以安全可靠的方式促进策略管理的数据共享。入侵检测系统(IDS)对于执行策略至关重要。提出了一种实时入侵检测系统，用于监控和分析运行容器的linux内核系统调用。我们采用一类支持向量机(OC-SVM)来检测异常。OC-SVM算法的训练数据是在安全的环境中收集和消毒的。我们使用定制的攻击数据集评估了我们提出的系统对现代攻击的检测能力，例如机器学习(ML)对抗性攻击。此外，我们还研究了各种特征提取方法、核函数和分割长度对四个度量的影响。我们的实验结果表明，当我们采用频项特征提取方法，选择分割长度为30000时，我们可以获得较低的FPR，最坏情况为0.12，对大多数攻击的TPR为1。此外，最优核函数取决于所检查的具体应用程序。
{"title":"A real-time intrusion detection system based on OC-SVM for containerized applications","authors":"Lu Zhang, R. Cushing, C. D. Laat, P. Grosso","doi":"10.1109/CSE53436.2021.00029","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00029","url":null,"abstract":"A Digital Data Marketplace (DDM) is a digital infrastructure to facilitate policy-governed data sharing in a secure and trustworthy manner with container-based virtualization technologies. An intrusion detection systems (IDS) is essential to enforce the policies. We propose a real-time intrusion detection system that monitors and analyzes the Linux-kernel system calls of a running container. We adopt the One-Class Support Vector Machine (OC-SVM) to detect anomalies. The training data of the OC-SVM algorithm is collected and sanitized in a secure environment. We evaluate the detection capability of our proposed system against modern attacks, e.g. Machine Learning (ML) adversarial attacks, with a customized attack dataset. In addition, we investigate the influence of various feature extraction methods, kernel functions and segmentation length with four metrics. Our experimental results show that we can achieve a low FPR, with a worst case of 0.12, and a TPR of 1 for most attacks, when we adopt the term-frequency feature extraction method and we choose segmentation length of 30000. Furthermore, the optimal kernel functions depend on the concrete application being examined.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"122 1","pages":"138-145"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89392232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00026
Xiangbin Shi, Jinwen Peng
In this paper, we addressed the problems of occlusion and crowding in large-scale object detection. First, large-scale detection is more complex and diverse than traditional object detection. The number of targets to be detected is larger and often clustered together. This will produce occlusion and dense detection problems, which brings a serious challenge to object detection. Secondly, current dominant object detection is rarely trained and inferred on large-scale labeled dataset, so it is unable to evaluate the performance of these detection models on large dataset. To solve the above problems, we propose L-YOLO large-scale object detection algorithm. We modified the structure of feature pyramid network, then the receptive field was increased by using four-scale detection. Next, we propose a new loss function designed specifically for large-scale scenarios, which keeps the prediction box that is not the target as far away from the target as possible. It prevents the fusion of adjacent boundary boxes in the inference process and improves the detection performance in the case of occlusion effectively. At last, we use a new non-maximum suppression rule to prevent suppression of the correct detection box during infer. We annotated new dataset for large-scale detection, retrained and evaluated our model. Experiments on our dataset show the superiority of our model. Compared to the original YOLOv4, our improved model increases 1.8% mAP.
{"title":"A Large-scale Detection Algorithm and Application Based on YOLOv4","authors":"Xiangbin Shi, Jinwen Peng","doi":"10.1109/CSE53436.2021.00026","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00026","url":null,"abstract":"In this paper, we addressed the problems of occlusion and crowding in large-scale object detection. First, large-scale detection is more complex and diverse than traditional object detection. The number of targets to be detected is larger and often clustered together. This will produce occlusion and dense detection problems, which brings a serious challenge to object detection. Secondly, current dominant object detection is rarely trained and inferred on large-scale labeled dataset, so it is unable to evaluate the performance of these detection models on large dataset. To solve the above problems, we propose L-YOLO large-scale object detection algorithm. We modified the structure of feature pyramid network, then the receptive field was increased by using four-scale detection. Next, we propose a new loss function designed specifically for large-scale scenarios, which keeps the prediction box that is not the target as far away from the target as possible. It prevents the fusion of adjacent boundary boxes in the inference process and improves the detection performance in the case of occlusion effectively. At last, we use a new non-maximum suppression rule to prevent suppression of the correct detection box during infer. We annotated new dataset for large-scale detection, retrained and evaluated our model. Experiments on our dataset show the superiority of our model. Compared to the original YOLOv4, our improved model increases 1.8% mAP.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"54 1","pages":"116-122"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86950742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00025
Shasha Li, Xiaodong Bai, Songjie Wei
Crowdsourcing relies on Internet-wide capability to solve the complicated or large-scale tasks that are difficult to accomplish separately by individuals. However, traditional centralized crowdsourcing systems highly depend on the centralized coordination server to operate, making it extremely vulnerable to the single-point bottleneck and failure. And the whole system lacks verifiable trustworthiness among the participants. This paper proposes a blockchain-based framework for the distributed crowdsourcing without relying solely on any single trusted entity. The solutions for the outsourced tasks are verified with consensus among the participants with a reputation mechanism. We prove by theoretical security analysis that the proposed scheme resists malicious attacks better comparing to other typical crowdsourcing schemes. A prototype system is implemented based on Ethereum to demonstrate the overhead performance in various aspects. Theoretical and experimental evaluations show that the proposed scheme possesses reliability, security, quality, and feasibility.
{"title":"Blockchain-based Crowdsourcing Task Management and Solution Verification Method","authors":"Shasha Li, Xiaodong Bai, Songjie Wei","doi":"10.1109/CSE53436.2021.00025","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00025","url":null,"abstract":"Crowdsourcing relies on Internet-wide capability to solve the complicated or large-scale tasks that are difficult to accomplish separately by individuals. However, traditional centralized crowdsourcing systems highly depend on the centralized coordination server to operate, making it extremely vulnerable to the single-point bottleneck and failure. And the whole system lacks verifiable trustworthiness among the participants. This paper proposes a blockchain-based framework for the distributed crowdsourcing without relying solely on any single trusted entity. The solutions for the outsourced tasks are verified with consensus among the participants with a reputation mechanism. We prove by theoretical security analysis that the proposed scheme resists malicious attacks better comparing to other typical crowdsourcing schemes. A prototype system is implemented based on Ethereum to demonstrate the overhead performance in various aspects. Theoretical and experimental evaluations show that the proposed scheme possesses reliability, security, quality, and feasibility.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"1 1","pages":"108-115"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83449270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00018
Sami Alenezi, Chunbo Luo, G. Min
Device-to-Device (D2D) communication has emerged as an evolving communication technology in 5G networks, enabling a pair of user equipment units to communicate without passing through the base station. However, the introduction of a D2D link can cause interference with other cellular user links, which highlights the difficulty of guaranteeing the communication quality of the whole system. In addition, when a large number of cellular users are connected to the network through D2D devices at the same time, the circuit consumption of the mobile devices will greatly increase and affect the user experience. In this paper, we focus on improving the energy efficiency of D2D devices in a cellular network served by one base station, through the adjustment of D2D link transmission power. We propose a centralised power control algorithm based on reinforcement learning to optimise the energy utilisation, while minimising the interference on cellular users, to maintain the quality of service (QoS). Simulation results show that the proposed approach can significantly increase the system energy efficiency and maintain the cellular user QoS, compared with the benchmark algorithm.
{"title":"Energy-Efficient D2D Communications Based on Centralised Reinforcement Learning Techniques","authors":"Sami Alenezi, Chunbo Luo, G. Min","doi":"10.1109/CSE53436.2021.00018","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00018","url":null,"abstract":"Device-to-Device (D2D) communication has emerged as an evolving communication technology in 5G networks, enabling a pair of user equipment units to communicate without passing through the base station. However, the introduction of a D2D link can cause interference with other cellular user links, which highlights the difficulty of guaranteeing the communication quality of the whole system. In addition, when a large number of cellular users are connected to the network through D2D devices at the same time, the circuit consumption of the mobile devices will greatly increase and affect the user experience. In this paper, we focus on improving the energy efficiency of D2D devices in a cellular network served by one base station, through the adjustment of D2D link transmission power. We propose a centralised power control algorithm based on reinforcement learning to optimise the energy utilisation, while minimising the interference on cellular users, to maintain the quality of service (QoS). Simulation results show that the proposed approach can significantly increase the system energy efficiency and maintain the cellular user QoS, compared with the benchmark algorithm.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"90 1","pages":"57-63"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80668861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00012
Dienul Paramarta, Juan Li
Subjective expert projections have been traditionally used to predict points in fantasy football, while machine prediction applications are limited. Memory-based collaborative filtering has been widely used in the recommender system domain to predict ratings and recommend items. In this study, user-based and item-based collaborative filtering were explored and implemented to predict the weekly statistics and fantasy points of NFL quarterbacks. The predictions from multiple seasons were compared against expert projections. On both weekly statistics and total fantasy points, the implementations could not make significantly better predictions than experts. However, the prediction from the implementation improved the accuracy of other regression models when used as additional features.
{"title":"CFP- A New Approach to Predicting Fantasy Points of NFL Quarterbacks","authors":"Dienul Paramarta, Juan Li","doi":"10.1109/CSE53436.2021.00012","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00012","url":null,"abstract":"Subjective expert projections have been traditionally used to predict points in fantasy football, while machine prediction applications are limited. Memory-based collaborative filtering has been widely used in the recommender system domain to predict ratings and recommend items. In this study, user-based and item-based collaborative filtering were explored and implemented to predict the weekly statistics and fantasy points of NFL quarterbacks. The predictions from multiple seasons were compared against expert projections. On both weekly statistics and total fantasy points, the implementations could not make significantly better predictions than experts. However, the prediction from the implementation improved the accuracy of other regression models when used as additional features.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"53 1","pages":"12-19"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74679144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/CSE53436.2021.00019
Han Xu, Haozhe Wang, Jia Hu, G. Min
The proliferation of advanced information technology applications such as Virtual/Augmented Reality and ultra-high-definition (UHD) multimedia services that demand high bandwidth and ultra-low latency put tremendous pressure on the current communication networks. To meet these pressing requirements, Information-Centric Networks (ICN), a promising future Internet paradigm has been attracting much research attention. ICN deploy ubiquitous in-network caching that could not only handle large content dissemination and retrieval but also expedite the content delivery. To investigate the performance of ICN, it is important to have an analytical model that can accurately characterize the content transfer in ICN under different network and traffic conditions. In this paper, we exploit the queueing network theory to develop a new analytical model for content transfer in ICN. We derive the mathematical expressions for calculating cache miss rate and content delivery time. The accuracy of our analytical model is validated by comparing the analytical results with those obtained from simulation experiments. We also use the model to investigate the content delivery time under various network and traffic conditions.
{"title":"Analytical Modelling of Content Transfer in Information Centric Networks","authors":"Han Xu, Haozhe Wang, Jia Hu, G. Min","doi":"10.1109/CSE53436.2021.00019","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00019","url":null,"abstract":"The proliferation of advanced information technology applications such as Virtual/Augmented Reality and ultra-high-definition (UHD) multimedia services that demand high bandwidth and ultra-low latency put tremendous pressure on the current communication networks. To meet these pressing requirements, Information-Centric Networks (ICN), a promising future Internet paradigm has been attracting much research attention. ICN deploy ubiquitous in-network caching that could not only handle large content dissemination and retrieval but also expedite the content delivery. To investigate the performance of ICN, it is important to have an analytical model that can accurately characterize the content transfer in ICN under different network and traffic conditions. In this paper, we exploit the queueing network theory to develop a new analytical model for content transfer in ICN. We derive the mathematical expressions for calculating cache miss rate and content delivery time. The accuracy of our analytical model is validated by comparing the analytical results with those obtained from simulation experiments. We also use the model to investigate the content delivery time under various network and traffic conditions.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"7 1","pages":"64-71"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82695724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Inquiry into labor from a Feminist Theological Perspective: Regarding the Issue of Basic Income","authors":"ChungMeehyun","doi":"10.21050/cse.2018.42.09","DOIUrl":"https://doi.org/10.21050/cse.2018.42.09","url":null,"abstract":"","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"7 1","pages":"241-264"},"PeriodicalIF":0.0,"publicationDate":"2018-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72934157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
DOORS is a distributed system proposal that provides execution and storage services in the form of "objects", which encapsulate both state and behaviour. We start by briefly describing the current state of the DOORS solution, as detailed in previous work. We then outline the class of problems that we aim to solve with DOORS, and provide brief motivations for the architectural choices. As direct consequences of the chosen architecture (distributed message passing in favour of shared memory) we analyse the following critical aspects: concurrency control, replication and the choice between consistency and availability. With the support of this analysis, we identify and present 3 different "kinds" of inter-node communication. In spite of their apparent similarity, these scenarios are addressed differently into the design, such that the implementation of DOORS remains correct, consistent and useful.
{"title":"Analysis and Design of DOORS, in the Context of Consistency, Availability, Partitioning and Latency","authors":"Dorin Mihai Palanciuc Mawas","doi":"10.1109/CSE.2018.00009","DOIUrl":"https://doi.org/10.1109/CSE.2018.00009","url":null,"abstract":"DOORS is a distributed system proposal that provides execution and storage services in the form of \"objects\", which encapsulate both state and behaviour. We start by briefly describing the current state of the DOORS solution, as detailed in previous work. We then outline the class of problems that we aim to solve with DOORS, and provide brief motivations for the architectural choices. As direct consequences of the chosen architecture (distributed message passing in favour of shared memory) we analyse the following critical aspects: concurrency control, replication and the choice between consistency and availability. With the support of this analysis, we identify and present 3 different \"kinds\" of inter-node communication. In spite of their apparent similarity, these scenarios are addressed differently into the design, such that the implementation of DOORS remains correct, consistent and useful.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"55 1","pages":"12-18"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78010575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Parameter Estimation of Phase Code and Linear Frequency Modulation Combined Signal Based on Fractional Autocorrelation and Haar Wavelet Transform","authors":"Zhaoyang Qiu, Jun Zhu, Pei Wang, B. Tang","doi":"10.1109/CSE.2014.188","DOIUrl":"https://doi.org/10.1109/CSE.2014.188","url":null,"abstract":"","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"43 1","pages":"936-939"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73872245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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