Pub Date : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703268
K. Ghanshala, Sachin Sharma, S. Mohan, Lata Nautiyal, P. Mishra, R. Joshi
The era of new and emerging technologies demand that the new challenges they bring about to be effectively tackled and resolved. One such key challenge is spectrum management, especially in Cognitive Radio Vehicular Adhoc Network (CRAVENET) environment. The large-scale deployment of multimedia and Internet of Things (IoT) applications generate the need to establish an efficient spectrum allocation mechanism. This paper proposes a centralized self-organizing spectrum management in the context of economic and social sustainability using reinforcement learning technique. The objective of the proposed approach facilitates economic and social justice. The social economic justice architecture is developed through a user demand level concepts. The spectrum management methodology has been developed in a CRAVENET environment for better quality of service (QoS) with low average latency. The proposed methodology is expected to be highly effective for its economic feasibility, social impact, user comfort, efficiency, and communication latency minimization requirements.
{"title":"Self-Organizing Sustainable Spectrum Management Methodology in Cognitive Radio Vehicular Adhoc Network (CRAVENET) Environment: A Reinforcement Learning Approach","authors":"K. Ghanshala, Sachin Sharma, S. Mohan, Lata Nautiyal, P. Mishra, R. Joshi","doi":"10.1109/ICSCCC.2018.8703268","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703268","url":null,"abstract":"The era of new and emerging technologies demand that the new challenges they bring about to be effectively tackled and resolved. One such key challenge is spectrum management, especially in Cognitive Radio Vehicular Adhoc Network (CRAVENET) environment. The large-scale deployment of multimedia and Internet of Things (IoT) applications generate the need to establish an efficient spectrum allocation mechanism. This paper proposes a centralized self-organizing spectrum management in the context of economic and social sustainability using reinforcement learning technique. The objective of the proposed approach facilitates economic and social justice. The social economic justice architecture is developed through a user demand level concepts. The spectrum management methodology has been developed in a CRAVENET environment for better quality of service (QoS) with low average latency. The proposed methodology is expected to be highly effective for its economic feasibility, social impact, user comfort, efficiency, and communication latency minimization requirements.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126006799","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703371
Dalwinder Singh, Birmohan Singh
Automatic bioacoustics monitoring has a great potential to assess the ecosystem health. However, such bioacoustics systems are not highly accurate because the classification of data involves a large number of species. In this paper, we have considered the related problem which involves classification of frog and toad species from their sounds. A publicly available large dataset is used for this purpose where performance is evaluated with leave-one-out cross-validation on the k-NN classifier. The dataset was prepared by extracting Mel-frequency cepstral coefficients (MFCCs)features from the recorded anurans calls, and it comprises the classification of anurans at family, genus and species levels. This paper presents the application of feature weighting to improve the classification of anurans calls. It is a continuous search problem where weights are assigned to features with respect to their contribution in classification. These weights are searched with the Ant Lion optimization along with the best parametric values of the k-NN classifier. The outcomes of experiments show that the proposed approach has successfully enhanced the classification accuracy at family, genus and species levels. The maximum classification accuracies of 95.01%, 88.38%,and 88.08% are achieved at family, genus and species levels respectively which has outperformed the feature selection approach as well as existing works.
{"title":"Feature Weighting for Improved Classification of Anuran Calls","authors":"Dalwinder Singh, Birmohan Singh","doi":"10.1109/ICSCCC.2018.8703371","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703371","url":null,"abstract":"Automatic bioacoustics monitoring has a great potential to assess the ecosystem health. However, such bioacoustics systems are not highly accurate because the classification of data involves a large number of species. In this paper, we have considered the related problem which involves classification of frog and toad species from their sounds. A publicly available large dataset is used for this purpose where performance is evaluated with leave-one-out cross-validation on the k-NN classifier. The dataset was prepared by extracting Mel-frequency cepstral coefficients (MFCCs)features from the recorded anurans calls, and it comprises the classification of anurans at family, genus and species levels. This paper presents the application of feature weighting to improve the classification of anurans calls. It is a continuous search problem where weights are assigned to features with respect to their contribution in classification. These weights are searched with the Ant Lion optimization along with the best parametric values of the k-NN classifier. The outcomes of experiments show that the proposed approach has successfully enhanced the classification accuracy at family, genus and species levels. The maximum classification accuracies of 95.01%, 88.38%,and 88.08% are achieved at family, genus and species levels respectively which has outperformed the feature selection approach as well as existing works.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127949632","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703359
Sourabh Sarkar, Geeta Sikka
Face recognition, recently, has been a fast and effective method of authentication with the advent of deep learning and powerful hardware. This paper investigates different classifiers used in classifying facial embeddings and evaluates their performance. The paper also focuses on an easily deployable pipeline for face recognition using Python which can be used to develop a face recognition system on portable low-power hardware devices. The methodology discussed uses pretrained models and frameworks which results in state-of-the-art performance without the need of any powerful hardware. The proposed methodology achieves an F1 score of 0. 9947with an AUC score of 0.9997 on LFW dataset.
{"title":"A comparative study of classifiers used in facial embedding classification","authors":"Sourabh Sarkar, Geeta Sikka","doi":"10.1109/ICSCCC.2018.8703359","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703359","url":null,"abstract":"Face recognition, recently, has been a fast and effective method of authentication with the advent of deep learning and powerful hardware. This paper investigates different classifiers used in classifying facial embeddings and evaluates their performance. The paper also focuses on an easily deployable pipeline for face recognition using Python which can be used to develop a face recognition system on portable low-power hardware devices. The methodology discussed uses pretrained models and frameworks which results in state-of-the-art performance without the need of any powerful hardware. The proposed methodology achieves an F1 score of 0. 9947with an AUC score of 0.9997 on LFW dataset.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125148708","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703312
Shailendra Singh, B. Raj
In this paper we study for the imminent novel Vertical Tunnel-FET(TFET) fascinating device for excessive low power digital circuit application because of its Subthreshold slope or swing (S) and low I-OFF current. As MOSFET are scaled down below the 45nm, the problems arises such as short channel effects, the I-OFF leakage current grow drastically because to the non-versatility of edge voltage as the Subthreshold Slope or swing (S) is restricted to 60mV/decade. As Tunnel FETs smothered the point of confinement of 60mV/decade by utilizing quantum-mechanical Band-2-Band Tunneling (B2BT) due to which the performance of this circuit for low power applications improved. This outline paper will examine about the substitution of the CMOS with different structures among which Vertical Tunnel Field Effect Transistor (TFET) found to be greater energy efficiency with improved $mathrm{I}_{mathrm{O}mathrm{N}}$ current which is thought to be the most basic plan parameter for pervasive and portable processing frameworks.
{"title":"Vertical Tunnel-FET Analysis for Excessive Low Power Digital Applications","authors":"Shailendra Singh, B. Raj","doi":"10.1109/ICSCCC.2018.8703312","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703312","url":null,"abstract":"In this paper we study for the imminent novel Vertical Tunnel-FET(TFET) fascinating device for excessive low power digital circuit application because of its Subthreshold slope or swing (S) and low I-OFF current. As MOSFET are scaled down below the 45nm, the problems arises such as short channel effects, the I-OFF leakage current grow drastically because to the non-versatility of edge voltage as the Subthreshold Slope or swing (S) is restricted to 60mV/decade. As Tunnel FETs smothered the point of confinement of 60mV/decade by utilizing quantum-mechanical Band-2-Band Tunneling (B2BT) due to which the performance of this circuit for low power applications improved. This outline paper will examine about the substitution of the CMOS with different structures among which Vertical Tunnel Field Effect Transistor (TFET) found to be greater energy efficiency with improved $mathrm{I}_{mathrm{O}mathrm{N}}$ current which is thought to be the most basic plan parameter for pervasive and portable processing frameworks.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124735634","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703365
M. Anwar, A. Khosla
Fog is not always homogeneous in nature. The fog density and distribution are varying in nature while capturing images through a camera or sensor. In contrast to homogeneity the fog may be treated as heterogeneous which depends upon the density variation of its constituents particles i.e water droplets. Classification is important and sometimes helpful to design a fog removal algorithm for vision enhancement while considering type of fog without knowing its density. Classification methods are applicable for both synthetic and camera images. This paper presents Support Vector Machine (SVM) that plays a key role to classify the synthetic data into two classes with accuracy measurement. Confusion matrix and Receiver Operational Characteristic (ROC) curve hold SVM to quantify the accuracy. The proposed method quantifies the type of fog with more than 92 percent accuracy for synthetically generated images containing various objects and environments in foggy situation. This acquaintance will finally help to generate a natural image dataset of homogeneous and heterogeneous foggy images.
{"title":"Fog Classification and Accuracy Measurement Using SVM","authors":"M. Anwar, A. Khosla","doi":"10.1109/ICSCCC.2018.8703365","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703365","url":null,"abstract":"Fog is not always homogeneous in nature. The fog density and distribution are varying in nature while capturing images through a camera or sensor. In contrast to homogeneity the fog may be treated as heterogeneous which depends upon the density variation of its constituents particles i.e water droplets. Classification is important and sometimes helpful to design a fog removal algorithm for vision enhancement while considering type of fog without knowing its density. Classification methods are applicable for both synthetic and camera images. This paper presents Support Vector Machine (SVM) that plays a key role to classify the synthetic data into two classes with accuracy measurement. Confusion matrix and Receiver Operational Characteristic (ROC) curve hold SVM to quantify the accuracy. The proposed method quantifies the type of fog with more than 92 percent accuracy for synthetically generated images containing various objects and environments in foggy situation. This acquaintance will finally help to generate a natural image dataset of homogeneous and heterogeneous foggy images.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133824946","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 : 2018-12-01DOI: 10.1109/icsccc.2018.8703297
{"title":"ICSCCC 2018 Author Index","authors":"","doi":"10.1109/icsccc.2018.8703297","DOIUrl":"https://doi.org/10.1109/icsccc.2018.8703297","url":null,"abstract":"","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654307","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703282
Harmanpreet Singh, Damanpreet Singh
Multi-level clustering offers energy efficient data gathering and much needed scalability in large-scale wireless sensor networks (WSNs). Although, few multi-level frameworks have been designed for static clustering and manually deployed WSNs, but no work has been done for randomly deployed WSN performing dynamic clustering. Moreover, there is a lack of structured framework for evolutionary optimization based multilevel clustering protocols. Design of multi-level clustering depends on two parameters: 1) optimal position of layers and 2) number of sensor nodes at each layer. Based on these parameters, a concentric layered architecture (CLA) is designed in this paper to perform multi-level clustering in randomly deployed WSN. CLA divide the network into layers based on node density and number of sensor nodes at each layer. Further, CLA is evaluated on an evolutionary optimization technique based clustering approach namely PSO-C. Simulation results show that the proposed CLA significantly improves the network lifetime and energy efficiency.
{"title":"Concentric Layered Architecture for Multi-Level Clustering in Large-Scale Wireless Sensor Networks","authors":"Harmanpreet Singh, Damanpreet Singh","doi":"10.1109/ICSCCC.2018.8703282","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703282","url":null,"abstract":"Multi-level clustering offers energy efficient data gathering and much needed scalability in large-scale wireless sensor networks (WSNs). Although, few multi-level frameworks have been designed for static clustering and manually deployed WSNs, but no work has been done for randomly deployed WSN performing dynamic clustering. Moreover, there is a lack of structured framework for evolutionary optimization based multilevel clustering protocols. Design of multi-level clustering depends on two parameters: 1) optimal position of layers and 2) number of sensor nodes at each layer. Based on these parameters, a concentric layered architecture (CLA) is designed in this paper to perform multi-level clustering in randomly deployed WSN. CLA divide the network into layers based on node density and number of sensor nodes at each layer. Further, CLA is evaluated on an evolutionary optimization technique based clustering approach namely PSO-C. Simulation results show that the proposed CLA significantly improves the network lifetime and energy efficiency.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116664169","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 : 2018-12-01DOI: 10.1109/icsccc.2018.8703367
{"title":"ICSCCC 2018 Committee and Message","authors":"","doi":"10.1109/icsccc.2018.8703367","DOIUrl":"https://doi.org/10.1109/icsccc.2018.8703367","url":null,"abstract":"","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129502865","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703344
Pooja Singh, Praveen Pawar, A. Trivedi
Rapid growth in the fifth generation (5G) wireless network applications demands new requirements on the data storage, computation, and networking. Thus, it will introduce new threats to the integrity, availability, and confidentiality. 5G is advantageous concerning high data rate, low latency, energy and spectrum efficient, higher capacity, and reliable connectivity. Currently, safeguarding information in the 5G wireless networks is the pivotal issue for research. In this paper, the importance of Physical Layer Security (PLS) for secure transmission of information in wireless networks are discussed. Some popular 5G technologies are studied in the context of security during transmission. With all this, significant issues and challenges are identified in the implementation of new technologies into reality. These technologies are mobile-health (m-health), cognitive radio networks (CRNs), constructive interference, massive multiple input multiple output (massive MIMO), non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT). Future challenges and direction for the further study of such technologies are also given. Moreover, one numerical result is presented for the spectral efficiency in MIMO communication system.
{"title":"Physical Layer Security Approaches in 5G Wireless Communication Networks","authors":"Pooja Singh, Praveen Pawar, A. Trivedi","doi":"10.1109/ICSCCC.2018.8703344","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703344","url":null,"abstract":"Rapid growth in the fifth generation (5G) wireless network applications demands new requirements on the data storage, computation, and networking. Thus, it will introduce new threats to the integrity, availability, and confidentiality. 5G is advantageous concerning high data rate, low latency, energy and spectrum efficient, higher capacity, and reliable connectivity. Currently, safeguarding information in the 5G wireless networks is the pivotal issue for research. In this paper, the importance of Physical Layer Security (PLS) for secure transmission of information in wireless networks are discussed. Some popular 5G technologies are studied in the context of security during transmission. With all this, significant issues and challenges are identified in the implementation of new technologies into reality. These technologies are mobile-health (m-health), cognitive radio networks (CRNs), constructive interference, massive multiple input multiple output (massive MIMO), non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT). Future challenges and direction for the further study of such technologies are also given. Moreover, one numerical result is presented for the spectral efficiency in MIMO communication system.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129145338","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703349
Ritu Singh, N. Rajpal, R. Mehta
Biomedical signals like Electrocardiogram (ECG) contains essential information related to the functionality of heart. The pre analysis of ECG disturbances, aided by computer designed algorithms can prove to be efficient support in reducing cardiac emergencies. In this present method, dual tree complex wavelet transform (DTCWT) with linear discriminate analysis (LDA) also known as hybrid feature extraction are employed for denoising and dimensionally reduced non linear feature extraction respectively. The classification and analysis of ECG dataset into normal and abnormal beats is done by independently deploying five classifiers like support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), feed forward neural network (FNNN) and K nearest neighbour (KNN). The outcomes of proposed work are compared with pre existing methods. The highest percentage accuracy of 99.7% is achieved using BPNN, SVM and KNN. The simulation results show that the shift invariance nature of DTCWT provides a robust technique for non linear and non stationary ECG signals.
{"title":"Abnormality detection in ECG using hybrid feature extraction approach","authors":"Ritu Singh, N. Rajpal, R. Mehta","doi":"10.1109/ICSCCC.2018.8703349","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703349","url":null,"abstract":"Biomedical signals like Electrocardiogram (ECG) contains essential information related to the functionality of heart. The pre analysis of ECG disturbances, aided by computer designed algorithms can prove to be efficient support in reducing cardiac emergencies. In this present method, dual tree complex wavelet transform (DTCWT) with linear discriminate analysis (LDA) also known as hybrid feature extraction are employed for denoising and dimensionally reduced non linear feature extraction respectively. The classification and analysis of ECG dataset into normal and abnormal beats is done by independently deploying five classifiers like support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), feed forward neural network (FNNN) and K nearest neighbour (KNN). The outcomes of proposed work are compared with pre existing methods. The highest percentage accuracy of 99.7% is achieved using BPNN, SVM and KNN. The simulation results show that the shift invariance nature of DTCWT provides a robust technique for non linear and non stationary ECG signals.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122203919","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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