Pub Date : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669040
Sina Fathi Kazerooni, Yagiz Kaymak, R. Rojas-Cessa
A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user’s Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user’s network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.
{"title":"Tracking User Application Activity by using Machine Learning Techniques on Network Traffic","authors":"Sina Fathi Kazerooni, Yagiz Kaymak, R. Rojas-Cessa","doi":"10.1109/ICAIIC.2019.8669040","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669040","url":null,"abstract":"A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user’s Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user’s network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121735499","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669006
Ju-won Pak, Min-koo Kim
People living near the airport are experiencing many inconveniences due to frequent aircraft noise. For these people, the government uses the aircraft noise evaluation unit (e.g., Lden) to calculate the degree of annoyance and then compensate for aircraft noise. Aircraft noise evaluation unit should be calculated only by aircraft noise, but the reality is not so. This is because the aircraft noise monitor measures not only aircraft noise but also loud background noise. Therefore, in this paper, we propose a method of recognizing only the aircraft noise among the stored noise from the noise monitor to calculate accurate aircraft noise evaluation unit. The proposal uses convolutional neural network, one of the deep learning techniques. Our proposal purposes less than 1% false-positive (FP) or false-negative (FN) rate.
{"title":"Convolutional Neural Network Approach for Aircraft Noise Detection","authors":"Ju-won Pak, Min-koo Kim","doi":"10.1109/ICAIIC.2019.8669006","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669006","url":null,"abstract":"People living near the airport are experiencing many inconveniences due to frequent aircraft noise. For these people, the government uses the aircraft noise evaluation unit (e.g., Lden) to calculate the degree of annoyance and then compensate for aircraft noise. Aircraft noise evaluation unit should be calculated only by aircraft noise, but the reality is not so. This is because the aircraft noise monitor measures not only aircraft noise but also loud background noise. Therefore, in this paper, we propose a method of recognizing only the aircraft noise among the stored noise from the noise monitor to calculate accurate aircraft noise evaluation unit. The proposal uses convolutional neural network, one of the deep learning techniques. Our proposal purposes less than 1% false-positive (FP) or false-negative (FN) rate.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122441231","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669090
Zhongyang Shen
Massive logs are generated in telecommunication networks. It is a challenge to analyze abnormal information in the big data logs quickly and effectively. We present a new outlier detection algorithm based on Unsupervised Learning Algorithm by geometric angle scanning judgment. First, calculate geometric center of measured data and several observation points around the measured data. Outliers can be segregated from normal area by density contrast method by angle based calculation. Results show that outlier geometric angle detection (OGAD) algorithm can separate anomaly from measured data effectively, and improve the accuracy of anomaly identification.
{"title":"Outlier Geometric Angle Detection Algorithm","authors":"Zhongyang Shen","doi":"10.1109/ICAIIC.2019.8669090","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669090","url":null,"abstract":"Massive logs are generated in telecommunication networks. It is a challenge to analyze abnormal information in the big data logs quickly and effectively. We present a new outlier detection algorithm based on Unsupervised Learning Algorithm by geometric angle scanning judgment. First, calculate geometric center of measured data and several observation points around the measured data. Outliers can be segregated from normal area by density contrast method by angle based calculation. Results show that outlier geometric angle detection (OGAD) algorithm can separate anomaly from measured data effectively, and improve the accuracy of anomaly identification.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115220145","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 : 2019-02-01DOI: 10.1109/icaiic.2019.8669013
{"title":"ICAIIC 2019 Committee","authors":"","doi":"10.1109/icaiic.2019.8669013","DOIUrl":"https://doi.org/10.1109/icaiic.2019.8669013","url":null,"abstract":"","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124400800","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669067
Qalab E. Abbas, Sung-Bong Jang
Blockchain is one of the technologies which appeared in the last decade and brought a lot of promise with it. Much researches are being conducted actively to explore the full capabilities of Blockchain. Some believe that Blockchain is key for a decentralized society. Especially, we are considering blockchain as the security scheme to protect the privacies of the objects to be augmented in intelligent mobile augmented reality (IMAR) project. To do that, this paper describe an overview of an blockchain.
{"title":"A Survey of Blockchain and Its Applications","authors":"Qalab E. Abbas, Sung-Bong Jang","doi":"10.1109/ICAIIC.2019.8669067","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669067","url":null,"abstract":"Blockchain is one of the technologies which appeared in the last decade and brought a lot of promise with it. Much researches are being conducted actively to explore the full capabilities of Blockchain. Some believe that Blockchain is key for a decentralized society. Especially, we are considering blockchain as the security scheme to protect the privacies of the objects to be augmented in intelligent mobile augmented reality (IMAR) project. To do that, this paper describe an overview of an blockchain.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663650","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669086
Jong-jin Jung, Kyung Won Kim, Jongbin Park
This paper introduces a framework of big data analysis about IoT home devices which are delivered to the consumer through several distribution channels, are used by a home user in the smart home, and are repaired in A/S center (repair shop). We collect big data and make an analysis at three major stages that are distribution stage, customer-usage stage, and A/S stage. The ultimate purpose of the presented framework is to help the small/medium companies to make an elastic strategy for the new product. Therefore they can make a more effective decision at three major stages. For example, they can reduce redundancy about a distribution channel, they can adjust a quantity of warehousing, release, stock. They can make a decision on what to upgrade the new next device, how to increase durability, and so on. For these purposes, this framework consists of three subsystems. 1) A data crawler that collects and stores big data about IoT-home devices at three major stages, 2) A big data analyzer about IoT-home device with an appreciate analytic model, 3) A visualization of insights, which help a user to understand the analytic output.
{"title":"Framework of Big data Analysis about IoT-Home-device for supporting a decision making an effective strategy about new product design","authors":"Jong-jin Jung, Kyung Won Kim, Jongbin Park","doi":"10.1109/ICAIIC.2019.8669086","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669086","url":null,"abstract":"This paper introduces a framework of big data analysis about IoT home devices which are delivered to the consumer through several distribution channels, are used by a home user in the smart home, and are repaired in A/S center (repair shop). We collect big data and make an analysis at three major stages that are distribution stage, customer-usage stage, and A/S stage. The ultimate purpose of the presented framework is to help the small/medium companies to make an elastic strategy for the new product. Therefore they can make a more effective decision at three major stages. For example, they can reduce redundancy about a distribution channel, they can adjust a quantity of warehousing, release, stock. They can make a decision on what to upgrade the new next device, how to increase durability, and so on. For these purposes, this framework consists of three subsystems. 1) A data crawler that collects and stores big data about IoT-home devices at three major stages, 2) A big data analyzer about IoT-home device with an appreciate analytic model, 3) A visualization of insights, which help a user to understand the analytic output.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848367","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668971
Songpu Ai, Antorweep Chakravorty, Chunming Rong
The popularization of electric vehicle, the commercialization of micro-generation, and the advance of local storage lead great challenges to the local power grid on household and neighbourhood level. A potential solution is to construct a home/neighbourhood energy management system (HEMS) to coordinate all available electrical equipment together using AI. As a portion of HEMS, peak demand prediction is critically important on triggering load scheduling among the household power environment to achieve better electricity usage curve. Long short-term memory (LSTM) network as an eminent type of machine learning method is generally considered to be capable on forecasting based on time series data including temporal dynamic behaviours with unknown lags. Various LSTM networks are adopted in existing researches to provide predictions in energy informatics field. However, the presented network structures are commonly selected through empirical or enumerative approaches. The utilized networks are generally carefully tuned as case by case studies. In this article, an evolutionary ensemble LSTM (EELSTM) method is proposed to pool LSTM networks with the same structure or with similar structures to obtain a more reliable prediction automatically. Experimental study demonstrates that networks with suitable structures and initialization are selected out through the learning process. A better performed peak demand prediction is achieved comparing with single LSTM unit network. In addition, the evolutionary parameters have variant impacts on the model performance.
{"title":"Evolutionary Ensemble LSTM based Household Peak Demand Prediction","authors":"Songpu Ai, Antorweep Chakravorty, Chunming Rong","doi":"10.1109/ICAIIC.2019.8668971","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668971","url":null,"abstract":"The popularization of electric vehicle, the commercialization of micro-generation, and the advance of local storage lead great challenges to the local power grid on household and neighbourhood level. A potential solution is to construct a home/neighbourhood energy management system (HEMS) to coordinate all available electrical equipment together using AI. As a portion of HEMS, peak demand prediction is critically important on triggering load scheduling among the household power environment to achieve better electricity usage curve. Long short-term memory (LSTM) network as an eminent type of machine learning method is generally considered to be capable on forecasting based on time series data including temporal dynamic behaviours with unknown lags. Various LSTM networks are adopted in existing researches to provide predictions in energy informatics field. However, the presented network structures are commonly selected through empirical or enumerative approaches. The utilized networks are generally carefully tuned as case by case studies. In this article, an evolutionary ensemble LSTM (EELSTM) method is proposed to pool LSTM networks with the same structure or with similar structures to obtain a more reliable prediction automatically. Experimental study demonstrates that networks with suitable structures and initialization are selected out through the learning process. A better performed peak demand prediction is achieved comparing with single LSTM unit network. In addition, the evolutionary parameters have variant impacts on the model performance.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261701","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669014
Tosin A. Adesuyi, Byeong-Man Kim
Datasets are sources of information mining where knowledge can be derived. The versatility of these dataset determines the quality of knowledge gained. However, several of these data contains personal sensitive information that can lead to infringement of privacy. Existing research tends to deliver DNN models that can preserve privacy of personal information but the accuracy of these models are rather much lower as compared to their non-privacy preserving counterparts. This is due to the degree of noise and the points where noise was added to perturb the model data. Consequently, this has led to minimal adoption of privacy preserving DNN models in the industrial world. In this paper, we present a layer-wise perturbation approach and differential privacy technique to determine points of perturbation and preserve privacy. Our approach was able to narrow down the accuracy gap between privacy-preserving and non-privacy preserving DNN model.
{"title":"A layer-wise Perturbation based Privacy Preserving Deep Neural Networks","authors":"Tosin A. Adesuyi, Byeong-Man Kim","doi":"10.1109/ICAIIC.2019.8669014","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669014","url":null,"abstract":"Datasets are sources of information mining where knowledge can be derived. The versatility of these dataset determines the quality of knowledge gained. However, several of these data contains personal sensitive information that can lead to infringement of privacy. Existing research tends to deliver DNN models that can preserve privacy of personal information but the accuracy of these models are rather much lower as compared to their non-privacy preserving counterparts. This is due to the degree of noise and the points where noise was added to perturb the model data. Consequently, this has led to minimal adoption of privacy preserving DNN models in the industrial world. In this paper, we present a layer-wise perturbation approach and differential privacy technique to determine points of perturbation and preserve privacy. Our approach was able to narrow down the accuracy gap between privacy-preserving and non-privacy preserving DNN model.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533542","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669060
Dengjun Zhu, Jinlong Yan, Haiwei Yuan, Yongjun Ma, Xufeng Hu
The safety of high speed sensor data transmission is an important part of smart grid. Due to the development of the diversity and scale, there has been an ever-increasing need of data transmission algorithms in both academia and industry. Past research shows that with the increasing types and number of sensors deployed, there are problems such as low transmission efficiency and excessive energy consumption. When the collected data are transferred back to the background server, sensor nodes face the problem of high storage pressure. Distributed technology can alleviate the transmission and storage pressure of signal nodes. Therefore, this paper proposes an optimization algorithm which can reduce the amount of data, energy consumption and improve transmission rate. In addition, for further improving the accuracy of restored data, principal component analysis (PCA) is utilized to generate adaptive sparse matrix for different types of sensors. Through selecting different sparse matrices, our experiments show that the technology can significantly reduce the transmission of data and ensure the accuracy of data reconstruction.
{"title":"DCS-PCA based Data Transmission in Smart Grid","authors":"Dengjun Zhu, Jinlong Yan, Haiwei Yuan, Yongjun Ma, Xufeng Hu","doi":"10.1109/ICAIIC.2019.8669060","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669060","url":null,"abstract":"The safety of high speed sensor data transmission is an important part of smart grid. Due to the development of the diversity and scale, there has been an ever-increasing need of data transmission algorithms in both academia and industry. Past research shows that with the increasing types and number of sensors deployed, there are problems such as low transmission efficiency and excessive energy consumption. When the collected data are transferred back to the background server, sensor nodes face the problem of high storage pressure. Distributed technology can alleviate the transmission and storage pressure of signal nodes. Therefore, this paper proposes an optimization algorithm which can reduce the amount of data, energy consumption and improve transmission rate. In addition, for further improving the accuracy of restored data, principal component analysis (PCA) is utilized to generate adaptive sparse matrix for different types of sensors. Through selecting different sparse matrices, our experiments show that the technology can significantly reduce the transmission of data and ensure the accuracy of data reconstruction.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969920","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669044
T. Wada, Takao Toma, Mursal Dawodi, J. Baktash
This paper proposes a channel estimation method for Orthogonal Frequency Division Multiple Access (OFDM) communication system by utilizing a Neural Network (NN) based a Machine Learning (ML). Especially, Autoencoder is utilized to estimate Channel Transfer Function (CTF) and to reduce a noise on the estimate. Japanese Digital TV broadcast system is assumed as target system. Then 8k FFT/IFFT is used and number of sub-carriers are 5617 such as mode3 in Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) spec. 5617 complex CTF points must be estimated by limited number of scattered pilot sub-carriers. Assumed channel condition is 2 wave multipath channel with Additive White Gaussian Noise (AWGN). The multipath parameters are randomly generated. To train the autoencoder, 5000 CTFs are generated and pre-training was performed. System performance was evaluated by measuring Bit Error Rate (BER). The system with conventional frequency-domain interpolator and the system with autoencoder based were compared. According to BER simulation results, the autoencoder based system has shown lower BER than the conventional. At BER=10$^{-5}$, autoencoder system shows roughly 2dB gain than conventional system.
{"title":"A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system","authors":"T. Wada, Takao Toma, Mursal Dawodi, J. Baktash","doi":"10.1109/ICAIIC.2019.8669044","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669044","url":null,"abstract":"This paper proposes a channel estimation method for Orthogonal Frequency Division Multiple Access (OFDM) communication system by utilizing a Neural Network (NN) based a Machine Learning (ML). Especially, Autoencoder is utilized to estimate Channel Transfer Function (CTF) and to reduce a noise on the estimate. Japanese Digital TV broadcast system is assumed as target system. Then 8k FFT/IFFT is used and number of sub-carriers are 5617 such as mode3 in Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) spec. 5617 complex CTF points must be estimated by limited number of scattered pilot sub-carriers. Assumed channel condition is 2 wave multipath channel with Additive White Gaussian Noise (AWGN). The multipath parameters are randomly generated. To train the autoencoder, 5000 CTFs are generated and pre-training was performed. System performance was evaluated by measuring Bit Error Rate (BER). The system with conventional frequency-domain interpolator and the system with autoencoder based were compared. According to BER simulation results, the autoencoder based system has shown lower BER than the conventional. At BER=10$^{-5}$, autoencoder system shows roughly 2dB gain than conventional system.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123964493","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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