Pub Date : 2018-11-01DOI: 10.1109/CSPIS.2018.8642725
M. Alkhodari, A. Rashed, Meera Alex, Nai-Shyong Yeh
In this paper, further investigations into a simpler automated use of Independent Component Analysis (ICA) in the process of Fetal ECG (FECG) extraction are performed. Extracting FECG signals through abdominal electrodes helps clinicians in diagnosing the overall health of the fetus non-invasively. In the ICA technique, FECG signals are separated from Abdominal ECG (AECG) mixtures containing maternal and noise signals. 300,000 Data samples of three AECG recordings are obtained from PhysioNet database at 1 kHz sampling frequency. Data are pre-processed through MATLAB software by centering, whitening, and filtering techniques. Then, a simpler Fast ICA algorithm is developed and used to smoothly distinguish between AECG components through automatic signal characteristics matching. Moreover, further analysis of the extracted FECG signal is performed to determine the fetus heart rate. Results successfully show efficient automatic separation between the FECG, Maternal ECG (MECG), and noise from the AECG recordings. In addition, the developed characteristics matching algorithm automatically identified the fetus signal and smoothed it to be ready for further fetal health observations. The integration of AECG signal characteristics as a prior information into the ICA algorithm promises to assist clinicians in decision making when diagnosing fetal health conditions non-invasively.
{"title":"Fetal ECG Extraction Using Independent Components and Characteristics Matching","authors":"M. Alkhodari, A. Rashed, Meera Alex, Nai-Shyong Yeh","doi":"10.1109/CSPIS.2018.8642725","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642725","url":null,"abstract":"In this paper, further investigations into a simpler automated use of Independent Component Analysis (ICA) in the process of Fetal ECG (FECG) extraction are performed. Extracting FECG signals through abdominal electrodes helps clinicians in diagnosing the overall health of the fetus non-invasively. In the ICA technique, FECG signals are separated from Abdominal ECG (AECG) mixtures containing maternal and noise signals. 300,000 Data samples of three AECG recordings are obtained from PhysioNet database at 1 kHz sampling frequency. Data are pre-processed through MATLAB software by centering, whitening, and filtering techniques. Then, a simpler Fast ICA algorithm is developed and used to smoothly distinguish between AECG components through automatic signal characteristics matching. Moreover, further analysis of the extracted FECG signal is performed to determine the fetus heart rate. Results successfully show efficient automatic separation between the FECG, Maternal ECG (MECG), and noise from the AECG recordings. In addition, the developed characteristics matching algorithm automatically identified the fetus signal and smoothed it to be ready for further fetal health observations. The integration of AECG signal characteristics as a prior information into the ICA algorithm promises to assist clinicians in decision making when diagnosing fetal health conditions non-invasively.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121168480","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642712
Daiki Takatsuki, S. Saiki, Masahide Nakamura
In this paper, we present a novel system, called Formroid, which facilitates answering online questionnaire surveys with the virtual agent technology. For a questionnaire given by an investigator, Formroid commands the virtual agent to ask each question to the respondent. Through conversation with the virtual agent, a respondent can answer the questionnaire. Thus, Formroid transforms the conventional form input into a face-to-face interview conducted by the virtual agent. In this paper, we especially address the design issues of Formroid, and the implementation of prototype system. We also introduce an experiment, where Formroid is extensively used for questionnaire-based assessment of quality of life.
{"title":"Using Virtual Agent for Facilitating Online Questionnaire Surveys","authors":"Daiki Takatsuki, S. Saiki, Masahide Nakamura","doi":"10.1109/CSPIS.2018.8642712","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642712","url":null,"abstract":"In this paper, we present a novel system, called Formroid, which facilitates answering online questionnaire surveys with the virtual agent technology. For a questionnaire given by an investigator, Formroid commands the virtual agent to ask each question to the respondent. Through conversation with the virtual agent, a respondent can answer the questionnaire. Thus, Formroid transforms the conventional form input into a face-to-face interview conducted by the virtual agent. In this paper, we especially address the design issues of Formroid, and the implementation of prototype system. We also introduce an experiment, where Formroid is extensively used for questionnaire-based assessment of quality of life.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816300","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642760
Ivana Kovacevic, A. Erdeljan, Miroslav Zarić, Nikola Dalčeković, I. Lendák
Consumption of electricity has grown, and that tendency will continue according to Energy Information Administration (EIA). Most modern distribution networks, evolving into Smart Grids, are managed through sophisticated software, such as advanced distribution management systems (ADMS). Their operations are based on gathering, analysis and transformation of data coming from the different devices in distribution network. Data volume in Smart Grids is increasing rapidly. Therefore, handling that growing amount of data may pose significant challenges for relational databases in the future, as they may struggle with demand for execution of complex queries. In some cases, like in modeling power system network, the data model is naturally represented by a graph, hence graph databases could provide viable, more efficient alternative. The paper is proposing an approach to include sensitive data access permissions in a graph oriented database – enabling us to decide who can access the sensitive data and who cannot. We have performed analysis on security controls to limit the access to personal data using a realistic data model derived from an existing network model of power distribution utility based in Europe, but described approach is also applicable to other sensitive data. We concluded that the proposed approach would provide ability for implementing access management security controls, while each approach would differently affect the levels of overall system performances.
{"title":"Modelling access control for CIM based graph model in Smart Grids","authors":"Ivana Kovacevic, A. Erdeljan, Miroslav Zarić, Nikola Dalčeković, I. Lendák","doi":"10.1109/CSPIS.2018.8642760","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642760","url":null,"abstract":"Consumption of electricity has grown, and that tendency will continue according to Energy Information Administration (EIA). Most modern distribution networks, evolving into Smart Grids, are managed through sophisticated software, such as advanced distribution management systems (ADMS). Their operations are based on gathering, analysis and transformation of data coming from the different devices in distribution network. Data volume in Smart Grids is increasing rapidly. Therefore, handling that growing amount of data may pose significant challenges for relational databases in the future, as they may struggle with demand for execution of complex queries. In some cases, like in modeling power system network, the data model is naturally represented by a graph, hence graph databases could provide viable, more efficient alternative. The paper is proposing an approach to include sensitive data access permissions in a graph oriented database – enabling us to decide who can access the sensitive data and who cannot. We have performed analysis on security controls to limit the access to personal data using a realistic data model derived from an existing network model of power distribution utility based in Europe, but described approach is also applicable to other sensitive data. We concluded that the proposed approach would provide ability for implementing access management security controls, while each approach would differently affect the levels of overall system performances.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132358027","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642728
Prajowal Manandhar, P. Marpu, Z. Aung
In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.
{"title":"Deep Learning Approach To Update Road Network using VGI Data","authors":"Prajowal Manandhar, P. Marpu, Z. Aung","doi":"10.1109/CSPIS.2018.8642728","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642728","url":null,"abstract":"In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121442761","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642764
Aamna Al Teneiji, Muhammed Saeed Khan, N. Ali, Ahmed A. Al-Tunaiji
Frequency modulated continuous wave radar is used to measure the target’s distance and velocity. This paper presents a comparison of different signal processing algorithms that improve FMCW radar detection. The algorithms are studied and validated by simulation. The radar is simulated to detect stationary targets at different considerable distances in order to prove the validity of the algorithms. Signal processing algorithms used in this paper are based on Fast Fourier Transform, windowing, zero-padding, Chirp-Z transform and Jacobsen’s frequency estimator. The paper shows the results found using each algorithm and offers a comparison among them.
{"title":"Improving the Detection Accuracy of Frequency Modulated Continuous Wave Radar","authors":"Aamna Al Teneiji, Muhammed Saeed Khan, N. Ali, Ahmed A. Al-Tunaiji","doi":"10.1109/CSPIS.2018.8642764","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642764","url":null,"abstract":"Frequency modulated continuous wave radar is used to measure the target’s distance and velocity. This paper presents a comparison of different signal processing algorithms that improve FMCW radar detection. The algorithms are studied and validated by simulation. The radar is simulated to detect stationary targets at different considerable distances in order to prove the validity of the algorithms. Signal processing algorithms used in this paper are based on Fast Fourier Transform, windowing, zero-padding, Chirp-Z transform and Jacobsen’s frequency estimator. The paper shows the results found using each algorithm and offers a comparison among them.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123161918","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642792
Hussein Walugembe, Chris Phillips, Jesús Requena-Carrión, T. Timotijevic
This paper concerns a rehabilitation framework that makes use of a low cost "off-the-shelf" device. The device is a visual markerless sensor system called the Leap Motion controller (LM). However, before deploying the LM, we investigate its accuracy and limitations in measuring finger joint angles. During a rehabilitation procedure, patients will be flexing and extending their fingers and accurate feedback is a prerequisite for them to benefit effectively from the exercises. During finger joint angle error analysis, we conducted a series of experiments to assess the accuracy of the LM in terms of parameters like elevation, lateral (side-to-side) positioning, forward-backward positioning, and rotation of the hand relative to the LM. We used an "artist’s hand" placed above the LM. The artist’s hand is more accurate than a human hand in performing static hand gestures as it can maintain a fixed posture as long as is necessary. According to the results of the error analysis, we apply Principal Component Analysis (PCA) to the LM raw data to see whether the algorithm can compensate for these errors. The experimental results show that the PCA algorithm is feasible, effective and can be applied such that fairly accurate measurements can be obtained and therefore suitable feedback can be provided to the patient using the LM for hand rehabilitation purposes.
{"title":"Characterizing and Compensating for Errors in a Leap Motion using PCA","authors":"Hussein Walugembe, Chris Phillips, Jesús Requena-Carrión, T. Timotijevic","doi":"10.1109/CSPIS.2018.8642792","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642792","url":null,"abstract":"This paper concerns a rehabilitation framework that makes use of a low cost \"off-the-shelf\" device. The device is a visual markerless sensor system called the Leap Motion controller (LM). However, before deploying the LM, we investigate its accuracy and limitations in measuring finger joint angles. During a rehabilitation procedure, patients will be flexing and extending their fingers and accurate feedback is a prerequisite for them to benefit effectively from the exercises. During finger joint angle error analysis, we conducted a series of experiments to assess the accuracy of the LM in terms of parameters like elevation, lateral (side-to-side) positioning, forward-backward positioning, and rotation of the hand relative to the LM. We used an \"artist’s hand\" placed above the LM. The artist’s hand is more accurate than a human hand in performing static hand gestures as it can maintain a fixed posture as long as is necessary. According to the results of the error analysis, we apply Principal Component Analysis (PCA) to the LM raw data to see whether the algorithm can compensate for these errors. The experimental results show that the PCA algorithm is feasible, effective and can be applied such that fairly accurate measurements can be obtained and therefore suitable feedback can be provided to the patient using the LM for hand rehabilitation purposes.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133377858","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642720
Martin Dlask, J. Kukal, P. Sovka
A number of biomedical data can be investigated using methods of fractal geometry. A measurement of their nonlinear character and chaoticity can be used for subsequent data classification or irregularity detection. In this paper, we introduce the method of the fractional Brownian bridge for the Hurst exponent estimation from a signal and apply it to the electroencephalogram (EEG) data. The technique is used to detect the early stages of Alzheimer’s disease, exhibiting significant performance when compared with control patients. The measures of variability where the most significant changes occur together with the recommended EEG channels are presented in the paper.
{"title":"Fractional Brownian Bridge Model for Alzheimer Disease Detection from EEG Signal","authors":"Martin Dlask, J. Kukal, P. Sovka","doi":"10.1109/CSPIS.2018.8642720","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642720","url":null,"abstract":"A number of biomedical data can be investigated using methods of fractal geometry. A measurement of their nonlinear character and chaoticity can be used for subsequent data classification or irregularity detection. In this paper, we introduce the method of the fractional Brownian bridge for the Hurst exponent estimation from a signal and apply it to the electroencephalogram (EEG) data. The technique is used to detect the early stages of Alzheimer’s disease, exhibiting significant performance when compared with control patients. The measures of variability where the most significant changes occur together with the recommended EEG channels are presented in the paper.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912898","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642772
Sinan Chen, S. Saiki, Masahide Nakamura
Cognitive API is API of emerging AI-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement smart and affordable context sensing services in a smart home. However, since the existing APIs are trained for general-purpose image recognition, they may not be of practical use in specific configuration of smart homes. In this paper, we therefore propose a method that evaluates the feasibility of cognitive APIs for the home context sensing. In the proposed method, we exploit document similarity measures to see how well tags extracted from given images characterize the original contexts. Using the proposed method, we evaluate practical APIs of Microsoft Azure, IBM Watson, and Google Cloud for recognizing 11 different contexts in our smart home.
认知API是一种新兴的基于人工智能的云服务API，它可以从包括图像和音频在内的非数字多媒体数据中提取各种上下文信息。我们的兴趣是应用基于图像的认知api，在智能家居中实现智能和负担得起的上下文感知服务。然而，由于现有的api是针对通用图像识别进行训练的，因此它们可能无法在智能家居的特定配置中实际使用。因此，在本文中，我们提出了一种评估家庭环境感知认知api可行性的方法。在提出的方法中，我们利用文档相似度度量来查看从给定图像中提取的标签如何很好地表征原始上下文。使用提出的方法，我们评估了Microsoft Azure, IBM Watson和谷歌Cloud的实用api，以识别我们智能家居中的11种不同环境。
{"title":"Evaluating Feasibility of Image-Based Cognitive APIs for Home Context Sensing","authors":"Sinan Chen, S. Saiki, Masahide Nakamura","doi":"10.1109/CSPIS.2018.8642772","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642772","url":null,"abstract":"Cognitive API is API of emerging AI-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement smart and affordable context sensing services in a smart home. However, since the existing APIs are trained for general-purpose image recognition, they may not be of practical use in specific configuration of smart homes. In this paper, we therefore propose a method that evaluates the feasibility of cognitive APIs for the home context sensing. In the proposed method, we exploit document similarity measures to see how well tags extracted from given images characterize the original contexts. Using the proposed method, we evaluate practical APIs of Microsoft Azure, IBM Watson, and Google Cloud for recognizing 11 different contexts in our smart home.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"37 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130485920","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 : 2018-11-01DOI: 10.1109/cspis.2018.8642719
{"title":"[Title page]","authors":"","doi":"10.1109/cspis.2018.8642719","DOIUrl":"https://doi.org/10.1109/cspis.2018.8642719","url":null,"abstract":"","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131949882","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 : 2018-11-01DOI: 10.1109/CSPIS.2018.8642743
Mina Talal, A. Panthakkan, Husameldin Mukhtar, W. Mansoor, S. Almansoori, Hussain Al-Ahmad
This paper proposes a semantic segmentation technique to automatically detect water-bodies from DubaiSat-2 images. The proposed method uses a deep convolutional neural network transfer-learning model. Several evaluation metrics such as accuracy, precision, and Jaccard coefficient are used to test our proposed algorithm. The overall accuracy for the prediction of water-bodies in DubaiSat-2 image dataset is 99.86%.
{"title":"Detection of Water-Bodies Using Semantic Segmentation","authors":"Mina Talal, A. Panthakkan, Husameldin Mukhtar, W. Mansoor, S. Almansoori, Hussain Al-Ahmad","doi":"10.1109/CSPIS.2018.8642743","DOIUrl":"https://doi.org/10.1109/CSPIS.2018.8642743","url":null,"abstract":"This paper proposes a semantic segmentation technique to automatically detect water-bodies from DubaiSat-2 images. The proposed method uses a deep convolutional neural network transfer-learning model. Several evaluation metrics such as accuracy, precision, and Jaccard coefficient are used to test our proposed algorithm. The overall accuracy for the prediction of water-bodies in DubaiSat-2 image dataset is 99.86%.","PeriodicalId":251356,"journal":{"name":"2018 International Conference on Signal Processing and Information Security (ICSPIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475322","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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