Pub Date : 2018-11-01DOI: 10.1109/IEMCON.2018.8614797
Detlef Streitferdt, A. Zimmermann, Jörg Schaffner, Michael Kallenbach
Safety certification became an increasingly important issue as well as a feature of industrial software development. The certification process for safe software causes enormous efforts and has to be repeatedly executed for any changes in the systems. Modular and component-based software architectures are very common, but cannot use their advantages in the certification process. This paper presents the results of an industrial software development and certification project in the railway domain and enhances a previous work to change components without a re-certification by additional requirements, which have to be met, to allow for changes in the basic framework of the system as well, again, without re-certification.
{"title":"Complete Component-Wise Software Certification for Safety-Critical Embedded Devices","authors":"Detlef Streitferdt, A. Zimmermann, Jörg Schaffner, Michael Kallenbach","doi":"10.1109/IEMCON.2018.8614797","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614797","url":null,"abstract":"Safety certification became an increasingly important issue as well as a feature of industrial software development. The certification process for safe software causes enormous efforts and has to be repeatedly executed for any changes in the systems. Modular and component-based software architectures are very common, but cannot use their advantages in the certification process. This paper presents the results of an industrial software development and certification project in the railway domain and enhances a previous work to change components without a re-certification by additional requirements, which have to be met, to allow for changes in the basic framework of the system as well, again, without re-certification.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"1 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":"128864116","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/IEMCON.2018.8614825
Tauseef Ahmad, Amr Almaddah
In image processing, edge detection concerns with the localization of discontinuity of the gray scale images, accurately detecting continuous edges is difficult in noisy images. Usually for accurate edge detection requires smoothing and differentiation, to localize edge pixels in intensity images. Smoothing images with median filter instead of Gaussian filter has more edge preserving tendency. In this proposed method, filtered images with non- linear filter, which is convolved with two new developed 3x3 operators for detecting gradient magnitude of images. The resulted thick binary edges were filter with two new developed structure matrices for enhancement in binary edges. The new algorithm is examined and compared with the traditional edge detectors. The comparison is based on two type of distributed noises Gaussian and salt and pepper. The results comparison suggests that the new algorithm detect edges more accurate thinner and smoother than the edges detected by traditional edge detector.
{"title":"Detection of continuous and thin edges of noisy images by new kernel approach","authors":"Tauseef Ahmad, Amr Almaddah","doi":"10.1109/IEMCON.2018.8614825","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614825","url":null,"abstract":"In image processing, edge detection concerns with the localization of discontinuity of the gray scale images, accurately detecting continuous edges is difficult in noisy images. Usually for accurate edge detection requires smoothing and differentiation, to localize edge pixels in intensity images. Smoothing images with median filter instead of Gaussian filter has more edge preserving tendency. In this proposed method, filtered images with non- linear filter, which is convolved with two new developed 3x3 operators for detecting gradient magnitude of images. The resulted thick binary edges were filter with two new developed structure matrices for enhancement in binary edges. The new algorithm is examined and compared with the traditional edge detectors. The comparison is based on two type of distributed noises Gaussian and salt and pepper. The results comparison suggests that the new algorithm detect edges more accurate thinner and smoother than the edges detected by traditional edge detector.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"96 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":"125298426","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/IEMCON.2018.8614742
O. Rehman, M. Ould-Khaoua
A radical transformation is foreseen in the automotive industry for which Vehicular Ad hoc NETworks (VANETs) is a major attraction. This is due to its potential to support a wide range of applications that can lead towards new driving and travelling experiences. Multi-hop broadcasting approach is expected to be the primary mode of communication among vehicles. Suitable choice of the next-hop relay nodes is an essential part for the design of multi-hop messaging schemes in VANETs. This greatly impacts the reception of the broadcasted messages, particularly when evaluated on high node density networks. This research proposes a link quality driven hybrid scheme that attempts to systematically combine multiple link quality based message dissemination schemes in a particular order. The proposed scheme attempts to improve message reception performance over stringent communication conditions. Our performance evaluation indicates that the suggested scheme improves messages reception over high node density networks compared to the existing conventional versions.
{"title":"Link Quality Driven Hybrid Scheme for Multi-hop Message Broadcast in VANETs","authors":"O. Rehman, M. Ould-Khaoua","doi":"10.1109/IEMCON.2018.8614742","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614742","url":null,"abstract":"A radical transformation is foreseen in the automotive industry for which Vehicular Ad hoc NETworks (VANETs) is a major attraction. This is due to its potential to support a wide range of applications that can lead towards new driving and travelling experiences. Multi-hop broadcasting approach is expected to be the primary mode of communication among vehicles. Suitable choice of the next-hop relay nodes is an essential part for the design of multi-hop messaging schemes in VANETs. This greatly impacts the reception of the broadcasted messages, particularly when evaluated on high node density networks. This research proposes a link quality driven hybrid scheme that attempts to systematically combine multiple link quality based message dissemination schemes in a particular order. The proposed scheme attempts to improve message reception performance over stringent communication conditions. Our performance evaluation indicates that the suggested scheme improves messages reception over high node density networks compared to the existing conventional versions.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"18 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":"125564937","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/IEMCON.2018.8614772
Qasim Alajmi, Ruzaini bin Abdullah Arshah, Adzhar Kamaludin, Mohammed A. Al-Sharafi
Many Higher Education Institutions are shifting to Cloud-Based E-Iearning (CBEL) due to its benefits. These benefits include reduced costs of accessing IT services, pooling of resources, scalability, and mobility as well as consumer satisfaction among others. However, some institutions especially in developing countries in general and Gulf Cooperation Council (GCC) in particular still reluctant to adopt CBEL due to many factors. There is necessity to find out the factors that drive the adoption of CBEL in higher education institutions. This study was carried out to find out the factors that determine whether these institutions in GCC would adopt the CBEL or not. The study involved a group of respondents from the GCC who provided with an On-line survey sent to identify the factors they thought played a role in determining whether institutions would adopt the CBEL or not. The base for this study was TOE and DOI adoption theories. The following factors were recommended by researchers; Technological Factors such as Relative advantage, Complexity, Compatibility which derived from DOI theory and Organizational factors such as Fit, Decision maker, Cost reduction, and IT readiness which derived from TOE framework, and Information culture behavioral such as Information Integrity, Information Formality, Information Control and Information Pro-activeness which derived from Information culture factors.
{"title":"Current State of Cloud-Based E-learning Adoption: Results from Gulf Cooperation Council's Higher Education Institutions","authors":"Qasim Alajmi, Ruzaini bin Abdullah Arshah, Adzhar Kamaludin, Mohammed A. Al-Sharafi","doi":"10.1109/IEMCON.2018.8614772","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614772","url":null,"abstract":"Many Higher Education Institutions are shifting to Cloud-Based E-Iearning (CBEL) due to its benefits. These benefits include reduced costs of accessing IT services, pooling of resources, scalability, and mobility as well as consumer satisfaction among others. However, some institutions especially in developing countries in general and Gulf Cooperation Council (GCC) in particular still reluctant to adopt CBEL due to many factors. There is necessity to find out the factors that drive the adoption of CBEL in higher education institutions. This study was carried out to find out the factors that determine whether these institutions in GCC would adopt the CBEL or not. The study involved a group of respondents from the GCC who provided with an On-line survey sent to identify the factors they thought played a role in determining whether institutions would adopt the CBEL or not. The base for this study was TOE and DOI adoption theories. The following factors were recommended by researchers; Technological Factors such as Relative advantage, Complexity, Compatibility which derived from DOI theory and Organizational factors such as Fit, Decision maker, Cost reduction, and IT readiness which derived from TOE framework, and Information culture behavioral such as Information Integrity, Information Formality, Information Control and Information Pro-activeness which derived from Information culture factors.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"88 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":"126200698","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}
Early and reliable detection of neurological disorders is important for effective treatment of patients. In spite of reasonable amount of research done in the field of early detection of epileptic seizure, still an effective model for predicting the same is absent. Motivated by this, in the current study the class imbalance problem associated with classification of patients into healthy and epilepsy affected ones is addressed. Two well established algorithms namely Synthetic Minority Oversampling Technique (SMOTE) and Selective Pre-Processing of Imbalanced Data Algorithm (SPIDER) have been used in order to combat the imbalanced classes. Afterwards, three different classifiers namely KNN, SVM and MLP-FFN have been used for the classification task. Experimental results revealed that addressing imbalances classes improved the classification accuracy to a greater extent.
{"title":"Improved Epilepsy Detection method by addressing Class Imbalance Problem","authors":"Siddhartha Haldar, R. Mukherjee, Pushpak Chakraborty, Shayan Banerjee, Shreyaasha Chaudhury, Sankhadeen Chatterjee","doi":"10.1109/IEMCON.2018.8614826","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614826","url":null,"abstract":"Early and reliable detection of neurological disorders is important for effective treatment of patients. In spite of reasonable amount of research done in the field of early detection of epileptic seizure, still an effective model for predicting the same is absent. Motivated by this, in the current study the class imbalance problem associated with classification of patients into healthy and epilepsy affected ones is addressed. Two well established algorithms namely Synthetic Minority Oversampling Technique (SMOTE) and Selective Pre-Processing of Imbalanced Data Algorithm (SPIDER) have been used in order to combat the imbalanced classes. Afterwards, three different classifiers namely KNN, SVM and MLP-FFN have been used for the classification task. Experimental results revealed that addressing imbalances classes improved the classification accuracy to a greater extent.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","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":"124901522","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/IEMCON.2018.8615039
M. Jamshidi, N. Alibeigi, Nahid Rabbani, Bahareh Oryani, A. Lalbakhsh
Recently, using the statistical methods for analysis of humans' relationships has experienced a dramatic growth by psychologists. Although, surveying such complex concepts is considered as a difficult calculation by mathematical or statistical methods, computational intelligence-based approaches are able to examine any kinds of complex functions. In this paper, a practical approach based on Artificial Neural Networks (ANN) as a helpful tool to analyze data in the field of cognitive psychology is demonstrated. To illustrate the proposed method, a psychology problem based on 5 questionnaires was designed and each of questionnaires was filled randomly by MATLAB. The errors of the network are shown by surface function, verifying the reliability of the proposed method.
{"title":"Artificial Neural Networks: A Powerful Tool for Cognitive Science","authors":"M. Jamshidi, N. Alibeigi, Nahid Rabbani, Bahareh Oryani, A. Lalbakhsh","doi":"10.1109/IEMCON.2018.8615039","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8615039","url":null,"abstract":"Recently, using the statistical methods for analysis of humans' relationships has experienced a dramatic growth by psychologists. Although, surveying such complex concepts is considered as a difficult calculation by mathematical or statistical methods, computational intelligence-based approaches are able to examine any kinds of complex functions. In this paper, a practical approach based on Artificial Neural Networks (ANN) as a helpful tool to analyze data in the field of cognitive psychology is demonstrated. To illustrate the proposed method, a psychology problem based on 5 questionnaires was designed and each of questionnaires was filled randomly by MATLAB. The errors of the network are shown by surface function, verifying the reliability of the proposed method.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"45 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":"131433914","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/IEMCON.2018.8614818
W. Haque
Assignment of teaching workload is an essential annual ritual in all academic institutions. Depending upon the structure and complexity, it can be a painful process for department heads and administrative deans who are responsible for resource allocation. We present a framework which uses business intelligence techniques to allow asynchronous data entry, analysis and reporting in a collaborative environment to assist with the decision-making process. To begin, the first-tier administrators make workload assignments in consultation with faculty using interactive web forms, data is pushed to the underlying database or cube, reports are rendered, and memos are auto-generated. Deans responsible for approval are able to review the information along several dimensions and make informed decisions regarding the assignments. Historical data remains available for future years for trends and analysis. Besides achieving the benefits from transparency of the process, the framework exploits both OLAP cubes and relational data stores for optimum performance.
{"title":"An Interactive Framework to Allocate and Manage Teaching Workload using Hybrid OLAP Cubes","authors":"W. Haque","doi":"10.1109/IEMCON.2018.8614818","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614818","url":null,"abstract":"Assignment of teaching workload is an essential annual ritual in all academic institutions. Depending upon the structure and complexity, it can be a painful process for department heads and administrative deans who are responsible for resource allocation. We present a framework which uses business intelligence techniques to allow asynchronous data entry, analysis and reporting in a collaborative environment to assist with the decision-making process. To begin, the first-tier administrators make workload assignments in consultation with faculty using interactive web forms, data is pushed to the underlying database or cube, reports are rendered, and memos are auto-generated. Deans responsible for approval are able to review the information along several dimensions and make informed decisions regarding the assignments. Historical data remains available for future years for trends and analysis. Besides achieving the benefits from transparency of the process, the framework exploits both OLAP cubes and relational data stores for optimum performance.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"60 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":"133718898","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/IEMCON.2018.8614866
K. S. Srujan
It is important to identify and to classify brain signals to diagnose brain diseases. This study uses Synchronized Brainwave Recordings or Electro Encephalography (EEG) signals data available from the University of California, Berkeley, School of Information, to understand features and to classify signals into eight different classes. First, Fast Fourier Transform (FFT) is used for feature extraction and then classifiers like Random Forest, Gradient Boost, Xgboost, Ensemble Voting and Logistic Regression are used to classify the signals. Next, the challenges in classifying using deep learning based approaches like Convolutional Neural Network (CNN) for multi-class classification are discussed.
{"title":"Classification of Synchronized Brainwave Recordings using Machine Learning and Deep Learning Approaches","authors":"K. S. Srujan","doi":"10.1109/IEMCON.2018.8614866","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614866","url":null,"abstract":"It is important to identify and to classify brain signals to diagnose brain diseases. This study uses Synchronized Brainwave Recordings or Electro Encephalography (EEG) signals data available from the University of California, Berkeley, School of Information, to understand features and to classify signals into eight different classes. First, Fast Fourier Transform (FFT) is used for feature extraction and then classifiers like Random Forest, Gradient Boost, Xgboost, Ensemble Voting and Logistic Regression are used to classify the signals. Next, the challenges in classifying using deep learning based approaches like Convolutional Neural Network (CNN) for multi-class classification are discussed.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"68 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":"133987238","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/IEMCON.2018.8614871
Debadri Dutta, Debpriyo Paul, Partha Ghosh
Diabetes is an uprising illness, particularly because of the kind of nourishment we are having these days and the conflicting eating regimen and schedule that we take after. Diabetes are fundamentally caused because of obesity or high glucose level, and so forth. So in this paper we will discover what are the critical elements for the reason for diabetes. Variable and feature choice have turned into the focal point of much research in regions of utilization for which datasets with tens or a huge number of factors are accessible. Likewise we will center around the most essential features to predict whether a person will have chances to develop diabetes in the future.
{"title":"Analysing Feature Importances for Diabetes Prediction using Machine Learning","authors":"Debadri Dutta, Debpriyo Paul, Partha Ghosh","doi":"10.1109/IEMCON.2018.8614871","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8614871","url":null,"abstract":"Diabetes is an uprising illness, particularly because of the kind of nourishment we are having these days and the conflicting eating regimen and schedule that we take after. Diabetes are fundamentally caused because of obesity or high glucose level, and so forth. So in this paper we will discover what are the critical elements for the reason for diabetes. Variable and feature choice have turned into the focal point of much research in regions of utilization for which datasets with tens or a huge number of factors are accessible. Likewise we will center around the most essential features to predict whether a person will have chances to develop diabetes in the future.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"1 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":"134516402","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/IEMCON.2018.8615015
Bikramjit Dasgupta, Damian Valles, S. McClellan
This paper shows that with bandwidth and round-trip time statistics, data analytics can be used to classify three characteristic phenomena in wireless signal use: decreases in bandwidth due to signal over-saturation, signal attenuation due to increasing distance, and signal improvement due to decreasing distance. Using a K-Means algorithm, bandwidth and round-trip time trends were clustered correctly by signal loss type with a 99.98% accuracy rating with 10,000 validation samples.
{"title":"A K-Means Algorithm Approach for Classifying Wireless Signal Loss Using RTT and Bandwidth","authors":"Bikramjit Dasgupta, Damian Valles, S. McClellan","doi":"10.1109/IEMCON.2018.8615015","DOIUrl":"https://doi.org/10.1109/IEMCON.2018.8615015","url":null,"abstract":"This paper shows that with bandwidth and round-trip time statistics, data analytics can be used to classify three characteristic phenomena in wireless signal use: decreases in bandwidth due to signal over-saturation, signal attenuation due to increasing distance, and signal improvement due to decreasing distance. Using a K-Means algorithm, bandwidth and round-trip time trends were clustered correctly by signal loss type with a 99.98% accuracy rating with 10,000 validation samples.","PeriodicalId":368939,"journal":{"name":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"390 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":"133863672","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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