Pub Date : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380123
Sadouni Salheddine, Sadouni Ouissal, M. Benslama, Messai Abderraouf, A. Beylot
In the last few years we have been witnessing with stupefaction the intensification of the majority of meteorological phenomena as a direct consequence of the global warming of the planet. The latter, occupies a preponderant place in scientific research, to define with precision its present and future repercussions on human life. For this purpose, a continuous and real-time monitoring of the physical quantities characterizing meteorological phenomena is necessary for their good understanding as well as their early detection and the triggering of alarms during emergency situations. Therefore, in this paper we focus on the development of an intelligent electronic sentinel, which will be able to collect, process environmental data to detect those meteorological phenomena that form in its direct vicinity. In this respect, our electronic sentinel will rely on artificial intelligence to enrich these knowledge bases and ensure the veracity of the phenomena it quantifies by avoiding triggering false alarms.
{"title":"Development of an intelligent electronic sentinel for the monitoring and detection of meteorological phenomena due to global climate change","authors":"Sadouni Salheddine, Sadouni Ouissal, M. Benslama, Messai Abderraouf, A. Beylot","doi":"10.1109/ICAASE51408.2020.9380123","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380123","url":null,"abstract":"In the last few years we have been witnessing with stupefaction the intensification of the majority of meteorological phenomena as a direct consequence of the global warming of the planet. The latter, occupies a preponderant place in scientific research, to define with precision its present and future repercussions on human life. For this purpose, a continuous and real-time monitoring of the physical quantities characterizing meteorological phenomena is necessary for their good understanding as well as their early detection and the triggering of alarms during emergency situations. Therefore, in this paper we focus on the development of an intelligent electronic sentinel, which will be able to collect, process environmental data to detect those meteorological phenomena that form in its direct vicinity. In this respect, our electronic sentinel will rely on artificial intelligence to enrich these knowledge bases and ensure the veracity of the phenomena it quantifies by avoiding triggering false alarms.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116789149","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380109
Nemouchi Warda Ismahene, Souheila Boudouda, N. Zarour
Cloud Computing has emerged as revolutionary paradigm for large-scale data intensive analysis over the last decade. In addition, Map Reduce and its implementation Hadoop have been successful at developing and running Big Data Distributed computations. However, their effect on datacenters energy efficiency has become significant; some of the servers are run without being used actively on daily basis. Making use of Cloud Computing advantages such as elasticity and scalability along with Hadoop’s powerful distributed architecture has been an important research axis. The ability of managing resources (adding/removing nodes that run Map Reduce jobs to the cluster) automatically based on workloads without affecting time response has been investigated. This paper presents an approach of auto-scaling in the Hadoop framework, we have focused on separating nodes to core/computation to avoid data loss and guarantee the ability to remove nodes smoothly and instantly.
{"title":"An Auto Scaling Energy Efficient Approach in Apache Hadoop","authors":"Nemouchi Warda Ismahene, Souheila Boudouda, N. Zarour","doi":"10.1109/ICAASE51408.2020.9380109","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380109","url":null,"abstract":"Cloud Computing has emerged as revolutionary paradigm for large-scale data intensive analysis over the last decade. In addition, Map Reduce and its implementation Hadoop have been successful at developing and running Big Data Distributed computations. However, their effect on datacenters energy efficiency has become significant; some of the servers are run without being used actively on daily basis. Making use of Cloud Computing advantages such as elasticity and scalability along with Hadoop’s powerful distributed architecture has been an important research axis. The ability of managing resources (adding/removing nodes that run Map Reduce jobs to the cluster) automatically based on workloads without affecting time response has been investigated. This paper presents an approach of auto-scaling in the Hadoop framework, we have focused on separating nodes to core/computation to avoid data loss and guarantee the ability to remove nodes smoothly and instantly.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122301991","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380115
Ben Yahmed Sarra, S. Merniz, S. Harous
Vehicular Ad hoc Network (VANET) has gained a lot of interest in academia and industry domains. High speed of vehicles, rapid change in topology and environmental characteristics of the city make the routing in VANET a very challenging problem. Traditional VANET routing protocols where decisions are made solely on local neighborhood observations have shown poor network performance. In contrast, the use of the Software Defined Network (SDN) paradigm, which is based on the global traffic view, has proven to be efficient in improving such performance. Internet of Vehicles (IoV) has emerged as a new technology that extends VANET architectures such that to meet the ever growing Intelligent Transport Systems (ITS) requirements. This paper surveys both traditional and SDN-based VANET routing schemes with a comparative study involving the main relevant performance parameters. It also gives an investigation of integration of SDN into an IoV environment that has shown to be very beneficial with respect to network performances.
{"title":"Ensuring QoS and Efficiency of Vehicular Networks by SDVN-IoV","authors":"Ben Yahmed Sarra, S. Merniz, S. Harous","doi":"10.1109/ICAASE51408.2020.9380115","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380115","url":null,"abstract":"Vehicular Ad hoc Network (VANET) has gained a lot of interest in academia and industry domains. High speed of vehicles, rapid change in topology and environmental characteristics of the city make the routing in VANET a very challenging problem. Traditional VANET routing protocols where decisions are made solely on local neighborhood observations have shown poor network performance. In contrast, the use of the Software Defined Network (SDN) paradigm, which is based on the global traffic view, has proven to be efficient in improving such performance. Internet of Vehicles (IoV) has emerged as a new technology that extends VANET architectures such that to meet the ever growing Intelligent Transport Systems (ITS) requirements. This paper surveys both traditional and SDN-based VANET routing schemes with a comparative study involving the main relevant performance parameters. It also gives an investigation of integration of SDN into an IoV environment that has shown to be very beneficial with respect to network performances.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114599778","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380117
Eric Cariou, Léa Brunschwig, Olivier Le Goaër, F. Barbier
We propose a model-based software development process based on UML state machines. State machines are executable models and such models offer the advantage to capture the behavior of a system at a high-level of abstraction. Besides, the business parts of the system can be specified and weave onto the executable models, applying a good separation of concerns. While advanced standards such as fUML enable to define the complete contents of an application at the model level, it leads to too much complexity and prevents flexibility in the use of existing code. For these reasons, we propose an intermediate and pragmatic approach where a UML state machine is compiled onto Java code for our lightweight execution engine PauWare. The business parts of the application are then implemented in standard Java.
{"title":"A software development process based on UML state machines","authors":"Eric Cariou, Léa Brunschwig, Olivier Le Goaër, F. Barbier","doi":"10.1109/ICAASE51408.2020.9380117","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380117","url":null,"abstract":"We propose a model-based software development process based on UML state machines. State machines are executable models and such models offer the advantage to capture the behavior of a system at a high-level of abstraction. Besides, the business parts of the system can be specified and weave onto the executable models, applying a good separation of concerns. While advanced standards such as fUML enable to define the complete contents of an application at the model level, it leads to too much complexity and prevents flexibility in the use of existing code. For these reasons, we propose an intermediate and pragmatic approach where a UML state machine is compiled onto Java code for our lightweight execution engine PauWare. The business parts of the application are then implemented in standard Java.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610969","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380108
R. Zaghdoudi, Hamid Seridi, A. boudiaf, S. Ziani
Efficient surface defect classification is one of the most important factors to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to its localization on a large surface, various defect appearance, large scale changes of defects, and random distribution. Therefore, in this paper, we proposed an efficient system for steel surface defects classification that can attain excellent classification accuracy. The presented system extracts local texture features from defect images, by application of the binary Gabor pattern (BGP) descriptor used for the first time on the steel surface defects classification. Then, a dimensionality reduction procedure, based on the principal component analysis (PCA) is employed to obtain compact representation of the defects image. Lastly, SVM multiclass classifier is utilized to give the final decision. A set of experiments was conducted on the NEU Surface Defects database to investigate the performance of the proposed system. The results obtained demonstrate the effectiveness of the proposed approach for steel surface defects classification.
{"title":"Binary Gabor pattern (BGP) descriptor and principal component analysis (PCA) for steel surface defects classification","authors":"R. Zaghdoudi, Hamid Seridi, A. boudiaf, S. Ziani","doi":"10.1109/ICAASE51408.2020.9380108","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380108","url":null,"abstract":"Efficient surface defect classification is one of the most important factors to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to its localization on a large surface, various defect appearance, large scale changes of defects, and random distribution. Therefore, in this paper, we proposed an efficient system for steel surface defects classification that can attain excellent classification accuracy. The presented system extracts local texture features from defect images, by application of the binary Gabor pattern (BGP) descriptor used for the first time on the steel surface defects classification. Then, a dimensionality reduction procedure, based on the principal component analysis (PCA) is employed to obtain compact representation of the defects image. Lastly, SVM multiclass classifier is utilized to give the final decision. A set of experiments was conducted on the NEU Surface Defects database to investigate the performance of the proposed system. The results obtained demonstrate the effectiveness of the proposed approach for steel surface defects classification.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129859493","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380110
Wayoud Bouzeraib, Afifa Ghenai, N. Zeghib
The rapid development of information and communication technologies makes the Internet of Things (IoT) devices much more complex and heterogeneous. In this context, the massive end devices (IoTs) and the large volume of data raise security and privacy challenges. To tackle these issues, the joint use of the Bockchain (BC) and Machine Learning (ML) seems attractive to achieve decentralized, secure, intelligent and efficient management of networks. On the one hand, the BC can greatly facilitate the sharing of training data and ML models, the decentralization of intelligence, security, privacy and reliable ML decision-making. On the other hand, ML may have significant impacts on the development of BC in communications and networking systems, including energy and resource efficiency, scalability, security, privacy and smart contracting. An important aspect of security intends to detect unusual and potentially inappropriate activities according to traffic patterns. This paper focuses on the problem of imbalance data where the number of abnormal samples is significantly lower than that of the normal (secure) ones. In particular, this paper presents a new equilibrium model based on an exciting recent innovation in ML namely Generator Adverse Networks (GANs) to address the problem of class imbalance and data noise to Intrusion Detection System (IDS) performance. The proposed approach use is illustrated by a case study: a smart house system-based scenario.
{"title":"A Blockchain Data Balance Using a Generative Adversarial Network Approach: Application to Smart House IDS","authors":"Wayoud Bouzeraib, Afifa Ghenai, N. Zeghib","doi":"10.1109/ICAASE51408.2020.9380110","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380110","url":null,"abstract":"The rapid development of information and communication technologies makes the Internet of Things (IoT) devices much more complex and heterogeneous. In this context, the massive end devices (IoTs) and the large volume of data raise security and privacy challenges. To tackle these issues, the joint use of the Bockchain (BC) and Machine Learning (ML) seems attractive to achieve decentralized, secure, intelligent and efficient management of networks. On the one hand, the BC can greatly facilitate the sharing of training data and ML models, the decentralization of intelligence, security, privacy and reliable ML decision-making. On the other hand, ML may have significant impacts on the development of BC in communications and networking systems, including energy and resource efficiency, scalability, security, privacy and smart contracting. An important aspect of security intends to detect unusual and potentially inappropriate activities according to traffic patterns. This paper focuses on the problem of imbalance data where the number of abnormal samples is significantly lower than that of the normal (secure) ones. In particular, this paper presents a new equilibrium model based on an exciting recent innovation in ML namely Generator Adverse Networks (GANs) to address the problem of class imbalance and data noise to Intrusion Detection System (IDS) performance. The proposed approach use is illustrated by a case study: a smart house system-based scenario.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116273454","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 : 2020-11-28DOI: 10.1109/icaase51408.2020.9380103
{"title":"ICAASE 2020 List of Authors","authors":"","doi":"10.1109/icaase51408.2020.9380103","DOIUrl":"https://doi.org/10.1109/icaase51408.2020.9380103","url":null,"abstract":"","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130928614","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380104
Fatima Bourebaa, M. Benmohammed
Deep learning in general and convolutional architectures, in particular, have pushed the limits of the current state of the art in the field of computer vision and the processing of natural languages and speech. Recently, these techniques have been applied to detect mobile malware and have once again shown their ability to remedy this type of problem. However, the most suitable deep network architecture for malware detection remains an open issue. In this paper, we investigate the possibilities of convolutional neural networks for efficient detection of mobile malware. Specifically, we address the impact of using inception based and multichannel architectures on network performance. We achieve an accuracy of 92% using a multichannel model on a set of 50000 malware and 50000 benign applications.
{"title":"Android Malware Detection using Convolutional Deep Neural Networks","authors":"Fatima Bourebaa, M. Benmohammed","doi":"10.1109/ICAASE51408.2020.9380104","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380104","url":null,"abstract":"Deep learning in general and convolutional architectures, in particular, have pushed the limits of the current state of the art in the field of computer vision and the processing of natural languages and speech. Recently, these techniques have been applied to detect mobile malware and have once again shown their ability to remedy this type of problem. However, the most suitable deep network architecture for malware detection remains an open issue. In this paper, we investigate the possibilities of convolutional neural networks for efficient detection of mobile malware. Specifically, we address the impact of using inception based and multichannel architectures on network performance. We achieve an accuracy of 92% using a multichannel model on a set of 50000 malware and 50000 benign applications.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129216484","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380119
Oussama Arki, Abdelhafid Zitouni, A. Hadjali
In the last years, cloud computing has emerged as a new information technology (IT) model. Which provides an easy way for data processing and storage remotely through a network. The common solution to secure data in the cloud is data encryption. However, treating all data with the same security policy does not appear to be a good practice, because they do not have the same sensitivity for their owners. In this paper, we propose a model that aims to solve the problem of data sensitivity in cloud storage using the classification. The proposal is based on the use of fuzzy logic that uses the CIA (Confidentiality, Integrity and Availability) triad of information security to classify data.
{"title":"A Cloud Data Classification Model Using Fuzzy Logic","authors":"Oussama Arki, Abdelhafid Zitouni, A. Hadjali","doi":"10.1109/ICAASE51408.2020.9380119","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380119","url":null,"abstract":"In the last years, cloud computing has emerged as a new information technology (IT) model. Which provides an easy way for data processing and storage remotely through a network. The common solution to secure data in the cloud is data encryption. However, treating all data with the same security policy does not appear to be a good practice, because they do not have the same sensitivity for their owners. In this paper, we propose a model that aims to solve the problem of data sensitivity in cloud storage using the classification. The proposal is based on the use of fuzzy logic that uses the CIA (Confidentiality, Integrity and Availability) triad of information security to classify data.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123067764","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 : 2020-11-28DOI: 10.1109/ICAASE51408.2020.9380125
Charaf Eddine Dridi, Zakaria Benzadri, F. Belala
Systems-of-Systems (SoSs) stand out from monolithic systems, because of their composed nature, their large scale, their decentralized control mechanism, their evolving environments, and their large number of stakeholders. Due to the varied methodologies and domains of applications in existing literature, there does not exist a single unified consensus for processes involved in System-of-Systems Engineering (SoSE). The purpose of this article is to provide a cursory description of the SoS basic concepts on the one hand, and then to analyse the main challenges in its development. Finally, we report the literature review showing various techniques and methods that have been modified from the conventional systems engineering to better fit the needs of SoSs design. We hope the findings of this work may encourage and inform the community researchers of the creation of a more holistic and unified engineering process that is tailored for the demands of these large-scale systems. Thus, the complexity of the SoS development lends itself nicely to a Model-Based Systems Engineering (MBSE) which provides communication and verification that transcends the levels of development. MBSE uses a model or set of models to document and communicate from the system requirements level down to the software implementation level.
{"title":"System of Systems Modelling: Recent work Review and a Path Forward","authors":"Charaf Eddine Dridi, Zakaria Benzadri, F. Belala","doi":"10.1109/ICAASE51408.2020.9380125","DOIUrl":"https://doi.org/10.1109/ICAASE51408.2020.9380125","url":null,"abstract":"Systems-of-Systems (SoSs) stand out from monolithic systems, because of their composed nature, their large scale, their decentralized control mechanism, their evolving environments, and their large number of stakeholders. Due to the varied methodologies and domains of applications in existing literature, there does not exist a single unified consensus for processes involved in System-of-Systems Engineering (SoSE). The purpose of this article is to provide a cursory description of the SoS basic concepts on the one hand, and then to analyse the main challenges in its development. Finally, we report the literature review showing various techniques and methods that have been modified from the conventional systems engineering to better fit the needs of SoSs design. We hope the findings of this work may encourage and inform the community researchers of the creation of a more holistic and unified engineering process that is tailored for the demands of these large-scale systems. Thus, the complexity of the SoS development lends itself nicely to a Model-Based Systems Engineering (MBSE) which provides communication and verification that transcends the levels of development. MBSE uses a model or set of models to document and communicate from the system requirements level down to the software implementation level.","PeriodicalId":405638,"journal":{"name":"2020 International Conference on Advanced Aspects of Software Engineering (ICAASE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123369438","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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