Pub Date : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002771
S. Nasih, Sara Arezki, T. Gadi
Due to its wide involvement in different fields: industry, maritime industry, trade, supply chain management has been greatly expanded to cover a large and complex network of stakeholders in the production and distribution process. The multitude of intermediaries in this process leads difficulties in communication, control, time saving… In this paper, we propose the Blockchain technology as a solution for decentralization and disintermediation of operations in the supply chain, and its effect on the maritime industry.
{"title":"Enhancement of supply chain management by integrating Blockchain technology","authors":"S. Nasih, Sara Arezki, T. Gadi","doi":"10.1109/ICSSD47982.2019.9002771","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002771","url":null,"abstract":"Due to its wide involvement in different fields: industry, maritime industry, trade, supply chain management has been greatly expanded to cover a large and complex network of stakeholders in the production and distribution process. The multitude of intermediaries in this process leads difficulties in communication, control, time saving… In this paper, we propose the Blockchain technology as a solution for decentralization and disintermediation of operations in the supply chain, and its effect on the maritime industry.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729331","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002846
Mimoun Lamrini, M. Chkouri
Data visualization has a crucial role in understanding and processing voluminous data (i.e., Big Data) and subsequently has become more important with the coincidence of the exponential growth of data analysis need.The problem of high-dimensional data visualization in a data processing software interface cannot be entirely displayed, in consideration that once the data size exceeds two-dimension, it cannot be projected into a two-dimension interface. Furthermore, the rough analysis and evaluation of high-dimensional data become considerably ambiguous, thus, making a precise decision on that data cannot be achieved. In order to overcome this anomaly, resorting to data dimensionality reduction is a plausible solution.In this paper, the integration of Principal Component Analysis (PCA) combined with the Matrix by Block Decomposition (MBD) method(A.K.A block segmentation). According to the literature, the MBD method turned out quite efficient in data segmentation, wherein a huge data can be divided into regular blocks. By doing so, it becomes easier to access and visualize a given part of data. In order to further enhance the visualization understanding, K-means segmentation has been integrated in our proposed algorithm.In our study, we took into account other data dimensionality reduction techniques such as Linear Discriminant Analysis (LDA), Multi-Dimensional Scaling(MDS).
{"title":"Decomposition and Visualization of High-Dimensional Data in a Two Dimensional Interface","authors":"Mimoun Lamrini, M. Chkouri","doi":"10.1109/ICSSD47982.2019.9002846","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002846","url":null,"abstract":"Data visualization has a crucial role in understanding and processing voluminous data (i.e., Big Data) and subsequently has become more important with the coincidence of the exponential growth of data analysis need.The problem of high-dimensional data visualization in a data processing software interface cannot be entirely displayed, in consideration that once the data size exceeds two-dimension, it cannot be projected into a two-dimension interface. Furthermore, the rough analysis and evaluation of high-dimensional data become considerably ambiguous, thus, making a precise decision on that data cannot be achieved. In order to overcome this anomaly, resorting to data dimensionality reduction is a plausible solution.In this paper, the integration of Principal Component Analysis (PCA) combined with the Matrix by Block Decomposition (MBD) method(A.K.A block segmentation). According to the literature, the MBD method turned out quite efficient in data segmentation, wherein a huge data can be divided into regular blocks. By doing so, it becomes easier to access and visualize a given part of data. In order to further enhance the visualization understanding, K-means segmentation has been integrated in our proposed algorithm.In our study, we took into account other data dimensionality reduction techniques such as Linear Discriminant Analysis (LDA), Multi-Dimensional Scaling(MDS).","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114846805","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9003119
Said Ouiazzane, M. Addou, Fatimazahra Barramou
The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).
{"title":"A Multi-Agent Model for Network Intrusion Detection","authors":"Said Ouiazzane, M. Addou, Fatimazahra Barramou","doi":"10.1109/ICSSD47982.2019.9003119","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9003119","url":null,"abstract":"The objective of this paper is to propose a distributed intrusion detection model based on a multi agent system. Mutli Agent Systems (MAS) are very suitable for intrusion detection systems as they meet the characteristics required by the networks and Big Data issues. The MAS agents cooperate and communicate with each other to ensure the effective detection of network intrusions without the intervention of an expert as used to be in the classical intrusion detection systems relying on signature matching to detect known attacks. The proposed model helped to detect known and unknown attacks within big computer infrastructure by responding to the network requirements in terms of distribution, autonomy, responsiveness and communication. The proposed model is capable of achieving a good and a real time intrusion detection using multi-agents paradigm and Hadoop Distributed File System (HDFS).","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129650613","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9003171
Oumaima Hourrane, Nouhaila Idrissi, E. Benlahmar
Sentiment classification is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, Deep learning models appeared to be effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings methods and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.
{"title":"An Empirical Study of Deep Neural Networks Models for Sentiment Classification on Movie Reviews","authors":"Oumaima Hourrane, Nouhaila Idrissi, E. Benlahmar","doi":"10.1109/ICSSD47982.2019.9003171","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9003171","url":null,"abstract":"Sentiment classification is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, Deep learning models appeared to be effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings methods and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325322","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002902
O. El Idrissi, Abdellatif Mezrioui, A. Belmekki
Industrial Control Systems (ICS) are currently integrated into critical infrastructures and are designed to support industrial processes, monitor and control in real time a large number of processes and operations such as gas and electricity distribution (conventional and nuclear), water treatment, etc. ICSs have evolved significantly in recent years and have embraced new technologies such as IoT and have been made accessible through the Internet to allow remote access to administrators, service providers, etc. The aim of this paper is to illustrate, through the use of EBIOS risk analysis method that such infrastructures are subject to vulnerabilities, overwhelming threats and potentials risks. The paper also proposes a number of recommendations and organizational and technological security measures to reduce these risks to an acceptable level and to decrease both their impacts and potentialities.
{"title":"A lightweight risk analysis of a critical infrastructure based ICSs","authors":"O. El Idrissi, Abdellatif Mezrioui, A. Belmekki","doi":"10.1109/ICSSD47982.2019.9002902","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002902","url":null,"abstract":"Industrial Control Systems (ICS) are currently integrated into critical infrastructures and are designed to support industrial processes, monitor and control in real time a large number of processes and operations such as gas and electricity distribution (conventional and nuclear), water treatment, etc. ICSs have evolved significantly in recent years and have embraced new technologies such as IoT and have been made accessible through the Internet to allow remote access to administrators, service providers, etc. The aim of this paper is to illustrate, through the use of EBIOS risk analysis method that such infrastructures are subject to vulnerabilities, overwhelming threats and potentials risks. The paper also proposes a number of recommendations and organizational and technological security measures to reduce these risks to an acceptable level and to decrease both their impacts and potentialities.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116116527","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002676
Mdarbi Fatima Ezzahra, Afifi Nadia, Hilal Imane
Big Data is a very large data set, its analysis exceeds the capabilities of traditional database management systems. Big Data is linked to the need for large computing and storage capacity.Big Data dependability is one of the major concerns of organizations. It reflects the confidence that can be placed in these data. Nowadays, companies find a major interest in Big Data, but dependability challenge remains a major obstacle.In this article, we present different works that have addressed Big Data dependability aspects. This study highlights new opportunities in this field as well as different challenges.
{"title":"Big Data Dependability Opportunities & Challenges","authors":"Mdarbi Fatima Ezzahra, Afifi Nadia, Hilal Imane","doi":"10.1109/ICSSD47982.2019.9002676","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002676","url":null,"abstract":"Big Data is a very large data set, its analysis exceeds the capabilities of traditional database management systems. Big Data is linked to the need for large computing and storage capacity.Big Data dependability is one of the major concerns of organizations. It reflects the confidence that can be placed in these data. Nowadays, companies find a major interest in Big Data, but dependability challenge remains a major obstacle.In this article, we present different works that have addressed Big Data dependability aspects. This study highlights new opportunities in this field as well as different challenges.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132734198","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002729
Kamal El Guemmat, Sara Ouahabi
Today, search engines play an important role in retrieving documents from a large database. The key success factors of these engines are their indexing and searching techniques.These engines have touched many areas to help their users to find the desired resources in a fast and accurate way. The field of teaching and research take advantage of these engines to offer them to the interested actors (students, teacher, staff, etc.) in order to find the desired learning objects.There are several prominent educational search engines in the field implementing the techniques of indexing and searching either classic, semantic, metadata. However, most engines do not mix all of these to achieve important results.The engine which will be presented in what follows benefits from the best techniques of the literature, and offers a more relevant searching.
{"title":"Towards a new educational search engine based on hybrid searching and indexing techniques","authors":"Kamal El Guemmat, Sara Ouahabi","doi":"10.1109/ICSSD47982.2019.9002729","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002729","url":null,"abstract":"Today, search engines play an important role in retrieving documents from a large database. The key success factors of these engines are their indexing and searching techniques.These engines have touched many areas to help their users to find the desired resources in a fast and accurate way. The field of teaching and research take advantage of these engines to offer them to the interested actors (students, teacher, staff, etc.) in order to find the desired learning objects.There are several prominent educational search engines in the field implementing the techniques of indexing and searching either classic, semantic, metadata. However, most engines do not mix all of these to achieve important results.The engine which will be presented in what follows benefits from the best techniques of the literature, and offers a more relevant searching.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132027768","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002770
Maissae Haddouchi, A. Berrado
Interpretability of highly performant Machine Learning [ML] methods, such as Random Forest [RF], is a key tool that attracts a great interest in datamining research. In the state of the art, RF is well-known as an efficient ensemble learning (in terms of predictive accuracy, flexibility and straightforwardness). Moreover, it is recognized as an intuitive and intelligible approach regarding to its building process. However it is also regarded as a Black Box model because of its hundreds of deep decision trees. This can be crucial for several fields of study, such as healthcare, biology and security, where the lack of interpretability could be a real disadvantage. Indeed, the interpretability of the RF models is, generally, necessary in such fields of applications because of different motivations. In fact, the more the ML users grasp what is going on inside a ML system (process and resulting model), the more they can trust it and take actions based on the knowledge extracted from it. Furthermore, ML models are increasingly constrained by new laws that require regulation and interpretation of the knowledge they provide.Several papers have tackled the interpretation of RF resulting models. It had been associated with different aspects depending on the specificity of the issue studied as well as the users concerned with explanations. Therefore, this paper aims to provide a survey of tools and methods used in literature in order to uncover insights in the RF resulting models. These tools are classified depending on different aspects characterizing the interpretability. This should guide, in practice, in the choice of the most useful tools for interpretation and deep analysis of the RF model depending on the interpretability aspect sought. This should also be valuable for researchers who aim to focus their work on the interpretability of RF, or ML in general.
{"title":"A survey of methods and tools used for interpreting Random Forest","authors":"Maissae Haddouchi, A. Berrado","doi":"10.1109/ICSSD47982.2019.9002770","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002770","url":null,"abstract":"Interpretability of highly performant Machine Learning [ML] methods, such as Random Forest [RF], is a key tool that attracts a great interest in datamining research. In the state of the art, RF is well-known as an efficient ensemble learning (in terms of predictive accuracy, flexibility and straightforwardness). Moreover, it is recognized as an intuitive and intelligible approach regarding to its building process. However it is also regarded as a Black Box model because of its hundreds of deep decision trees. This can be crucial for several fields of study, such as healthcare, biology and security, where the lack of interpretability could be a real disadvantage. Indeed, the interpretability of the RF models is, generally, necessary in such fields of applications because of different motivations. In fact, the more the ML users grasp what is going on inside a ML system (process and resulting model), the more they can trust it and take actions based on the knowledge extracted from it. Furthermore, ML models are increasingly constrained by new laws that require regulation and interpretation of the knowledge they provide.Several papers have tackled the interpretation of RF resulting models. It had been associated with different aspects depending on the specificity of the issue studied as well as the users concerned with explanations. Therefore, this paper aims to provide a survey of tools and methods used in literature in order to uncover insights in the RF resulting models. These tools are classified depending on different aspects characterizing the interpretability. This should guide, in practice, in the choice of the most useful tools for interpretation and deep analysis of the RF model depending on the interpretability aspect sought. This should also be valuable for researchers who aim to focus their work on the interpretability of RF, or ML in general.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133247705","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 : 2019-10-01DOI: 10.1109/ICSSD47982.2019.9002827
I. Lafram, N. Berbiche, Jamila El Alami
Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model’s performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.
{"title":"Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification","authors":"I. Lafram, N. Berbiche, Jamila El Alami","doi":"10.1109/ICSSD47982.2019.9002827","DOIUrl":"https://doi.org/10.1109/ICSSD47982.2019.9002827","url":null,"abstract":"Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model’s performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441158","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 : 2019-10-01DOI: 10.1109/icssd47982.2019.9002832
{"title":"ICSSD 2019 Committees","authors":"","doi":"10.1109/icssd47982.2019.9002832","DOIUrl":"https://doi.org/10.1109/icssd47982.2019.9002832","url":null,"abstract":"","PeriodicalId":342806,"journal":{"name":"2019 1st International Conference on Smart Systems and Data Science (ICSSD)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121838923","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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