Pub Date : 2019-07-01DOI: 10.1109/IISA.2019.8900772
N. Petrellis
The image processing technique described in this paper can be used for the classification of photographs that display an object of interest. Spots on the object having distinct color surrounded by a potential halo are segmented. The gray level, area and the number of the spots can determine the class of the object displayed in the photograph. The color histograms of the regions of interest are expected to have similar form in different photographs belonging to the same class. Instead of employing complicated pattern matching algorithms simple features are used including the position and the peaks of the lobes. Plant or skin disease diagnosis are indicative applications that can benefit from the proposed method. High speed classification is achieved with good accuracy in the cases where the proposed methods have been employed. However, their main advantage is the simplicity that allows extensibility since new classes can be supported after a draft statistical processing of a small number of photographs.
{"title":"Image Processing and Classification Method Appropriate for Extensible Mobile Applications","authors":"N. Petrellis","doi":"10.1109/IISA.2019.8900772","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900772","url":null,"abstract":"The image processing technique described in this paper can be used for the classification of photographs that display an object of interest. Spots on the object having distinct color surrounded by a potential halo are segmented. The gray level, area and the number of the spots can determine the class of the object displayed in the photograph. The color histograms of the regions of interest are expected to have similar form in different photographs belonging to the same class. Instead of employing complicated pattern matching algorithms simple features are used including the position and the peaks of the lobes. Plant or skin disease diagnosis are indicative applications that can benefit from the proposed method. High speed classification is achieved with good accuracy in the cases where the proposed methods have been employed. However, their main advantage is the simplicity that allows extensibility since new classes can be supported after a draft statistical processing of a small number of photographs.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134262914","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900714
Eirini Christodoulou, S. Moustakidis, Nikolaos I. Papandrianos, D. Tsaopoulos, E. Papageorgiou
This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the joints revealing a variation in symptoms' intensity, frequency and pattern. A large number of features/factors need to be assessed for knee OA, mainly related with medical risks factors including advanced age, gender, hormonal status, body weight or size, family history of disease etc. The main goal of this research study is to implement deep neural networks as a new efficient machine learning approach for this classification task taking into account the large number of medical factors affecting OA. The potential of the proposed methodology was demonstrated by classifying different subgroups of control participants from self-reported clinical data and providing a category of knee OA diagnosis. The investigated subgroups were defined by gender, age and obesity. Furthermore, to validate the proposed deep learning methodology, a comparison analysis between the proposed DNN and some benchmark machine learning techniques recommended for classification was conducted and the results showed the effectiveness of deep learning in the diagnosis of knee OA.
{"title":"Exploring deep learning capabilities in knee osteoarthritis case study for classification","authors":"Eirini Christodoulou, S. Moustakidis, Nikolaos I. Papandrianos, D. Tsaopoulos, E. Papageorgiou","doi":"10.1109/IISA.2019.8900714","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900714","url":null,"abstract":"This research study is devoted to the investigation of deep neural networks (DNN) for classification of the complex problem of knee osteoarthritis diagnosis. Osteoarthritis (OA) is the most common chronic condition of the joints revealing a variation in symptoms' intensity, frequency and pattern. A large number of features/factors need to be assessed for knee OA, mainly related with medical risks factors including advanced age, gender, hormonal status, body weight or size, family history of disease etc. The main goal of this research study is to implement deep neural networks as a new efficient machine learning approach for this classification task taking into account the large number of medical factors affecting OA. The potential of the proposed methodology was demonstrated by classifying different subgroups of control participants from self-reported clinical data and providing a category of knee OA diagnosis. The investigated subgroups were defined by gender, age and obesity. Furthermore, to validate the proposed deep learning methodology, a comparison analysis between the proposed DNN and some benchmark machine learning techniques recommended for classification was conducted and the results showed the effectiveness of deep learning in the diagnosis of knee OA.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975996","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900783
Sotiria Foutsitzi, S. Asteriadis, G. Caridakis
Emotion, human intelligence and learning have inextricable connections. Making sure learners’ emotions are positive during the learning procedure can increase and optimize the learning outcome. However, until recently, cognition and emotion were viewed as two separate notions. Learning materials and pedagogical strategies focusing more on how to increase and sustain the volume of knowledge, rather than how to actively engage the learner, through positive and enjoyable learning experiences, were in the focus of attention. However, in the last years, the advent of a wide variety of learning (digital) resources, such as serious games, robots, mobile devices, virtual and augmented reality, has provided the means to involve the learner in more immersive and active contexts, that place engagement and human emotions in the centre of the interaction. Moreover, the advances in artificial intelligence are now allowing for a wide availability of instruments that allow for estimating emotions based on a plethora of means, such as facial expressions, heart rate measurements, digital log files, personality analysis. The above are leading to personalized learning that tailors the learning procedure to the (emotional and cognitive) needs of the individual learner. This paper is presenting an introduction to the role of emotion in educational settings and describes influential and promising emotional models. A brief overview of ways to infer emotions follows, while examples of works intended to make use of measured emotion in learning conditions is presented at the end of this work.
{"title":"An overview of Affective Models and ICT in Education","authors":"Sotiria Foutsitzi, S. Asteriadis, G. Caridakis","doi":"10.1109/IISA.2019.8900783","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900783","url":null,"abstract":"Emotion, human intelligence and learning have inextricable connections. Making sure learners’ emotions are positive during the learning procedure can increase and optimize the learning outcome. However, until recently, cognition and emotion were viewed as two separate notions. Learning materials and pedagogical strategies focusing more on how to increase and sustain the volume of knowledge, rather than how to actively engage the learner, through positive and enjoyable learning experiences, were in the focus of attention. However, in the last years, the advent of a wide variety of learning (digital) resources, such as serious games, robots, mobile devices, virtual and augmented reality, has provided the means to involve the learner in more immersive and active contexts, that place engagement and human emotions in the centre of the interaction. Moreover, the advances in artificial intelligence are now allowing for a wide availability of instruments that allow for estimating emotions based on a plethora of means, such as facial expressions, heart rate measurements, digital log files, personality analysis. The above are leading to personalized learning that tailors the learning procedure to the (emotional and cognitive) needs of the individual learner. This paper is presenting an introduction to the role of emotion in educational settings and describes influential and promising emotional models. A brief overview of ways to infer emotions follows, while examples of works intended to make use of measured emotion in learning conditions is presented at the end of this work.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132032715","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900768
Georgia Theocharopoulou, Konstantinos Giannakis, Christos Papalitsas, Sofia Fanarioti, T. Andronikos
Unconventional computing is a relatively recent research field in which new computation paradigms are studied, with emphasis on bio-inspired architectures and algorithms. The physical limitations of traditional systems make the exploration for new, alternative solutions a quest of great importance. In this direction, traditional and well-studied concepts are re-examined. In this paper, the connection between game theory, an established scientific field, and a bio-inspired model of computation based on P systems is studied. To this end, a novel bio-inspired game on a membrane system is introduced, in which the rules are inspired by fundamental mitochondrial processes. Furthermore, a general framework for connecting game-theoretic notions with a bio-inspired model of computation based on P systems is proposed. Finally, possibilities and further extensions that could shed light on the deeper connection among these fields are highlighted.
{"title":"Elements of Game Theory in a Bio-inspired Model of Computation","authors":"Georgia Theocharopoulou, Konstantinos Giannakis, Christos Papalitsas, Sofia Fanarioti, T. Andronikos","doi":"10.1109/IISA.2019.8900768","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900768","url":null,"abstract":"Unconventional computing is a relatively recent research field in which new computation paradigms are studied, with emphasis on bio-inspired architectures and algorithms. The physical limitations of traditional systems make the exploration for new, alternative solutions a quest of great importance. In this direction, traditional and well-studied concepts are re-examined. In this paper, the connection between game theory, an established scientific field, and a bio-inspired model of computation based on P systems is studied. To this end, a novel bio-inspired game on a membrane system is introduced, in which the rules are inspired by fundamental mitochondrial processes. Furthermore, a general framework for connecting game-theoretic notions with a bio-inspired model of computation based on P systems is proposed. Finally, possibilities and further extensions that could shed light on the deeper connection among these fields are highlighted.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130111955","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900681
M. Stefani, Vassilios Stefanis, J. Garofalakis
Fashion has a great impact in everyday life and therefore, people pay close attention to the way they dress. Fashion item recommendation is typically a manual, curated process, where experts recommend items and trends to large populations. However, there is increasing use of automated, personalized recommendation systems, which have valuable applications in e-commerce websites. In this paper, we propose a collaborative fashion recommendation system, called CFRS. Apart from classic features, we propose a new metric, called trend score. Trend score shows how trendy a product is and is calculated taking into account the ratings provided by CFRS users (fashion experts and registered users). In particular, users rate (like/ dislike scale) current trends about colors, prints and materials. Finally, trend score is used a) for sorting products of each category from trendiest options to classic ones and b) to recommend trendy products from different clothing categories.
{"title":"CFRS: A Trends-Driven Collaborative Fashion Recommendation System","authors":"M. Stefani, Vassilios Stefanis, J. Garofalakis","doi":"10.1109/IISA.2019.8900681","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900681","url":null,"abstract":"Fashion has a great impact in everyday life and therefore, people pay close attention to the way they dress. Fashion item recommendation is typically a manual, curated process, where experts recommend items and trends to large populations. However, there is increasing use of automated, personalized recommendation systems, which have valuable applications in e-commerce websites. In this paper, we propose a collaborative fashion recommendation system, called CFRS. Apart from classic features, we propose a new metric, called trend score. Trend score shows how trendy a product is and is calculated taking into account the ratings provided by CFRS users (fashion experts and registered users). In particular, users rate (like/ dislike scale) current trends about colors, prints and materials. Finally, trend score is used a) for sorting products of each category from trendiest options to classic ones and b) to recommend trendy products from different clothing categories.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130153734","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900744
Jian Wu, Venkatesh Srinivasan, Alex Thomo
Graph libraries containing already implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analytics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this paper, we introduce Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX.
{"title":"Graph-XLL: a Graph Library for Extra Large Graph Analytics on a Single Machine","authors":"Jian Wu, Venkatesh Srinivasan, Alex Thomo","doi":"10.1109/IISA.2019.8900744","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900744","url":null,"abstract":"Graph libraries containing already implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analytics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this paper, we introduce Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114552683","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}
In this study we evaluate the research performance of the Computer Science departments in United Kingdom. We consider the research activity as a series process with two components, where the first component portrays the research productivity and the second component portrays the impact of the research outcomes of each department. The analysis is based on the data drawn from the Research Excellence Framework 2014 (REF 2014), which is the system for assessing the quality of research in the higher education institutions of United Kingdom. We carry out the assessment by employing the composition approach of Network Data Envelopment Analysis (Network DEA). Also, we encompass a qualitative aspect into the exercise based on the categorization of the publications provided by REF 2014.
{"title":"Measuring the Research Performance of UK Computer Science departments via Network DEA","authors":"Gregory Koronakos, Lucie Chytilová, Dimitris Sotiros","doi":"10.1109/IISA.2019.8900759","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900759","url":null,"abstract":"In this study we evaluate the research performance of the Computer Science departments in United Kingdom. We consider the research activity as a series process with two components, where the first component portrays the research productivity and the second component portrays the impact of the research outcomes of each department. The analysis is based on the data drawn from the Research Excellence Framework 2014 (REF 2014), which is the system for assessing the quality of research in the higher education institutions of United Kingdom. We carry out the assessment by employing the composition approach of Network Data Envelopment Analysis (Network DEA). Also, we encompass a qualitative aspect into the exercise based on the categorization of the publications provided by REF 2014.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134494162","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 : 2019-07-01DOI: 10.1109/iisa.2019.8900671
{"title":"IISA 2019 Cover Page","authors":"","doi":"10.1109/iisa.2019.8900671","DOIUrl":"https://doi.org/10.1109/iisa.2019.8900671","url":null,"abstract":"","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280463","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900765
M. Fragkaki, Ioannis Hatzligeroydis, Z. Pálková, Kostantinos Kovas
The core aim of the paper is to present the results of facing the challenge of instructional design in a Virtual Reality environment in the context of the capacity building Erasmus+ KA2 project “Virtual Reality as an Innovative and Immersive Tool for HEIs in Palestine (TESLA)”. The instructional design concerns the development of educational courses based on Virtual Worlds technology for use in Palestinian Higher Educational Institutions (HEIs). Given that existing instructional design models were not sufficient for the above task, the team of the University of Patras (UPAT) developed the project’s Instructional Design Model, as an integration of existing models, supporting the technological design with an efficient pedagogical well-structured framework.
{"title":"Instructional Design in Virtual Reality Environments: The case of Palestinian HEIs","authors":"M. Fragkaki, Ioannis Hatzligeroydis, Z. Pálková, Kostantinos Kovas","doi":"10.1109/IISA.2019.8900765","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900765","url":null,"abstract":"The core aim of the paper is to present the results of facing the challenge of instructional design in a Virtual Reality environment in the context of the capacity building Erasmus+ KA2 project “Virtual Reality as an Innovative and Immersive Tool for HEIs in Palestine (TESLA)”. The instructional design concerns the development of educational courses based on Virtual Worlds technology for use in Palestinian Higher Educational Institutions (HEIs). Given that existing instructional design models were not sufficient for the above task, the team of the University of Patras (UPAT) developed the project’s Instructional Design Model, as an integration of existing models, supporting the technological design with an efficient pedagogical well-structured framework.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133859247","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 : 2019-07-01DOI: 10.1109/IISA.2019.8900730
P. López-Iturri, E. Aguirre, Edgar Batista, M. Celaya-Echarri, L. Azpilicueta, A. Solanas, F. Falcone
Rail transportation is evolving in order to provide enhanced user experience, with the aid of enabling context aware environments, in order to provide multiple services, such as passenger information, entertainment, guidance/assistance or location-based marketing. Wireless systems play a key role in such service provision. In this work, wireless channel analysis for intra-wagon communications will be analyzed, as a function of network topology, indoor wagon characteristics and different wireless systems to be employed. The results provide radio planning analysis which in turn derive in system level indicators, which aid in the definition of quality of service and quality of experience metrics.
{"title":"Intra-Train Connectivity Analysis to Enable Context Aware Passenger Environments","authors":"P. López-Iturri, E. Aguirre, Edgar Batista, M. Celaya-Echarri, L. Azpilicueta, A. Solanas, F. Falcone","doi":"10.1109/IISA.2019.8900730","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900730","url":null,"abstract":"Rail transportation is evolving in order to provide enhanced user experience, with the aid of enabling context aware environments, in order to provide multiple services, such as passenger information, entertainment, guidance/assistance or location-based marketing. Wireless systems play a key role in such service provision. In this work, wireless channel analysis for intra-wagon communications will be analyzed, as a function of network topology, indoor wagon characteristics and different wireless systems to be employed. The results provide radio planning analysis which in turn derive in system level indicators, which aid in the definition of quality of service and quality of experience metrics.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196057","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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