Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558976
Yasir Kilic, Ahmet Büyükeke
Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Turkish texts. In this study, we observe performance of the GCN model on the sentiment classification problem of Turkish texts as first research. Since the structure of Turkish language is agglutinative, different preprocessing approaches are presented and performance results on three real-world Turkish sentiment datasets are shown. It is observed that the TripAdv dataset, which was used in this study, yielded a 0.76 F-measure value. This can be considered a reasonable success for a sentiment classification with three sentiment classes. On the other hand, this study is presented as an exploratory case study in preparation for more detailed and extensive research in the future.
{"title":"An Exploratory Case Study for Turkish Sentiment Classification Using Graph Convolutional Neural Networks","authors":"Yasir Kilic, Ahmet Büyükeke","doi":"10.1109/UBMK52708.2021.9558976","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558976","url":null,"abstract":"Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Turkish texts. In this study, we observe performance of the GCN model on the sentiment classification problem of Turkish texts as first research. Since the structure of Turkish language is agglutinative, different preprocessing approaches are presented and performance results on three real-world Turkish sentiment datasets are shown. It is observed that the TripAdv dataset, which was used in this study, yielded a 0.76 F-measure value. This can be considered a reasonable success for a sentiment classification with three sentiment classes. On the other hand, this study is presented as an exploratory case study in preparation for more detailed and extensive research in the future.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960458","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558997
Okan Tunali, Ahmet Tugrul Bayrak
In today’s business world, where competition is increasing with the increase in product and service diversity, companies are in search of smart methods to bring the right products to their customers. Product bundle generation, in which products that are likely to be purchased together, are collected and presented is one of these methods. In our study, a product bundle production engine is developed based on the sales data of a pioneering chain in the fast-food industry. In the study, which is a component of the product recommendation system, data patterns are learned by extracting product basket statistics and using a customized Gaussian Mixture Model according to the targets. Suitable product bundles for the targets are produced with the depth-first search algorithm, which uses mixture models as a prioritization tool. The study also produces output by considering weighted targets specific to certain customer groups, general purchasing preferences and sales periods. Although the developed model is independent of the sector, it allows for expansion according to business needs, as it consists of discrete modules.
{"title":"Targeted Personalized Product Bundle Generation","authors":"Okan Tunali, Ahmet Tugrul Bayrak","doi":"10.1109/UBMK52708.2021.9558997","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558997","url":null,"abstract":"In today’s business world, where competition is increasing with the increase in product and service diversity, companies are in search of smart methods to bring the right products to their customers. Product bundle generation, in which products that are likely to be purchased together, are collected and presented is one of these methods. In our study, a product bundle production engine is developed based on the sales data of a pioneering chain in the fast-food industry. In the study, which is a component of the product recommendation system, data patterns are learned by extracting product basket statistics and using a customized Gaussian Mixture Model according to the targets. Suitable product bundles for the targets are produced with the depth-first search algorithm, which uses mixture models as a prioritization tool. The study also produces output by considering weighted targets specific to certain customer groups, general purchasing preferences and sales periods. Although the developed model is independent of the sector, it allows for expansion according to business needs, as it consists of discrete modules.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133151035","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558909
Hasan Han, O. Karadeniz, Elena Battini Sönmez, Tuǧba Dalyan, B. Sarıoǧlu
One of the major problems with robot companions is their lack of credibility. Since emotions play a key role in human behaviour their implementation in virtual agents is a conditio sine-qua-non for realistic models. That is, correct classification of facial expressions in the wild is a necessary preprocessing step for implementing artificial empathy. The aim of this work is to implement a robust Facial Expression Recognition (FER) module into a robot. Considering the results of an empirical comparison among the most successful deep learning algorithms used for FER, this study fixes the state-of the-art performance of 75% on the FER2013 database with the ensemble method. With a single model, the best performance of 70.8% has been reached using the VGG16 architecture. Finally, the VGG16-based FER module has been been implemented into a robot and reached a performance of 70% when tested with wild expressive faces.
{"title":"Facial Expression Recognition in the Wild with Application in Robotics","authors":"Hasan Han, O. Karadeniz, Elena Battini Sönmez, Tuǧba Dalyan, B. Sarıoǧlu","doi":"10.1109/UBMK52708.2021.9558909","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558909","url":null,"abstract":"One of the major problems with robot companions is their lack of credibility. Since emotions play a key role in human behaviour their implementation in virtual agents is a conditio sine-qua-non for realistic models. That is, correct classification of facial expressions in the wild is a necessary preprocessing step for implementing artificial empathy. The aim of this work is to implement a robust Facial Expression Recognition (FER) module into a robot. Considering the results of an empirical comparison among the most successful deep learning algorithms used for FER, this study fixes the state-of the-art performance of 75% on the FER2013 database with the ensemble method. With a single model, the best performance of 70.8% has been reached using the VGG16 architecture. Finally, the VGG16-based FER module has been been implemented into a robot and reached a performance of 70% when tested with wild expressive faces.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116426245","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558935
Doruk Tıktıklar, Gürsel Baltaoğlu, Efsa Çakır, Zeynep Küçük, M. Aktaş
This paper examines existing sequence mining algorithms. Sequence mining algorithms are used in many domains, including cyber-security, telecommunications, user behaviour, and air quality patterns. We draw the underlying principles of the representative sequence mining algorithms and introduce a comparative analysis methodology for them. To test the methodology, we provide a prototype testing framework. We conduct a comprehensive experimental study on publicly available data sets, real-life telecommunication data set and data sets generated by a data generator. We compare GSP, PrefixSpan and CMRules algorithms. Comparing these sequence mining algorithms, we conclude that the fastest among the targeted three algorithms may differ for different data sets. Furthermore, we search for situations where sequential pattern mining algorithms can be used instead of sequential rule mining algorithms.
{"title":"On the Comparative Analysis of Sequence Mining Algorithms: Case Study in Telecommunications","authors":"Doruk Tıktıklar, Gürsel Baltaoğlu, Efsa Çakır, Zeynep Küçük, M. Aktaş","doi":"10.1109/UBMK52708.2021.9558935","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558935","url":null,"abstract":"This paper examines existing sequence mining algorithms. Sequence mining algorithms are used in many domains, including cyber-security, telecommunications, user behaviour, and air quality patterns. We draw the underlying principles of the representative sequence mining algorithms and introduce a comparative analysis methodology for them. To test the methodology, we provide a prototype testing framework. We conduct a comprehensive experimental study on publicly available data sets, real-life telecommunication data set and data sets generated by a data generator. We compare GSP, PrefixSpan and CMRules algorithms. Comparing these sequence mining algorithms, we conclude that the fastest among the targeted three algorithms may differ for different data sets. Furthermore, we search for situations where sequential pattern mining algorithms can be used instead of sequential rule mining algorithms.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117210462","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558925
Zeynep Yetiştiren, Can Özbey, Hakki Eren Arkangil
Nowadays, machine learning and deep learning models are used in many fields and giving promising results. Large amounts of labeled data are needed to increase the performance of these models, which become more complex and growing as technology advances. Although a large amount of data is produced every day, labeling this data is a major challenge in the development of these models, as it takes a lot of time and is costly. Active learning is a semi-supervised learning method which helps us overcome this problem. The purpose of active learning is to select and label the most informative examples from unlabeled data. Therefore, same success is achieved with less labeled data. At this stage, it has been observed that query strategies greatly affect the increase in accuracy, and this fact makes us think that the accuracy may increase further if new query strategies are used. In this study, we compare the cosine similarity strategy that we propose with different scenarios, as well as classical query strategies that measure the informativeness of the data. However, higher accuracy increase comparing to classical query strategies could not be observed.
{"title":"Different Scenarios and Query Strategies in Active Learning for Document Classification","authors":"Zeynep Yetiştiren, Can Özbey, Hakki Eren Arkangil","doi":"10.1109/UBMK52708.2021.9558925","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558925","url":null,"abstract":"Nowadays, machine learning and deep learning models are used in many fields and giving promising results. Large amounts of labeled data are needed to increase the performance of these models, which become more complex and growing as technology advances. Although a large amount of data is produced every day, labeling this data is a major challenge in the development of these models, as it takes a lot of time and is costly. Active learning is a semi-supervised learning method which helps us overcome this problem. The purpose of active learning is to select and label the most informative examples from unlabeled data. Therefore, same success is achieved with less labeled data. At this stage, it has been observed that query strategies greatly affect the increase in accuracy, and this fact makes us think that the accuracy may increase further if new query strategies are used. In this study, we compare the cosine similarity strategy that we propose with different scenarios, as well as classical query strategies that measure the informativeness of the data. However, higher accuracy increase comparing to classical query strategies could not be observed.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117341449","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558991
A. Gatiatullin, Lenara Kubedinova, N. Prokopyev
The article describes a multilingual linguistic database for Turkic languages, which is an integral model that includes taxonomic and situational ontologies. The basis of necessity for the development is the fact that the proposed linguistic database is a resource base for a whole set of linguistic processors. This is especially important for the Turkic languages since most of them belong to the type of low-resource languages, precisely because of the lack of linguistic bases, especially of the semantic-syntactic level. At the same time, the proposed model of the linguistic database developed with due regard to the structural and functional features of the Turkic languages, which will increase the efficiency of the linguistic software.
{"title":"Database of Frame Type in the “Turkic Morpheme” Portal","authors":"A. Gatiatullin, Lenara Kubedinova, N. Prokopyev","doi":"10.1109/UBMK52708.2021.9558991","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558991","url":null,"abstract":"The article describes a multilingual linguistic database for Turkic languages, which is an integral model that includes taxonomic and situational ontologies. The basis of necessity for the development is the fact that the proposed linguistic database is a resource base for a whole set of linguistic processors. This is especially important for the Turkic languages since most of them belong to the type of low-resource languages, precisely because of the lack of linguistic bases, especially of the semantic-syntactic level. At the same time, the proposed model of the linguistic database developed with due regard to the structural and functional features of the Turkic languages, which will increase the efficiency of the linguistic software.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115215939","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558957
Asım Kerem Hancı
In our study, we predicted software projects’ risk group by using machine learning algorithms. We conducted ID3 and Naïve Bayes algorithms using ‘development source as count’, ‘software development lifecycle model’ and ‘project size’ parameters. We obtained different accuracy ratios by implementing holdout model.
{"title":"Risk Group Prediction of Software Projects Using Machine Learning Algorithm","authors":"Asım Kerem Hancı","doi":"10.1109/UBMK52708.2021.9558957","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558957","url":null,"abstract":"In our study, we predicted software projects’ risk group by using machine learning algorithms. We conducted ID3 and Naïve Bayes algorithms using ‘development source as count’, ‘software development lifecycle model’ and ‘project size’ parameters. We obtained different accuracy ratios by implementing holdout model.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423142","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558888
Bahar Bender, Mehmet Emre Atasoy, Fatih Semiz
Nowadays, the detection and tracking of stationary or moving objects have begun to be of great importance for military applications as well as for civilian applications. In this case, it is necessary to use deep learning methodologies in order to effectively meet the emerging needs. This study, it is aimed to detect the people and vehicles in the videos recorded by drones in an environment suitable for field conditions. For this purpose, DarkNet-53 architecture in YOLOv3 was used to detect the presence of people and vehicles in motion in videos with 25 (Frame Per Second) images transferred to the screen in one second. The convolutional neural network has been developed by supporting it with various hyperparameter optimizations and an accuracy rate of 78 percent has been achieved.
{"title":"Deep Learning-Based Human and Vehicle Detection in Drone Videos","authors":"Bahar Bender, Mehmet Emre Atasoy, Fatih Semiz","doi":"10.1109/UBMK52708.2021.9558888","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558888","url":null,"abstract":"Nowadays, the detection and tracking of stationary or moving objects have begun to be of great importance for military applications as well as for civilian applications. In this case, it is necessary to use deep learning methodologies in order to effectively meet the emerging needs. This study, it is aimed to detect the people and vehicles in the videos recorded by drones in an environment suitable for field conditions. For this purpose, DarkNet-53 architecture in YOLOv3 was used to detect the presence of people and vehicles in motion in videos with 25 (Frame Per Second) images transferred to the screen in one second. The convolutional neural network has been developed by supporting it with various hyperparameter optimizations and an accuracy rate of 78 percent has been achieved.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122018373","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559018
Şalak Öksüzer, Gükhan Dalkılıç, Cem Kösemen
Nowadays, the Internet of things systems became integrated into our lives. Data produced by the sensors on these systems can be considered personal data that is private. Legal regulations such as the general data protection regulation GDPR secure the storage and sharing of these personal data. It is not easy to automatically control these systems with legal regulations. In this study, we present a new privacy-sharing method of providing privacy for personal data sharing. This method ensures that using blockchain technology secures all privacy sharing steps. We used Quorum as the blockchain infrastructure that is an enterprise blockchain, making data transparently available to the peers in a private and permissioned network.
{"title":"Enterprise Blockchain-Based Privacy Sharing on Internet of Things Devices","authors":"Şalak Öksüzer, Gükhan Dalkılıç, Cem Kösemen","doi":"10.1109/UBMK52708.2021.9559018","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559018","url":null,"abstract":"Nowadays, the Internet of things systems became integrated into our lives. Data produced by the sensors on these systems can be considered personal data that is private. Legal regulations such as the general data protection regulation GDPR secure the storage and sharing of these personal data. It is not easy to automatically control these systems with legal regulations. In this study, we present a new privacy-sharing method of providing privacy for personal data sharing. This method ensures that using blockchain technology secures all privacy sharing steps. We used Quorum as the blockchain infrastructure that is an enterprise blockchain, making data transparently available to the peers in a private and permissioned network.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126974068","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558879
Sercan Sari, Mehmet Çakir, S. Baydere
The main goal of the Internet of Things (IoT) platforms is to create different kinds of smart cloud data services that can respond in real-time to large amounts of sensor data received across these links. Although it has been more than twenty years since the first IoT networks were used, there are still several performance issues that need to be mitigated. This is a challenging issue since IoT scenarios have a wide range of requirements from the network. This led researchers to design different application layer messaging protocols to satisfy these requirements. While several studies investigate the messaging protocols in terms of performance, reliability, security, and energy consumption, almost none of them consider mobility in their comparisons. In this study, we analyze the effect of mobility on the performance of IoT networks. We established a simulation testbed using Netsim Simulator and conducted a set of experiments to observe the effect of mobility on the application layer protocols; HTTP and CoAP. Our results show that mobility can have a significant effect on the throughput, delay and battery consumption of the messaging protocols under consideration.
{"title":"Effect of Mobility on the Performance of IoT Networks","authors":"Sercan Sari, Mehmet Çakir, S. Baydere","doi":"10.1109/UBMK52708.2021.9558879","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558879","url":null,"abstract":"The main goal of the Internet of Things (IoT) platforms is to create different kinds of smart cloud data services that can respond in real-time to large amounts of sensor data received across these links. Although it has been more than twenty years since the first IoT networks were used, there are still several performance issues that need to be mitigated. This is a challenging issue since IoT scenarios have a wide range of requirements from the network. This led researchers to design different application layer messaging protocols to satisfy these requirements. While several studies investigate the messaging protocols in terms of performance, reliability, security, and energy consumption, almost none of them consider mobility in their comparisons. In this study, we analyze the effect of mobility on the performance of IoT networks. We established a simulation testbed using Netsim Simulator and conducted a set of experiments to observe the effect of mobility on the application layer protocols; HTTP and CoAP. Our results show that mobility can have a significant effect on the throughput, delay and battery consumption of the messaging protocols under consideration.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441172","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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