Pub Date : 2019-12-01DOI: 10.1109/ACIT47987.2019.8990974
Alaa Jaradat, A. Qusef
Unit testing allows developers to refactor their code confidently, these tests act as a safety net against producing bugs and provide immediate feedback during the refactoring process and furthermore help developers overcome the fear of change. When performing refactoring, the design of code is changed or restructured according to a predefined plan, after refactoring is applied, the alignment between source code and its corresponding unit tests could be broken which creates a problem that needs to be solved.This paper introduces an approach in which code refactoring can maintain the integrity of the previous unit tests; the tool called GreenRef demonstrates this work. This tool supports an automatic recovery for the unit tests after performing of three particular refactoring types for Java programming language: Rename Method, Add Parameter and Remove Parameter.The achieved results indicate that GreenRef facilitates consistent use of refactoring and unit tests, and save about 43% of the time required to recover broken unit tests manually.
{"title":"Automatic Recovery of Unit Tests after Code Refactoring","authors":"Alaa Jaradat, A. Qusef","doi":"10.1109/ACIT47987.2019.8990974","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8990974","url":null,"abstract":"Unit testing allows developers to refactor their code confidently, these tests act as a safety net against producing bugs and provide immediate feedback during the refactoring process and furthermore help developers overcome the fear of change. When performing refactoring, the design of code is changed or restructured according to a predefined plan, after refactoring is applied, the alignment between source code and its corresponding unit tests could be broken which creates a problem that needs to be solved.This paper introduces an approach in which code refactoring can maintain the integrity of the previous unit tests; the tool called GreenRef demonstrates this work. This tool supports an automatic recovery for the unit tests after performing of three particular refactoring types for Java programming language: Rename Method, Add Parameter and Remove Parameter.The achieved results indicate that GreenRef facilitates consistent use of refactoring and unit tests, and save about 43% of the time required to recover broken unit tests manually.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432842","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991064
A. Odeh, Raghad Abdelhadi, Hussien Odeh
Taking an appointment means go to the medical center, asking about the suitable doctor for your case, spend a lot of time, or make a phone call, or take an appointment with general doctor, after that let he/she decide to which specialists you have to go; it is very long, and boring process. The main aim of this research is supporting Smart Cities Approach in UAE by designing and implementing system and mobile application “Mwa3edk” to add new concepts for the process of taking appointments with doctors in hospitals and medical clinics by transferring this process into the online world technology. This system will be able to connect a huge number of hospitals and clinics with users over UAE; and enable people to look for doctors in different locations and take appointments that suite them. In addition, users can describe their symptoms then the application will give them recommendations according to what they described using the embedded artificial intelligent method, this will help users to avoid one step, where they can take appointment directly with a specialized doctor instead of meeting the general doctor first.
{"title":"Medical patient appointments management using smart software system in UAE","authors":"A. Odeh, Raghad Abdelhadi, Hussien Odeh","doi":"10.1109/ACIT47987.2019.8991064","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991064","url":null,"abstract":"Taking an appointment means go to the medical center, asking about the suitable doctor for your case, spend a lot of time, or make a phone call, or take an appointment with general doctor, after that let he/she decide to which specialists you have to go; it is very long, and boring process. The main aim of this research is supporting Smart Cities Approach in UAE by designing and implementing system and mobile application “Mwa3edk” to add new concepts for the process of taking appointments with doctors in hospitals and medical clinics by transferring this process into the online world technology. This system will be able to connect a huge number of hospitals and clinics with users over UAE; and enable people to look for doctors in different locations and take appointments that suite them. In addition, users can describe their symptoms then the application will give them recommendations according to what they described using the embedded artificial intelligent method, this will help users to avoid one step, where they can take appointment directly with a specialized doctor instead of meeting the general doctor first.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124147","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991099
Mostafa Abdallah, Ali Saad, M. Ayache
Freezing of gait (FoG) is one of the Parkinson’s disease (PD) symptoms that appears as an episodic incapability to walk. It usually occurs in patients with advanced PD, and it is a common reason for falls and injury in Parkinson’s disease patients. Freezing of gait must be carefully monitored because it not only decreases the patient’s quality of life, but also significantly rises the risk of injury. In this work, we presented an automatic freezing of gait detection system that is based on the convolutional neural networks (CNNs). The proposed system can perform automatic feature learning and distinguish between freezing events and normal gait. The proposed system eliminates the need for manually extract features and feature selection. The data was collected using five sensors: two telemeters, two accelerometers, and one goniometer. The proposed architecture discriminated the freezing events from the normal walking with an accuracy, specificity, and sensitivity more than 95%.
{"title":"Freezing of Gait Detection: Deep Learning Approach","authors":"Mostafa Abdallah, Ali Saad, M. Ayache","doi":"10.1109/ACIT47987.2019.8991099","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991099","url":null,"abstract":"Freezing of gait (FoG) is one of the Parkinson’s disease (PD) symptoms that appears as an episodic incapability to walk. It usually occurs in patients with advanced PD, and it is a common reason for falls and injury in Parkinson’s disease patients. Freezing of gait must be carefully monitored because it not only decreases the patient’s quality of life, but also significantly rises the risk of injury. In this work, we presented an automatic freezing of gait detection system that is based on the convolutional neural networks (CNNs). The proposed system can perform automatic feature learning and distinguish between freezing events and normal gait. The proposed system eliminates the need for manually extract features and feature selection. The data was collected using five sensors: two telemeters, two accelerometers, and one goniometer. The proposed architecture discriminated the freezing events from the normal walking with an accuracy, specificity, and sensitivity more than 95%.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063612","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991114
Mohammad Abuthawabeh, Khaled W. Mahmoud
The number of malware in Android environment is increasing. As a result, the conventional detection algorithms that employ signature detection methods are facing challenges to cope with the huge number of attacks. In this respect, a supervised-based model that can enhance the accuracy and the depth of the malware detection and categorization process using a conversation-level feature is presented. The ensemble learning technique was employed in order to select the most useful features. A comparison between the methods provided in this research and the results of other studies that used the same dataset is given. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75% for malware detection and 79.97% for malware categorization. Finally, this study has achieved significant enhancement in malware categorization rate by 30.2% for precision and 31.14% recall in comparison with other studies that used the same dataset.
{"title":"Android Malware Detection and Categorization Based on Conversation-level Network Traffic Features","authors":"Mohammad Abuthawabeh, Khaled W. Mahmoud","doi":"10.1109/ACIT47987.2019.8991114","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991114","url":null,"abstract":"The number of malware in Android environment is increasing. As a result, the conventional detection algorithms that employ signature detection methods are facing challenges to cope with the huge number of attacks. In this respect, a supervised-based model that can enhance the accuracy and the depth of the malware detection and categorization process using a conversation-level feature is presented. The ensemble learning technique was employed in order to select the most useful features. A comparison between the methods provided in this research and the results of other studies that used the same dataset is given. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75% for malware detection and 79.97% for malware categorization. Finally, this study has achieved significant enhancement in malware categorization rate by 30.2% for precision and 31.14% recall in comparison with other studies that used the same dataset.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116732187","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 : 2019-12-01DOI: 10.1109/acit47987.2019.8991017
{"title":"ACIT 2019 Technical Committee","authors":"","doi":"10.1109/acit47987.2019.8991017","DOIUrl":"https://doi.org/10.1109/acit47987.2019.8991017","url":null,"abstract":"","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190671","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991048
Ismail Hmiedi, Hassan M. Najadat, Zain A. Halloush, Ibtihal Jalabneh
Educational Data Mining (EDM) is a developing research field that has driven many researchers’ interests. The advancement in applying statistical and conventional measurements on the academic process has taken huge leaps in the past few years. In this paper, a robust prediction model based on the Random Forest Algorithm is provided. In this paper, a data set for graduate students in the University of California in Los Angeles was utilized to predict the admission acceptance. The model uses semi supervised learning for prediction and shows promising results with 91% accuracy. The suggested model provides a list of important features to be considered when applying for a university.
{"title":"Semi Supervised Prediction Model in Educational Data Mining","authors":"Ismail Hmiedi, Hassan M. Najadat, Zain A. Halloush, Ibtihal Jalabneh","doi":"10.1109/ACIT47987.2019.8991048","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991048","url":null,"abstract":"Educational Data Mining (EDM) is a developing research field that has driven many researchers’ interests. The advancement in applying statistical and conventional measurements on the academic process has taken huge leaps in the past few years. In this paper, a robust prediction model based on the Random Forest Algorithm is provided. In this paper, a data set for graduate students in the University of California in Los Angeles was utilized to predict the admission acceptance. The model uses semi supervised learning for prediction and shows promising results with 91% accuracy. The suggested model provides a list of important features to be considered when applying for a university.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132475219","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991014
Hussein Salem Alsrehan
The objective of this study is to understand the factors that contribute to students’ involvement in online courses and to predict community satisfaction with this modern approach of education. By providing an opportunity to listen to students’ online experiences, this study highlights students’ experiences in receiving online courses in UAE universities. It is critical that we remain to rise developed teaching in conducts that enhance the effectiveness of Forms of student involvement. Student participation is particularly significant in with regard to online-supported learning, given the assessments associated with it, on the other hand, The purpose of this research is to focus and create a connection between community Satisfaction (CS) and Factors influencing education and e-learning technology (EET) among students in higher education universities in the United Arab Emirates (UAE); it is possible to create recommendations for enthusiastic trainers and educational designers; Improve students’ participation in their online courses. Student participation is also essential for students to learn and succeed in online sessions. this study also describes the impact of information technology on the participation of UAE universities and used the online community and surveyed the scope of participation to share student testing in three types of interactions, student instructor (SI), student content (SC), and student information technology (SIT) in undergraduate and postgraduate, courses online highlighted the results of the factors analysis highlighted the factors that contribute to the interaction of the student and the teacher and interaction with the student’s content. Data from 439 students at UAE universities were used and community satisfaction (CS) was predicted after a series of analysis, which showed that there was a positive relationship between CS and the factors that create EET.
{"title":"Factors Influencing Education and E-learning Technology in UAE Universities as a Predictor of Community Satisfaction","authors":"Hussein Salem Alsrehan","doi":"10.1109/ACIT47987.2019.8991014","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991014","url":null,"abstract":"The objective of this study is to understand the factors that contribute to students’ involvement in online courses and to predict community satisfaction with this modern approach of education. By providing an opportunity to listen to students’ online experiences, this study highlights students’ experiences in receiving online courses in UAE universities. It is critical that we remain to rise developed teaching in conducts that enhance the effectiveness of Forms of student involvement. Student participation is particularly significant in with regard to online-supported learning, given the assessments associated with it, on the other hand, The purpose of this research is to focus and create a connection between community Satisfaction (CS) and Factors influencing education and e-learning technology (EET) among students in higher education universities in the United Arab Emirates (UAE); it is possible to create recommendations for enthusiastic trainers and educational designers; Improve students’ participation in their online courses. Student participation is also essential for students to learn and succeed in online sessions. this study also describes the impact of information technology on the participation of UAE universities and used the online community and surveyed the scope of participation to share student testing in three types of interactions, student instructor (SI), student content (SC), and student information technology (SIT) in undergraduate and postgraduate, courses online highlighted the results of the factors analysis highlighted the factors that contribute to the interaction of the student and the teacher and interaction with the student’s content. Data from 439 students at UAE universities were used and community satisfaction (CS) was predicted after a series of analysis, which showed that there was a positive relationship between CS and the factors that create EET.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"25 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652261","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991097
M. Shurman, Rami Khrais, Abdulrahman A. Yateem
the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.
{"title":"IoT Denial-of-Service Attack Detection and Prevention Using Hybrid IDS","authors":"M. Shurman, Rami Khrais, Abdulrahman A. Yateem","doi":"10.1109/ACIT47987.2019.8991097","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991097","url":null,"abstract":"the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306247","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991120
Ahmad Al-qerem, Yasmeen Shaher Alsalman, Khalid Mansour
Generative adversarial networks (GANs) can be used in modeling highly complex distributions for real world data, especially images. This paper compares between two different models of the Generative Adversarial Networks: the Multi-Agent Diverse Generative Adversarial Networks (MAD-GAN) which consists of multi-generator and one discriminator and the Generative Multi-Adversarial Networks (GMAN) that has multiple discriminators and one generator. The results show that both MAD-GAN and GMAN outperformed the DCGAN. In addition, MAD-GAN performs better than GMAN when avoiding mode collapse or when the dataset contains many different modes.
{"title":"Image Generation Using Different Models Of Generative Adversarial Network","authors":"Ahmad Al-qerem, Yasmeen Shaher Alsalman, Khalid Mansour","doi":"10.1109/ACIT47987.2019.8991120","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991120","url":null,"abstract":"Generative adversarial networks (GANs) can be used in modeling highly complex distributions for real world data, especially images. This paper compares between two different models of the Generative Adversarial Networks: the Multi-Agent Diverse Generative Adversarial Networks (MAD-GAN) which consists of multi-generator and one discriminator and the Generative Multi-Adversarial Networks (GMAN) that has multiple discriminators and one generator. The results show that both MAD-GAN and GMAN outperformed the DCGAN. In addition, MAD-GAN performs better than GMAN when avoiding mode collapse or when the dataset contains many different modes.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116477277","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 : 2019-12-01DOI: 10.1109/ACIT47987.2019.8991016
Mohammed Mahmod Shuaib
The capability of overtaking moving obstacles is an essential factor for accomplishing several aspects of pedestrian walking flow. In the Hajj area, for example, overtaking dynamic structures constituted by groups, barriers, and other moving obstacles is a vital phenomenon emerged while performing Hajj rituals. In emergency situation, the awareness of the dynamic behavior of moving obstacles is indispensable for achieving typical evacuation. This article proposes an essential intelligence approach to performing further realistic evacuation simulations. We provide each agent with the capability of selecting intermediate destination that enables him reaching his preferred destination; the agent continuously adapts his own trajectory that enables him to overtake such dynamic obstacles by selecting intermediate destinations to pass through. A collision avoidance field which composes of two-dimension grid of cells is proposed to cover the floor of the physical environment. The agent selects the optimal cells which achieve less potential of collision and minimize the distance to the original destination. The proposed model is integrated in a microscopic crowd dynamics model, and simulations are performed to examine the impact of the extended model on introducing further realistic and efficient evacuation.
{"title":"Modeling Collision Avoidance Field for Overtaking Moving Obstacles","authors":"Mohammed Mahmod Shuaib","doi":"10.1109/ACIT47987.2019.8991016","DOIUrl":"https://doi.org/10.1109/ACIT47987.2019.8991016","url":null,"abstract":"The capability of overtaking moving obstacles is an essential factor for accomplishing several aspects of pedestrian walking flow. In the Hajj area, for example, overtaking dynamic structures constituted by groups, barriers, and other moving obstacles is a vital phenomenon emerged while performing Hajj rituals. In emergency situation, the awareness of the dynamic behavior of moving obstacles is indispensable for achieving typical evacuation. This article proposes an essential intelligence approach to performing further realistic evacuation simulations. We provide each agent with the capability of selecting intermediate destination that enables him reaching his preferred destination; the agent continuously adapts his own trajectory that enables him to overtake such dynamic obstacles by selecting intermediate destinations to pass through. A collision avoidance field which composes of two-dimension grid of cells is proposed to cover the floor of the physical environment. The agent selects the optimal cells which achieve less potential of collision and minimize the distance to the original destination. The proposed model is integrated in a microscopic crowd dynamics model, and simulations are performed to examine the impact of the extended model on introducing further realistic and efficient evacuation.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116446466","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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