Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096805
A. eman, H. Ramdane
Localizing the mobile robot in an indoor environment is one of the problems encountered repeatedly. Achieving the target precisely in any environment is not an easy task since there are noises and obstacles in the surrounding environment. Therefore, filtering the signals to reduce noises is essential for more accurate and precise motion. In this paper, we selected the extended Kalman filter, which is used for non-linear models’ signals to predict the coordinates of a wheeled mobile robot. We tested the efficiency of this filter under three noise cases: no noise, Gaussian noise and non-Gaussian noise using MATLAB software.
{"title":"Mobile Robot Localization Using Extended Kalman Filter","authors":"A. eman, H. Ramdane","doi":"10.1109/ICCAIS48893.2020.9096805","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096805","url":null,"abstract":"Localizing the mobile robot in an indoor environment is one of the problems encountered repeatedly. Achieving the target precisely in any environment is not an easy task since there are noises and obstacles in the surrounding environment. Therefore, filtering the signals to reduce noises is essential for more accurate and precise motion. In this paper, we selected the extended Kalman filter, which is used for non-linear models’ signals to predict the coordinates of a wheeled mobile robot. We tested the efficiency of this filter under three noise cases: no noise, Gaussian noise and non-Gaussian noise using MATLAB software.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125177639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096723
Aljawharah A. Almjawel, Nourah A. Alerbeed, Ahlam S. Alogily, Ghaliah M. Alotaibi
Nowadays, many students and staff struggle to find specific information about classes or teachers whether in their colleges or any other institution on campus. Therefore, in this research, a mobile application (College Guide) was built to address this need. College Guide was developed as a guide for members and visitors to the College of Computer and Information Technology (CC&IT). The underlying technology upon which the application was based is augmented reality, which was used to enhance the quality and ease of use of the application, as users could get the information easily and contact others effectively. Five steps were carried out to develop the application: determine the problem and write the objectives and solution, analyze the system, gather requirements, design the interfaces, and implement the app. The results of using the system indicated that most users found it easy to use as the complete process included just three steps: open app, scan QR code, and show information. In addition, users found the design of the interfaces to be user-friendly, and no one suffered from using the application. Many users found it fun to use the augmented reality technique. Communication with staff was much faster than in the traditional way. In future work, we intend to extend the system to include all faculties and add more features, like 3D objects and live chat with the administration. We also want to involve the IT department in developing the application so that any faults can be reported and data updated directly.
{"title":"Campus Guide Using Augmented Reality Techniques","authors":"Aljawharah A. Almjawel, Nourah A. Alerbeed, Ahlam S. Alogily, Ghaliah M. Alotaibi","doi":"10.1109/ICCAIS48893.2020.9096723","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096723","url":null,"abstract":"Nowadays, many students and staff struggle to find specific information about classes or teachers whether in their colleges or any other institution on campus. Therefore, in this research, a mobile application (College Guide) was built to address this need. College Guide was developed as a guide for members and visitors to the College of Computer and Information Technology (CC&IT). The underlying technology upon which the application was based is augmented reality, which was used to enhance the quality and ease of use of the application, as users could get the information easily and contact others effectively. Five steps were carried out to develop the application: determine the problem and write the objectives and solution, analyze the system, gather requirements, design the interfaces, and implement the app. The results of using the system indicated that most users found it easy to use as the complete process included just three steps: open app, scan QR code, and show information. In addition, users found the design of the interfaces to be user-friendly, and no one suffered from using the application. Many users found it fun to use the augmented reality technique. Communication with staff was much faster than in the traditional way. In future work, we intend to extend the system to include all faculties and add more features, like 3D objects and live chat with the administration. We also want to involve the IT department in developing the application so that any faults can be reported and data updated directly.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115102221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096786
R. Latypov, E. Stolov
Watermark inserted in an audio file may be damaged after attacking this file. The goal of the paper is the development of a class of watermarks which can be recognized by human being even if the watermark saved only a part of the original information. A picture is leveraged as a watermark, and effective ternary coding of such a picture is suggested. The insertion of the watermark is based on the modulation of the container. The resistance of the watermark to various attacks is investigated. The original container is used while watermark extracted.
{"title":"Ternary Picture as Watermark for Audio Files","authors":"R. Latypov, E. Stolov","doi":"10.1109/ICCAIS48893.2020.9096786","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096786","url":null,"abstract":"Watermark inserted in an audio file may be damaged after attacking this file. The goal of the paper is the development of a class of watermarks which can be recognized by human being even if the watermark saved only a part of the original information. A picture is leveraged as a watermark, and effective ternary coding of such a picture is suggested. The insertion of the watermark is based on the modulation of the container. The resistance of the watermark to various attacks is investigated. The original container is used while watermark extracted.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122704940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096876
Asmaa S. Alswayed, H. Alhichri, Y. Bazi
Scene classification is an important problem in remote sensing (RS) since it is a prerequisite to other more intelligent analysis operations. Given an RS scene, not all of its parts are important for classification. Thus, using an attention mechanism that directs the classification system to focus on the parts that are important and ignore the irrelevant background should enhance the system’s accuracy. In this work we propose a deep CNN architecture based on the pre-trained SqueezeNet CNN. This CNN is composed of nine fire modules (fire 1 to fire 9) each consisting of Squeeze followed by expansion convolution layers. First, we improve the SqueezeNet CNN by introducing several modifications to the architecture. Then we introduce a separate branch to the network that implements an attention mechanism. Each neuron in this activation map of the fire 9 module covers a different receptive field in the original scene. An attention mechanism is applied to these neurons to learn the appropriate weighing scheme for merging the feature vectors corresponding to each neuron. Feature vectors that are assigned a higher weight indicate that the network has given more attention to the receptive field in the scene corresponding to that feature vector. Preliminary results are presenting on five popular scene datasets, namely UC Merced, KSA, AID, Whurs19, and Optimal31 datasets.
{"title":"SqueezeNet with Attention for Remote Sensing Scene Classification","authors":"Asmaa S. Alswayed, H. Alhichri, Y. Bazi","doi":"10.1109/ICCAIS48893.2020.9096876","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096876","url":null,"abstract":"Scene classification is an important problem in remote sensing (RS) since it is a prerequisite to other more intelligent analysis operations. Given an RS scene, not all of its parts are important for classification. Thus, using an attention mechanism that directs the classification system to focus on the parts that are important and ignore the irrelevant background should enhance the system’s accuracy. In this work we propose a deep CNN architecture based on the pre-trained SqueezeNet CNN. This CNN is composed of nine fire modules (fire 1 to fire 9) each consisting of Squeeze followed by expansion convolution layers. First, we improve the SqueezeNet CNN by introducing several modifications to the architecture. Then we introduce a separate branch to the network that implements an attention mechanism. Each neuron in this activation map of the fire 9 module covers a different receptive field in the original scene. An attention mechanism is applied to these neurons to learn the appropriate weighing scheme for merging the feature vectors corresponding to each neuron. Feature vectors that are assigned a higher weight indicate that the network has given more attention to the receptive field in the scene corresponding to that feature vector. Preliminary results are presenting on five popular scene datasets, namely UC Merced, KSA, AID, Whurs19, and Optimal31 datasets.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124067715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096810
Aminah Alqahtani, Manal Alnefaie, Nourah Alamri, Ahmad Khorsi
Arabic language is one of the most complex languages in Natural Language Processing (NLP). Solr is an Information Retrieval System (IRS) that is widely known for its accurate results and high performance in English. However, Arabic stemmer that is currently used by Solr is called Light-10 which has some deficiencies. In this approach, we evaluated two light stemmers (Assem, Tashaphyne) and two root stemmers (Khoja, ISRI) and chose the two stemmers that the experiments show the best; in addition to Light-10 stemmer. The highest two stemmers are Assem and Khoja. So, we used these two stemmers and Light-10 to evaluate the search retrieval accuracy of Solr in Arabic, then evaluated them again with synonyms. The evaluation is based on using two metrics Precision and Normalized Discounted Cumulative Gain (NDCG). Assem stemmer has the highest accuracy which is 86%, Light-10 is 83% and Khoja is 81%. Finally, Assem stemmer has been used as the stemmer for Almufed search engine that we developed in this approach based on Solr for more than 6000 Arabic books from Alshamela Library.
{"title":"Enhancing the Capabilities of Solr Information Retrieval System: Arabic Language","authors":"Aminah Alqahtani, Manal Alnefaie, Nourah Alamri, Ahmad Khorsi","doi":"10.1109/ICCAIS48893.2020.9096810","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096810","url":null,"abstract":"Arabic language is one of the most complex languages in Natural Language Processing (NLP). Solr is an Information Retrieval System (IRS) that is widely known for its accurate results and high performance in English. However, Arabic stemmer that is currently used by Solr is called Light-10 which has some deficiencies. In this approach, we evaluated two light stemmers (Assem, Tashaphyne) and two root stemmers (Khoja, ISRI) and chose the two stemmers that the experiments show the best; in addition to Light-10 stemmer. The highest two stemmers are Assem and Khoja. So, we used these two stemmers and Light-10 to evaluate the search retrieval accuracy of Solr in Arabic, then evaluated them again with synonyms. The evaluation is based on using two metrics Precision and Normalized Discounted Cumulative Gain (NDCG). Assem stemmer has the highest accuracy which is 86%, Light-10 is 83% and Khoja is 81%. Finally, Assem stemmer has been used as the stemmer for Almufed search engine that we developed in this approach based on Solr for more than 6000 Arabic books from Alshamela Library.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130094894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096881
Mehwash Farooqui, Maha N. Aldughreer, Abeer I. Alsomali, Khadijah H. Alhyder, Sarah A. Alzayed, N. Aslam, Mohammed D. Alahmri, Muneera A. Alhajri
Our propose system is a Non-invasive device and automated monitoring system to assist patients and healthcare provider in tracking the patient condition. Asthma patients need to constantly perform self-monitoring at home using a device called "peak flow meter". The device results are recorded during a period to be reviewed later with a doctor. The application uses the collected PEF scores and determine the patient’s status, then shows recommendations based on the action plan. The system also implements machine learning (ML) to predict if the patient will decline then sending him alerts, it also helps the patient adhere to his treatment plan by reminding him of his medications times and recording if he took it.
{"title":"A Non-invasive device and automated monitoring system using peak flow meter for asthma patients","authors":"Mehwash Farooqui, Maha N. Aldughreer, Abeer I. Alsomali, Khadijah H. Alhyder, Sarah A. Alzayed, N. Aslam, Mohammed D. Alahmri, Muneera A. Alhajri","doi":"10.1109/ICCAIS48893.2020.9096881","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096881","url":null,"abstract":"Our propose system is a Non-invasive device and automated monitoring system to assist patients and healthcare provider in tracking the patient condition. Asthma patients need to constantly perform self-monitoring at home using a device called \"peak flow meter\". The device results are recorded during a period to be reviewed later with a doctor. The application uses the collected PEF scores and determine the patient’s status, then shows recommendations based on the action plan. The system also implements machine learning (ML) to predict if the patient will decline then sending him alerts, it also helps the patient adhere to his treatment plan by reminding him of his medications times and recording if he took it.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096721
Najd Alosaimi, H. Alhichri
Scene classification problem in remote sensing (RS) images has attracted many researchers recently. Different fusion methods have been widely used by the machine learning community to fuse classifiers. In this paper, a decision-level fusion method has been proposed to fuse a set of stat-of-the-art CNN classifiers, namely VGG-16, SqueezeNet, and DenseNet. First, the experiment proves that these classifiers do not make the same classification mistakes, i.e. most of the time at least one of them provides correct classification. Thus these three classifiers are diverse and hence complement each other. To exploit this discovery, a novel decision-level fusion method that combines the classification decisions using voting and confidence fusion techniques has been developed. To show the effectiveness of the proposed fusion method, the results demonstrate how the accuracy of the classification can be enhanced using fusion versus training individual networks. The preliminary results for the UC Merced dataset, the KSA multisensor dataset, Aerial Image Datasets (AID), Optimal31 dataset and Whurs19 dataset have been presented. Preliminary comparison to state-of-the-art methods show the promising capabilities of this solution and encourages to investigate this method further.
{"title":"Fusion of CNN ensemble for Remote Sensing Scene Classification","authors":"Najd Alosaimi, H. Alhichri","doi":"10.1109/ICCAIS48893.2020.9096721","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096721","url":null,"abstract":"Scene classification problem in remote sensing (RS) images has attracted many researchers recently. Different fusion methods have been widely used by the machine learning community to fuse classifiers. In this paper, a decision-level fusion method has been proposed to fuse a set of stat-of-the-art CNN classifiers, namely VGG-16, SqueezeNet, and DenseNet. First, the experiment proves that these classifiers do not make the same classification mistakes, i.e. most of the time at least one of them provides correct classification. Thus these three classifiers are diverse and hence complement each other. To exploit this discovery, a novel decision-level fusion method that combines the classification decisions using voting and confidence fusion techniques has been developed. To show the effectiveness of the proposed fusion method, the results demonstrate how the accuracy of the classification can be enhanced using fusion versus training individual networks. The preliminary results for the UC Merced dataset, the KSA multisensor dataset, Aerial Image Datasets (AID), Optimal31 dataset and Whurs19 dataset have been presented. Preliminary comparison to state-of-the-art methods show the promising capabilities of this solution and encourages to investigate this method further.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128616192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096824
S. Sheikh, Nadhir Ben Halima
The fourth industrial revolution (4IR) is expected to change our lives. One of the main players towards 4IR will be Internet of Things (IoT). The main limiting factor for minimum IoT application implementation in Sub-Saharan Africa rural areas is that many areas do not have communication infrastructures mainly due to budget constraints. Recently Long Range (LoRA) has been gaining research for such applications. In this paper we present the concept that multi-hop networks based on IEEE802.11 and Schedule before Contention Scheduling strategies can also provide backhaul communication infrastructure. The use of the RWS-AGE Strategy tested in multi-hop networks in this paper has shown a reduction in the number of collisions, packet loss and end-to-end latency compared to Enhanced Distributed Channel Access (EDCA).
{"title":"Building Backhaul Networks for Rural Area Connectivity towards the Fourth Industrial Revolution","authors":"S. Sheikh, Nadhir Ben Halima","doi":"10.1109/ICCAIS48893.2020.9096824","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096824","url":null,"abstract":"The fourth industrial revolution (4IR) is expected to change our lives. One of the main players towards 4IR will be Internet of Things (IoT). The main limiting factor for minimum IoT application implementation in Sub-Saharan Africa rural areas is that many areas do not have communication infrastructures mainly due to budget constraints. Recently Long Range (LoRA) has been gaining research for such applications. In this paper we present the concept that multi-hop networks based on IEEE802.11 and Schedule before Contention Scheduling strategies can also provide backhaul communication infrastructure. The use of the RWS-AGE Strategy tested in multi-hop networks in this paper has shown a reduction in the number of collisions, packet loss and end-to-end latency compared to Enhanced Distributed Channel Access (EDCA).","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127245350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/ICCAIS48893.2020.9096867
Abdelhameed S. Eltanany, M. Safy
As the first step in image processing operations, corner detection is a vital procedure where it can be applied to many applications such as matching features, image registration, image mosaicking, change detection…. Registration of images can be described as the process of getting the pixel location misaligned between two or more images. A modified corner detector is proposed in this paper based on a combination of both phase congruence, later named PC, and Harris corner detector where PC image can provide fundamental and meaningful features despite complex changes in intensity. The performance was similar to detectors for the Shi-Tomasi, FAST, and Harris corner. Experiments are carried out using simulated images. As metric metrics, MSE (mean square error) and PSNR (peak signal-to-noise ratio) are used. The experimental results verify the effectiveness where the advantages of image constitutional representation are utilized, allowing the extraction of the powerful key points since it preserves the robustness of the coregistration process using image frequency properties that are not variant to illumination. It also has the ability to produce reasonable results as opposed to state-of-the-art such as Shi-Tomasi, FAST, and Harris algorithms.
{"title":"SAR Images Co-registration Based on Phase Congruency Algorithm","authors":"Abdelhameed S. Eltanany, M. Safy","doi":"10.1109/ICCAIS48893.2020.9096867","DOIUrl":"https://doi.org/10.1109/ICCAIS48893.2020.9096867","url":null,"abstract":"As the first step in image processing operations, corner detection is a vital procedure where it can be applied to many applications such as matching features, image registration, image mosaicking, change detection…. Registration of images can be described as the process of getting the pixel location misaligned between two or more images. A modified corner detector is proposed in this paper based on a combination of both phase congruence, later named PC, and Harris corner detector where PC image can provide fundamental and meaningful features despite complex changes in intensity. The performance was similar to detectors for the Shi-Tomasi, FAST, and Harris corner. Experiments are carried out using simulated images. As metric metrics, MSE (mean square error) and PSNR (peak signal-to-noise ratio) are used. The experimental results verify the effectiveness where the advantages of image constitutional representation are utilized, allowing the extraction of the powerful key points since it preserves the robustness of the coregistration process using image frequency properties that are not variant to illumination. It also has the ability to produce reasonable results as opposed to state-of-the-art such as Shi-Tomasi, FAST, and Harris algorithms.","PeriodicalId":422184,"journal":{"name":"2020 3rd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130797463","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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