Pub Date : 2014-12-01DOI: 10.1109/HIS.2014.7086208
M. Al-Berry, H. M. Ebied, A. S. Hussein, M. Tolba
Multi-scale methods, especially wavelets, are being used in various computer vision applications, including surveillance, robotics, and human-centered computing. Human action recognition is one of the core areas that dominate the aforementioned applications. In this paper, the 3D multi-scale stationary wavelet analysis is used to build a view-based multi-scale spatio-temporal representation of the human actions. The proposed representation benefits from the ability of the 3D stationary wavelet transform to fuse the spatio-temporal information highlighted at different scales and orientations. Experimental results using Weizmann and KTH datasets revealed a good performance in various scenarios with different conditions.
{"title":"Human action recognition via multi-scale 3D stationary wavelet analysis","authors":"M. Al-Berry, H. M. Ebied, A. S. Hussein, M. Tolba","doi":"10.1109/HIS.2014.7086208","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086208","url":null,"abstract":"Multi-scale methods, especially wavelets, are being used in various computer vision applications, including surveillance, robotics, and human-centered computing. Human action recognition is one of the core areas that dominate the aforementioned applications. In this paper, the 3D multi-scale stationary wavelet analysis is used to build a view-based multi-scale spatio-temporal representation of the human actions. The proposed representation benefits from the ability of the 3D stationary wavelet transform to fuse the spatio-temporal information highlighted at different scales and orientations. Experimental results using Weizmann and KTH datasets revealed a good performance in various scenarios with different conditions.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114185893","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086197
Wiam Elleuch, A. Wali, A. Alimi
This paper describes a process of converting raw Global Positioning System (GPS) data to a routable road map. In fact, it is a large scale database collected from thousands of vehicles circulating on Tunisian public roads. Moreover, the paper contains the architecture used to collect GPS data from these vehicles using GPRS connection and all the steps until getting the road traces. The data flow is composed of many steps which are: Collecting data which consists of extracting National Marine Electronics Association(NMEA) sentences; Filtering raw GPS nodes to eliminate outliers and noise caused by several sources of errors; Clustering step, in which we used two methods partitional (k-means)and hierarchical (agglomerative)clustering techniques. We compare them and we choose the most suitable for our work. In fact, K-means algorithm is carried out in order to partition data and facilitate handling the big data sets; Generating a Tunisian map network from our database and map-matching it with Google maps in order to make a comparison between them.
{"title":"Mining road map from big database of GPS data","authors":"Wiam Elleuch, A. Wali, A. Alimi","doi":"10.1109/HIS.2014.7086197","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086197","url":null,"abstract":"This paper describes a process of converting raw Global Positioning System (GPS) data to a routable road map. In fact, it is a large scale database collected from thousands of vehicles circulating on Tunisian public roads. Moreover, the paper contains the architecture used to collect GPS data from these vehicles using GPRS connection and all the steps until getting the road traces. The data flow is composed of many steps which are: Collecting data which consists of extracting National Marine Electronics Association(NMEA) sentences; Filtering raw GPS nodes to eliminate outliers and noise caused by several sources of errors; Clustering step, in which we used two methods partitional (k-means)and hierarchical (agglomerative)clustering techniques. We compare them and we choose the most suitable for our work. In fact, K-means algorithm is carried out in order to partition data and facilitate handling the big data sets; Generating a Tunisian map network from our database and map-matching it with Google maps in order to make a comparison between them.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129299729","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086211
M. Duarte, Estevam Hruschka
The first Never-Ending Learning system reported in the literature, which is called NELL (Never-Ending Language Learner), was designed to perform the task of autonomously building an knowledge base as a result of continuously reading the web. NELL is based on a learning paradigm in which, the learner, in an autonomous way, manages to constantly, incrementally and continuously evolve with time. But, most important than just keep evolving, in this paradigm acquired knowledge is used, in a dynamic way, to expand the scope and improve the performance of the learning task as a whole. Coreference resolution plays a key role in any system based on the Never-Ending Learning paradigm. In this paper two diferente views of correference resolution are applied to NELL's knowledge base and empirical evidence is obtained to show that combining morphological and semantic features in a hybrid model can be more effective than using only one of the feature views.
{"title":"Exploring two views of coreference resolution in a never-ending learning system","authors":"M. Duarte, Estevam Hruschka","doi":"10.1109/HIS.2014.7086211","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086211","url":null,"abstract":"The first Never-Ending Learning system reported in the literature, which is called NELL (Never-Ending Language Learner), was designed to perform the task of autonomously building an knowledge base as a result of continuously reading the web. NELL is based on a learning paradigm in which, the learner, in an autonomous way, manages to constantly, incrementally and continuously evolve with time. But, most important than just keep evolving, in this paradigm acquired knowledge is used, in a dynamic way, to expand the scope and improve the performance of the learning task as a whole. Coreference resolution plays a key role in any system based on the Never-Ending Learning paradigm. In this paper two diferente views of correference resolution are applied to NELL's knowledge base and empirical evidence is obtained to show that combining morphological and semantic features in a hybrid model can be more effective than using only one of the feature views.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"73 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125988912","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086213
S. Kéfi, I. Kallel, A. Alimi
Autonomous MultiRobot Systems are developing useful capabilities in several fields of applications as surveillance, exploration and space cleaning. Moreover, important features are of robotics' environments like avoid collision and planning should be handled. Furthermore, the distributed planning approaches, considered as MultiAgent planning, can be thought as a specialization of distributed problem solving. Therefore, this paper started by propounds a review on some planning approaches for MultiRobot Systems and describes the subsumption architecture of the mobile robot control in the MultiRobot system by highlighting the lowest level which is the obstacle avoidance using the soft computing technique. We present also in this research a simulation of MultiRobot for parallel spaces cleaning.
{"title":"Hybrid planning approaches for multirobot systems: A review and a proposal of a MultiAgent subsumption simulation","authors":"S. Kéfi, I. Kallel, A. Alimi","doi":"10.1109/HIS.2014.7086213","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086213","url":null,"abstract":"Autonomous MultiRobot Systems are developing useful capabilities in several fields of applications as surveillance, exploration and space cleaning. Moreover, important features are of robotics' environments like avoid collision and planning should be handled. Furthermore, the distributed planning approaches, considered as MultiAgent planning, can be thought as a specialization of distributed problem solving. Therefore, this paper started by propounds a review on some planning approaches for MultiRobot Systems and describes the subsumption architecture of the mobile robot control in the MultiRobot system by highlighting the lowest level which is the obstacle avoidance using the soft computing technique. We present also in this research a simulation of MultiRobot for parallel spaces cleaning.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908052","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086189
D. Khattab, H. M. Ebied, A. S. Hussein, M. Tolba
This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.
{"title":"Multi-label automatic GrabCut for image segmentation","authors":"D. Khattab, H. M. Ebied, A. S. Hussein, M. Tolba","doi":"10.1109/HIS.2014.7086189","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086189","url":null,"abstract":"This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"1103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127430990","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086166
Yassine Aribi, F. Hamza, A. Wali, F. Guermazi, A. Alimi
In this paper, we propose, a semi-automatic approach for the identification of Regions Of Interest (ROI) of the kidneys (healthy and diseased) on dynamic scintigraphic images. The triggering of the method depend on the intervention of an expert, such as a specialist in nuclear medicine. Our contribution is made obvious here through referring to the adaptive threshold relying on the calculation of the gradients histogram and thus accurately detecting the places of the region of interest. The adaptive threshold will be the average of the histogram of the matrix of the calculated gradients. This approach was tested on dynamic scintigraphic images acquired clinically and satisfactory results are obtained.
{"title":"ARG: A semi-automatic system for ROI detection on Renal Scintigraphic images","authors":"Yassine Aribi, F. Hamza, A. Wali, F. Guermazi, A. Alimi","doi":"10.1109/HIS.2014.7086166","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086166","url":null,"abstract":"In this paper, we propose, a semi-automatic approach for the identification of Regions Of Interest (ROI) of the kidneys (healthy and diseased) on dynamic scintigraphic images. The triggering of the method depend on the intervention of an expert, such as a specialist in nuclear medicine. Our contribution is made obvious here through referring to the adaptive threshold relying on the calculation of the gradients histogram and thus accurately detecting the places of the region of interest. The adaptive threshold will be the average of the histogram of the matrix of the calculated gradients. This approach was tested on dynamic scintigraphic images acquired clinically and satisfactory results are obtained.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115656098","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086167
S. S. Sohail, Jamshed Siddiqui, R. Ali
The intense growth of the modern technologies has caused data overload over the Internet. The increasing data over the World Wide Web has created the problems for the users to extract the exact information. The growth of the Internet has also boosted the e-commerce. The popularity of online shopping has grown up rapidly. Online shopping has become much more popular. While browsing the e-marketing portals, multiple options are presented before users; hence picking the right item is a difficult job. In this paper we propose a recommendation method for books. We have adopted link mining approach to recommend books using Ordered Ranked Weighted Averaging (ORWA) aggregation operator. ORWA is a modified form of Ordered Weighted Aggregated averaging (OWA) operator, a multi criteria decision making procedure. The weight generation using guided quantifier does not take into account the value of the voters, here, rankers which recommend the products, i.e. universities' ranking. Therefore the top ranked universities are considered and their recommended books are listed. We propose an algorithm to score the ranked books. By applying ORWA operator, best ranked books are recommended. This method may fulfill the requirement of the millions of students and academician who seek for their desired books.
{"title":"Ordered ranked weighted aggregation based book recommendation technique: A link mining approach","authors":"S. S. Sohail, Jamshed Siddiqui, R. Ali","doi":"10.1109/HIS.2014.7086167","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086167","url":null,"abstract":"The intense growth of the modern technologies has caused data overload over the Internet. The increasing data over the World Wide Web has created the problems for the users to extract the exact information. The growth of the Internet has also boosted the e-commerce. The popularity of online shopping has grown up rapidly. Online shopping has become much more popular. While browsing the e-marketing portals, multiple options are presented before users; hence picking the right item is a difficult job. In this paper we propose a recommendation method for books. We have adopted link mining approach to recommend books using Ordered Ranked Weighted Averaging (ORWA) aggregation operator. ORWA is a modified form of Ordered Weighted Aggregated averaging (OWA) operator, a multi criteria decision making procedure. The weight generation using guided quantifier does not take into account the value of the voters, here, rankers which recommend the products, i.e. universities' ranking. Therefore the top ranked universities are considered and their recommended books are listed. We propose an algorithm to score the ranked books. By applying ORWA operator, best ranked books are recommended. This method may fulfill the requirement of the millions of students and academician who seek for their desired books.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124429839","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086170
Daniyal Usmani, Tanveer Ahmad, M. Akram, Abdullah Danyal Saeed
Digital fundus images are commonly used for computer aided diagnosis of different eye disease such as diabetic retinopathy, glaucoma, age related macular degeneration. One issue with fundus cameras is that they provide fundus image only for a small field of view (FOV). This paper presents a novel method to increase the FOV by stitching different fundus images from same patient. The proposed system uses ASIFT based descriptors and generates a blended image by combining all available images. The paper also compares the proposed system with corner, SURF and SIFT based descriptors for same application.
{"title":"Fundus image mosaic generation for large field of view","authors":"Daniyal Usmani, Tanveer Ahmad, M. Akram, Abdullah Danyal Saeed","doi":"10.1109/HIS.2014.7086170","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086170","url":null,"abstract":"Digital fundus images are commonly used for computer aided diagnosis of different eye disease such as diabetic retinopathy, glaucoma, age related macular degeneration. One issue with fundus cameras is that they provide fundus image only for a small field of view (FOV). This paper presents a novel method to increase the FOV by stitching different fundus images from same patient. The proposed system uses ASIFT based descriptors and generates a blended image by combining all available images. The paper also compares the proposed system with corner, SURF and SIFT based descriptors for same application.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128617337","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086202
A. B. Babu, R. Sivakumar
There is currently lot of work in Ambient Intelligence particularly in context awareness. Context awareness enables service discovery and adaptation of computing devices for Ambient Intelligence application. In the same time, there is a common agreement of the fact that context aware systems should be responsive to Multi agents, assisting a large number of people, covering a large number of devices, and serving a large number of purposes. In an attempt to achieve such context aware systems with scalable scenario implementations, we propose an adaptive and autonomous context aware middleware using Ontology. Formal expressiveness and reasoning characteristics of Ontology make this middleware supportive to divergent programming applications. Our model provides a meta-model for context description that includes context collection, context processing and applications reactions to significant context changes. The advantage of the proposed ontology based middleware architecture is to improve the context awareness ability of the system and support divergent applications.
{"title":"Development of ontology based middleware for context awareness in ambient intelligence","authors":"A. B. Babu, R. Sivakumar","doi":"10.1109/HIS.2014.7086202","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086202","url":null,"abstract":"There is currently lot of work in Ambient Intelligence particularly in context awareness. Context awareness enables service discovery and adaptation of computing devices for Ambient Intelligence application. In the same time, there is a common agreement of the fact that context aware systems should be responsive to Multi agents, assisting a large number of people, covering a large number of devices, and serving a large number of purposes. In an attempt to achieve such context aware systems with scalable scenario implementations, we propose an adaptive and autonomous context aware middleware using Ontology. Formal expressiveness and reasoning characteristics of Ontology make this middleware supportive to divergent programming applications. Our model provides a meta-model for context description that includes context collection, context processing and applications reactions to significant context changes. The advantage of the proposed ontology based middleware architecture is to improve the context awareness ability of the system and support divergent applications.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122555023","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 : 2014-12-01DOI: 10.1109/HIS.2014.7086187
L. Gabralla, Talaat M. Wahby, Varun Ojha, A. Abraham
Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to predict the price of West Texas Intermediate (WTI), which is a well-known benchmark. We propose an ensemble of Adaptive Neuro-Fuzzy Inference System using a Particle Swarm Optimization algorithm for oil price prediction and the empirical results illustrate high performance and accurate results.
{"title":"Ensemble of adaptive neuro-fuzzy inference system using particle swarm optimization for prediction of crude oil prices","authors":"L. Gabralla, Talaat M. Wahby, Varun Ojha, A. Abraham","doi":"10.1109/HIS.2014.7086187","DOIUrl":"https://doi.org/10.1109/HIS.2014.7086187","url":null,"abstract":"Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to predict the price of West Texas Intermediate (WTI), which is a well-known benchmark. We propose an ensemble of Adaptive Neuro-Fuzzy Inference System using a Particle Swarm Optimization algorithm for oil price prediction and the empirical results illustrate high performance and accurate results.","PeriodicalId":161103,"journal":{"name":"2014 14th International Conference on Hybrid Intelligent Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134149715","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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