Pub Date : 2017-11-01DOI: 10.1109/ICSAI.2017.8248524
Novie Joy, C. Pelobello, Raul Vincent W. Lumapas, Adrian D. Ablazo
This research runs a predictive model using a simple decision tree of the twitter mined about the opinion of the people towards the new K-12 education program of the Philippines. The initial study which acquired sentiments from Twitter microblogs was utilized to find out whether a predictive model can substantially generate knowledge to support the K to 12 educational reforms in the Philippines. RapidMiner was used as tool to perform analytics on Twitter data. Various RapidMiner operators were used to process the Twitter microblogs to perform clustering and predictive analytics. It also utilized AYLIEN as an extension module of RapidMiner for text analysis and extract insights from these tweets. The experiment reveals in word cluster analysis that users who expressed sentiments about K-12 used similar words on the messages they posted. Overall, the results suggest that tweet data have a quite peculiar nature. Words used in discussed topic create a sort of Twitter culture. The results showed that in the decision tree generated, only favorites variable or the number of likes on a K-12 tweet provides a strong indication of classifying a K-12 tweet as subjective or objective.
{"title":"Knowledge creation opportunities for the K to 12 educationaltransformation in the Philippines using predictive analytics","authors":"Novie Joy, C. Pelobello, Raul Vincent W. Lumapas, Adrian D. Ablazo","doi":"10.1109/ICSAI.2017.8248524","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248524","url":null,"abstract":"This research runs a predictive model using a simple decision tree of the twitter mined about the opinion of the people towards the new K-12 education program of the Philippines. The initial study which acquired sentiments from Twitter microblogs was utilized to find out whether a predictive model can substantially generate knowledge to support the K to 12 educational reforms in the Philippines. RapidMiner was used as tool to perform analytics on Twitter data. Various RapidMiner operators were used to process the Twitter microblogs to perform clustering and predictive analytics. It also utilized AYLIEN as an extension module of RapidMiner for text analysis and extract insights from these tweets. The experiment reveals in word cluster analysis that users who expressed sentiments about K-12 used similar words on the messages they posted. Overall, the results suggest that tweet data have a quite peculiar nature. Words used in discussed topic create a sort of Twitter culture. The results showed that in the decision tree generated, only favorites variable or the number of likes on a K-12 tweet provides a strong indication of classifying a K-12 tweet as subjective or objective.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133448415","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248475
Shengyong Li, X. Ai, Ronghua Wu, Nianlong Zeng
Edge detection and extraction is very important in image processing and recognition whose algorithm directly affect the performance of the entire detection system. The ability of image denoising and the accuracy of edge detection are both required high, especially in the complex natural environment at sea. Most of image edge examination algorithms before have limitations and disadvantages of their own, so there is still room for improvement in this area. I'd like to put forward a sea-skyline detection algorithm and give simulation examples based on image filtering processing and gray image corrosion in the complex background of natural environment at sea, aiming at acquiring preferable ability of denoising and target extraction on the premise of ensuring the detection accuracy.
{"title":"Infrared target edge detectionin in sea sky backgrand","authors":"Shengyong Li, X. Ai, Ronghua Wu, Nianlong Zeng","doi":"10.1109/ICSAI.2017.8248475","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248475","url":null,"abstract":"Edge detection and extraction is very important in image processing and recognition whose algorithm directly affect the performance of the entire detection system. The ability of image denoising and the accuracy of edge detection are both required high, especially in the complex natural environment at sea. Most of image edge examination algorithms before have limitations and disadvantages of their own, so there is still room for improvement in this area. I'd like to put forward a sea-skyline detection algorithm and give simulation examples based on image filtering processing and gray image corrosion in the complex background of natural environment at sea, aiming at acquiring preferable ability of denoising and target extraction on the premise of ensuring the detection accuracy.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125453789","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248554
Yang Chen, Yipeng Wu
This paper presents a nonlinear piezoelectric oscillator structure capable of collecting multi-directional vibrational energy. The structure is mainly composed of universal coupling, piezoelectric spring, flexible hinge and mass block. The dynamical response of the structure is similar to the spring pendulum system. The oscillator has two degrees of freedom: swing angle and the length of elastic. The forced vibration model of the multidirectional piezoelectric oscillator structure is established.
{"title":"Forced vibration of a multidirectional nonlinear oscillator","authors":"Yang Chen, Yipeng Wu","doi":"10.1109/ICSAI.2017.8248554","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248554","url":null,"abstract":"This paper presents a nonlinear piezoelectric oscillator structure capable of collecting multi-directional vibrational energy. The structure is mainly composed of universal coupling, piezoelectric spring, flexible hinge and mass block. The dynamical response of the structure is similar to the spring pendulum system. The oscillator has two degrees of freedom: swing angle and the length of elastic. The forced vibration model of the multidirectional piezoelectric oscillator structure is established.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132328056","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248505
Shuo Xiong, Long Zuo, Zeliang Zhang, H. Iida
Game refinement theory is a new game theory which concerns about the entertaining aspects of games using a game sophistication measure that is derived from a game progress model. This paper explores a generic model of game progress for scoring games based on the concept of swings. Then, we synthesize the game refinement theory, game swing and game unexpectedness together to establish the signal processing model to analyze the individual game information impact. In the future, human can use this method to judge the target match/game is interesting or not.
{"title":"Individual game information evaluation using signal processing measurement","authors":"Shuo Xiong, Long Zuo, Zeliang Zhang, H. Iida","doi":"10.1109/ICSAI.2017.8248505","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248505","url":null,"abstract":"Game refinement theory is a new game theory which concerns about the entertaining aspects of games using a game sophistication measure that is derived from a game progress model. This paper explores a generic model of game progress for scoring games based on the concept of swings. Then, we synthesize the game refinement theory, game swing and game unexpectedness together to establish the signal processing model to analyze the individual game information impact. In the future, human can use this method to judge the target match/game is interesting or not.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"645 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131921127","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248408
Kainan Zhu, Xu Zhu, Yufei Jiang, Weiqiang Xu
In this paper, we propose a hybrid video sensor network, which comprises both power line nodes and wireless nodes to extend the network lifetime. We formulate the network lifetime maximization problem by considering video encoding rate, aggregate energy consumption, channel access control, and link rate allocation jointly. We develop a fully distributed algorithm which achieves very close performance compared to the centralized algorithm, while saving significant communication overhead required by the centralized algorithm. Numerical results show that the network lifetime is extended by 35% and 42% in the proposed hybrid video sensor network, compared to pure wireless video sensor networks with different network configurations.
{"title":"Novel hybrid wireless-power line video sensor networks with distributed cross-layer optimization","authors":"Kainan Zhu, Xu Zhu, Yufei Jiang, Weiqiang Xu","doi":"10.1109/ICSAI.2017.8248408","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248408","url":null,"abstract":"In this paper, we propose a hybrid video sensor network, which comprises both power line nodes and wireless nodes to extend the network lifetime. We formulate the network lifetime maximization problem by considering video encoding rate, aggregate energy consumption, channel access control, and link rate allocation jointly. We develop a fully distributed algorithm which achieves very close performance compared to the centralized algorithm, while saving significant communication overhead required by the centralized algorithm. Numerical results show that the network lifetime is extended by 35% and 42% in the proposed hybrid video sensor network, compared to pure wireless video sensor networks with different network configurations.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132622551","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248284
Kevin Lee, D. Moloney
Stereo vision is a very active field in the realm of computer vision and in recent years Convolutional Neural Networks (CNNs) have proven to be very competitive against the state-of-the-art. However the performance of these networks are limited by the quality of the data that is used when training the CNNs. Data acquisition of high quality labelled images is a time-consuming and expensive process. By exploiting the power of modern-day powerful GPUs, we present a synthetic dataset with fully rectified stereo image pairs and accompanying accurate ground truth information that can be used for training and testing stereo algorithms. We provide validation of the quality of our dataset by performing quantitative experiments that suggest pre-training deep learning algorithms on synthetic data can perform competitively against networks trained on real life data. Testing on the KITTI data-set[1], we found the accuracy performance difference between the real and synthetically trained networks was within a margin of 1.8%. We also illustrate the functionality synthetic data can provide, by conducting a key performance index on a selection of conventional and deep learning stereo algorithms available on embedded platforms and compared them under common metrics. We also focused on power consumption and performance and we were able to achieve a compute the matching cost from a CNN performing inference on an embedded device at 11.9 FPS at 1.2 Watts.
{"title":"Evaluation of synthetic data for deep learning stereo depth algorithms on embedded platforms","authors":"Kevin Lee, D. Moloney","doi":"10.1109/ICSAI.2017.8248284","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248284","url":null,"abstract":"Stereo vision is a very active field in the realm of computer vision and in recent years Convolutional Neural Networks (CNNs) have proven to be very competitive against the state-of-the-art. However the performance of these networks are limited by the quality of the data that is used when training the CNNs. Data acquisition of high quality labelled images is a time-consuming and expensive process. By exploiting the power of modern-day powerful GPUs, we present a synthetic dataset with fully rectified stereo image pairs and accompanying accurate ground truth information that can be used for training and testing stereo algorithms. We provide validation of the quality of our dataset by performing quantitative experiments that suggest pre-training deep learning algorithms on synthetic data can perform competitively against networks trained on real life data. Testing on the KITTI data-set[1], we found the accuracy performance difference between the real and synthetically trained networks was within a margin of 1.8%. We also illustrate the functionality synthetic data can provide, by conducting a key performance index on a selection of conventional and deep learning stereo algorithms available on embedded platforms and compared them under common metrics. We also focused on power consumption and performance and we were able to achieve a compute the matching cost from a CNN performing inference on an embedded device at 11.9 FPS at 1.2 Watts.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133657122","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248346
M. Valenzuela, Alvaro Peña, Luis Lopez, H. Pinto
Many problems addressed in operational research are combinatorial and NP-hard type. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the percentile concept. We apply the percentile concept to multi-verse optimizer algorithm to solve set covering problem (SCP). Experiments are designed to demonstrate the utility of the percentile concept in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that Binary multi-verse Optimizer (BMVO) obtains adequate results when it is evaluated against another state of the art algorithm.
{"title":"A binary multi-verse optimizer algorithm applied to the set covering problem","authors":"M. Valenzuela, Alvaro Peña, Luis Lopez, H. Pinto","doi":"10.1109/ICSAI.2017.8248346","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248346","url":null,"abstract":"Many problems addressed in operational research are combinatorial and NP-hard type. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the percentile concept. We apply the percentile concept to multi-verse optimizer algorithm to solve set covering problem (SCP). Experiments are designed to demonstrate the utility of the percentile concept in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that Binary multi-verse Optimizer (BMVO) obtains adequate results when it is evaluated against another state of the art algorithm.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117198775","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248512
Bo Li, Xiang Zhao, Shuai Wang, Weihong Lin, W. Xiao
Relation classification plays an important part in the structuralization of text data via information extraction. Lately, neural network-based methods have been applied to relation classification, which use neural networks to encode and extract features from the input text. As convolution neural network based method can extract high-level features through convolution filters, it achieves competitive performance with other complex-structured networks by only using a standard convolution layer, a pooling layer, and a regression layer. However, it failed to capture the hierarchical and syntax information of sentence. Inspired by this, We introduce the hierarchical convolutional layers and dependency embedding to the CNN based methods. The hierarchical convolution layers capture the detail feature and high-level hierarchical features and concatenate these features as the sentence representation. The dependency embeddings help CNN capture the dependency structure in the window size, which improve the classification results. Experiments verify that the revised relation classification method provide state-of-the-art performance, even without additional artificial features.
{"title":"Relation classification using revised convolutional neural networks","authors":"Bo Li, Xiang Zhao, Shuai Wang, Weihong Lin, W. Xiao","doi":"10.1109/ICSAI.2017.8248512","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248512","url":null,"abstract":"Relation classification plays an important part in the structuralization of text data via information extraction. Lately, neural network-based methods have been applied to relation classification, which use neural networks to encode and extract features from the input text. As convolution neural network based method can extract high-level features through convolution filters, it achieves competitive performance with other complex-structured networks by only using a standard convolution layer, a pooling layer, and a regression layer. However, it failed to capture the hierarchical and syntax information of sentence. Inspired by this, We introduce the hierarchical convolutional layers and dependency embedding to the CNN based methods. The hierarchical convolution layers capture the detail feature and high-level hierarchical features and concatenate these features as the sentence representation. The dependency embeddings help CNN capture the dependency structure in the window size, which improve the classification results. Experiments verify that the revised relation classification method provide state-of-the-art performance, even without additional artificial features.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114930035","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248355
Alessandro Palla, D. Moloney, L. Fanucci
In this work, we propose a 3D scene reconstruction algorithm based on a fully convolutional 3D denoising autoencoder neural network. The network is capable of reconstructing a full scene from a single depth image by creating a 3D representation of it and automatically filling holes and inserting hidden elements. We exploit the fact that our neural network is capable of generalizing object shapes by inferring similarities in geometry. Our fully convolutional architecture enables the network to be unconstrained by a fixed 3D shape, and so it is capable of successfully reconstructing arbitrary scene sizes. Our algorithm was evaluated on a real word dataset of tabletop scenes acquired using a Kinect and processed using KinectFusion software in order to obtain ground truth for network training and evaluation. Extensive measurements show that our deep neural network architecture outperforms the previous state of the art both in terms of precision and recall for the scene reconstruction task. The network has been broadly profiled in terms of memory footprint, number of floating point operations, inference time and power consumption in CPU, GPU and embedded devices. Its small memory footprint and its low computation requirements enable low power, memory constrained, real time always-on embedded applications such as autonomous vehicles, warehouse robots, interactive gaming controllers and drones.
{"title":"Fully convolutional denoising autoencoder for 3D scene reconstruction from a single depth image","authors":"Alessandro Palla, D. Moloney, L. Fanucci","doi":"10.1109/ICSAI.2017.8248355","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248355","url":null,"abstract":"In this work, we propose a 3D scene reconstruction algorithm based on a fully convolutional 3D denoising autoencoder neural network. The network is capable of reconstructing a full scene from a single depth image by creating a 3D representation of it and automatically filling holes and inserting hidden elements. We exploit the fact that our neural network is capable of generalizing object shapes by inferring similarities in geometry. Our fully convolutional architecture enables the network to be unconstrained by a fixed 3D shape, and so it is capable of successfully reconstructing arbitrary scene sizes. Our algorithm was evaluated on a real word dataset of tabletop scenes acquired using a Kinect and processed using KinectFusion software in order to obtain ground truth for network training and evaluation. Extensive measurements show that our deep neural network architecture outperforms the previous state of the art both in terms of precision and recall for the scene reconstruction task. The network has been broadly profiled in terms of memory footprint, number of floating point operations, inference time and power consumption in CPU, GPU and embedded devices. Its small memory footprint and its low computation requirements enable low power, memory constrained, real time always-on embedded applications such as autonomous vehicles, warehouse robots, interactive gaming controllers and drones.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203419","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}
Many resources today are shared freely through social network or cloud storage platforms, which are helpful for uses to acquire data or exchange information. Unfortunately, due to the unrestricted participations, some resources with advertisements or fraud are also uploaded, which force users to hit the ad websites or steal users' data. Therefore, the quality evaluation of one resource is needed for users to judge whether to utilize or install it. In this paper, we implement a system to evaluate the quality based on software install packages, which applies four algorithms to forecast the quality scores. We conduct an extensive experimental study on a real dataset and find that the prediction can be performed in less than one second (0.002s∼0.04s) and with a high accuracy (82.84%∼90.52%).
{"title":"Resource quality prediction based on machine learning algorithms","authors":"Yu Wang, Dingyu Yang, Yunfan Shi, Yizhen Wang, Wanli Chen","doi":"10.1109/ICSAI.2017.8248529","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248529","url":null,"abstract":"Many resources today are shared freely through social network or cloud storage platforms, which are helpful for uses to acquire data or exchange information. Unfortunately, due to the unrestricted participations, some resources with advertisements or fraud are also uploaded, which force users to hit the ad websites or steal users' data. Therefore, the quality evaluation of one resource is needed for users to judge whether to utilize or install it. In this paper, we implement a system to evaluate the quality based on software install packages, which applies four algorithms to forecast the quality scores. We conduct an extensive experimental study on a real dataset and find that the prediction can be performed in less than one second (0.002s∼0.04s) and with a high accuracy (82.84%∼90.52%).","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115115640","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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