Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949201
Xin Yuan, M. Liebelt, Peng Shi, B. Phillips
Association Rules Mining is an approach to discover rules from data sets, and it can establish relationships among elements in a data set. Our research is focused on rule-based agents with Artificial General Intelligence (AGI), which are developed based on the overall environment to achieve functions with cognition. In this paper, we use a modified Association Rules Mining method to find out characteristic rules from data recorded in the training of customized parking scenarios. Fuzzy symbolic elements are recorded during training, and Association Rule Mining selects rules for the AI agent. Experiments have been conducted in a virtual environment to demonstrate the effectiveness of the proposed new algorithm.
{"title":"Development of Rule-Based Agents for Autonomous Parking Systems by Association Rules Mining","authors":"Xin Yuan, M. Liebelt, Peng Shi, B. Phillips","doi":"10.1109/ICMLC48188.2019.8949201","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949201","url":null,"abstract":"Association Rules Mining is an approach to discover rules from data sets, and it can establish relationships among elements in a data set. Our research is focused on rule-based agents with Artificial General Intelligence (AGI), which are developed based on the overall environment to achieve functions with cognition. In this paper, we use a modified Association Rules Mining method to find out characteristic rules from data recorded in the training of customized parking scenarios. Fuzzy symbolic elements are recorded during training, and Association Rule Mining selects rules for the AI agent. Experiments have been conducted in a virtual environment to demonstrate the effectiveness of the proposed new algorithm.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643236","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949309
Chuen-Min Huang, Yi-Jun Jiang
This study compares Chinese news classification results of machine learning (ML) and deep learning (DL). In processing ML, we chose Support Vector Machine (SVM) and Naive Bayes (NB) to form three models: Word2Vec-SVM, TFIDF-SVM, and TFIDF-NB. Since NB assumes that the words are independent, this is different from the concept of related word distribution in Word2Vec, so the combination with NB is excluded. In processing DL, we adopted Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and used Word2Vec for word embedding. Experimental results showed that with proper word preprocessing, the difference of classification accuracy of ML and DL models is actually very small. Although the results show that Bi-LSTM performs the most accurate and has the lowest Loss compared to other DL techniques, its implementation process is the most time consuming. This study affirms the excellent results of CNN, while its Loss is the highest of the DL models. We also found that Word2Vec-SVM was superior to TFIDF-SVM in terms of efficiency, but its accuracy is not as good as expected. To summarize the classification accuracy in Bi-LSTM, LSTM, CNN, Word2vec-SVM, TFIDF-SVM, and NB are 89.3%, 88%, and 87.54%, 85.32%, 87.35%, 86.56%, respectively.
{"title":"An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques","authors":"Chuen-Min Huang, Yi-Jun Jiang","doi":"10.1109/ICMLC48188.2019.8949309","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949309","url":null,"abstract":"This study compares Chinese news classification results of machine learning (ML) and deep learning (DL). In processing ML, we chose Support Vector Machine (SVM) and Naive Bayes (NB) to form three models: Word2Vec-SVM, TFIDF-SVM, and TFIDF-NB. Since NB assumes that the words are independent, this is different from the concept of related word distribution in Word2Vec, so the combination with NB is excluded. In processing DL, we adopted Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and used Word2Vec for word embedding. Experimental results showed that with proper word preprocessing, the difference of classification accuracy of ML and DL models is actually very small. Although the results show that Bi-LSTM performs the most accurate and has the lowest Loss compared to other DL techniques, its implementation process is the most time consuming. This study affirms the excellent results of CNN, while its Loss is the highest of the DL models. We also found that Word2Vec-SVM was superior to TFIDF-SVM in terms of efficiency, but its accuracy is not as good as expected. To summarize the classification accuracy in Bi-LSTM, LSTM, CNN, Word2vec-SVM, TFIDF-SVM, and NB are 89.3%, 88%, and 87.54%, 85.32%, 87.35%, 86.56%, respectively.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114236310","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949326
P. Tseng, Fu-Yi Yang, Meng-Han Yang
While industrial pollutions cause changes in the environment and gradually has a strong impact on human physiologies, the relationship between air pollutants and disease occurrences is a subject worthy of exploration. Therefore, based on the nationwide datasets, this study would use non-parametric regression methods to analyze the impact of air pollutants on various psychiatric & neurological illnesses. Through these regression models, the time lag effect of environmental factors on the target diseases would also be taken into account. According to the evaluation outcomes of correlation coefficients, the targets diseases were mainly associated with air pressure, CH4, and SO2. Moreover, observing the coefficients of non-parametric regression models, influences from the environmental factors, i.e. meteorological items and air pollutants, were not limited to the current occurrence (0~1-day lag) but might also accumulate after a period of time (5~7-day lag). In summary, the relationships between air pollutants and psychiatric/neurological illnesses have been verified in this study.
{"title":"Using Non-Parametric Regression Methods to Analyze the Impact of air Pollutants on Psychiatric & Neurological Illnesses","authors":"P. Tseng, Fu-Yi Yang, Meng-Han Yang","doi":"10.1109/ICMLC48188.2019.8949326","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949326","url":null,"abstract":"While industrial pollutions cause changes in the environment and gradually has a strong impact on human physiologies, the relationship between air pollutants and disease occurrences is a subject worthy of exploration. Therefore, based on the nationwide datasets, this study would use non-parametric regression methods to analyze the impact of air pollutants on various psychiatric & neurological illnesses. Through these regression models, the time lag effect of environmental factors on the target diseases would also be taken into account. According to the evaluation outcomes of correlation coefficients, the targets diseases were mainly associated with air pressure, CH4, and SO2. Moreover, observing the coefficients of non-parametric regression models, influences from the environmental factors, i.e. meteorological items and air pollutants, were not limited to the current occurrence (0~1-day lag) but might also accumulate after a period of time (5~7-day lag). In summary, the relationships between air pollutants and psychiatric/neurological illnesses have been verified in this study.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116466859","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949253
Shilong Feng, H. Xie, Hongbo Yin, Xiaopeng Chen, Deshun Yang, P. Chan
The objective of metric learning is to search a suitable metric for measuring distance or similarity between samples. Usually, it aims to minimize the distance between samples of same class and maximizes the distance between samples of different classes. However, most metric learning methods do not consider the sizes of classes, which may cause negative impact on the performance in classification since the size of a cluster is usually ignored in the distance comparison. In this work, we propose a triplet loss with variance constraint. Our method focuses not only on the distances between samples but also on the sizes of classes. The size difference between classes is also minimized in our objective function. The experimental results confirm that our method outperforms the one without the class size variance.
{"title":"Class Size Variance Minimization to Metric Learning for Dish Identification","authors":"Shilong Feng, H. Xie, Hongbo Yin, Xiaopeng Chen, Deshun Yang, P. Chan","doi":"10.1109/ICMLC48188.2019.8949253","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949253","url":null,"abstract":"The objective of metric learning is to search a suitable metric for measuring distance or similarity between samples. Usually, it aims to minimize the distance between samples of same class and maximizes the distance between samples of different classes. However, most metric learning methods do not consider the sizes of classes, which may cause negative impact on the performance in classification since the size of a cluster is usually ignored in the distance comparison. In this work, we propose a triplet loss with variance constraint. Our method focuses not only on the distances between samples but also on the sizes of classes. The size difference between classes is also minimized in our objective function. The experimental results confirm that our method outperforms the one without the class size variance.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116697360","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}
Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledge-aware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long short-term memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.
{"title":"Rhyming Knowledge-Aware Deep Neural Network for Chinese Poetry Generation","authors":"Wen-Chao Yeh, Yung-Chun Chang, Yu-Hsuan Li, Wei-Chieh Chang","doi":"10.1109/ICMLC48188.2019.8949208","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949208","url":null,"abstract":"Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledge-aware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long short-term memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116732441","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949229
Lu Liu, Shuling Yang, D. Shi
Convolutional neural network is a multi-layer neural network with robust pattern recognition ability. However, when the activation function is sigmoid, the convolutional neural network produces gradient vanishing problem. First, this paper analyzes the gradient vanishing problem, and then based on the balance of excitation and inhibition mechanism in neurology, it is proposed to use feed-forward inhibition to reduce activition value and wipe off the scale effect of weights, so that the model can accelerate convergence under the premise of maintaining the nonlinear fitting ability. The results show that the improved convolutional neural network can effectively relieve the gradient vanishing problem.
{"title":"Advanced Convolutional Neural Network With Feedforward Inhibition","authors":"Lu Liu, Shuling Yang, D. Shi","doi":"10.1109/ICMLC48188.2019.8949229","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949229","url":null,"abstract":"Convolutional neural network is a multi-layer neural network with robust pattern recognition ability. However, when the activation function is sigmoid, the convolutional neural network produces gradient vanishing problem. First, this paper analyzes the gradient vanishing problem, and then based on the balance of excitation and inhibition mechanism in neurology, it is proposed to use feed-forward inhibition to reduce activition value and wipe off the scale effect of weights, so that the model can accelerate convergence under the premise of maintaining the nonlinear fitting ability. The results show that the improved convolutional neural network can effectively relieve the gradient vanishing problem.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114447251","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949267
Cheng-Yuan Tang, Whei-Wen Cheng, Tzu-Yen Hsu, C. Jeng, Yi-Leh Wu
The landslides and flows cause significant direct damage to lives and property. A system for monitoring these signs can be the most powerful tool for disaster prevention. In the natural hazard of slope, the signs for rain warning is very useful for disaster prevention. Labeling the rain warning seems to be an important and useful job for disaster prevention. In this paper, two neural network models are used for labeling the rain warning. These two models are the multilayer perceptron (MLP) and the long short-term memory (LSTM). The raw data consist of four observations such as time (time), rainfall (rain), groundwater level (W1) and displacements of inclinometers (SAA-11 and SAA-20). The RMSE (Root Mean Squared Error) using LSTM is 0.161 and RMSE using MLP is 0.212. In the experimental results, LSTM is better than MLP.
{"title":"Using Neural Networks to Label Rain Warning for Natural Hazard of Slope","authors":"Cheng-Yuan Tang, Whei-Wen Cheng, Tzu-Yen Hsu, C. Jeng, Yi-Leh Wu","doi":"10.1109/ICMLC48188.2019.8949267","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949267","url":null,"abstract":"The landslides and flows cause significant direct damage to lives and property. A system for monitoring these signs can be the most powerful tool for disaster prevention. In the natural hazard of slope, the signs for rain warning is very useful for disaster prevention. Labeling the rain warning seems to be an important and useful job for disaster prevention. In this paper, two neural network models are used for labeling the rain warning. These two models are the multilayer perceptron (MLP) and the long short-term memory (LSTM). The raw data consist of four observations such as time (time), rainfall (rain), groundwater level (W1) and displacements of inclinometers (SAA-11 and SAA-20). The RMSE (Root Mean Squared Error) using LSTM is 0.161 and RMSE using MLP is 0.212. In the experimental results, LSTM is better than MLP.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116567200","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949296
Masami Nakamura, Yuta Aoto, Shunji Maeda
In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.
{"title":"A Basic Study on Railway Facility Extraction Using a Single-Shot Multi-Box Detector","authors":"Masami Nakamura, Yuta Aoto, Shunji Maeda","doi":"10.1109/ICMLC48188.2019.8949296","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949296","url":null,"abstract":"In railway facilities, there are numerous types and electric train-line facilities. It is difficult to visually inspect all of them, so automatic visual inspection is expected. To achieve automatic inspection, it is important to extract and diagnose the target facilities. This study focuses on facilities extraction by utilizing single-shot multi-box detector (SSD), which can be used as a discriminator for human, car and boat object detection, etc. Diagnosis using Local Subspace Classifier (LSC) is proposed. Herein, we present the evaluation results and the issues applying SSD to the equipment called hangers connecting overhead lines. Some diagnosis results are also explained.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137518","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949241
H. Ferng, Muhammad Abdullah
Owing to high mobility and a large amount of vehicles in a vehicular ad hoc network (VANET), it is challenging to overcome the issues of frequent topology changes and network scalability. To mitigate these issues, a vehicle clustering and management scheme can be applied to VANETs. Towards this goal, a mobility-based clustering scheme with a clustering link quality estimation (CLQE) metric considering both mobility information and link quality estimation is proposed in this paper. The proposed clustering scheme is evaluated through the NS-3 simulator and our simulation results show that our proposed scheme outperforms the closely related schemes in most scenarios.
{"title":"Mobility-Based Clustering With Link Quality Estimation for Urban Vanets","authors":"H. Ferng, Muhammad Abdullah","doi":"10.1109/ICMLC48188.2019.8949241","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949241","url":null,"abstract":"Owing to high mobility and a large amount of vehicles in a vehicular ad hoc network (VANET), it is challenging to overcome the issues of frequent topology changes and network scalability. To mitigate these issues, a vehicle clustering and management scheme can be applied to VANETs. Towards this goal, a mobility-based clustering scheme with a clustering link quality estimation (CLQE) metric considering both mobility information and link quality estimation is proposed in this paper. The proposed clustering scheme is evaluated through the NS-3 simulator and our simulation results show that our proposed scheme outperforms the closely related schemes in most scenarios.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122284343","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949181
T. Arai, Y. Toda, N. Kubota
In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.
{"title":"Behavior Acquisition on a Mobile Robot Using Reinforcement Learning With Continuous State Space","authors":"T. Arai, Y. Toda, N. Kubota","doi":"10.1109/ICMLC48188.2019.8949181","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949181","url":null,"abstract":"In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126012690","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}