Alzheimer's disease (AD), a kind of dementia, is marked by progressive cognitive and behavioural problems that appear in middle or late life. Alzheimer's disease must be detected early in order to create more effective therapies. Dr. Alois Alzheimer was the first doctor in the medical field to notice an unusual state of change in the brains of his deceased patients with mental illness, which marked the start of Alzheimer's study. Machine learning (ML) techniques nowadays employ a variety of probabilistic and optimization strategies to allow computers to learn from vast and complex datasets. Because of the limited number of labelled data and the prevalence of outliers in the current datasets, accurate dementia prediction is extremely difficult. In this research, we propose a sustainable framework for dementia prediction based on ML techniques such as Support Vector Machine, Decision Tree, AdaBoost, Random Forest, and XGmodel. All the experiments, in this literature, were conducted under the same experimental conditions using the longitudinal MRI Dataset.
{"title":"Machine Learning GUI based For Detecting Alzheimer’s","authors":"Fatema Nafa, Evelyn RodriguezArgueta, Annie Dequit, Changqing Chen","doi":"10.5121/csit.2022.121813","DOIUrl":"https://doi.org/10.5121/csit.2022.121813","url":null,"abstract":"Alzheimer's disease (AD), a kind of dementia, is marked by progressive cognitive and behavioural problems that appear in middle or late life. Alzheimer's disease must be detected early in order to create more effective therapies. Dr. Alois Alzheimer was the first doctor in the medical field to notice an unusual state of change in the brains of his deceased patients with mental illness, which marked the start of Alzheimer's study. Machine learning (ML) techniques nowadays employ a variety of probabilistic and optimization strategies to allow computers to learn from vast and complex datasets. Because of the limited number of labelled data and the prevalence of outliers in the current datasets, accurate dementia prediction is extremely difficult. In this research, we propose a sustainable framework for dementia prediction based on ML techniques such as Support Vector Machine, Decision Tree, AdaBoost, Random Forest, and XGmodel. All the experiments, in this literature, were conducted under the same experimental conditions using the longitudinal MRI Dataset.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75419206","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 : 2022-10-29DOI: 10.5121/csit.2022.121806
Tiffany Zhan
Blockchain-based cryptocurrency has attracted the immersive attention of individuals and businesses. With distributed ledger technology (DLT) consisting of growing list of record blocks and securely linked together using cryptography, each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. The timestamp proves that the transaction data existed when the block was created. Since each block contains information about the block previous to it, they effectively form a chain, with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks. The blockchain-based technologies have been emerging with a fleet speed. In this paper, the trustworthy Artificial Intelligence will be explored for blockchain-based cryptocurrency where the prohibitive price leap creates a challenge for financial analysis and prediction.
{"title":"Trustworthy Artificial Intelligence for Blockchain-based Cryptocurrency","authors":"Tiffany Zhan","doi":"10.5121/csit.2022.121806","DOIUrl":"https://doi.org/10.5121/csit.2022.121806","url":null,"abstract":"Blockchain-based cryptocurrency has attracted the immersive attention of individuals and businesses. With distributed ledger technology (DLT) consisting of growing list of record blocks and securely linked together using cryptography, each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. The timestamp proves that the transaction data existed when the block was created. Since each block contains information about the block previous to it, they effectively form a chain, with each additional block linking to the ones before it. Consequently, blockchain transactions are irreversible in that, once they are recorded, the data in any given block cannot be altered retroactively without altering all subsequent blocks. The blockchain-based technologies have been emerging with a fleet speed. In this paper, the trustworthy Artificial Intelligence will be explored for blockchain-based cryptocurrency where the prohibitive price leap creates a challenge for financial analysis and prediction.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84539063","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 : 2022-10-29DOI: 10.5121/csit.2022.121822
Ruize Yu, Yu Sun
Due to the ever growing popularity of music as a part of everyday life, and with the continuous advances in AI technology, it is now possible for computers to listen to and recognize music [1]. However, there still exist limitations on machines’ ability to recognize audio. This paper proposes an application to simplify the process of music transcription and reduce its runtime [2]. This application was tested in a different range of settings and evaluated. The results show what can be further improved on this application.
{"title":"An Automatic Sheet Music Generating Algorithm based on Machine Learning and Artificial Intelligence","authors":"Ruize Yu, Yu Sun","doi":"10.5121/csit.2022.121822","DOIUrl":"https://doi.org/10.5121/csit.2022.121822","url":null,"abstract":"Due to the ever growing popularity of music as a part of everyday life, and with the continuous advances in AI technology, it is now possible for computers to listen to and recognize music [1]. However, there still exist limitations on machines’ ability to recognize audio. This paper proposes an application to simplify the process of music transcription and reduce its runtime [2]. This application was tested in a different range of settings and evaluated. The results show what can be further improved on this application.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76305112","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 : 2022-10-29DOI: 10.5121/csit.2022.121804
Hang Cheng, Shixiong Wang, Naiyang Guan
Non-negative matrix factorization (NMF) is an effective dimension reduction tool widely used in pattern recognition and computer vision. However, conventional NMF models are neither robust enough, as their objective functions are sensitive to outliers, nor discriminative enough, as they completely ignore the discriminative information in data. In this paper, we proposed a robust discriminative NMF model (RDNMF) for learning an effective discriminative subspace from noisy dataset. In particular, RDNMF approximates observations by their reconstructions in the subspace via maximum correntropy criterion to prohibit outliers from influencing the subspace. To incorporate the discriminative information, RDNMF builds adjacent graphs by using maximum correntropy criterion based robust representation, and regularizes the model by margin maximization criterion. We developed a multiplicative update rule to optimize RDNMF and theoretically proved its convergence. Experimental results on popular datasets verify the effectiveness of RDNMF comparing with conventional NMF models, discriminative NMF models, and robust NMF models.
{"title":"Robust Discriminative Non-Negative Matrix Factorization with Maximum Correntropy Criterion","authors":"Hang Cheng, Shixiong Wang, Naiyang Guan","doi":"10.5121/csit.2022.121804","DOIUrl":"https://doi.org/10.5121/csit.2022.121804","url":null,"abstract":"Non-negative matrix factorization (NMF) is an effective dimension reduction tool widely used in pattern recognition and computer vision. However, conventional NMF models are neither robust enough, as their objective functions are sensitive to outliers, nor discriminative enough, as they completely ignore the discriminative information in data. In this paper, we proposed a robust discriminative NMF model (RDNMF) for learning an effective discriminative subspace from noisy dataset. In particular, RDNMF approximates observations by their reconstructions in the subspace via maximum correntropy criterion to prohibit outliers from influencing the subspace. To incorporate the discriminative information, RDNMF builds adjacent graphs by using maximum correntropy criterion based robust representation, and regularizes the model by margin maximization criterion. We developed a multiplicative update rule to optimize RDNMF and theoretically proved its convergence. Experimental results on popular datasets verify the effectiveness of RDNMF comparing with conventional NMF models, discriminative NMF models, and robust NMF models.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83033246","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 : 2022-10-29DOI: 10.5121/csit.2022.121825
Ziheng Guan, Angqian Li
Family conflicts between parents and their children are nothing new and are something experienced by many in such situations [1]. These conflicts can even be exacerbated by cultural differences that exist between the two parties, especially in cases where the parents and child were raised in different countries, cultures and/or generations [2]. This description illustrates my personal experiences of conflict with my parents, which is what inspired me to create this app: The Problem Solver app. The app differs from other methods that could be applied to resolve these conflicts in that it facilitates more direct communication between the two conflicting parties, which would hopefully result in a more rapid and successful conflict resolution [3]. Naturally, there were challenges I faced in the making of the app, but I was eventually able to work through these and build a working product. I will also explore some related works and research into this topic that were helpful in supporting the idea that cultural differences between differently raised generations can have an impact on familial relations [4]. Then, I give a general overview of the system of the app and finally delve into possible limitations of the app and further steps I could take in the development of the app.
{"title":"The Problem Solver: A Mobile Platform to Mediate Teenager Family Relationship using Dart and Machine Learning","authors":"Ziheng Guan, Angqian Li","doi":"10.5121/csit.2022.121825","DOIUrl":"https://doi.org/10.5121/csit.2022.121825","url":null,"abstract":"Family conflicts between parents and their children are nothing new and are something experienced by many in such situations [1]. These conflicts can even be exacerbated by cultural differences that exist between the two parties, especially in cases where the parents and child were raised in different countries, cultures and/or generations [2]. This description illustrates my personal experiences of conflict with my parents, which is what inspired me to create this app: The Problem Solver app. The app differs from other methods that could be applied to resolve these conflicts in that it facilitates more direct communication between the two conflicting parties, which would hopefully result in a more rapid and successful conflict resolution [3]. Naturally, there were challenges I faced in the making of the app, but I was eventually able to work through these and build a working product. I will also explore some related works and research into this topic that were helpful in supporting the idea that cultural differences between differently raised generations can have an impact on familial relations [4]. Then, I give a general overview of the system of the app and finally delve into possible limitations of the app and further steps I could take in the development of the app.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"97 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83355278","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 : 2022-10-29DOI: 10.5121/csit.2022.121801
Aadil Ahamed, Kamran Alipour, Sateesh Kumar, Severine Soltani, M. Pazzani
In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. Recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.
{"title":"Improving Explanations of Image Classification with Ensembles of Learners","authors":"Aadil Ahamed, Kamran Alipour, Sateesh Kumar, Severine Soltani, M. Pazzani","doi":"10.5121/csit.2022.121801","DOIUrl":"https://doi.org/10.5121/csit.2022.121801","url":null,"abstract":"In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. Recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89899277","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 : 2022-10-29DOI: 10.5121/csit.2022.121818
R. Delmonte, Nicolò Busetto
In this paper we present an experiment carried out with BERT on a small number of Italian sentences taken from two domains: newspapers and poetry domain. They represent two levels of increasing difficulty in the possibility to predict the masked word that we intended to test. The experiment is organized on the hypothesis of increasing difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we alternate canonical and non-canonical versions of the same sentence before processing them with the same DL model. The result shows that DL models are highly sensitive to presence of non-canonical structures and to local non-literal meaning compositional effect. However, DL are also very sensitive to word frequency by predicting preferentially function vs content words, collocates vs infrequent word phrases. To measure differences in performance we created a linguistically based “predictability parameter” which is highly correlated with a cosine based classification but produces better distinctions between classes.
{"title":"Word Predictability is Based on Context - and/or Frequency","authors":"R. Delmonte, Nicolò Busetto","doi":"10.5121/csit.2022.121818","DOIUrl":"https://doi.org/10.5121/csit.2022.121818","url":null,"abstract":"In this paper we present an experiment carried out with BERT on a small number of Italian sentences taken from two domains: newspapers and poetry domain. They represent two levels of increasing difficulty in the possibility to predict the masked word that we intended to test. The experiment is organized on the hypothesis of increasing difficulty in predictability at the three levels of linguistic complexity that we intend to monitor: lexical, syntactic and semantic level. To test this hypothesis we alternate canonical and non-canonical versions of the same sentence before processing them with the same DL model. The result shows that DL models are highly sensitive to presence of non-canonical structures and to local non-literal meaning compositional effect. However, DL are also very sensitive to word frequency by predicting preferentially function vs content words, collocates vs infrequent word phrases. To measure differences in performance we created a linguistically based “predictability parameter” which is highly correlated with a cosine based classification but produces better distinctions between classes.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86347426","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 : 2022-10-29DOI: 10.5121/csit.2022.121816
Wenxin Tian
Cartoons are an important art style, which not only has a unique drawing effect but also reflects the character itself, which is gradually loved by people. With the development of image processing technology, people's research on image research is no longer limited to image recognition, target detection, and tracking, but also images In this paper, we use deep learning based image processing to generate cartoon caricatures of human faces. Therefore, this paper investigates the use of deep learning-based methods to learn face features and convert image styles while preserving the original content features, to automatically generate natural cartoon avatars. In this paper, we study a face cartoon generation method based on content invariance. In the task of image style conversion, the content is fused with different style features based on the invariance of content information, to achieve the style conversion.
{"title":"Converting Real Human Avatar to Cartoon Avatar using CycleGAN","authors":"Wenxin Tian","doi":"10.5121/csit.2022.121816","DOIUrl":"https://doi.org/10.5121/csit.2022.121816","url":null,"abstract":"Cartoons are an important art style, which not only has a unique drawing effect but also reflects the character itself, which is gradually loved by people. With the development of image processing technology, people's research on image research is no longer limited to image recognition, target detection, and tracking, but also images In this paper, we use deep learning based image processing to generate cartoon caricatures of human faces. Therefore, this paper investigates the use of deep learning-based methods to learn face features and convert image styles while preserving the original content features, to automatically generate natural cartoon avatars. In this paper, we study a face cartoon generation method based on content invariance. In the task of image style conversion, the content is fused with different style features based on the invariance of content information, to achieve the style conversion.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79898343","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 : 2022-10-29DOI: 10.5121/csit.2022.121803
Joshua Tian, Y. Sun
In recent years, we have seen a huge increase in air conditioning usage [4]. However, much of this energy put into air conditioning is being wasted, which contributes to a far less environmentally friendly world and is inconvenient for many [5][6]. This paper develops a smart vent and a mobile app to regulate temperatures in different rooms of a home to create an efficient solution to save energy. This conservation of energy allows both the environment to be preserved as well as the financial burden of families in need to be alleviated. Controlled studies of the system provide evidence of the system's automated ability to be energy efficient.
{"title":"A Context-Aware and Adaptive System to Automate the Control of the AC Windshield using AI and Internet of Things","authors":"Joshua Tian, Y. Sun","doi":"10.5121/csit.2022.121803","DOIUrl":"https://doi.org/10.5121/csit.2022.121803","url":null,"abstract":"In recent years, we have seen a huge increase in air conditioning usage [4]. However, much of this energy put into air conditioning is being wasted, which contributes to a far less environmentally friendly world and is inconvenient for many [5][6]. This paper develops a smart vent and a mobile app to regulate temperatures in different rooms of a home to create an efficient solution to save energy. This conservation of energy allows both the environment to be preserved as well as the financial burden of families in need to be alleviated. Controlled studies of the system provide evidence of the system's automated ability to be energy efficient.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72936648","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 : 2022-10-29DOI: 10.5121/csit.2022.121809
Tiffany Zhan
Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.
{"title":"Hyper-Parameter Tuning in Deep Neural Network Learning","authors":"Tiffany Zhan","doi":"10.5121/csit.2022.121809","DOIUrl":"https://doi.org/10.5121/csit.2022.121809","url":null,"abstract":"Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include penalizing parameters during training or trimming connectivity. CNNs use relatively little pre-processing compared to other image classification algorithms. Given the rise in popularity and use of deep neural network learning, the problem of tuning hyperparameters is increasingly prominent tasks in constructing efficient deep neural networks. In this paper, the tuning of deep neural network learning (DNN) hyper-parameters is explored using an evolutionary based approach popularized for use in estimating solutions to problems where the problem space is too large to get an exact solution.","PeriodicalId":91205,"journal":{"name":"Artificial intelligence and applications (Commerce, Calif.)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009137","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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