With the development of deep neural networks in pattern classification for recognizing handwritten digits on cheques, object classification for the automated surveillance, and autonomous vehicles, the problem of DNNs confront malicious inputs has been a hot topic. In this paper, we introduced a security-enhanced framework for DNNs to conduct classification based on moving target defense (MTDNNF). Also, we presented three pivotal characteristics to realize the framework, heterogeneity, selectivity, and adaptability, which enabled MTDNNF and guaranteed security and veracity. Also, we analyzed the security and performance of MTDNNF. Those analyses show that the MTDNNF can provide significant security improvements against malicious inputs, and extra cost in performance is inessential under both massive and minimum scenarios.
{"title":"MTDNNF: Building the Security Framework for Deep Neural Network by Moving Target Defense*","authors":"Weiwei Wang, Xinli Xiong, Songhe Wang, Jingye Zhang","doi":"10.1145/3446132.3446178","DOIUrl":"https://doi.org/10.1145/3446132.3446178","url":null,"abstract":"With the development of deep neural networks in pattern classification for recognizing handwritten digits on cheques, object classification for the automated surveillance, and autonomous vehicles, the problem of DNNs confront malicious inputs has been a hot topic. In this paper, we introduced a security-enhanced framework for DNNs to conduct classification based on moving target defense (MTDNNF). Also, we presented three pivotal characteristics to realize the framework, heterogeneity, selectivity, and adaptability, which enabled MTDNNF and guaranteed security and veracity. Also, we analyzed the security and performance of MTDNNF. Those analyses show that the MTDNNF can provide significant security improvements against malicious inputs, and extra cost in performance is inessential under both massive and minimum scenarios.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127007974","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}
In recent years, leveraging the characteristics of users’ historical behavior to predict click-through rates (CTRs) has become a key point of interest in studies of recommender systems. Although theoretical and experimental investigations of CTR models have increased substantially, most models focus on linear feature interaction; however, crucial user characteristics in the real world are discovered implicitly by non-linear features. In this paper, we propose a novel model that integrates the advantages of linear and non-linear feature interaction. Our deep factorization machines network with non-linear interaction for recommend systems (DFNR) model identifies non-linear feature interactions by designing a new Non-linear interaction (NL-interaction) layer. We also incorporate a deeper multilayer perceptron (MLP) than other CTR models, which yields more accurate information about higher-order feature interactions. The MLP in the proposed model is unique because we use the residual structure to correct problems caused by a deeper network structure. Findings show that our DFNR model performs better on a CTR prediction task compared to other models. Results demonstrate the effective-ness of our model based on its non-linear interaction layer and deeper neural network architecture.
{"title":"Deep Factorization Machines network with Non-linear interaction for Recommender System","authors":"Chuchu Yu, Xinmei Yang, Han Jiang","doi":"10.1145/3446132.3446134","DOIUrl":"https://doi.org/10.1145/3446132.3446134","url":null,"abstract":"In recent years, leveraging the characteristics of users’ historical behavior to predict click-through rates (CTRs) has become a key point of interest in studies of recommender systems. Although theoretical and experimental investigations of CTR models have increased substantially, most models focus on linear feature interaction; however, crucial user characteristics in the real world are discovered implicitly by non-linear features. In this paper, we propose a novel model that integrates the advantages of linear and non-linear feature interaction. Our deep factorization machines network with non-linear interaction for recommend systems (DFNR) model identifies non-linear feature interactions by designing a new Non-linear interaction (NL-interaction) layer. We also incorporate a deeper multilayer perceptron (MLP) than other CTR models, which yields more accurate information about higher-order feature interactions. The MLP in the proposed model is unique because we use the residual structure to correct problems caused by a deeper network structure. Findings show that our DFNR model performs better on a CTR prediction task compared to other models. Results demonstrate the effective-ness of our model based on its non-linear interaction layer and deeper neural network architecture.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132895638","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}
Electronic medical records(EMR) contain a lot of medical diagnosis information; In order to mine the value of data, it is necessary to extract the attributes of the electronic medical record. The deep learning method has been widely used in attribute extraction tasks and has achieved remarkable results in general text datasets. However, in specific medical fields, such as our electronic medical record extraction task, attribute extraction often lacks a lot of high-quality annotation data; besides, the attributes in the corpus can be divided into two types: discriminative attribute and extractive attribute, there is a strong correlation between some attributes. Independent Modeling each attribute cannot use this information, which will lead to insufficient information that the model can learn. This paper proposes a unified framework for medical record attribute extraction based on ALBERT, uses a large amount of general corpus as external knowledge for pre-training and fine-tuning, and adopts multi-task learning to make all attributes share the underlying cod-ing and train. Experiments show that this framework is greatly improved than the traditional LSTM-CRF model; it performs better in practical application scenarios.
{"title":"A unified framework for attribute extraction in electronic medical records","authors":"Ming Du, Wenkun Wang, Sufen Wang, Bo Xu","doi":"10.1145/3446132.3446410","DOIUrl":"https://doi.org/10.1145/3446132.3446410","url":null,"abstract":"Electronic medical records(EMR) contain a lot of medical diagnosis information; In order to mine the value of data, it is necessary to extract the attributes of the electronic medical record. The deep learning method has been widely used in attribute extraction tasks and has achieved remarkable results in general text datasets. However, in specific medical fields, such as our electronic medical record extraction task, attribute extraction often lacks a lot of high-quality annotation data; besides, the attributes in the corpus can be divided into two types: discriminative attribute and extractive attribute, there is a strong correlation between some attributes. Independent Modeling each attribute cannot use this information, which will lead to insufficient information that the model can learn. This paper proposes a unified framework for medical record attribute extraction based on ALBERT, uses a large amount of general corpus as external knowledge for pre-training and fine-tuning, and adopts multi-task learning to make all attributes share the underlying cod-ing and train. Experiments show that this framework is greatly improved than the traditional LSTM-CRF model; it performs better in practical application scenarios.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369081","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}
Unlike Emotion Cause Extraction (ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction (ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network (IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.
{"title":"A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction","authors":"Xiangyu Jia, Xinhai Chen, Qian Wan, Jie Liu","doi":"10.1145/3446132.3446195","DOIUrl":"https://doi.org/10.1145/3446132.3446195","url":null,"abstract":"Unlike Emotion Cause Extraction (ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction (ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network (IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129705855","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}
Deep Linear Discriminative Analysis (DeepLDA) is an effective feature learning method that combines LDA with deep neural network. The core of DeepLDA is putting a LDA based loss function on the top of deep neural network, which is constructed by fully-connected layers. Generally speaking, fully-connected layers will lead to a large consumption of computing resource. What’s more, capacity of the deep neural network may too large to fit training data properly when fully-connected layers are used. Thus, performance of DeepLDA may be improved by increasing sparsity of the deep neural network. In this paper, a sparse training strategy is exploited to train DeepLDA. Dense layers in DeepLDA are replaced by a Erdös-Rényi random graph based sparse topology first. Then, sparse evolutionary training (SET) strategy is employed to train DeepLDA. Preliminary experiments show that DeepLDA trained with SET strategy outperforms DeepLDA trained with fully-connected layers on MINST classification task.
深度线性判别分析(Deep Linear Discriminative Analysis, DeepLDA)是一种将深度线性判别分析与深度神经网络相结合的有效特征学习方法。DeepLDA的核心是将一个基于LDA的损失函数放在由全连接层构成的深度神经网络的顶层。一般来说，全连接层会导致大量的计算资源消耗。此外，当使用全连接层时，深度神经网络的容量可能太大而无法正确拟合训练数据。因此，可以通过增加深度神经网络的稀疏度来提高DeepLDA的性能。本文采用稀疏训练策略对DeepLDA进行训练。DeepLDA中的密集层首先被基于稀疏拓扑的Erdös-Rényi随机图所取代。然后，采用稀疏进化训练(SET)策略对DeepLDA进行训练。初步实验表明，SET策略训练的DeepLDA在MINST分类任务上优于全连接层训练的DeepLDA。
{"title":"A Sparse Deep Linear Discriminative Analysis using Sparse Evolutionary Training","authors":"Xuefeng Bai, Lijun Yan","doi":"10.1145/3446132.3446167","DOIUrl":"https://doi.org/10.1145/3446132.3446167","url":null,"abstract":"Deep Linear Discriminative Analysis (DeepLDA) is an effective feature learning method that combines LDA with deep neural network. The core of DeepLDA is putting a LDA based loss function on the top of deep neural network, which is constructed by fully-connected layers. Generally speaking, fully-connected layers will lead to a large consumption of computing resource. What’s more, capacity of the deep neural network may too large to fit training data properly when fully-connected layers are used. Thus, performance of DeepLDA may be improved by increasing sparsity of the deep neural network. In this paper, a sparse training strategy is exploited to train DeepLDA. Dense layers in DeepLDA are replaced by a Erdös-Rényi random graph based sparse topology first. Then, sparse evolutionary training (SET) strategy is employed to train DeepLDA. Preliminary experiments show that DeepLDA trained with SET strategy outperforms DeepLDA trained with fully-connected layers on MINST classification task.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130678055","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}
Cunchao Zhu, Guangquan Cheng, Yang Ma, Jiuyao Jiang, M. Wang, Tingfei Huang
Link prediction is an important application in complex networks. It predicts existing but undiscovered associations or possible future relationships in the network. However, networks in real life have much noise. The networks we observe are incomplete or redundant which interfere with the effect of link prediction. This paper summarizes and constructs four kinds of common noises in social networks, then analyzes the robustness of traditional link prediction methods and methods based on network representation under the influence of different kinds and different degrees of noises on multiple social networks. The experimental results confirm that algorithms using local network properties have higher link accuracy, while methods based on the global properties have higher robustness. CCS CONCEPTS • Networks∼Network performance evaluation∼Network performance analysis • Networks∼Network performance evaluation∼Network experimentation • Networks∼Network performance evaluation∼Network performance modeling
{"title":"Robustness analysis of noise network link prediction","authors":"Cunchao Zhu, Guangquan Cheng, Yang Ma, Jiuyao Jiang, M. Wang, Tingfei Huang","doi":"10.1145/3446132.3446143","DOIUrl":"https://doi.org/10.1145/3446132.3446143","url":null,"abstract":"Link prediction is an important application in complex networks. It predicts existing but undiscovered associations or possible future relationships in the network. However, networks in real life have much noise. The networks we observe are incomplete or redundant which interfere with the effect of link prediction. This paper summarizes and constructs four kinds of common noises in social networks, then analyzes the robustness of traditional link prediction methods and methods based on network representation under the influence of different kinds and different degrees of noises on multiple social networks. The experimental results confirm that algorithms using local network properties have higher link accuracy, while methods based on the global properties have higher robustness. CCS CONCEPTS • Networks∼Network performance evaluation∼Network performance analysis • Networks∼Network performance evaluation∼Network experimentation • Networks∼Network performance evaluation∼Network performance modeling","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130705716","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}
Based on the subject's keyboard typing time series dataset, an long short term (LSTM) network model was developed to predict the early-stage Parkinson's disease. The training and test results show that the area under ROC curve (AUC) is 0.82, accuracy rate (ACC) is 0.84, precision (PRE) is 0.85, recall rate (REC) is 0.98, and F1 score is 0.90. This indicates that the LSTM prediction model can botain high accuracy, precision and sensitivity results by automatically extracting keyboard typing time series characteristics of keyboard typing time series data.
{"title":"An application of LSTM prediction model based on keystroke data","authors":"O. Min, Zhang Wei, Zhou Nian, Xie Su","doi":"10.1145/3446132.3446191","DOIUrl":"https://doi.org/10.1145/3446132.3446191","url":null,"abstract":"Based on the subject's keyboard typing time series dataset, an long short term (LSTM) network model was developed to predict the early-stage Parkinson's disease. The training and test results show that the area under ROC curve (AUC) is 0.82, accuracy rate (ACC) is 0.84, precision (PRE) is 0.85, recall rate (REC) is 0.98, and F1 score is 0.90. This indicates that the LSTM prediction model can botain high accuracy, precision and sensitivity results by automatically extracting keyboard typing time series characteristics of keyboard typing time series data.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115646282","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}
The paper describes the development a corpus of an English variety, i.e. China English, in or-der to provide a linguistic resource for researchers in the field of China English. The Corpus of China English (CCE) was built with due consideration given to its representativeness and authenticity. It was composed of more than 13,962,102 tokens in 15,333 texts evenly divided between the following four genres: newspapers, magazines, fiction and academic writings. The texts cover a wide range of domains, such as news, financial, politics, environment, social, culture, technology, sports, education, philosophy, literary, etc. It is a helpful resource for research on China English, computational linguistics, natural language processing, corpus linguistics and English language education.
{"title":"The Design and Construction of the Corpus of China English","authors":"L. Xia, Yun Xia","doi":"10.1145/3446132.3446398","DOIUrl":"https://doi.org/10.1145/3446132.3446398","url":null,"abstract":"The paper describes the development a corpus of an English variety, i.e. China English, in or-der to provide a linguistic resource for researchers in the field of China English. The Corpus of China English (CCE) was built with due consideration given to its representativeness and authenticity. It was composed of more than 13,962,102 tokens in 15,333 texts evenly divided between the following four genres: newspapers, magazines, fiction and academic writings. The texts cover a wide range of domains, such as news, financial, politics, environment, social, culture, technology, sports, education, philosophy, literary, etc. It is a helpful resource for research on China English, computational linguistics, natural language processing, corpus linguistics and English language education.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045150","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}
Xin Shi, Xiaoyang Zeng, Jie Wu, Mengshu Hou, Hao Zhu
Extracting valuable information from text has always been a hot point for research and event detection is an essential subtask of information extraction. Most existing methods of event detection only focus on sentence-level information and do not consider the correlation between different event types. To address these problems, in this paper, we propose a novel pre-trained language model based event detection framework named CFEE that utilizes document-level information and event correlation to enhance the event detection task. To obtain event correlation, we project all event types into a shared semantic space through a Skip-gram model, where the event correlation can be represented as the distance between event embeddings. In order to capture document-level information, we utilize a bidirectional recurrent neural network to fuse the context information. Experiments on the ACE2005 dataset demonstrate that our proposed model is better than most existing methods, and also demonstrate the effectiveness of event correlation and document-level information.
{"title":"Context Event Features and Event Embedding Enhanced Event Detection","authors":"Xin Shi, Xiaoyang Zeng, Jie Wu, Mengshu Hou, Hao Zhu","doi":"10.1145/3446132.3446397","DOIUrl":"https://doi.org/10.1145/3446132.3446397","url":null,"abstract":"Extracting valuable information from text has always been a hot point for research and event detection is an essential subtask of information extraction. Most existing methods of event detection only focus on sentence-level information and do not consider the correlation between different event types. To address these problems, in this paper, we propose a novel pre-trained language model based event detection framework named CFEE that utilizes document-level information and event correlation to enhance the event detection task. To obtain event correlation, we project all event types into a shared semantic space through a Skip-gram model, where the event correlation can be represented as the distance between event embeddings. In order to capture document-level information, we utilize a bidirectional recurrent neural network to fuse the context information. Experiments on the ACE2005 dataset demonstrate that our proposed model is better than most existing methods, and also demonstrate the effectiveness of event correlation and document-level information.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131751366","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}
In this paper, the genetic algorithm and the dynamic programming algorithm are used to solve the 0-1 knapsack problem, and the principles and implementation process of the two methods are analyzed. For the two methods, the initial condition values are changed respectively, and the running time, the number of iterations and the accuracy of the running results of each algorithm under different conditions are compared and analyzed, with the reasons for the differences are studied to show the characteristics in order to find different features of these algorithms.
{"title":"Comparison of genetic algorithm and dynamic programming solving knapsack problem","authors":"Yan Wang, M. Wang, Jia Li, Xiang Xu","doi":"10.1145/3446132.3446142","DOIUrl":"https://doi.org/10.1145/3446132.3446142","url":null,"abstract":"In this paper, the genetic algorithm and the dynamic programming algorithm are used to solve the 0-1 knapsack problem, and the principles and implementation process of the two methods are analyzed. For the two methods, the initial condition values are changed respectively, and the running time, the number of iterations and the accuracy of the running results of each algorithm under different conditions are compared and analyzed, with the reasons for the differences are studied to show the characteristics in order to find different features of these algorithms.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134394333","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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