Pub Date : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559011
M. Ozkan-Okay, R. Samet, Ömer Aslan
These days, various devices including computers, smartphones, internet of things (IoT), and cloud services are using computer networks for data communications. As the computer network is being used extensively, it becomes the target of many attacks. It can be different attacks such as denial of service attack (DoS), remote to user attack (R2L), user to remote attack (U2R), and probing attack. To protect communication networks from network-based attacks, intrusion detection systems (IDSs) have been proposed in many studies. However, today IDSs are not good enough to detect new attack types in the communication networks. To increase the efficiency of the current IDSs, a subset of features needs to be obtained before performing the machine learning classifiers. In this study, a new feature selection method is proposed for current IDSs. In addition, the proposed method is combined with machine learning classifiers and tested on KDD ’99 dataset and %99.81 accuracy rate was obtained. The obtained performance is pretty high to separate network attacks from the normal traffic.
{"title":"A New Feature Selection Approach and Classification Technique for Current Intrusion Detection System","authors":"M. Ozkan-Okay, R. Samet, Ömer Aslan","doi":"10.1109/UBMK52708.2021.9559011","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559011","url":null,"abstract":"These days, various devices including computers, smartphones, internet of things (IoT), and cloud services are using computer networks for data communications. As the computer network is being used extensively, it becomes the target of many attacks. It can be different attacks such as denial of service attack (DoS), remote to user attack (R2L), user to remote attack (U2R), and probing attack. To protect communication networks from network-based attacks, intrusion detection systems (IDSs) have been proposed in many studies. However, today IDSs are not good enough to detect new attack types in the communication networks. To increase the efficiency of the current IDSs, a subset of features needs to be obtained before performing the machine learning classifiers. In this study, a new feature selection method is proposed for current IDSs. In addition, the proposed method is combined with machine learning classifiers and tested on KDD ’99 dataset and %99.81 accuracy rate was obtained. The obtained performance is pretty high to separate network attacks from the normal traffic.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128540981","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558992
Zekeriya Anil Guven, Murat Osman Unalir
Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn’t use Named Entity Recognition technique sufficiently.
{"title":"Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering","authors":"Zekeriya Anil Guven, Murat Osman Unalir","doi":"10.1109/UBMK52708.2021.9558992","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558992","url":null,"abstract":"Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn’t use Named Entity Recognition technique sufficiently.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127256904","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559029
E. Yalcin, Derya Yiltas-Kaplan
In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.
{"title":"Classification and Irrigation of Different Kinds of Plants with Mobile Application","authors":"E. Yalcin, Derya Yiltas-Kaplan","doi":"10.1109/UBMK52708.2021.9559029","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559029","url":null,"abstract":"In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130067556","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558945
Gokhan Bakal, Orhan Abar
Due to the impressive information dissemination power of social networks such as Twitter, people tend to check social networks and Web pages more than other traditional news sources, including newspapers, TV news programs, or radio channels. In that sense, the information carried by the content of the shared social media posts becomes much more considerable. However, most of the posts are commonly either irrelevant or inaccurate. Besides, the more critical case than the correctness of the information is the diffusion speed on Twitter through the reply or retweet actions. These activities make the initial situation even more complicated than itself due to the unregulated nature of the social networks and the lack of an immediate verification mechanism for the correctness of the posts. When we consider the current Covid-19 pandemic period (causing the coronavirus disease), one of the most utilized information resources is Twitter except the official health administration institutions. Thereupon, examining the correctness of the information related to the Covid-19 pandemic by computational techniques (e.g., Data Mining, Machine Learning, and Deep Learning) has been gaining popularity and remains a substantial task. Hence, we mainly focused on analyzing the correctness of the posts related to the current pandemic shared on the Twitter platform. Therefore, the overall goal of this work is to classify the relevant tweets using linear and non-linear machine learning models. We achieved the best F1 performance score (99%) with the neural network model using the unigram features & threshold value of 50 among all model configurations.
{"title":"On Comparative Classification of Relevant Covid-19 Tweets","authors":"Gokhan Bakal, Orhan Abar","doi":"10.1109/UBMK52708.2021.9558945","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558945","url":null,"abstract":"Due to the impressive information dissemination power of social networks such as Twitter, people tend to check social networks and Web pages more than other traditional news sources, including newspapers, TV news programs, or radio channels. In that sense, the information carried by the content of the shared social media posts becomes much more considerable. However, most of the posts are commonly either irrelevant or inaccurate. Besides, the more critical case than the correctness of the information is the diffusion speed on Twitter through the reply or retweet actions. These activities make the initial situation even more complicated than itself due to the unregulated nature of the social networks and the lack of an immediate verification mechanism for the correctness of the posts. When we consider the current Covid-19 pandemic period (causing the coronavirus disease), one of the most utilized information resources is Twitter except the official health administration institutions. Thereupon, examining the correctness of the information related to the Covid-19 pandemic by computational techniques (e.g., Data Mining, Machine Learning, and Deep Learning) has been gaining popularity and remains a substantial task. Hence, we mainly focused on analyzing the correctness of the posts related to the current pandemic shared on the Twitter platform. Therefore, the overall goal of this work is to classify the relevant tweets using linear and non-linear machine learning models. We achieved the best F1 performance score (99%) with the neural network model using the unigram features & threshold value of 50 among all model configurations.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164257","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558936
Melisa Uçkuner, H. Erol
Cancer is a group of diseases that damage tissues by the uncontrolled proliferation of cells. The difficulty of distinguishing skin cancer, which is a common type of cancer, without technical support necessitates studies that can help specialists in the diagnosis phase. In this study, a deep learning model with 7 convolution layers and 3 neural layers was designed to classify the HAM10000 dataset, which consists of 7 classes and includes dermoscopic images. The accuracy rate for the test data of the proposed model was calculated as 99.01%. This result shows that the proposed model can help experts in diagnosing skin cancer.
{"title":"A New Deep Learning Model for Skin Cancer Classification","authors":"Melisa Uçkuner, H. Erol","doi":"10.1109/UBMK52708.2021.9558936","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558936","url":null,"abstract":"Cancer is a group of diseases that damage tissues by the uncontrolled proliferation of cells. The difficulty of distinguishing skin cancer, which is a common type of cancer, without technical support necessitates studies that can help specialists in the diagnosis phase. In this study, a deep learning model with 7 convolution layers and 3 neural layers was designed to classify the HAM10000 dataset, which consists of 7 classes and includes dermoscopic images. The accuracy rate for the test data of the proposed model was calculated as 99.01%. This result shows that the proposed model can help experts in diagnosing skin cancer.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128979103","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558902
Aysegül Senol Çalim, Cüneyt Kaya, Hakan Yüksel
The size of data in institutions such as banks is increasing rapidly due to the fact that the number of new products is put into service, the number of customers is increasing rapidly, the number of new applications is put into use due to regulations, and the data that must be kept compulsory such as audit trail records are excessive. When these data remain in existing systems for years, systems and applications become heavy, and the costs of operational processes such as backup and system maintenance increase. For all these problems, the data should be classified and categorized according to the frequency of access, those that do not need instant access to the categorized data should be archived by moving them to secondary and less costly systems and deleted from the source system. The large data-based archiving management system will be developed as a software product, providing more effective access to structural or unstructured data to be archived in the Hadoop ecosystem and bringing cheaper storage costs.
{"title":"Big Data Based Archiving Management System","authors":"Aysegül Senol Çalim, Cüneyt Kaya, Hakan Yüksel","doi":"10.1109/UBMK52708.2021.9558902","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558902","url":null,"abstract":"The size of data in institutions such as banks is increasing rapidly due to the fact that the number of new products is put into service, the number of customers is increasing rapidly, the number of new applications is put into use due to regulations, and the data that must be kept compulsory such as audit trail records are excessive. When these data remain in existing systems for years, systems and applications become heavy, and the costs of operational processes such as backup and system maintenance increase. For all these problems, the data should be classified and categorized according to the frequency of access, those that do not need instant access to the categorized data should be archived by moving them to secondary and less costly systems and deleted from the source system. The large data-based archiving management system will be developed as a software product, providing more effective access to structural or unstructured data to be archived in the Hadoop ecosystem and bringing cheaper storage costs.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130669598","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558943
Ali Erdem Altinbaş, Y. Yalman
Today, the development of data hiding techniques for information security or confidential communication is a subject of great demand and interest. The main purpose of most studies is to develop imperceptible hiding techniques and to hide more information with as little distortion as possible. Similarly, the main purpose of the presented study is to hide an image inside an audio file with less distortion. For this purpose, a bit reduction-based approach is used. The difference between the original audio file and the bit-reduced audio file can be classified as insignificant mostly. By adding the small changes made on this difference to the reduced audio file and saving it while preserving the original bit depth, the audio file containing the hidden image (stego-audio) is obtained. At the stage of data extracting, bit reduction is performed on the stego-audio first. The difference between stego-audio and reduced stego-audio includes the hidden image. As a result of this study, relatively higher bits can be hidden inside an audio file with very low distortion. The application results show that the developed algorithm is successful in terms of SNR and PSNR. The MATLAB codes of the developed application and the cover audio are available in the link: https://bit.ly/31Nynfe
{"title":"Bit Reduction based Audio Steganography Algorithm","authors":"Ali Erdem Altinbaş, Y. Yalman","doi":"10.1109/UBMK52708.2021.9558943","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558943","url":null,"abstract":"Today, the development of data hiding techniques for information security or confidential communication is a subject of great demand and interest. The main purpose of most studies is to develop imperceptible hiding techniques and to hide more information with as little distortion as possible. Similarly, the main purpose of the presented study is to hide an image inside an audio file with less distortion. For this purpose, a bit reduction-based approach is used. The difference between the original audio file and the bit-reduced audio file can be classified as insignificant mostly. By adding the small changes made on this difference to the reduced audio file and saving it while preserving the original bit depth, the audio file containing the hidden image (stego-audio) is obtained. At the stage of data extracting, bit reduction is performed on the stego-audio first. The difference between stego-audio and reduced stego-audio includes the hidden image. As a result of this study, relatively higher bits can be hidden inside an audio file with very low distortion. The application results show that the developed algorithm is successful in terms of SNR and PSNR. The MATLAB codes of the developed application and the cover audio are available in the link: https://bit.ly/31Nynfe","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132267553","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558971
Can Öz, N. Y. Topaloglu
The advancement of internet technologies has made it easier for local devices to access the internet. Smart systems are also becoming widespread in transportation systems. Device management using standard protocols becomes important for transportation systems; mainly responsible for vehicle tracking, in-vehicle sensor tracking and trip management. The presence of different solution providers in these systems makes it difficult for applications to work together and to manage in interaction with each other. This complexity can be managed by using IoT approaches. In our study, we provide a message-based topic structure for device management problems. Pub/Sub and event driven framework was realized with the help of frequently used IoT tools MQTT and WebSocket protocols.
{"title":"Message Based Communication Framework for Public Transportation Systems","authors":"Can Öz, N. Y. Topaloglu","doi":"10.1109/UBMK52708.2021.9558971","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558971","url":null,"abstract":"The advancement of internet technologies has made it easier for local devices to access the internet. Smart systems are also becoming widespread in transportation systems. Device management using standard protocols becomes important for transportation systems; mainly responsible for vehicle tracking, in-vehicle sensor tracking and trip management. The presence of different solution providers in these systems makes it difficult for applications to work together and to manage in interaction with each other. This complexity can be managed by using IoT approaches. In our study, we provide a message-based topic structure for device management problems. Pub/Sub and event driven framework was realized with the help of frequently used IoT tools MQTT and WebSocket protocols.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130483437","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9559007
Zekeriya Anil Guven
Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
{"title":"The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews","authors":"Zekeriya Anil Guven","doi":"10.1109/UBMK52708.2021.9559007","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9559007","url":null,"abstract":"Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131683876","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 : 2021-09-15DOI: 10.1109/UBMK52708.2021.9558968
M. Karakaya
Automatic tagging of images is an important research topic in the field of image processing. Another area similar to this is the automatic generation of picture captions. In this study, a deep learning model that automatically tags the pictures is used to detect errors in image captions. As a result of the initial experiments, it is observed that the proposed system can find up to 80% of the errors in the image captions.
{"title":"Detecting Errors in Automatic Image Captioning by Deep Learning","authors":"M. Karakaya","doi":"10.1109/UBMK52708.2021.9558968","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558968","url":null,"abstract":"Automatic tagging of images is an important research topic in the field of image processing. Another area similar to this is the automatic generation of picture captions. In this study, a deep learning model that automatically tags the pictures is used to detect errors in image captions. As a result of the initial experiments, it is observed that the proposed system can find up to 80% of the errors in the image captions.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126673400","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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