Pub Date : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274665
L. Susanto, T. Ahmad
Since a long time ago, biometrics has become a popular authentication method to verify users easily. Despite this advantage, the user’s biometric data must be stored securely because those data cannot be replaced once it has been compromised. It is because humans cannot alter parts of his or her body. Many methods can be used to secure biometric data. One of them is the cancelable fingerprint template. However, its matching computation process is quite expensive, as many minutia points need to compare. In this paper, we optimize the number of minutia points in its selection process using a new threshold. It is to reduce the computational cost of pair-polar coordinate-based cancelable fingerprint template’s matching. The experiment result shows that the proposed method achieves five times better performance than the previous method.
{"title":"Reducing Computational Cost of Pair-Polar Coordinate-based Cancelable Fingerprint Template Matching","authors":"L. Susanto, T. Ahmad","doi":"10.1109/IC2IE50715.2020.9274665","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274665","url":null,"abstract":"Since a long time ago, biometrics has become a popular authentication method to verify users easily. Despite this advantage, the user’s biometric data must be stored securely because those data cannot be replaced once it has been compromised. It is because humans cannot alter parts of his or her body. Many methods can be used to secure biometric data. One of them is the cancelable fingerprint template. However, its matching computation process is quite expensive, as many minutia points need to compare. In this paper, we optimize the number of minutia points in its selection process using a new threshold. It is to reduce the computational cost of pair-polar coordinate-based cancelable fingerprint template’s matching. The experiment result shows that the proposed method achieves five times better performance than the previous method.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132070501","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274658
F. Kurniadi, Desty Septyani, I. Pratama
Lampung characters are one of the region-heritage characters from Indonesia. However, the current trend makes these characters becoming unknown. The concern about the extinction of these characters makes several researchers tried to digitized the documents which contained Lampung Character. Nevertheless, the process of digitalization is not free from noise. This problem makes us want to handle the noise from the scanned documents using binarization and noise removal techniques, especially in Lampung characters' documents. In this paper, we implemented local adaptive thresholding using the Niblack method and the Sauvola method for thresholding value. We also implemented Adaptive Wavelet Thresholding for Bayes Shrink for removing salt and pepper noise from the binarization process. The result showed that the Sauvola thresholding gives better results compared to Niblack thresholding. Our contribution in this paper is the implementation of both processes in Lampung Characters document
{"title":"Local Adaptive Thresholding Techniques for Binarizing Scanned Lampung Aksara Document Images","authors":"F. Kurniadi, Desty Septyani, I. Pratama","doi":"10.1109/IC2IE50715.2020.9274658","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274658","url":null,"abstract":"Lampung characters are one of the region-heritage characters from Indonesia. However, the current trend makes these characters becoming unknown. The concern about the extinction of these characters makes several researchers tried to digitized the documents which contained Lampung Character. Nevertheless, the process of digitalization is not free from noise. This problem makes us want to handle the noise from the scanned documents using binarization and noise removal techniques, especially in Lampung characters' documents. In this paper, we implemented local adaptive thresholding using the Niblack method and the Sauvola method for thresholding value. We also implemented Adaptive Wavelet Thresholding for Bayes Shrink for removing salt and pepper noise from the binarization process. The result showed that the Sauvola thresholding gives better results compared to Niblack thresholding. Our contribution in this paper is the implementation of both processes in Lampung Characters document","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132230514","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274564
Hana Rizky Herdika, E. K. Budiardjo
The user story widely studied insufficiently describes the software requirements. Many studies discuss the challenges of requirements specifications and proposed solutions. However, there are many agile methods and each method embraces different practices. Therefore, the solution has to follow each method’s ceremonies. Only a few studies discuss conducted practices in requirements specification development with the agile method and there are no studies that discuss practices comparisons among these methods. In this paper, we performed a systematic literature review to gather the available studies in procuring a current insight into the requirements engineering practices incorporate into the agile method. This study aims to map the variability and commonality practices among agile methods. The various and standard agile RE practices show crucial practices in the requirements specification development process. Moreover, it gives information on practices from other methods that increase the success of requirements specification development. From the systematic literature review, we found a total of 12 studies relevant to the study. There are eight variability and commonality practices between Scrum, XP, and Lean methods.
{"title":"Variability and Commonality Requirement Specification on Agile Software Development: Scrum, XP, Lean, and Kanban","authors":"Hana Rizky Herdika, E. K. Budiardjo","doi":"10.1109/IC2IE50715.2020.9274564","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274564","url":null,"abstract":"The user story widely studied insufficiently describes the software requirements. Many studies discuss the challenges of requirements specifications and proposed solutions. However, there are many agile methods and each method embraces different practices. Therefore, the solution has to follow each method’s ceremonies. Only a few studies discuss conducted practices in requirements specification development with the agile method and there are no studies that discuss practices comparisons among these methods. In this paper, we performed a systematic literature review to gather the available studies in procuring a current insight into the requirements engineering practices incorporate into the agile method. This study aims to map the variability and commonality practices among agile methods. The various and standard agile RE practices show crucial practices in the requirements specification development process. Moreover, it gives information on practices from other methods that increase the success of requirements specification development. From the systematic literature review, we found a total of 12 studies relevant to the study. There are eight variability and commonality practices between Scrum, XP, and Lean methods.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131418363","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274596
Nandiwardhana Waranugraha, M. Suryanegara
This paper aims to develop the Internet-of Things (IoT) Smart-Basket, working on 2 different systems, i.e. Edge Computing and Cloud Computing. To identify the best system, we compare the performance between “Edge Computing” system and “Cloud Computing” system. The system consists of Raspberry Pi hardware and webcam. Python, TFLite, OpenCV, and Google Cloud Vision API software to detect shopping objects. The object detection results are calculated and sent to end-users through the Telegram application. Discussions are presented concerning the Time Performance and RSSI Value between two systems. The results show “Edge Computing” systems have a more stable system with an average processing time of 1.74 sec on Line-of-Sight (LOS) condition and 1.75 sec on Non-Line-of-Sight (NLOS) condition compared to “Cloud Computing” systems with an average processing time of 10.46 sec on LOS condition and 5.36 sec on NLOS condition.
{"title":"The Development of IoT-Smart Basket: Performance Comparison between Edge Computing and Cloud Computing System","authors":"Nandiwardhana Waranugraha, M. Suryanegara","doi":"10.1109/IC2IE50715.2020.9274596","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274596","url":null,"abstract":"This paper aims to develop the Internet-of Things (IoT) Smart-Basket, working on 2 different systems, i.e. Edge Computing and Cloud Computing. To identify the best system, we compare the performance between “Edge Computing” system and “Cloud Computing” system. The system consists of Raspberry Pi hardware and webcam. Python, TFLite, OpenCV, and Google Cloud Vision API software to detect shopping objects. The object detection results are calculated and sent to end-users through the Telegram application. Discussions are presented concerning the Time Performance and RSSI Value between two systems. The results show “Edge Computing” systems have a more stable system with an average processing time of 1.74 sec on Line-of-Sight (LOS) condition and 1.75 sec on Non-Line-of-Sight (NLOS) condition compared to “Cloud Computing” systems with an average processing time of 10.46 sec on LOS condition and 5.36 sec on NLOS condition.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130525473","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 : 2020-09-15DOI: 10.1109/ic2ie50715.2020.9274652
{"title":"Welcome Editorial Remarks","authors":"","doi":"10.1109/ic2ie50715.2020.9274652","DOIUrl":"https://doi.org/10.1109/ic2ie50715.2020.9274652","url":null,"abstract":"","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116163675","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274682
Latifah Alhaura, I. Budi
The rapid growth of social networks indeed triggers an increase in malicious activities, including the spread of false information, the creation of fake accounts, spamming, and malware distribution. However, developing a detection system that can identify accounts precisely becomes quite challenging. In this paper, we present a study related to the detection of malicious accounts on Twitter users from Indonesia. Our study objective is to propose a simple feature set to detect malicious accounts using only a few metadata and the tweet content itself from Twitter. We divided the classification level into three: account level classification, tweet level classification, and combination of account and tweet level classification. To get the classification results, we applied some popular machine learning algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Neural Network, and Logistic Regression to each classification level. The results show that Random Forest achieved high classification accuracy (AUC >80%) in each classification level using our proposed feature set.
{"title":"Malicious Account Detection on Indonesian Twitter Account","authors":"Latifah Alhaura, I. Budi","doi":"10.1109/IC2IE50715.2020.9274682","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274682","url":null,"abstract":"The rapid growth of social networks indeed triggers an increase in malicious activities, including the spread of false information, the creation of fake accounts, spamming, and malware distribution. However, developing a detection system that can identify accounts precisely becomes quite challenging. In this paper, we present a study related to the detection of malicious accounts on Twitter users from Indonesia. Our study objective is to propose a simple feature set to detect malicious accounts using only a few metadata and the tweet content itself from Twitter. We divided the classification level into three: account level classification, tweet level classification, and combination of account and tweet level classification. To get the classification results, we applied some popular machine learning algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Neural Network, and Logistic Regression to each classification level. The results show that Random Forest achieved high classification accuracy (AUC >80%) in each classification level using our proposed feature set.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124051362","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274607
T. Mantoro, M. A. Ayu, R. Handayanto
Crime information is usually announced periodically. To get the information in real-time, the information should be retrieved through the information retrieval system automatically. The system should choose some appropriate keywords for retrieving the crime data. Eight keywords have been chosen which represented the most viral topic. The keywords in this study were analyzed regarding their sentiment from the hashtags in twitter posts. The Machine Learning algorithms were utilised such as Multinomal Naive Bayes, Random Forest Classifier, Linear SVM, and Nearestneighborhood (kNN) finding a better classifier. Sentiments, both positive and negative, are usually have been used by the website content designer to attract the reader. Most keywords showed negative sentiment which showed the negative reaction from the people. The sentiment analysis in Bahasa Indonesia also useful for understanding the people’s view on the types of crime as well as for keyword selection in the crime information retrieval system. As the result, near-repeat crime effect as a condition where criminal activity tends to repeat in the near place and time, can be predicted.
{"title":"Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval","authors":"T. Mantoro, M. A. Ayu, R. Handayanto","doi":"10.1109/IC2IE50715.2020.9274607","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274607","url":null,"abstract":"Crime information is usually announced periodically. To get the information in real-time, the information should be retrieved through the information retrieval system automatically. The system should choose some appropriate keywords for retrieving the crime data. Eight keywords have been chosen which represented the most viral topic. The keywords in this study were analyzed regarding their sentiment from the hashtags in twitter posts. The Machine Learning algorithms were utilised such as Multinomal Naive Bayes, Random Forest Classifier, Linear SVM, and Nearestneighborhood (kNN) finding a better classifier. Sentiments, both positive and negative, are usually have been used by the website content designer to attract the reader. Most keywords showed negative sentiment which showed the negative reaction from the people. The sentiment analysis in Bahasa Indonesia also useful for understanding the people’s view on the types of crime as well as for keyword selection in the crime information retrieval system. As the result, near-repeat crime effect as a condition where criminal activity tends to repeat in the near place and time, can be predicted.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124256430","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274685
I. Priyanto, C. A. Hartanto, A. M. Arymurthy
The aquaculture method uses floating net cages are the most productive fish farming techniques. We utilize deep learning for change detection and monitoring of floating net cages quantities by detecting & counting the number of floating net cages plots on the same Region of Interest (RoI) in different years using google earth satellite imagery. The proposed methods apply Faster R-CNN for detection purposes and compare Faster R-CNN between using NASNet-A and inception-v2 as the feature extractor. Our experiments have been conducted on annotation images by cropping google earth images to demonstrate the effectiveness and efficiency of the proposed method. The results show that Faster R-CNN using NASNet-A achieves higher accuracy with longer training time. In addition, Faster R-CNN with inception-v2 network also provided promising results with lower training time.
{"title":"Change Detection of Floating Net Cages Quantities Utilizing Faster R-CNN","authors":"I. Priyanto, C. A. Hartanto, A. M. Arymurthy","doi":"10.1109/IC2IE50715.2020.9274685","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274685","url":null,"abstract":"The aquaculture method uses floating net cages are the most productive fish farming techniques. We utilize deep learning for change detection and monitoring of floating net cages quantities by detecting & counting the number of floating net cages plots on the same Region of Interest (RoI) in different years using google earth satellite imagery. The proposed methods apply Faster R-CNN for detection purposes and compare Faster R-CNN between using NASNet-A and inception-v2 as the feature extractor. Our experiments have been conducted on annotation images by cropping google earth images to demonstrate the effectiveness and efficiency of the proposed method. The results show that Faster R-CNN using NASNet-A achieves higher accuracy with longer training time. In addition, Faster R-CNN with inception-v2 network also provided promising results with lower training time.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115362126","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274643
Ferryansa, Avon Budiono, A. Almaarif
The use of a very wide windows operating system is undeniably also followed by increasing attacks on the operating system. Universal Serial Bus (USB) is one of the mechanisms used by many people with plug and play functionality that is very easy to use, making data transfers fast and easy compared to other hardware. Some research shows that the Windows operating system has weaknesses so that it is often exploited by using various attacks and malware. There are various methods used to exploit the Windows operating system, one of them by using a USB device. By using a USB device, a criminal can plant a backdoor reverse shell to exploit the victim’s computer just by connecting the USB device to the victim’s computer without being noticed. This research was conducted by planting a reverse shell backdoor through a USB device to exploit the victim’s device, especially the webcam and microphone device on the target computer. From 35 experiments that have been carried out, it was found that 83% of spying attacks using USB devices on the Windows operating system were successfully carried out.
{"title":"Analysis of USB Based Spying Method Using Arduino and Metasploit Framework in Windows Operating System","authors":"Ferryansa, Avon Budiono, A. Almaarif","doi":"10.1109/IC2IE50715.2020.9274643","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274643","url":null,"abstract":"The use of a very wide windows operating system is undeniably also followed by increasing attacks on the operating system. Universal Serial Bus (USB) is one of the mechanisms used by many people with plug and play functionality that is very easy to use, making data transfers fast and easy compared to other hardware. Some research shows that the Windows operating system has weaknesses so that it is often exploited by using various attacks and malware. There are various methods used to exploit the Windows operating system, one of them by using a USB device. By using a USB device, a criminal can plant a backdoor reverse shell to exploit the victim’s computer just by connecting the USB device to the victim’s computer without being noticed. This research was conducted by planting a reverse shell backdoor through a USB device to exploit the victim’s device, especially the webcam and microphone device on the target computer. From 35 experiments that have been carried out, it was found that 83% of spying attacks using USB devices on the Windows operating system were successfully carried out.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115120064","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274627
P. Oktivasari, Fikri Vallian, Riandini
The heart sound detector using an android-based electronic stethoscope is a development of an acoustic stethoscope. This tool converts the sound of the heartbeat into an electrical signal by wirelessly displaying the first heart sound signal S1 (Lub), and the second heart sound signal S2 (Dub) on the android application. A low-cost electronic stethoscope consisting of a condenser microphone that converts the sound of the heartbeat captured by the stethoscope into a heartbeat signal, a signal conditioning circuit that corrects the detected signal. The output signal from the conditioner circuit is processed by the ADC on the Arduino, which will then be sent to Android using Bluetooth. The open source software used to display heart rate detection results is Android Studio. S1 and S2 heart sound signal patterns have been recorded in real-time with intervals of 2-3 seconds duration, and no time delay. The resulting signals can be saved as graphs and WAV files.
{"title":"Pattern Detection in Heart Sound Signal Based on Android Application","authors":"P. Oktivasari, Fikri Vallian, Riandini","doi":"10.1109/IC2IE50715.2020.9274627","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274627","url":null,"abstract":"The heart sound detector using an android-based electronic stethoscope is a development of an acoustic stethoscope. This tool converts the sound of the heartbeat into an electrical signal by wirelessly displaying the first heart sound signal S1 (Lub), and the second heart sound signal S2 (Dub) on the android application. A low-cost electronic stethoscope consisting of a condenser microphone that converts the sound of the heartbeat captured by the stethoscope into a heartbeat signal, a signal conditioning circuit that corrects the detected signal. The output signal from the conditioner circuit is processed by the ADC on the Arduino, which will then be sent to Android using Bluetooth. The open source software used to display heart rate detection results is Android Studio. S1 and S2 heart sound signal patterns have been recorded in real-time with intervals of 2-3 seconds duration, and no time delay. The resulting signals can be saved as graphs and WAV files.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122069052","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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