Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711414
Y. Ishii, Eisuke Saneyoshi, Mitsuru Sendoda, Reishi Kondo
This paper proposes an anomaly identification method for a liquid-coffee vending machine using electrical current waveforms. The method consists of preprocessing of a series of current values collected from the machine, training of multiple classifiers corresponding to individual target anomalous operations, and anomaly detection by means of the classifiers. Preprocessing improves detection accuracy by excluding current values that represent non-target operations. Multiple classifiers corresponding to individual target operations are trained using pre-processed data and the ground truth. An operation with the maximum likelihood normalized by the total number of individual operations is identified as the current anomaly. Evaluations using electrical current values obtained from an actual coffee vending machine shows a false positive rate and a false negative rate of, respectively, 0% and 6.7%, for lack of beans and 2% and 0% for water leakage, both of which are major reasons for degraded coffee quality.
{"title":"Anomaly Identification in A Liquid-Coffee Vending Machine Using Electrical Current Waveforms","authors":"Y. Ishii, Eisuke Saneyoshi, Mitsuru Sendoda, Reishi Kondo","doi":"10.1109/INFOCT.2019.8711414","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711414","url":null,"abstract":"This paper proposes an anomaly identification method for a liquid-coffee vending machine using electrical current waveforms. The method consists of preprocessing of a series of current values collected from the machine, training of multiple classifiers corresponding to individual target anomalous operations, and anomaly detection by means of the classifiers. Preprocessing improves detection accuracy by excluding current values that represent non-target operations. Multiple classifiers corresponding to individual target operations are trained using pre-processed data and the ground truth. An operation with the maximum likelihood normalized by the total number of individual operations is identified as the current anomaly. Evaluations using electrical current values obtained from an actual coffee vending machine shows a false positive rate and a false negative rate of, respectively, 0% and 6.7%, for lack of beans and 2% and 0% for water leakage, both of which are major reasons for degraded coffee quality.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124845054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711324
H. Kettani, Polly Wainwright
The technological innovation of cyber systems and increase dependence of individuals, societies and nations on them has brought new, real and everchanging threat landscapes. In fact, the threats evolving faster than they can be assessed. The technological innovation that brought ease and efficiency to our lives, has been met by similar innovation to take advantage of cyber systems for other gains. More threat actors are noted to be sponsored by nation-states and the skills and capabilities of organizations to defend against these attacks are lagging. This warrants an increase in automation of threat analysis and response as well as increased adoption of security measures by at-risk organizations. Thus, to properly prepare defenses and mitigations to the threats introduced by cyber, it is necessary to understand these threats. Accordingly, this paper provides an overview of top cyber security threats in together with current and emerging trends. The analyses include general trends in the complexity of attacks, actors, and the maturity of skills and capabilities of organizations to defend against attacks. Top threats are discussed with regard to instances of attacks and strategies for mitigation within the kill chain. A brief discussion of threat agents and attack vectors adds context to the threats.
{"title":"On the Top Threats to Cyber Systems","authors":"H. Kettani, Polly Wainwright","doi":"10.1109/INFOCT.2019.8711324","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711324","url":null,"abstract":"The technological innovation of cyber systems and increase dependence of individuals, societies and nations on them has brought new, real and everchanging threat landscapes. In fact, the threats evolving faster than they can be assessed. The technological innovation that brought ease and efficiency to our lives, has been met by similar innovation to take advantage of cyber systems for other gains. More threat actors are noted to be sponsored by nation-states and the skills and capabilities of organizations to defend against these attacks are lagging. This warrants an increase in automation of threat analysis and response as well as increased adoption of security measures by at-risk organizations. Thus, to properly prepare defenses and mitigations to the threats introduced by cyber, it is necessary to understand these threats. Accordingly, this paper provides an overview of top cyber security threats in together with current and emerging trends. The analyses include general trends in the complexity of attacks, actors, and the maturity of skills and capabilities of organizations to defend against attacks. Top threats are discussed with regard to instances of attacks and strategies for mitigation within the kill chain. A brief discussion of threat agents and attack vectors adds context to the threats.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711269
Jian-ao Lian
Two locally adaptive schemes for image sharpening are established. With appropriate selections of parameters, the schemes also smoothen the image in the area with flat-valued pixels, which leads to more pleasant-looking images. Demonstration by using standard images shows that the schemes are effective and comparable to some commonly used image-sharpening techniques in the literature.
{"title":"Two Adaptive Schemes for Image Sharpening","authors":"Jian-ao Lian","doi":"10.1109/INFOCT.2019.8711269","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711269","url":null,"abstract":"Two locally adaptive schemes for image sharpening are established. With appropriate selections of parameters, the schemes also smoothen the image in the area with flat-valued pixels, which leads to more pleasant-looking images. Demonstration by using standard images shows that the schemes are effective and comparable to some commonly used image-sharpening techniques in the literature.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122096326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711104
Jia Wu, Chao Liu, Qiyu Long, Weiyan Hou
In this paper, we propose a Personal Identity Verification (PIV) method based on 2-D convolutional neural network (CNN) by using electrocardiosignal (ECG singles). CNN shows outstanding performance in the field of image recognition nowadays, in order to make better use of this advantage, we innovatively convert electrocardiosignal into 2-D grayscale instead of traditional ECG. While ensuring that the image contains a complete cardiac cycle, it also enables the network to fully learn both the characteristics of the electrocardiosignal period and characteristics between each electrocardiosignal period. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. As a result, our classifier achieved 99.90% average accuracy. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent identification accuracy.
{"title":"Research on Personal Identity Verification Based on Convolutional Neural Network","authors":"Jia Wu, Chao Liu, Qiyu Long, Weiyan Hou","doi":"10.1109/INFOCT.2019.8711104","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711104","url":null,"abstract":"In this paper, we propose a Personal Identity Verification (PIV) method based on 2-D convolutional neural network (CNN) by using electrocardiosignal (ECG singles). CNN shows outstanding performance in the field of image recognition nowadays, in order to make better use of this advantage, we innovatively convert electrocardiosignal into 2-D grayscale instead of traditional ECG. While ensuring that the image contains a complete cardiac cycle, it also enables the network to fully learn both the characteristics of the electrocardiosignal period and characteristics between each electrocardiosignal period. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. As a result, our classifier achieved 99.90% average accuracy. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent identification accuracy.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"os-39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711036
Kemachart Kemavuthanon, O. Uchida
Twitter is the most popular social media platform in Japan for social interactions and real-time information exchanges during disasters; however, almost all information is in Japanese. This paper describes the data collection and analysis process associated with the planned construction of a real-time disaster-related information providing system for foreign tourists. To this end, characteristics of the tweets during the 2018 Osaka North Earthquake were analyzed. Despite there being thousands of tweets during the earthquake, information was hardly transmitted to foreign tourists. In this study, a data set of more than 9,000,000 tweets was used to analyze the information being shared and identify the most frequently used terms.
{"title":"Social Media Messages During Disasters in Japan : An Empirical Study of 2018 Osaka North Earthquake in Japan","authors":"Kemachart Kemavuthanon, O. Uchida","doi":"10.1109/INFOCT.2019.8711036","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711036","url":null,"abstract":"Twitter is the most popular social media platform in Japan for social interactions and real-time information exchanges during disasters; however, almost all information is in Japanese. This paper describes the data collection and analysis process associated with the planned construction of a real-time disaster-related information providing system for foreign tourists. To this end, characteristics of the tweets during the 2018 Osaka North Earthquake were analyzed. Despite there being thousands of tweets during the earthquake, information was hardly transmitted to foreign tourists. In this study, a data set of more than 9,000,000 tweets was used to analyze the information being shared and identify the most frequently used terms.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129782276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711122
S. Bravo, Ángel H. Moreno
In this work, a model has been developed for the prediction of the fundamental variables of the microwave drying process of amaranth seeds, using the initial mass of seeds and the temperature of the process as input data. The model was developed by using the RandomForestRegressor classifier, which is found in the module sklearn.ensemble of the Python programming language. For the training and prediction of the model, the data of the measurements made of the drying time and energy consumption in the drying experiments carried out at three temperatures (35, 45, 55 ° C) in a domestic microwave oven were used, as well as the germination rate of the amaranth seeds obtained in the germination tests. The predictions made by the model have a precision of 99.6% for the drying time, 98.5% for energy consumption and 92.2% for the germination rate of the seeds.
{"title":"A Random Forest Approach for Predicting the Microwave Drying Process of Amaranth Seeds","authors":"S. Bravo, Ángel H. Moreno","doi":"10.1109/INFOCT.2019.8711122","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711122","url":null,"abstract":"In this work, a model has been developed for the prediction of the fundamental variables of the microwave drying process of amaranth seeds, using the initial mass of seeds and the temperature of the process as input data. The model was developed by using the RandomForestRegressor classifier, which is found in the module sklearn.ensemble of the Python programming language. For the training and prediction of the model, the data of the measurements made of the drying time and energy consumption in the drying experiments carried out at three temperatures (35, 45, 55 ° C) in a domestic microwave oven were used, as well as the germination rate of the amaranth seeds obtained in the germination tests. The predictions made by the model have a precision of 99.6% for the drying time, 98.5% for energy consumption and 92.2% for the germination rate of the seeds.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711369
S. P. Nanda, Emanuel S. Grant
Safety critical systems are systems where a failure to find a fault can cause serious harm to the environment and people or even can lead to loss of life. The most important requirement of the system is to keep it fault free. This will be possible if the system is subject to development and verification in a systematic approach. Formal specification methods, as the name suggests, are truly formal with a strong mathematical background that can be trusted to facilitate the development of fault-free systems. The paper will discuss examples of safety-critical systems and some common type of errors that are found in the development of such systems will be discussed. The paper will examine how different domains affect the standards of formal specification methods in different applications. The approach will be to survey various papers in the related fields.
{"title":"A Survey of Formal Specification Application to Safety Critical Systems","authors":"S. P. Nanda, Emanuel S. Grant","doi":"10.1109/INFOCT.2019.8711369","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711369","url":null,"abstract":"Safety critical systems are systems where a failure to find a fault can cause serious harm to the environment and people or even can lead to loss of life. The most important requirement of the system is to keep it fault free. This will be possible if the system is subject to development and verification in a systematic approach. Formal specification methods, as the name suggests, are truly formal with a strong mathematical background that can be trusted to facilitate the development of fault-free systems. The paper will discuss examples of safety-critical systems and some common type of errors that are found in the development of such systems will be discussed. The paper will examine how different domains affect the standards of formal specification methods in different applications. The approach will be to survey various papers in the related fields.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134234453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711179
Songhao Lou, Shaoyin Cheng, J. Huang, Fan Jiang
With explosive growth of Android malware and due to the severity of its damages to smart phone users, efficient Android malware detection methods are urgently needed. As is known to us, different categories of applications divided by their functions use sensitive data in distinct ways. Besides, in each category, malicious applications treat sensitive data differently from benign applications. We thus propose TFDroid, a novel machine learning-based approach to detect malware using the related topics and data flows of Android applications. We test TFDroid on thousands of benign and malicious applications. The results show that TFDroid can correctly identify 93.7% of all malware.
{"title":"TFDroid: Android Malware Detection by Topics and Sensitive Data Flows Using Machine Learning Techniques","authors":"Songhao Lou, Shaoyin Cheng, J. Huang, Fan Jiang","doi":"10.1109/INFOCT.2019.8711179","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711179","url":null,"abstract":"With explosive growth of Android malware and due to the severity of its damages to smart phone users, efficient Android malware detection methods are urgently needed. As is known to us, different categories of applications divided by their functions use sensitive data in distinct ways. Besides, in each category, malicious applications treat sensitive data differently from benign applications. We thus propose TFDroid, a novel machine learning-based approach to detect malware using the related topics and data flows of Android applications. We test TFDroid on thousands of benign and malicious applications. The results show that TFDroid can correctly identify 93.7% of all malware.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8710918
Tomoka Azakami, Chihiro Shibata, R. Uda, T. Kinoshita
The accuracy of the image classification by the convolutional neural network is exceeding the ability of human being and contributes to various fields. However, the improvement of the image recognition technology gives a great blow to security system with an image such as CAPTCHA. In particular, since the character string CAPTCHA has already added distortion and noise in order not to be read by the computer, it becomes a problem that the human readability is lowered. Adversarial examples is a technique to produce an image letting an image classification by the machine learning be wrong intentionally. The best feature of this technique is that when human beings compare the original image with the adversarial examples, they cannot understand the difference on appearance. However, Adversarial examples that is created with conventional FGSM cannot completely misclassify strong nonlinear networks like CNN. Osadchy et al. have researched to apply this adversarial examples to CAPTCHA and attempted to let CNN misclassify them. However, they could not let CNN misclassify character images. In this research, we propose a method to apply FGSM to the character string CAPTCHAs and to let CNN misclassified them.
{"title":"Creation of Adversarial Examples with Keeping High Visual Performance","authors":"Tomoka Azakami, Chihiro Shibata, R. Uda, T. Kinoshita","doi":"10.1109/INFOCT.2019.8710918","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8710918","url":null,"abstract":"The accuracy of the image classification by the convolutional neural network is exceeding the ability of human being and contributes to various fields. However, the improvement of the image recognition technology gives a great blow to security system with an image such as CAPTCHA. In particular, since the character string CAPTCHA has already added distortion and noise in order not to be read by the computer, it becomes a problem that the human readability is lowered. Adversarial examples is a technique to produce an image letting an image classification by the machine learning be wrong intentionally. The best feature of this technique is that when human beings compare the original image with the adversarial examples, they cannot understand the difference on appearance. However, Adversarial examples that is created with conventional FGSM cannot completely misclassify strong nonlinear networks like CNN. Osadchy et al. have researched to apply this adversarial examples to CAPTCHA and attempted to let CNN misclassify them. However, they could not let CNN misclassify character images. In this research, we propose a method to apply FGSM to the character string CAPTCHAs and to let CNN misclassified them.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132431152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-14DOI: 10.1109/INFOCT.2019.8711059
M. Masinde, Portia Naledi Thothela
Information and communication technologies (ICTs), especially mobile phone technology, have great potential in improving livelihoods and alleviating poverty among Africa’s small-scale farmers. In particular, an effective decision support system that can aid farmers’ tactical and routine level decisions has been proven to lead to increased agricultural production. In this paper, we present such a tool – ITIKI Plus intelligently integrates indigenous and scientific data and information to provide contextualised micro-level drought forecast and cropping decision information to small-scale farmers in Kenya, Mozambique and South Africa. By providing the farmers, especially women, with both tactical-level and day-to-day decision support, the farmers have been able to make decisions that are up to 98% accurate. They have also been able to increase their food production by up to 10%.
{"title":"ITIKI Plus: A Mobile Based Application for Integrating Indigenous Knowledge and Scientific Agro-Climate Decision Support for Africa’s Small-Scale Farmers","authors":"M. Masinde, Portia Naledi Thothela","doi":"10.1109/INFOCT.2019.8711059","DOIUrl":"https://doi.org/10.1109/INFOCT.2019.8711059","url":null,"abstract":"Information and communication technologies (ICTs), especially mobile phone technology, have great potential in improving livelihoods and alleviating poverty among Africa’s small-scale farmers. In particular, an effective decision support system that can aid farmers’ tactical and routine level decisions has been proven to lead to increased agricultural production. In this paper, we present such a tool – ITIKI Plus intelligently integrates indigenous and scientific data and information to provide contextualised micro-level drought forecast and cropping decision information to small-scale farmers in Kenya, Mozambique and South Africa. By providing the farmers, especially women, with both tactical-level and day-to-day decision support, the farmers have been able to make decisions that are up to 98% accurate. They have also been able to increase their food production by up to 10%.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116572506","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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