Pub Date : 2021-12-01DOI: 10.1109/cic52973.2021.00005
{"title":"Message from IEEE 2021 CIC General Chairs and PC Chairs","authors":"","doi":"10.1109/cic52973.2021.00005","DOIUrl":"https://doi.org/10.1109/cic52973.2021.00005","url":null,"abstract":"","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126616037","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00014
Krishna Bharadwaj, A. Burks, Andrew E. Johnson, Lance Long, L. Renambot, Maxine D. Brown, Dylan Kobayashi, Mahdi Belcaid, Nurit Kirshenbaum, Roderick S. Tabalba, Ryan Theriot, J. Leigh
SAGE2 (Scalable Amplified Group Environment) is the de facto platform to support group work on wide-band display environments. Unlike most web applications, the SAGE environment, due to the nature of its collaborative model, needs a nuanced handling of security aspects. This paper details the security requirements of SAGE2, the Identity and Access Control model that was developed to address those requirements, and the details of the Identity and Access Management system that the SAGE team implemented based on this new model. Further, we present a comparison of this new system with some of the popular collaboration platforms to highlight the uniqueness of SAGE2 integrated with this new Identity and Access Management system.
{"title":"Securing Collaborative Work in Wide-band Display Environments","authors":"Krishna Bharadwaj, A. Burks, Andrew E. Johnson, Lance Long, L. Renambot, Maxine D. Brown, Dylan Kobayashi, Mahdi Belcaid, Nurit Kirshenbaum, Roderick S. Tabalba, Ryan Theriot, J. Leigh","doi":"10.1109/CIC52973.2021.00014","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00014","url":null,"abstract":"SAGE2 (Scalable Amplified Group Environment) is the de facto platform to support group work on wide-band display environments. Unlike most web applications, the SAGE environment, due to the nature of its collaborative model, needs a nuanced handling of security aspects. This paper details the security requirements of SAGE2, the Identity and Access Control model that was developed to address those requirements, and the details of the Identity and Access Management system that the SAGE team implemented based on this new model. Further, we present a comparison of this new system with some of the popular collaboration platforms to highlight the uniqueness of SAGE2 integrated with this new Identity and Access Management system.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133239963","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}
Recognizing disordered speech is a challenge to Automatic Speech Recognition (ASR) systems. This research focuses on classifying disordered speech vs. non-disordered speech through signal processing coupled with machine learning techniques. We have found little evidence of ASR that correctly classifies disordered vs. ordered speech at the level of expert-based classification. This research supports the Automated Phonetic Transcription - Grading Tool (APTgt). APTgt is an online E-Learning system that supports Communications Disorders (CMDS) faculty during linguistic courses and provides reinforcement activities for phonetic transcription with the potential to improve the quality of students' learning efficacy and teachers' pedagogical experience. In addition, APTgt generates interactive practice sessions and exams, automatic grading, and exam analysis. This paper will focus on the classification module to classify disordered speech and non-disordered speech supporting APTgt. We utilize Mel-frequency cepstral coefficients (MFCCs) and dynamic time warping (DTW) to preprocess the audio files and calculate the similarity, and the Support Vector Machine (SVM) algorithm for classification and regression.
{"title":"Speech Disorders Classification in Phonetic Exams with MFCC and DTW","authors":"Jueting Liu, Marisha Speights, Dallin J Bailey, Sicheng Li, Huanyi Zhou, Yaoxuan Luan, Tianshi Xie, Cheryl D. Seals","doi":"10.1109/CIC52973.2021.00015","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00015","url":null,"abstract":"Recognizing disordered speech is a challenge to Automatic Speech Recognition (ASR) systems. This research focuses on classifying disordered speech vs. non-disordered speech through signal processing coupled with machine learning techniques. We have found little evidence of ASR that correctly classifies disordered vs. ordered speech at the level of expert-based classification. This research supports the Automated Phonetic Transcription - Grading Tool (APTgt). APTgt is an online E-Learning system that supports Communications Disorders (CMDS) faculty during linguistic courses and provides reinforcement activities for phonetic transcription with the potential to improve the quality of students' learning efficacy and teachers' pedagogical experience. In addition, APTgt generates interactive practice sessions and exams, automatic grading, and exam analysis. This paper will focus on the classification module to classify disordered speech and non-disordered speech supporting APTgt. We utilize Mel-frequency cepstral coefficients (MFCCs) and dynamic time warping (DTW) to preprocess the audio files and calculate the similarity, and the Support Vector Machine (SVM) algorithm for classification and regression.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133986986","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00023
Arnett Campbell, Sean S. E. Thorpe, Tyrone Edwards, Christopher Panther, Sean Ramsey, David White
This paper posits the need for an integrated micro services framework for handling all business and student services at our local University. We present the research question, “to what extent do the functionalities of the micro service frameworks provide beneficial considerations for the implementation of a micro service system within the University campus environment?” We discuss the response to this question regarding using a use case implementation now in progress from our enterprise systems solution - ISAS (Integrated Student Assessment System). The assumption is to implement functional micro-services to support our student and staff environments like never before. As such, the pivot from traditionally monolithic legacy systems to one that is component-based and service-driven is urgently necessary to allow our University to support all its application layered services continuously. The adaption of scalable micro services architectures focuses on Universities like ours to keep delivering a sustainable virtual presence. We use the summary perspectives presented in this paper to inform other institutions seeking to make these changes part of driving workable virtualized infrastructure, both containerized and serverless in design.
{"title":"Towards an Integrated Micro-services Architecture for Campus environments","authors":"Arnett Campbell, Sean S. E. Thorpe, Tyrone Edwards, Christopher Panther, Sean Ramsey, David White","doi":"10.1109/CIC52973.2021.00023","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00023","url":null,"abstract":"This paper posits the need for an integrated micro services framework for handling all business and student services at our local University. We present the research question, “to what extent do the functionalities of the micro service frameworks provide beneficial considerations for the implementation of a micro service system within the University campus environment?” We discuss the response to this question regarding using a use case implementation now in progress from our enterprise systems solution - ISAS (Integrated Student Assessment System). The assumption is to implement functional micro-services to support our student and staff environments like never before. As such, the pivot from traditionally monolithic legacy systems to one that is component-based and service-driven is urgently necessary to allow our University to support all its application layered services continuously. The adaption of scalable micro services architectures focuses on Universities like ours to keep delivering a sustainable virtual presence. We use the summary perspectives presented in this paper to inform other institutions seeking to make these changes part of driving workable virtualized infrastructure, both containerized and serverless in design.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115279807","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 : 2021-12-01DOI: 10.1109/cic52973.2021.00006
{"title":"Organizing Committee CIC 2021","authors":"","doi":"10.1109/cic52973.2021.00006","DOIUrl":"https://doi.org/10.1109/cic52973.2021.00006","url":null,"abstract":"","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123148926","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00022
Mohammad Al-Saad, Madeleine Lucas, Lakshmish Ramaswamy
Nowadays, large companies including Fitbit, Garmin, and Apple provide consumers with highly accurate and real-time activity trackers. An individual can simply wear a watch or handheld IoT device to automatically detect and track any movement throughout their day. Using sensor data obtained from Arizona State's Kinesiology department, this study presents the privacy concerns that activity-tracker devices pose due to the extensive amount of user data they obtain. We input unidentified user sensor data from six different recorded activities to an LSTM to show how accurately the model can match the data to the individual who completed it. We show that for three out of the six activities, the model can accurately match 88-92% of the timestep samples to the correct subject that performed them and 60-70% for the remaining three activities studied. Additionally, we present a voting based mechanism that improves the accuracy of sensor data classification to an average of 93%. Replacing the data of the participants with fake data can potentially enhance the privacy and anonymize the identities of those participants. One promising way to generate fake data with high quality data is to use generative adversarial networks (GANs). GANs have gained attention in the research community due to its ability to learn rich data distribution from samples and its outstanding experimental performance as a generative model. However, applying GANs by itself on sensitive data could raise a privacy concern since the density of the learned generative distribution could concentrate on the training data points. This means that GANs can easily remember training samples due to the high model complexity of deep networks. To mitigate the privacy risks, we combine ideas from the literature to implement a differentially private GAN model (HDP-GAN) that is capable of generating private synthetic streaming data before residing at its final destination in the tracker's company cloud. Two experiments were conducted to show that HDP-GAN can have promising results in protecting the individuals who performed the activities.
{"title":"Privacy Vulnerabilities of Wearable Activity Monitors: Threat and Potential Defence","authors":"Mohammad Al-Saad, Madeleine Lucas, Lakshmish Ramaswamy","doi":"10.1109/CIC52973.2021.00022","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00022","url":null,"abstract":"Nowadays, large companies including Fitbit, Garmin, and Apple provide consumers with highly accurate and real-time activity trackers. An individual can simply wear a watch or handheld IoT device to automatically detect and track any movement throughout their day. Using sensor data obtained from Arizona State's Kinesiology department, this study presents the privacy concerns that activity-tracker devices pose due to the extensive amount of user data they obtain. We input unidentified user sensor data from six different recorded activities to an LSTM to show how accurately the model can match the data to the individual who completed it. We show that for three out of the six activities, the model can accurately match 88-92% of the timestep samples to the correct subject that performed them and 60-70% for the remaining three activities studied. Additionally, we present a voting based mechanism that improves the accuracy of sensor data classification to an average of 93%. Replacing the data of the participants with fake data can potentially enhance the privacy and anonymize the identities of those participants. One promising way to generate fake data with high quality data is to use generative adversarial networks (GANs). GANs have gained attention in the research community due to its ability to learn rich data distribution from samples and its outstanding experimental performance as a generative model. However, applying GANs by itself on sensitive data could raise a privacy concern since the density of the learned generative distribution could concentrate on the training data points. This means that GANs can easily remember training samples due to the high model complexity of deep networks. To mitigate the privacy risks, we combine ideas from the literature to implement a differentially private GAN model (HDP-GAN) that is capable of generating private synthetic streaming data before residing at its final destination in the tracker's company cloud. Two experiments were conducted to show that HDP-GAN can have promising results in protecting the individuals who performed the activities.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127654544","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00019
Balsam Alkouz, Babar Shahzaad, A. Bouguettaya
Service delivery is set to experience a major paradigm shift with fast advances in drone technologies coupled with higher expectations from customers and increased competition. We propose a novel service-oriented approach to enable the ubiquitous delivery of packages in a drone-operated skyway network. We discuss the benefits, framework and architecture, contemporary approaches, open challenges and future visioned directions of service-based drone deliveries.
{"title":"Service-Based Drone Delivery","authors":"Balsam Alkouz, Babar Shahzaad, A. Bouguettaya","doi":"10.1109/CIC52973.2021.00019","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00019","url":null,"abstract":"Service delivery is set to experience a major paradigm shift with fast advances in drone technologies coupled with higher expectations from customers and increased competition. We propose a novel service-oriented approach to enable the ubiquitous delivery of packages in a drone-operated skyway network. We discuss the benefits, framework and architecture, contemporary approaches, open challenges and future visioned directions of service-based drone deliveries.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131457257","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00018
A. Mahmood, Quan Z. Sheng, S. A. Siddiqui, S. Sagar, Wei Emma Zhang, Hajime Suzuki, Wei Ni
Recent technological breakthroughs in vehicular ad hoc networks and the Internet of Things (IoT) have transformed vehicles into smart objects thus paving the way for the evolution of the promising paradigm of the Internet of Vehicles (IoV), which is an integral constituent of the modern intelligent transportation systems. Simply put, IoV attributes to the IoT-on-wheels, wherein vehicles broadcast safety-critical information among one another (and their immediate ambiences) for guaranteeing highly reliable and efficacious traffic flows. This, therefore, necessitates the need to fully secure an IoV network since a single malicious message is capable enough of jeopardizing the safety of the nearby vehicles (and their respective passengers) and vulnerable pedestrians. It is also pertinent to mention that a malicious attacker, i.e., vehicle, is not only able to send counterfeited safety-critical messages to its nearby vehicles and the traffic management authorities but could further enable a compromised vehicle to broadcast both spoofed coordinates and speed-related information. It is, therefore, of the utmost importance that malicious entities and their messages be identified and subsequently eliminated from the network before they are able to manipulate the entire network for their malicious gains. This paper, therefore, delineates on the convergence of the notion of trust with the IoV primarily in terms of its underlying rationale. It further highlights the opportunities which transpire as a result of this convergence to secure an IoV network. Finally, open research challenges, together with the recommendations for addressing the same, have been discussed.
{"title":"When Trust Meets the Internet of Vehicles: Opportunities, Challenges, and Future Prospects","authors":"A. Mahmood, Quan Z. Sheng, S. A. Siddiqui, S. Sagar, Wei Emma Zhang, Hajime Suzuki, Wei Ni","doi":"10.1109/CIC52973.2021.00018","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00018","url":null,"abstract":"Recent technological breakthroughs in vehicular ad hoc networks and the Internet of Things (IoT) have transformed vehicles into smart objects thus paving the way for the evolution of the promising paradigm of the Internet of Vehicles (IoV), which is an integral constituent of the modern intelligent transportation systems. Simply put, IoV attributes to the IoT-on-wheels, wherein vehicles broadcast safety-critical information among one another (and their immediate ambiences) for guaranteeing highly reliable and efficacious traffic flows. This, therefore, necessitates the need to fully secure an IoV network since a single malicious message is capable enough of jeopardizing the safety of the nearby vehicles (and their respective passengers) and vulnerable pedestrians. It is also pertinent to mention that a malicious attacker, i.e., vehicle, is not only able to send counterfeited safety-critical messages to its nearby vehicles and the traffic management authorities but could further enable a compromised vehicle to broadcast both spoofed coordinates and speed-related information. It is, therefore, of the utmost importance that malicious entities and their messages be identified and subsequently eliminated from the network before they are able to manipulate the entire network for their malicious gains. This paper, therefore, delineates on the convergence of the notion of trust with the IoV primarily in terms of its underlying rationale. It further highlights the opportunities which transpire as a result of this convergence to secure an IoV network. Finally, open research challenges, together with the recommendations for addressing the same, have been discussed.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116941856","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 : 2021-12-01DOI: 10.1109/cic52973.2021.00010
{"title":"Conference Panel: Pandemic 2023 – An Information Technology Retrospective","authors":"","doi":"10.1109/cic52973.2021.00010","DOIUrl":"https://doi.org/10.1109/cic52973.2021.00010","url":null,"abstract":"","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738559","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 : 2021-12-01DOI: 10.1109/CIC52973.2021.00016
Joffrey L. Leevy, John T. Hancock, T. Khoshgoftaar, Naeem Seliya
The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our “6 Agree” technique.
{"title":"IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection","authors":"Joffrey L. Leevy, John T. Hancock, T. Khoshgoftaar, Naeem Seliya","doi":"10.1109/CIC52973.2021.00016","DOIUrl":"https://doi.org/10.1109/CIC52973.2021.00016","url":null,"abstract":"The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our “6 Agree” technique.","PeriodicalId":170121,"journal":{"name":"2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133175586","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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