Although the boom of social media in the past decade has enabled the creation, distribution, and consumption of information at a remarkable rate, it has also led to the growth of different forms of online abuse. Since the outbreak of COVID-19, hate against Chinese or Sinophobia has increased significantly in real world as well as on online platforms making it necessary to design ways to combat it. In this paper, we design a platform-agnostic model to detect Sinophobic content on social media websites automatically. We use pre-trained word embeddings with several machine learning classifiers to detect Sinophobia on three platforms---Parler, Reddit, and Twitter. Our results demonstrate that the BERT model shows the best performance among all the models by achieving an accuracy of 98.51% on Parler, 95.36% on Reddit, and 88.12% on Twitter datasets.
{"title":"Platform-agnostic Model to Detect Sinophobia on Social Media","authors":"Matthew Morgan, Adita Kulkarni","doi":"10.1145/3564746.3587024","DOIUrl":"https://doi.org/10.1145/3564746.3587024","url":null,"abstract":"Although the boom of social media in the past decade has enabled the creation, distribution, and consumption of information at a remarkable rate, it has also led to the growth of different forms of online abuse. Since the outbreak of COVID-19, hate against Chinese or Sinophobia has increased significantly in real world as well as on online platforms making it necessary to design ways to combat it. In this paper, we design a platform-agnostic model to detect Sinophobic content on social media websites automatically. We use pre-trained word embeddings with several machine learning classifiers to detect Sinophobia on three platforms---Parler, Reddit, and Twitter. Our results demonstrate that the BERT model shows the best performance among all the models by achieving an accuracy of 98.51% on Parler, 95.36% on Reddit, and 88.12% on Twitter datasets.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128084665","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}
Vyas Padmanabhan, Jared Harvey, Asit Singh, Jeff Gray, Sanford White
In a manufacturing factory, eliminating seconds off a task that is repeated often can reduce the overall manufacturing time and improve the profitability of each unit produced. It is advantageous to understand the average amount of time required to complete each task. In order to scientifically determine the desired time required to complete a task, the individual subtasks also can be timed. This can be made more efficient by automating the process of recording the basic physical motions of factory workers that are involved in a task. Our paper shows how it is possible to use a machine learning based approach to classify the basic motions. This paper describes two approaches that we implemented in order to automate the process of motion classification. We contrast the two approaches and analyze the tradeoffs between each approach. The context for the application of our project is Mercedes-Benz US International, a large automotive manufacturing facility in the Southeastern United States. Additionally, we discuss the limitations of the two approaches and future work that can address these issues.
{"title":"A Comparison of Two Approaches to Support Methods Time Measurement in an Automotive Factory","authors":"Vyas Padmanabhan, Jared Harvey, Asit Singh, Jeff Gray, Sanford White","doi":"10.1145/3564746.3587008","DOIUrl":"https://doi.org/10.1145/3564746.3587008","url":null,"abstract":"In a manufacturing factory, eliminating seconds off a task that is repeated often can reduce the overall manufacturing time and improve the profitability of each unit produced. It is advantageous to understand the average amount of time required to complete each task. In order to scientifically determine the desired time required to complete a task, the individual subtasks also can be timed. This can be made more efficient by automating the process of recording the basic physical motions of factory workers that are involved in a task. Our paper shows how it is possible to use a machine learning based approach to classify the basic motions. This paper describes two approaches that we implemented in order to automate the process of motion classification. We contrast the two approaches and analyze the tradeoffs between each approach. The context for the application of our project is Mercedes-Benz US International, a large automotive manufacturing facility in the Southeastern United States. Additionally, we discuss the limitations of the two approaches and future work that can address these issues.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132885332","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}
Technological advances have made notable changes in the way healthcare services can be received and delivered today. Telemedicine is a form of virtual healthcare service that is convenient in context of time commitments, cost, and in-person availability. Despite its benefits, the adoption of telemedicine remains challenged with technology constraints, including security and privacy issues that pose risks. Existing research literature shows that there have been some prior survey studies on the security and privacy risks in telemedicine systems. However, none of these previous works have holistically surveyed the telemedicine security and privacy issues, using their networking layer locations, their causes, and their potential impacts on information flow. In this research study, we survey existing works on telemedicine security plus privacy, and come up with a unique way of classifying the surveyed literature using a noteworthy W3H2 analysis approach, which reviews and organizes the prior literature in terms of the locations, causes, potential impacts, and remedies related to the telemedicine security issues. We also discuss existing research gaps plus open research directions on the topic. The highlight of this study is our nifty W3H2 analysis approach, which makes use of simple yet notable research questions, as well as the seven layered OSI network architecture model, unlike previous survey studies on telemedicine security. We end up creating a multi-layer, novel taxonomy framework for effective presentation plus categorization of our surveyed literature.
{"title":"A W3H2 Analysis of Security and Privacy Issues in Telemedicine: A Survey Study","authors":"Ankur Chattopadhyay, Thuong Ho, Nahom Beyene","doi":"10.1145/3564746.3587109","DOIUrl":"https://doi.org/10.1145/3564746.3587109","url":null,"abstract":"Technological advances have made notable changes in the way healthcare services can be received and delivered today. Telemedicine is a form of virtual healthcare service that is convenient in context of time commitments, cost, and in-person availability. Despite its benefits, the adoption of telemedicine remains challenged with technology constraints, including security and privacy issues that pose risks. Existing research literature shows that there have been some prior survey studies on the security and privacy risks in telemedicine systems. However, none of these previous works have holistically surveyed the telemedicine security and privacy issues, using their networking layer locations, their causes, and their potential impacts on information flow. In this research study, we survey existing works on telemedicine security plus privacy, and come up with a unique way of classifying the surveyed literature using a noteworthy W3H2 analysis approach, which reviews and organizes the prior literature in terms of the locations, causes, potential impacts, and remedies related to the telemedicine security issues. We also discuss existing research gaps plus open research directions on the topic. The highlight of this study is our nifty W3H2 analysis approach, which makes use of simple yet notable research questions, as well as the seven layered OSI network architecture model, unlike previous survey studies on telemedicine security. We end up creating a multi-layer, novel taxonomy framework for effective presentation plus categorization of our surveyed literature.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"311 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132862603","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}
Improved pedagogy is urgently needed that helps computer science majors better recognize how their products will affect societies where computing is increasingly permeating all aspects of life. A more suitable approach than frequently updating materials to reflect the effect of rapidly changing computing paradigms on continuously evolving societies is to integrate the assessment of current events into the classroom and coursework. Because current events evolve so quickly, often the only source of information is the news media for which trustworthiness and accuracy have become complex issues. To accurately target instructional changes that account for both the benefits and challenges of incorporating news reporting, instructors must first understand how computer science majors consume news. Unfortunately, research addressing the habits and perceptions of computer science students when consuming news reporting is limited. To help fill this gap, we present results from a survey investigating the method, frequency, and perceptions of news consumption among CS majors at a primarily undergraduate university in the United States. We also report if students appreciate the effect of computing on current events and provide analysis from investigating the impact of news stories influenced by computing.
{"title":"News Consumption Among CS Majors: Habits, Perceptions, and Challenges","authors":"S. Barlowe, Andrew S. Scott","doi":"10.1145/3564746.3587023","DOIUrl":"https://doi.org/10.1145/3564746.3587023","url":null,"abstract":"Improved pedagogy is urgently needed that helps computer science majors better recognize how their products will affect societies where computing is increasingly permeating all aspects of life. A more suitable approach than frequently updating materials to reflect the effect of rapidly changing computing paradigms on continuously evolving societies is to integrate the assessment of current events into the classroom and coursework. Because current events evolve so quickly, often the only source of information is the news media for which trustworthiness and accuracy have become complex issues. To accurately target instructional changes that account for both the benefits and challenges of incorporating news reporting, instructors must first understand how computer science majors consume news. Unfortunately, research addressing the habits and perceptions of computer science students when consuming news reporting is limited. To help fill this gap, we present results from a survey investigating the method, frequency, and perceptions of news consumption among CS majors at a primarily undergraduate university in the United States. We also report if students appreciate the effect of computing on current events and provide analysis from investigating the impact of news stories influenced by computing.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) is transforming the world. On the one hand, its rapid integration into many systems is making automation easier, but on the other hand dependence of many processes on IoT is also making the IoT an attractive target for exploitation. One of the attacks that IoT devices can suffer from is device impersonation. To verify that the sender of a piece of information is who it claims to be, digital signatures are a solution. Applying digital signatures requires some overhead, and that overhead may impact the performance of an IoT network. In this paper, we observed the computational impact of using the Elliptic Curve Digital Signature Algorithm (ECDSA) to create and verify IoT devices' digital signatures. We used two criteria to evaluate the performance of our small IoT network: the packet loss and the average time needed to sign and verify a packet in a small IoT network. We also analyzed the same system without using digital signatures. Our evaluations show that in a small IoT sensor network, ECDSA computational impact is quite low.
{"title":"Analysis of ECDSA's Computational Impact on IoT Network Performance","authors":"Joseph Clark, F. Ali","doi":"10.1145/3564746.3587013","DOIUrl":"https://doi.org/10.1145/3564746.3587013","url":null,"abstract":"The Internet of Things (IoT) is transforming the world. On the one hand, its rapid integration into many systems is making automation easier, but on the other hand dependence of many processes on IoT is also making the IoT an attractive target for exploitation. One of the attacks that IoT devices can suffer from is device impersonation. To verify that the sender of a piece of information is who it claims to be, digital signatures are a solution. Applying digital signatures requires some overhead, and that overhead may impact the performance of an IoT network. In this paper, we observed the computational impact of using the Elliptic Curve Digital Signature Algorithm (ECDSA) to create and verify IoT devices' digital signatures. We used two criteria to evaluate the performance of our small IoT network: the packet loss and the average time needed to sign and verify a packet in a small IoT network. We also analyzed the same system without using digital signatures. Our evaluations show that in a small IoT sensor network, ECDSA computational impact is quite low.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116196453","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}
As instructors in courses that introduce unit testing, we want to provide autograded practice problems that guide students in how they write their unit test methods, similar to how existing autograder tools can provide students with traditional coding problems. For courses taught in Java, these existing autograder systems often use instructor-supplied JUnit tests to evaluate student submissions of non-unit-test code; our approach is designed to integrate into these JUnit-based systems and expand their capabilities to include practice coding unit tests. We do this by writing special instructor-provided unit tests that evaluate students' submitted unit tests; we call these "meta-tests" to distinguish them from the students' work. This paper describes the use of meta-tests, the technology that facilitates them, and strategies for writing them. Previous work in this space focused on using coverage metrics (e.g., lines-of-code covered or numbers of bugs caught) that evaluate the aggregate performance of suites of unit tests. Our approach instead examines the internal structure of individual unit test methods and provides feedback on whether, for example, the test method creates the correct object(s), calls the correct method-under-test, and/or calls appropriate assertions.
{"title":"Automated Evaluation of the Structure of Student-Written Unit Tests","authors":"L. Baumstark","doi":"10.1145/3564746.3587002","DOIUrl":"https://doi.org/10.1145/3564746.3587002","url":null,"abstract":"As instructors in courses that introduce unit testing, we want to provide autograded practice problems that guide students in how they write their unit test methods, similar to how existing autograder tools can provide students with traditional coding problems. For courses taught in Java, these existing autograder systems often use instructor-supplied JUnit tests to evaluate student submissions of non-unit-test code; our approach is designed to integrate into these JUnit-based systems and expand their capabilities to include practice coding unit tests. We do this by writing special instructor-provided unit tests that evaluate students' submitted unit tests; we call these \"meta-tests\" to distinguish them from the students' work. This paper describes the use of meta-tests, the technology that facilitates them, and strategies for writing them. Previous work in this space focused on using coverage metrics (e.g., lines-of-code covered or numbers of bugs caught) that evaluate the aggregate performance of suites of unit tests. Our approach instead examines the internal structure of individual unit test methods and provides feedback on whether, for example, the test method creates the correct object(s), calls the correct method-under-test, and/or calls appropriate assertions.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124054355","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}
Social recovery schemes enable the recovery of decentralized digital assets like Bitcoin and Ethereum through a social network. These schemes suffer from security issues and limitations including centralization, a conspiracy of one's network, large transaction fees for the user and network, and verifiable connections between the user and the network. To mitigate these issues, the work proposes and implements a novel social recovery scheme based on verifiable secret sharing, smart contract code, encrypted communication, and biometric encryption. The security and performance analysis versus key existing implementations is evaluated in terms of security features and gas fees.
{"title":"Smart Contract-Based Social Recovery Wallet Management Scheme for Digital Assets","authors":"Allan B. Pedin, N. Siasi, Mohammad Sameni","doi":"10.1145/3564746.3587016","DOIUrl":"https://doi.org/10.1145/3564746.3587016","url":null,"abstract":"Social recovery schemes enable the recovery of decentralized digital assets like Bitcoin and Ethereum through a social network. These schemes suffer from security issues and limitations including centralization, a conspiracy of one's network, large transaction fees for the user and network, and verifiable connections between the user and the network. To mitigate these issues, the work proposes and implements a novel social recovery scheme based on verifiable secret sharing, smart contract code, encrypted communication, and biometric encryption. The security and performance analysis versus key existing implementations is evaluated in terms of security features and gas fees.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"388 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122351335","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}
ABM.Adnan Azmee, Manohar Murikipudi, Md Abdullah Al Hafiz Khan
Electroencephalography (EEG) signals can be captured with the help of Brain-Computer Interfaces. When properly analyzed and applied, the information in these EEG signals can serve various purposes. People who are paralyzed or partially paralyzed and have difficulty communicating as a result of their condition can benefit immensely from the use of EEG. By detecting the motor imagery movement from EEG, we can determine the intent of a subject who is unable to perform motor functions (e.g., paralyzed patient) but is imagining them. However, detecting motor movement from EEG signals is challenging since EEG is susceptible to noise. Moreover, the complex relationship between motor activities and EEG data makes it difficult to classify. Deep neural networks excel at comprehending intricate features and executing complex computations. Using the capabilities of deep neural networks, we develop a hybrid neural network model in this paper that can accurately detect motor activity movement from EEG data; our model outperforms the state-of-the-art models and generates a classification accuracy of 98%.
{"title":"Detecting Motor Imagery Movement from EEG Signal","authors":"ABM.Adnan Azmee, Manohar Murikipudi, Md Abdullah Al Hafiz Khan","doi":"10.1145/3564746.3587009","DOIUrl":"https://doi.org/10.1145/3564746.3587009","url":null,"abstract":"Electroencephalography (EEG) signals can be captured with the help of Brain-Computer Interfaces. When properly analyzed and applied, the information in these EEG signals can serve various purposes. People who are paralyzed or partially paralyzed and have difficulty communicating as a result of their condition can benefit immensely from the use of EEG. By detecting the motor imagery movement from EEG, we can determine the intent of a subject who is unable to perform motor functions (e.g., paralyzed patient) but is imagining them. However, detecting motor movement from EEG signals is challenging since EEG is susceptible to noise. Moreover, the complex relationship between motor activities and EEG data makes it difficult to classify. Deep neural networks excel at comprehending intricate features and executing complex computations. Using the capabilities of deep neural networks, we develop a hybrid neural network model in this paper that can accurately detect motor activity movement from EEG data; our model outperforms the state-of-the-art models and generates a classification accuracy of 98%.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130177543","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}
Petri Nets with Players, Strategies, and Costs (PNPSC) is an extension of Petri nets specifically designed to model cyberattacks. The PNPSC formalism includes a representation of the strategies for the competing "players," i.e., the attacker and defender. Developing well-performing strategies for players in PNPSC nets is challenging for both game tree and reinforcement learning algorithms. This paper presents a method of modeling the PNPSC net player strategies as a game tree and using a combination of Monte Carlo Tree Search (MCTS) and deep reinforcement learning to effectively improve the players' strategies. The performance of this combination method is compared with standard action selection with the deep Q-learning algorithm.
{"title":"A Deep Reinforcement Learning Technique for PNPSC Net Player Strategies","authors":"E. M. Bearss, Mikel D. Petty","doi":"10.1145/3564746.3587011","DOIUrl":"https://doi.org/10.1145/3564746.3587011","url":null,"abstract":"Petri Nets with Players, Strategies, and Costs (PNPSC) is an extension of Petri nets specifically designed to model cyberattacks. The PNPSC formalism includes a representation of the strategies for the competing \"players,\" i.e., the attacker and defender. Developing well-performing strategies for players in PNPSC nets is challenging for both game tree and reinforcement learning algorithms. This paper presents a method of modeling the PNPSC net player strategies as a game tree and using a combination of Monte Carlo Tree Search (MCTS) and deep reinforcement learning to effectively improve the players' strategies. The performance of this combination method is compared with standard action selection with the deep Q-learning algorithm.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415416","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}
This paper discusses the development of a new pseudorandom number generator (PRNG) based on chaotic billiards and particle randomness. In this new system, two massless particles bounce, teleport, and collide with each other inside of the classic Sinai Billiard. Random sequences are generated based on the collision coordinates of two particles as they bounce off of the circular center billiard wall. Three statistical tests conducted on the generated sequences indicate the generator produces sequences comparable to truly random sequences. In addition to discussing the results of the three statistical analysis, this paper details how the model is set up and how the pseudorandom sequence is produced.
{"title":"Modeling Particle Randomness: A Pseudorandom Number Generator","authors":"Chloe Dunmire, Amber Wagner","doi":"10.1145/3564746.3587025","DOIUrl":"https://doi.org/10.1145/3564746.3587025","url":null,"abstract":"This paper discusses the development of a new pseudorandom number generator (PRNG) based on chaotic billiards and particle randomness. In this new system, two massless particles bounce, teleport, and collide with each other inside of the classic Sinai Billiard. Random sequences are generated based on the collision coordinates of two particles as they bounce off of the circular center billiard wall. Three statistical tests conducted on the generated sequences indicate the generator produces sequences comparable to truly random sequences. In addition to discussing the results of the three statistical analysis, this paper details how the model is set up and how the pseudorandom sequence is produced.","PeriodicalId":322431,"journal":{"name":"Proceedings of the 2023 ACM Southeast Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127570436","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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