Using a robot to guide a non-medically skilled human helper to perform neurorehabilitation post-stroke therapies is a challenging task. Much information needs to be expressed in a quick manner and it needs to be precise to empower the helper with the feeling of "doing the right thing" during a therapy session. This doctoral research paper aims to highlight current efforts of modelling the interaction in such a situation and presents the setup for its research. We suggest a robot system setup which will be used for the "arm basis training" (ABT). We will present selected research questions for modelling both users and the role of the robot. On the whole, we aim to make patient-helper interaction more engaging and easier. This could hopefully enable even non-medical helpers to perform this therapy and keep both participants' motivation high throughout the whole therapy.
{"title":"Developing a Patient-Therapist-Robot User Model for Motivation in Neurorehabilitation Therapies","authors":"Alexandru Bundea","doi":"10.1145/3340631.3398679","DOIUrl":"https://doi.org/10.1145/3340631.3398679","url":null,"abstract":"Using a robot to guide a non-medically skilled human helper to perform neurorehabilitation post-stroke therapies is a challenging task. Much information needs to be expressed in a quick manner and it needs to be precise to empower the helper with the feeling of \"doing the right thing\" during a therapy session. This doctoral research paper aims to highlight current efforts of modelling the interaction in such a situation and presents the setup for its research. We suggest a robot system setup which will be used for the \"arm basis training\" (ABT). We will present selected research questions for modelling both users and the role of the robot. On the whole, we aim to make patient-helper interaction more engaging and easier. This could hopefully enable even non-medical helpers to perform this therapy and keep both participants' motivation high throughout the whole therapy.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273245","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}
Market cannibalization is inevitable when there are two or more competing marketing approaches to the same customer base. The cannibalization problem has been discussed in the context of search advertising of individual advertisers, whereas in this paper we discuss the problem that advertising platform companies face in dealing with multiple advertisers. In online advertising, they must properly serve ads with varying mass appeal to users with various interests. For them, it is important to maximize the value of the ads for advertisers and also for the platform. To do so, they deploy user models to serve ads. However, shortsighted models could lead to a decrease in overall performance in an attempt to improve certain ads' performance while slightly impairing the rest. We consider this phenomenon from the perspective of cannibalization and confirm the existence of a cannibalization problem in optimizing the delivery of ads in minor categories. To resolve this problem, we propose new methods, apply them to an ad delivery system, and conduct an A/B test. Our methods overcame the cannibalization problem and increased revenue by + 0.6% compared with the baseline method.
{"title":"Tackling Cannibalization Problems for Online Advertisement","authors":"Yutaro Ueoka, K. Tsubouchi, Nobuyuki Shimizu","doi":"10.1145/3340631.3394875","DOIUrl":"https://doi.org/10.1145/3340631.3394875","url":null,"abstract":"Market cannibalization is inevitable when there are two or more competing marketing approaches to the same customer base. The cannibalization problem has been discussed in the context of search advertising of individual advertisers, whereas in this paper we discuss the problem that advertising platform companies face in dealing with multiple advertisers. In online advertising, they must properly serve ads with varying mass appeal to users with various interests. For them, it is important to maximize the value of the ads for advertisers and also for the platform. To do so, they deploy user models to serve ads. However, shortsighted models could lead to a decrease in overall performance in an attempt to improve certain ads' performance while slightly impairing the rest. We consider this phenomenon from the perspective of cannibalization and confirm the existence of a cannibalization problem in optimizing the delivery of ads in minor categories. To resolve this problem, we propose new methods, apply them to an ad delivery system, and conduct an A/B test. Our methods overcame the cannibalization problem and increased revenue by + 0.6% compared with the baseline method.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133698301","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}
An increasing body of research indicates that transparency in recommender systems affects trust of users. Additionally, a vast amount of studies already showed that personality impacts the way users perceive a recommender system. However, only recently, research has begun to investigate the effects of cognitive style on the perception of recommender systems. Furthermore, it is still unclear whether this cognitive style also affects the interaction strategies of users, and whether the reason why and when users want transparency is affected by this cognitive style. Additionally, despite the ubiquitous presence of recommender systems on mobile environments, no study has investigated the effect of transparency for mobile music recommender systems. In this paper, we report the results of a within-subject study (N=25) on a mobile music recommender system where we investigated the effect of cognitive styles on three different aspects: the interaction strategies with the different applications, the reasons why and when users want transparency and the effect of transparency on the trust of users. The results show that users with a rational thinking style put more effort in seeking the best recommendations and that they want scrutable explanations to adjust the recommendation. In contrast, intuitive thinkers only need explanations when they search for a very specific kind of music.
{"title":"Cogito ergo quid? The Effect of Cognitive Style in a Transparent Mobile Music Recommender System","authors":"Martijn Millecamp, Robin Haveneers, K. Verbert","doi":"10.1145/3340631.3394871","DOIUrl":"https://doi.org/10.1145/3340631.3394871","url":null,"abstract":"An increasing body of research indicates that transparency in recommender systems affects trust of users. Additionally, a vast amount of studies already showed that personality impacts the way users perceive a recommender system. However, only recently, research has begun to investigate the effects of cognitive style on the perception of recommender systems. Furthermore, it is still unclear whether this cognitive style also affects the interaction strategies of users, and whether the reason why and when users want transparency is affected by this cognitive style. Additionally, despite the ubiquitous presence of recommender systems on mobile environments, no study has investigated the effect of transparency for mobile music recommender systems. In this paper, we report the results of a within-subject study (N=25) on a mobile music recommender system where we investigated the effect of cognitive styles on three different aspects: the interaction strategies with the different applications, the reasons why and when users want transparency and the effect of transparency on the trust of users. The results show that users with a rational thinking style put more effort in seeking the best recommendations and that they want scrutable explanations to adjust the recommendation. In contrast, intuitive thinkers only need explanations when they search for a very specific kind of music.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130534166","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}
G. Delzanno, Giovanna Guerrini, Daniele Traversaro
A wide range of tools and applications have been developed for supporting Computer Science Education, ranging from visual programming languages to web applications. In this setting it is crucial to model user needs and provide personalized support to improve the effectiveness and satisfaction of learning experiences. This summary gives a brief overview of the workshop Adaptation and Personalization in Computer Science Education organized at UMAP 2020 in order to bring together researchers, practitioners and education stakeholders interested in these topics. The workshop program consists of a keynote speech by Wolfgang Slany head of the Catrobat Project and by three technical sessions offering different perspectives on the main themes of the workshop.
{"title":"Adaptation and Personalization in Computer Science Education: APCSE '20","authors":"G. Delzanno, Giovanna Guerrini, Daniele Traversaro","doi":"10.1145/3340631.3398675","DOIUrl":"https://doi.org/10.1145/3340631.3398675","url":null,"abstract":"A wide range of tools and applications have been developed for supporting Computer Science Education, ranging from visual programming languages to web applications. In this setting it is crucial to model user needs and provide personalized support to improve the effectiveness and satisfaction of learning experiences. This summary gives a brief overview of the workshop Adaptation and Personalization in Computer Science Education organized at UMAP 2020 in order to bring together researchers, practitioners and education stakeholders interested in these topics. The workshop program consists of a keynote speech by Wolfgang Slany head of the Catrobat Project and by three technical sessions offering different perspectives on the main themes of the workshop.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310167","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}
Intelligent computer systems aim at providing user-assistance for challenging tasks, like decision-making, planning, or learning. For offering optimal assistance, it is essential for such systems to decide when to be reactive or proactive and how active system behaviour should be designed. Especially, as this decision may greatly influence the user's trust in the system. Therefore, we conducted a mixed-factorial study which examines how different levels of proactivity (none, notification, suggestion, and intervention) as well as timing strategies (fixed-timing and insecurity-based) are trusted by subjects while performing a planning task. The results showed, that proactive system behaviour is perceived trustworthy in insecure situations independent of the timing. However, proactive dialogue showed strong effects on cognition-based trust (system's perceived competence and reliability) depending on task difficulty. Furthermore, fully autonomous system behaviour fails to establish an adequate human-computer trust relationship, in contrast to conservative strategies.
{"title":"Effects of Proactive Dialogue Strategies on Human-Computer Trust","authors":"Matthias Kraus, Nicolas Wagner, W. Minker","doi":"10.1145/3340631.3394840","DOIUrl":"https://doi.org/10.1145/3340631.3394840","url":null,"abstract":"Intelligent computer systems aim at providing user-assistance for challenging tasks, like decision-making, planning, or learning. For offering optimal assistance, it is essential for such systems to decide when to be reactive or proactive and how active system behaviour should be designed. Especially, as this decision may greatly influence the user's trust in the system. Therefore, we conducted a mixed-factorial study which examines how different levels of proactivity (none, notification, suggestion, and intervention) as well as timing strategies (fixed-timing and insecurity-based) are trusted by subjects while performing a planning task. The results showed, that proactive system behaviour is perceived trustworthy in insecure situations independent of the timing. However, proactive dialogue showed strong effects on cognition-based trust (system's perceived competence and reliability) depending on task difficulty. Furthermore, fully autonomous system behaviour fails to establish an adequate human-computer trust relationship, in contrast to conservative strategies.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121815914","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}
Eye-tracking has been extensively used both in psychology for understanding various aspects of human cognition, as well as in human computer interaction (HCI) for evaluation of interface design or as a form of direct input. In recent years, eye-tracking has also been investigated as a source of information for machine learning models that predict relevant user states and traits (e.g., attention, confusion, learning, perceptual abilities). These predictions can then be leveraged by AI agents to model their users and personalize the interaction accordingly. In this talk, Dr. Conati will provide an overview of the research her lab has done in this area, including detecting and modeling user cognitive skills, and affective states, with applications to user-adaptive visualizations, intelligent tutoring systems and health.
{"title":"The Eyes Are the Windows to the Mind: Implications for AI-Driven Personalized Interaction","authors":"C. Conati","doi":"10.1145/3340631.3395385","DOIUrl":"https://doi.org/10.1145/3340631.3395385","url":null,"abstract":"Eye-tracking has been extensively used both in psychology for understanding various aspects of human cognition, as well as in human computer interaction (HCI) for evaluation of interface design or as a form of direct input. In recent years, eye-tracking has also been investigated as a source of information for machine learning models that predict relevant user states and traits (e.g., attention, confusion, learning, perceptual abilities). These predictions can then be leveraged by AI agents to model their users and personalize the interaction accordingly. In this talk, Dr. Conati will provide an overview of the research her lab has done in this area, including detecting and modeling user cognitive skills, and affective states, with applications to user-adaptive visualizations, intelligent tutoring systems and health.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115572547","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}
Ekaterina Muravyeva, J. Janssen, K. Dirkx, M. Specht
The current study investigated the role of trust in students' attitudes towards personal data sharing in the context of e-assessment, and whether this is different for students with special educational needs and disabilities (SEND). SEND students were included as a special target group because they may feel more dependent on e-assessment technologies, and thus, more easily consent to personal data sharing. A mixed methods research design was adopted combining an online survey and a focus group interview to collect quantitative and qualitative data. The findings suggest that a considerable number of students trust e-assessment technology that does not require the physical presence of a supervisor. Students who trust are more likely to perceive e-assessment technology as having no disadvantages, and are more willing to share their personal data for e-assessment purposes. The responses of SEND and non-SEND students do not differ significantly in terms of trust. However, the results diverge regarding the relation between trust and perception of e-assessment technology as having no disadvantages. Practical implications for informed consent are discussed.
{"title":"The Role of Trust in Personal Data Sharing in the Context of e-Assessment and the Moderating Effect of Special Educational Needs","authors":"Ekaterina Muravyeva, J. Janssen, K. Dirkx, M. Specht","doi":"10.1145/3340631.3394876","DOIUrl":"https://doi.org/10.1145/3340631.3394876","url":null,"abstract":"The current study investigated the role of trust in students' attitudes towards personal data sharing in the context of e-assessment, and whether this is different for students with special educational needs and disabilities (SEND). SEND students were included as a special target group because they may feel more dependent on e-assessment technologies, and thus, more easily consent to personal data sharing. A mixed methods research design was adopted combining an online survey and a focus group interview to collect quantitative and qualitative data. The findings suggest that a considerable number of students trust e-assessment technology that does not require the physical presence of a supervisor. Students who trust are more likely to perceive e-assessment technology as having no disadvantages, and are more willing to share their personal data for e-assessment purposes. The responses of SEND and non-SEND students do not differ significantly in terms of trust. However, the results diverge regarding the relation between trust and perception of e-assessment technology as having no disadvantages. Practical implications for informed consent are discussed.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633844","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}
Michael Barz, Kristin Altmeyer, Sarah Malone, Luisa Lauer, Daniel Sonntag
Digital pen signals were shown to be predictive for cognitive states, cognitive load and emotion in educational settings. We investigate whether low-level pen-based features can predict the difficulty of tasks in a cognitive test and the learner's performance in these tasks, which is inherently related to cognitive load, without a semantic content analysis. We record data for tasks of varying difficulty in a controlled study with children from elementary school. We include two versions of the Trail Making Test (TMT) and six drawing patterns from the Snijders-Oomen Non-verbal intelligence test (SON) as tasks that feature increasing levels of difficulty. We examine how accurately we can predict the task difficulty and the user performance as a measure for cognitive load using support vector machines and gradient boosted decision trees with different feature selection strategies. The results show that our correlation-based feature selection is beneficial for model training, in particular when samples from TMT and SON are concatenated for joint modelling of difficulty and time. Our findings open up opportunities for technology-enhanced adaptive learning.
{"title":"Digital Pen Features Predict Task Difficulty and User Performance of Cognitive Tests","authors":"Michael Barz, Kristin Altmeyer, Sarah Malone, Luisa Lauer, Daniel Sonntag","doi":"10.1145/3340631.3394839","DOIUrl":"https://doi.org/10.1145/3340631.3394839","url":null,"abstract":"Digital pen signals were shown to be predictive for cognitive states, cognitive load and emotion in educational settings. We investigate whether low-level pen-based features can predict the difficulty of tasks in a cognitive test and the learner's performance in these tasks, which is inherently related to cognitive load, without a semantic content analysis. We record data for tasks of varying difficulty in a controlled study with children from elementary school. We include two versions of the Trail Making Test (TMT) and six drawing patterns from the Snijders-Oomen Non-verbal intelligence test (SON) as tasks that feature increasing levels of difficulty. We examine how accurately we can predict the task difficulty and the user performance as a measure for cognitive load using support vector machines and gradient boosted decision trees with different feature selection strategies. The results show that our correlation-based feature selection is beneficial for model training, in particular when samples from TMT and SON are concatenated for joint modelling of difficulty and time. Our findings open up opportunities for technology-enhanced adaptive learning.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114379656","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 tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation. Then, we practically show two use cases, addressing biases that lead to disparate exposure of items based on their popularity and to systematically discriminate against a legally-protected class of users. Finally, we cover a range of techniques for evaluating and mitigating the impact of these biases on the recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world platforms.
{"title":"Hands on Data and Algorithmic Bias in Recommender Systems","authors":"Ludovico Boratto, M. Marras","doi":"10.1145/3340631.3398669","DOIUrl":"https://doi.org/10.1145/3340631.3398669","url":null,"abstract":"This tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation. Then, we practically show two use cases, addressing biases that lead to disparate exposure of items based on their popularity and to systematically discriminate against a legally-protected class of users. Finally, we cover a range of techniques for evaluating and mitigating the impact of these biases on the recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world platforms.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123395360","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}
Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.
{"title":"Inferring Cognitive Style from Eye Gaze Behavior During Information Visualization Usage","authors":"B. Steichen, Bo Fu, Tho Nguyen","doi":"10.1145/3340631.3394881","DOIUrl":"https://doi.org/10.1145/3340631.3394881","url":null,"abstract":"Information Visualization is a key technique to assist users in data analysis tasks, by creating visual representations of data to amplify human cognition. However, while human cognitive abilities and styles have been shown to differ significantly, Information Visualizations have traditionally been designed in a manner that does not consider such individual user differences. Recent research has started to address this issue, by identifying individual user characteristics that influence individual users' interactions with Information Visualizations, as well as developing novel Information Visualization systems that provide more personalized support. This paper presents a set of experiments aimed towards building such User-Adaptive Information Visualization systems, by studying the extent to which a user's cognitive style can be inferred from a user's interaction with an Information Visualization system. Results show that a user's eye gaze data can be used to infer a user's cognitive style during information visualization usage with up to 86% accuracy, and that the most informative features relate to a user's saccade angles and fixation durations.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125281498","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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