Bettina Berendt, Veronika Bogina, R. Burke, Michael D. Ekstrand, Alan Hartman, S. Kleanthous, T. Kuflik, B. Mobasher, Jahna Otterbacher
It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observation that these two fields increasingly impact one another. Personalization has become a ubiquitous and essential part of systems that help users find relevant information in today's highly complex, information-rich online environments. Machine learning techniques applied to big data, as done by recommender systems, and user modeling in general, are key enabling technologies that allow intelligent systems to learn from users and adapt their output to users' needs and preferences. However, there has been a growing recognition that these underlying technologies raise novel ethical, legal, and policy challenges. It has become apparent that a single-minded focus on user characteristics has obscured other important and beneficial outcomes such systems must be able to deliver. System properties such as fairness, transparency, balance, and other social welfare considerations are not captured by typical metrics based on which data-driven personalized models are optimized. Indeed, widely-used personalization systems in popular sites such as Facebook, Google News and YouTube have been heavily criticized for personalizing information delivery too heavily at the cost of these other objectives.
{"title":"FairUMAP 2019 Chairs' Welcome Overview","authors":"Bettina Berendt, Veronika Bogina, R. Burke, Michael D. Ekstrand, Alan Hartman, S. Kleanthous, T. Kuflik, B. Mobasher, Jahna Otterbacher","doi":"10.1145/3314183.3323842","DOIUrl":"https://doi.org/10.1145/3314183.3323842","url":null,"abstract":"It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observation that these two fields increasingly impact one another. Personalization has become a ubiquitous and essential part of systems that help users find relevant information in today's highly complex, information-rich online environments. Machine learning techniques applied to big data, as done by recommender systems, and user modeling in general, are key enabling technologies that allow intelligent systems to learn from users and adapt their output to users' needs and preferences. However, there has been a growing recognition that these underlying technologies raise novel ethical, legal, and policy challenges. It has become apparent that a single-minded focus on user characteristics has obscured other important and beneficial outcomes such systems must be able to deliver. System properties such as fairness, transparency, balance, and other social welfare considerations are not captured by typical metrics based on which data-driven personalized models are optimized. Indeed, widely-used personalization systems in popular sites such as Facebook, Google News and YouTube have been heavily criticized for personalizing information delivery too heavily at the cost of these other objectives.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455735","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}
Sylvain Castagnos, F. Marchal, Alexandre Bertrand, Morgane Colle, Djalila Mahmoudi
This paper is a first step towards identifying the links between the characteristics of gaze behaviour and visitor preferences in a museum. In the long term, the real-time analysis of visitors' gaze should allow a fine estimation of their interest for the different artworks exhibited and should replace the fastidious and time-consuming elicitation of preferences commonly used in traditional recommender systems. To study these links, we carried out a user study at the Nancy Museum of Fine Arts in the North-East of France. This pilot study involved 13 volunteers who had the opportunity to freely explore the museum and contemplate hundreds of artworks for more than 50 minutes on average in May 2018. We were able to analyze millions of fixation points so as to find correlations between the number of fixation points per painting, the time spent looking at a painting, and whether or not this painting is appreciated. We plan to extend this study to 100 visitors in the coming months.
{"title":"Inferring Art Preferences from Gaze Exploration in a Museum","authors":"Sylvain Castagnos, F. Marchal, Alexandre Bertrand, Morgane Colle, Djalila Mahmoudi","doi":"10.1145/3314183.3323871","DOIUrl":"https://doi.org/10.1145/3314183.3323871","url":null,"abstract":"This paper is a first step towards identifying the links between the characteristics of gaze behaviour and visitor preferences in a museum. In the long term, the real-time analysis of visitors' gaze should allow a fine estimation of their interest for the different artworks exhibited and should replace the fastidious and time-consuming elicitation of preferences commonly used in traditional recommender systems. To study these links, we carried out a user study at the Nancy Museum of Fine Arts in the North-East of France. This pilot study involved 13 volunteers who had the opportunity to freely explore the museum and contemplate hundreds of artworks for more than 50 minutes on average in May 2018. We were able to analyze millions of fixation points so as to find correlations between the number of fixation points per painting, the time spent looking at a painting, and whether or not this painting is appreciated. We plan to extend this study to 100 visitors in the coming months.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133743718","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}
Personalization in principle cannot happen without information about individuals, requiring personalization systems to comply with official privacy regulations. However, in order to design personalization systems that provide the best possible privacy-related user experience, a more human-centered perspective has to be taken into account. As a first step towards this goal, in the present work we show the setup and results of an online survey investigating the relation between the intention to disclose certain categories of personal data and the type of benefit promised by personalization.
{"title":"Privacy and Personalization: The Trade-off between Data Disclosure and Personalization Benefit","authors":"Lisa-Marie Wadle, Noemi Martin, Daniel Ziegler","doi":"10.1145/3314183.3323672","DOIUrl":"https://doi.org/10.1145/3314183.3323672","url":null,"abstract":"Personalization in principle cannot happen without information about individuals, requiring personalization systems to comply with official privacy regulations. However, in order to design personalization systems that provide the best possible privacy-related user experience, a more human-centered perspective has to be taken into account. As a first step towards this goal, in the present work we show the setup and results of an online survey investigating the relation between the intention to disclose certain categories of personal data and the type of benefit promised by personalization.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130973719","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}
Carine Pierrette Mukamakuza, Dimitris Sacharidis, H. Werthner
Personalization is typically based on preferences extracted from the interactions of users with the system. A recent trend is to also account for the social influence among users, which may play a non-negligible role in shaping one's individual preferences. The underlying assumptions are that friends tend to develop similar taste, i.e., homophily, and that similar users tend to connect to each other, i.e., social selection. In this work, we investigate the conditions under which social influence has a significant impact on the preferences of users. We find that pairs of friends, where one is socially very active whereas the other is not, exhibit stronger correlations in their preferences compared to other pairs of friends, implying thus a stronger mechanism of influence.
{"title":"The Impact of Social Connections in Personalization","authors":"Carine Pierrette Mukamakuza, Dimitris Sacharidis, H. Werthner","doi":"10.1145/3314183.3323675","DOIUrl":"https://doi.org/10.1145/3314183.3323675","url":null,"abstract":"Personalization is typically based on preferences extracted from the interactions of users with the system. A recent trend is to also account for the social influence among users, which may play a non-negligible role in shaping one's individual preferences. The underlying assumptions are that friends tend to develop similar taste, i.e., homophily, and that similar users tend to connect to each other, i.e., social selection. In this work, we investigate the conditions under which social influence has a significant impact on the preferences of users. We find that pairs of friends, where one is socially very active whereas the other is not, exhibit stronger correlations in their preferences compared to other pairs of friends, implying thus a stronger mechanism of influence.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134647146","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 studies an approach for predicting an individual's perception of the advertising appeal of ad design. Although previous research has shown that people perceive an ad as more appealing when its design matches their psychological traits, the matching required the help of psychology experts, relying on their implicit knowledge. To exclude such dependence, we examined how the psychological traits affect perceived advertising appeal by conducting a questionnaire survey. Analyzing the survey results, we confirmed that psychological traits have significant moderating effects on both visual and linguistic features of an ad design in terms of how an individual perceives advertising appeal. We also confirmed that the moderating effects as well as main effects of visual and linguistic features have significant predictive utility for perceived appeal. The model in which the both effects are incorporated predicted the most appealing ad for each person more accurately than did a human without psychology expertise (accuracy lift: mean - 1.35, max - 2.48). While further study is necessary on whether the studied approach can serve as a substitute for psychology experts, we consider the present study took the first step toward realizing this goal.
{"title":"Predicting Advertising Appeal from Receiver's Psychological Traits and Ad Design Features","authors":"Yuichi Ishikawa, Akihiro Kobayashi, A. Minamikawa","doi":"10.1145/3314183.3324979","DOIUrl":"https://doi.org/10.1145/3314183.3324979","url":null,"abstract":"This paper studies an approach for predicting an individual's perception of the advertising appeal of ad design. Although previous research has shown that people perceive an ad as more appealing when its design matches their psychological traits, the matching required the help of psychology experts, relying on their implicit knowledge. To exclude such dependence, we examined how the psychological traits affect perceived advertising appeal by conducting a questionnaire survey. Analyzing the survey results, we confirmed that psychological traits have significant moderating effects on both visual and linguistic features of an ad design in terms of how an individual perceives advertising appeal. We also confirmed that the moderating effects as well as main effects of visual and linguistic features have significant predictive utility for perceived appeal. The model in which the both effects are incorporated predicted the most appealing ad for each person more accurately than did a human without psychology expertise (accuracy lift: mean - 1.35, max - 2.48). While further study is necessary on whether the studied approach can serve as a substitute for psychology experts, we consider the present study took the first step toward realizing this goal.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115497267","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}
When modelling for the social we need to consider more than one medium. Little is known as to how platform community characteristics shape the discussion and how communicators could best engage each community, taking into consideration these characteristics. We consider comments on TED videos featuring roboticists, shared at TED.com and YouTube. We find evidence of different social norms and importantly, approaches to comment writing. The emotional tone is more positive at TED; however, there is little emotional escalation in either platform. The study highlights the importance of considering the community characteristics of a medium, when communicating with the public in a case study of emerging technologies.
{"title":"Shaping the Reaction: Community Characteristics and Emotional Tone of Citizen Responses to Robotics Videos at TED versus YouTube","authors":"S. Kleanthous, Jahna Otterbacher","doi":"10.1145/3314183.3323673","DOIUrl":"https://doi.org/10.1145/3314183.3323673","url":null,"abstract":"When modelling for the social we need to consider more than one medium. Little is known as to how platform community characteristics shape the discussion and how communicators could best engage each community, taking into consideration these characteristics. We consider comments on TED videos featuring roboticists, shared at TED.com and YouTube. We find evidence of different social norms and importantly, approaches to comment writing. The emotional tone is more positive at TED; however, there is little emotional escalation in either platform. The study highlights the importance of considering the community characteristics of a medium, when communicating with the public in a case study of emerging technologies.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114666890","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}
In many settings, it is required that items are recommended to a group of users instead of a single user. Most often, when the decision criteria and preferences of the group as a whole are not known, the gold standard is to aggregate individual member preferences or recommendations. Such techniques typically presuppose some process under which group members reach consensus, e.g., least misery, maximum satisfaction, disregarding any uncertainty on whether this presumption is accurate. We propose a different approach that explicitly models the system's uncertainty in the way members might agree on a group ranking. The basic idea is to quantify the likelihood of hypothetical group rankings based on the observed member's individual rankings. Then, the systems recommends a ranking that has the highest expected reward with respect to the hypothetical rankings. Experiments with real and synthetic groups demonstrate the superiority of this approach compared to previous work based on aggregation strategies and to recent fairness-aware techniques.
{"title":"Modeling Uncertainty in Group Recommendations","authors":"Dimitris Sacharidis","doi":"10.1145/3314183.3324987","DOIUrl":"https://doi.org/10.1145/3314183.3324987","url":null,"abstract":"In many settings, it is required that items are recommended to a group of users instead of a single user. Most often, when the decision criteria and preferences of the group as a whole are not known, the gold standard is to aggregate individual member preferences or recommendations. Such techniques typically presuppose some process under which group members reach consensus, e.g., least misery, maximum satisfaction, disregarding any uncertainty on whether this presumption is accurate. We propose a different approach that explicitly models the system's uncertainty in the way members might agree on a group ranking. The basic idea is to quantify the likelihood of hypothetical group rankings based on the observed member's individual rankings. Then, the systems recommends a ranking that has the highest expected reward with respect to the hypothetical rankings. Experiments with real and synthetic groups demonstrate the superiority of this approach compared to previous work based on aggregation strategies and to recent fairness-aware techniques.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"534 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116233495","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}
Panagiotis Germanakos, Maria Kasinidou, Marios Constantinides, G. Samaras
A critical phase in teaching is the effective design of educational contents. Instructors are phased with the dilemma of compensating on the volume and complexity that academic curriculum may entail, to easily accommodating educational content to learners. InfoVis or Infographics feature as a viable method to alleviate this problem through rich information and structured visual stories by taking advantage of the visual thinking of individuals and difficulties in information processing. A challenge, however, is the creation of personalized educational content that will dynamically adapt to users' intrinsic cognitive and emotional characteristics. We present a preliminary user study that explores two human factors, i.e., metacognition and motivation, which could enrich user models and guide the personalization process of learning material devised as infographic. Our results revealed strong influence of the two human factors in the learning process, while in cases suggest that may also be used as good predictors of academic achievement.
{"title":"A Metacognitive Perspective of InfoVis in Education","authors":"Panagiotis Germanakos, Maria Kasinidou, Marios Constantinides, G. Samaras","doi":"10.1145/3314183.3323674","DOIUrl":"https://doi.org/10.1145/3314183.3323674","url":null,"abstract":"A critical phase in teaching is the effective design of educational contents. Instructors are phased with the dilemma of compensating on the volume and complexity that academic curriculum may entail, to easily accommodating educational content to learners. InfoVis or Infographics feature as a viable method to alleviate this problem through rich information and structured visual stories by taking advantage of the visual thinking of individuals and difficulties in information processing. A challenge, however, is the creation of personalized educational content that will dynamically adapt to users' intrinsic cognitive and emotional characteristics. We present a preliminary user study that explores two human factors, i.e., metacognition and motivation, which could enrich user models and guide the personalization process of learning material devised as infographic. Our results revealed strong influence of the two human factors in the learning process, while in cases suggest that may also be used as good predictors of academic achievement.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122589553","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}
It is our great pleasure to welcome you to the UMAP 2019 LBR and Demo Track, in conjunction with the 27th Conference on User Modelling, Adaptation and Personalization, held in Larnaca, Cyprus on June 9-12th, 2019. This track encompasses two categories: (i) Demos, which showcase research prototypes and commercially available products of UMAP-based systems, (ii) Late-breaking Results (LBR), which contain original and unpublished accounts of innovative research ideas, preliminary results, industry showcases, and system prototypes, addressing both the theory and practice of UMAP. The submissions spanned a wide scope of topics, ranging from novel techniques for user and group modeling, to adaptation and personalization implementations across different application scenarios. We received 46 LBR and 4 Demo submissions. Each submission was carefully reviewed by members of the Demo and LBR program committee, which consisted of 89 members. Each submission was reviewed by at least 3 PC members. Out of this total of 50 submissions, 15 LBR and 3 Demos were deemed of good quality by the reviewers, and were consequently accepted (36% overall acceptance rate). They were presented in the UMAP poster sessions, which collectively showcased the wide spectrum of novel ideas and latest results in user modelling, adaptation and personalization.
{"title":"UMAP 2019 Demo and Late-Breaking Results - Chairs' Preface","authors":"S. Kleanthous, M. Bieliková, B. Steichen","doi":"10.1145/3314183.3324969","DOIUrl":"https://doi.org/10.1145/3314183.3324969","url":null,"abstract":"It is our great pleasure to welcome you to the UMAP 2019 LBR and Demo Track, in conjunction with the 27th Conference on User Modelling, Adaptation and Personalization, held in Larnaca, Cyprus on June 9-12th, 2019. This track encompasses two categories: (i) Demos, which showcase research prototypes and commercially available products of UMAP-based systems, (ii) Late-breaking Results (LBR), which contain original and unpublished accounts of innovative research ideas, preliminary results, industry showcases, and system prototypes, addressing both the theory and practice of UMAP. The submissions spanned a wide scope of topics, ranging from novel techniques for user and group modeling, to adaptation and personalization implementations across different application scenarios. We received 46 LBR and 4 Demo submissions. Each submission was carefully reviewed by members of the Demo and LBR program committee, which consisted of 89 members. Each submission was reviewed by at least 3 PC members. Out of this total of 50 submissions, 15 LBR and 3 Demos were deemed of good quality by the reviewers, and were consequently accepted (36% overall acceptance rate). They were presented in the UMAP poster sessions, which collectively showcased the wide spectrum of novel ideas and latest results in user modelling, adaptation and personalization.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129488132","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}
Moayad Mokatren, Veronika Bogina, A. Wecker, T. Kuflik
This paper describes an exploratory study that attempts to classify museum visitors by taking into consideration indoor behavior and demographic features. We discuss different approaches of using such data for improving the user experience in the museum. Moreover, we try to explain user's behavior by creating different user groups using a novel data set. Our findings indicate that knowing user age, education and her museum visits frequency, together with the current visit signals (total standing time and listening to a mobile guide time) can be used for visitors classification that might be useful in designing new intelligent user interfaces that can improve the visitor's indoor experience.
{"title":"A Museum Visitors Classification Based On Behavioral and Demographic Features","authors":"Moayad Mokatren, Veronika Bogina, A. Wecker, T. Kuflik","doi":"10.1145/3314183.3323864","DOIUrl":"https://doi.org/10.1145/3314183.3323864","url":null,"abstract":"This paper describes an exploratory study that attempts to classify museum visitors by taking into consideration indoor behavior and demographic features. We discuss different approaches of using such data for improving the user experience in the museum. Moreover, we try to explain user's behavior by creating different user groups using a novel data set. Our findings indicate that knowing user age, education and her museum visits frequency, together with the current visit signals (total standing time and listening to a mobile guide time) can be used for visitors classification that might be useful in designing new intelligent user interfaces that can improve the visitor's indoor experience.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121514361","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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