Virtual reality exergames have been demonstrated to provide high levels of exertion compared to traditional exercise, while players perceive less exertion. However, it is hard for people to be confident of whether they get the recommended levels of exercise. In this work, we present the "HappyFit" aesthetic interface that has been designed to be a pleasing ambient display that enables people to see their long-term user model of physical activity. The user model interface has been particularly designed to distinguish the source of the exercise and the user model is inferred from combinations of sensor data from worn devices that track steps and heart-rate. We show how "HappyFit" enables a person to gain an overview of the relative contributions of their exercise from both walking in daily life and playing virtual reality exergames. Our core contribution is the exploration of how to harness long term sensor data to build user models with aesthetic user interfaces that enable people to review and reflect on their physical activity.
{"title":"HappyFit: Time-aware Visualization for Daily Physical Activity and Virtual Reality Games","authors":"Soojeong Yoo, Lichen Xue, J. Kay","doi":"10.1145/3099023.3099102","DOIUrl":"https://doi.org/10.1145/3099023.3099102","url":null,"abstract":"Virtual reality exergames have been demonstrated to provide high levels of exertion compared to traditional exercise, while players perceive less exertion. However, it is hard for people to be confident of whether they get the recommended levels of exercise. In this work, we present the \"HappyFit\" aesthetic interface that has been designed to be a pleasing ambient display that enables people to see their long-term user model of physical activity. The user model interface has been particularly designed to distinguish the source of the exercise and the user model is inferred from combinations of sensor data from worn devices that track steps and heart-rate. We show how \"HappyFit\" enables a person to gain an overview of the relative contributions of their exercise from both walking in daily life and playing virtual reality exergames. Our core contribution is the exploration of how to harness long term sensor data to build user models with aesthetic user interfaces that enable people to review and reflect on their physical activity.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117102481","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 large volume of data available in location-based social networks (LBSNs) enables Point-of-Interest (POI) recommendation services. On another hand, the heterogeneous information (e.g., user check-in records, geographical features of POIs, social network and user reviews) imposes great challenges on effective POI recommendation. In this paper, we focus on leveraging such rich information in an integrated manner to improve POI recommendation performance. We exploit not only the geographical and social information, but also aspects extracted from user reviews to better model users' preferences. More specifically, to fully utilize various types of information, we construct a novel heterogeneous graph, Aspect-aware Geo-Social influence Graph (AGSG), by fusing various relations among the three types of nodes, i.e., users, POIs and aspects. The personalized POI recommendation task is then transformed as a graph node ranking problem in AGSG. We design a graph-based recommendation algorithm based on both personalized PageRank (PPR) and meta paths, to fully exploit the heterogeneous graph structure as well as to capture the semantic relations among the various nodes. Experiments on three real-world datasets show that our proposed approach outperforms the state-of-art methods.
{"title":"Aspect-aware Point-of-Interest Recommendation with Geo-Social Influence","authors":"Q. Guo, Zhu Sun, Jie Zhang, Qi Chen, Y. Theng","doi":"10.1145/3099023.3099066","DOIUrl":"https://doi.org/10.1145/3099023.3099066","url":null,"abstract":"The large volume of data available in location-based social networks (LBSNs) enables Point-of-Interest (POI) recommendation services. On another hand, the heterogeneous information (e.g., user check-in records, geographical features of POIs, social network and user reviews) imposes great challenges on effective POI recommendation. In this paper, we focus on leveraging such rich information in an integrated manner to improve POI recommendation performance. We exploit not only the geographical and social information, but also aspects extracted from user reviews to better model users' preferences. More specifically, to fully utilize various types of information, we construct a novel heterogeneous graph, Aspect-aware Geo-Social influence Graph (AGSG), by fusing various relations among the three types of nodes, i.e., users, POIs and aspects. The personalized POI recommendation task is then transformed as a graph node ranking problem in AGSG. We design a graph-based recommendation algorithm based on both personalized PageRank (PPR) and meta paths, to fully exploit the heterogeneous graph structure as well as to capture the semantic relations among the various nodes. Experiments on three real-world datasets show that our proposed approach outperforms the state-of-art methods.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116310311","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 this paper we present a simple, yet powerful approach to generating labeled datasets of Twitter1 users. Our focus falls on sensitive personal details, shared as background information in tweets. Such tweets avoid the focus of user's attention and also tend to resist the vast amounts of humor, wishes or hypothetical thinking typical for tweets. Our approach combines selecting search queries, followed up by a semi-supervised filtering of indicative messages. We create datasets in several unrelated domains and prove that all sorts of target groups can be built with minimal manual annotator effort. The generated datasets include separate groups of users with specific characteristics: pet ownership, blood pressure, diabetes and psychotropic medicine usage, for which to our knowledge manually labeled data was previously not available. Our search-based approach is also used to generate a cross-domain corpus, matching Twitter users with their Yelp2 profiles.
{"title":"Generating Labeled Datasets of Twitter Users","authors":"Yasen Kiprov, Pepa Gencheva, Ivan Koychev","doi":"10.1145/3099023.3099048","DOIUrl":"https://doi.org/10.1145/3099023.3099048","url":null,"abstract":"In this paper we present a simple, yet powerful approach to generating labeled datasets of Twitter1 users. Our focus falls on sensitive personal details, shared as background information in tweets. Such tweets avoid the focus of user's attention and also tend to resist the vast amounts of humor, wishes or hypothetical thinking typical for tweets. Our approach combines selecting search queries, followed up by a semi-supervised filtering of indicative messages. We create datasets in several unrelated domains and prove that all sorts of target groups can be built with minimal manual annotator effort. The generated datasets include separate groups of users with specific characteristics: pet ownership, blood pressure, diabetes and psychotropic medicine usage, for which to our knowledge manually labeled data was previously not available. Our search-based approach is also used to generate a cross-domain corpus, matching Twitter users with their Yelp2 profiles.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118070","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}
L. Ardissono, M. Lucenteforte, Noemi Mauro, Adriano Savoca, Angioletta Voghera, L. Riccia
This demo paper describes the semantic query interpretation model adopted in the OnToMap Participatory GIS and presents its benefits to information retrieval and personalized information presentation.
{"title":"Semantic Interpretation of Search Queries for Personalization","authors":"L. Ardissono, M. Lucenteforte, Noemi Mauro, Adriano Savoca, Angioletta Voghera, L. Riccia","doi":"10.1145/3099023.3099030","DOIUrl":"https://doi.org/10.1145/3099023.3099030","url":null,"abstract":"This demo paper describes the semantic query interpretation model adopted in the OnToMap Participatory GIS and presents its benefits to information retrieval and personalized information presentation.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130917777","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}
C. Musto, A. Rapp, Veronika Bogina, F. Cena, F. Hopfgartner, J. Kay, D. Konopnicki, T. Kuflik, B. Mobasher, G. Semeraro
The importance of user modeling and personalization is taken for granted in several scenarios. According to this widespread paradigm, each user can be modeled through some (explicitly or implicitly gathered) information about her knowledge or about her preferences, in order to adapt the behavior of a generic intelligent system to her specific characteristics. However, the recent spread of social network and self-tracking devices has totally changed the rules for personalization. On one side, the spread of social network platforms radically changed and renewed many consolidated behavioral paradigms. Thanks to the heterogeneous nature of the discussions that take place on social networks, a lot of new data are continuously available and can be gathered and exploited to build richer and more complete user models, to discover latent communities, to infer information about users' emotions and personality traits, and also to study very complex phenomena, such as those related to the psycho-social sphere, in a totally new way. At the same time, self-tracking devices are becoming more and more pervasive, and a plethora of personal data is today available by exploiting such tools. These devices model and track a lot of signals that pure content-based information which is commonly spread on social networks can't actually handle. Reasoning on these data can enable predictions about the user's behavior, health, and goals. As a consequence, it is very important to think about a new generation of user models that are able to effectively merge the information coming from both information sources, while also taking into account the fact that user models evolve over time.
{"title":"UMAP 2017 THUM Workshop Chairs' Welcome & Organization","authors":"C. Musto, A. Rapp, Veronika Bogina, F. Cena, F. Hopfgartner, J. Kay, D. Konopnicki, T. Kuflik, B. Mobasher, G. Semeraro","doi":"10.1145/3099023.3099100","DOIUrl":"https://doi.org/10.1145/3099023.3099100","url":null,"abstract":"The importance of user modeling and personalization is taken for granted in several scenarios. According to this widespread paradigm, each user can be modeled through some (explicitly or implicitly gathered) information about her knowledge or about her preferences, in order to adapt the behavior of a generic intelligent system to her specific characteristics. However, the recent spread of social network and self-tracking devices has totally changed the rules for personalization. On one side, the spread of social network platforms radically changed and renewed many consolidated behavioral paradigms. Thanks to the heterogeneous nature of the discussions that take place on social networks, a lot of new data are continuously available and can be gathered and exploited to build richer and more complete user models, to discover latent communities, to infer information about users' emotions and personality traits, and also to study very complex phenomena, such as those related to the psycho-social sphere, in a totally new way. At the same time, self-tracking devices are becoming more and more pervasive, and a plethora of personal data is today available by exploiting such tools. These devices model and track a lot of signals that pure content-based information which is commonly spread on social networks can't actually handle. Reasoning on these data can enable predictions about the user's behavior, health, and goals. As a consequence, it is very important to think about a new generation of user models that are able to effectively merge the information coming from both information sources, while also taking into account the fact that user models evolve over time.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131242567","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}
A music listener's mainstreaminess indicates the extent to which her listening preferences correspond to those of the population at large. However, formal definitions to quantify the level of mainstreaminess of a listener are rare and those available define mainstreaminess based on fractions between some kind of individual and global listening profiles. We argue, in contrast, that measures based on a modified version of the well-established Kullback-Leibler (KL) divergence as well as rank-order correlation coefficient may be better suited to capture the mainstreaminess of listeners. We therefore propose two measures adopting KL divergence and rank-order correlation and show, on a real-world dataset of over one billion user-generated listening events (LFM-1b), that music recommender systems can notably benefit when grouping users according to their level of mainstreaminess with respect to these two measures. This particularly holds for the frequently neglected listener group which is characterized by low mainstreaminess.
{"title":"Distance- and Rank-based Music Mainstreaminess Measurement","authors":"M. Schedl, Christine Bauer","doi":"10.1145/3099023.3099098","DOIUrl":"https://doi.org/10.1145/3099023.3099098","url":null,"abstract":"A music listener's mainstreaminess indicates the extent to which her listening preferences correspond to those of the population at large. However, formal definitions to quantify the level of mainstreaminess of a listener are rare and those available define mainstreaminess based on fractions between some kind of individual and global listening profiles. We argue, in contrast, that measures based on a modified version of the well-established Kullback-Leibler (KL) divergence as well as rank-order correlation coefficient may be better suited to capture the mainstreaminess of listeners. We therefore propose two measures adopting KL divergence and rank-order correlation and show, on a real-world dataset of over one billion user-generated listening events (LFM-1b), that music recommender systems can notably benefit when grouping users according to their level of mainstreaminess with respect to these two measures. This particularly holds for the frequently neglected listener group which is characterized by low mainstreaminess.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323557","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}
Obesity is a serious health problem that has been linked to the major cause of death worldwide, ischemic heart disease. There is a lot of research on influencing people to live healthier lives by being active and eating healthy foods. However, there is little research on influencing people to buy healthier foods at the point of sale especially online. Because people tend to cook and eat what they buy, making healthier choices when grocery shopping online could lead to healthier eating habits for consumers. To advance research in this area, we propose a framework that uses gamification elements to influence consumers to purchase healthier foods in e-commerce. In this position paper, we present our proposed framework and describe the implementation of some of the influence strategies and game design elements such as rewards, personalization, suggestion, self-monitoring and feedback. This paper contributes to the area of game design by describing possible guidelines that could lead to healthier food shopping habits for e-commerce consumers.
{"title":"A Gamified System for Influencing Healthy E-commerce Shopping Habits","authors":"I. Adaji, Julita Vassileva","doi":"10.1145/3099023.3099110","DOIUrl":"https://doi.org/10.1145/3099023.3099110","url":null,"abstract":"Obesity is a serious health problem that has been linked to the major cause of death worldwide, ischemic heart disease. There is a lot of research on influencing people to live healthier lives by being active and eating healthy foods. However, there is little research on influencing people to buy healthier foods at the point of sale especially online. Because people tend to cook and eat what they buy, making healthier choices when grocery shopping online could lead to healthier eating habits for consumers. To advance research in this area, we propose a framework that uses gamification elements to influence consumers to purchase healthier foods in e-commerce. In this position paper, we present our proposed framework and describe the implementation of some of the influence strategies and game design elements such as rewards, personalization, suggestion, self-monitoring and feedback. This paper contributes to the area of game design by describing possible guidelines that could lead to healthier food shopping habits for e-commerce consumers.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132641159","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}
Nowadays we assist to a significant innovation of the teaching practises due to the crisis of the classical teaching approach, the availability of low cost mobile technology and the easy access to global knowledge and information. Learning Design systems represent valuable tools to support teachers in the delicate task of organizing the teaching-learning activities in active student-centered approaches. There are many active projects in this field, but the available tools do not always fulfill the expectations. Furthermore, there is a rapid growth of Web 2.0 apps to create digital artefacts with a strong potential impact in learning activities, but current LD platforms don't guide teachers and students in choosing best apps to carry on a specific task. This paper provides an overview of the state of the art LD tools and developing perspective in this area.
{"title":"Modeling and Developing a Learning Design System based on Graphic Organizers","authors":"M. Corbatto","doi":"10.1145/3099023.3099028","DOIUrl":"https://doi.org/10.1145/3099023.3099028","url":null,"abstract":"Nowadays we assist to a significant innovation of the teaching practises due to the crisis of the classical teaching approach, the availability of low cost mobile technology and the easy access to global knowledge and information. Learning Design systems represent valuable tools to support teachers in the delicate task of organizing the teaching-learning activities in active student-centered approaches. There are many active projects in this field, but the available tools do not always fulfill the expectations. Furthermore, there is a rapid growth of Web 2.0 apps to create digital artefacts with a strong potential impact in learning activities, but current LD platforms don't guide teachers and students in choosing best apps to carry on a specific task. This paper provides an overview of the state of the art LD tools and developing perspective in this area.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116725223","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}
Research has shown that the perceived credibility of a website is critical to its success. However, little is known about how individual differences influence this important factor of web design. In this paper, we investigate how personality traits affect the perceived credibility of a website in the mobile domain. Using a sample of 323 participants, we developed a model showing how the Big Five personality traits influence the perceived credibility of a website through its perceived aesthetics and perceived usability. Our model reveals that Agreeableness is the strongest predictor of aesthetics and/or usability, followed by Conscientiousness. This suggests that the more agreeable and/or the more conscientious users are easily more satisfied aesthetically and usability-wise by a mobile websites than the less agreeable and/or the less conscientious users respectively. Consequently, designers of mobile sites may have to do more in user interface design in order to attract the less agreeable and/or the less conscientious users to their sites based on its hedonic (aesthetics-inspired) and utilitarian (usability-inspired) appeal.
{"title":"The Influence of Personality on Mobile Web Credibility","authors":"Kiemute Oyibo, Rita Orji, Julita Vassileva","doi":"10.1145/3099023.3099074","DOIUrl":"https://doi.org/10.1145/3099023.3099074","url":null,"abstract":"Research has shown that the perceived credibility of a website is critical to its success. However, little is known about how individual differences influence this important factor of web design. In this paper, we investigate how personality traits affect the perceived credibility of a website in the mobile domain. Using a sample of 323 participants, we developed a model showing how the Big Five personality traits influence the perceived credibility of a website through its perceived aesthetics and perceived usability. Our model reveals that Agreeableness is the strongest predictor of aesthetics and/or usability, followed by Conscientiousness. This suggests that the more agreeable and/or the more conscientious users are easily more satisfied aesthetically and usability-wise by a mobile websites than the less agreeable and/or the less conscientious users respectively. Consequently, designers of mobile sites may have to do more in user interface design in order to attract the less agreeable and/or the less conscientious users to their sites based on its hedonic (aesthetics-inspired) and utilitarian (usability-inspired) appeal.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115247960","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 this paper, we investigate differences between recipes uploaded by users and recipes bookmarked by users. The results indicate that uploaded recipes outperform bookmarked recipes in terms of healthiness. Further, health scores and nutritional values of these recipes are highly related to the stated cooking interests: for example, Southern Food lovers eat not as healthy as those who prefer the Mediterranean or Middle-Eastern cuisine. A disturbing finding is that interest in the category `Kids' is associated with bad values for all nutritional measures. We also found some interactions between hobbies such as biking, hunting or knitting and nutritional values. These insights pave way to the design of health-aware recipe recommender systems that take a user's food preferences into account; in addition, taking a user's lifestyle and hobbies into account would provide valuable input to persuasive systems.
{"title":"On the Relations Between Cooking Interests, Hobbies and Nutritional Values of Online Recipes: Implications for Health-Aware Recipe Recommender Systems","authors":"C. Trattner, Markus Rokicki, E. Herder","doi":"10.1145/3099023.3099072","DOIUrl":"https://doi.org/10.1145/3099023.3099072","url":null,"abstract":"In this paper, we investigate differences between recipes uploaded by users and recipes bookmarked by users. The results indicate that uploaded recipes outperform bookmarked recipes in terms of healthiness. Further, health scores and nutritional values of these recipes are highly related to the stated cooking interests: for example, Southern Food lovers eat not as healthy as those who prefer the Mediterranean or Middle-Eastern cuisine. A disturbing finding is that interest in the category `Kids' is associated with bad values for all nutritional measures. We also found some interactions between hobbies such as biking, hunting or knitting and nutritional values. These insights pave way to the design of health-aware recipe recommender systems that take a user's food preferences into account; in addition, taking a user's lifestyle and hobbies into account would provide valuable input to persuasive systems.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122044214","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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