The personalization of feedback by an Intelligent Tutoring System has the potential to greatly improve learner motivation. This PhD investigates how an Intelligent Tutoring System can adapt to the cultural background of learners when giving feedback. The research uses the user-as-wizard method for investigation. To convey the cultural background of the learner in user studies, validated cultural stories (using Hofstede cultural dimensions) are required. These stories are then used to conduct qualitative and empirical studies to investigate how participants from a range of different cultures believe the culture of a learner should affect the kind of feedback given. The insights gathered from these studies will be unified to inspire an algorithm to allow an intelligent tutoring system to utilise these adaptations, and the effects tested on real learners.
{"title":"Adaptive E-Learning: Motivating Learners whilst Adapting Feedback to Cultural Background","authors":"Muhammad Adamu Sidi-Ali","doi":"10.1145/3320435.3323464","DOIUrl":"https://doi.org/10.1145/3320435.3323464","url":null,"abstract":"The personalization of feedback by an Intelligent Tutoring System has the potential to greatly improve learner motivation. This PhD investigates how an Intelligent Tutoring System can adapt to the cultural background of learners when giving feedback. The research uses the user-as-wizard method for investigation. To convey the cultural background of the learner in user studies, validated cultural stories (using Hofstede cultural dimensions) are required. These stories are then used to conduct qualitative and empirical studies to investigate how participants from a range of different cultures believe the culture of a learner should affect the kind of feedback given. The insights gathered from these studies will be unified to inspire an algorithm to allow an intelligent tutoring system to utilise these adaptations, and the effects tested on real learners.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852348","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 Mavridis, Owen Huang, S. Qiu, U. Gadiraju, A. Bozzon
Conversational interfaces can facilitate human-computer interactions. Whether or not conversational interfaces can improve worker experience and work quality in crowdsourcing marketplaces has remained unanswered. We investigate the suitability of text-based conversational interfaces for microtask crowdsourcing. We designed a rigorous experimental campaign aimed at gauging the interest and acceptance by crowdworkers for this type of work interface. We compared Web and conversational interfaces for five common microtask types and measured the execution time, quality of work, and the perceived satisfaction of 316 workers recruited from the FigureEight platform. We show that conversational interfaces can be used effectively for crowdsourcing microtasks, resulting in a high satisfaction from workers, and without having a negative impact on task execution time or work quality.
{"title":"Chatterbox: Conversational Interfaces for Microtask Crowdsourcing","authors":"Panagiotis Mavridis, Owen Huang, S. Qiu, U. Gadiraju, A. Bozzon","doi":"10.1145/3320435.3320439","DOIUrl":"https://doi.org/10.1145/3320435.3320439","url":null,"abstract":"Conversational interfaces can facilitate human-computer interactions. Whether or not conversational interfaces can improve worker experience and work quality in crowdsourcing marketplaces has remained unanswered. We investigate the suitability of text-based conversational interfaces for microtask crowdsourcing. We designed a rigorous experimental campaign aimed at gauging the interest and acceptance by crowdworkers for this type of work interface. We compared Web and conversational interfaces for five common microtask types and measured the execution time, quality of work, and the perceived satisfaction of 316 workers recruited from the FigureEight platform. We show that conversational interfaces can be used effectively for crowdsourcing microtasks, resulting in a high satisfaction from workers, and without having a negative impact on task execution time or work quality.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"569 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117232106","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}
Oludamilare Matthews, Sukru Eraslan, Victoria Yaneva, Alan Davies, Y. Yeşilada, Markel Vigo, S. Harper
People with autism often exhibit different visual behaviours from neurotypical users. To explore how these differences are exhibited on the Web, we model visual behaviour by combining pupillary response, which is an unobtrusive measure of physiological arousal, with eye-tracking scan paths that indicate visual attention. We evaluated our approach with two populations: 19 neurotypical users and 19 users with autism. We observe differences in their visual behaviours as, in certain instances, individuals with autism exhibit a lower arousal response to affective contents. While this is consistent with the literature on autism, we confirm this phenomenon on the Web. We discuss how our modelling method can be used to identify possible UX issues such as the presence of stress, cognitive load and differences in the perception of Web elements in relation to physiological arousal.
{"title":"Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web: A Case Study of Neurotypical People vs People with Autism","authors":"Oludamilare Matthews, Sukru Eraslan, Victoria Yaneva, Alan Davies, Y. Yeşilada, Markel Vigo, S. Harper","doi":"10.1145/3320435.3320446","DOIUrl":"https://doi.org/10.1145/3320435.3320446","url":null,"abstract":"People with autism often exhibit different visual behaviours from neurotypical users. To explore how these differences are exhibited on the Web, we model visual behaviour by combining pupillary response, which is an unobtrusive measure of physiological arousal, with eye-tracking scan paths that indicate visual attention. We evaluated our approach with two populations: 19 neurotypical users and 19 users with autism. We observe differences in their visual behaviours as, in certain instances, individuals with autism exhibit a lower arousal response to affective contents. While this is consistent with the literature on autism, we confirm this phenomenon on the Web. We discuss how our modelling method can be used to identify possible UX issues such as the presence of stress, cognitive load and differences in the perception of Web elements in relation to physiological arousal.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121069843","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}
People with Multiple Sclerosis (pwMS) suffer from a diverse set of symptoms such as fatigue, pain, depression, and decline in motor and cognitive function. It has been proven that physical activity has a positive effect on most of these symptoms. However, many pwMS lead sedentary lives, and do not meet the guidelines for physical activity. We propose WalkWithMe, a mobile application that supports pwMS in walking. WalkWithMe coaches pwMS in achieving a personal goal over a period of 10 weeks. We conducted a workshop with pwMS and brainstorm sessions with experts in rehabilitation to define the design choices of WalkWithMe. We examined the impact of WalkWithMe in a 10-week field study with 13 pwMS. The study revealed insights in walking habits, and positive trends in walking capacity. In this paper, we present the design aspects of WalkWithMe, findings of our 10-week evaluation, and resulting insights on goal setting for pwMS.
{"title":"WalkWithMe: Personalized Goal Setting and Coaching for Walking in People with Multiple Sclerosis","authors":"Eva Geurts, F. V. Geel, P. Feys, K. Coninx","doi":"10.1145/3320435.3320459","DOIUrl":"https://doi.org/10.1145/3320435.3320459","url":null,"abstract":"People with Multiple Sclerosis (pwMS) suffer from a diverse set of symptoms such as fatigue, pain, depression, and decline in motor and cognitive function. It has been proven that physical activity has a positive effect on most of these symptoms. However, many pwMS lead sedentary lives, and do not meet the guidelines for physical activity. We propose WalkWithMe, a mobile application that supports pwMS in walking. WalkWithMe coaches pwMS in achieving a personal goal over a period of 10 weeks. We conducted a workshop with pwMS and brainstorm sessions with experts in rehabilitation to define the design choices of WalkWithMe. We examined the impact of WalkWithMe in a 10-week field study with 13 pwMS. The study revealed insights in walking habits, and positive trends in walking capacity. In this paper, we present the design aspects of WalkWithMe, findings of our 10-week evaluation, and resulting insights on goal setting for pwMS.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127307979","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}
Chelsea M. Myers, David Grethlein, Anushay Furqan, Santiago Ontañón, Jichen Zhu
Voice User Interfaces (VUIs) are becoming increasingly popular. However, how VUIs can adapt to user differences remains insufficiently understood. We analyze usage data from a user study (n=50) where participants interacted with an unfamiliar VUI. Through automated clustering and statistical analysis, we present user models of their behavior patterns. We found user behavior can be grouped into three clusters: people who become proficient with the system and typically stay proficient while completing different tasks, people who exhibit an exploratory approach to completing tasks, and people who struggled to complete tasks. We discuss design implications based on these behavior clusters.
语音用户界面(Voice User Interfaces, VUIs)正变得越来越流行。然而，ui如何适应用户差异仍然没有得到充分的理解。我们分析了来自用户研究(n=50)的使用数据，其中参与者与不熟悉的VUI进行交互。通过自动聚类和统计分析，我们给出了用户行为模式的模型。我们发现用户行为可以分为三类:精通系统并且在完成不同任务时通常保持精通的人，表现出探索性方法来完成任务的人，以及努力完成任务的人。我们将讨论基于这些行为集群的设计含义。
{"title":"Modeling Behavior Patterns with an Unfamiliar Voice User Interface","authors":"Chelsea M. Myers, David Grethlein, Anushay Furqan, Santiago Ontañón, Jichen Zhu","doi":"10.1145/3320435.3320475","DOIUrl":"https://doi.org/10.1145/3320435.3320475","url":null,"abstract":"Voice User Interfaces (VUIs) are becoming increasingly popular. However, how VUIs can adapt to user differences remains insufficiently understood. We analyze usage data from a user study (n=50) where participants interacted with an unfamiliar VUI. Through automated clustering and statistical analysis, we present user models of their behavior patterns. We found user behavior can be grouped into three clusters: people who become proficient with the system and typically stay proficient while completing different tasks, people who exhibit an exploratory approach to completing tasks, and people who struggled to complete tasks. We discuss design implications based on these behavior clusters.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131586010","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}
Adriano Arra, Alessio Bianchini, Joana Chavez, Pietro Ciravolo, Fatjon Nebiu, Martina Olivelli, Gabriele Scoma, Simone Tavoletta, Matteo Zagaglia, Alessio Vecchio
Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.
{"title":"Personalized Gait-based Authentication Using UWB Wearable Devices","authors":"Adriano Arra, Alessio Bianchini, Joana Chavez, Pietro Ciravolo, Fatjon Nebiu, Martina Olivelli, Gabriele Scoma, Simone Tavoletta, Matteo Zagaglia, Alessio Vecchio","doi":"10.1145/3320435.3320473","DOIUrl":"https://doi.org/10.1145/3320435.3320473","url":null,"abstract":"Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114509994","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 Clock Drawing Test is used as a cognitive assessment tool in geriatrics to detect signs of dementia or to model the progress of stroke recovery. The result is scored manually by a trained professional. We implement the Mendez scoring scheme and create a hierarchy of error categories that model the test characteristics of the clock drawing test, based on a set of impaired clock examples provided by a geriatrics clinic. Using a digital pen we recorded 120 clock samples for evaluating the automatic scoring system, with a total of 2400 error samples distributed over the 20 error classes of the Mendez scoring scheme. Error classes are scored automatically using a handwriting and gesture recognition framework. Results show that we provide a clinically relevant cognitive model for each subject. In addition, we heavily reduce the time spent on manual scoring. We compare manual scoring results with results produced by our automated system.
{"title":"Modeling Cognitive Status through Automatic Scoring of a Digital Version of the Clock Drawing Test","authors":"Alexander Prange, Daniel Sonntag","doi":"10.1145/3320435.3320452","DOIUrl":"https://doi.org/10.1145/3320435.3320452","url":null,"abstract":"The Clock Drawing Test is used as a cognitive assessment tool in geriatrics to detect signs of dementia or to model the progress of stroke recovery. The result is scored manually by a trained professional. We implement the Mendez scoring scheme and create a hierarchy of error categories that model the test characteristics of the clock drawing test, based on a set of impaired clock examples provided by a geriatrics clinic. Using a digital pen we recorded 120 clock samples for evaluating the automatic scoring system, with a total of 2400 error samples distributed over the 20 error classes of the Mendez scoring scheme. Error classes are scored automatically using a handwriting and gesture recognition framework. Results show that we provide a clinically relevant cognitive model for each subject. In addition, we heavily reduce the time spent on manual scoring. We compare manual scoring results with results produced by our automated system.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692724","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 methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination ofnatural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.
{"title":"Justifying Recommendations through Aspect-based Sentiment Analysis of Users Reviews","authors":"C. Musto, P. Lops, M. Degemmis, G. Semeraro","doi":"10.1145/3320435.3320457","DOIUrl":"https://doi.org/10.1145/3320435.3320457","url":null,"abstract":"In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination ofnatural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130673958","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}
Michal Shmueli-Scheuer, Jonathan Herzig, D. Konopnicki, T. Sandbank
Persuasion is one of the most frequent, albeit challenging, tasks in human interaction. In a textual argument, one party (author) aims to change the view of the other party (reader). In this paper, we propose to detect persuasive textual arguments while considering the parties personality traits. We find that we can substantially improve accuracy by introducing features that capture author-reader personality traits and their interaction. Our model improves performance of state-of-the-art baselines from 66% to 71% on a new dataset of more than 19K arguments we collected.
{"title":"Detecting Persuasive Arguments based on Author-Reader Personality Traits and their Interaction","authors":"Michal Shmueli-Scheuer, Jonathan Herzig, D. Konopnicki, T. Sandbank","doi":"10.1145/3320435.3320467","DOIUrl":"https://doi.org/10.1145/3320435.3320467","url":null,"abstract":"Persuasion is one of the most frequent, albeit challenging, tasks in human interaction. In a textual argument, one party (author) aims to change the view of the other party (reader). In this paper, we propose to detect persuasive textual arguments while considering the parties personality traits. We find that we can substantially improve accuracy by introducing features that capture author-reader personality traits and their interaction. Our model improves performance of state-of-the-art baselines from 66% to 71% on a new dataset of more than 19K arguments we collected.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130055163","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}
Argyris Constantinides, Marios Belk, C. Fidas, A. Pitsillides
Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.
{"title":"On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical Passwords","authors":"Argyris Constantinides, Marios Belk, C. Fidas, A. Pitsillides","doi":"10.1145/3320435.3320474","DOIUrl":"https://doi.org/10.1145/3320435.3320474","url":null,"abstract":"Graphical passwords leverage the picture superiority effect to enhance memorability, and reflect today's haptic users' interaction realms. Images related to users' past sociocultural experiences (e.g., retrospective) enable the creation of memorable and secure passwords, while randomly system-assigned images (e.g., generic) lead to easy-to-predict hotspot regions within graphical password schemes. What remains rather unexplored is whether the image type could be inferred during the password creation. In this work, we present a between-subjects user study in which 37 participants completed a recall-based graphical password creation task with retrospective and generic images, while we were capturing their visual behavior. We found that the image type can be inferred within a few seconds in real-time. User adaptive mechanisms might benefit from our work's findings, by providing users early feedback whether they are moving towards the creation of a weak graphical password.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133955125","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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