ACM PATCH 2020, organized in conjunction with the 28th International Conference on User Modeling, Adaptation and Personalization, is the latest event of the PATCH series, started in 2007 and held within the UMAP and IUI Conference series. We summarize the main ideas addressed in the papers accepted for publication in the workshop proceedings and for presentation at the event.
{"title":"Workshop on Personalized Access to Cultural Heritage: PATCH'20","authors":"L. Ardissono, Noemi Mauro, G. Raptis, A. Wecker","doi":"10.1145/3340631.3398670","DOIUrl":"https://doi.org/10.1145/3340631.3398670","url":null,"abstract":"ACM PATCH 2020, organized in conjunction with the 28th International Conference on User Modeling, Adaptation and Personalization, is the latest event of the PATCH series, started in 2007 and held within the UMAP and IUI Conference series. We summarize the main ideas addressed in the papers accepted for publication in the workshop proceedings and for presentation at the event.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133175144","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}
Interactive dashboards enable viewing and interacting with complex underlying data using visualisations such as charts, tables, maps, or even text typically on a single display. By bringing the most important information in a single place, dashboards enable performance monitoring and support decision making. Although nowadays dashboards are widely adopted in many domains, they involve challenges that prevent users from utilising them as they were intended. For example, having a dashboard with too much data can negatively affect decision making and lead to misleading interpretation. Through this research, we identify and investigate the challenges associated with dashboards, what users do in response to those challenges, and what adaptations can be applied to mitigate these challenges. Consequently, we aim to examine and evaluate a set of adaptation techniques that can improve the experience of users interacting with dashboards.
{"title":"Challenges, Strategies and Adaptations on Interactive Dashboards","authors":"M. Alhamadi","doi":"10.1145/3340631.3398678","DOIUrl":"https://doi.org/10.1145/3340631.3398678","url":null,"abstract":"Interactive dashboards enable viewing and interacting with complex underlying data using visualisations such as charts, tables, maps, or even text typically on a single display. By bringing the most important information in a single place, dashboards enable performance monitoring and support decision making. Although nowadays dashboards are widely adopted in many domains, they involve challenges that prevent users from utilising them as they were intended. For example, having a dashboard with too much data can negatively affect decision making and lead to misleading interpretation. Through this research, we identify and investigate the challenges associated with dashboards, what users do in response to those challenges, and what adaptations can be applied to mitigate these challenges. Consequently, we aim to examine and evaluate a set of adaptation techniques that can improve the experience of users interacting with dashboards.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"79 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":"123993802","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}
Nico Herbig, Tim Düwel, M. Helali, Lea Eckhart, P. Schuck, Subhabrata Choudhury, A. Krüger
In this paper, we analyze a wide range of physiological, behavioral, performance, and subjective measures to estimate cognitive load (CL) during e-learning. To the best of our knowledge, the analyzed sensor measures comprise the most diverse set of features from a variety of modalities that have to date been investigated in the e-learning domain. Our focus lies on predicting the subjectively reported CL and difficulty as well as intrinsic content difficulty based on the explored features. A study with 21 participants, who learned through videos and quizzes in a Moodle environment, shows that classifying intrinsic content difficulty works better for quizzes than for videos, where participants actively solve problems instead of passively consuming videos. Regression analysis for predicting the subjectively reported level of CL and difficulty also works with very low error within content topics. Among the explored feature modalities, eye-based features yield the best results, followed by heart-based and then skin-based measures. Furthermore, combining multiple modalities results in better performance compared to using a single modality. The presented results can guide researchers and developers of cognition-aware e-learning environments by suggesting modalities and features that work particularly well for estimating difficulty and CL.
{"title":"Investigating Multi-Modal Measures for Cognitive Load Detection in E-Learning","authors":"Nico Herbig, Tim Düwel, M. Helali, Lea Eckhart, P. Schuck, Subhabrata Choudhury, A. Krüger","doi":"10.1145/3340631.3394861","DOIUrl":"https://doi.org/10.1145/3340631.3394861","url":null,"abstract":"In this paper, we analyze a wide range of physiological, behavioral, performance, and subjective measures to estimate cognitive load (CL) during e-learning. To the best of our knowledge, the analyzed sensor measures comprise the most diverse set of features from a variety of modalities that have to date been investigated in the e-learning domain. Our focus lies on predicting the subjectively reported CL and difficulty as well as intrinsic content difficulty based on the explored features. A study with 21 participants, who learned through videos and quizzes in a Moodle environment, shows that classifying intrinsic content difficulty works better for quizzes than for videos, where participants actively solve problems instead of passively consuming videos. Regression analysis for predicting the subjectively reported level of CL and difficulty also works with very low error within content topics. Among the explored feature modalities, eye-based features yield the best results, followed by heart-based and then skin-based measures. Furthermore, combining multiple modalities results in better performance compared to using a single modality. The presented results can guide researchers and developers of cognition-aware e-learning environments by suggesting modalities and features that work particularly well for estimating difficulty and CL.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"30 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":"122584856","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}
Educational Recommender Systems (EdRecSys) are different in nature from conventional Recommender Systems (RecSys) --mostly related to e-commerce-- as the main goal of EdRecSys is supporting students learning' instead of maximizing users' satisfaction from consuming the recommended items. Thus, research on transparency for traditional RecSys is hard to transfer from e-commerce contexts to educational scenarios, as the level of knowledge of the end-user (i.e. the student) is crucial for generating and evaluating the impact of the recommendations on students' learning. In this paper I present the main idea of my thesis proposal, which aims to fill this gap by taking a user-centered approach that combines design and evaluation of personalized recommender algorithms and explanatory interfaces with students in real learning contexts.
{"title":"Exploring the Need for Transparency in Educational Recommender Systems","authors":"Jordan Barria-Pineda","doi":"10.1145/3340631.3398676","DOIUrl":"https://doi.org/10.1145/3340631.3398676","url":null,"abstract":"Educational Recommender Systems (EdRecSys) are different in nature from conventional Recommender Systems (RecSys) --mostly related to e-commerce-- as the main goal of EdRecSys is supporting students learning' instead of maximizing users' satisfaction from consuming the recommended items. Thus, research on transparency for traditional RecSys is hard to transfer from e-commerce contexts to educational scenarios, as the level of knowledge of the end-user (i.e. the student) is crucial for generating and evaluating the impact of the recommendations on students' learning. In this paper I present the main idea of my thesis proposal, which aims to fill this gap by taking a user-centered approach that combines design and evaluation of personalized recommender algorithms and explanatory interfaces with students in real learning contexts.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"27 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":"127892929","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, N. Tintarev, O. Inel, Marco Polignano, G. Semeraro, J. Ziegler
Adaptive and personalized systems have become pervasive technologies which are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal assistants able to proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of the explainability and the transparency of the model. The main research questions which arise from this scenario is simple and straightforward: How can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability? The workshop aims to provide a forum for discussing such problems, challenges and innovative research approaches in the area, by investigating the role of transparency and explainability on the recent methodologies for building user models or for developing personalized and adaptive systems.
{"title":"Workshop on Explainable User Models and Personalized Systems (ExUM 2020)","authors":"C. Musto, N. Tintarev, O. Inel, Marco Polignano, G. Semeraro, J. Ziegler","doi":"10.1145/3340631.3398673","DOIUrl":"https://doi.org/10.1145/3340631.3398673","url":null,"abstract":"Adaptive and personalized systems have become pervasive technologies which are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal assistants able to proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of the explainability and the transparency of the model. The main research questions which arise from this scenario is simple and straightforward: How can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability? The workshop aims to provide a forum for discussing such problems, challenges and innovative research approaches in the area, by investigating the role of transparency and explainability on the recent methodologies for building user models or for developing personalized and adaptive systems.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"13 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":"114641115","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}
Sebastian Zepf, Neska El Haouij, Jinmo Lee, Asma Ghandeharioun, Javier Hernández, Rosalind W. Picard
Driving can occupy a considerable part of our daily lives and is often associated with high levels of stress. Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help manage stress. In particular, we implemented and evaluated a closed-loop system that monitored the breathing rate of drivers in real-time and delivered either a conscious or an unconscious personalized acoustic breathing guide whenever needed. In a study with 24 participants, we observed that conscious interventions more effectively reduced the breathing rate but also increased the number of driving mistakes. We observed that prior driving experience as well as personality are significantly associated with the effect of the interventions, which highlights the importance of considering user profiles for in-car stress management interventions.
{"title":"Studying Personalized Just-in-time Auditory Breathing Guides and Potential Safety Implications during Simulated Driving","authors":"Sebastian Zepf, Neska El Haouij, Jinmo Lee, Asma Ghandeharioun, Javier Hernández, Rosalind W. Picard","doi":"10.1145/3340631.3394854","DOIUrl":"https://doi.org/10.1145/3340631.3394854","url":null,"abstract":"Driving can occupy a considerable part of our daily lives and is often associated with high levels of stress. Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help manage stress. In particular, we implemented and evaluated a closed-loop system that monitored the breathing rate of drivers in real-time and delivered either a conscious or an unconscious personalized acoustic breathing guide whenever needed. In a study with 24 participants, we observed that conscious interventions more effectively reduced the breathing rate but also increased the number of driving mistakes. We observed that prior driving experience as well as personality are significantly associated with the effect of the interventions, which highlights the importance of considering user profiles for in-car stress management interventions.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"328 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":"131813779","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 talk covers two main research directions based on the iCub humanoid robot. The iCub is a humanoid robot designed to support research in embodied AI. At 104 cm tall, the iCub has the size of a five-year-old child. It can crawl on all fours, walk, and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub is distributed as Open Source following the GPL license and can now count on a worldwide community of enthusiastic developers. There are more than 40 robots available in laboratories across the globe. The iCub sensory system allows seeing, hearing and feeling physical contacts. It is one of the few platforms in the world with a sensitive full-body skin. The iCub is being used at the Italian Institute of Technology as a model platform to develop the technology of future interactive service robots. In particular, I will describe our work in the field of physical and social interaction. For example, through extensive use of machine learning, we developed algorithms to interpret and use external contact information in a variety of tasks as well as contactless cues - vision, sound - to ease interaction between the user and the robot.
{"title":"Physical and Social Human-robot Interaction","authors":"G. Metta","doi":"10.1145/3340631.3395383","DOIUrl":"https://doi.org/10.1145/3340631.3395383","url":null,"abstract":"This talk covers two main research directions based on the iCub humanoid robot. The iCub is a humanoid robot designed to support research in embodied AI. At 104 cm tall, the iCub has the size of a five-year-old child. It can crawl on all fours, walk, and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub is distributed as Open Source following the GPL license and can now count on a worldwide community of enthusiastic developers. There are more than 40 robots available in laboratories across the globe. The iCub sensory system allows seeing, hearing and feeling physical contacts. It is one of the few platforms in the world with a sensitive full-body skin. The iCub is being used at the Italian Institute of Technology as a model platform to develop the technology of future interactive service robots. In particular, I will describe our work in the field of physical and social interaction. For example, through extensive use of machine learning, we developed algorithms to interpret and use external contact information in a variety of tasks as well as contactless cues - vision, sound - to ease interaction between the user and the robot.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"252 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":"117300357","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}
Requirements engineering is one of the most critical phases in the context of software development. Unclear textual specifications of requirements, hidden dependencies between requirements, and suboptimal prioritizations and release plans represent the major reasons for project delays and even cancellation. In this paper, we show how group recommender user interfaces can help to improve the quality of requirements engineering processes. To that end, we developed a novel group recommendation approach that focuses on the aspect of improving requirements prioritization by making preference elicitation processes more flexible as well as by introducing innovative user interfaces that foster information exchange among stakeholders. We conducted a large user study (N=313 participants) to evaluate our approach. The evaluation results indicate that argumentation-based user interfaces in a group setting trigger more rating and communication activity among the group members which significantly improves the quality of the prioritization process. Our main contributions are twofold: (1) more flexibility of the requirements evaluation by supporting the delegation of votes to experts and (2) an increased engagement of the stakeholders responsible for the requirements.
{"title":"Group Recommender User Interfaces for Improving Requirements Prioritization","authors":"Ralph Samer, Martin Stettinger, A. Felfernig","doi":"10.1145/3340631.3394851","DOIUrl":"https://doi.org/10.1145/3340631.3394851","url":null,"abstract":"Requirements engineering is one of the most critical phases in the context of software development. Unclear textual specifications of requirements, hidden dependencies between requirements, and suboptimal prioritizations and release plans represent the major reasons for project delays and even cancellation. In this paper, we show how group recommender user interfaces can help to improve the quality of requirements engineering processes. To that end, we developed a novel group recommendation approach that focuses on the aspect of improving requirements prioritization by making preference elicitation processes more flexible as well as by introducing innovative user interfaces that foster information exchange among stakeholders. We conducted a large user study (N=313 participants) to evaluate our approach. The evaluation results indicate that argumentation-based user interfaces in a group setting trigger more rating and communication activity among the group members which significantly improves the quality of the prioritization process. Our main contributions are twofold: (1) more flexibility of the requirements evaluation by supporting the delegation of votes to experts and (2) an increased engagement of the stakeholders responsible for the requirements.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"42 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":"127611242","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}
Past studies have shown that personality has a significant association with user behaviour and preferences, not least towards music. This makes personality information a promising aspect for user modelling in personalised recommender systems and similar domains. In contrast to existing studies, which investigate personality correlates of music preferences via genres or styles, we study such correlates by modelling music preferences at a finer-grained content level, using audio features of the music users listen to. Leveraging listening and personality information of more than 1,300 Last.fm users, we identify several significant medium and weak correlations between music audio features and personality traits, the latter defined by the five-factor model. Our results provide useful insights into the relationship between personality and music preference, which can be valuable for music recommender systems in terms of more personalised recommendations.
{"title":"Personality Correlates of Music Audio Preferences for Modelling Music Listeners","authors":"Alessandro B. Melchiorre, M. Schedl","doi":"10.1145/3340631.3394874","DOIUrl":"https://doi.org/10.1145/3340631.3394874","url":null,"abstract":"Past studies have shown that personality has a significant association with user behaviour and preferences, not least towards music. This makes personality information a promising aspect for user modelling in personalised recommender systems and similar domains. In contrast to existing studies, which investigate personality correlates of music preferences via genres or styles, we study such correlates by modelling music preferences at a finer-grained content level, using audio features of the music users listen to. Leveraging listening and personality information of more than 1,300 Last.fm users, we identify several significant medium and weak correlations between music audio features and personality traits, the latter defined by the five-factor model. Our results provide useful insights into the relationship between personality and music preference, which can be valuable for music recommender systems in terms of more personalised recommendations.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"88 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":"133923583","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}
Autism spectrum disorder (ASD) is a long-standing mental condition characterized by hindered mental growth and development and is a lifelong disability for the majority of affected individuals. In 2018, 2-3% of children in the USA have been diagnosed with autism. As these children move to adulthood, they have difficulty in developing a well-functioning motor skill. Some of these abnormalities, however, can be gradually improved if they are treated appropriately during their adulthood. Studies have shown that people with ASD enjoy playing video games. Educational games, however, have been primarily developed for children with ASD, which are too primitive for adults with ASD. We have developed a gaming and personalized recommender system that suggests therapeutic games to adults with ASD which can improve their social-interactive skills. The gaming system maintains the entertainment value of the games to ensure that adults are interested in playing them, whereas the recommender system suggests appropriate games for adults with ASD to play. The effectiveness and merit of our gaming and recommender system is backed up by an empirical study.
{"title":"Recommending Video Games to Adults with Autism Spectrum Disorder for Social-Skill Enhancement","authors":"Alisha Banskota, Yiu-Kai Ng","doi":"10.1145/3340631.3394867","DOIUrl":"https://doi.org/10.1145/3340631.3394867","url":null,"abstract":"Autism spectrum disorder (ASD) is a long-standing mental condition characterized by hindered mental growth and development and is a lifelong disability for the majority of affected individuals. In 2018, 2-3% of children in the USA have been diagnosed with autism. As these children move to adulthood, they have difficulty in developing a well-functioning motor skill. Some of these abnormalities, however, can be gradually improved if they are treated appropriately during their adulthood. Studies have shown that people with ASD enjoy playing video games. Educational games, however, have been primarily developed for children with ASD, which are too primitive for adults with ASD. We have developed a gaming and personalized recommender system that suggests therapeutic games to adults with ASD which can improve their social-interactive skills. The gaming system maintains the entertainment value of the games to ensure that adults are interested in playing them, whereas the recommender system suggests appropriate games for adults with ASD to play. The effectiveness and merit of our gaming and recommender system is backed up by an empirical study.","PeriodicalId":417607,"journal":{"name":"Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization","volume":"38 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":"124095525","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：