Pub Date : 2022-07-01DOI: 10.1109/iv56949.2022.00010
{"title":"Reviewers: IV 2022","authors":"","doi":"10.1109/iv56949.2022.00010","DOIUrl":"https://doi.org/10.1109/iv56949.2022.00010","url":null,"abstract":"","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"8 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132077499","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}
Pub Date : 2022-07-01DOI: 10.1109/iv56949.2022.00055
Tomoya Onuki, Kazuo Misue
Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. Visualization of movement data helps intuitive analysis. However, it is not easy for seabird experts to design suitable visual representations for comparison. The purpose of our study is to develop a tool to support the visualization process for comparison of seabird movement. There are three components to visualization techniques for comparison: Juxtaposition, Superposition, and Explicit Encodings. In our study, we aim to support comparative analysis under complex conditions using flexible Small Multiples based on juxtaposition and superposition. We designed visual representations for the comparative analysis of seabird movement data. We implemented such visual representations into a tool and confirmed its effectiveness for comparative analysis of seabird movement data.
{"title":"Visualization Tool for Comparative Analysis of Seabird Movement Data","authors":"Tomoya Onuki, Kazuo Misue","doi":"10.1109/iv56949.2022.00055","DOIUrl":"https://doi.org/10.1109/iv56949.2022.00055","url":null,"abstract":"Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. Visualization of movement data helps intuitive analysis. However, it is not easy for seabird experts to design suitable visual representations for comparison. The purpose of our study is to develop a tool to support the visualization process for comparison of seabird movement. There are three components to visualization techniques for comparison: Juxtaposition, Superposition, and Explicit Encodings. In our study, we aim to support comparative analysis under complex conditions using flexible Small Multiples based on juxtaposition and superposition. We designed visual representations for the comparative analysis of seabird movement data. We implemented such visual representations into a tool and confirmed its effectiveness for comparative analysis of seabird movement data.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122342870","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00048
Antonella Gabriella Montanaro, F. Sciarrone, M. Temperini
Concept maps are graphic tools to organize, represent and share knowledge. In particular, a concept map can explicitly express the knowledge of a person or group, about a given domain of interest. Concept maps are used effectively to support learning of any topic, at any level: from Primary School to University, and to professional/vocational training, it can stimulate and unveil the occurrence of meaningful learning. In an educational context, having the possibility to compare Concept Maps coming from different students, also by means of an automated computation of map similarity, can reveal to be a great asset for a teacher. And this is so much more true when the number of students is very high, like in Massive Open Online Course. Here we propose a similarity measure based on two deep learning techniques that produce embeddings of the single structures that make up a concept map. We also report about a preliminary experiment, having encouraging results.
{"title":"A Deep Learning Approach to Concept Maps Similarity","authors":"Antonella Gabriella Montanaro, F. Sciarrone, M. Temperini","doi":"10.1109/IV56949.2022.00048","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00048","url":null,"abstract":"Concept maps are graphic tools to organize, represent and share knowledge. In particular, a concept map can explicitly express the knowledge of a person or group, about a given domain of interest. Concept maps are used effectively to support learning of any topic, at any level: from Primary School to University, and to professional/vocational training, it can stimulate and unveil the occurrence of meaningful learning. In an educational context, having the possibility to compare Concept Maps coming from different students, also by means of an automated computation of map similarity, can reveal to be a great asset for a teacher. And this is so much more true when the number of students is very high, like in Massive Open Online Course. Here we propose a similarity measure based on two deep learning techniques that produce embeddings of the single structures that make up a concept map. We also report about a preliminary experiment, having encouraging results.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122717624","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00051
Yuki Nakai, T. Itoh
Lyrics are one of the most important components of music, and they have a great impact on the appreciation of songs such as J-POP. Therefore, it is useful to classify and search songs based on lyrics. However, the impression of lyrics is subjective and may be influenced by musical elements other than lyrics, so the criteria for searching for lyrics required by users may vary from person to person. To address this issue, we are working on a research project to support active lyric search by visualizing the distribution of lyrics. Here, it is often difficult to appropriately calculate the distribution of the lyrics because lyrics have a higher degree of lexical freedom than articles and papers. In this study, we propose a method to visualize the distribution of lyrics calculated applying guided LDA (Latent Dirichlet Allocation) that interactively consumes guided words. This method facilitates the iterative visualization of lyric classification results based on the users' viewpoints. It also makes it possible to search for songs by focusing only on lyrics without taking other musical elements into account. Users can observe the differences in individuality and tendency of songs and artists, and the diversity of lyrics, by using the visualization results.
{"title":"Classification and Visualization of Lyric Collections Using Guided LDA","authors":"Yuki Nakai, T. Itoh","doi":"10.1109/IV56949.2022.00051","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00051","url":null,"abstract":"Lyrics are one of the most important components of music, and they have a great impact on the appreciation of songs such as J-POP. Therefore, it is useful to classify and search songs based on lyrics. However, the impression of lyrics is subjective and may be influenced by musical elements other than lyrics, so the criteria for searching for lyrics required by users may vary from person to person. To address this issue, we are working on a research project to support active lyric search by visualizing the distribution of lyrics. Here, it is often difficult to appropriately calculate the distribution of the lyrics because lyrics have a higher degree of lexical freedom than articles and papers. In this study, we propose a method to visualize the distribution of lyrics calculated applying guided LDA (Latent Dirichlet Allocation) that interactively consumes guided words. This method facilitates the iterative visualization of lyric classification results based on the users' viewpoints. It also makes it possible to search for songs by focusing only on lyrics without taking other musical elements into account. Users can observe the differences in individuality and tendency of songs and artists, and the diversity of lyrics, by using the visualization results.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116075867","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00013
R. S. Vieira, H. A. D. Nascimento, J. M. Ferreira, L. Foulds
One of the commonly used techniques to improve the readability of large graph drawings is called edge bundling, which groups edges in such a way that reduces the visual complexity of the drawing. This paper proposes to treat this task as a clustering problem, using compatibility metrics to evaluate the generated solutions in an optimization pipeline, combined with a clustering ensemble approach. The goal was to solve the General-based Edge Bundling (GBEB) problem with relatively low computational costs using a method called Clustering Ensemble-based Edge Bundling (CEBEB) and evaluate the results. CEBEB proved to be a very promising alternative to solve GBEB, since it is capable of generating relatively good solutions with shorter run-times compared to an existing, well-established GBEB method.
{"title":"Clustering Ensemble-based Edge Bundling to Improve the Readability of Graph Drawings","authors":"R. S. Vieira, H. A. D. Nascimento, J. M. Ferreira, L. Foulds","doi":"10.1109/IV56949.2022.00013","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00013","url":null,"abstract":"One of the commonly used techniques to improve the readability of large graph drawings is called edge bundling, which groups edges in such a way that reduces the visual complexity of the drawing. This paper proposes to treat this task as a clustering problem, using compatibility metrics to evaluate the generated solutions in an optimization pipeline, combined with a clustering ensemble approach. The goal was to solve the General-based Edge Bundling (GBEB) problem with relatively low computational costs using a method called Clustering Ensemble-based Edge Bundling (CEBEB) and evaluate the results. CEBEB proved to be a very promising alternative to solve GBEB, since it is capable of generating relatively good solutions with shorter run-times compared to an existing, well-established GBEB method.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216588","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00053
Andrea Rocco Racca, Emilio Sulis, Sara Capecchi
This paper introduces an experiment and the first results of a research on computer programming education using process mining methods. A web-based tutorial addresses the topic of agent-based modeling by introducing a guided exercise with NetLogo, a widely used tool for modeling natural and social phenomena. A goal of the project is to analyze the goodness of the learning process, also through appropriate tests placed between the pages and at the end of the tutorial. Actual data extracted on student behavior (e.g., length of time spent on different parts of each web page, movements on the page, mouse position and mouse clicks) are examined using process discovery technique. Special attention is given to the return of student learning outcomes through visualization. Our solution includes heatmaps and direct-follow graphs of the real processes. Initial results are encouraging on the possibility of improving the assessment of learning processes by relying on techniques from the discipline of process mining, as shown by the case of web-based behaviour tracking data.
{"title":"Behavioral Web Tracking in e-Learning: An Educational Process Mining Application","authors":"Andrea Rocco Racca, Emilio Sulis, Sara Capecchi","doi":"10.1109/IV56949.2022.00053","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00053","url":null,"abstract":"This paper introduces an experiment and the first results of a research on computer programming education using process mining methods. A web-based tutorial addresses the topic of agent-based modeling by introducing a guided exercise with NetLogo, a widely used tool for modeling natural and social phenomena. A goal of the project is to analyze the goodness of the learning process, also through appropriate tests placed between the pages and at the end of the tutorial. Actual data extracted on student behavior (e.g., length of time spent on different parts of each web page, movements on the page, mouse position and mouse clicks) are examined using process discovery technique. Special attention is given to the return of student learning outcomes through visualization. Our solution includes heatmaps and direct-follow graphs of the real processes. Initial results are encouraging on the possibility of improving the assessment of learning processes by relying on techniques from the discipline of process mining, as shown by the case of web-based behaviour tracking data.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114687453","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00036
Megumi Kawase, M. Nakayama
An emotional intensity regression estimation model was created using calculated perceived intensity values and deep learning. In our previous study, we considered emotional intensity using 10 categories and estimated emotional intensity by categorization, but the flexibility of the method was insufficient. In order to solve this problem, an emotional intensity estimation model which takes into account differences in the perceptual intensity value of each category of emotional intensity was used in this study. For this purpose, two types of perceived intensity values were calculated for a Japanese speech corpus of sounds uttered in an emotion perception rating experiment. In the results, the average correlation coefficient between the estimated intensity value and the set intensity value of the sounds was 0.73 for the emotional intensity estimation model when perceived intensity values were used. These results suggest the possibility of successfully estimating emotional intensity using regression.
{"title":"Regression estimation model for emotion and intensity of speech using perception rating","authors":"Megumi Kawase, M. Nakayama","doi":"10.1109/IV56949.2022.00036","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00036","url":null,"abstract":"An emotional intensity regression estimation model was created using calculated perceived intensity values and deep learning. In our previous study, we considered emotional intensity using 10 categories and estimated emotional intensity by categorization, but the flexibility of the method was insufficient. In order to solve this problem, an emotional intensity estimation model which takes into account differences in the perceptual intensity value of each category of emotional intensity was used in this study. For this purpose, two types of perceived intensity values were calculated for a Japanese speech corpus of sounds uttered in an emotion perception rating experiment. In the results, the average correlation coefficient between the estimated intensity value and the set intensity value of the sounds was 0.73 for the emotional intensity estimation model when perceived intensity values were used. These results suggest the possibility of successfully estimating emotional intensity using regression.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127538940","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00050
Aleksandra Maslennikova, D. Rotelli, A. Monreale
Due to the flexibility of online learning courses, students organise and manage their own learning time by deciding when, what, and how to study. Each individual has distinctive learning habits that identify their behaviours and set them apart from others. To explore how students behave over time, in this work we seek to identify adequate time-windows that could be used to investigate the temporal behaviour of students in online learning environments. We first propose a novel perspective to identify various types of sessions based on individual requirements. Most of the works in the literature address this problem by setting an arbitrary session timeout threshold. In this paper we propose an algorithm that helps us in determining the most suitable threshold for the session. Then, based on the identified sessions, we determine time-windows using data-driven methods. To this end, we created a visual tool that assists data scientists and researchers in determining the optimal settings for the session identification and locating suitable time-windows.
{"title":"Visual Analytics for Session-based Time-Windows Identification in Virtual Learning Environments","authors":"Aleksandra Maslennikova, D. Rotelli, A. Monreale","doi":"10.1109/IV56949.2022.00050","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00050","url":null,"abstract":"Due to the flexibility of online learning courses, students organise and manage their own learning time by deciding when, what, and how to study. Each individual has distinctive learning habits that identify their behaviours and set them apart from others. To explore how students behave over time, in this work we seek to identify adequate time-windows that could be used to investigate the temporal behaviour of students in online learning environments. We first propose a novel perspective to identify various types of sessions based on individual requirements. Most of the works in the literature address this problem by setting an arbitrary session timeout threshold. In this paper we propose an algorithm that helps us in determining the most suitable threshold for the session. Then, based on the identified sessions, we determine time-windows using data-driven methods. To this end, we created a visual tool that assists data scientists and researchers in determining the optimal settings for the session identification and locating suitable time-windows.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124494610","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00063
D. Kyselica, S. Krajcovic, J. Silha, R. Ďurikovič
During astronomical observations, images of selected part of the sky are made by the Slovak 70cm telescope specialized on space debris tracking. Every pixel of this frame can be represented by three data: position on the horizontal $X$ axis, vertical $Y$ axis, respectively and the intensity value that can range from 0 to 65536. The intensity value in the order of thousands or higher indicates presence of an orbital or extraterrestrial object such as a star, planet, space debris, or even electromagnetic field interference, celestial plane background and other artefacts. In this paper, we present the methodology and proof of concept of our design for processing of astronomical images and a novel space debris tracklet building process using a machine learning method by exploiting Long Short Term Memory (LSTM) architectures. Machine learning models need a fair amount of data examples for training. However, there are not enough sequences captured by the telescope, therefore we train a neural network with synthetic artificial training data based on known sky observations. Information about moving objects in the Earth's orbit is visualized as sequences of positions in time.
{"title":"Advanced Algorithms for Segmentation of Space Debris Astronomical Images","authors":"D. Kyselica, S. Krajcovic, J. Silha, R. Ďurikovič","doi":"10.1109/IV56949.2022.00063","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00063","url":null,"abstract":"During astronomical observations, images of selected part of the sky are made by the Slovak 70cm telescope specialized on space debris tracking. Every pixel of this frame can be represented by three data: position on the horizontal $X$ axis, vertical $Y$ axis, respectively and the intensity value that can range from 0 to 65536. The intensity value in the order of thousands or higher indicates presence of an orbital or extraterrestrial object such as a star, planet, space debris, or even electromagnetic field interference, celestial plane background and other artefacts. In this paper, we present the methodology and proof of concept of our design for processing of astronomical images and a novel space debris tracklet building process using a machine learning method by exploiting Long Short Term Memory (LSTM) architectures. Machine learning models need a fair amount of data examples for training. However, there are not enough sequences captured by the telescope, therefore we train a neural network with synthetic artificial training data based on known sky observations. Information about moving objects in the Earth's orbit is visualized as sequences of positions in time.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132660699","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}
Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00034
Mariko Sasakura, Syouta Toda, Akito Monden
The ability to see and find things is very important in our daily lives. For example, when looking for mistakes in debugging a program, or when looking for misspellings in documents, etc., the visual sense is mainly used. The search may or may not be successful. Is there any difference in the way of searching when the search is successful or unsuccessful? The aim of this study is to analyse the gaze while searching and to clarify the differences between successful and unsuccessful searches, using ‘spot the difference’ as a subject. We have developed an experimental application to measure people's gaze while they are looking ‘spot the difference'. In the experiment conducted in this study, 29 subjects have asked to perform ‘spot the difference’ of multiple problems and their gaze have been measured. Analysis of the data obtained from this experiment shows that in many cases, subjects who could not find a difference were not looking at the location of the difference. On the other hand, the existence of ‘cases of looking but not finding’, in which the difference is not detected even though the difference is fully looked at, is also identified. In the present experiment, ‘looking but not finding’ cases account for 15% of all non-correct responses in all questions.
{"title":"Gaze Analysis in Spot the Difference","authors":"Mariko Sasakura, Syouta Toda, Akito Monden","doi":"10.1109/IV56949.2022.00034","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00034","url":null,"abstract":"The ability to see and find things is very important in our daily lives. For example, when looking for mistakes in debugging a program, or when looking for misspellings in documents, etc., the visual sense is mainly used. The search may or may not be successful. Is there any difference in the way of searching when the search is successful or unsuccessful? The aim of this study is to analyse the gaze while searching and to clarify the differences between successful and unsuccessful searches, using ‘spot the difference’ as a subject. We have developed an experimental application to measure people's gaze while they are looking ‘spot the difference'. In the experiment conducted in this study, 29 subjects have asked to perform ‘spot the difference’ of multiple problems and their gaze have been measured. Analysis of the data obtained from this experiment shows that in many cases, subjects who could not find a difference were not looking at the location of the difference. On the other hand, the existence of ‘cases of looking but not finding’, in which the difference is not detected even though the difference is fully looked at, is also identified. In the present experiment, ‘looking but not finding’ cases account for 15% of all non-correct responses in all questions.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134314937","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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