Pub Date : 2022-07-01DOI: 10.1109/IV56949.2022.00054
V. Albano, D. Firmani, L. Laura, Anna Lucia Paoletti, Irene Torrente
Knowledge assessment in online platforms is widely based on multiple-choice questions (MCQs). In this paper we describe our proposal for a NLP-based system designed to support the management of large repositories of MCQs. Indeed, within large repositories of MCQs, it is common to have similar if not almost duplicated questions, and coping with them is a time consuming and error prone task. We propose an approach, based on Natural Language Processing (NLP), that i) computes the similarity between the items and ii) checks the similarity between the questions and, if available, the areas of the syllabus. The results of the analysis are also displayed in a graph (i.e. network) based view, providing a clear picture to the user.
{"title":"Managing Large Multiple-choice Test Items Repositories","authors":"V. Albano, D. Firmani, L. Laura, Anna Lucia Paoletti, Irene Torrente","doi":"10.1109/IV56949.2022.00054","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00054","url":null,"abstract":"Knowledge assessment in online platforms is widely based on multiple-choice questions (MCQs). In this paper we describe our proposal for a NLP-based system designed to support the management of large repositories of MCQs. Indeed, within large repositories of MCQs, it is common to have similar if not almost duplicated questions, and coping with them is a time consuming and error prone task. We propose an approach, based on Natural Language Processing (NLP), that i) computes the similarity between the items and ii) checks the similarity between the questions and, if available, the areas of the syllabus. The results of the analysis are also displayed in a graph (i.e. network) based view, providing a clear picture to the user.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"155 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":"125228625","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/iv56949.2022.00007
{"title":"Organizing Committee: IV 2022","authors":"","doi":"10.1109/iv56949.2022.00007","DOIUrl":"https://doi.org/10.1109/iv56949.2022.00007","url":null,"abstract":"","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"34 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":"125912425","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/IV56949.2022.00059
Lennart B. Sina, Kawa Nazemi
Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.
{"title":"Visual Analytics for Systematic Reviews According to PRISMA","authors":"Lennart B. Sina, Kawa Nazemi","doi":"10.1109/IV56949.2022.00059","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00059","url":null,"abstract":"Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"92 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":"124644404","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/IV56949.2022.00047
M. Nakayama, Satoru Kikuchi, Hiroh Yamamoto
Sentences in student's essay reports were analysed to examine the feasibility of evaluating ability of critical thinking disposition in order to develop this ability during a fully online course in a university class. Features of essay reports, dependency of terms, and the representation of selected terms between levels of critical thinking disposition which were measured using a questionnaire were compared. The results show that the frequency of dependencies reflects the level of factor scores of critical thinking disposition and that multi-dimensional scales of these frequencies can illustrate the relationships between dependencies and levels of ability. Also, senses which contained affirmative or negative contents are evaluated using a corpus of affirmative and negative words used in essay reports. These frequencies are influenced by the level of ability as well.
{"title":"Phrase Features in Essay Report Sentences for Developing Critical Thinking Ability in a Fully Online Course","authors":"M. Nakayama, Satoru Kikuchi, Hiroh Yamamoto","doi":"10.1109/IV56949.2022.00047","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00047","url":null,"abstract":"Sentences in student's essay reports were analysed to examine the feasibility of evaluating ability of critical thinking disposition in order to develop this ability during a fully online course in a university class. Features of essay reports, dependency of terms, and the representation of selected terms between levels of critical thinking disposition which were measured using a questionnaire were compared. The results show that the frequency of dependencies reflects the level of factor scores of critical thinking disposition and that multi-dimensional scales of these frequencies can illustrate the relationships between dependencies and levels of ability. Also, senses which contained affirmative or negative contents are evaluated using a corpus of affirmative and negative words used in essay reports. These frequencies are influenced by the level of ability as well.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"65 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114011345","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/IV56949.2022.00078
Aymen Afli, Nessrine Elloumi, Aicha Ben Makhlouf, B. Louhichi, M. Jaidane, J. M. R. Tavares
With the emergence of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), three-dimensional images facilitate the generation of 3D models of a patient, providing a new practical and accurate assistance, particularly for surgical planning. These images can be manipulated to produce an accurate 3D representation of an organ. The reconstructed mesh can be used to generate and visualize a deformable model during surgical intervention using Augmented Reality (AR) technology. To obtain an efficient reconstruction, a segmentation of these medical images using deep learning architecture can be used to extract the target organ's properties. Many methods were proposed based on the captured pre-operative patient's CT scans. Generally, the segmentation process is done manually using image processing software. In this context several approaches were proposed, these methods are not efficient and need human interaction to select the segmentation area correctly. This work aims to develop a deep learning method using a Convolutional Neural Network (CNN) that captures the liver organ from a set of CT scans. Given preoperative patient-specific data (CT scans), the U-net architecture is implemented to detect the liver organ. As a result, the segmented 2D images are used to generate a 3D patient-specific liver model.
{"title":"Preoperative Image Segmentation for Organ Visualization Using Augmented Reality Technology During Open Liver Surgery","authors":"Aymen Afli, Nessrine Elloumi, Aicha Ben Makhlouf, B. Louhichi, M. Jaidane, J. M. R. Tavares","doi":"10.1109/IV56949.2022.00078","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00078","url":null,"abstract":"With the emergence of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), three-dimensional images facilitate the generation of 3D models of a patient, providing a new practical and accurate assistance, particularly for surgical planning. These images can be manipulated to produce an accurate 3D representation of an organ. The reconstructed mesh can be used to generate and visualize a deformable model during surgical intervention using Augmented Reality (AR) technology. To obtain an efficient reconstruction, a segmentation of these medical images using deep learning architecture can be used to extract the target organ's properties. Many methods were proposed based on the captured pre-operative patient's CT scans. Generally, the segmentation process is done manually using image processing software. In this context several approaches were proposed, these methods are not efficient and need human interaction to select the segmentation area correctly. This work aims to develop a deep learning method using a Convolutional Neural Network (CNN) that captures the liver organ from a set of CT scans. Given preoperative patient-specific data (CT scans), the U-net architecture is implemented to detect the liver organ. As a result, the segmented 2D images are used to generate a 3D patient-specific liver model.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"47 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":"114016068","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/IV56949.2022.00028
Shane Sheehan, M. Masoodian, S. Luz
Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of automated and semi-automated tools to analyze the content of text corpora. Concordancing - or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords - is one of the oldest and still most widely used forms of text analysis. Concordance Mosaic is an interactive concordance visualization which emphasises quantitative information such as word frequency. While Concordance Mosaic is in active use by humanities scholars, no quantitative evaluation of the technique exists. In this paper, the Concordance Mosaic is quantitatively evaluated in comparison to a typical concordance browser. The comparison is evaluated using speed and accuracy on identified corpus analysis actions.
{"title":"Task-based Quantitative Evaluation of the Concordance Mosaic Visualization","authors":"Shane Sheehan, M. Masoodian, S. Luz","doi":"10.1109/IV56949.2022.00028","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00028","url":null,"abstract":"Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of automated and semi-automated tools to analyze the content of text corpora. Concordancing - or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords - is one of the oldest and still most widely used forms of text analysis. Concordance Mosaic is an interactive concordance visualization which emphasises quantitative information such as word frequency. While Concordance Mosaic is in active use by humanities scholars, no quantitative evaluation of the technique exists. In this paper, the Concordance Mosaic is quantitatively evaluated in comparison to a typical concordance browser. The comparison is evaluated using speed and accuracy on identified corpus analysis actions.","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":"127563737","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/IV56949.2022.00031
Andreas Stöckl
In this work we investigate the possibilities of integrating a Deep Learning language model for a Natural Language Interface (NLI) of an information visualisation software. For this purpose, we have developed a prototype web application that uses the deep learning model OpenAI Codex from the GPT3 family to create visualisations from text input. For comparison, we created a second prototype with a classical NLP approach based on NL4DV toolkit (with subtasks like part-of-speech (POS) tagging, entity recognition, and dependency parsing) and an almost identical interface. The two variants were subjected to a study with test persons, and the advantages and disadvantages of the two approaches and the suitability for the most common visualisation types were investigated. The Deep Learning approach offers greater expressiveness for describing the graphics, but also the danger of not always being entirely comprehensible. The participants were able to use it to create more complex visualisations, but also sometimes had problems finding the right text input to solve the tasks. In our preliminary usability study, the Deep Learning prototype performed slightly better than the comparison prototype and achieved a useful usability score.
{"title":"Natural Language Interface for Data Visualization with Deep Learning Based Language Models","authors":"Andreas Stöckl","doi":"10.1109/IV56949.2022.00031","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00031","url":null,"abstract":"In this work we investigate the possibilities of integrating a Deep Learning language model for a Natural Language Interface (NLI) of an information visualisation software. For this purpose, we have developed a prototype web application that uses the deep learning model OpenAI Codex from the GPT3 family to create visualisations from text input. For comparison, we created a second prototype with a classical NLP approach based on NL4DV toolkit (with subtasks like part-of-speech (POS) tagging, entity recognition, and dependency parsing) and an almost identical interface. The two variants were subjected to a study with test persons, and the advantages and disadvantages of the two approaches and the suitability for the most common visualisation types were investigated. The Deep Learning approach offers greater expressiveness for describing the graphics, but also the danger of not always being entirely comprehensible. The participants were able to use it to create more complex visualisations, but also sometimes had problems finding the right text input to solve the tasks. In our preliminary usability study, the Deep Learning prototype performed slightly better than the comparison prototype and achieved a useful usability score.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"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":"117188102","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/IV56949.2022.00021
Alexandre Abreu de Freitas, Walbert Cunha Monteiro, Thiago Augusto Soares de Sousa, V. F. Queiroz, Tiago Araújo, B. Meiguins
The process of creating physical data visualizations is not a trivial task. In general, it may require skills from the user in information visualization, tangible interaction, 3D modeling, fabrication of physical objects, etc. In addition, few works have presented computational support to the entire digital and physical rendering pipeline of data visualization, characterizing many steps of this process as manual. From this context, this work presents a process that facilitates the generation of physical data visualization. Besides that, It allows one to define which type of physical visualization to create, among passive, rearrangeable, and dynamic physicalization types. Finally, the pipelines for each physicalization type are presented, with scenarios including physical bar charts, stacked bar charts, and grouped bar charts.
{"title":"A Flexible Pipeline to Create Different Types of Data Physicalizations","authors":"Alexandre Abreu de Freitas, Walbert Cunha Monteiro, Thiago Augusto Soares de Sousa, V. F. Queiroz, Tiago Araújo, B. Meiguins","doi":"10.1109/IV56949.2022.00021","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00021","url":null,"abstract":"The process of creating physical data visualizations is not a trivial task. In general, it may require skills from the user in information visualization, tangible interaction, 3D modeling, fabrication of physical objects, etc. In addition, few works have presented computational support to the entire digital and physical rendering pipeline of data visualization, characterizing many steps of this process as manual. From this context, this work presents a process that facilitates the generation of physical data visualization. Besides that, It allows one to define which type of physical visualization to create, among passive, rearrangeable, and dynamic physicalization types. Finally, the pipelines for each physicalization type are presented, with scenarios including physical bar charts, stacked bar charts, and grouped bar charts.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"1 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":"116479466","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/iv56949.2022.00009
{"title":"D-Art Gallery: IV 2022","authors":"","doi":"10.1109/iv56949.2022.00009","DOIUrl":"https://doi.org/10.1109/iv56949.2022.00009","url":null,"abstract":"","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"28 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":"122182590","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/IV56949.2022.00032
Laine G. Jeston-Fenton, Shadi Abpeikar, Kathryn E. Kasmarik
Swarming robots have the potential to perform many different tasks like coverage, exploration, and navigation in industry, healthcare, military and transportation. However, human control of large numbers of robots is difficult. It is not yet clear which metrics may be beneficial for human-swarm interaction or how to display them. This paper presents an Android platform for permitting human-swarm interaction, while also displaying metrics describing the swarm behavior. The visualization includes charts to represent the changes in boids' grouping, alignment, fragmentation, and coverage metrics. Experiments show how the visualization responds to different behaviors of swarm triggered by human interaction.
{"title":"Visualisation of Swarm Metrics on a Handheld Device for Human-Swarm Interaction","authors":"Laine G. Jeston-Fenton, Shadi Abpeikar, Kathryn E. Kasmarik","doi":"10.1109/IV56949.2022.00032","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00032","url":null,"abstract":"Swarming robots have the potential to perform many different tasks like coverage, exploration, and navigation in industry, healthcare, military and transportation. However, human control of large numbers of robots is difficult. It is not yet clear which metrics may be beneficial for human-swarm interaction or how to display them. This paper presents an Android platform for permitting human-swarm interaction, while also displaying metrics describing the swarm behavior. The visualization includes charts to represent the changes in boids' grouping, alignment, fragmentation, and coverage metrics. Experiments show how the visualization responds to different behaviors of swarm triggered by human interaction.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"35 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":"129513282","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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