| 1. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Empirical Study : Visual Analytics for Comparing Stacking to Blending Ensemble Learning
- 2021
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Ingår i: Proceedings of the 23rd International Conference on Control Systems and Computer Science (CSCS23), 26–28 May 2021, Bucharest, Romania. - : IEEE. - 9781665439404 - 9781665439398 ; , s. 1-8
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Stacked generalization (also called stacking) is an ensemble method in machine learning that uses a metamodel to combine the predictive results of heterogeneous base models arranged in at least one layer. K-fold cross-validation is employed at the various stages of training in this method. Nonetheless, another validation strategy is to try out several splits of data leading to different train and test sets for the base models and then use only the latter to train the metamodel—this is known as blending. In this work, we present a modification of an existing visual analytics system, entitled StackGenVis, that now supports the process of composing robust and diverse ensembles of models with both aforementioned methods. We have built multiple ensembles using our system with the two respective methods, and we tested the performance with six small- to large-sized data sets. The results indicate that stacking is significantly more powerful than blending based on three performance metrics. However, the training times of the base models and the final ensembles are lower and more stable during various train/test splits in blending rather than stacking.
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| 2. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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FeatureEnVi : Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
- 2022
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 28:4, s. 1773-1791
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Tidskriftsartikel (refereegranskat)abstract
- The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
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| 3. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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StackGenVis : Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
- 2021
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE Computer Society Digital Library. - 1077-2626 .- 1941-0506. ; 27:2, s. 1547-1557
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Tidskriftsartikel (refereegranskat)abstract
- In machine learning (ML), ensemble methods—such as bagging, boosting, and stacking—are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called "stacked generalization") is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and then employs another metamodel to summarize the predictions of those models. Although it may be a highly-effective approach for increasing the predictive performance of ML, generating a stack of models from scratch can be a cumbersome trial-and-error process. This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to various metrics. In this work, we present a knowledge generation model, which supports ensemble learning with the use of visualization, and a visual analytics system for stacked generalization. Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance. In consequence, our proposed tool helps users to decide between distinct models and to reduce the complexity of the resulting stack by removing overpromising and underperforming models. The applicability and effectiveness of StackGenVis are demonstrated with two use cases: a real-world healthcare data set and a collection of data related to sentiment/stance detection in texts. Finally, the tool has been evaluated through interviews with three ML experts.
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| 4. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
- 2020
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 39:3, s. 713-756
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
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| 5. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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VisEvol : Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
- 2021
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 40:3, s. 201-214
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Tidskriftsartikel (refereegranskat)abstract
- During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
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| 6. |
- Chatzimparmpas, Angelos, 1994-, et al.
(författare)
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Visualization for Trust in Machine Learning Revisited : The State of the Field in 2023
- 2024
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Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 44:3, s. 99-113
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Tidskriftsartikel (refereegranskat)abstract
- Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance.
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| 7. |
- Fatemi, Masoud, 1990-, et al.
(författare)
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Self-Similarity of Twitter Users
- 2021
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Ingår i: Proceedings of the 2021 Swedish Workshop on Data Science (SweDS). - : IEEE. - 9781665418300
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Konferensbidrag (refereegranskat)abstract
- Earlier studies have established that the (perceived) similarity of users is highly subjective and reflects more on how people respect/admire others rather than their characteristics or behavioral similarities. We study this phenomenon among Twitter users, and while confirm that it is indeed the case, we further explore the components of similarity by investigating it using data from three categories (interactions between egos and alters, profile-based activity history, and linguistic content in the messages). We use interactions as estimation for admiration and observe that it has more impact and a higher correlation to the perceived similarity than other objective measures, including similarity based on user profiles and their use of hashtags.
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| 8. |
- Huang, Zeyang, 1998-, et al.
(författare)
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Towards a Visual Analytics System for Emotion Trajectories in Multiparty Conversations
- 2024
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Ingår i: Poster Proceedings of the 26th Eurographics Conference on Visualization (EuroVis 2024 Posters). - : Eurographics - European Association for Computer Graphics. - 9783038682585
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Konferensbidrag (refereegranskat)abstract
- Visualizing sentiments in textual data has received growing interest; however, representing emotions within interlocutor relationships and associating them with the temporal progression of dialogues remains challenging. In this poster abstract, we describe the ongoing work on a visual analytics tool designed for analyzing emotion trajectories within dialogue collections composed of utterances from multiple speakers. The proposed tool provides exploration at different levels of detail to complex multigraphs, where edges represent direct responses between speakers through their utterances. Our approach includes several selection strategies for connecting different views: summaries of emotion transitions across dialogue groups, detailed analyses of individual utterances within specific dialogues of interest in interlocutor networks, and close reading. The tool aims to support model development in natural language processing by allowing users to explore text corpora interactively.
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| 9. |
- Huang, Zeyang, 1998-, et al.
(författare)
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Towards an Exploratory Visual Analytics System for Multivariate Subnetworks in Social Media Analysis
- 2022
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Ingår i: Poster Abstracts, IEEE Visualization and Visual Analytics (VIS '22). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Identifying sociolinguistic attributes of inter-community interactions is essential for understanding the polarization of social network communities. A wide range of computational text and network analysis methods may be applicable for this task, however, interpretation of the respective results and investigation of particularly interesting cases and subnetworks are difficult due to the scale and complexity of the data, e.g., for the Reddit platform. In this poster paper, we present an interactive visual analysis interface that facilitates network exploration and comparison at different topological and multivariate attribute scales. Users are able to investigate text- and network-based properties of social network community interactions, identify anomalies of conflict starters, or gain insight into multivariate anomalies behind groups of negative social media posts.
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| 10. |
- Huang, Zeyang, 1998-, et al.
(författare)
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VA + Embeddings STAR : A State-of-the-Art Report on the Use of Embeddings in Visual Analytics
- 2023
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 42:3, s. 539-571
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Tidskriftsartikel (refereegranskat)abstract
- Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.
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| 11. |
- Kerren, Andreas, 1971-, et al.
(författare)
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BioVis Explorer : A visual guide for biological data visualization techniques
- 2017
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Ingår i: PLOS ONE. - : PLOS. - 1932-6203. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.
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| 12. |
- Kerren, Andreas, 1971-, et al.
(författare)
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MDS-based Visual Survey of Biological Data Visualization Techniques
- 2017
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Ingår i: EuroVis 2017 - Posters. - : Eurographics - European Association for Computer Graphics. - 9783038680444 ; , s. 85-87
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Konferensbidrag (refereegranskat)abstract
- Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for biological data visualization have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers as well as visualization researchers. To address this challenge, we have reviewed visualization research for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods.
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| 13. |
- Kopacheva, Elizaveta, et al.
(författare)
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Using Social-Media-Network Ties for Predicting Intended Protest Participation in Russia
- 2023
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Ingår i: Online Social Networks and Media. - : Elsevier. - 2468-6964. ; 37-38
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Tidskriftsartikel (refereegranskat)abstract
- Previous research has highlighted the importance of network structures in information diffusion on social media. In this study, we explore the role of an individual’s social network structure in predicting publicly announced intention of protest participation. Using the case of ecological protests in Russia and applying machine learning to publicly-available VKontakte data, we classify users into protesters and non-protesters. We have found that personal social networks have a high predictive power allowing user classification with an accuracy of 81%. Meanwhile, using all public VKontakte data, including memberships in activist groups and friendship ties to protesters, we were able to classify users into protesters and non-protesters with a higher accuracy of 96%. Our study contributes to the political-participation literature by demonstrating the importance of personal social networks in predicting protest participation. Our results suggest that in some cases, the likelihood of participating in protests can be significantly influenced by elements of a personal-network structure, inter alia, network density and size. Further explanatory research should be done to explore the mechanisms underlying these relationships.
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| 14. |
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| 15. |
- Kucher, Kostiantyn, et al.
(författare)
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Active learning and visual analytics for stance classification with ALVA
- 2017
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Ingår i: ACM Transactions on Interactive Intelligent Systems. - New York, NY, USA : Association for Computing Machinery. - 2160-6455 .- 2160-6463. ; 7:3
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Tidskriftsartikel (refereegranskat)abstract
- The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers' attitudes toward their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics to improve the understanding of stance phenomena and to build a st ance classifier for applications such as social media monitoring.
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| 16. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper
- 2022
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Ingår i: Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond — Methodological Approaches to Visualization (BELIV '22). - : IEEE. - 9798350396294 - 9798350396300 ; , s. 28-37
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Konferensbidrag (refereegranskat)abstract
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.
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| 17. |
- Kucher, Kostiantyn, et al.
(författare)
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An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond com- putational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and “big picture” concerns) and provide suggestions for research oppor- tunities from an interdisciplinary perspective.
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| 18. |
- Kucher, Kostiantyn, et al.
(författare)
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Analysis of VINCI 2009–2017 Proceedings
- 2018
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Ingår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 97-101
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Konferensbidrag (refereegranskat)abstract
- Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.
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| 19. |
- Kucher, Kostiantyn, et al.
(författare)
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Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
- 2018
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Ingår i: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18). - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 102-103
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Konferensbidrag (refereegranskat)abstract
- The analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, stances, arguments, topics, terms, and text documents are usually connected with many-to-many relationships for such tasks. Exploratory visual analysis with interactive tools can help the analysts to get an overview of the topics and opinions, identify particularly interesting documents, and describe main themes of various arguments. In our previous work, we introduced an interactive tool called Topics2Themes that was used for topic and theme analysis of vaccination-related discussion texts with a limited set of stance categories. In this poster paper, we describe an application of Topics2Themes to a different genre of data, namely, political comments from Reddit, and multiple sentiment and stance categories detected with automatic classifiers.
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| 20. |
- Kucher, Kostiantyn, et al.
(författare)
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DoSVis : Document Stance Visualization
- 2018
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Ingår i: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18). - : SciTePress. - 9789897582899 ; , s. 168-175
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Konferensbidrag (refereegranskat)abstract
- Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer’s attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature.
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| 21. |
- Kucher, Kostiantyn, et al.
(författare)
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Methodology and Applications of Visual Stance Analysis : An Interactive Demo
- 2016
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Ingår i: International Symposium on Digital Humanities, Växjö 7-8 November 2016. - : Linnaeus University. ; , s. 56-57
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Konferensbidrag (refereegranskat)abstract
- Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages.
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| 22. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
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Project in Visualization and Data Analysis : Experiences in Designing and Coordinating the Course
- 2021
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Ingår i: Proceedings of 42nd Annual Conference of the European Association for Computer Graphics (EG '21) — Education Papers. - : Eurographics - European Association for Computer Graphics. - 9783038681328 ; , s. 39-44
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Konferensbidrag (refereegranskat)abstract
- Visual analytics involves both visual and computational components for empowering human analysts who face the challenges of making sense and making use of large and heterogeneous data sets in various application domains. In order to facilitate the learning process for the students at higher education institutions with regard to both the theoretical knowledge and practical skills in visual analytics, the respective courses must cover a variety of topics and include multiple assessment methods and activities. In this paper, we report on the design and first instantiation of a full term project-based course in visualization and data analysis, which was recently offered to graduate and post-graduate students at our department and met with positive feedback from the course participants.
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| 23. |
- Kucher, Kostiantyn, 1989-
(författare)
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Sentiment and Stance Visualization of Textual Data for Social Media
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media platforms typically rely on textual data produced or shared by the users in multiple timestamped posts. Analyses of such data are challenging for traditional manual methods that are unable to scale up to the volume and the variety of the data. While computational methods can partially address these challenges, they have to be used together with the methods developed within information visualization and visual analytics to gain knowledge from the text data by using interactive visual representations.One of the most interesting aspects of text data is related to expressions of sentiments and opinions. The corresponding task of sentiment analysis has been studied within computational linguistics, and sentiment visualization techniques exist as well. However, there are gaps in research on the related task of stance analysis, dedicated to subjectivity that is not expressible only in terms of sentiment. Research on stance is an area of interest in linguistics, but support by computational and visual methods has been limited so far. The challenges related to definition, analysis, and visualization of stance in textual data call for an interdisciplinary research effort. The StaViCTA project addressed these challenges with a focus on written text in English. The corresponding results in the area of visualization are reported in this work, based on multiple publications.The main goal of this dissertation is to define, categorize, and implement means for visual analysis of sentiment and stance in textual data, in particular, for social media. Our work is based on the theoretical framework and automatic classifier of stance developed by our project collaborators, involving multiple non-exclusive stance categories such as certainty and prediction. We define a design space for sentiment and stance visualization techniques based on literature surveys. We discuss multiple visualization and visual analytics approaches developed by us to facilitate the underlying research on stance analysis, data collection and annotation, and visual analysis of sentiment and stance in real-world text data from several social media sources. The work described in this dissertation was carried out in cooperation with domain experts in linguistics and computational linguistics, and our approaches were validated with case studies, expert user reviews, and critical discussion. The results of this work open up further opportunities for research in text visualization and visual text analytics. The potential application areas are academic research, business intelligence, social media monitoring, and journalism.
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| 24. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
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StanceVis Prime : Visual Analysis of Sentiment and Stance in Social Media Texts
- 2020
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Ingår i: Journal of Visualization. - : Springer. - 1343-8875 .- 1875-8975. ; 23:6, s. 1015-1034
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Tidskriftsartikel (refereegranskat)abstract
- Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest for this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by existing approaches. The challenges associated with this problem include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.
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| 25. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Supporting University Research and Administration via Interactive Visual Exploration of Bibliographic Data
- 2023
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Ingår i: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '23). - : SciTePress. - 9789897586347 ; , s. 248-255
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Konferensbidrag (refereegranskat)abstract
- Bibliographic data and bibliometric analyses play an important role in the professional life of academic researchers, and the quality of the respective publication records is essential for establishing the big picture of the relationships between particular publications, their authors and affiliations, or further data facets associated with publications. In this paper, we report on the design and outcomes of an interactive visual data exploration project conducted within the scope of a university with the goal of gaining overview of the university publication data. The project has been carried out by information visualization researchers in collaboration with several groups of stakeholders, including the university library and administration staff. We describe the design considerations, the resulting interactive visual interface, and the feedback received from the stakeholders with respect to the tool functionality and the insights discovered in the bibliographic data.
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| 26. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
-
Task-Based Evaluation of Sentiment Visualization Techniques
- 2022
-
Ingår i: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '22). - : SciTePress. - 9789897585555 ; , s. 187-194
-
Konferensbidrag (refereegranskat)abstract
- Sentiment visualization is concerned with visual representation of sentiments, emotions, opinions, and stances typically detected in textual data, for example, charts or diagrams representing negative and positive opinions in online customer reviews or Twitter discussions. Such approaches have been applied for the purposes of academic research and practical applications in the past years. But the question of usability of these various techniques still remains generally unsolved, as the existing research typically addresses individual design alternatives for a particular technique implementation only. This work focuses on evaluation of the effectiveness and efficiency of common visual representations for low-level visualization tasks in the context of sentiment visualization. More specifically, we describe a task-based within-subject user study for various tasks, carried out as an online survey and taking the task completion time and error rate into account for most questions. The study involved 50 participants, and we present and discuss their responses and free-form comments. The results provide evidence of strengths and weaknesses of particular representations and visual variables with respect to different tasks, as well as specific user preferences, in the context of sentiment visualization.
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| 27. |
- Kucher, Kostiantyn, et al.
(författare)
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Text Visualization Browser : A Visual Survey of Text Visualization Techniques
- 2014
-
Ingår i: Poster Abstracts of IEEE VIS 2014.
-
Konferensbidrag (refereegranskat)abstract
- Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this poster, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends.
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| 28. |
- Kucher, Kostiantyn, et al.
(författare)
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Text Visualization Revisited : The State of the Field in 2019
- 2019
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Ingår i: Posters of the 21th EG/VGTC Conference on Visualization (EuroVis '19). - : Eurographics - European Association for Computer Graphics. - 9783038680888 ; , s. 29-31
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Konferensbidrag (refereegranskat)abstract
- Text and document data visualization is an important research field within information visualization and visual analytics with multiple application domains including digital humanities and social media, for instance. During the past five years, we have been collecting text visualization techniques described in peer-reviewed literature, categorizing them according to a detailed categorization schema, and providing the resulting manually curated collection in an online survey browser. In this poster paper, we present the updated results of analyses of this data set as of spring 2019. Compared to the recent surveys and meta-analyses that mainly focus on particular aspects and problems related to text visualization, our results provide an overview of the current state of the text visualization field and the respective research community in general.
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| 29. |
- Kucher, Kostiantyn, et al.
(författare)
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Text Visualization Techniques : Taxonomy, Visual Survey, and Community Insights
- 2015
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Ingår i: Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15). - : IEEE. - 9781467368797 ; , s. 117-121
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Konferensbidrag (refereegranskat)abstract
- Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data.
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| 30. |
- Kucher, Kostiantyn, et al.
(författare)
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The State of the Art in Sentiment Visualization
- 2018
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Ingår i: Computer graphics forum (Print). - : John Wiley & Sons. - 0167-7055 .- 1467-8659. ; 37:1, s. 71-96
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Tidskriftsartikel (refereegranskat)abstract
- Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.
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| 31. |
- Kucher, Kostiantyn, 1989-, et al.
(författare)
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Towards Visual Sociolinguistic Network Analysis
- 2021
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Ingår i: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '21). - : SciTePress. - 9789897584886 ; , s. 248-255
-
Konferensbidrag (refereegranskat)abstract
- Investigation of social networks formed by individuals in various contexts provides numerous interesting and important challenges for researchers and practitioners in multiple disciplines. Within the field of variationist sociolinguistics, social networks are analyzed in order to reveal the patterns of language variation and change while taking the social, cultural, and geographical aspects into account. In this field, traditional approaches usually focusing on small, manually collected data sets can be complemented with computational methods and large digital data sets extracted from online social network and social media sources. However, increasing data size does not immediately lead to the qualitative improvement in the understanding of such data. In this position paper, we propose to address this issue by a joint effort combining variationist sociolinguistics and computational network analyses with information visualization and visual analytics. In order to lay the foundation for this interdisciplinary collaboration, we analyse the previous relevant work and discuss the challenges related to operationalization, processing, and exploration of such social networks and associated data. As the result, we propose a roadmap towards realization of visual sociolinguistic network analysis.
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| 32. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Visual Analysis of Humor Assessment Annotations for News Headlines in the Humicroedit Data Set
- 2024
-
Ingår i: Proceedings of the First Workshop on Visualization for Natural Language Processing (Vis4NLP 2024). - : Eurographics - European Association for Computer Graphics.
-
Konferensbidrag (refereegranskat)abstract
- Effective utilization of training data is a critical component for the success of any artificial intelligence algorithm, including natural language processing (NLP) tasks. One particular task of interest is related to detecting or ranking humor in texts, as exemplified by the Humicroedit data set used for the SemEval 2020 task of assessing humor in micro-edited news headlines. Rather than focusing on text classification or prediction, in this study, we focus on gaining a deeper understanding and utilization of the data through the use of information visualization techniques facilitated by the established NLP methods such as sentiment analysis and topic modeling. We describe the design of an interactive visualization tool prototype that relies on multiple coordinated views to allow the user explore and analyze the relationships between the annotated humor scores, sentiments, and topics. Evaluation of the proposed approach involves a case study with the Humicroedit data set as well as domain expert reviews with four participants. The experts deemed the prototype useful for its purpose and saw potential in exploring similar data sets with it, as well as further potential applications in their line of work. Our study thus contributes to the body of work on visual text analytics for supporting computational humor analysis as well as annotated text data analysis in general.
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| 33. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena
- 2016
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116
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Tidskriftsartikel (refereegranskat)abstract
- Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.
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| 34. |
- Kucher, Kostiantyn, et al.
(författare)
-
Visual Analysis of Sentiment and Stance in Social Media Texts
- 2018
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Ingår i: EuroVis 2018 - Posters. - : Eurographics - European Association for Computer Graphics. - 9783038680659 ; , s. 49-51
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Konferensbidrag (refereegranskat)abstract
- Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.
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| 35. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Stance Markers in Online Social Media
- 2014
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Ingår i: Poster Abstracts of IEEE VIS 2014. - : IEEE. ; , s. 259-260
-
Konferensbidrag (refereegranskat)abstract
- Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.
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| 36. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Text Annotations for Stance Classification with ALVA
- 2016
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Ingår i: EuroVis Posters 2016. - : Eurographics - European Association for Computer Graphics. - 9783038680154 ; , s. 49-51
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Konferensbidrag (refereegranskat)abstract
- The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.
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| 37. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(författare)
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Visualization of Swedish News Articles: A Design Study
- 2024
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Ingår i: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '24). - : SciTePress. ; , s. 670-677
-
Konferensbidrag (refereegranskat)abstract
- The amount of available text data has increased rapidly in the past years, making it difficult for many users to find relevant information. To solve this, natural language processing (NLP) and text visualization methods have been developed, however, they typically focus on English texts only, while the support for low-resource languages is limited. The aim of this design study was to implement a visualization prototype for exploring a large number of Swedish news articles (made available by industrial collaborators), including the temporal and relational data aspects. Sketches of three visual representations were designed and evaluated through user tests involving both our collaborators and end-users (journalists). Next, an NLP pipeline was designed in order to support dynamic and hierarchical topic modeling. The final part of the study resulted in an interactive visualization prototype that uses a variation of area charts to represent topic evolution. The prototype was evaluated thr ough an internal case study and user tests with two groups of participants with the background in journalism and NLP. The evaluation results reveal the participants’ preference for the representation focusing on top topics rather than the topic hierarchy, while suggestions for future work relevant for Swedish text data visualization are also provided.
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| 38. |
- Kucher, Kostiantyn, et al.
(författare)
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Visualizing Excitement of Individuals and Groups
- 2016
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Ingår i: Proceedings of the ACM IUI 2016 Workshop on Emotion and Visualization (EmoVis '16). - Linköping, Sweden : Linköping University Electronic Press. - 9789176858172 ; , s. 15-22
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Konferensbidrag (refereegranskat)abstract
- Excitement or arousal is one of the main emotional dimensions that affects our lives on a daily basis. We win a tennis match, watch a great movie, get into an argument with a colleague—all of these are instances when most of us experience excitement, yet we do not pay much attention to it. Today, there are few systems that capture our excitement levels and even fewer that actually promote awareness of our most exciting moments. In this paper, we propose a visualization concept for representing individual and group-level excitement for emotional self-awareness and group-level awareness. The data used for the visualization is obtained from smart wristbands worn by each of the users. The visualization uses animated glyphs to generate a real-time representation for each individual’s excitement levels. We introduce two types of encodings for these glyphs: one focusing on capturing both the current excitement and the excitement history, as well as another focusing only on real-time values and previous peaks. The excitement levels are computed based on measurements of the user’s galvanic skin response and accelerometer data from the wristbands, allowing for a classification of the excitement levels into experienced (excitement without physical manifestation) and manifested excitement. A dynamic clustering of the individual glyphs supports the scalability of our visualization, while at the same time offering an overview of the group-level excitement and its distribution. The results of a preliminary evaluation suggest that the visualization allows users to intuitively and accurately perceive both individual and group-level excitement.
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| 39. |
- Martins, Rafael Messias, et al.
(författare)
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StanceXplore : Visualization for the Interactive Exploration of Stance in Social Media
- 2017
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Konferensbidrag (refereegranskat)abstract
- The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier.
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| 40. |
- Neset, Tina-Simone, Professor, et al.
(författare)
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AI för klimatanpassning : Hur kan nya digitala teknologier stödja klimatanpassning?
- 2024
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Tillgång till vädervarningar med information om förväntade konsekvenser av vädret är nödvändigt för god krisberedskap hos myndigheter, kommuner, näringsliv och privatpersoner. Vidareutveckling av varningssystem som fokuserar på förväntade störningar (konsekvensbaserade varningssystem) är därför en viktig komponent i samhällets hantering av klimatförändringar. Forskningsprojektet AI för klimatanpassning (AI4CA) har analyserat möjligheter och hinder med att inkludera AI-baserad text- och bildanalys som stöd till SMHI:s konsekvensbaserade vädervarningssystem och på sikt även stödja långsiktig klimatanpassning.
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| 41. |
- Neves, Tácito Trindade de Araújo Tiburtino, et al.
(författare)
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Fast and Reliable Incremental Dimensionality Reduction for Streaming Data
- 2022
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Ingår i: Computers & graphics. - : Elsevier. - 0097-8493 .- 1873-7684. ; 102, s. 233-244
-
Tidskriftsartikel (refereegranskat)abstract
- Streaming data applications are becoming more common due to the ability ofdifferent information sources to continuously capture or produce data, such as sensors and social media. Although there are recent advances, most visualization approaches, particularly Dimensionality Reduction (DR) techniques, cannot be directly applied in such scenarios due to the transient nature of streaming data. A few DR methods currently address this limitation using online or incremental strategies, continuously updating the visualization as data is received. Despite their relative success, most impose the need to store and access the data multiple times to produce a complete projection, not being appropriate for streaming where data continuously grow. Others do not impose such requirements but cannot update the position of the data already projected, potentially resulting in visual artifacts. This paper presents Xtreaming, a novel incremental DR technique that continuously updates the visual representation to reflect new emerging structures or patterns without visiting the high-dimensional data more than once. Our tests show that in streaming scenarios where data is not fully stored in-memory, Xtreaming is competitive in terms of quality compared to other streaming and incremental techniques while being orders of magnitude faster.
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| 42. |
- Nylin, Magnus, 1978-
(författare)
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Flexible Automation in Air Traffic Control Through Adaptation of Human-Automation Collaboration
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many domains currently experience an increase in level of automation of their technical systems. However, the increased level of automation must be accompanied with the development of well-functioning human-automation collaboration to avoid undesirable phenomena such as automation surprises or reduced situational awareness. In safety critical domains like air traffic control, this may be more than an inconvenience or loss of efficiency: in the worst case, it can jeopardize flight safety. Although these issues and risks related to increased automation are well-known, they continue to appear in new systems. This indicates that they are not sufficiently addressed in the design of new automation, and that better methods and ideas for this must be developed. This dissertation explores how the concept of flexible automation can be used to increase the understanding of how to design for better human-automation collaboration. The fundamental idea of the flexible automation concept is to provide the automation with knowledge about the human’s situation and let the automation combine that with its understanding of the overall situation, including mutual work- and control processes. At a conceptual level, this also widens the view on automation as merely a way to replace human labor, to a view on automation where it enhances the joint performance of the human-automation team. It also emphasizes the human as an essential piece of a successful automation. Though being important and applicable to many areas and domains, this dissertation is mainly focused on the air traffic control domain, but also includes elements from the domains of vessel traffic service and train traffic management. The approach for the dissertation has been to use the known problems and issues with automation as a starting point, and then decompose that complex of problems and explore them to find possible solutions. Initiation of communication between automation and human, how and when it is performed, were identified as one critical point in the human-automation collaboration. This insight formed the basis for the two main concepts presented in this dissertation. The first concept is the Reduced Autonomy Workspace (RAW), a design approach that describes the process of when a highly autonomous automation needs to consult the human working together with the automation. Focus is on the temporal aspects, but it also includes transformation of the information content to reduce the cognitive effort on the human receiving the information. A visualization for future automation using glyphs based on the same principles was developed to support the RAW design approach. The glyphs were evaluated in workshops with operators from air traffic control, vessel traffic service, and train traffic management, respectively. The second main concept is a new type of attention support, Soft Visual Cues (SVC). The SVC complements other types of attention support which may, due to the automation’s lack of knowledge of the overall situation, draw the operator’s attention away from something potentially more important. Different designs were evaluated in operator workshops and tested by other air traffic controllers in a series of real-time simulations. In addition to these concepts, studies have also been performed to explore and gain a deeper knowledge of how the involved control processes can be understood, the effect of entangled control processes, and how domain specific prerequisites are reflected in the control processes. The studies have involved subject matter experts, such as air traffic controllers, to understand the processes that underlies human-automation team-work. However, data collection through workshops and simulations has been challenging because of the pandemic, even though most activities were possible to perform. The results have contributed to a better understanding of the design for human-automation collaboration, the importance of temporal aspects in this, and how it can be visualized. While the RAW and the glyph visualizations aims at future automations, the SVC is a concept that could be implemented in a near future. The problems and proposed solutions in this dissertation are mainly focused on applications in air traffic control. However, the problems and issues addressed here are similar in many other areas focused on control of real-time processes. Therefore, the presented ideas and concepts should be of value for those as well, and the results are a good starting point for the development of tomorrow’s flexible automation, where the human is still a major asset.
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| 43. |
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| 44. |
-
Proceedings of the 2021 Swedish Workshop on Data Science (SweDS) : Växjö, SwedenDecember 2–3, 2021
- 2021
-
Proceedings (redaktörskap) (refereegranskat)abstract
- Welcome to the 9th Swedish Workshop on Data Science (SweDS21) held (virtually) in Växjö, Sweden during December 2–3, 2021. SweDS is a national event with a focus of maintaining and developing Swedish data science research and its applications by fostering the exchange of ideas and promoting collaboration within and across disciplines. This annual workshop brings together researchers and practitioners of data science working in a variety of academic, commercial, industrial, or other sectors. The current and past workshops have included presentations from a variety of domains, e.g., computer science, linguistics, eco- nomics, archaeology, environmental science, education, journalism, medicine, healthcare, biology, sociology, psychology, history, physics, chemistry, geography, forestry, design, and music. SweDS is hosted by Linnaeus University (Växjö, Sweden) this year. Due to the yet ongoing COVID-19 pandemic, travel restrictions, and public health concerns, the workshop is conducted online-only, which has allowed authors both within and outside of Sweden to submit and present their work.
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| 45. |
-
Proceedings of the 2021 Swedish Workshop on Data Science (SweDS)
- 2021
-
Proceedings (redaktörskap) (refereegranskat)abstract
- Welcome to the 9th Swedish Workshop on Data Science (SweDS21) held (virtually) in Växjö, Sweden during December 2–3, 2021. SweDS is a national event with a focus of maintaining and developing Swedish data science research and its applications by fostering the exchange of ideas and promoting collaboration within and across disciplines. This annual workshop brings together researchers and practitioners of data science working in a variety of academic, commercial, industrial, or other sectors. The current and past workshops have included presentations from a variety of domains, e.g., computer science, linguistics, eco- nomics, archaeology, environmental science, education, journalism, medicine, healthcare, biology, sociology, psychology, history, physics, chemistry, geography, forestry, design, and music. SweDS is hosted by Linnaeus University (Växjö, Sweden) this year. Due to the yet ongoing COVID-19 pandemic, travel restrictions, and public health concerns, the workshop is conducted online-only, which has allowed authors both within and outside of Sweden to submit and present their work.
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| 46. |
- Rashik, Mashrur, et al.
(författare)
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Beyond Text and Speech in Conversational Agents: Mapping the Design Space of Avatars
- 2024
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Ingår i: Proceedings of the 2024 ACM Designing Interactive Systems Conference (DIS '24). - : Association for Computing Machinery (ACM). ; , s. 1875-1894
-
Konferensbidrag (refereegranskat)abstract
- Conversational agents have gained widespread popularity due to their ability to simulate and sustain contextual conversations. Prior works predominantly focused on computational challenges. However, avatars — the representation of the agent — impact user interactions and perception of conversational agents’ trustworthiness and usefulness. Despite their importance, we lack a holistic understanding of conversational agent avatar design space. In this work, we address this gap by defining a categorization of 10 dimensions that is based on the analysis and iterative coding of 266 conversational agent papers from 160 venues spanning 2003 to the present. In addition, we built an interactive browser to facilitate exploration and interaction with these dimensions and their interrelationships. Our categorization lays the groundwork for researchers, designers, and practitioners to discern task-specific and contextual aspects of conversational agent avatar design. Our work fosters innovative ideas to facilitate new interactions with avatars by surfacing current patterns and highlighting open challenges.
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| 47. |
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| 48. |
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| 49. |
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| 50. |
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