| 1. |
- Andreasson, Rebecca, et al.
(författare)
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Effects of Visualizing Missing Data : An Empirical Evaluation
- 2014
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Ingår i: 18th International Conference on Information Visualisation (IV). - : IEEE conference proceedings. - 9781479941032 ; , s. 132-138
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Konferensbidrag (refereegranskat)abstract
- This paper presents an empirical study that evaluates the effects of visualizing missing data on decision-making tasks. A comparison between three visualization techniques: (1) emptiness, (2) fuzziness, and (3) emptiness plus explanation, revealed that the latter technique induced significantly higher degree of decision-confidence than the visualization technique fuzziness. Moreover, emptiness plus explanation yield the highest number of risky choices of the three. This result suggests that uncertainty visualization techniques affect the decision-maker and the decisionconfidence. Additionally, the results indicate a possible relation between the degree of decision-confidence and the decision-maker's displayed risk behavior.
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| 2. |
- Annavarjula, Vaishnavi, et al.
(författare)
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Implicit user data in fashion recommendation systems
- 2020
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Ingår i: Developments of Artificial Intelligence Technologies in Computation and Robotics. - : World Scientific. - 9789811223334 - 9789811223341 - 9789811223327 ; , s. 614-621
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Konferensbidrag (refereegranskat)abstract
- Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.
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| 3. |
- Arnab, Sylvester, et al.
(författare)
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A Conceptual Model Towards the Scaffolding of Learning Experience
- 2015. - 1
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Ingår i: Games and Learning Alliance. - Cham : Springer. - 9783319229591 - 9783319229607 ; , s. 83-96
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Bokkapitel (refereegranskat)abstract
- The challenge of delivering personalized learning experiences is amplified by the size of classrooms and of online learning communities. In turn, serious games are increasingly recognized for their potential to improve education, but a typical requirement from instructors is to gain insight into how the students are playing. When we bring games into the rapidly growing online learning communities, the challenges multiply and hinder the potential effectiveness of serious games. There is a need to deliver a comprehensive, flexible and intelligent learning framework that facilitates better understanding of learners’ knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model discussed aims to highlight key considerations that may advance the current state of learning analytics, adaptive learning and serious games, by leveraging serious games as an ideal medium for gathering data and performing adaptations. This opportunity has the potential to affect the design and deployment of education and training in the future.
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| 4. |
- Bae, Juhee, et al.
(författare)
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Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
- 2017
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Ingår i: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017). - : SciTePress. - 9789897582288 ; , s. 64-74
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Konferensbidrag (refereegranskat)abstract
- This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.
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| 5. |
- Bae, Juhee, et al.
(författare)
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Identifying Root Cause and Derived Effects in Causal Relationships
- 2017
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Ingår i: Human Interface and the Management of Information: Information, Knowledge and Interaction Design. - Cham : Springer. - 9783319585208 - 9783319585215 ; , s. 22-34
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Konferensbidrag (refereegranskat)abstract
- This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.
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| 6. |
- Bae, Juhee, et al.
(författare)
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Interactive clustering : A comprehensive review
- 2020
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Ingår i: ACM Computing Surveys. - New York, NY : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 53:1
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Tidskriftsartikel (refereegranskat)abstract
- In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.
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| 7. |
- Bae, Juhee, et al.
(författare)
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On the Visualization of Discrete Non-additive Measures
- 2018
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Ingår i: Aggregation Functions in Theory and in Practice AGOP 2017. - Cham : Springer. - 9783319593067 - 9783319593050 ; , s. 200-210
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Konferensbidrag (refereegranskat)abstract
- Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.
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| 8. |
- Bae, Juhee, et al.
(författare)
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Understanding Indirect Causal Relationships in Node-Link Graphs
- 2017
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Ingår i: Computer graphics forum (Print). - : Wiley. - 0167-7055 .- 1467-8659. ; 36:3, s. 411-421
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Tidskriftsartikel (refereegranskat)abstract
- To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.
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| 9. |
- Bae, Juhee, et al.
(författare)
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Visual Data Analysis
- 2019
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Ingår i: Data science in Practice. - Cham : Springer. - 9783319975566 - 9783319975559 ; , s. 133-155
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Bokkapitel (refereegranskat)abstract
- Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.
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| 10. |
- Beauxis-Aussalet, Emma, et al.
(författare)
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The Role of Interactive Visualization in Fostering Trust in AI
- 2021
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Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 41:6, s. 7-12
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Tidskriftsartikel (refereegranskat)abstract
- The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
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