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  • 1.
    Andreasson, Rebecca
    et al.
    University of Skövde, School of Informatics.
    Riveiro, Maria
    University of Skövde, School of Informatics.
    Effects of Visualizing Missing Data: An Empirical Evaluation2014In: 18th International Conference on Information Visualisation (IV) / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Francis T. Marchese, Muhammad Sarfraz, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, Urska Cvek, Marjan Trutschl, Georges Grinstein, Vladimir Geroimenko, Sarah Kenderdine & Fatma Bouali, 2014, p. 132-138Conference paper (Refereed)
    Abstract [en]

    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 decision-confidence. Additionally, the results indicate a possible relation between the degree of decision-confidence and the decision-maker's displayed risk behavior.

  • 2.
    Andreasson, Rebecca
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Effects of Visualizing Missing Data: An Empirical Evaluation2014In: 18th International Conference on Information Visualisation (IV) / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Francis T. Marchese, Muhammad Sarfraz, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, Urska Cvek, Marjan Trutschl, Georges Grinstein, Vladimir Geroimenko, Sarah Kenderdine & Fatma Bouali, IEEE conference proceedings, 2014, p. 132-138Conference paper (Refereed)
    Abstract [en]

    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.

  • 3.
    Andreasson, Rebecca
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Effects of Visualizing Missing Data: An Empirical Evaluation2014In: 18th International Conference on Information Visualisation (IV) / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Francis T. Marchese, Muhammad Sarfraz, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, Urska Cvek, Marjan Trutschl, Georges Grinstein, Vladimir Geroimenko, Sarah Kenderdine & Fatma Bouali, IEEE conference proceedings , 2014, p. 132-138Conference paper (Refereed)
    Abstract [en]

    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.

  • 4.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visual Data Analysis2019In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
    Abstract [en]

    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.

  • 5.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Identifying Root Cause and Derived Effects in Causal Relationships2017In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer , 2017, p. 22-34Conference paper (Refereed)
    Abstract [en]

    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.

  • 6.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde .
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Skövde.
    Identifying Root Cause and Derived Effects in Causal Relationships2017In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer, 2017, p. 22-34Conference paper (Refereed)
    Abstract [en]

    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.

  • 7.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Understanding Indirect Causal Relationships in Node-Link Graphs2017In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed)
    Abstract [en]

    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.

  • 8.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Understanding Indirect Causal Relationships in Node-Link Graphs2017In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed)
    Abstract [en]

    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.

  • 9.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Evaluating Multi-Attributes on Cause and Effect Relationship Visualization2017In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress, 2017, p. 64-74Conference paper (Refereed)
    Abstract [en]

    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.

  • 10.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ventocilla, Elio
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Evaluating Multi-Attributes on Cause and Effect Relationship Visualization2017In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress , 2017, p. 64-74Conference paper (Refereed)
    Abstract [en]

    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.

  • 11.
    Bae, Juhee
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ventocilla, Elio
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On the Visualization of Discrete Non-additive Measures2018In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper (Refereed)
    Abstract [en]

    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.

  • 12.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ventocilla, Elio
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi.
    On the Visualization of Discrete Non-additive Measures2018In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper (Refereed)
    Abstract [en]

    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.

  • 13.
    Bergström, Erik
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Carlén, Urban
    University West.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    How to Include Female Students in Technical Computer Science Study Programs2016In: NU2016 Högskolan i samhället - samhället i högskolan, 2016, article id BT30Conference paper (Refereed)
    Abstract [en]

    This study examines why female students do not register in computer sciencerelated programs after submitting their application. In design for inclusion in academic culture, pedagogical implications are based on empirical findings that intend to foster a more heterogeneous group, which can be introduced to promote student’s interest in technology.

  • 14.
    Bergström, Erik
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Carlén, Urban
    University West.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    How to Include Female Students in Technical Computer Science Study Programs2016In: NU2016 Högskolan i samhället - samhället i högskolan, 2016, article id BT30Conference paper (Refereed)
    Abstract [en]

    This study examines why female students do not register in computer sciencerelated programs after submitting their application. In design for inclusion in academic culture, pedagogical implications are based on empirical findings that intend to foster a more heterogeneous group, which can be introduced to promote student’s interest in technology.

  • 15.
    Dahlbom, Anders
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Situation Modeling and Visual Analytics for Decision Support in Sports2014In: Proceedings of the 16th International Conference on Enterprise Information Systems: Volume 1 / [ed] Slimane Hammoudi, Leszek Maciaszek, José Cordeiro, SciTePress, 2014, p. 539-544Conference paper (Refereed)
    Abstract [en]

    High performance is a goal in most sporting activities, for elite athletes as well as for recreational practitioners, and the process of measuring, evaluating and improving performance is one fundamental aspect to why people engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous data in order to apply actions that change important properties for improved outcome. The number of computer based decision support systems in the context of data analysis for performance improvement is scarce. In this position paper we briefly review the literature, and we propose the use of information fusion, situation modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to elite, in the process of performance evaluation.

  • 16.
    Dahlbom, Anders
    et al.
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Situation Modeling and Visual Analytics for Decision Support in Sports2014In: Proceedings of the 16th International Conference on Enterprise Information Systems: Volume 1 / [ed] Slimane Hammoudi, Leszek Maciaszek, José Cordeiro, SciTePress , 2014, p. 539-544Conference paper (Refereed)
    Abstract [en]

    High performance is a goal in most sporting activities, for elite athletes as well as for recreational practitioners, and the process of measuring, evaluating and improving performance is one fundamental aspect to why people engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous data in order to apply actions that change important properties for improved outcome. The number of computer based decision support systems in the context of data analysis for performance improvement is scarce. In this position paper we briefly review the literature, and we propose the use of information fusion, situation modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to elite, in the process of performance evaluation.

  • 17.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    König, Rikard
    University of Borås.
    Johansson, Ulf
    University of Borås.
    Brattberg, Peter
    University of Borås.
    Supporting Golf Coaching with 3D Modeling of Swings2014In: Sportinformatik X: Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien / [ed] Arnold Baca & Michael Stöckl, Hamburg: Feldhaus Verlag GmbH & Co. KG , 2014, p. 142-148Chapter in book (Refereed)
  • 18.
    Dahlbom, Anders
    et al.
    Högskolan i Skövde.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    König, Rikard
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Johansson, Ulf
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Brattberg, Peter
    Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
    Supporting Golf Coaching with 3D Modeling of Swings2014In: Sportinformatik X: Jahrestagung der dvs-Sektion Sportinformatik, Hamburg: Feldhaus Verlag , 2014, 10, p. 142-148Chapter in book (Refereed)
  • 19.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Lebram, Mikael
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Transparency of military threat evaluation through visualizing uncertainty and system rationale2013In: Engineering Psychology and Cognitive Ergonomics: Applications and Services / [ed] Don Harris, Springer Berlin/Heidelberg, 2013, no PART 2, p. 263-272Conference paper (Refereed)
    Abstract [en]

    Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

  • 20.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Dahlbom, Anders
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Lebram, Mikael
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Transparency of military threat evaluation through visualizing uncertainty and system rationale2013In: Engineering Psychology and Cognitive Ergonomics: Applications and Services / [ed] Don Harris, Springer, 2013, p. 263-272Conference paper (Refereed)
    Abstract [en]

    Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

  • 21.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Davidsson, Staffan
    Volvo Car Corporation, Gothenburg, Sweden.
    Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving2013In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), New York: Association for Computing Machinery (ACM), 2013, p. 210-217Conference paper (Refereed)
    Abstract [en]

    To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

  • 22.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Davidsson, Staffan
    Volvo Car Corporation, Gothenburg, Sweden.
    Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving2013In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), New York: Association for Computing Machinery (ACM) , 2013, p. 210-217Conference paper (Refereed)
    Abstract [en]

    To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

  • 23.
    Helldin, Tove
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ohlander, Ulrika
    Saab Aeronautics, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Transparency of Automated Combat Classification2014In: Engineering Psychology and Cognitive Ergonomics: 11th International Conference, EPCE 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings / [ed] Don Harris, Springer, 2014, p. 22-33Conference paper (Refereed)
    Abstract [en]

    We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots’ performance and initial trust in the system were evaluated. The levels of transparency consisted of (1) only presenting text–based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators’ trust ratings were the highest when using display condition 3. No difference in the pilots’ decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots’ general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period.

  • 24.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ohlander, Ulrika
    Saab Aeronautics, Sweden.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Transparency of Automated Combat Classification2014In: Engineering Psychology and Cognitive Ergonomics: 11th International Conference, EPCE 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014. Proceedings / [ed] Don Harris, Springer , 2014, p. 22-33Conference paper (Refereed)
    Abstract [en]

    We present an empirical study where the effects of three levels of system transparency of an automated target classification aid on fighter pilots’ performance and initial trust in the system were evaluated. The levels of transparency consisted of (1) only presenting text–based information regarding the specific object (without any automated support), (2) accompanying the text-based information with an automatically generated object class suggestion and (3) adding the incorporated sensor values with associated (uncertain) historic values in graphical form. The results show that the pilots needed more time to make a classification decision when being provided with display condition 2 and 3 than display condition 1. However, the number of correct classifications and the operators’ trust ratings were the highest when using display condition 3. No difference in the pilots’ decision confidence was found, yet slightly higher workload was reported when using display condition 3. The questionnaire results report on the pilots’ general opinion that an automatic classification aid would help them make better and more confident decisions faster, having trained with the system for a longer period.

  • 25.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Explanation Methods for Bayesian Networks: review and application to a maritime scenario2009In: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009), University of Skövde , 2009, p. 28-32Conference paper (Refereed)
    Abstract [en]

    Surveillance systems analyze and present vast amounts of heterogeneous sensor data. In order to support operators while monitoring such systems, the identification of anomalous behavior or situations that might need further investigation may reduce operators’ cognitive load. Bayesian networks can be used in order to detect anomalies in data. In order to understand the outcome generated from an anomaly detection application based on Bayesian networks, proper explanations must be given to operators.

    This paper presents the findings of a literature analysis regarding what constitutes an explanation, which properties an explanation may have and a review of different explanation methods for Bayesian networks. Moreover, we present the empirical tests conducted with two of these methods in a maritime scenario. Findings from the survey and the experiments show that explanation methods for Bayesian networks can be used in order to provide operators with more detailed information to base their decisions on.

  • 26.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Explanation Methods for Bayesian Networks: review and application to a maritime scenario2009In: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009), University of Skövde , 2009, p. 28-32Conference paper (Refereed)
    Abstract [en]

    Surveillance systems analyze and present vast amounts of heterogeneous sensor data. In order to support operators while monitoring such systems, the identification of anomalous behavior or situations that might need further investigation may reduce operators’ cognitive load. Bayesian networks can be used in order to detect anomalies in data. In order to understand the outcome generated from an anomaly detection application based on Bayesian networks, proper explanations must be given to operators.

    This paper presents the findings of a literature analysis regarding what constitutes an explanation, which properties an explanation may have and a review of different explanation methods for Bayesian networks. Moreover, we present the empirical tests conducted with two of these methods in a maritime scenario. Findings from the survey and the experiments show that explanation methods for Bayesian networks can be used in order to provide operators with more detailed information to base their decisions on.

  • 27.
    Helldin, Tove
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Pashami, Sepideh
    Halmstad University, Halmstad, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Byttner, Stefan
    Halmstad University, Halmstad, Sweden.
    Nowaczyk, Slawomir
    Halmstad University, Halmstad, Sweden.
    Supporting analytical reasoning: A study from the automotive industry2016In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Springer, 2016, p. 20-31Conference paper (Refereed)
    Abstract [en]

    In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.

  • 28.
    Helldin, Tove
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Pashami, Sepideh
    Halmstad University, Halmstad, Sweden.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Byttner, Stefan
    Halmstad University, Halmstad, Sweden.
    Nowaczyk, Slawomir
    Halmstad University, Halmstad, Sweden.
    Supporting analytical reasoning: A study from the automotive industry2016In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Springer, 2016, p. 20-31Conference paper (Refereed)
    Abstract [en]

    In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.

  • 29.
    Huhnstock, Nikolas Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    An Infinite Replicated Softmax Model for Topic Modeling2019In: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, p. 307-318Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

    The full text will be freely available from 2020-07-25 00:00
  • 30.
    Huhnstock, Nikolas Alexander
    et al.
    University of Skövde, Skövde, Sweden.
    Karlsson, Alexander
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. University of Skövde, Skövde, Sweden.
    Steinhauer, H. Joe
    University of Skövde, Skövde, Sweden.
    An Infinite Replicated Softmax Model for Topic Modeling2019In: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, p. 307-318Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

    The full text will be freely available from 2020-07-24 00:00
  • 31.
    Huhnstock, Nikolas Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On the behavior of the infinite restricted boltzmann machine for clustering2018In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [ed] Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir, New York, NY, USA: Association for Computing Machinery (ACM), 2018, p. 461-470Conference paper (Refereed)
    Abstract [en]

    Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.

  • 32.
    Huhnstock, Nikolas Alexander
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi.
    On the behavior of the infinite restricted boltzmann machine for clustering2018In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [ed] Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir, New York, NY, USA: Association for Computing Machinery (ACM) , 2018, p. 461-470Conference paper (Refereed)
    Abstract [en]

    Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.

  • 33.
    Johansson, Ulf
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Konig, R.
    Department of Information Technology, University of Borås, Sweden.
    Brattberg, P.
    Department of Information Technology, University of Borås, Sweden.
    Dahlbom, A.
    School of Informatics, University of Skövde, Sweden.
    Riveiro, Maria
    Department of Information Technology, University of Borås, Sweden.
    Mining trackman golf data2016In: Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015, IEEE, 2016, p. 380-385Conference paper (Refereed)
    Abstract [en]

    Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.

  • 34.
    Johansson, Ulf
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Köning, Rikard
    Department of Information Technology, University of Borås, Sweden.
    Brattberg, Peter
    Department of Information Technology, University of Borås, Sweden.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mining Trackman Golf Data2015In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI) / [ed] Hamid R. Arabnia, Leonidas Deligiannidis & Quoc-Nam Tran, Los Alamitos, CA: IEEE Computer Society, 2015, p. 380-385Conference paper (Refereed)
    Abstract [en]

    Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.

  • 35.
    Kinkeldey, Christoph
    et al.
    Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg, Hamburg, Germany.
    MacEachren, Alan M.
    GeoVISTA Center and Department of Geography, Penn State University, University Park, PA, USA.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Schiewe, Jochen
    Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg, Hamburg, Germany.
    Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations2017In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 44, no 1, p. 1-21Article, review/survey (Refereed)
    Abstract [en]

    For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making.

  • 36.
    Kinkeldey, Christoph
    et al.
    Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg, Hamburg, Germany.
    MacEachren, Alan M.
    GeoVISTA Center and Department of Geography, Penn State University, University Park, PA, USA.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Schiewe, Jochen
    Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg, Hamburg, Germany.
    Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations2017In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 44, no 1, p. 1-21Article, review/survey (Refereed)
    Abstract [en]

    For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making.

  • 37.
    König, Rikard
    et al.
    University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). University of Borås, Borås, Sweden.
    Riveiro, Maria
    University of Skövde, Skövde, Sweden.
    Brattberg, Peter
    University of Borås, Borås, Sweden.
    Modeling golf player skill using machine learning2017In: Machine Learning and Knowledge Extraction, Springer, 2017, p. 275-294Conference paper (Refereed)
    Abstract [en]

    In this study we apply machine learning techniques to Modeling Golf Player Skill using a dataset consisting of 277 golfers. The dataset includes 28 quantitative metrics, related to the club head at impact and ball flight, captured using a Doppler-radar. For modeling, cost-sensitive decision trees and random forest are used to discern between less skilled players and very good ones, i.e., Hackers and Pros. The results show that both random forest and decision trees achieve high predictive accuracy, with regards to true positive rate, accuracy and area under the ROC-curve. A detailed interpretation of the decision trees shows that they concur with modern swing theory, e.g., consistency is very important, while face angle, club path and dynamic loft are the most important evaluated swing factors, when discerning between Hackers and Pros. Most of the Hackers could be identified by a rather large deviation in one of these values compared to the Pros. Hackers, which had less variation in these aspects of the swing, could instead be identified by a steeper swing plane and a lower club speed. The importance of the swing plane is an interesting finding, since it was not expected and is not easy to explain. © 2017, IFIP International Federation for Information Processing.

  • 38.
    Lagerstedt, Erik
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Thill, Serge
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Agent Autonomy and Locus of Responsibility for Team Situation Awareness2017In: HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction, New York: Association for Computing Machinery (ACM) , 2017, p. 261-269Conference paper (Refereed)
    Abstract [en]

    Rapid technical advancements have led to dramatically improved abilities for artificial agents, and thus opened up for new ways of cooperation between humans and them, from disembodied agents such as Siris to virtual avatars, robot companions, and autonomous vehicles. It is therefore relevant to study not only how to maintain appropriate cooperation, but also where the responsibility for this resides and/or may be affected. While there are previous organisations and categorisations of agents and HAI research into taxonomies, situations with highly responsible artificial agents are rarely covered. Here, we propose a way to categorise agents in terms of such responsibility and agent autonomy, which covers the range of cooperation from humans getting help from agents to humans providing help for the agents. In the resulting diagram presented in this paper, it is possible to relate different kinds of agents with other taxonomies and typical properties. A particular advantage of this taxonomy is that it highlights under what conditions certain effects known to modulate the relationship between agents (such as the protégé effect or the "we"-feeling) arise.

  • 39.
    Lagerstedt, Erik
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Thill, Serge
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Plymouth University, United Kingdom.
    Agent Autonomy and Locus of Responsibility for Team Situation Awareness2017In: HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction, New York: Association for Computing Machinery (ACM), 2017, p. 261-269Conference paper (Refereed)
    Abstract [en]

    Rapid technical advancements have led to dramatically improved abilities for artificial agents, and thus opened up for new ways of cooperation between humans and them, from disembodied agents such as Siris to virtual avatars, robot companions, and autonomous vehicles. It is therefore relevant to study not only how to maintain appropriate cooperation, but also where the responsibility for this resides and/or may be affected. While there are previous organisations and categorisations of agents and HAI research into taxonomies, situations with highly responsible artificial agents are rarely covered. Here, we propose a way to categorise agents in terms of such responsibility and agent autonomy, which covers the range of cooperation from humans getting help from agents to humans providing help for the agents. In the resulting diagram presented in this paper, it is possible to relate different kinds of agents with other taxonomies and typical properties. A particular advantage of this taxonomy is that it highlights under what conditions certain effects known to modulate the relationship between agents (such as the protégé effect or the "we"-feeling) arise.

  • 40.
    Lagerstedt, Erik
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Thill, Serge
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Interacting with Artificial Agents2015In: Thirteenth Scandinavian Conference on Artificial Intelligence / [ed] Sławomir Nowaczyk, IOS Press, 2015, p. 184-185Conference paper (Refereed)
  • 41.
    Lagerstedt, Erik
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Thill, Serge
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Interacting with Artificial Agents2015In: Thirteenth Scandinavian Conference on Artificial Intelligence / [ed] Sławomir Nowaczyk, IOS Press, 2015, Vol. 278, p. 184-185Conference paper (Refereed)
  • 42.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Saab Microwave Systems, Skövde, Sweden.
    A Unified Situation Analysis Model for Human and Machine Situation Awareness2007In: INFORMATIK 2007: Informatik trifft Logistik: Band 2: Beiträge der 37. Jahrestagung der Gesellschaft für Informatik e.V. (GI) 24. - 27. September 2007 in Bremen / [ed] Otthein Herzog, Karl-Heinz Rödiger, Marc Ronthaler, Rainer Koschke, Bonn: Gesellschaft für Informatik , 2007, p. 105-109Conference paper (Refereed)
  • 43.
    Niklasson, Lars
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Fredrik
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Dahlbom, Anders
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Ziemke, Tom
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Brax, Christoffer
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A Unified Situation Analysis Model for Human and Machine Situation Awareness2007In: INFORMATIK 2007: Informatik trifft Logistik: Band 2: Beiträge der 37. Jahrestagung der Gesellschaft für Informatik e.V. (GI) 24. - 27. September 2007 in Bremen / [ed] Otthein Herzog, Karl-Heinz Rödiger, Marc Ronthaler, Rainer Koschke, Bonn: Gesellschaft für Informatik , 2007, p. 105-109Conference paper (Refereed)
  • 44.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Extending the scope of Situation Analysis2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), Cologne, Germany, June 30–July 3, 2008, IEEE Press, 2008, p. 454-461Conference paper (Refereed)
    Abstract [en]

    The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

  • 45.
    Niklasson, Lars
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Johansson, Fredrik
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Dahlbom, Anders
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Falkman, Göran
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Ziemke, Tom
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Brax, Christoffer
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Extending the scope of Situation Analysis2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), Cologne, Germany, June 30–July 3, 2008, IEEE Press , 2008, p. 454-461Conference paper (Refereed)
    Abstract [en]

    The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

  • 46.
    Nilsson, Maria
    et al.
    University of Skövde, School of Humanities and Informatics.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics.
    Investigating human-computer interaction issues in information-fusion-based decision support2008Report (Other academic)
    Abstract [en]

    Information fusion is a research area which focuses on how to combine information from many different sources to support decision making. Commonly used information fusion systems are often complex and used in military and crises management domains. The focus of information fusion research so far has been mainly on the technological aspects. There is still a lack of understanding relevant user aspects that affect the information fusion systems as a whole. This paper presents a framework of HCI issues which considers users as embedded in the context of information fusion systems. The framework aims at providing insights regarding factors that affect user interaction to inform the development of future information fusion systems. Design considerations are presented together with a heuristic evaluation of an information fusion prototype.

  • 47.
    Nilsson, Maria
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Ziemke, Tom
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Investigating human-computer interaction issues in information-fusion-based decision support2008Report (Other academic)
    Abstract [en]

    Information fusion is a research area which focuses on how to combine information from many different sources to support decision making. Commonly used information fusion systems are often complex and used in military and crises management domains. The focus of information fusion research so far has been mainly on the technological aspects. There is still a lack of understanding relevant user aspects that affect the information fusion systems as a whole. This paper presents a framework of HCI issues which considers users as embedded in the context of information fusion systems. The framework aims at providing insights regarding factors that affect user interaction to inform the development of future information fusion systems. Design considerations are presented together with a heuristic evaluation of an information fusion prototype.

  • 48.
    Ohlander, Ulrika
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Saab Aeronautics, Saab AB, Linköping.
    Alfredson, Jens
    Saab Aeronautics, Saab AB, Linköping.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A Teamwork Model for Fighter Pilots2016In: Engineering Psychology and Cognitive Ergonomics: 13th International Conference, EPCE 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings / [ed] Don Harris, Springer, 2016, Vol. 9736, p. 221-230Conference paper (Refereed)
    Abstract [en]

    Fighter pilots depend on collaboration and teamwork to perform successful air missions. However, such collaboration is challenging due to limitations in communication and the amount of data that can be shared between aircraft. In order to design future support systems for fighter pilots, this paper aims at characterizing how pilots collaborate while performing real-world missions. Our starting point is the “Big Five” model for effective teamwork, put forth by Salas et al. [1]. Fighter pilots were interviewed about their teamwork, and how they prepare and perform missions in teams. The results from the interviews were used to describe how pilots collaborate in teams, and to suggest relationships between the teamwork elements of the “Big Five” model for fighter pilots performing missions. The results presented in this paper are intended to inform designers and developers of cockpit displays, data links and decision support systems for fighter aircraft.

  • 49.
    Ohlander, Ulrika
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Alfredson, Jens
    Saab Aeronautics, Saab AB, Linköping.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Falkman, Göran
    Högskolan i Skövde, Institutionen för informationsteknologi.
    A Teamwork Model for Fighter Pilots2016In: Engineering Psychology and Cognitive Ergonomics: 13th International Conference, EPCE 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings / [ed] Don Harris, Springer, 2016, p. 221-230Conference paper (Refereed)
    Abstract [en]

    Fighter pilots depend on collaboration and teamwork to perform successful air missions. However, such collaboration is challenging due to limitations in communication and the amount of data that can be shared between aircraft. In order to design future support systems for fighter pilots, this paper aims at characterizing how pilots collaborate while performing real-world missions. Our starting point is the “Big Five” model for effective teamwork, put forth by Salas et al. [1]. Fighter pilots were interviewed about their teamwork, and how they prepare and perform missions in teams. The results from the interviews were used to describe how pilots collaborate in teams, and to suggest relationships between the teamwork elements of the “Big Five” model for fighter pilots performing missions. The results presented in this paper are intended to inform designers and developers of cockpit displays, data links and decision support systems for fighter aircraft.

  • 50.
    Ohlander, Ulrika
    et al.
    Saab Aeronautics, Saab AB, Linköping.
    Alfredson, Jens
    Saab Aeronautics, Saab AB, Linköping.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Elements of team effectiveness: A qualitative study with pilots2016In: 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), IEEE Computer Society, 2016, p. 21-27Conference paper (Refereed)
    Abstract [en]

    Fighter pilots performing air missions rely heavily on teamwork for successful outcomes. Designing systems that support such teamwork in highly dynamic missions is a challenging task, and to the best of our knowledge, current teamwork models are not specifically adapted for this domain. This paper presents a model of task performance for military fighter pilots based on the teamwork model “Big Five” proposed by Salas, Sims, and Burke [1]. The “Big Five” model consists of eight teamwork elements that are essential for successful team performance. In-depth interviews were performed with fighter pilots to explore and describe the teamwork elements for the fighter aircraft domain. The findings from these interviews are used to suggest where in the task cycle of mission performance each teamwork element comes in to play.

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