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  • 1.
    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.

  • 2.
    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.

  • 3.
    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.

  • 4.
    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.

  • 5.
    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.

  • 6.
    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.

  • 7.
    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, 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.

  • 8.
    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)
  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.

  • 16.
    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.

  • 17.
    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)
  • 18.
    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)
  • 19.
    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.

  • 20.
    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.

  • 21.
    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.

  • 22.
    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.

  • 23.
    Ohlander, Ulrika
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Saab Aeronautics, Saab AB, Linköping, Sweden.
    Alfredson, Jens
    Saab Aeronautics, Saab AB, Linköping, Sweden.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Understanding Team Effectiveness in a Tactical Air Unit2015In: Engineering Psychology and Cognitive Ergonomics: 12th International Conference, EPCE 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Don Harris, Springer, 2015, Vol. 9174, p. 472-479Conference paper (Refereed)
    Abstract [en]

    Effective team work is regarded as a key factor for success in missions performed by fighter aircraft in a Tactical Air Unit (TAU). Many factors contrib-ute to how a team will succeed in their mission. From the existing literature on teamwork, Salas, Sims and Burke [1], suggested five main factors and three sup-porting mechanisms for effective team work. These were proposed as the “Big Five” of teamwork. This article investigates if the model offered by Salas et al. is applicable to a TAU of fighter aircraft. Semi-structured interviews were carried out with six fighter pilots. The results of these interviews imply that the model has relevance for the teamwork in a TAU. Moreover, this paper discusses impli-cations for the design of future decision-support systems that support team effec-tiveness. 

  • 24.
    Ohlander, Ulrika
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Saab Aeronautics, Saab AB.
    Alfredson, Jens
    Saab Aeronautics, Saab AB.
    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.
    User Participation in the Design of Cockpit Interfaces2017In: Advances in Ergonomics Modeling, Usability & Special Populations / [ed] Marcelo Soares, Christianne Falcão & Tareq Z. Ahram, Springer, 2017, Vol. 486, p. 51-58Conference paper (Refereed)
    Abstract [en]

    This paper investigates the nature of user participation in the process of designing fighter aircraft cockpits. The role of the users, i.e. pilots, in the design of cockpit interfaces is explored. We present the results of an on-line questionnaire with twelve designers of cockpit interfaces for fighter aircraft. The results show that the designers have highlighted the need for more opportunities to observe the pilots, and they wish to obtain more information and ideas from them. Moreover, a larger involvement from users as examiners and testers in the evaluation process was desirable. Access to users was considered unproblematic and the risk of misunderstandings was reported to be low. Moreover, the designers did not support the idea that users should design or take design decisions.

  • 25.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Cognitive Evaluation of Uncertainty Visualization Methods for Decision Making2007In: Symposium on Applied Perception in Graphics and Visualization (APGV 2007), ACM Press, 2007, p. 133-133Conference paper (Refereed)
    Abstract [en]

    Uncertainty constitutes a major obstacle to effective decision making. This work presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as results from user experiments for the theoretical evaluation of uncertainty visualization techniques that aid decision making. These principles can be used in future theoretical evaluations of existing or newly developed uncertainty visualization methods before usability testing with actual users.

  • 26.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Evaluation of Normal Model Visualization for Anomaly Detection in Maritime Traffic2014In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 4, no 1, article id 5Article in journal (Refereed)
    Abstract [en]

    Monitoring dynamic objects in surveillance applications is normally a demanding activity for operators, not only because of the complexity and high dimensionality of the data but also because of other factors like time constraints and uncertainty. Timely detection of anomalous objects or situations that need further investigation may reduce operators' cognitive load. Surveillance applications may include anomaly detection capabilities, but their use is not widespread, since they usually generate a high number of false alarms, they do not provide appropriate cognitive support for operators, and their outcomes can be difficult to comprehend and trust. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in traffic data, making this process more transparent. As a step toward this goal of transparency, this article presents an evaluation that assesses whether visualizations of normal behavioral models of vessel traffic support two of the main analytical tasks specified during our field work in maritime control centers. The evaluation combines quantitative and qualitative usability assessments. The quantitative evaluation, which was carried out with a proof-of-concept prototype, reveals that participants who used the visualization of normal behavioral models outperformed the group which did not do so. The qualitative assessment shows that domain experts have a positive attitude towards the provision of automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time and increase trust in and comprehensibility of the system

  • 27.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Evaluation of Uncertainty Visualization Techniques for Information Fusion2007In: 10th International Conference on Information Fusion, 2007, IEEE Press, 2007, p. 1-8Conference paper (Refereed)
    Abstract [en]

    This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles can be used in future theoretical evaluations of existing or newly developed uncertainty visualization techniques before usability testing with actual users.

  • 28.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics.
    Research proposal: Information Visualization for Information Fusion2007Report (Other academic)
    Abstract [en]

    Information fusion is a field of research that strives to establish theories, techniques and tools that exploit synergies in data retrieved from multiple sources. In many real-world applications huge amounts of data need to be gathered, evaluated and analyzed in order to make the right decisions. An important key element of information fusion is the adequate presentation of the data that guides decision-making processes efficiently. This is where theories and tools developed in information visualization, visual data mining and human computer interaction (HCI) research can be of great support. This report presents an overview of information fusion and information visualization, highlighting the importance of the latter in information fusion research. Information visualization techniques that can be used in information fusion are presented and analyzed providing insights into its strengths and weakness. Problems and challenges regarding the presentation of information that the decision maker faces in the ground situation awareness scenario (GSA) lead to open questions that are assumed to be the focus of further research

  • 29.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    The importance of visualization and interaction in the anomaly detection process2013In: Innovative approaches of data visualization and visual analytics / [ed] Mao Lin Huang & Weidong Huang, Information Science Reference, 2013, p. 133-150Chapter in book (Refereed)
  • 30.
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Visual Analytics for Maritime Anomaly Detection2011Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The surveillance of large sea areas typically involves  the analysis of huge quantities of heterogeneous data.  In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purely computational methods. This thesis suggests and investigates the adoption of visual analytics principles to support the detection of anomalous vessel behavior in maritime traffic data. This adoption involves studying the analytical reasoning process that needs to be supported,  using combined automatic and visualization approaches to support such process, and evaluating such integration. The analysis of data gathered during interviews and participant observations at various maritime control centers and the inspection of video recordings of real anomalous incidents lead to a characterization of the analytical reasoning process that operators go through when monitoring traffic. These results are complemented with a literature review of anomaly detection techniques applied to sea traffic. A particular statistical-based technique is implemented, tested, and embedded in a proof-of-concept prototype that allows user involvement in the detection process. The quantitative evaluation carried out by employing the prototype reveals that participants who used the visualization of normal behavioral models outperformed the group without aid. The qualitative assessment shows that  domain experts are positive towards providing automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time, as well as increase trust and comprehensibility in the system. Based on the lessons learned, this thesis provides recommendations for designers and developers of maritime control and anomaly detection systems, as well as guidelines for carrying out evaluations of visual analytics environments.

  • 31.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Visually supported reasoning under uncertain conditions: Effects of domain expertise on air traffic risk assessment2016In: Spatial Cognition and Computation, ISSN 1387-5868, E-ISSN 1573-9252, Vol. 16, no 2, p. 133-153Article in journal (Refereed)
    Abstract [en]

    This article investigates the impact that domain expertise has on risk assessment when analyzing uncertain geographical and sensor data. The differences between novice and expert air traffic operators were examined taking into account the performance of identifying and classifying threatening targets, the time needed to carry out such classifications, and the confidence reported for each decision. The results show that confidence was significantly higher for the expert group. This was supported by the after-test questionnaire because none of the novice participants reported being more confident with the visualizations of uncertainty provided. No significant differences regarding time and performance were found between the groups, even if experts needed, on average, more time to make a decision. Based on the collected logs, the experienced participants more often accessed the detailed information for each object presented by the tool tip. Both the time taken and the data accessed might indicate that experts had better situation awareness. Finally, the experts reported higher workload values related to performance.

  • 32.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics.
    Bergström, Erik
    University of Skövde, School of Humanities and Informatics.
    Carlén, Urban
    University of Skövde, School of Humanities and Informatics.
    Inför en ökad jämställdhet i datavetenskapliga utbildningsprogram2012Conference paper (Refereed)
  • 33.
    Riveiro, Maria
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    König, Rikard
    Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Department of Information Technology, University of Borås, Borås, Sweden.
    Brattberg, Peter
    Department of Information Technology, University of Borås, Borås, Sweden / PGA Sweden, Bara, Sweden.
    Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems2015In: Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Panayiotis Zaphiris & Andri Ioannou, Springer International Publishing Switzerland , 2015, Vol. 9192, p. 279-290Conference paper (Refereed)
    Abstract [en]

    Golf is a popular sport around the world. Since an accomplished golf swing is essential for succeeding in this sport, golf players spend a considerable amount of time perfecting their swing. In order to guide the design of future computer-based training systems that support swing instruction, this paper analyzes the data gathered during interviews with golf instructors and participant observations of actual swing coaching sessions. Based on our field work, we describe the characteristics of a proficient swing, how the instructional sessions are normally carried out and the challenges professional instructors face. Taking into account these challenges, we outline which desirable capabilities future computer-based training systems for professional golf instructors should have.

  • 34.
    Riveiro, Maria
    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.
    Detecting anomalous behavior in sea traffic: A study of analytical strategies and their implications for surveillance systems2014In: International Journal of Information Technology and Decision Making, ISSN 0219-6220, ISSN 0219-6220, Vol. 13, no 2, p. 317-360Article in journal (Refereed)
  • 35.
    Riveiro, Maria
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    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).
    Empirical evaluation of visualizations of normal behavioral models for supporting maritime anomaly detection2011In: Abstracts of GeoViz: Linking Geovisualization with Spatial Analysis and Modeling, March 10–11, 2011, Hamburg, Germany, 2011, p. 1-2Conference paper (Refereed)
  • 36.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Evaluating the usability of visualizations of normal behavioral models for analytical reasoning2010In: Proceedings 2010 Seventh International Conference on Computer Graphics, Imaging and Visualization: CGIV 2010: 7-10 August 2010 Sydney, Australia / [ed] Ebad Banissi, Muhammad Sarfraz and Mao Lin Huang, IEEE Computer Society, 2010, p. 179-185Conference paper (Refereed)
    Abstract [en]

    Many approaches for anomaly detection use statistical based methods that build profiles of normality. In these cases, anomalies are defined as deviations from normal models build from representative data. Detection systems based solely on these approaches typically generate high false alarm rates due to the difficulty of creating flawless models. In order to support the comprehension, validation and update of such models, this paper is devoted to the visualization of normal behavioral models of sea traffic and their usability evaluation. First, we present geographical projections of the different probability density functions that represent the normal traffic behavior and second, we outline results from a usability assessment carried out in order to evaluate the ability of such visualizations to support representative tasks related to the establishment of normal situational picture.

  • 37.
    Riveiro, Maria
    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, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Interactive Visualization of Normal Behavioral Models and Expert Rules for Maritime Anomaly Detection2009In: Computer graphics, imaging & visualisation: New advances and trends / [ed] Ebad Banissi, Muhammad Sarfraz, Jiawan Zhang, Anna Ursyn, Wong Chow Jeng, Mark W. McK. Bannatyne, Jian J. Zhang, Lim Hwee San, and Mao Lin Huang, IEEE Computer Society, 2009, p. 459-466Conference paper (Refereed)
    Abstract [en]

    Maritime surveillance systems analyze vast amounts of heterogeneous sensor data from a large number of objects. In order to support the operator while monitoring such systems, the identification of anomalous vessels or situations that might need further investigation may reduce the operator’s cognitive load. While it is worth acknowledgingthat many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world, since the detection of anomalous behavior is normally not a welldefined problem and therefore, human expert knowledge is needed. This calls for the development of interaction components that can support the user in the detection process.

    In order to support the comprehension of the knowledge embedded in the system, we propose an interactive way of visualizing expert rules and normal behavioral models built from the data. The overall goal is to facilitate the validation and update of these models and signatures, supporting the insertion of human expert knowledge while improving confidence and trust in the system.

  • 38.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Supporting the analytical reasoning process in maritime anomaly detection: evaluation and experimental design2010In: Proceedings 2010 14th International Conference Information Visualisation: IV 2010: 26-29 July 2010 London, United Kingdom, IEEE Computer Society, 2010, p. 170-178Conference paper (Refereed)
    Abstract [en]

    Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies include investigations on the analytical reasoning process that needs to be supported. In this paper, we present an approach to evaluate the ability of certain visual representations from an integrated visual-computational environment to support the completion of representative tasks. The problem area studied is the detection and identification of anomalous vessels and situations while monitoring maritime traffic data. This paper presents: (1) a brief review of current evaluation methodologies within information visualization and visual analytics, (2) an analysis of operator’s analytical reasoning process (derived from field work in maritime control centers and a literature review on analytical reasoning theories), (3) a list of representative tasks for usability evaluation and (4) an approach to evaluate the use of normal behavioral models representations during the detection process.

  • 39.
    Riveiro, Maria
    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, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    The role of visualization and interaction in maritime anomaly detection2011In: Visualization and Data Analysis 2011: Proceedings of SPIE / IS & T Electronic Imaging / [ed] Pak Chung Wong, Jinah Park, Ming C. Hao, Chaomei Chen, Katy Börner, David L. Kao, Jonathan C. Roberts, SPIE - International Society for Optical Engineering, 2011, p. Article number 78680M, 1-12Conference paper (Refereed)
    Abstract [en]

    The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems for area surveillance are rarely used in the real world. We argue that such capabilities and applications present two critical challenges: (1) they need to provide adequate user support and (2) they need to involve the user in the underlying detection process.

    In order to encourage the use of anomaly detection capabilities in surveillance systems, this paper analyzes the challenges that existing anomaly detection and behavioral analysis approaches present regarding their use and maintenance by users. We analyze input parameters, detection process, model representation and outcomes. We discuss the role of visualization and interaction in the anomaly detection process. Practical examples from our current research within the maritime domain illustrate key aspects presented.

  • 40.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    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.
    Improving maritime anomaly detection and situation awareness through interactive visualization2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), IEEE Computer Society, 2008, p. 47-54Conference paper (Refereed)
    Abstract [en]

    Surveillance of large land, air or sea areas with a multitude of sensor and sensor types typically generates huge amounts of data. Human operators trying to establish individual or collective maritime situation awareness are often overloaded by this information. In order to help them cope with this information overload, we have developed a combined methodology of data visualization, interaction and mining techniques that allows filtering out anomalous vessels, by building a model over normal behavior from which the user can detect deviations. The methodology includes a set of interactive visual representations that support the insertion of the user’s knowledge and experience in the creation, validation and continuous update of the normal model. Additionally, this paper presents a software prototype that implements the suggested methodology.

     

  • 41.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    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.
    Visual Analytics for the Detection of Anomalous Maritime Behavior2008In: Proceedings of 12th International Conference on Information Visualisation IV08 / [ed] Ebad Banissi, Liz Stuart, Mikael Jern, Gennady Andrienko, Francis T. Marchese, Nasrullah Memon, Reda Alhajj, Theodor G. Wyeld, Remo Aslak Burkhard, Georges Grinstein, Dennis Groth, Anna Ursyn, Carsten Maple, Anthony Faiola, and Brock Craft, IEEE Computer Society, 2008, p. 273-279Conference paper (Refereed)
    Abstract [en]

    The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information. Based on a visual analytics process model, we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations. The system uses an interactive data mining module that supports the insertion of the user's knowledge and experience in the creation, validation and continuous update of the normal model of the environment.

  • 42.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Kronhamn, Thomas
    Saab AB.
    Reasoning about anomalies: a study of the analytical process of detecting and identifying anomalous behavior in maritime traffic data2009In: Visual Analytics for Homeland Defense and Security: Proceedings of SPIE Defense, Security, and Sensing 2009 / [ed] William J Tolone, William Ribarsky, SPIE , 2009, p. Article ID 73460A-Conference paper (Refereed)
    Abstract [en]

    The goal of visual analytical tools is to support the analytical reasoning process, maximizing human perceptual, understanding and reasoning capabilities in complex and dynamic situations. Visual analytics software must be built upon an understanding of the reasoning process, since it must provide appropriate interactions that allow a true discourse with the information. In order to deepen our understanding of the human analytical process and guide developers in the creation of more efficient anomaly detection systems, this paper investigates how is the human analytical process of detecting and identifying anomalous behavior in maritime traffic data. The main focus of this work is to capture the entire analysis process that an analyst goes through, from the raw data to the detection and identification of anomalous behavior.

    Three different sources are used in this study: a literature survey of the science of analytical reasoning, requirements specified by experts from organizations with interest in port security and user field studies conducted in different marine surveillance control centers. Furthermore, this study elaborates on how to support the human analytical process using data mining, visualization and interaction methods.

    The contribution of this paper is twofold: (1) within visual analytics, contribute to the science of analytical reasoning with practical understanding of users tasks in order to develop a taxonomy of interactions that support the analytical reasoning process and (2) within anomaly detection, facilitate the design of future anomaly detector systems when fully automatic approaches are not viable and human participation is needed.

  • 43.
    Riveiro, Maria
    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).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Warston, Håkan
    Saab Microwave Systems AB (Sweden).
    VISAD: an interactive and visual analytical tool for the detection of behavioural anomalieis in maritime traffic data2009In: Visual Analytics for Homeland Defense and Security: Proceedings of SPIE Defense, Security, and Sensing 2009 / [ed] William J. Tolone, William Ribarsky, SPIE - International Society for Optical Engineering, 2009, p. Article ID 734607-Conference paper (Refereed)
    Abstract [en]

    Monitoring the surveillance of large sea areas normally involves the analysis of huge quantities of heterogeneous data from multiple sources (radars, cameras, automatic identification systems, reports, etc.). The rapid identification of anomalous behavior or any threat activity in the data is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world. There are two main reasons: (1) the detection of anomalous behavior is normally not a well-defined and structured problem and therefore, automatic data mining approaches do not work well and (2) the difficulties that these systems have regarding the representation and employment of the prior knowledge that the users bring to their tasks. In order to overcome these limitations, we believe that human involvement in the entire discovery process is crucial.

    Using a visual analytics process model as a framework, we present VISAD: an interactive, visual knowledge discovery tool for supporting the detection and identification of anomalous behavior in maritime traffic data. VISAD supports the insertion of human expert knowledge in (1) the preparation of the system, (2) the establishment of the normal picture and (3) in the actual detection of rare events. For each of these three modules, VISAD implements different layers of data mining, visualization and interaction techniques. Thus, the detection procedure becomes transparent to the user, which increases his/her confidence and trust in the system and overall, in the whole discovery process.

  • 44.
    Riveiro, Maria
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Gustavsson, Per M.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Combitech AB.
    Lebram, Mikael
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bengstsson, Mats
    Saab Training Systems, Saab AB, Huskvarna, Jönköping, Sweden.
    Blomqvist, Pär
    Saab Training Systems, Saab AB, Huskvarna, Jönköping, Sweden.
    Wallinius, Martin
    Saab Training Systems, Saab AB, Helsingborg, Sweden.
    Enhanced Training through Interactive Visualization of Training Objectives and Models2016In: Proceedings of the STO-MP-MSG-143, Ready for the Predictable, Prepared for the Unexpected: M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts, NATO Science & Technology Organization (STO) , 2016Conference paper (Refereed)
    Abstract [en]

    Military forces operate in complex and dynamic environments [1] where bad decisions might have fatal consequences. A key ability of the commander, team and individual warfighter is to quickly adapt to novel situations. Live, Virtual and Constructive training environments all provide elements of best practices for this type of training. However, many of the virtual training are designed without thorough consideration of the effectiveness and efficiency of embedded instructional strategies [2], and without considering the cognitive capabilities and limitations of trainees. As highlighted recently by Stacy and Freeman [3], large military training exercises require a significant commitment of resources, and to net a return on that investment, training scenarios for these events should systematically address well-specified training objectives, even if they often, do not.

    In order to overcome these shortcomings with both Live and Virtual training systems and following our previous work [4,5,6], this paper presents a design solution for a proof-of-concept prototype that visualizes and manages training objectives and performance measures, at individual and collective levels. To illustrate its functionality we use real-world data from Live training exercises. Finally, this paper discusses how to learn from previous training experiences using data mining methods in order to build training models to provide instructional personalized feedback to trainees.

  • 45.
    Riveiro, Maria
    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.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Influence of Meta-Information on Decision-Making: Lessons Learned from Four Case Studies2014In: Proceedings of the 4th International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2014), IEEE Communications Society, 2014Conference paper (Refereed)
    Abstract [en]

    This paper discusses the results of four empirical evaluations that assess the effects that visualizing system metainformation have on decision-making, particularly on confidence, trust, workload, time and performance. These four case studies correspond to the analysis of (1) the effects that visualizing uncertainty associated with sensor values (position, speed, altitude, etc. and track quality) have on decision-making on a ground to air defense scenario; (2) the effects that the visualization of the car’s certainty on its own capability of driving autonomously have on drivers’ trust and performance; (3) the influence that the visualization of various qualifiers associated with the proposals given by the support system has on air traffic operators carrying out identification tasks and (4) the effects that the presentation of different abstraction levels of information have on classification tasks carried out by fighter pilots. We summarize the results of these four case studies and discuss lessons learned for the design of future computerized support systems regarding the visualization of meta-information.

  • 46.
    Riveiro, Maria
    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.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Lebram, Mikael
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Effects of visualizing uncertainty on decision-making in a target identification scenario2014In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 41, no 1, p. 84-98Article in journal (Refereed)
    Abstract [en]

    This paper presents an empirical study that addresses the effects the visualization of uncertainty has on decision-making. We focus our investigations on an area where uncertainty plays an important role and the decision time is limited. For that, we selected an air defense scenario, where expert operators have a few minutes to make a well-informed decision based on uncertain sensor data regarding the identity of an object and where the consequences of a late or wrong decision are severe. An approach for uncertainty visualization is proposed and tested using a prototype that supports the interactive analysis of multivariate spatio-temporal sensor data. The uncertainty visualization embeds the accuracy of the sensor data values using the thickness of the lines in the graphical representation of the sensor values. Semi-transparent filled circles represent the uncertain position, while a track quality value between 0 and 1 accounts for the quality of the estimated track for each target. Twenty-two experienced air traffic operators were divided into two groups (with and without uncertainty visualization) and carried out identification and prioritization tasks using the prototype. The results show that the group aided by visualizations of uncertainty needed significantly fewer attempts to make a final identification, and a significant difference between the groups when considering the identities and priorities assigned was observed (participants with uncertainty visualization selected higher priority values and more hostile and suspect identities). These results may show that experts put themselves in the ``worst-case scenario" in the presence of uncertainty when safety is an issue. Additionally, the presentation of uncertainty neither increased the participants' expressed workload, nor the time needed to make a classification. However, the inclusion of the uncertainty information did not have a significant effect on the performance (true positives, false negatives and false positives) or the participants' expressed confidence in their decisions.

  • 47.
    Riveiro, Maria
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Helldin, Tove
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Lebram, Mikael
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Towards future threat evaluation systems: user study, proposal and precepts for design2013In: Proceedings of the 16th International Conference on Information Fusion, FUSION 2013, IEEE Press, 2013, p. 1863-1870Conference paper (Refereed)
    Abstract [en]

    In the defense domain, to estimate if a targetis threatening and to which degree is a complex task, thatis typically carried out by human operators due to the highrisks and uncertainties associated. To their aid, different supportsystems have been implemented to analyze the data and providerecommendations for actions. Since the ultimate responsibilitylies in human operators, it is of utmost importance that theytrust and know how to use these systems, as well as have anunderstanding of their inner workings, strengths and limitations.This paper presents, first, a formative user study to char-acterize how air traffic operators carry out threat evaluationrelated tasks. Grounded in these findings and in guidelinesfound in the literature, we present a transparent and highlyinteractive prototype that aims at reducing operator’s cognitiveload and support threat assessment activities. The literaturereview provided on design guidelines, the outcomes of the userstudy, the design of the prototype as well as the results of aninitial evaluation can provide guidance for both researchers andprospective developers of future threat evaluation systems.

  • 48.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Supporting Maritime Situation Awareness Using Self Organizing Maps and Gaussian Mixture Models2008In: Proceedings of the Tenth Scandinavian Conference on Artificial Intelligence (SCAI 2008) / [ed] Anders Holst, Per Kreuger, Peter Funk, Amsterdam: IOS Press, 2008, p. 84-91Conference paper (Refereed)
    Abstract [en]

    Maritime situation awareness is of importance in a lot of areas – e.g. detection of weapon smuggling in military peacekeeping operations, and harbor traffic control missions for the coast guard. In this paper, we have combined the use of Self Organizing Maps with Gaussian Mixture Models, in order to enable situation awareness by detecting deviations from normal behavior in an unsupervised way. Initial results show that simple anomalies can be detected using this approach.

  • 49.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics.
    Johansson, Ronnie
    University of Skövde, School of Humanities and Informatics.
    Karlsson, Alexander
    University of Skövde, School of Humanities and Informatics.
    Modeling and analysis of energy data: state-of-the-art and practical results from an application scenario2011Report (Other academic)
    Abstract [en]

    This paper presents a comprehensive summary of the state-of-the-art of energy efficiency research. The literature review carried out focuses on the application of data mining and data analysis techniques to energy consumption data, as well as  descriptions  of  tools, applications and research prototypes to manage the consumption of energy. Moreover, preliminary results of the application of a clustering technique to energy consumption data illustrate the review.

  • 50.
    Riveiro, Maria
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Lebram, Mikael
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Andersson, Christian X.
    Takara Bio Europe, Gothenburg, Sweden.
    Sartipy, Peter
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. Astra Zeneca, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Interactive visualization of large-scale gene expression data2016In: Information Visualisation: Computer Graphics, Imaging and Visualisation / [ed] Ebad Banissi, Mark W. McK. Bannatyne, Fatma Bouali, Remo Burkhard, John Counsell, Urska Cvek, Martin J. Eppler, Georges Grinstein, Wei Dong Huang, Sebastian Kernbach, Chun-Cheng Lin, Feng Lin, Francis T. Marchese, Chi Man Pun, Muhammad Sarfraz, Marjan Trutschl, Anna Ursyn, Gilles Venturini, Theodor G. Wyeld, and Jian J. Zhang, IEEE Computer Society, 2016, p. 348-354Conference paper (Refereed)
    Abstract [en]

    In this article, we present an interactive prototype that aids the interpretation of large-scale gene expression data, showing how visualization techniques can be applied to support knowledge extraction from large datasets. The developed prototype was evaluated on a dataset of human embryonic stem cell-derived cardiomyocytes. The visualization approach presented here supports the analyst in finding genes with high similarity or dissimilarity across different experimental groups. By using an external overview in combination with filter windows, and various color scales for showing the degree of similarity, our interactive visual prototype is able to intuitively guide the exploration processes over the large amount of gene expression data.

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