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  • 1.
    Baalsrud Hauge, Jannicke Madeleine
    et al.
    University of Bremen, Bremer Institut für Produktion und Logistik, Germany.
    Stanescu, Ioana
    Carol I National Defence University, Romania.
    Arnab, Sylvester
    Disruptive Media Learning Lab, Coventry University, UK.
    Moreno Ger, Pablo
    Universidad Complutense de Madrid, Spain.
    Lim, Theodore
    Herriot-Watt University, UK.
    Serrano-Laguna, Angel
    Universidad Complutense de Madrid, Spain.
    Lameras, Petros
    Serious Games Institute, Coventry University, UK.
    Hendrix, Maurice
    School of Science and Technology, University of Northampton, UK.
    Kiili, Kristian
    Tampere University of Technology, Finland.
    Ninaus, Manuel
    University of Graz, Austria.
    de Freitas, Sara
    Curtin University, Australia.
    Mazzetti, Alessandro
    Gruppo SIGLA S.r.l, Italy.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Degano, Cristiana
    Gruppo SIGLA S.r.l, Italy.
    Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods2015In: International Journal of Serious Games, ISSN 2384-8766, Vol. 2, no 1, p. 29-44Article in journal (Refereed)
    Abstract [en]

    The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs) are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture) discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.

  • 2.
    Brax, Christoffer
    et al.
    Training Systems and Information Fusion, Business Area Electronic Defence Systems, Saab AB, Skövde, Sweden.
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    A Study of Anomaly Detection in Data from Urban Sensor Networks2012In: Modeling Decisions for Artificial Intelligence: 9th International Conference, MDAI 2012, Girona, Catalonia, Spain, November 21-23, 2012. Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Beatriz López, Mateu Villaret, Springer Berlin/Heidelberg, 2012, p. 185-196Conference paper (Refereed)
    Abstract [en]

    In many sensor systems used in urban environments, the amount of data produced can be vast. To aid operators of such systems, high-level information fusion can be used for automatically analyzing the surveillance information. In this paper an anomaly detection approach for finding areas with traffic patterns that deviate from what is considered normal is evaluated. The use of such approaches could help operators in identifying areas with an increased risk for ambushes  or improvised explosive devices (IEDs).

  • 3.
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    A comparison of two approaches for situation detection in an air-to-air combat scenario2013In: Modeling Decisions for Artificial Intelligence, Springer Berlin/Heidelberg, 2013, p. 70-81Conference paper (Refereed)
    Abstract [en]

    Combat survivability is an important objective in military air operations, which involves not being shot down by e.g. enemy aircraft. This involves analyzing data and information, detecting and estimating threats, and implementing actions to counteract threats. Beyond visual range missiles can today be fired from one hundred kilometers away. At such distances, missiles are difficult to detect and track. The use of techniques for recognizing hostile aircraft behaviors can possibly be used to infer the presence and for providing early warnings of such threats. In this paper we compare the use of dynamic Bayesian networks and fuzzy logic for detecting hostile aircraft behaviors.

  • 4.
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Petri nets for Situation Recognition2011Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Situation recognition is a process with the goal of identifying a priori defined situations in a flow of data and information. The purpose is to aid decision makers with focusing on relevant information by filtering out situations of interest. This is an increasingly important and non trivial problem to solve since the amount of information in various decision making situations constantly grow. Situation recognition thus addresses the information gap, i.e. the problem of finding the correct information at the correct time. Interesting situations may also evolve over time and they may consist of multiple participating objects and their actions. This makes the problem even more complex to solve. This thesis explores situation recognition and provides a conceptualization and a definition of the problem, which allow for situations of partial temporal definition to be described. The thesis then focuses on investigating how Petri nets can be used for recognising situations. Existing Petri net based approaches for recognition have some limitations when it comes to fulfilling requirements that can be put on solutions to the situation recognition problem. An extended Petri net based technique that addresses these limitations is therefore introduced. It is shown that this technique can be as efficient as a rule based techniques using the Rete algorithm with extensions for explicitly representing temporal constraints. Such techniques are known to be efficient; hence, the Petri net based technique is efficient too. The thesis also looks at the problem of learning Petri net situation templates using genetic algorithms. Results points towards complex dynamic genome representations as being more suited for learning complex concepts, since these allow for promising solutions to be found more quickly compared with classical bit string based representations. In conclusion, the extended Petri net based technique is argued to offer a viable approach for situation recognition since it: (1) can achieve good recognition performance, (2) is efficient with respect to time, (3) allows for manually constructed situation templates to be improved and (4) can be used with real world data to find real world situations.

  • 5.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Supporting threat evaluation through visual analytics2013In: 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2013), IEEE Press, 2013, p. 155-162Conference paper (Refereed)
    Abstract [en]

    Threat evaluation is concerned with estimating the level of threat posed by enemy units to one's own assets. This is an impact analysis problem which is important to address for supporting operators in achieving situation awareness. Due to the risky and complex nature of the threat evaluation tasks, it is imperative that the operators are supported by computerized systems as well as that they are an integral part of the threat evaluation process. To do so, the operators have to understand and be able to provide their input to the process, hence the need to make the threat evaluation process transparent to the operators. In order to implement a transparent threat evaluation support system, we argue that the process of visual analytics should provide valuable guidance. In this paper we suggest a model for using visual analytics in a threat evaluation context. We also investigate the potential of recognized threat evaluation models to be used within a visual analytics context. © 2013 IEEE.

  • 6.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Evolving Petri Net Situation Templates for Situation Recognition2009In: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009) / [ed] Ronnie Johansson, Joeri van Laere and Jonas Mellin, University of Skövde , 2009, p. 11-16Conference paper (Refereed)
    Abstract [en]

     

    Situation recognition is an important problem to address in order to enhance the capabilities of modern surveillance systems. Situation recognition is concerned with finding a priori defined situations that possibly are instantiated in the present flow of information. It can be a rather tricky task to manually define templates for situations that evolve over time, and to at the same time achieve good results with respect to recall and precision on a situation recognition task. In this paper we present some initial results concerning the task of applying genetic algorithms to evolve Petri net based situation templates of interesting situations. Our results show that it is possible to evolve Petri nets that are on par with manually defined templates. However, more research is needed in order to establish the actual effects it has on recall and precision.

     

  • 7.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Goal-Directed Hierarchical Dynamic Scripting for RTS Games2006In: Proceedings of the Second Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE -06) / [ed] John Laird & Jonathan Schaeffer, 2006, p. 21-28Conference paper (Refereed)
    Abstract [en]

    Learning how to defeat human players is a challenging task in today’s commercial computer games. This paper suggests a goal-directed hierarchical dynamic scripting approach for incorporating learning into real-time strategy games. Two alternatives for shortening the re-adaptation time when using dynamic scripting are also presented. Finally, this paper presents an effective way of throttling the performance of the adaptive artificial intelligence system. Put together, the approach entails the possibility of an artificial intelligence opponent to be challenging for a human player, but not too challenging.

  • 8.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Trajectory Clustering for Coastal Surveillance2007In: 10th International Conference on Information Fusion (FUSION 2007),, IEEE Press, 2007, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Achieving superior situation awareness is a key task for military, as well as civilian, decision makers. Today, automatic systems provide us with an excellent opportunity for assisting the human decision maker in achieving this awareness. Due to the potential of information overload one important aspect is to understand where to focus attention. Anomaly detection is concerned with finding deviations from normalcy and it is an increasingly important topic when providing decision support, since it can give hints towards where more analysis is needed. In this paper we explore trajectory clustering as a means for representing normal behavior in a coastal surveillance scenario. Trajectory clustering however suffers from some drawbacks in this type of setting and we therefore propose a new approach, spline-based clustering, with a potential for solving the task of representing the normal course of events.

  • 9.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    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.
    A Component-based Simulator for Supporting Research on Situation Recognition2009In: Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing: Proceedings of SPIE Defense, Security, and Sensing 2009 / [ed] John F Koegel Buford, SPIE - International Society for Optical Engineering, 2009, p. Article ID 735206-Conference paper (Refereed)
    Abstract [en]

    Research on information fusion and situation management within the military domain, is often focused on data-driven approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new algorithms it is of utmost importance to have data for studying the problem at hand (as well as for evaluation purposes). This often become a problem within the military domain as there is a high level of secrecy, resulting in a lack of data, and instead one often needs to resort to artificial data. Many tools and simulation environments can be used for constructing scenarios in virtual worlds. Most of these are however data-centered, that is, their purpose is to simulate the real-world as accurately as possible, in contrast to simulating complex scenarios. In high-level information fusion we can however often assume that lower-level problems have already been solved - thus the separation of abstraction - and we should instead focus on solving problems concerning complex relationships, i.e. situations and threats. In this paper we discuss requirements that research on situation recognition puts on simulation tools. Based on these requirements we present a component-based simulator for quickly adapting the simulation environment to the needs of the research problem at hand. This is achieved by defining new components that define behaviors of entities in the simulated world.

  • 10.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    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.
    Attempting to increase the Performance of Petri net based Situation Recognition2010In: Proceedings of the 22nd Benelux Conference on Artificial Intelligence, Benelux Association for Artificial Intelligence , 2010Conference paper (Refereed)
    Abstract [en]

    Situation recognition is an important problem to solve for introducing new capabilities in surveillance applications. It is concerned with recognizing a priori defined situations of interest, which are characterized as being of temporal and concurrent nature. The purpose is to aid decision makers with focusing on information that is known to likely be important for them, given their goals. Besides the two most important problems: knowing what to recognize and being able to recognize it, there are three main problems coupled to real time recognition of situations. Computational complexity — we need to process data and information within bounded time. Tractability — human operators must be able to easily understand what is being modelled. Expressability — we must be able to express situations at suitable levels of abstraction. In this paper we attempt to lower the computational complexity of a Petri net based approach for situation.

  • 11.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    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.
    DESIRER: a Development Environment for Situation Recognition Research2010In: Proceedings 2010 Second WRI Global Congress on Intelligent Systems: GCIS 2010: Volume 1 / [ed] Xinhan Huang, Li Da Xu, Zu De Zhou, Zhun Fan, M. M. Gupta, & Pan Wang, Los Alamitos, CA: IEEE Computer Society, 2010, p. 143-148Conference paper (Refereed)
    Abstract [en]

    Situation recognition is an important problem within the surveillance domain, which addresses the problem of recognizing a priori defined patterns of interesting situations that may be of concurrent and temporal nature, and which possibly are occurring in the present flow of data and information. There may be many viable approaches, with different properties, for addressing this problem however, something they must have in common is good efficiency and high performance. In order to determine if a potential solution has these properties, it is a necessity to have access to test and development environments. In this paper we present DESIRER, a development environment for working with situation recognition, and for evaluating and comparing different approaches.

  • 12.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    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.
    Evolving Petri Nets for Situation Recognition2010In: GEM 2010: Proceedings of the 2010 International Conference on Genetic and Evolutionary Methods / [ed] Hamid R. Arabnia, Ray R. Hashemi, Ashu M. G. Solo, CSREA Press, 2010, p. 29-35Conference paper (Refereed)
    Abstract [en]

    Situation recognition is an important problem to address for developing newcapabilities in the surveillance domain.  It is concerned with recognizing a priori defined situations of interest, which can be of concurrent and temporal nature, possibly occurring in a continuous flow of data and information. It is however a complex task to manually define what constitutes an interesting situation, and we therefore investigate the possibility of using genetic algorithms for evolving Petri nets for situation recognition. Our results show that: (1) it is possible to evolve complex Petri nets, (2) it is possible to increase the performance of manually  designed Petri nets, and (3) a dynamic genome representation consisting of  complex genes is beneficial compared to a representation consisting of bit strings.

  • 13.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Situation Recognition and Hypothesis Management Using Petri Nets2009In: Modeling Decisions for Artificial Intelligence: Proceedings of the 6th International Conference (MDAI 2009) / [ed] Vicenç Torra, Yasuo Narukawa, Masahiro Inuiguchi, Springer Berlin/Heidelberg, 2009, p. 303-314Conference paper (Refereed)
    Abstract [en]

    Situation recognition – the task of tracking states and identifying situations - is a problem that is important to look into for aiding decision makers in achieving enhanced situation awareness. The purpose of situation recognition is, in contrast to producing more data and information, to aid decision makers in focusing on information that is important for them, i.e. to detect potentially interesting situations. In this paper we explore the applicability of a Petri net based approach for modeling and recognizing situations, as well as for managing the hypothesis space coupled to matching situation templates with the present stream of data.

     

  • 14.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Niklasson, Lars
    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.
    Loutfi, Amy
    Örebro University.
    Towards Template-based Situation Recognition2009In: Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing: Proceedings of SPIE Defense, Security, and Sensing 2009 / [ed] Stephen Mott, John F. Buford, Gabe Jakobson, Michael J. Mendenhall, SPIE - International Society for Optical Engineering, 2009, p. Article ID 735205-Conference paper (Refereed)
    Abstract [en]

    The process of tracking and identifying developing situations is an ability of importance within the surveillance domain. We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers. Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are interesting, how to represent these situations, and which inferable events and states that can be used for representing them. We also need to know how to track and identify situations and how to determine the correlation between present information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals concerning template-based situation recognition. We provide a categorization of approaches for situation recognition together with a formalization of the template-based situation recognition problem. We also discuss this formalization in the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of decision makers.

  • 15.
    Dahlbom, Anders
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Nordlund, Per-Johan
    Aeronautics, Saab AB, Linköping, Sweden.
    Detection of Hostile Aircraft Behaviors using Dynamic Bayesian Networks2013In: Proceedings of the 16th International Conference on Information Fusion, IEEE Press, 2013, p. 2033-2040Conference paper (Refereed)
    Abstract [en]

    Aircraft Combat Survivability in military air operations is concerned with survival of the own aircraft. This entails analysis of information, detection and estimation of threats, and the implementation of actions to counteract detected threats. Beyond visual range weapons can today be fired from one hundred kilometers away, making them difficult to detect and track. One approach for providing early warnings of such threats is to analyze the kinematic behavior of enemy aircraft in order to detect situations that may point to malicious intent. In this paper we investigate the use of dynamic Bayesian networks for detecting hostile aircraft behaviors.

  • 16.
    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.

  • 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.
    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.

  • 19.
    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.

  • 20.
    Karlsson, Alexander
    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.
    Zhong, Hui
    Advanced Technology and Research, Volvo Group Trucks Technology, Gothenburg, Sweden.
    Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems2014In: Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings, 2014, p. 194-203Conference paper (Refereed)
  • 21.
    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)
  • 22.
    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.

  • 23.
    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.

  • 24.
    Tavara, Shirin
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Department of Information Technology, University of Borås, Borås, Sweden.
    Sundell, Håkan
    Department of Information Technology, University of Borås, Borås, Sweden.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM2015In: Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1 / [ed] Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno, Printed in the United States of America: CSREA Press, 2015, Vol. 1, p. 177-183Conference paper (Refereed)
    Abstract [en]

    We present a significantly improved implementation of a parallel SVM algorithm (PSVM) together with a comprehensive experimental study. Support Vector Machines (SVM) is one of the most well-known machine learning classification techniques. PSVM employs the Interior Point Method, which is a solver used for SVM problems that has a high potential of parallelism. We improve PSVM regarding its structure and memory management for contemporary processor architectures. We perform a number of experiments and study the impact of the reduced column size p and other important parameters as C and gamma on the class-prediction accuracy and training time. The experimental results show that there exists a threshold between the number of computational cores and the training time, and that choosing an appropriate value of p effects the choice of the C and gamma parameters as well as the accuracy.

  • 25.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Aliahmadipour, Laya
    Departement of Computer Science Shahid Bahonar University of Kerman, Iran.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters2016In: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2016, p. 524-530Conference paper (Refereed)
    Abstract [en]

    In this paper we discuss how three types of fuzzy partitions can be used to describe the results of three types of cluster structures. Standard fuzzy partitions are suitable for centroid based clusters, and I-fuzzy partitions for clusters represented by segments or lines (e.g., c-varieties). In this paper, we introduce hesitant fuzzy partitions. They are suitable for clusters defined by sets of centroids. Because of that, we show that they are useful for hierarchical clustering. We also establish the relationship between hesitant fuzzy partitions and I-fuzzy partitions.

  • 26.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Narukawa, Yasuo
    Toho Gakuen, Naka, Kunitachi, Tokyo, Japan / Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Nagatuta, Midori-ku, Yokohama, Japan.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    On this book: Clustering, multisets, rough sets and fuzzy sets2017In: Fuzzy sets, rough sets, multisets and clustering / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, p. 1-5Chapter in book (Other academic)
    Abstract [en]

    This chapter gives an overview of the content of this book, and links them with the work of Prof. Sadaaki Miyamoto, to whom this book is dedicated.

  • 27.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Narukawa, YasuoDahlbom, AndersUniversity of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings2015Conference proceedings (editor) (Refereed)
1 - 27 of 27
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