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  • 51.
    Johansson, Fredrik
    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.
    SWARD: System for Weapon Allocation Research & Development2010In: FUSION 2010: 13th international Conference on Information Fusion, 26-29 July 2010, EICC, Edinburgh, UK, IEEE conference proceedings, 2010, p. Article number 5712067-Conference paper (Refereed)
    Abstract [en]

    The allocation of firing units to hostile targets is an important process within the air defense domain. Many algorithms have been proposed for solving various weapon allocation problems, but evaluation of the performance of such algorithms is problematic, since it does not exist any standard scenarios on which to test the algorithms. It is to a large extent unknown how weapon allocation algorithms compare to each other when it comes to solution quality. We have developed the testbed SWARD, making it possible to systematically compare algorithm performance, and to support the development of new weapon allocation algorithms.

  • 52.
    Jontell, Mats
    et al.
    Göteborgs universitet.
    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).
    Gustafsson, Marie
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Torgersson, Olof
    Chalmers.
    Elektroniskt verktyg för klinik, utbildning och forskning2008In: Tandläkartidningen, ISSN 0039-6982, Vol. 100, no 12, p. 78-81Article in journal (Refereed)
    Abstract [sv]

    Att praktisera evidensbaserad odontologi innebär att integrera expertisen hos individuella kliniker med bästa vetenskapliga evidens från externa kunskapskällor.

  • 53.
    Karlsson, Alexander
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Hammarfelt, Björn
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Steinhauer, H. Joe
    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.
    Olson, Nasrine
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Nelhans, Gustaf
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Nolin, Jan
    Swedish School of Library and Information Science (SSLIS), University of Borås, Sweden.
    Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach2015In: Scientometrics, ISSN 0138-9130, E-ISSN 1588-2861, Vol. 102, no 3, p. 2255-2274Article in journal (Refereed)
  • 54.
    Khan, Fahad Shabhaz
    et al.
    Chalmers University of Technology.
    Anwer, Rao Muhammad
    Chalmers University of Technology.
    Torgersson, Olof
    Chalmers University of Technology.
    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.
    Data Mining in Oral Medicine Using Decision Trees2008In: Proceedings of the 5th International Conference on Computer, Electrical, and Systems Science, and Engineering (CESSE 2008), Cairo, Egypt, February 6–8, 2008, World Academy of Science Engineering and Technology - WASET , 2008, p. 225-230Conference paper (Refereed)
    Abstract [en]

    Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert’s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context over time.

  • 55.
    Kolbeinsson, Ari
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Virtual Systems Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Lindblom, Jessica
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Showing uncertainty in aircraft cockpits using icons2015In: Procedia Manufacturing, ISSN 2351-9789, Vol. 3, p. 2905-2912Article in journal (Refereed)
    Abstract [en]

    This paper examines an icon set designed for displaying uncertainty surrounding threat levels of an approaching object in anaircraft cockpit. This is done through an experiment that compares an icon set designed for this experiment with two icon setsfrom existing research that were tested in static laboratory conditions. The experiment used a flight simulator to simulate realisticflight conditions. The results showed that the icon set designed for this experiment was easier to read. Guidelines for the designof icons for displaying uncertainty are presented based on the results of the experiment.

  • 56.
    Laxhammar, Rikard
    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.
    Conformal Prediction for Distribution-Independent Anomaly Detection in Streaming Vessel Data2010In: StreamKDD '10: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, Association for Computing Machinery (ACM), 2010, p. 47-55Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection. We exploit the fact that conformal predictors give valid prediction sets at specified confidence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distribution. If the actual observation is not included in the possibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods.

  • 57.
    Laxhammar, Rikard
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Saab Security and Defence Solutions, Järfälla, Sweden.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories2015In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 74, no 1-2, p. 67-94Article in journal (Refereed)
  • 58.
    Laxhammar, Rikard
    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.
    Online Detection of Anomalous Sub-Trajectories: A Sliding Window Approach based on Conformal Anomaly Detection and Local Outlier Factor2012In: Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27–30, 2012, Proceedings, Part II / [ed] Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas, Springer, 2012, p. 192-202Conference paper (Refereed)
    Abstract [en]

    Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local sub-trajectory anomalies. Third, they involve tuning of many parameters and may suffer from high false alarm rates. The main contribution of this paper is the proposal and discussion of the Sliding Window Local Outlier Conformal Anomaly Detector (SWLO-CAD), which is an algorithm for online detection of local sub-trajectory anomalies. It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate. Moreover, SWLO-CAD is based on Local outlier factor, which is a previously proposed outlier measure that is sensitive to local anomalies. Thus, SWLO-CAD has a unique set of properties that address the issues above.

  • 59.
    Laxhammar, Rikard
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics. Saab AB.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Informatics.
    Online Learning and Sequential Anomaly Detection in Trajectories2014In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 36, no 6, p. 1158-1173Article in journal (Refereed)
  • 60.
    Laxhammar, Rikard
    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.
    Sequential Conformal Anomaly Detection in Trajectories based on Hausdorff Distance2011In: Proceedings of the 14th International Conference on Information Fusion (FUSION 2011), IEEE Computer Society, 2011, p. 153-160Conference paper (Refereed)
    Abstract [en]

    Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with wellcalibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameterfree dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.

  • 61.
    Laxhammar, Rikard
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. Saab Systems, Saab AB, Järfälla, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Sviestins, Egils
    Saab Systems, Saab AB, Järfälla, Sweden.
    Anomaly detection in sea traffic - a comparison of the Gaussian Mixture Model and the Kernel Density Estimator2009In: Proceedings of the 12th International Conference on Information Fusion, ISIF , 2009, p. 756-763Conference paper (Refereed)
    Abstract [en]

    This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model (GMM) and the adaptive Kernel Density Estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and

    simulated anomalous trajectories. The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Part of the explanation is that the methods are based on a rather artificial division of data into geographical cells. The paper therefore discusses other clustering approaches based on more informed features of data and more background knowledge regarding the structure and natural classes of the data.

  • 62.
    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)
  • 63.
    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.

  • 64.
    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.

  • 65.
    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.

  • 66.
    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. 

  • 67.
    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, Linköping, Sweden.
    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.

  • 68.
    Olson, Nasrine
    et al.
    University of Borås.
    Steinhauer, H. Joe
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Nelhans, Gustaf
    University of Borås.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Nolin, Jan
    University of Borås.
    Little Scientist, Big Data Information fusion towards meeting the information needs of scholars2014In: Assessing Libraries and Library Users and Use, 2014Conference paper (Other academic)
  • 69.
    Rana, Rakesh
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A framework for identifying and evaluating technologies of interest for effective business strategy: Using text analytics to augment technology forecasting2017In: 5th International Symposium on Computational and Business Intelligence (ISCBI 2017), IEEE, 2017, p. 110-115, article id 8053555Conference paper (Refereed)
  • 70.
    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)
  • 71.
    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)
  • 72.
    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.

  • 73.
    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.

  • 74.
    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.

  • 75.
    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.

  • 76.
    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.

     

  • 77.
    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.

  • 78.
    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.

  • 79.
    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.

  • 80.
    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.

  • 81.
    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.

  • 82.
    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.

  • 83.
    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.

  • 84.
    Ståhl, Niclas
    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.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Boström, Jonas
    Department of Medicinal Chemistry, CVMD iMED, AstraZeneca, Mölndal, Sweden.
    Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data2018In: Article in journal (Refereed)
    Abstract [en]

    We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

  • 85.
    Ståhl, Niclas
    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.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Boström, Jonas
    Department of Medicinal Chemistry, CVMD iMED, AstraZeneca, Sweden.
    Improving the use of deep convolutional neural networks for the prediction of molecular properties2019In: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Springer, 2019, p. 71-79Conference paper (Refereed)
    Abstract [en]

    We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

  • 86.
    Ståhl, Niclas
    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.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A self-organizing ensemble of deep neural networks for the classification of data from complex processes2018Conference paper (Refereed)
    Abstract [en]

    We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

  • 87.
    Torgersson, Olof
    et al.
    Department of Computing Science, Chalmers University of Technology, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Using text generation to access clinical data in a variety of contexts2002In: Health Data in the Information Society: Proceedings of MIE2002, IOS Press, 2002, p. 460-465Conference paper (Refereed)
12 51 - 87 of 87
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