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Ståhl, N., Falkman, G., Mathiason, G. & Karlsson, A. (2018). A self-organizing ensemble of deep neural networks for the classication of data from complex processes. In: : . Paper presented at 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain.
Open this publication in new window or tab >>A self-organizing ensemble of deep neural networks for the classication of data from complex processes
2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15008 (URN)
Conference
17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-06-11
Rana, R., Karlsson, A. & Falkman, G. (2017). A framework for identifying and evaluating technologies of interest for effective business strategy: Using text analytics to augment technology forecasting. In: 5th International Symposium on Computational and Business Intelligence (ISCBI 2017): . Paper presented at 5th International Symposium on Computational and Business Intelligence (ISCBI), August 11-14, 2017, Dubai, United Arab Emirates (pp. 110-115). IEEE, Article ID 8053555.
Open this publication in new window or tab >>A framework for identifying and evaluating technologies of interest for effective business strategy: Using text analytics to augment technology forecasting
2017 (English)In: 5th International Symposium on Computational and Business Intelligence (ISCBI 2017), IEEE, 2017, p. 110-115, article id 8053555Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
Keyword
text analytics, technology intelligence, forecasting, emerging technologies, technology forecasting, trend analysis
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14647 (URN)10.1109/ISCBI.2017.8053555 (DOI)000427097000023 ()2-s2.0-85034807729 (Scopus ID)978-1-5386-1772-4 (ISBN)978-1-5386-1771-7 (ISBN)978-1-5386-1770-0 (ISBN)978-1-5386-1773-1 (ISBN)
Conference
5th International Symposium on Computational and Business Intelligence (ISCBI), August 11-14, 2017, Dubai, United Arab Emirates
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-06-11Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M., Helldin, T. & Falkman, G. (2017). Evaluating Multi-Attributes on Cause and Effect Relationship Visualization. In: Alexandru Telea, Jose Braz, Lars Linsen (Ed.), Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP. Paper presented at 8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal (pp. 64-74). SciTePress
Open this publication in new window or tab >>Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
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2017 (English)In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress, 2017, p. 64-74Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Place, publisher, year, edition, pages
SciTePress, 2017
Keyword
Cause and effect, uncertainty, evaluation, graph visualization
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14190 (URN)10.5220/0006102300640074 (DOI)978-989-758-228-8 (ISBN)
Conference
8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal
Funder
Knowledge Foundation
Available from: 2017-10-02 Created: 2017-10-02 Last updated: 2018-06-11Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M. & Falkman, G. (2017). User Participation in the Design of Cockpit Interfaces. In: Marcelo Soares, Christianne Falcão & Tareq Z. Ahram (Ed.), Advances in Ergonomics Modeling, Usability & Special Populations: . Paper presented at AHFE 2016 International Conference on Ergonomics Modeling, Usability & Special Populations, July 27-31, 2016, Walt Disney World®, Florida, USA (pp. 51-58). Springer, 486
Open this publication in new window or tab >>User Participation in the Design of Cockpit Interfaces
2017 (English)In: Advances in Ergonomics Modeling, Usability & Special Populations / [ed] Marcelo Soares, Christianne Falcão & Tareq Z. Ahram, Springer, 2017, Vol. 486, p. 51-58Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2017
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 486
Keyword
User participation, User involvement, Fighter aircraft
National Category
Human Computer Interaction Engineering and Technology
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12962 (URN)10.1007/978-3-319-41685-4_5 (DOI)2-s2.0-84992650798 (Scopus ID)978-3-319-41685-4 (ISBN)978-3-319-41684-7 (ISBN)
Conference
AHFE 2016 International Conference on Ergonomics Modeling, Usability & Special Populations, July 27-31, 2016, Walt Disney World®, Florida, USA
Funder
VINNOVA
Available from: 2016-09-24 Created: 2016-09-24 Last updated: 2018-03-28Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M. & Falkman, G. (2016). A Teamwork Model for Fighter Pilots. In: Don Harris (Ed.), 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. Paper presented at 13th International Conference, EPCE 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016 (pp. 221-230). Springer, 9736
Open this publication in new window or tab >>A Teamwork Model for Fighter Pilots
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9736
Keyword
Team effectiveness, Teamwork, Fighter aircraft, Fighter pilots
National Category
Engineering and Technology Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12961 (URN)10.1007/978-3-319-40030-3_23 (DOI)000389412900023 ()2-s2.0-84978218415 (Scopus ID)978-3-319-40029-7 (ISBN)978-3-319-40030-3 (ISBN)
Conference
13th International Conference, EPCE 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016
Funder
VINNOVA
Available from: 2016-09-24 Created: 2016-09-24 Last updated: 2018-03-28Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M. & Falkman, G. (2016). Elements of team effectiveness: A qualitative study with pilots. In: 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA): . Paper presented at IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, CA, 21-25 March 2016 (pp. 21-27). IEEE Computer Society
Open this publication in new window or tab >>Elements of team effectiveness: A qualitative study with pilots
2016 (English)In: 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), IEEE Computer Society, 2016, p. 21-27Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Keyword
fighter aircraft, teamwork, fighter pilots, team effectiveness
National Category
Engineering and Technology Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12960 (URN)10.1109/COGSIMA.2016.7497781 (DOI)000390774200004 ()2-s2.0-84981298083 (Scopus ID)978-1-5090-0632-8 (ISBN)
Conference
IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, CA, 21-25 March 2016
Funder
VINNOVA
Available from: 2016-09-24 Created: 2016-09-24 Last updated: 2018-03-28Bibliographically approved
Helldin, T., Riveiro, M., Pashami, S., Falkman, G., Byttner, S. & Nowaczyk, S. (2016). Supporting analytical reasoning: A study from the automotive industry. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II. Paper presented at 18th International Conference on Human Interface and the Management of Information (HCI International 2016), Toronto, Canada, July 17-22, 2016. (pp. 20-31). Springer
Open this publication in new window or tab >>Supporting analytical reasoning: A study from the automotive industry
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2016 (English)In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Springer, 2016, p. 20-31Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9735
Keyword
analytical reasoning, sense-making, visual analytics, truck data analysis, big data
National Category
Human Computer Interaction
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12801 (URN)10.1007/978-3-319-40397-7_3 (DOI)000389467600003 ()2-s2.0-84978877445 (Scopus ID)978-3-319-40396-0 (ISBN)978-3-319-40397-7 (ISBN)
Conference
18th International Conference on Human Interface and the Management of Information (HCI International 2016), Toronto, Canada, July 17-22, 2016.
Projects
BIDAF - A Big Data Analytics Framework for a Smart Society
Funder
Knowledge Foundation, BIDAF 2014/32
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2018-03-28Bibliographically approved
Laxhammar, R. & Falkman, G. (2015). Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories. Annals of Mathematics and Artificial Intelligence, 74(1-2), 67-94
Open this publication in new window or tab >>Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories
2015 (English)In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 74, no 1-2, p. 67-94Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer, 2015
Keyword
Anomaly detection, Conformal prediction, Local outlier factor, Maritime surveillance, Trajectory data
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-9034 (URN)10.1007/s10472-013-9381-7 (DOI)000355747600005 ()2-s2.0-84930417109 (Scopus ID)
Available from: 2014-05-01 Created: 2014-05-01 Last updated: 2018-01-11Bibliographically approved
Karlsson, A., Hammarfelt, B., Steinhauer, H. J., Falkman, G., Olson, N., Nelhans, G. & Nolin, J. (2015). Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach. Scientometrics, 102(3), 2255-2274
Open this publication in new window or tab >>Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach
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2015 (English)In: Scientometrics, ISSN 0138-9130, E-ISSN 1588-2861, Vol. 102, no 3, p. 2255-2274Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Springer Netherlands, 2015
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-10224 (URN)10.1007/s11192-014-1481-6 (DOI)000350337000028 ()2-s2.0-84925497206 (Scopus ID)
Projects
Information Fusion as an E-Service in Scholarly Information Use (INCITE)
Available from: 2014-11-26 Created: 2014-11-26 Last updated: 2018-03-28Bibliographically approved
Kolbeinsson, A., Falkman, G. & Lindblom, J. (2015). Showing uncertainty in aircraft cockpits using icons. Paper presented at 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, 26–30 July 2015, Las Vegas, United States. Procedia Manufacturing, 3, 2905-2912
Open this publication in new window or tab >>Showing uncertainty in aircraft cockpits using icons
2015 (English)In: Procedia Manufacturing, ISSN 2351-9789, Vol. 3, p. 2905-2912Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
uncertainty visualisation, aviation, information visualisation, interaction design, icon design
National Category
Interaction Technologies
Research subject
Technology; User Centred Product Design; Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-11359 (URN)10.1016/j.promfg.2015.07.805 (DOI)000383740303004 ()2-s2.0-85009918026 (Scopus ID)
Conference
6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, 26–30 July 2015, Las Vegas, United States
Note

Arbetet som ligger till grund för detta konferensbidrag utfördes av Ari när han var student på masterprogrammet i datavetenskap (nuvarande IIT), därav har Ari fått den affileringen för detta bidrag. Göran Falkman var handledare och Jessica Lindblom var examinator. ISBN 978-1-4951-6042-4

Available from: 2015-08-17 Created: 2015-08-17 Last updated: 2018-04-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8884-2154

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