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Publications (10 of 30) Show all publications
Koloseni, D., Helldin, T. & Torra, V. (2018). Absolute and relative preferences in AHP-like matrices. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018). Paper presented at Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018 (pp. 260-267). World Scientific Publishing Co. Pte. Ltd., 11
Open this publication in new window or tab >>Absolute and relative preferences in AHP-like matrices
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, World Scientific Publishing Co. Pte. Ltd. , 2018, Vol. 11, p. 260-267Conference paper, Published paper (Refereed)
Abstract [en]

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

Place, publisher, year, edition, pages
World Scientific Publishing Co. Pte. Ltd., 2018
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 11
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16409 (URN)10.1142/9789813273238_0035 (DOI)978-981-3273-22-1 (ISBN)978-981-3273-24-5 (ISBN)
Conference
Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-08Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2018). Anomaly Detection in Telecommunication Networks using Topic Models. In: : . Paper presented at Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018.
Open this publication in new window or tab >>Anomaly Detection in Telecommunication Networks using Topic Models
2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16491 (URN)
Conference
Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018
Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-02-14
Helldin, T., Steinhauer, H. J., Karlsson, A. & Mathiason, G. (2018). Situation Awareness in Telecommunication Networks Using Topic Modeling. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom (pp. 549-556). IEEE
Open this publication in new window or tab >>Situation Awareness in Telecommunication Networks Using Topic Modeling
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, p. 549-556Conference paper, Published paper (Refereed)
Abstract [en]

For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Data handling, Data visualization, Information fusion, Design suggestions, Exploratory data analysis, Human operator, Information fusion techniques, Situation awareness, Telecommunication operators, Topic Modeling, Wireless telecommunications, Data mining
National Category
Computer and Information Sciences Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16297 (URN)10.23919/ICIF.2018.8455529 (DOI)2-s2.0-85054066646 (Scopus ID)978-0-9964527-6-2 (ISBN)978-0-9964527-7-9 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom
Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2019-02-08Bibliographically approved
Steinhauer, H. J., Helldin, T. & Mathiason, G. (2018). Spatio-Temporal Awareness for Wireless Telecommunication Networks. In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge. Paper presented at International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden) (pp. 49-50).
Open this publication in new window or tab >>Spatio-Temporal Awareness for Wireless Telecommunication Networks
2018 (English)In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge, 2018, p. 49-50Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16490 (URN)
Conference
International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden)
Note

http://www.iiia.csic.es/LR2018/talks_proceedings

Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-02-14Bibliographically 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
Keywords
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)000444939500005 ()2-s2.0-85040593124 (Scopus ID)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: 2018-12-20Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Identifying Root Cause and Derived Effects in Causal Relationships. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I. Paper presented at Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017 (pp. 22-34). Springer
Open this publication in new window or tab >>Identifying Root Cause and Derived Effects in Causal Relationships
2017 (English)In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer, 2017, p. 22-34Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10273
Keywords
cause and effect, strenght and significance, graph visualization, user study
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14191 (URN)10.1007/978-3-319-58521-5_2 (DOI)2-s2.0-85025150109 (Scopus ID)978-3-319-58520-8 (ISBN)978-3-319-58521-5 (ISBN)
Conference
Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017
Projects
BIDAF
Funder
Knowledge Foundation
Available from: 2017-10-02 Created: 2017-10-02 Last updated: 2018-06-11Bibliographically approved
Steinhauer, H. J., Helldin, T., Karlsson, A. & Mathiason, G. (2017). Topic Modeling for Situation Understanding in Telecommunication Networks. In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC): . Paper presented at 27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia (pp. 73-78). IEEE
Open this publication in new window or tab >>Topic Modeling for Situation Understanding in Telecommunication Networks
2017 (English)In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, p. 73-78Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
Series
International Telecommunication Networks and Applications Conference (ITNAC), E-ISSN 2474-154X
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14591 (URN)10.1109/ATNAC.2017.8215362 (DOI)000427574400013 ()2-s2.0-85046642703 (Scopus ID)978-1-5090-6796-1 (ISBN)978-1-5090-6795-4 (ISBN)978-1-5090-6797-8 (ISBN)
Conference
27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2019-03-05Bibliographically approved
Helldin, T., Pernestig, A.-K. & Tilevik, D. (2017). Towards a Clinical Support System for the Early Diagnosis of Sepsis. In: Vincent G. Duffy (Ed.), Digital Human Modeling - Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety: 8th International Conference, DHM 2017 Held as Part of HCI International 2017 Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part II. Paper presented at 8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017, Vancouver, Canada, July 9–14, 2017 (pp. 23-35). Springer
Open this publication in new window or tab >>Towards a Clinical Support System for the Early Diagnosis of Sepsis
2017 (English)In: Digital Human Modeling - Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety: 8th International Conference, DHM 2017 Held as Part of HCI International 2017 Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part II / [ed] Vincent G. Duffy, Springer, 2017, p. 23-35Conference paper, Published paper (Refereed)
Abstract [en]

Early and accurate diagnosis of sepsis is critical for patientsafety. However, this is a challenging task due to the very general symptomsassociated with sepsis, the immaturity of the tools used by theclinicians as well as the time-delays associated with the diagnostic methodsused today. This paper explores current literature regarding guidelinesfor clinical decision support, and support for sepsis diagnosis inparticular, together with guidelines extracted from interviews with fourclinicians and one biomedical analyst working at a hospital and clinicallaboratory in Sweden. The results indicate the need for the developmentof visual and interactive aids for enabling early and accurate diagnosisof sepsis.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10287
Keywords
Clinical decision support, sepsis, guidelines, system transparency, electronic health record
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Infection Biology; INF301 Data Science; INF502 Biomarkers
Identifiers
urn:nbn:se:his:diva-13975 (URN)10.1007/978-3-319-58466-9_3 (DOI)2-s2.0-85025140829 (Scopus ID)978-3-319-58466-9 (ISBN)978-3-319-58465-2 (ISBN)
Conference
8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017, Vancouver, Canada, July 9–14, 2017
Projects
SepsIT
Available from: 2017-08-11 Created: 2017-08-11 Last updated: 2018-11-16Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Understanding Indirect Causal Relationships in Node-Link Graphs. Paper presented at 19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN. Computer graphics forum (Print), 36(3), 411-421
Open this publication in new window or tab >>Understanding Indirect Causal Relationships in Node-Link Graphs
2017 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed) Published
Abstract [en]

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

National Category
Human Computer Interaction Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-13970 (URN)10.1111/cgf.13198 (DOI)000404881200038 ()2-s2.0-85022207775 (Scopus ID)
Conference
19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN
Available from: 2017-08-10 Created: 2017-08-10 Last updated: 2018-06-11Bibliographically approved
Steinhauer, H. J., Karlsson, A., Mathiason, G. & Helldin, T. (2016). Root-Cause Localization using Restricted Boltzmann Machines. In: 2016 19th International Conference on Information Fusion Proceedings: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016 (pp. 248-255). IEEE Computer Society
Open this publication in new window or tab >>Root-Cause Localization using Restricted Boltzmann Machines
2016 (English)In: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, p. 248-255Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12884 (URN)000391273400034 ()2-s2.0-84992092150 (Scopus ID)9780996452748 (ISBN)978-1-5090-2012-6 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016
Available from: 2016-09-07 Created: 2016-09-07 Last updated: 2018-04-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6245-5850

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