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Publications (10 of 69) Show all publications
Huhnstock, N. A., Karlsson, A., Riveiro, M. & Steinhauer, H. J. (2019). An Infinite Replicated Softmax Model for Topic Modeling. In: Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani (Ed.), Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings. Paper presented at 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019 (pp. 307-318). Springer
Open this publication in new window or tab >>An Infinite Replicated Softmax Model for Topic Modeling
2019 (English)In: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, p. 307-318Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11676
Keywords
Restricted Boltzmann machine, Unsupervised learning, Topic modeling, Adaptive Neural Network
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17664 (URN)10.1007/978-3-030-26773-5_27 (DOI)978-3-030-26772-8 (ISBN)978-3-030-26773-5 (ISBN)
Conference
16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019
Available from: 2019-09-09 Created: 2019-09-10 Last updated: 2019-11-08Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M. & Falkman, G. (2019). Fighter pilots' teamwork: a descriptive study. Ergonomics, 62(7), 880-890
Open this publication in new window or tab >>Fighter pilots' teamwork: a descriptive study
2019 (English)In: Ergonomics, ISSN 0014-0139, E-ISSN 1366-5847, Vol. 62, no 7, p. 880-890Article in journal (Refereed) Published
Abstract [en]

The execution of teamwork varies widely depending on the domain and task in question. Despite the considerable diversity of teams and their operation, researchers tend to aim for unified theories and models regardless of field. However, we argue that there is a need for translation and adaptation of the theoretical models to each specific domain. To this end, a case study was carried out on fighter pilots and it was investigated how teamwork is performed in this specialised and challenging environment, with a specific focus on the dependence on technology for these teams. The collaboration between the fighter pilots is described and analysed using a generic theoretical model for effective teamwork from the literature. The results show that domain-specific application and modification is needed in order for the model to capture fighter pilot's teamwork. The study provides deeper understanding of the working conditions for teams of pilots and gives design implications for how tactical support systems can enhance teamwork in the domain. Practitioner summary: This article presents a qualitative interview study with fighter pilots based on a generic theoretical teamwork model applied to the fighter domain. The purpose is to understand the conditions under which teams of fighter pilots work and to provide guidance for the design of future technological aids.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2019
Keywords
Teamwork, team effectiveness, fighter pilot, fighter aircraft
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems, Social aspects
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16880 (URN)10.1080/00140139.2019.1596319 (DOI)000465935600001 ()31002026 (PubMedID)2-s2.0-85064645549 (Scopus ID)
Available from: 2019-05-10 Created: 2019-05-10 Last updated: 2019-09-30Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M. & Falkman, G. (2019). Informing the Design of Fighter Aircraft Cockpits Using a Teamwork Perspective. In: Neville Stanton (Ed.), Advances in Human Aspects of Transportation: Proceedings of the AHFE 2018 International Conference on Human Factors in Transportation, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA. Paper presented at The AHFE 2018 International Conference on Human Factors in Transportation, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA (pp. 3-10). Cham: Springer
Open this publication in new window or tab >>Informing the Design of Fighter Aircraft Cockpits Using a Teamwork Perspective
2019 (English)In: Advances in Human Aspects of Transportation: Proceedings of the AHFE 2018 International Conference on Human Factors in Transportation, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA / [ed] Neville Stanton, Cham: Springer, 2019, p. 3-10Conference paper, Published paper (Refereed)
Abstract [en]

We describe a research process where fighter pilots’ behaviors were investigated from a teamwork perspective and the findings conveyed to the designers of cockpit interfaces in order to improve the fighter aircraft system. The teamwork perspective was selected because fighter aircraft are complex systems that require an advanced and trained pilot, who also, in addition to managing the aircraft systems needs to be a team player, collaborating with team members during dynamic and fast-paced circumstances to achieve the mission goals. A generic theoretical model for effective teamwork was selected as a starting point and a survey was conducted in order to investigate how fighter pilots collaborate during missions. The teamwork model and the survey results were then presented at workshops with designers of cockpit interfaces participating. The focus on the workshops was pilot teamwork and several design ideas aiming at improving the system for collaboration were generated.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 786
Keywords
Teamwork, Fighter pilots, System design
National Category
Computer and Information Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17931 (URN)10.1007/978-3-319-93885-1_1 (DOI)2-s2.0-85049688231 (Scopus ID)978-3-319-93884-4 (ISBN)978-3-319-93885-1 (ISBN)
Conference
The AHFE 2018 International Conference on Human Factors in Transportation, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA
Funder
Vinnova, NFFP6-2013-01201
Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2019-12-03
Thill, S. & Riveiro, M. (2019). Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems. In: : . Paper presented at Workshop on Robust Articial Intelligence for Neurorobotics (RAI-NR) 2019, University of Edinburgh, Edinburgh, United Kingdom.
Open this publication in new window or tab >>Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems
2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-17786 (URN)
Conference
Workshop on Robust Articial Intelligence for Neurorobotics (RAI-NR) 2019, University of Edinburgh, Edinburgh, United Kingdom
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-11
Bae, J., Falkman, G., Helldin, T. & Riveiro, M. (2019). Visual Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 133-155). Springer
Open this publication in new window or tab >>Visual Data Analysis
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
Abstract [en]

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16810 (URN)10.1007/978-3-319-97556-6_8 (DOI)000464719500009 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-09-30Bibliographically approved
Ventocilla, E. & Riveiro, M. (2019). Visual Growing Neural Gas for Exploratory Data Analysis. In: Andreas Kerren, Christophe Hurter, Jose Braz (Ed.), Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: Volume 3: IVAPP, 58-71, 2019, Prague, Czech Republic. Paper presented at 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 25-27, 2019, Prague, Czech Republic (pp. 58-71). SciTePress, 3
Open this publication in new window or tab >>Visual Growing Neural Gas for Exploratory Data Analysis
2019 (English)In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: Volume 3: IVAPP, 58-71, 2019, Prague, Czech Republic / [ed] Andreas Kerren, Christophe Hurter, Jose Braz, SciTePress, 2019, Vol. 3, p. 58-71Conference paper, Published paper (Refereed)
Abstract [en]

This paper argues for the use of a topology learning algorithm, the Growing Neural Gas (GNG), for providing an overview of the structure of large and multidimensional datasets that can be used in exploratory data analysis. We introduce a generic, off-the-shelf library, Visual GNG, developed using the Big Data framework Apache Spark, which provides an incremental visualization of the GNG training process, and enables user-in-the-loop interactions where users can pause, resume or steer the computation by changing optimization parameters. Nine case studies were conducted with domain experts from different areas, each working on unique real-world datasets. The results show that Visual GNG contributes to understanding the distribution of multidimensional data; finding which features are relevant in such distribution; estimating the number of k clusters to be used in traditional clustering algorithms, such as K-means; and finding outliers.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Growing Neural Gas, Dimensionality Reduction, Multidimensional Data, Visual Analytics, Exploratory Data Analysis
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16756 (URN)10.5220/0007364000580071 (DOI)2-s2.0-85064748097 (Scopus ID)978-989-758-354-4 (ISBN)
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 25-27, 2019, Prague, Czech Republic
Available from: 2019-04-08 Created: 2019-04-08 Last updated: 2019-09-30Bibliographically approved
Thill, S., Riveiro, M., Lagerstedt, E., Lebram, M., Hemeren, P., Habibovic, A. & Klingegård, M. (2018). Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 420-435
Open this publication in new window or tab >>Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study
Show others...
2018 (English)In: Transportation Research Part F: Traffic Psychology and Behaviour, ISSN 1369-8478, E-ISSN 1873-5517, Vol. 56, p. 420-435Article in journal (Refereed) Published
Abstract [en]

This paper presents a large-scale simulator study on driver adherence to recommendationsgiven by driver support systems, specifically eco-driving support and navigation support.123 participants took part in this study, and drove a vehicle simulator through a pre-defined environment for a duration of approximately 10 min. Depending on the experi-mental condition, participants were either given no eco-driving recommendations, or asystem whose provided support was either basic (recommendations were given in theform of an icon displayed in a manner that simulates a heads-up display) or informative(the system additionally displayed a line of text justifying its recommendations). A naviga-tion system that likewise provided either basic or informative support, depending on thecondition, was also provided.

Effects are measured in terms of estimated simulated fuel savings as well as engine brak-ing/coasting behaviour and gear change efficiency. Results indicate improvements in allvariables. In particular, participants who had the support of an eco-driving system spenta significantly higher proportion of the time coasting. Participants also changed gears atlower engine RPM when using an eco-driving support system, and significantly more sowhen the system provided justifications. Overall, the results support the notion that pro-viding reasons why a support system puts forward a certain recommendation improvesadherence to it over mere presentation of the recommendation.

Finally, results indicate that participants’ driving style was less eco-friendly if the navi-gation system provided justifications but the eco-system did not. This may be due to par-ticipants considering the two systems as one whole rather than separate entities withindividual merits. This has implications for how to design and evaluate a given driver sup-port system since its effectiveness may depend on the performance of other systems in thevehicle.

Keywords
Driver behaviour, System awareness, Eco-friendly behaviour, Driver recommendation systems
National Category
Psychology Human Computer Interaction Information Systems
Research subject
Interaction Lab (ILAB); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-15279 (URN)10.1016/j.trf.2018.05.009 (DOI)000437997700037 ()2-s2.0-85048505654 (Scopus ID)
Projects
TIEB
Funder
Swedish Energy Agency
Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2019-11-19Bibliographically approved
Riveiro, M., Pallotta, G. & Vespe, M. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 8(5), Article ID e1266.
Open this publication in new window or tab >>Maritime anomaly detection: A review
2018 (English)In: Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, ISSN 1942-4787, Vol. 8, no 5, article id e1266Article, review/survey (Refereed) Published
Abstract [en]

The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
anomaly detection, data mining, maritime anomaly detection, maritime traffic, review, situation awareness
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16182 (URN)10.1002/widm.1266 (DOI)000441767200004 ()2-s2.0-85051797167 (Scopus ID)
Funder
Knowledge Foundation, 20140294
Available from: 2018-09-11 Created: 2018-09-11 Last updated: 2018-09-13Bibliographically approved
Huhnstock, N. A., Karlsson, A., Riveiro, M. & Steinhauer, H. J. (2018). On the behavior of the infinite restricted boltzmann machine for clustering. In: Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir (Ed.), SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing: . Paper presented at SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018 (pp. 461-470). New York, NY, USA: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>On the behavior of the infinite restricted boltzmann machine for clustering
2018 (English)In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [ed] Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir, New York, NY, USA: Association for Computing Machinery (ACM), 2018, p. 461-470Conference paper, Published paper (Refereed)
Abstract [en]

Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2018
Keywords
clustering, unsupervised, machine learning, restricted boltzmann machine
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16505 (URN)10.1145/3167132.3167183 (DOI)000455180700067 ()2-s2.0-85050522612 (Scopus ID)978-1-4503-5191-1 (ISBN)
Conference
SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-02-14Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M. & Torra, V. (2018). On the Visualization of Discrete Non-additive Measures. In: Torra V, Mesiar R, Baets B (Ed.), Aggregation Functions in Theory and in Practice AGOP 2017: . Paper presented at 9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017 (pp. 200-210). Springer
Open this publication in new window or tab >>On the Visualization of Discrete Non-additive Measures
2018 (English)In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper, Published paper (Refereed)
Abstract [en]

Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

Place, publisher, year, edition, pages
Springer, 2018
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 581
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15590 (URN)10.1007/978-3-319-59306-7_21 (DOI)000432811600021 ()2-s2.0-85019989762 (Scopus ID)978-3-319-59306-7 (ISBN)978-3-319-59305-0 (ISBN)
Conference
9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017
Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-10-02Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

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