his.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 66) Show all publications
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: 2019-06-05Bibliographically approved
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: 2019-06-10Bibliographically 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: 2019-05-09Bibliographically 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: 2019-02-14Bibliographically 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
Ventocilla, E., Bae, J., Riveiro, M. & Said, A. (2017). A Billiard Metaphor for Exploring Complex Graphs. In: Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz (Ed.), Second Workshop on Supporting Complex Search Tasks: . Paper presented at Second Workshop on Supporting Complex Search Tasks co-located with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2017), Oslo, Norway, March 11, 2017 (pp. 37-40). , 1798
Open this publication in new window or tab >>A Billiard Metaphor for Exploring Complex Graphs
2017 (English)In: Second Workshop on Supporting Complex Search Tasks / [ed] Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz, 2017, Vol. 1798, p. 37-40Conference paper, Published paper (Refereed)
Abstract [en]

Exploring and revealing relations between the elements is a fre-quent task in exploratory analysis and search. Examples includethat of correlations of attributes in complex data sets, or facetedsearch. Common visual representations for such relations are di-rected graphs or correlation matrices. These types of visual encod-ings are often - if not always - fully constructed before being shownto the user. This can be thought of as a top-down approach, whereusers are presented with a full picture for them to interpret andunderstand. Such a way of presenting data could lead to a visualoverload, specially when it results in complex graphs with highdegrees of nodes and edges. We propose a bottom-up alternativecalled Billiard where few elements are presented at rst and fromwhich a user can interactively construct the rest based on whats/he nds of interest. The concept is based on a billiard metaphorwhere a cue ball (node) has an eect on other elements (associatednodes) when stroke against them.

Series
CEUR Workshop Proceedings, E-ISSN 1613-0073 ; 1798
Keywords
Visualization, interaction, correlation
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14775 (URN)2-s2.0-85019592292 (Scopus ID)
Conference
Second Workshop on Supporting Complex Search Tasks co-located with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2017), Oslo, Norway, March 11, 2017
Available from: 2018-02-27 Created: 2018-02-27 Last updated: 2018-09-24Bibliographically approved
Ulfenborg, B., Karlsson, A., Riveiro, M., Améen, C., Åkesson, K., Andersson, C. X., . . . Synnergren, J. (2017). A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells. PLoS ONE, 12(6), Article ID e0179613.
Open this publication in new window or tab >>A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells
Show others...
2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179613Article in journal (Refereed) Published
Abstract [en]

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Bioinformatics; Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF501 Integration of -omics Data
Identifiers
urn:nbn:se:his:diva-14015 (URN)10.1371/journal.pone.0179613 (DOI)000404541500020 ()28654683 (PubMedID)2-s2.0-85021324072 (Scopus ID)
Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2018-11-16Bibliographically approved
Zhen, R., Riveiro, M. & Jin, Y. (2017). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492-501
Open this publication in new window or tab >>A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance
2017 (English)In: Ocean Engineering, ISSN 0029-8018, E-ISSN 1873-5258, Vol. 145, p. 492-501Article in journal (Refereed) Published
Abstract [en]

Multi-vessel collision risk assessment for maritime traffic surveillance is a key technique to ensure the safety and security of maritime traffic and transportation. This paper proposes a framework of real-time multi-vessel collision assessment that combines a spatial clustering process (DBSCAN) for detecting clusters of encounter vessels and a multi-vessel collision risk index model for encounter vessels within each cluster from the large amounts of monitored vessels in a surveyed sea area. First, the vessels monitored are clustered using DBSCAN to obtain the clusters of encounter vessels, filtering out the relatively safe vessels. Then, the dynamic motion relation between encounter vessels within each cluster is modeled to obtain DCPA and TCPA. The semantic and mathematical relationship of vessel collision risk index for each cluster of encounter vessels with DCPA and TCAP is constructed using a negative exponential function. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within the west coastal waters of Sweden. The results show that our framework is effective and efficient at detecting and ranking collision risk indexes between encounter vessels within each duster, which allows an automatic risk prioritization of encounter vessels for further investigation by operators. Hence, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of lives and property.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Maritime transportation, Vessel traffic, AIS, Collision risk index, Maritime surveillance
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14544 (URN)10.1016/j.oceaneng.2017.09.015 (DOI)000414886600041 ()2-s2.0-85029783882 (Scopus ID)
Projects
KK Prospekt NOVA 2014/0294China Scholarship 366 Council, Grant number 201608310093PhD candidate 367 in Shanghai Maritime University, Grant number 2016ycx077
Funder
Knowledge Foundation, 20140294
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

Search in DiVA

Show all publications