his.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 62) Show all publications
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)
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: 2018-09-11Bibliographically 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
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
Lagerstedt, E., Riveiro, M. & Thill, S. (2017). Agent Autonomy and Locus of Responsibility for Team Situation Awareness. In: HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction. Paper presented at 5th International Conference on Human Agent Interaction, Bielefeld, October 17-20, 2017 (pp. 261-269). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Agent Autonomy and Locus of Responsibility for Team Situation Awareness
2017 (English)In: HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction, New York: Association for Computing Machinery (ACM), 2017, p. 261-269Conference paper, Published paper (Refereed)
Abstract [en]

Rapid technical advancements have led to dramatically improved abilities for artificial agents, and thus opened up for new ways of cooperation between humans and them, from disembodied agents such as Siris to virtual avatars, robot companions, and autonomous vehicles. It is therefore relevant to study not only how to maintain appropriate cooperation, but also where the responsibility for this resides and/or may be affected. While there are previous organisations and categorisations of agents and HAI research into taxonomies, situations with highly responsible artificial agents are rarely covered. Here, we propose a way to categorise agents in terms of such responsibility and agent autonomy, which covers the range of cooperation from humans getting help from agents to humans providing help for the agents. In the resulting diagram presented in this paper, it is possible to relate different kinds of agents with other taxonomies and typical properties. A particular advantage of this taxonomy is that it highlights under what conditions certain effects known to modulate the relationship between agents (such as the protégé effect or the "we"-feeling) arise.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2017
Keywords
HAI, Locus of Responsibility, Agent Relationship, Classification of Artificial Agents
National Category
Interaction Technologies
Research subject
Interaction Lab (ILAB); Skövde Artificial Intelligence Lab (SAIL); INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-14269 (URN)10.1145/3125739.3125768 (DOI)2-s2.0-85034847392 (Scopus ID)978-1-4503-5113-3 (ISBN)
Conference
5th International Conference on Human Agent Interaction, Bielefeld, October 17-20, 2017
Projects
Dreams4Cars
Funder
EU, Horizon 2020, 731593
Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2018-11-16Bibliographically approved
Riveiro, M., Lebram, M. & Elmer, M. (2017). Anomaly Detection for Road Traffic: A Visual Analytics Framework. IEEE transactions on intelligent transportation systems (Print), 18(8), 2260-2270, Article ID 7887700.
Open this publication in new window or tab >>Anomaly Detection for Road Traffic: A Visual Analytics Framework
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 8, p. 2260-2270, article id 7887700Article in journal (Refereed) Published
Abstract [en]

The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidimensional road traffic data; 2) the analysis of normal behavioral models built from data; 3) the detection of anomalous events; and 4) the explanation of anomalous events. We illustrate the use of this framework with examples from a large database of real road traffic data collected from several areas in Europe. Finally, we report on feedback provided by expert analysts from Volvo Group Trucks Technology, regarding its design and usability.

Keywords
Anomaly detection, visual analytics, normal traffic model, intelligent transport systems
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB); INF301 Data Science; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-14111 (URN)10.1109/TITS.2017.2675710 (DOI)000407347300022 ()2-s2.0-85017131904 (Scopus ID)
Funder
Knowledge Foundation, 20140294
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2018-11-16Bibliographically 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
Show others...
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: 2018-10-19Bibliographically approved
Kinkeldey, C., MacEachren, A. M., Riveiro, M. & Schiewe, J. (2017). Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations. Cartography and Geographic Information Science, 44(1), 1-21
Open this publication in new window or tab >>Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations
2017 (English)In: Cartography and Geographic Information Science, ISSN 1523-0406, E-ISSN 1545-0465, Vol. 44, no 1, p. 1-21Article, review/survey (Refereed) Published
Abstract [en]

For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making.

Keywords
Uncertainty visualization, literature review, evaluation, user studies, decision-making
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-11557 (URN)10.1080/15230406.2015.1089792 (DOI)000388603400001 ()2-s2.0-84941686201 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2015-09-25 Created: 2015-09-25 Last updated: 2018-06-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

Search in DiVA

Show all publications