Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 67) 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 and Information Sciences Human Computer Interaction
Identifiers
urn:nbn:se:hj:diva-45795 (URN)10.1007/978-3-030-26773-5_27 (DOI)PP JTH 2019 (Local ID)978-3-030-26772-8 (ISBN)978-3-030-26773-5 (ISBN)PP JTH 2019 (Archive number)PP JTH 2019 (OAI)
Conference
16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-17Bibliographically 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, 2019
Keywords
Teamwork, team effectiveness, fighter pilot, fighter aircraft
National Category
Computer Systems
Identifiers
urn:nbn:se:hj:diva-43551 (URN)10.1080/00140139.2019.1596319 (DOI)000465935600001 ()31002026 (PubMedID)2-s2.0-85064645549 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-08-23Bibliographically approved
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 Robust Artificial Intelligence for Neurorobotics, 26 – 28 August 2019, Edinburgh, Scotland.
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)
Abstract [en]

In this contribution, we briefly examine the role of end users in the evaluation and characterisation of sophisticated AI-based systems, such as autonomous vehicles or near-future robots. Indeed, when trying to ensure the safety of learning, perception and control in real world settings, one aspect that needs consideration is that human end users are often part of such settings. We argue that current approaches for considering end users in this respect are insufficient, not the least from a safety perspective, and that this insufficiency will become more acute when transitioning to neuromorphic and/or strongly cognitively inspired solutions. We demonstrate this by borrowing examples from the field of enactivism, which demonstrate that human end users might change the system dynamics of advanced neuromorphic systems when interacting with them, which needs to be taken into consideration. Enactivism might also provide clues as to how to design future evaluation metrics for human-machine teams.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:hj:diva-45855 (URN)
Conference
Robust Artificial Intelligence for Neurorobotics, 26 – 28 August 2019, Edinburgh, Scotland
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-09Bibliographically approved
Ventocilla, E. & Riveiro, M. (2019). Visual Growing Neural Gas for Exploratory Data Analysis. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: . Paper presented at 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 25 - 27 February, 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, 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 and Information Sciences
Identifiers
urn:nbn:se:hj:diva-43350 (URN)10.5220/0007364000580071 (DOI)0;0;miljJAIL (Local ID)978-989-758-354-4 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 25 - 27 February, 2019, Prague, Czech Republic
Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-08-23Bibliographically 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:hj:diva-43234 (URN)10.1016/j.trf.2018.05.009 (DOI)000437997700037 ()2-s2.0-85048505654 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Projects
TIEB
Funder
Swedish Energy Agency
Available from: 2018-06-04 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically 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:hj:diva-43235 (URN)10.1002/widm.1266 (DOI)000441767200004 ()2-s2.0-85051797167 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Funder
Knowledge Foundation, 20140294
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically 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:hj:diva-43236 (URN)10.1145/3167132.3167183 (DOI)000455180700067 ()2-s2.0-85050522612 (Scopus ID)0;0;miljJAIL (Local ID)978-1-4503-5191-1 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018
Available from: 2018-12-17 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically 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:hj:diva-43237 (URN)10.1007/978-3-319-59306-7_21 (DOI)000432811600021 ()2-s2.0-85019989762 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-59306-7 (ISBN)978-3-319-59305-0 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017
Available from: 2018-06-14 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically 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).
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, 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:hj:diva-43238 (URN)2-s2.0-85019592292 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
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: 2019-03-05 Last updated: 2019-08-23Bibliographically 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:hj:diva-43239 (URN)10.1371/journal.pone.0179613 (DOI)000404541500020 ()28654683 (PubMedID)2-s2.0-85021324072 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

Search in DiVA

Show all publications