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Vitoria, Aida
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Publications (10 of 22) Show all publications
Muthumanickam, P., Nordman, A., Meyer, L., Boonsong, S., Lundberg, J. & Cooper, M. (2019). Analysis of Long Duration Eye-Tracking Experiments in a Remote Tower Environment. In: : . Paper presented at Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), Vienna, Austria, June 17-21, 2019.
Open this publication in new window or tab >>Analysis of Long Duration Eye-Tracking Experiments in a Remote Tower Environment
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Eye-Tracking experiments have proven to be of great assistance in understanding human computer interaction across many fields. Most eye-tracking experiments are non-intrusive and so do not affect the behaviour of the subject. Such experiments usually last for just a few minutes and so the spatio- temporal data generated by the eye-tracker is quite easy to analyze using simple visualization techniques such as heat maps and animation. Eye tracking experiments in air traffic control, or maritime or driving simulators can, however, last for several hours and the analysis of such long duration data becomes much more complex. We have developed an analysis pipeline, where we identify visual spatial areas of attention over a user interface using clustering and hierarchical cluster merging techniques. We have tested this technique on eye tracking datasets generated by air traffic controllers working with Swedish air navigation services, where each eye tracking experiment lasted for ∼90 minutes. We found that our method is interactive and effective in identification of interesting patterns of visual attention that would have been very difficult to locate using manual analysis.

Keywords
Remote tower, Eye tracking, Spatio-temporal clustering
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-160959 (URN)
Conference
Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), Vienna, Austria, June 17-21, 2019
Funder
Swedish Transport AdministrationSwedish Research Council
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-11-25Bibliographically approved
Muthumanickam, P., Helske, J., Vitoria, A., Johansson, J. & Cooper, M. (2019). Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest. In: : . Paper presented at Hawaii International Conference on System Sciences.
Open this publication in new window or tab >>Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging techniques that require minimal user input, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover common and distinct behaviour between the users which, until now, has been prohibitively difficult to achieve.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-161999 (URN)
Conference
Hawaii International Conference on System Sciences
Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-25
Vrotsou, K. & Nordman, A. (2019). Exploratory visual sequence mining based on pattern-growth. IEEE Transactions on Visualization and Computer Graphics, 25(8), 2597-2610
Open this publication in new window or tab >>Exploratory visual sequence mining based on pattern-growth
2019 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 8, p. 2597-2610Article in journal (Refereed) Published
Abstract [en]

Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large number of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a "transparent box" execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users' capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals' activity sequences.

Keywords
Sequential pattern mining, interactive mining, visual data mining, mining with constraints
National Category
Computer and Information Sciences Media and Communication Technology
Identifiers
urn:nbn:se:liu:diva-153938 (URN)10.1109/TVCG.2018.2848247 (DOI)000473597800007 ()29994660 (PubMedID)
Note

Funding agencies: CENIIT, Center for Industrial Information Technology at Linkoping University; RESKILL project - public research and innovation funds from the Swedish Transport Administration; Swedish Maritime Administration; Swedish Air Navigation Service Provider LFV

Available from: 2019-01-20 Created: 2019-01-20 Last updated: 2019-07-19
Muthumanickam, P., Vrotsou, K., Vitoria, A., Johansson, J. & Cooper, M. (2019). Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets. IEEE Transactions on Visualization and Computer Graphics, 87-97
Open this publication in new window or tab >>Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets
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2019 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, p. 87-97Article in journal (Refereed) Published
Abstract [en]

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects’ behaviour, making the analyst’s task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Eye-tracking data, areas of interest, clustering, minimum spanning tree, temporal data, spatio-temporal data
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-152714 (URN)10.1109/TVCG.2018.2865042 (DOI)000452640000009 ()30183636 (PubMedID)2-s2.0-85052788669 (Scopus ID)
Note

Funding agencies: Swedish Research Council [2013-4939]; RESKILL project - Swedish Transport Administration; Swedish Maritime Administration; Swedish Air Navigation Service Provider LFV

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2019-11-25Bibliographically approved
Dieckmann, M. E., Falk, M., Steneteg, P., Folini, D., Hotz, I., Nordman, A., . . . Walder, R. (2019). Structure of a collisionless pair jet in a magnetizedelectron-proton plasma: Flow-aligned magnetic field. In: High Energy Phenomena in Relativistic Outflows VII (HEPRO VII): Formation and propagation of relativistic outflows. Paper presented at High Energy Phenomena in Relativistic Outflows VII (HEPRO VII). Sissa Medialab srl, Article ID 006.
Open this publication in new window or tab >>Structure of a collisionless pair jet in a magnetizedelectron-proton plasma: Flow-aligned magnetic field
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2019 (English)In: High Energy Phenomena in Relativistic Outflows VII (HEPRO VII): Formation and propagation of relativistic outflows, Sissa Medialab srl, 2019, article id 006Conference paper, Published paper (Refereed)
Abstract [en]

We present the results from a particle-in-cell (PIC) simulation that models the interaction between a spatially localized electron-positron cloud and an electron-ion plasma. The latter is permeated by a magnetic field that is initially spatially uniform and aligned with the mean velocity vector of the pair cloud. The pair cloud expels the magnetic field and piles it up into an electromagnetic piston. Its electromagnetic field is strong enough to separate the pair cloud from the ambient plasma in the direction that is perpendicular to the cloud propagation direction. The piston propagates away from the spine of the injected pair cloud and it accelerates the protons to a high nonrelativistic speed. The accelerated protons form an outer cocoon that will eventually become separated from the unperturbed ambient plasma by a fast magnetosonic shock. No electromagnetic piston forms at the front of the cloud and a shock is mediated here by the filamentation instability. The final plasma distribution resembles that of a hydrodynamic jet. Collisionless plasma jets may form in the coronal plasma of accreting black holes and the interaction between the strong magnetic field of the piston and the hot pair cloud may contribute to radio emissions by such objects.

Place, publisher, year, edition, pages
Sissa Medialab srl: , 2019
Keywords
PIC simulation, collisionless plasma, relativistic jet
National Category
Fusion, Plasma and Space Physics
Identifiers
urn:nbn:se:liu:diva-164075 (URN)10.22323/1.354.0006 (DOI)
Conference
High Energy Phenomena in Relativistic Outflows VII (HEPRO VII)
Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-11
Westin, C., Vrotsou, K., Nordman, A., Lundberg, J. & Meyer, L. (2019). Visual Scan Patterns in Tower Control: Foundations for an Instructor Support Tool. In: Dirk Schaefer (Ed.), : . Paper presented at SESAR Innovation Days.
Open this publication in new window or tab >>Visual Scan Patterns in Tower Control: Foundations for an Instructor Support Tool
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2019 (English)In: / [ed] Dirk Schaefer, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Although where to look, when, and in what order is crucial for situation awareness and task performance in tower control, instructors are lacking support systems that can help them understand operators’ visual scan behaviours. As a way forward, this paper investigates the existence and characteristics of visual scan patterns in tower control and explores a novel support tool that can help instructors in searching for and exploring these patterns. First, eye-tracking data from two controllers were collected in a high-fidelity tower simulator. Second, a workshop was conducted with three instructors to discuss specific scan patterns that can be expected in relation to the approach scenarios used in the eye-tracking data collection. Six template visual scan patterns were identified during the workshop. Finally, an interactive visual sequence mining tool was used to identify and explore instances of the template scan patterns in the recorded eye-tracking data. Four of these could be detected using the tool: runway scans, landing clearance, touchdown and landing roll, and phases of visual focus. The identification of template scan patterns provides additional insight for formalising controllers’ visual work in tower control. The ability to detect and explore visual scan patterns in the proposed tool shows promise for improving instructors’ understanding of controllers’ visual scan behaviours, and for improving training effectiveness.

Series
SESAR Innovation Days, ISSN 0770-1268 ; 9
Keywords
Automation, Training, Eye-tracking, Air Traffic Control, Sequence mining, Visual perception
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-162712 (URN)
Conference
SESAR Innovation Days
Projects
RESKILL
Funder
Swedish Transport Administration, TRV2017764269
Note

This study was part of the RESKILL project, funded by public research and innovation funds from the Swedish Air Navigation Service Provider LFV, the Swedish Maritime Administration, and the Swedish Transport Administration.

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-24
Vrotsou, K. & Vitoria, A. (2014). Interactive Visual Sequence Mining Based on Pattern-Growth. In: IEEE Conference on Visual Analytics Science and Technology (VAST): . Paper presented at IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 285-286). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Interactive Visual Sequence Mining Based on Pattern-Growth
2014 (English)In: IEEE Conference on Visual Analytics Science and Technology (VAST), Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 285-286Conference paper, Published paper (Refereed)
Abstract [en]

Sequential pattern mining aims to discover valuable patterns from datasets and has a vast number of applications in various fields. Due to the combinatorial nature of the problem, the existing algorithms tend to output long lists of patterns that often suffer from a lack offocus from the user perspective. Our aim is to tackle this problemby combining interactive visualization techniques with sequential pattern mining to create a “transparent box” execution model for existing algorithms. This paper describes our first step in this direction and gives an overview of a system that allows the user to guide the execution of a pattern-growth algorithm at suitable points, through a powerful visual interface.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-115876 (URN)10.1109/VAST.2014.7042532 (DOI)000380474000057 ()9781479962273 (ISBN)
Conference
IEEE Conference on Visual Analytics Science and Technology (VAST)
Note

VAST HONORABLE MENTION AWARD

Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2019-11-29
Vitória, A. (2010). Reasoning with Rough Sets and Paraconsistent Rough Sets. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Reasoning with Rough Sets and Paraconsistent Rough Sets
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents an approach to knowledge representation combining rough sets and para-consistent logic programming.

The rough sets framework proposes a method to handle a specific type of uncertainty originating from the fact that an agent may perceive different objects of the universe as being similar, although they may have di®erent properties. A rough set is then defined by approximations taking into account the similarity between objects. The number of applications and the clear mathematical foundation of rough sets techniques demonstrate their importance.

Most of the research in the rough sets field overlooks three important aspects. Firstly, there are no established techniques for defining rough concepts (sets) in terms of other rough concepts and for reasoning about them. Secondly, there are no systematic methods for integration of domain and expert knowledge into the definition of rough concepts. Thirdly, some additional forms of uncertainty are not considered: it is assumed that knowledge about similarities between objects is precise, while in reality it may be incomplete and contradictory; and, for some objects there may be no evidence about whether they belong to a certain concept.

The thesis addresses these problems using the ideas of paraconsistent logic programming, a recognized technique which makes it possible to represent inconsistent knowledge and to reason about it. This work consists of two parts, each of which proposes a di®erent language. Both languages cater for the definition of rough sets by combining lower and upper approximations and boundaries of other rough sets. Both frameworks take into account that membership of an object into a concept may be unknown.

The fundamental difference between the languages is in the treatment of similarity relations. The first language assumes that similarities between objects are represented by equivalence relations induced from objects with similar descriptions in terms of a given number of attributes. The second language allows the user to define similarity relations suitable for the application in mind and takes into account that similarity between objects may be imprecise. Thus, four-valued similarity relations are used to model indiscernibility between objects, which give rise to rough sets with four-valued approximations, called paraconsistent rough sets. The semantics of both languages borrows ideas and techniques used in paraconsistent logic programming. Therefore, a distinctive feature of our work is that it brings together two major fields, rough sets and paraconsistent logic programming.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. p. 43
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1307
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-60794 (URN)978-91-7393-411-4 (ISBN)
Public defence
2010-11-19, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15
Opponent
Supervisors
Available from: 2010-10-26 Created: 2010-10-26 Last updated: 2020-02-19Bibliographically approved
Vitoria, A., Maluszynski, J. & Szalas, A. (2009). Modelling and Reasoning with Paraconsistent Rough Sets. Fundamenta Informaticae, 97(4), 405-438
Open this publication in new window or tab >>Modelling and Reasoning with Paraconsistent Rough Sets
2009 (English)In: Fundamenta Informaticae, ISSN 0169-2968, E-ISSN 1875-8681, Vol. 97, no 4, p. 405-438Article in journal (Refereed) Published
Abstract [en]

We present a language for defining paraconsistent rough sets and reasoning about them. Our framework relates and brings together two major fields: rough sets [23] and paraconsistent logic programming [9]. To model inconsistent and incomplete information we use a four-valued logic. The language discussed in this paper is based on ideas of our previous work [21, 32, 22] developing a four-valued framework for rough sets. In this approach membership function, set containment and set operations are four-valued, where logical values are t (true), f (false), i (inconsistent) and u (unknown). We investigate properties of paraconsistent rough sets as well as develop a paraconsistent rule language, providing basic computational machinery for our approach.

Keywords
approximate reasoning, rough sets, paraconsistent reasoning, four-valued logics
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-53059 (URN)10.3233/FI-2009-209 (DOI)
Available from: 2010-01-15 Created: 2010-01-15 Last updated: 2017-12-12
Vitoria, A., Szalas, A. & Maluszynski, J. (2008). Four-valued Extension of Rough Sets. In: Proceedings of the 3rd International Conference Rough Sets and Knowledge Technology (RSKT) (pp. 106-114). Springer
Open this publication in new window or tab >>Four-valued Extension of Rough Sets
2008 (English)In: Proceedings of the 3rd International Conference Rough Sets and Knowledge Technology (RSKT), Springer , 2008, p. 106-114Conference paper, Published paper (Refereed)
Abstract [en]

Rough set approximations of Pawlak [15] are sometimes generalized by using similarities between objects rather than elementary sets. In practical applications, both knowledge about properties of objects and knowledge of similarity between objects can be incomplete and inconsistent. The aim of this paper is to define set approximations when all sets, and their approximations, as well as similarity relations are four-valued. A set is four-valued in the sense that its membership function can have one of the four logical values: unknown (u), false (f), inconsistent (i), or true (t). To this end, a new implication operator and set-theoretical operations on four-valued sets, such as set containment, are introduced. Several properties of lower and upper approximations of four-valued sets are also presented.

Place, publisher, year, edition, pages
Springer, 2008
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5009
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-43561 (URN)10.1007/978-3-540-79721-0_19 (DOI)74200 (Local ID)978-3-540-79720-3 (ISBN)74200 (Archive number)74200 (OAI)
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2011-03-04
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