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  • 1. Andersson, Robin
    et al.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Maluszynski, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Komorowski, Henryk Jan
    RoSy: A Rough Knowledge Base System2005In: Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing,2005, Berlin: Springer , 2005, p. 48-Conference paper (Refereed)
  • 2.
    Dell-Acqua, Pierangelo
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Pereira, Luis Moniz
    Universidade Nova de Lisboa, Caparica, Portugal.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    User Preference Information in Query Answering2002In: Flexible Query Answering Systems: 5th International Conference, FQAS 2002 Copenhagen, Denmark, October 27–29, 2002 Proceedings / [ed] Jaime G. Carbonell, Jörg Siekmann, Troels Andreasen, Henning Christiansen, Amihai Motro and Henrik Legind Larsen, Springer Berlin/Heidelberg, 2002, Vol. 2522, p. 163-173Chapter in book (Refereed)
    Abstract [en]

    This paper discusses the use of usage information to enhance cooperative behaviour from query answering systems. A user can pose a query to the system by providing (at query time) update and preference information. Updates allow us to model dynamically evolving worlds and preferences allow us to facilitate the. retrieval of information by targeting the answers of the system with respect to users' interests in a given context.

  • 3.
    Dieckmann, Mark E
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Falk, Martin
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Steneteg, Peter
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Folini, Doris
    CRAL, École Normale Supérieure, 69622 Lyon, France.
    Hotz, Ingrid
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nordman, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Dell'Acqua, Pierangelo
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Walder, Rolf
    CRAL, École Normale Supérieure, 69622 Lyon, France.
    Structure of a collisionless pair jet in a magnetizedelectron-proton plasma: Flow-aligned magnetic field2019In: High Energy Phenomena in Relativistic Outflows VII (HEPRO VII): Formation and propagation of relativistic outflows, Sissa Medialab srl, 2019, article id 006Conference 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.

  • 4.
    Maluszynski, Jan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Szalas, Andrzej
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    A Four-Valued Logic for Rough Set-Like Approximate Reasoning2007In: Transactions on Rough Sets VI / [ed] James F. Peters, Andrzej Skowron, Ivo Düntsch, Jerzy Grzymala-Busse, Ewa Orlowska and Lech Polkowski, Springer, 2007, Vol. 6, p. 176-190Chapter in book (Refereed)
    Abstract [en]

    Annotation The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume VI of the Transactions on Rough Sets (TRS) commemorates the life and work of Zdzislaw Pawlak (1926-2006). His legacy is rich and varied. Prof. Pawlak's research contributions have had far-reaching implications inasmuch as his works are fundamental in establishing new perspectives for scientific research in a wide spectrum of fields. This volume of the TRS presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak. In particular, this volume introduces a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science. These advances have significant implications in a number of research areas such as the foundations of rough sets, approximate reasoning, bioinformatics, computational intelligence, cognitive science, data mining, information systems, intelligent systems, machine intelligence, and security.

  • 5.
    Maluszynski, Jan
    et al.
    Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory. Linköping University, The Institute of Technology.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Defining Rough Sets by Extended Logic Programs2002In: PCL02 Paraconsistent Computational Logic, Roskilde: Roskilde University , 2002, p. 81-90Conference paper (Refereed)
  • 6.
    Maluszynski, Jan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Towards Rough Datalog: Embedding Rough Sets in Prolog2004In: Rough-Neural Computing: Techniques for Computing with Words / [ed] Sankar K Pal; Lech Polkowski; Andrzej Skowron, Berlin: Springer Verlag , 2004, p. 297-332Chapter in book (Other academic)
    Abstract [en]

    Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms.

    It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

  • 7.
    Maluszynski, Jan
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Szalas, Andrzej
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Paraconsistent Logic Programs with Four-valued Rough Sets2008In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Germany: Springer , 2008, p. 41-51Conference paper (Refereed)
    Abstract [en]

    This paper presents a language for defining four-valued rough sets and to reason about them. Our framework brings together two major fields: rough sets and paraconsistent logic programming. On the one hand it provides a paraconsistent approach, based on four-valued rough sets, for integrating knowledge from different sources and reasoning in the presence of inconsistencies. On the other hand, it also caters for a specific type of uncertainty that originates from the fact that an agent may perceive different objects of the universe as being indiscernible. This paper extends the ideas presented in [9]. Our language allows the user to define similarity relations and use the approximations induced by them in the definition of other four-valued sets. A positive aspect is that it allows users to tune the level of uncertainty or the source of uncertainty that best suits applications.

  • 8.
    Muthumanickam, Prithiviraj
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Helske, Jouni
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Centre for Climate Science and Policy Research, CSPR.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest2019Conference 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.

  • 9.
    Muthumanickam, Prithiviraj
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nordman, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Meyer, Lothar
    LFV.
    Boonsong, Supathida
    LFV.
    Lundberg, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Analysis of Long Duration Eye-Tracking Experiments in a Remote Tower Environment2019Conference 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.

  • 10.
    Muthumanickam, Prithiviraj
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Vrotsou, Katerina
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Centre for Climate Science and Policy Research, CSPR.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets2019In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, p. 87-97Article in journal (Refereed)
    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. 

  • 11.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    A framework for reasoning with rough sets2005Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability.

    Most of the current rough sets techniques and software systems based on them only consider rough sets defined explicitly by concrete examples given in tabular form. The previous research mostly disregards the following two problems. The first problem is related with how to define rough sets in terms of other rough sets. The second problem is related with how to incorporate domain or expert knowledge.

    This thesis proposes a language that caters for implicit definitions of rough sets obtained by combining different regions of other rough sets. In this way, concept approximations can be derived by taking into account domain knowledge. A declarative semantics for the language is also discussed. It is then shown that programs in the proposed language can be compiled to extended logic programs under the paraconsistent stable model semantics. The equivalence between the declarative semantics of the language and the declarative semantics of the compiled programs is proved. This transformation provides the computational basis for implementing our ideas. A query language for retrieving information about the concepts represented through the defined rough sets is also defined. Several motivating applications are described. Finally, an extension of the proposed language with numerical measures is discussed. This extension is motivated by the fact that numerical measures are an important aspect in data mining applications.

  • 12.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    A framework for reasoning with rough sets2005In: Transactions on Rough Sets IV / [ed] James F. Peters and Andrzej Skowron, Springer Berlin/Heidelberg, 2005, Vol. 3700, p. 178-276Chapter in book (Refereed)
    Abstract [en]

    Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability. This thesis(1) proposes a language that caters for implicit definitions of rough sets obtained by combining different regions of other rough sets. In this way, concept approximations can be derived by taking into account domain knowledge. A declarative semantics for the language is also discussed. It is then shown that programs in the proposed language can be compiled to extended logic programs under the paraconsistent stable model semantics. The equivalence between the declarative semantics of the language and the declarative semantics of the compiled programs is proved. This transformation provides the computational basis for implementing our ideas. A query language for retrieving information about the concepts represented through the defined rough sets is also discussed. Several motivating applications are described. Finally, an extension of the proposed language with numerical measures is presented. This extension is motivated by the fact that numerical measures are an important aspect in data mining applications.

  • 13.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Damasio, C. V.
    Universidade Nova de Lisboa.
    Maluszynski, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Toward rough knowledge bases with quantitative measures2004In: 4th International Conference on Rough Sets and Current Trends in Computing,2004, New York: Springer , 2004, p. 153-Conference paper (Refereed)
  • 14.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Damasio, Carlos V
    Departamento de Informática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa.
    Maluszynski, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    From Rough Sets to Rough Knowledge Bases2003In: Fundamenta Informaticae, ISSN 0169-2968, E-ISSN 1875-8681, Vol. 57, no 2-4, p. 215-246Article in journal (Refereed)
    Abstract [en]

    This paper presents an expressive language for representing knowledge about vague concepts. It is based on the rough set formalism and it caters for implicit definition of rough relations by combining different regions (e.g. upper approximation, lower approximation, boundary) of other rough relations. The semantics of the proposed language is obtained by translating it to the language of extended logic programs whose meaning is captured by paraconsistent stable models. A query language is also discussed to retrieve information about the defined rough relations. Motivating examples illustrating the use of the language are described.

  • 15.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Damasio, Carlos Viegas
    Universidade Nova de Lisboa, Caparica, Portugal.
    Maluszyniski, Jan
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Query answering in rough knowledge bases2003In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 9th International Conference, RSFDGrC 2003, Chongqing, China, May 26–29, 2003 Proceedings / [ed] Guoyin Wang, Qing Liu, Yiyu Yao, Andrzej Skowron, Springer Berlin/Heidelberg, 2003, Vol. 2639, p. 197-204Conference paper (Refereed)
    Abstract [en]

    We propose a logic programming language which makes it possible to define and to reason about rough sets. In particular we show how to test for rough inclusion and rough equality. This extension to our previous work [7] is motivated by the need of these concepts in practical applications.

  • 16.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Maluszynski, Jan
    Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory. Linköping University, The Institute of Technology.
    A logic programming framework for rough sets2002In: Rough Sets and Current Trends in Computing: Third International Conference, RSCTC 2002 Malvern, PA, USA, October 14–16, 2002 Proceedings / [ed] James J. Alpigini, James F. Peters, Andrzej Skowron and Ning Zhong, Springer Berlin/Heidelberg, 2002, Vol. 2475, p. 205-212Chapter in book (Refereed)
    Abstract [en]

    We propose a framework for defining and reasoning about rough sets based on definite extended logic programs. Moreover, we introduce a rough-set-specific query language. Several motivating examples are also presented. Thus, we establish a link between rough set theory and logic programming that makes possible transfer of expertise between these fields and combination of the techniques originating from both fields.

  • 17.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Maluszynski, Jan
    Coll Econ and Comp Science, PL-10061 Olsztyn, Poland .
    Szalas, Andrzej
    Warsaw University, Institute Informat, PL-02097 Warsaw, Poland .
    Modelling and Reasoning with Paraconsistent Rough Sets2009In: Fundamenta Informaticae, ISSN 0169-2968, E-ISSN 1875-8681, Vol. 97, no 4, p. 405-438Article in journal (Refereed)
    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.

  • 18.
    Vitoria, Aida
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Szalas, Andrzej
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Maluszynski, Jan
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, TCSLAB - Theoretical Computer Science Laboratory.
    Four-valued Extension of Rough Sets2008In: Proceedings of the 3rd International Conference Rough Sets and Knowledge Technology (RSKT), Springer , 2008, p. 106-114Conference 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.

  • 19. Order onlineBuy this publication >>
    Vitória, Aida
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Media and Information Technology.
    Reasoning with Rough Sets and Paraconsistent Rough Sets2010Doctoral 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.

    List of papers
    1. A framework for reasoning with rough sets
    Open this publication in new window or tab >>A framework for reasoning with rough sets
    2005 (English)In: Transactions on Rough Sets IV / [ed] James F. Peters and Andrzej Skowron, Springer Berlin/Heidelberg, 2005, Vol. 3700, p. 178-276Chapter in book (Refereed)
    Abstract [en]

    Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability. This thesis(1) proposes a language that caters for implicit definitions of rough sets obtained by combining different regions of other rough sets. In this way, concept approximations can be derived by taking into account domain knowledge. A declarative semantics for the language is also discussed. It is then shown that programs in the proposed language can be compiled to extended logic programs under the paraconsistent stable model semantics. The equivalence between the declarative semantics of the language and the declarative semantics of the compiled programs is proved. This transformation provides the computational basis for implementing our ideas. A query language for retrieving information about the concepts represented through the defined rough sets is also discussed. Several motivating applications are described. Finally, an extension of the proposed language with numerical measures is presented. This extension is motivated by the fact that numerical measures are an important aspect in data mining applications.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2005
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3700
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 3700
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-48115 (URN)10.1007/11574798_10 (DOI)978-3-540-29830-4 (ISBN)978-3-540-32016-6 (ISBN)3-540-29830-4 (ISBN)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2018-02-15Bibliographically approved
    2. Modelling and Reasoning with Paraconsistent Rough Sets
    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
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    Reasoning with Rough Sets and Paraconsistent Rough Sets
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    omslag
  • 20.
    Vrotsou, Katerina
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nordman, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Exploratory visual sequence mining based on pattern-growth2019In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 8, p. 2597-2610Article in journal (Refereed)
    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.

  • 21.
    Vrotsou, Katerina
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Vitoria, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Interactive Visual Sequence Mining Based on Pattern-Growth2014In: IEEE Conference on Visual Analytics Science and Technology (VAST), Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 285-286Conference 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.

  • 22.
    Westin, Carl
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Vrotsou, Katerina
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Nordman, Aida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Lundberg, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Meyer, Lothar
    Research & Innovation LFV, Norrköping, Sweden.
    Visual Scan Patterns in Tower Control: Foundations for an Instructor Support Tool2019In: / [ed] Dirk Schaefer, 2019Conference 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.

1 - 22 of 22
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