Tanja Urbančič

Genetic algorithms in controller design and tuning

A three-phased framework for learning dynamic system control is presented. A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on a benchmark problem of inverted pendulum control, with special emphasis on robustness and reliability. It is also shown that the proposed framework enables exploiting available domain knowledge. In this case, genetic algorithm makes qualitative control rules operational by providing interpretation of symbols in terms of numerical values. >

Literature Mining: Towards Better Understanding of Autism

In this article we present a literature mining method RaJoLink that upgrades Swansons ABC model approach to uncovering hidden relations from a set of articles in a given domain. When these relations are interesting from medical point of view and can be verified by medical experts, they represent new pieces of knowledge and can contribute to better understanding of diseases. In our study we analyzed biomedical literature about autism, which is a very complex and not yet sufficiently understood domain. On the basis of word frequency statistics several rare terms were identified with the aim of generating potentially new explanations for the impairments that are observed in the affected population. Calcineurin was discovered as a joint term in the intersection of their corresponding literature. Similarly, NF-kappaB was recognized as a joint term. Pairs of documents that point to potential relations between the identified joint terms and autism were also automatically detected. Expert evaluation confirmed the relevance of these relations.

Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining

This paper investigates the role of outliers in literature-based knowledge discovery. It shows that detecting interesting outliers which appear in the literature on a given phenomenon can help the expert to find implicit relationships among concepts of different domains. The underlying assumption is that while the majority of articles in the given scientific domain describe matters related to a common understanding of the domain, the exploration of outliers may lead to the detection of scientifically interesting bridging concepts among disjoint sets of scientific articles. The proposed approach contributes to cross-context link discovery by proving the utility of outlier detection for finding bisociative links in the process of autism literature exploration, as well as by uncovering implicit relationships in the articles from the migraine domain.

RaJoLink: A Method for Finding Seeds of Future Discoveries in Nowadays Literature

In this article we present a study which demonstrates the ability of the method RaJoLink to uncover candidate hypotheses for future discoveries from rare terms in existing literature. The method is inspired by Swansons ABC model approach to finding hidden relations from a set of articles in a given domain. The main novelty is in a semi-automated way of suggesting which relations might have more potential for new discoveries and are therefore good candidates for further investigations. In our previous articles we reported on a successful application of the method RaJoLink in the autism domain. To support the evaluation of the method with a well-known example from the literature, we applied it to the migraine domain, aiming at reproducing Swansons finding of magnesium deficiency as a possible cause of migraine. Only literature which was available at the time of the Swansons experiment was used in our test. As described in this study, in addition to actually uncovering magnesium as a candidate for formulating the hypothesis, RaJoLink pointed also to interferon, interleukin and tumor necrosis factor as candidates for potential discoveries connecting them with migraine. These connections were not published in the titles contemporary to the ones used in the experiment, but have been recently reported in several scientific articles. This confirms the ability of the RaJoLink method to uncover seeds of future discoveries in existing literature by using rare terms as a beacon.

Bisociative knowledge discovery by literature outlier detection

The aim of this chapter is to present the role of outliers in literature-based knowledge discovery that can be used to explore potential bisociative links between different domains of expertise. The proposed approach upgrades the RaJoLink method which provides a novel framework for effectively guiding the knowledge discovery from literature, based on the principle of rare terms from scientific articles. This chapter shows that outlier documents can be successfully used as means of detecting bridging terms that connect documents of two different literature sources. This linking process, known also as closed discovery, is incorporated as one of the steps of the RaJoLink methodology, and is performed by using publicly available topic ontology construction tool OntoGen. We chose scientific articles about autism as the application example with which we demonstrated the proposed approach.

Bisociative literature mining by ensemble heuristics

In literature mining, the identification of bridging concepts that link two diverse domains has been shown to be a promising approach for finding bisociations as distinct, yet unexplored cross-domain connections which could lead to new scientific discoveries. This chapter introduces the system CrossBee (on line Cross-Context Bisociation Explorer) which implements a methodology that supports the search for hidden links connecting two different domains. The methodology is based on an ensemble of specially tailored text mining heuristics which assign the candidate bridging concepts a bisociation score. Using this score, the user of the system can primarily explore only the most promising concepts with high bisociation scores. Besides improved bridging concept identification and ranking, CrossBee also provides various content presentations which further speed up the process of bisociation hypotheses examination. These presentations include side-by-side document inspection, emphasizing of interesting text fragments, and uncovering similar documents. The methodology is evaluated on two problems: the standard migraine-magnesium problem well-known in literature mining, and a more recent autism-calcineurin literature mining problem.

Developing a Deeper Understanding of Autism: Connecting Knowledge through Literature Mining

In the field of autism, an enormous increase in available information makes it very difficult to connect fragments of knowledge into a more coherent picture. We present a literature mining method, RaJoLink, to search for matched themes in unrelated literature that may contribute to a better understanding of complex pathological conditions, such as autism. 214 full text articles on autism, published in PubMed, served as a source of data. Using ontology construction, we identified the main concepts of what is already known about autism. Then, the RaJoLink method, based on Swansons ABC model, was used to reveal potentially interesting, but not yet investigated, connections between different concepts in research. Among the more interesting concepts identified with RaJoLink in our study were calcineurin and NF-kappaB. Both terms can be linked to neuro-immune abnormalities in the brain of patients with autism. Further research is needed to provide stronger evidence about calcineurin and NF-kappaB involvement in autism. However, the analysis presented confirms that this method could support experts on their way towards discovering hidden relationships and towards a better understanding of the disorder.

Enhancing Human Choice by Information Technologies

The paper discusses what the research community in the field of information technologies can do to improve quality of life by offering more alternatives both to individuals and to the society. Moreover, attention is given to the present IT tools which can be used to support decision making and choice among possible alternatives in complex settings. The role of ITS in achieving this goal is explained and illustrated by examples in different fields, including environmental decision-making and health care. Enhanced possibilities of choice in collaborative settings supported by new media and computer networks are also shown. Besides the role of ITS that support this enhancement, the importance of non-technological aspects is presented with special emphasis on responsibility and network intelligence.

Virtual Enterprise for Data Mining and Decision Support: A Model for Networking Academia and Business

This paper presents an organizational model for networking of international expert teams from academia and business in the area of data mining and decision support. The described virtual enterprise model was chosen as a basis for enabling a flexible collaboration of academic institutions and business entities which (although having different motivations for this partnership) share the main objective of promoting and selling advanced services offered by a pool of partners.

What can videolectures.net and translectures do for opening higher education to the multicultural world

The exploration of new forms of teaching that improve learning ability is often motivated by its positive influence on economic growth and social prosperity. In this paper we present a set of IT tools which can empower small nations to contribute to the treasure of widely accessible educational resources, and at the same time enhance their ability for using these resources with no fears of endangering their identity, so tightly attached to culture, tradition and language. The context of our study is a national initiative to develop and apply technological as well as pedagogical solutions to serve teachers and learners at all levels of education in Slovenia. We demonstrate the post-processing abilities such as automatic translation on a case study video lecture. The preliminary evaluation of the results indicates that the used tools can typically improve both the quality and the speed of the process of automatic transcription and translation.