Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Magnitude-Preserving Ranking for Structured Outputs.

Abstract: In this paper, we present a novel method for solving structured prediction problems, based on combining Input Output Kernel Regression (IOKR) with an extension of magnitudepreserving ranking to structured output spaces. In particular, we concentrate on the case where a set of candidate outputs has been given, and the associated pre-image problem calls for ranking the set of candidate outputs. Our method, called magnitude-preserving IOKR, both aims to produce a good approximation of the output feature vectors, and to preserve the magnitude differences of the output features in the candidate sets. For the case where the candidate set does not contain corresponding ’correct’ inputs, we propose a method for approximating the inputs through application of IOKR in the reverse direction. We apply our method to two learning problems: cross-lingual document retrieval and metabolite identification. Experiments show that the proposed approach improves performance over IOKR, and in the latter application obtains the current state-of-the-art accuracy.

Multivariate analysis of Finnish dialect data: an overview of lexical variation

Abstract: During the process of writing a comprehensive dictionary of Finnish dialects, a large set of maps describing the regional distribution of the dialect words have been compiled in electronic form. In this article, we set out to analyse this corpus of data in order to gain new insight on the variation of Finnish dialects. We use a wide range of multivariate data analysis methods, including principal components analysis, independent components analysis, clustering, and multidimensional scaling. We explain how to preprocess the data to overcome the problem of uneven sampling caused by the way the data has been collected. We discuss the results obtained by these methods and compare them to the traditional view of Finnish dialect groups.

Modeling Uncertainty in Semantic Web Taxonomies

Abstract: Summary. Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. However, Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies. Degrees of overlap indicate how well an individual data item matches the query concept, which can be used as a well-defined measure of relevance in information retrieval tasks.

The role of the learning platform in student-centred e-learning

Abstract: It is often a case that technologically-oriented research on e-learning stresses the tools and individual features used in e-learning platforms rather than the pedagogical model and the underlying course structures. This study compares the outcomes from a similar course in a similar setting using very different learning platforms designed by the same research group. The tool used in the first course, EDUCO, offers awareness of other learners by real-time social navigation features. The tool used in the second course, EDUCOSM, relies on easy-to-make joint asynchronous annotations on documents. The pedagogical model for the courses was the same: student-centered learning in self-organizing and self-evolving groups using peer support to tackle open-ended large problems. The results suggest that the learning outcomes can vary, especially in a case where the environment is open and transparent in a sense that it enables learners to easily rely on and help each other in peer-to-peer fashion.