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Institution

Helsinki Institute for Information Technology

FacilityEspoo, Finland
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.


Papers
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Proceedings ArticleDOI
05 May 2012
TL;DR: In this article, the authors discuss the state of music interaction as a part of digital media research and discuss why music interaction research has become so marginal in HCI and discuss how to revive it.
Abstract: The ubiquity of music consumption is overarching. Statistics for digital music sales, streaming video videos, computer games, and illegal sharing all speak of a huge interest. At the same, an incredible amount of data about every day interactions (sales and use) with music is accumulating through new cloud services. However, there is an amazing lack of public knowledge about everyday music interaction. This panel discusses the state of music interaction as a part of digital media research. We consider why music interaction research has become so marginal in HCI and discuss how to revive it. Our two discussion themes are: orientation towards design vs. research in music related R&D, and the question if and how private, big data on music interactions could enlighten our understanding of ubiquitous media culture.

14 citations

Journal ArticleDOI
TL;DR: Mere booklet information is beneficial for employees who report mild LBP in the occupational health (OH) setting, and is also cost saving for the health care system.
Abstract: Evidence shows that low back specific patient information is effective in sub-acute low back pain (LBP), but effectiveness and cost-effectiveness (CE) of information in early phase symptoms is not clear. We assessed effectiveness and CE of patient information in mild LBP in the occupational health (OH) setting in a quasi-experimental study. A cohort of employees (N = 312, aged <57) with non-specific, mild LBP (Visual Analogue Scale between 10–34 mm) was selected from the respondents of an employee survey (N = 2480; response rate 71 %). A random sample, representing the natural course of LBP (NC, N = 83; no intervention), was extracted as a control group. Remaining employees were invited (181 included, 47 declined, one excluded) into a randomised controlled study with two 1:1 allocated parallel intervention arms (“Booklet”, N = 92; “Combined”, N = 89). All participants received the “Back Book” patient information booklet and the Combined also an individual verbal review of the booklet. Physical impairment (PHI), LBP, health care (HC) utilisation, and all-cause sickness absence (SA) were assessed at two years. CE of the interventions on SA days was analysed by using direct HC costs in one year, two years from baseline. Multiple imputation was used for missing values. Compared to NC, the Booklet reduced HC costs by 196€ and SA by 3.5 days per year. In 81 % of the bootstrapped cases the Booklet was both cost saving and effective on SA. Compared to NC, in the Combined arm, the figures were 107€, 0.4 days, and 54 %, respectively. PHI decreased in both interventions. Booklet information alone was cost-effective in comparison to natural course of mild LBP. Combined information reduced HC costs. Both interventions reduced physical impairment. Mere booklet information is beneficial for employees who report mild LBP in the OH setting, and is also cost saving for the health care system. ClinicalTrials.gov NCT00908102

14 citations

Journal ArticleDOI
TL;DR: A probabilistic latent grouping model for predicting the relevance of a document to a user and compares it against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure.
Abstract: We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. Analogously, generalization over documents improves predictions in the case of new users. We show that if either users and documents or both are new, two-way generalization becomes necessary. We demonstrate the benefits of grouping of users, grouping of documents, and two-way grouping, with artificial data and in two case studies with real data. We have introduced a probabilistic latent grouping model for predicting the relevance of a document to a user. The model assumes a latent group structure for both users and items. We compare the model against a state-of-the-art method, the User Rating Profile model, where only the users have a latent group structure. We compute the posterior of both models by Gibbs sampling. The Two-Way Model predicts relevance more accurately when the target consists of both new documents and new users. The reason is that generalization over documents becomes beneficial for new documents and at the same time generalization over users is needed for new users.

14 citations

Proceedings Article
01 Jan 2011
TL;DR: Results show that from a probabilistic matrix with more than one million rows and columns, a small set of meaningful patterns that accurately characterize the data distribution of any probable world can be extracted.
Abstract: Motivated by sensor networks, mobility data, biology and life sciences, the area of mining uncertain data has recently received a great deal of attention. While various papers have focused on efficiently mining frequent patterns from uncertain data, the problem of discovering a small set of interesting patterns that provide an accurate and condensed description of a probabilistic database is still unexplored. In this paper we study the problem of discovering characteristic patterns in uncertain data through information theoretic lenses. Adopting the possible worlds interpretation of probabilistic data and a compression scheme based on the MDL principle, we formalize the problem of mining patterns that compress the database well in expectation. Despite its huge search space, we show that this problem can be accurately approximated. In particular, we devise a sequence of three methods where each new method improves the memory requirements orders of magnitudes compared to its predecessor, while giving up only a little in terms of approximation accuracy. We empirically compare our methods on both synthetic data and real data from life science. Results show that from a probabilistic matrix with more than one million rows and columns, we can extract a small set of meaningful patterns that accurately characterize the data distribution of any probable world.

14 citations

Journal ArticleDOI
TL;DR: The software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.
Abstract: Summary: Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals. Availability and Implementation: Implementation in R freely available at www.iki.fi/mpirinen. Contact: matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online.

14 citations


Authors

Showing all 632 results

NameH-indexPapersCitations
Dimitri P. Bertsekas9433285939
Olli Kallioniemi9035342021
Heikki Mannila7229526500
Jukka Corander6641117220
Jaakko Kangasjärvi6214617096
Aapo Hyvärinen6130144146
Samuel Kaski5852214180
Nadarajah Asokan5832711947
Aristides Gionis5829219300
Hannu Toivonen5619219316
Nicola Zamboni5312811397
Jorma Rissanen5215122720
Tero Aittokallio522718689
Juha Veijola5226119588
Juho Hamari5117616631
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20224
202185
202097
2019140
2018127