Institution
Helsinki Institute for Information Technology
Facility•Espoo, 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 published on a yearly basis
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01 Jan 2004
TL;DR: InformationRadar attempts to combine both public and in-group messaging into one system by providing a novel radar interface for accessing messages, desktop-like temporal storage for messages, location-independent message threading, filtering functionality, contextual audience addressing, multimedia messaging, social activity indicator, and voting.
Abstract: Previous research has sought to utilize everyday messaging metaphors, such as the notice board, in location-based messaging systems. Unfortunately, many of the restrictions associated with the metaphors have been unnecessarily reintroduced to interaction, and results from the previous field trials have been disheartening. InfoRadar builds on experiences with these systems by presenting improvements in user interface functionality and services. By providing a novel radar interface for accessing messages, desktop-like temporal storage for messages, location-independent message threading, filtering functionality, contextual audience addressing, multimedia messaging, social activity indicator, and voting, InfoRadar attempts to combine both public and in-group messaging into one system. A preliminary field trial indicates that location-based aspects may have a role in facilitating mobile communication, particularly when it comes to engaging in social interaction with unknown people.
54 citations
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TL;DR: The authors combine conditional independencies and independent component analysis to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible.
Abstract: An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on conditional independencies (Spirtes et al. 1993; Pearl 2000) cannot distinguish between independence-equivalent models, whereas approaches purely based on Independent Component Analysis (Shimizu et al. 2006) are inapplicable to data which is partially Gaussian. In this paper, we generalize and combine the two approaches, to yield a method able to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible. We give exact graphical conditions for when two distinct models represent the same family of distributions, and empirically demonstrate the power of our method through thorough simulations.
53 citations
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TL;DR: A combination of existing and new probabilistic machine learning techniques are used to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results.
Abstract: Motivation: As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.
Results: We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.
Availability: The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.
Contact: jose.caldas@tkk.fi
53 citations
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19 Mar 2011TL;DR: This study investigated profile work in Last.fm, an SNS that automatically publishes music listening information, and found that, instead of simply not publishing things they might rather keep private, users tend to change their music listening behavior in order to control their self-presentation.
Abstract: We offer the concept of profile work to illustrate the effort people invest in their public profiles in social network services (SNSs). In our explorative study, we investigated profile work in Last.fm, an SNS that automatically publishes music listening information. We found that, instead of simply not publishing things they might rather keep private, users tend to change their music listening behavior in order to control their self-presentation. Four dimensions of profile work were identified, including detailed mechanisms to regulate one's profile. We suggest ways to support users' profile work in the context of automated sharing of behavior information.
52 citations
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TL;DR: A new algebraic sieving technique to detect constrained multilinear monomials in multivariate polynomial generating functions given by an evaluation oracle is introduced and shown to show an $$O^*(2^k)$$O∗(2k)-time polynomials space algorithm for the k-sized Graph Motif problem.
Abstract: We introduce a new algebraic sieving technique to detect constrained multilinear monomials in multivariate polynomial generating functions given by an evaluation oracle. The polynomials are assumed to have coefficients from a field of characteristic two. As applications of the technique, we show an $$O^*(2^k)$$O?(2k)-time polynomial space algorithm for the $$k$$k-sized Graph Motif problem. We also introduce a new optimization variant of the problem, called Closest Graph Motif and solve it within the same time bound. The Closest Graph Motif problem encompasses several previously studied optimization variants, like Maximum Graph Motif, Min-Substitute Graph Motif, and Min-Add Graph Motif. Finally, we provide a piece of evidence that our result might be essentially tight: the existence of an $$O^*((2-\epsilon )^k)$$O?((2-∈)k)-time algorithm for the Graph Motif problem implies an $$O((2-\epsilon ')^n)$$O((2-∈?)n)-time algorithm for Set Cover.
52 citations
Authors
Showing all 632 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dimitri P. Bertsekas | 94 | 332 | 85939 |
Olli Kallioniemi | 90 | 353 | 42021 |
Heikki Mannila | 72 | 295 | 26500 |
Jukka Corander | 66 | 411 | 17220 |
Jaakko Kangasjärvi | 62 | 146 | 17096 |
Aapo Hyvärinen | 61 | 301 | 44146 |
Samuel Kaski | 58 | 522 | 14180 |
Nadarajah Asokan | 58 | 327 | 11947 |
Aristides Gionis | 58 | 292 | 19300 |
Hannu Toivonen | 56 | 192 | 19316 |
Nicola Zamboni | 53 | 128 | 11397 |
Jorma Rissanen | 52 | 151 | 22720 |
Tero Aittokallio | 52 | 271 | 8689 |
Juha Veijola | 52 | 261 | 19588 |
Juho Hamari | 51 | 176 | 16631 |