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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 Article
01 Jan 2019
TL;DR: It is shown rigorously that in the case of bivariate causal discovery, such non-linear ICA can be used to infer the causal direction via a series of independence tests, and an alternative measure of causal direction based on asymptotic approximations to the likelihood ratio is proposed.
Abstract: We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make the assumption of additive noise. Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships. The proposed method is based on recent progress in non-linear independent component analysis and exploits the non-stationarity of observations in order to recover the underlying sources or latent disturbances. We show rigorously that in the case of bivariate causal discovery, such non-linear ICA can be used to infer the causal direction via a series of independence tests. We further propose an alternative measure of causal direction based on asymptotic approximations to the likelihood ratio, as well as an extension to multivariate causal discovery. We demonstrate the capabilities of the proposed method via a series of simulation studies and conclude with an application to neuroimaging data.

25 citations

Proceedings ArticleDOI
06 Jan 2003
TL;DR: A requirement analysis is presented and a wireless terminal-based transaction manager (TM) architecture based on the assumption that there is an application that supports certain business transaction(s) and that it uses the TM to store transactional state information and retrieve it after a communication link, application, or terminal crash is presented.
Abstract: Although there has been a lot of discussion of "transactions" in mobile e-commerce (m-commerce), very little attention has been paid for distributed transactional properties of the computations facilitating m-commerce. In this paper, we first present a requirement analysis and then present a wireless terminal-based transaction manager (TM) architecture. This architecture is based on the assumption that there is an application that supports certain business transaction(s) and that it uses the TM to store transactional state information and retrieve it after a communication link, application, or terminal crash. We present the design of such a TM, including the application interface, modules and log structure. A pilot implementation of this TM for the location-based application is also discussed. We further discuss other alternatives to design such a TM that together can be called "ontological transaction monitor". This acts as an intelligent component between the application and the servers accessed during m-commerce transactions and controls the perceivable communication behavior of the terminal towards the servers, maintains the state information and takes care of tight coupling of transactional properties of the computations as well as of security and privacy.

25 citations

Proceedings Article
27 Jul 2014
TL;DR: The empirical results, based on the largest evaluation of state-of-the-art BNS learning algorithms to date, demonstrate that they can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance.
Abstract: There are various algorithms for finding a Bayesian network structure (BNS) that is optimal with respect to a given scoring function. No single algorithm dominates the others in speed, and, given a problem instance, it is a priori unclear which algorithm will perform best and how fast it will solve the problem. Estimating the runtimes directly is extremely difficult as they are complicated functions of the instance. The main contribution of this paper is characterization of the empirical hardness of an instance for a given algorithm based on a novel collection of non-trivial, yet efficiently computable features. Our empirical results, based on the largest evaluation of state-of-the-art BNS learning algorithms to date, demonstrate that we can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance. Moreover, we also show how the results can be utilized in building a portfolio algorithm that combines several individual algorithms in an almost optimal manner.

25 citations

Proceedings ArticleDOI
30 Sep 2009
TL;DR: A set of community innovation principles relevant for social media design is introduced that are based on the comparison between Closed and Open Innovation introduced by Chesbrough and tested against two real world cases.
Abstract: This paper introduces a set of community innovation principles relevant for social media design. By drawing on the comparison between Closed and Open Innovation introduced by Chesbrough, we develop a set of hypotheses that explore the nature of community innovation practices and their related principles. Furthermore we test those principles against two real world cases.

25 citations

22 Nov 2016
TL;DR: The 2018 edition of the MediaEval 2018 Emotional Impact of Movies Task as mentioned in this paper focused on predicting the emotional impact that video content will have on viewers, in terms of valence, arousal and fear.
Abstract: This paper provides a description of the MediaEval 2018 “Emotional Impact of Movies task". It continues to build on last year’s edition, integrating the feedback of previous participants. The goal is to create systems that automatically predict the emotional impact that video content will have on viewers, in terms of valence, arousal and fear. Here we provide a description of the use case, task challenges, dataset and ground truth, task run requirements and evaluation metrics.

25 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