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.

The psychophysiology of James Bond: phasic emotional responses to violent video game events

Abstract: The authors examined emotional valence- and arousal-related phasic psychophysiological responses to different violent events in the first-person shooter video game "James Bond 007: NightFire" among 36 young adults. Event-related changes in zygomaticus major, corrugator supercilii, and orbicularis oculi electromyographic (EMG) activity and skin conductance level (SCL) were recorded, and the participants rated their emotions and the trait psychoticism based on the Psychoticism dimension of the Eysenck Personality Questionnaire--Revised, Short Form. Wounding and killing the opponent elicited an increase in SCL and a decrease in zygomatic and orbicularis oculi EMG activity. The decrease in zygomatic and orbicularis oculi activity was less pronounced among high Psychoticism scorers compared with low Psychoticism scorers. The wounding and death of the player's own character (James Bond) elicited an increase in SCL and zygomatic and orbicularis oculi EMG activity and a decrease in corrugator activity. Instead of joy resulting from victory and success, wounding and killing the opponent may elicit high-arousal negative affect (anxiety), with high Psychoticism scorers experiencing less anxiety than low Psychoticism scorers. Although counterintuitive, the wounding and death of the player's own character may increase some aspect of positive emotion.

Bayesian Canonical correlation analysis

Abstract: Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attractive for their potential in hierarchical extensions and for coping with the combination of large dimensionalities and small sample sizes. The existing methods have not been particularly successful in fulfilling the promise yet; we introduce a novel efficient solution that imposes group-wise sparsity to estimate the posterior of an extended model which not only extracts the statistical dependencies (correlations) between data sets but also decomposes the data into shared and data set-specific components. In statistics literature the model is known as inter-battery factor analysis (IBFA), for which we now provide a Bayesian treatment.

Correcting errors in short reads by multiple alignments

Abstract: Motivation: Current sequencing technologies produce a large number of erroneous reads. The sequencing errors present a major challenge in utilizing the data in de novo sequencing projects as assemblers have difficulties in dealing with errors. Results: We present Coral which corrects sequencing errors by forming multiple alignments. Unlike previous tools for error correction, Coral can utilize also bases distant from the error in the correction process because the whole read is present in the alignment. Coral is easily adjustable to reads produced by different sequencing technologies like Illumina Genome Analyzer and Roche/454 Life Sciences sequencing platforms because the sequencing error model can be defined by the user. We show that our method is able to reduce the error rate of reads more than previous methods. Availability: The source code of Coral is freely available at http://www.cs.helsinki.fi/u/lmsalmel/coral/. Contact: leena.salmela@cs.helsinki.fi

Self-organizing maps of document collections: A new approach to interactive exploration

Abstract: Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, WEBSOM, for automatic organization of full-text document collections using the self-organizing map (SOM) algorithm. The document collection is ordered onto a map in an unsupervised manner utilizing statistical information of short word contexts. The resulting ordered map where similar documents lie near each other thus presents a general view of the document space. With the aid of a suitable (WWW-based) interface, documents in interesting areas of the map can be browsed. The browsing can also be interactively extended to related topics, which appear in nearby areas on the map. Along with the method we present a case study of its use.