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.

Learning in balance: Using oscillatory EEG biomarkers of attention, motivation and vigilance to interpret game-based learning

Abstract: Motivated by the link between play and learning, proposed in literature to have a neurobiological basis, we study the electroencephalogram and associated psychophysiology of “learning game” players. Forty-five players were tested for topic comprehension by a questionnaire administered before and after solo playing of the game Peacemaker (Impact Games 2007), during which electroencephalography and other physiological signals were measured. Play lasted for one hour, with a break at half time. We used the Bloom taxonomy to distinguish levels of difficulty in demonstrated learning—with the first five levels assigned to fixed questions—and “gain” scores to measure actual value of demonstrated learning. We present the analysis of the physiological signals recorded during game play and their relationship to learning scores. Main effects related to biomarkers of vigilance and motivation—including decreased delta power and relatively balanced fronto-hemispheric alpha power—predicted learning at the analyse...

Security for medical sensor networks in mobile health systems

Abstract: Emerging Internet of Things (IoT) technologies and mobile health scenarios provide opportunities for enhancing traditional healthcare systems. Yet current development meets the challenge of sensing patient's health data with strong security guarantees in mobile and resource-constrained settings as well as in emergency situations. This paper presents a generic IoT-aware system architecture that enables security of personal mobile data and their transfer to healthcare services. Our security solutions apply the Host Identity Protocol. We validate the efficiency using a prototype implementation.

A preliminary analysis of TCP performance in an enterprise network

Abstract: Although TCP behavior is one of the most studied aspects of Internet traffic, little is known about TCP performance within modern enterprise networks. In this paper we analyze aspects of TCP performance observed in packet traces taken over four months from a medium-sized enterprise. We assess the prevalence of broken TCP transactions, applications used, throughput of TCP connections, and phenomena that influence performance, such as retransmissions, out-of-order delivery, and packet corruption. While much remains to explore, this work represents a first step towards understanding TCP performance in the under-studied environment.

Compression-based similarity measures in symbolic, polyphonic music

Abstract: We present a novel compression-based method for measuring similarity between sequences of symbolic, polyphonic music. The method is based on mapping the values of binary chromagrams extracted from MIDI files to tonal centroids, then quantizing the tonal centroid representation values to sequences, and finally measuring the similarity between the quantized sequences using Normalized Compression Distance (NCD). The method is comprehensively evaluated with a test set of classical music variations, and the highest achieved precision and recall values suggest that the proposed method can be applied for similarity measuring. Also, we analyze the performance of the method and discuss what should be taken into consideration when applying the method for measurement tasks.

Classes for the masses: Systematic classification of unknowns using fragmentation spectra

Abstract: Metabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.