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.

A shared document-based annotation tool to support learner-centred collaborative learning.

Abstract: A shared document-based annotation tool was presented, and its usefulness in two different real-life web-based university-level courses (adult learners, n= 27 and adolescent learners, n= 23) was empirically investigated. The study design embodied three data collection phases: (1) a pretest measuring self-rated motivation, learning strategies, and social ability; (2) log file data analysis showing actual use of the system features; and (3) a posttest in a form of an email survey. For both groups, the results showed that the level of motivation has a positive effect on activity in the system and the final grade. The learners, who reported to have good time-management strategies, were the most active users of the system. The level of social ability predicted both the number of consecutive comments in the documents and the threads in document-related newsgroup discussions. Log file data analysis showed that user activity in the system was positively related to the final grade in both samples. Results of the posttest showed that all the respondents agreed when asked: (1) if the system brought added value to the learning process; (2) if the use of the system changed their studying habits favourably; and (3) if they would like to use the system in other courses. [ABSTRACT FROM AUTHOR]

The Opponent Matters: Elevated fMRI Reward Responses to Winning Against a Human Versus a Computer Opponent During Interactive Video Game Playing

Abstract: Winning against an opponent in a competitive video game can be expected to be more rewarding than losing, especially when the opponent is a fellow human player rather than a computer. We show that winning versus losing in a first-person video game activates the brain's reward circuit and the ventromedial prefrontal cortex (vmPFC) differently depending on the type of the opponent. Participants played a competitive tank shooter game against alleged human and computer opponents while their brain activity was measured with functional magnetic resonance imaging. Brain responses to wins and losses were contrasted by fitting an event-related model to the hemodynamic data. Stronger activation to winning was observed in ventral and dorsal striatum as well as in vmPFC. Activation in ventral striatum was associated with participants' self-ratings of pleasure. During winning, ventral striatum showed stronger functional coupling with right insula, and weaker coupling with dorsal striatum, sensorimotor pre- and postcentral gyri, and visual association cortices. The vmPFC and dorsal striatum responses were stronger to winning when the subject was playing against a human rather than a computer. These results highlight the importance of social context in the neural encoding of reward value.

High-Quality Self-Supervised Deep Image Denoising

Abstract: We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a "blind spot" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data.

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance

Abstract: Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway. Contact: koen.deforche@uz.kuleuven.ac.be Supplementary information: Supplementary data are available at Bioinformatics online.

Energy-Efficient Multichannel Cooperative Sensing Scheduling With Heterogeneous Channel Conditions for Cognitive Radio Networks

Abstract: Spectrum sensing is an important aspect of cognitive radio networks (CRNs). Secondary users (SUs) should periodically sense the channels to ensure primary-user (PU) protection. Sensing with cooperation among several SUs is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energy-efficient (EE) spectrum sensing scheduling with satisfactory PU protection. Our model exploits the diversity of SUs in their received signal-to-noise ratio (SNR) of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the given problem as an optimization problem with two different objectives. The first objective is to minimize the energy consumption, and the second objective is to minimize the spectrum sensing duration to maximize the remaining time for data transmission. We solve both problems using the outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various numbers of SUs, average channel SNR, and channel sampling frequency. Our analysis reveals that all proposals with an energy minimization perspective provide significant energy savings compared with a pure transmission-time maximization (TXT) technique.