Using modern technology to support museum activities. Case study: Estonians Deported to Siberia: Soviet Red Terror 1940-1960

in Proceedings of ICOM, Milano 2016 (2016)

Using multi-member panels to tackle RSD complexities

in Forced Migration Review (2020), (65), 32-35

Using Ontologies to Model Data Protection Requirements in Workflows

Scientific Conference (2015, November)

Data protection, currently under the limelight at the European level, is undergoing a long and complex reform that is finally approaching its completion. Consequently, there is an urgent need to customize ... [more ▼]

Data protection, currently under the limelight at the European level, is undergoing a long and complex reform that is finally approaching its completion. Consequently, there is an urgent need to customize semantic standards towards the prospective legal framework. The aim of this paper is to provide a bottom-up ontology describing the constituents of data protection domain and its relationships. Our contribution envisions a methodology to highlight the (new) duties of data controllers and foster the transition of IT-based systems, services/tools and businesses to comply with the new General Data Protection Regulation. This structure may serve as the foundation in the design of present and future information systems abiding to data protection legal requirements. [less ▲]

Using opcode-sequences to detect malicious Android applications

in IEEE International Conference on Communications, ICC 2014, Sydney Australia, June 10-14, 2014 (2014, June)

Recently, the Android platform has seen its number of malicious applications increased sharply. Motivated by the easy application submission process and the number of alternative market places for ... [more ▼]

Recently, the Android platform has seen its number of malicious applications increased sharply. Motivated by the easy application submission process and the number of alternative market places for distributing Android applications, rogue authors are developing constantly new malicious programs. While current anti-virus software mainly relies on signature detection, the issue of alternative malware detection has to be addressed. In this paper, we present a feature based detection mechanism relying on opcode-sequences combined with machine learning techniques. We assess our tool on both a reference dataset known as Genome Project as well as on a wider sample of 40,000 applications retrieved from the Google Play Store. [less ▲]

Using Polyurethane to Reduce the Production Cost of Hydrokinetic Turbine Foils

in Proceedings International Conference on Hydropower for Sustainable Development (2015, February 05)

Polyurethane made profiles can be used for hydrokinetic oscillating foil micro turbines to reduce their production cost. For this purpose, a NACA 0015 profile is designed with a computer aided design ... [more ▼]

Polyurethane made profiles can be used for hydrokinetic oscillating foil micro turbines to reduce their production cost. For this purpose, a NACA 0015 profile is designed with a computer aided design software and milled from aluminium. From this sample a negative mould of silicone is produced. This negative was used to produce finally polyurethane foils. Holes and grooves can be simply implemented in the negative by placing corresponding shapes made of metal within the silicone form. A massive reduction of the production cost of about 1600 Euro for a polyurethane (PUR) foil of 1000 mm length compared to an aluminium foil of the same shape is accomplished. [less ▲]

Using presence to evaluate an augmented reality location aware game

in Personal and Ubiquitous Computing (2011), 15(1), 25-35

Location-aware augmented reality games provide players with a rich and potentially unlimited range of interaction possibilities. In this paper, a study is described which uses a number of measurement ... [more ▼]

Location-aware augmented reality games provide players with a rich and potentially unlimited range of interaction possibilities. In this paper, a study is described which uses a number of measurement techniques including questionnaires, direct observation, semi-structured interviews and video analysis to measure player’s sense of presence. The paper points to the importance of the availability of actions within augmented reality games and how this shapes their sense of presence. The findings indicate that such an approach to measuring presence can provide valuable information on the structure of augmented reality location-aware games. [less ▲]

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

in Briefings in Bioinformatics (2015)

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical ... [more ▼]

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example. [less ▲]

Using process data to explain group differences in complex problem solving

in Journal of Educational Psychology (2020), 122

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.

in Frontiers in physiology (2018), 9

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down ... [more ▼]

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information. [less ▲]

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

in PLoS ONE (2012), 7(7), 39932-39932

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find ... [more ▼]

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL’s classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes. [less ▲]