#Machine Learning

In Machine Learning, artificial systems learn from experience – similar to humans. This technology can be used in various applications in research and practice, for example to analyse medical data or fend off IT attacks.

Projects

SBA Research

K1 competence center for Information Security

Scribe ID AI

Active Machine Learning for automatic identification of handwriting in 12th century manuscripts

Plant Monitoring AI

Leveraging machine learning and predictive analytics for early detection of plant stress for the benefit of sustainability in farming

Digital Makers Hub

Platform for digital makers, for interaction, efficient sharing of knowledge & experiences and nationwide coordination of activities

DIHOST - Digital Innovation Hub Lower Austria/Vienna/Burgenland

Comprehensive service programme to increase the transformation capacity and transformation speed of SMEs in Eastern Austria with regard to digital innovations

IntelliGait 3D- Gait Data Mining

Establishing advanced analysis methods for modelling, classification and similarity retrieval of gait patterns to enable novel data-driven ways to access 3D gait databases

Smart Companion

Using everyday low-cost robot sensors and smart speech recognition in assistance services for elderly people

ReMoCap-Lab

The „Laboratory for Capturing Motion and Augmenting Environment in Motor Rehabilitation“ will enable innovative projects and excellent research in Austria

Ressel Center: Horizons of personalized music therapy 2

Exploration of music therapeutic processes and relationships in selected areas of neurological rehabilitation

Big-Data Analytics

Developing methods for the analysis of large amounts of data without compromising data protection.

Publications

Slijepcevic, D., Zeppelzauer, M., Schwab, Caterine, Raberger, A.-M., Breitender, C., & Horsak, B. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203. https://doi.org/https://doi.org/10.1016/j.gaitpost.2019.10.021
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. In Abstractband des 3. GAMMA Kongress. München, Deutschland.
Koch, D., Despotovic, M., Thaler, S., & Zeppelzauer, M. (2020). Estimating Graduate Density in Satellite Images - A Machine Learning Approach. Elsevier Computers, Environment and Urban Systems, Submitted.
Oliveira, V. A. D. J., Stoiber, C., Grüblbauer, J., Musik, C., Ringot, A., & Gebesmair, A. (2020). SAMBAVis: Design Study of a Visual Analytics Tool for the Music Industry Powered by YouTube Comments (p. 5). Presented at the Eurovis 2020, Norrköping, Sweden.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., Worisch, M., & Zeppelzauer, M. (2020). GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Scientific Data, 7(1), 143. https://doi.org/10.1038/s41597-020-0481-z
Eresheim, S. (2020). Reinforcement Learning for Incident Protection in IT. Presented at the First Conference on Mathematics of Data Science (MDS20).
Eresheim, S. (2020). Cybersecurity Containment Agent. Presented at the Machine Learning Prague 2020.
Luh, R., & Schrittwieser, S. (2019). Advanced threat intelligence: detection and classification of anomalous behavior in system processes. E \& i Elektrotechnik Und Informationstechnik, Springer, 1–7.
Pirker, M. (2019, November). More Data - More Security? Invited Talk presented at the TOP Alumni Club, TU Wien.
Pirker, M. (2019, October). Digitale Probleme....für Alle! Presented at the PrivacyWeek, Wien.

News

06/26/2020

A Data Treasure for Gait Analysis

06/19/2020

New Science Format: Science@Home

01/07/2020

Launch of Lower Austrian Science Academy