#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

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.

Data Science Bootcamp

Training experts from within companies to become Data Scientists who are able to the best use of artificial intelligence methods

Publications

Horst, F., Slijepcevic, D., Lapuschkin, S., Raberger, A.-M., Zeppelzauer, M., Samek, W., … Horsak, B. (2020). On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. Frontiers in Bioengineering and Biotechnology, Submitted.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., & Zeppelzauer, M. (2020). GaitRec, a large-scale walking GRF dataset for a healthy cohort and patients with musculo-skeletal impairments. Scientific Data, Submitted.
Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Breitender, C., Horsak, B., & Horsak, Brian. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203.
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.
Luh, Robert, & 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.
Schrittwieser, S. (2019, September). Sicherheit von Container-Virtualisierung. Invited Talk presented at the IDC Security Roadshow Vienna, Wien.
Luh, R., Janicke, H., & Schrittwieser, S. (2019). AIDIS: Detecting and classifying anomalous behavior in ubiquitous kernel processes. Computers & Security, (84), 120–147. https://doi.org/https://doi.org/10.1016/j.cose.2019.03.015
Luh, R. (2019). Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes (Dissertation). De Monfort University Leicester.

News

01/07/2020

Launch of Lower Austrian Science Academy

12/11/2019

Research from the World of Media Technology

11/21/2019

Secure Data Transmission with Ultrasound