Dr. Tobias Baur
|Telefon:||+49 821 – 598 2305|
|Adresse:||Universitätsstraße 6a, 86159 Augsburg|
- Affective Computing
- Artificial Emotional Intelligence
- eXplainable AI
- Semi-Supervised Active Machine Learning
|Human-Centered Artificial Intelligence for Health Care Applications||Sommersemester 2020||Praktikum|
- 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019) - Best Paper Award
- ACM Multimedia 2013 Open Source Challenge - Honorable Mention
- UMAP 2013 - Most Participative Demo Award
- IDGEI 2013, FDG - Best Paper Award
Talks / Keynotes
NOVA is a tool for annotating and analyzing behaviours in social interactions. A main feature of NOVA is that it allows to employ a collaborative annotation database where annotation work can be split between multiple sides, but also between a human annotator and a machine by supporting human annotators with machine learning techniques already during the annotation task - A process we call Collaborative Machine Learning.
NOVA allows framewise labeling for a precise coding experience, and value-continuous annotations for labeling e.g emotions or social attitudes. The interface is customizable and allows loading and labeling data of multiple persons. The Cooperative Machine Learning capabilities allow to train and evaluate machine learning models, such as Support Vector machines or Artificial neural networks directly from the interface with both, a session completion step, where a model is trained on the first minutes of an annotated sessions to predict the remaining session, and a session transfer step where a model is trained on multiple sessions to predict completly unknown data. With the help of human input the models can then be refined. NOVA further integrates eXplainable AI algorithms to give inisghts into a Machine Learning model’s inner workings.
The Social Signal Interpretation Framework
The Social Signal Interpretation (SSI) framework offers tools to record, analyse and recognize human behavior in real-time, such as gestures, mimics, head nods, and emotional speech. Following a patch-based design pipelines are set up from autonomic components and allow the parallel and synchronized processing of sensor data from multiple input devices. In particularly SSI supports the machine learning pipeline in its full length and offers a graphical interface that assists a user to collect own training corpora and obtain personalized models. In addition to a large set of built-in components SSI also encourages developers to extend available tools with new functions. For inexperienced users an easy-to-use XML editor is available to draft and run pipelines without special programming skills. SSI is written in C++ and optimized to run on computer systems with multiple CPUs.