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combination of different HCI methods. Furthermore, these methods shall be founded by legal as well as learning and motivational psychological viewpoints. Within the frameworks of this project, a simulation of
Section: FB3
recognized, human experts instruct and continuously improve the CNN to be developed using supervised learning.
health environments in complex multi-device scenarios. This includes the application of machine learning (ML) techniques for data-driven long-term evaluation and prediction. This will enable users to play
experiences of household robots so that they can identify their own reasoning errors. They will also learn in which situations certain reasoning processes are useful or unnecessary. Previously, He worked in
sports, feedback systems, and motion analysis in weight training. His focus is on applying machine learning methods to motion data recorded using different sensors. He studied computer science at the University
Nguyen more © Samuel Wiest Samuel Wiest more News Jun. 24: Our paper "Embodied Runtime Monitoring of Learning-enabled Robot Perception Components" (Deebul Sivarajan Nair, Sathwik Panchangam, Miguel A. Oliv
narratives in social media texts Evaluating (deep) language understanding: e.g. in comparing machine learning/symbolic AI/hybrid AI Visualization for NLU: e.g. interactive topic networks or narrative graphs
218-64424 ORCID Research Interests Knowledge representation and ontologies Formal semantics Machine learning models in Natural Language Processing Human Computer Interaction (HCI) Possible thesis projects:
stimuli, their modality, and participants’ response options. Second, signal processing and machine learning methods will be leveraged to identify and differentiate hate speech from other types of speech for
measurement and interpretation and potential usefulness in human-machine interfaces. They should learn to analyze and formally describe the problems, challenges and potential of using these signals for