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Dissertation
Algorithms for Emotion Recognition
(2023)
Technological advancements have increasingly facilitated emotion recognition through physiological sensors integrated into intelligent devices, such as earables and wristbands. People commonly wear these devices in their everyday lives (i.e., in the wild). Patterns can be extracted from various physiological signals, enabling the recognition of emotions. This capability can be integrated into diverse applications, such as attention management systems, human-robot interaction, and stress detection, enhancing them to ...
Dissertation
Investigation and Elimination of Substructures in Formal Concept Analysis focusing on Boolean Suborders and Subcontexts
(2023)
In the field of Formal Concept Analysis, data is mainly presented in so-called formal contexts, which assign to a set of objects their respective attributes. From those concept lattices can be generated, where the objects are grouped with respect to their common attributes to represent the relationships in the data in a way that enhances the understandability for humans. However, since a concept lattice can be of exponential size compared to its associated formal context, the presented relationships often become hard ...
Dissertation
Trajectory Mapping in Physical and Topical Spaces
(2023)
In this dissertation, two different types of trajectories are investigated. In the engfirst part of this work, we investigate methods for the analysis of physical trajectories. We focus on scenarios, in which signal strengths ofWiFi access points are recorded. In detail, they are recorded through smartphone devices by people moving through a building. By means of theWiFi signal strengths, (physical) trajectories of humans are reconstructed, which reflect the locations visited over time. Accurate localization based ...
Dissertation
Orometry, Intrinsic Dimensionality and Learning: Novel Insights into Network Data
(2023-11)
Today, networks are an integral part of our world. Let it be real-life friendship networks or social connections that are based on social media. In this thesis, we contribute to the understanding of networks by studying networks from three different perspectives. First, we adapt notions and concepts from orometry to metric data and networks to gain novel insights from a local perspective. Specifically, we study measures of local outstandingness and propose concepts to derive small hierarchies from larger networks. ...