Thomas Walther

Matthias Geihs, Oleg Nikiforov, Denise Demirel et al.

Sensitive digital data, such as health information or governmental archives, are often stored for decades or centuries. The processing of such data calls for long-term security. Secure channels on the Internet require robust key establishment methods. Currently used key distribution protocols are either vulnerable to future attacks based on Shor's algorithm, or vulnerable in principle due to their reliance on computational problems. Quantum-based key distribution protocols are information-theoretically secure and offer long-term security. However, significant obstacles to their real-world use remain. This paper, which results from a multidisciplinary project involving computer scientists and physicists, systematizes knowledge about obstacles to and strategies for the realization of long-term secure Internet communication from quantum-based key distribution. We discuss performance and security particulars, consider the specific challenges arising from multi-user network settings, and identify key challenges for actual deployment.

Thomas Walther, Rolf P. Würtz

Unsupervised learning of a generalizable model of the visual appearance of humans from video data is of major importance for computing systems interacting naturally with their users and others. We propose a step towards automatic behavior understanding by integrating principles of Organic Computing into the posture estimation cycle, thereby relegating the need for human intervention while simultaneously raising the level of system autonomy. The system extracts coherent motion from moving upper bodies and autonomously decides about limbs and their possible spatial relationships. The models from many videos are integrated into meta-models, which show good generalization to different individuals, backgrounds, and attire. These models allow robust interpretation of single video frames without temporal continuity and posture mimicking by an android robot.

Christopher Schymura, Thomas Walther, Dorothea Kolossa

This study describes a binaural machine hearing system that is capable of performing auditory stream segregation in scenarios where multiple sound sources are present. The process of stream segregation refers to the capability of human listeners to group acoustic signals into sets of distinct auditory streams, corresponding to individual sound sources. The proposed computational framework mimics this ability via a probabilistic clustering scheme for joint localization and segregation. This scheme is based on mixtures of von Mises distributions to model the angular positions of the sound sources surrounding the listener. The distribution parameters are estimated using block-wise processing of auditory cues extracted from binaural signals. Additionally, the proposed system can conduct rotational head movements to improve localization and stream segregation performance. Evaluation of the system is conducted in scenarios containing multiple simultaneously active speech and non-speech sounds placed at different positions relative to the listener.