Naturally gaze is used for visual perception of our environment and gaze movements are mainly controlled subconsciously. Forcing the user to consciously diverge from that natural gaze behavior for interaction purposes causes high cognitive workload and destroys information contained in natural gaze movements. Instead of proposing a new gaze-based interaction technique, we analyze natural gaze behavior during an object manipulation task and show ways how it can be used for intention recognition, which provides a universal basis for integrating gaze into multimodal interfaces for different applications. We propose a model for multimodal integration of natural gaze behavior and evaluate it for two different use cases, namely for improvement of robustness of other potentially noisy input cues and for the design of proactive interaction techniques.

The increased use and interconnection of electronic components in automobiles has made communication behavior in automotive networks drastically more complex. Both communication designs at application level and complex communication scenarios are often under-specified or out of scope of existing analysis techniques. We extend traditional protocol analyzers in order to capture communication at the level of abstraction that reflects application design and show that the same technique can be used to specify, monitor and test complex scenarios. We present CFR (Channel Filter Rule) models, a novel approach for the specification of analyzers and a domain-specific language that implements this approach. From CFR models, we can fully generate powerful analyzers that extract design intentions, abstract protocol layers and even complex scenarios from low level communication data. We show that three basic concepts (channels, filters and rules) are sufficient to build such powerful analyzers and identify possible areas of application.