![]() ![]() Our solution is based on a central fact base in combination with a declarative rule-based language to derive new facts at different abstraction levels. We present Mudra, a unified multimodal interaction framework supporting the integrated processing of low-level data streams as well as high-level semantic inferences. However, there is a lack of multimodal interaction engines offering native fusion support across different levels of abstractions to fully exploit the power of multimodal interactions. Existing multimodal fusion engines and frameworks range from low-level data stream-oriented approaches to high-level semantic in\-fer\-ence-based solutions. In recent years, multimodal interfaces have gained momentum as an alternative to traditional WIMP interaction styles.
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