A ubiquitous context-sensitive multimodal multimedia computing system and its machine learning-based reconfiguration at the architectural level

In this paper, we present our work on a ubiquitous context-sensitive multimodal multimedia computing system that progressively acquires machine knowledge. This ubiquitous computing system supports an automatic selection of media and modalities deemed appropriate for the user's context and user's profile. The ability of the system to do so constitutes its acquired knowledge. The decision making for media/modality selection takes into account if the user has some special needs due to disability. The architecture of the system is designed to be pervasive and is conceived to resist failure. In case of one or more components being missing or found defective, the machine would resist failure by reconfiguring itself dynamically in the architectural level. It finds alternative replacement to the failed component using its acquired knowledge.