Design of an Incremental Machine Learning Component of a Ubiquitous Multimodal Multimedia Computing System

Machine learning (ML) allows a machine to acquire basic and decision-making knowledge so that it can be delegated with tasks that are otherwise left to human for implementation. Incremental learning is a form of continuous ML wherein the knowledge acquisition can go on indefinitely for as long as there is some new knowledge to learn. In effect, a machine becomes smarter after gaining added knowledge from training. Our paper details the design of an incremental ML component of a ubiquitous multimodal multimedia computing system. Our system chooses the media and modalities that are appropriate for the user situation; the situation itself is based upon user´s context, user´s profile and the user´s environment and hardware profile. The user context itself is dependent on the user´s location, the noise level in the workplace and the presence or absence of other people in the vicinity, and the user´s computing device. If applicable, the user´s special need (i.e. handicap) is also taken into consideration in the media and modality selection. Incremental ML allows our system to become fault-tolerant, capable of finding replacement to a missing or defective component