Modeling Abstract Concepts For Internet of Everything: A Cognitive Artificial System

Nowadays, people, things, and processes are all connected to internet, and Internet of Everything (IoE) is addressing them conjointly. It is expected that these entities of IoE will communicate to one another, and produce a huge amount of invaluable data, thus generating a need to understand this kind of data. Computational approaches have shown their capabilities in extracting valuable information from such data. Usually these techniques focus on determining concrete concepts (particular activities such as driving, sitting, or entities like digit `1', `2' etc.). On the contrary, abstract concept identification (actions such as Motion-state, Static-state, or entity like `Number') is relatively less explored. In this article we present a methodology to model abstract representation of concepts without supervision, along with a mechanism to utilize generated models in an artificial system. Primarily, we illustrate a Regulated Activation Networks (RANs) approach that identifies abstract concepts, and dynamically builds a hierarchy to represent them. Further, we describe RANs learning, a novel way of associating concepts in two subsequent layers. We experimentally demonstrate how our approach is unsupervised, and able to model abstract activity (static-state and motion-state) of the subjects, using the Smart-phones Dataset from UCI machine learning repository for Human Activity Recognition problem.