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Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

There are many models of hypertext, distinguished by a number of factors such as the underlying semantic data model (link typing and node typing), the degree of dynamic linking in the hypertext, and how dynamism and other behaviours are implemented. This essay examines a particular approach to dynamism in hypertext, based on the degree of similarity between a text passage in a source node and the text of a target node. It reviews work carried out over the past decade in creating systems for markup-based querying and dynamic hypertext, with particular emphasis on a model of dynamic hypertext that computes hypertext links on the fly using queries.