Abstract— We show how to efficiently extract truly random bits from two independent sources of linear min-entropy, under a computational assumption. The assumption we rely on is...
Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain e...
Jonathan D. Pfautz, Zach Cox, Geoffrey Catto, Davi...
—This paper describes a unified data model that represents multimedia, timeline, and simulation data utilizing a single set of related data modeling constructs. A uniform model f...
There are a number of genuinely open questions concerning the use of domain models in nlp. It would be great if contributors to Applied Ontology could help addressing them rather ...
We compute AM-FM models for infrared video frames depicting military targets immersed in structured clutter backgrounds. We show that independent correlation based detection proce...
Nick A. Mould, Chuong T. Nguyen, Colin M. Johnston...