In supervised machine learning, variable ranking aims at sorting the input variables according to their relevance w.r.t. an output variable. In this paper, we propose a new relevan...
Global variable promotion, i.e. allocating unaliased globals to registers, can significantly reduce the number of memory operations. This results in reduced cache activity and less...
We address the problem of obtaining good variable orderings for the BDD representation of a system of interacting finite state machines (FSMs). Orderings are derived from the comm...
In this paper we propose an approach to support dynamic or runtime variability in systems that must adapt dynamically to changing runtime context. The approach is founded on refle...
Nelly Bencomo, Gordon S. Blair, Carlos A. Flores-C...
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...