Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
A variety of compilers, static analyses, and testing frameworks rely heavily on path frequency information. Uses for such information range from optimizing transformations to bug ...
Nowadays, large-scale industrial software systems may involve hundreds of developers working on hundreds of different but related models representing parts of the same system spec...
In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to ini...
The issue logic of a superscalar processor dissipates a large amount of static and dynamic power. Furthermore, its power density makes it a hot-spot requiring expensive cooling sy...