Most manifold learning methods consider only one similarity matrix to induce a low-dimensional manifold embedded in data space. In practice, however, we often use multiple sensors...
The POMDP is considered as a powerful model for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model precisely the real-...
Uncertainty in data occurs in domains ranging from natural science to medicine to computer science. By developing ways to include uncertainty in our information visualizations we ...
Meredith M. Skeels, Bongshin Lee, Greg Smith, Geor...
In this paper we present a software framework which supports the construction of mixed-fidelity (from sketch-based to software) prototypes for mobile devices. The framework is ava...
Management of virtual machines (VMs) on a large scale remains a significant challenge today. We lack general and vendor-independent quantitative criteria/metrics by which to desc...