Development of a rich hand-gesture-based interface is currently a tedious process, requiring expertise in computer vision and/or machine learning. We address this problem by introducing a simple language for pose and gesture description, a set of development tools for using it, and an algorithmic pipeline that recognizes it with high accuracy. The language is based on a small set of basic propositions, obtained by applying four predicate types to the fingers and to palm center: direction, relative location, finger touching and finger folding state. This enables easy development of a gesture-based interface, using coding constructs, gesture definition files or an editing GUI. The language is recognized from 3D camera input with an algorithmic pipeline composed of multiple classification/regression stages, trained on a large annotated dataset. Our experimental results indicate that the pipeline enables successful gesture recognition with a very low computational load, thus enabling a gesture-based interface on low-end processors.

Mega grids span several continents and may consist of millions of nodes and billions of tasks executing at any point in time. This setup calls for scalable and highly available resource utilization control that adapts itself to dynamic changes in the grid environment as they occur. In this paper, we address the problem of enforcing upper bounds on the consumption of grid resources. We propose a grid-wide quota enforcement system, called GWiQ-P. GWiQ-P is light-weight, and in practice is infinitely scalable, satisfying concurrently any number of resource demands, all within the limits of a global quota assigned to each user. GWiQ-P adapts to dynamic changes in the grid as they occur, improving future performance by means of improved locality. This improved performance does not impair the system's ability to respond to current requests, tolerate failures, or maintain the allotted quota levels.