We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links repres...
Michael Hay, Gerome Miklau, David Jensen, Donald F...
Enabling virtual agents to quickly and accurately infer users’ psychological characteristics such as their personality could support a broad range of applications in education, t...
Jennifer L. Robison, Jonathan P. Rowe, Scott W. Mc...
Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compou...
We introduce a refinement strategy to bring the parallel performance analysis closer to the user. The analysis starts with a simple high-level performance model. It is based on fir...
Jan Lemeire, Andy Crijns, John Crijns, Erik F. Dir...
We investigate generalizations of the allsubtrees "DOP" approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences ...