This paper describes a novel approach to generate an optimized schedule to run threads on distributed shared memory (DSM) systems. The approach relies upon a binary instrumentatio...
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
This paper addresses the issue of unsupervised network anomaly detection. In recent years, networks have played more and more critical roles. Since their outages cause serious eco...
Crowdsourcing has recently become popular among machine learning researchers and social scientists as an effective way to collect large-scale experimental data from distributed w...
This paper presents an extensive analysis of the client workloads for educational media servers at two major U.S. universities. The goals of the analysis include providing data fo...
Jussara M. Almeida, Jeffrey Krueger, Derek L. Eage...