We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
We present a scalable approach to recognizing and describing complex activities in video sequences. We are interested in long-term, sequential activities that may have several par...
Mobile ad hoc networks (MANETs) are vulnerable to routing attacks, especially attacks launched by non-cooperative (selfish or compromised) network members and appear to be protoco...
This paper addresses the difficult problem of selecting representative samples of peer properties (e.g., degree, link bandwidth, number of files shared) in unstructured peer-to-p...
Daniel Stutzbach, Reza Rejaie, Nick G. Duffield, S...