Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
While the problem of analyzing network traffic at the granularity of individual connections has seen considerable previous work and tool development, understanding traffic at a ...
Recently, peer-to-peer (P2P) networks have emerged as a covert communication platform for malicious programs known as bots. As popular distributed systems, they allow bots to comm...
Duc T. Ha, Guanhua Yan, Stephan Eidenbenz, Hung Q....
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Abstract— Road network information (RNI) simplifies autonomous driving by providing strong priors about driving environments. Its usefulness has been demonstrated in the DARPA U...