Chris Rorres

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This paper describes a collection of algorithms that we developed and implemented to facilitate the automatic recovery of the modular structure of a software system from its source code. We treat automatic modularization as an optimization problem. Our algorithms make use of traditional hillclimbing and genetic algorithms.
Three different estimators are presented for the types of parameters present in mathematical models of animal epidemics. The estimators make use of the data collected during an epidemic, which may be limited, incomplete, or under collection on an ongoing basis. When data are being collected on an ongoing basis, the estimated parameters can be used to(More)
State-scale and premises-scale gravity models for the spread of highly pathogenic avian influenza (H5N1) in Nigeria and Ghana were used to provide a basis for risk maps for future epidemics and to compare and rank plausible culling and vaccination strategies for control. Maximum likelihood methods were used to fit the models to the 2006–2007 outbreaks. The(More)
We formulate a stochastic, spatial, discrete-time model of viral "Susceptible, Exposed, Infectious, Recovered" animal epidemics and apply it to an avian influenza epidemic in Pennsylvania in 1983-84. Using weekly data for the number of newly infectious cases collected during the epidemic, we find estimates for the latent period of the virus and the values(More)
Despite advances in software engineering, software faults continue to cause system downtime. Software faults are difficult to detect before the system fails, especially since the first symptom of a fault is often system failure itself. This paper presents a computational geometry technique and a supporting tool to tackle the problem of timely fault(More)