Dependability modeling and assessment in UML-based software development.

Assessment of software nonfunctional properties (NFP) is an important problem in software development. In the context of model-driven development, an emerging approach for the analysis of different NFPs consists of the following steps: (a) to extend the software models with annotations describing the NFP of interest; (b) to transform automatically the annotated software model to the formalism chosen for NFP analysis; (c) to analyze the formal model using existing solvers; (d) to assess the software based on the results and give feedback to designers. Such a modeling→analysis→assessment approach can be applied to any software modeling language, be it general purpose or domain specific.

Predicting high-risk preterm birth using artificial neural networks.

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations.

Capturing behaviour for the use of avatars in virtual environments.

Avatars, representations of people in virtual environments, are subject to human control. However, for most applications, it is impractical for a person to directly control each joint in a complex avatar. Rather, people must be allowed to specify complex behaviours with simple instructions and the avatar permitted to select the correct movements in sequence to execute the instruction.