As the most attractive realization of component-based software, composite Web service asks a heavy demand for reliability prediction in the early stage for service discovery and service composition. Although it is believed that input should be one of the most important aspects affecting the reliability of a component-based system, research of both giving more details on an operational profile and considering software architecture to get more accurate reliability prediction is scant. This paper describes an algebra-based reliability prediction approach in three aspects: (i) a sub-set of OWL-S that provides reliability estimation information is discussed; (ii) a framework that describes syntax and reliability prediction semantics of the sub-set is explained and implemented in Maude; (iii) an example of how to use the framework to calculate reliability is studied.

Many features can be used to describe a human face but few have been used in combination. Extending the feature vector using orthogonal sets of measurements can reduce the variance of a matching measure, to improve discrimination capability. This paper investigates how different features can be used for discrimination, alone or when integrated into an extended feature vector. This study concentrates on improving feature definition and extraction from a frontal view image, incorporating and extending established measurements. These form an extended feature vector based on four feature sets: geometric (distance) measurements, the eye region, the outline contour, and the profile. The profile, contour, and eye region are described by the Walsh power spectrum, normalized Fourier descriptors, and normalized moments, respectively. Although there is some correlation between the geometrical measures and the other sets, their bases (distance, shape description, sequency, and statistics) are orthogonal and hence appropriate for this research. A database of face images was analyzed using two matching measures which were developed to control differently the contributions of elements of the feature sets. The match was evaluated for both measures for the separate feature sets and for the extended feature vector. Results demonstrated that no feature set alone was sufficient for recognition whereas the extended feature vector could discriminate between subjects successfully.