Encapsulating and Manipulating Component Object Graphics (COGs) using SVG

Scalable Vector Graphics (SVG) has an imaging model similar to that of PostScript and PDF but the XML basis of SVG allows it to participate fully, via namespaces, in generalised XML documents.There is increasing interest in using SVG as a Page Description Language and we examine ways in which SVG document components can be encapsulated in contexts where SVG will be used as a rendering technology for conventional page printing.Our aim is to encapsulate portions of SVG content (SVG COGs) so that the COGs are mutually independent and can be moved around a page, while maintaining invariant graphic properties and with guaranteed freedom from side effects and mutual interference. Parellels are drawn between COG implementation within SVG's tree-based inheritance mechanisms and an earlier COG implementation using PDF.

A Component Based Heuristic Search Method with AdaptivePerturbations for Hospital Personnel Scheduling

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with adaptive perturbations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then mimic a natural evolutionary process on these components to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.