Semantic Web Technologies in Support of Service Oriented Architecture Governance

As Service Oriented Architecture (SOA) deployments gradually mature they also grow in size and complexity. The number of service providers, services, and service consumers increases, and so do the dependencies among these entities and the various artefacts that describe how services operate, or how they are meant to operate under specific conditions. Appropriate governance over the various phases and activities associated with the service lifecycle is therefore indispensable in order to prevent a SOA deployment from dissolving into an unmanageable infrastructure. The employment of Semantic Web technologies for describing and reasoning about service properties and governance requirements has the potential to greatly enhance the effectiveness and efficiency of SOA Governance solutions by increasing the levels of automation in a wide-range of tasks relating to service lifecycle management. The goal of the proposed research work is to investigate the application of Semantic Web technologies in the context of service lifecycle management, and propose a concrete theoretical and technological approach for supporting SOA Governance through the realisation of semantically-enhanced registry and repository solutions

Building a refinement checker for Z

In previous work we have described how refinements can be checked using a temporal logic based model-checker, and how we have built a model-checker for Z by providing a translation of Z into the SAL input language. In this paper we draw these two strands of work together and discuss how we have implemented refinement checking in our Z2SAL toolset. The net effect of this work is that the SAL toolset can be used to check refinements between Z specifications supplied as input files written in the LaTeX mark-up. Two examples are used to illustrate the approach and compare it with a manual translation and refinement check.Comment: In Proceedings Refine 2011, arXiv:1106.348

Borrow, Copy or Steal? Loans and Larceny in the Orthodox Canonical Form

C++ libraries designed in the orthodox canonical form (OCF) ensure that classes which manage any kind of heap structures faithfully copy and delete these. However, in certain common circumstances, OCF heap structures are wastefully copied multiple times. General reference counting is not an option in OCF, since a shared body violates the intended value semantics; although a copy-on-write policy can be made to work with borrowed heap structures. A simpler ownership policy, based on larceny, allows low-level memory manager objects to steal heap structures from temporary variables, in properly isolated circumstances. Various strategies for regulating theft are presented, ranging from pilfer-constructors to locks on heap data. Larceny has similarities with other transfer of ownership patterns, but is more a core implementation technique designed to improve the efficiency and effectiveness of OCF-conformant libraries. Conference Stream Research paper 8 478 words; 20pp article + 2pp referenc..

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable