The Object Management Group Ontology Definition Metamodel

Report of a submission being made to a major international software engineering standards group, the Object Management Group which ties together OMG standards with World-Wide Web Consortium and International Standards Organization standards. Major industry bodies including IBM are collaborating, and the submission has the support of 24 companies. OMG, W3C and ISO standards strongly influence the industry, especially in combination. Colomb was a major contributor, responsible for 30% of the submission, and the primary author of the paper.

Embodied women at work in neo-liberal times and places

In this article five women explore (female) embodiment in academic work in current workplaces. In a week-long collective biography workshop they produced written memories of themselves in their various workplaces and memories of themselves as children and as students. These memories then became the texts out of which the analysis was generated. The authors examine the constitutive and seductive effects of neoliberal discourses and practices, and in particular, the assembling of academic bodies as particular kinds of working bodies. They use the concept of chiasma, or crossing over, to trouble some aspects of binary thinking about bodies and about the relations between bodies and discourses. They examine the way that we simultaneously resist and appropriate, and are seduced by and appropriated within, neoliberal discourses and practices.

MDA-based re-engineering with Object-Z

This paper describes a practical application of MDA and reverse engineering based on a domain-specific modelling language. A well defined metamodel of a domain-specific language is useful for verification and validation of associated tools. We apply this approach to SIFA, a security analysis tool. SIFA has evolved as requirements have changed, and it has no metamodel. Hence, testing SIFA’s correctness is difficult. We introduce a formal metamodelling approach to develop a well-defined metamodel of the domain. Initially, we develop a domain model in EMF by reverse engineering the SIFA implementation. Then we transform EMF to Object-Z using model transformation. Finally, we complete the Object-Z model by specifying system behavior. The outcome is a well-defined metamodel that precisely describes the domain and the security properties that it analyses. It also provides a reliable basis for testing the current SIFA implementation and forward engineering its successor.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.