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The database inference problem is a well-known problem in database security and information system security in general. In order to prevent an adversary from inferring classified information from combinations of unclassified information, a database inference analyst must be able to detect and prevent possible inferences. Detecting database inference problems at database design time provides great power in reducing problems over the lifetime of a database. We have developed and constructed a system called Wizard to analyze databases for their inference problems. The system takes as input a database schema, its constituent instances (if available) and additional human-supplied domain information, and provides a set of associations between entities and/or activities that can be grouped by their potential severity of inference vulnerability. A knowledge acquisition process called microanalysis permits semantic knowledge of a database to be incorporated into the analysis using conceptual graphs. These graphs are then analyzed with respect to inference-relevant domains we call facets using tools we have developed. We can determine inference problems within single facets as well as some inference problems between two or more facets. The architecture of the system is meant to be general so that further refinements of inference information subdomains can be easily incorporated into the system. Index Terms - Information security, conceptual graphs, database inference, inference detection, inference analysis, transitive associations.

Byline: Harry S. Delugach This paper presents a standards-based approach to specifying and reasoning about metadata constraints based on existing metadata registry standards, existing terminologies and Common Logic. The paper introduces Common Logic's basic principles and theory, and applies it to several examples where we might want to reason about metadata constraints. It is our expectation that these additional constraints will be developed by the makers of the metadata for a given data source, and will be included in a metadata registry for exchange with any subscriber to the metadata.

Multiple-viewed requirements modeling allows modelers to focus on different aspects of a system's requirements and express them in appropriate modeling notations. As a result, the requirements are scattered in a set of different models. The semantic overlap among them makes model transformation possible, and such a transformation can be used for acquiring requirements knowledge. In this paper, we demonstrate the process of deriving a set of sequence diagrams with requirements knowledge acquisition opportunities from a state diagram. This set can be used as a requirements elicitation medium for sequence diagram modelers. The transformation is based on the rich semantic relationship between the two diagrams and proved graph theory algorithms for finding sequences. The set of derived sequence diagrams is consistent with the state model and achieves minimum state transition path coverage in the state diagram. Such a set of sequence diagrams with knowledge acquisition opportunities can be used as a modest spur to induce human modelers to provide valuable requirements knowledge. [ABSTRACT FROM AUTHOR]

When comparing alternative courses of action, modern military decision makers often must consider both the military effectiveness and the ethical consequences of the available alternatives. The basis, design, calibration, and performance of a principles-based computational model of ethical considerations in military decision making are reported in this article. The relative ethical violation (REV) model comparatively evaluates alternative military actions based upon the degree to which they violate contextually relevant ethical principles. It is based on a set of specific ethical principles deemed by philosophers and ethicists to be relevant to military courses of action. A survey of expert and non-expert human decision makers regarding the relative ethical violation of alternative actions for a set of specially designed calibration scenarios was conducted to collect data that was used to calibrate the REV model. Perhaps unsurprisingly, the survey showed that people, even experts, disagreed greatly amongst themselves regarding the scenarios’ ethical considerations. Despite this disagreement, two significant results emerged. First, after calibration the REV model performed very well in terms of replicating the ethical assessments of human experts for the calibration scenarios. The REV model outperformed an earlier model that was based on tangible consequences rather than ethical principles, that earlier model performed comparably to human experts, the experts outperformed human non-experts, and the non-experts outperformed random selection of actions. All of these performance comparisons were measured quantitatively and confirmed with suitable statistical tests. Second, although humans tended to value some principles over others, none of the ethical principles involved—even the principle of not harming civilians—completely overshadowed all of the other principles. [ABSTRACT FROM AUTHOR]

Empirical studies have shown complexity metrics to be good predictors of testing effort and maintainability in traditional, imperative programming languages. Empirical validation studies have also shown that complexity is a good predictor of initial quality and reliability in object-oriented (OO) software. To date, one of the most empirically validated OO complexity metrics is the Chidamber and Kemerer Weighted Methods in a Class (WMC). However, there are many more OO complexity metrics whose predictive power has not been as extensively explored. In this study, we explore the predictive ability of several complexity-related metrics for OO software that have not been heavily validated. We do this by exploring their ability to measure quality in an evolutionary software process, by correlating these metrics to defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. Using statistical techniques such as Spearman's correlation, principal component analysis, binary logistic regression models and their respective validations, we show that some lesser known complexity metrics including Michura et al.'s standard deviation method complexity and Etzkorn et al.'s average method complexity are more consistent predictors of OO quality than any variant of the Chidamber and Kemerer WMC metric. We also show that these metrics are useful in identifying fault-prone classes in software developed using highly iterative or agile software development processes. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]