An Assurance Framework for Independent Co-assurance of Safety and Security

Integrated safety and security assurance for complex systems is difficult for many technical and socio-technical reasons such as mismatched processes, inadequate information, differing use of language and philosophies, etc.. Many co-assurance techniques rely on disregarding some of these challenges in order to present a unified methodology. Even with this simplification, no methodology has been widely adopted primarily because this approach is unrealistic when met with the complexity of real-world system development. This paper presents an alternate approach by providing a Safety-Security Assurance Framework (SSAF) based on a core set of assurance principles. This is done so that safety and security can be co-assured independently, as opposed to unified co-assurance which has been shown to have significant drawbacks. This also allows for separate processes and expertise from practitioners in each domain. With this structure, the focus is shifted from simplified unification to integration through exchanging the correct information at the right time using synchronisation activities

A Bayesian Hierarchical Approach to Multirater Correlated ROC Analysis

In a common ROC study design, several readers are asked to rate diagnostics of the same cases processed under different modalities. We describe a Bayesian hierarchical model that facilitates the analysis of this study design by explicitly modeling the three sources of variation inherent to it. In so doing, we achieve substantial reductions in the posterior uncertainty associated with estimates of the differences in areas under the estimated ROC curves and corresponding reductions in the mean squared error (MSE) of these estimates. Based on simulation studies, both the widths of confidence intervals and MSE of estimates of differences in the area under the curves appear to be reduced by a factor that often exceeds two. Thus, our methodology has important implications for increasing the power of analyses based on ROC data collected from an available study population

Detecting Pulsatile Hormone Secretion Events: A Bayesian Approach

Many challenges arise in the analysis of pulsatile, or episodic, hormone concentration time series data. Among these challenges is the determination of the number and location of pulsatile events and the discrimination of events from noise. Analyses of these data are typically performed in two stages. In the first stage, the number and approximate location of the pulses are determined. In the second stage, a model (typically a deconvolution model) is fit to the data conditional on the number of pulses. Any error made in the first stage is carried over to the second stage. Furthermore, current methods, except two, assume that the underlying basal concentration is constant. We present a fully Bayesian deconvolution model that simultaneously estimates the number of secretion episodes, as well as their locations, and a non-constant basal concentration. This model obviates the need to determine the number of events a priori. Furthermore, we estimate probabilities for all ``candidate\u27\u27 event locations. We demonstrate our method on a real data set

Favoring the job applications of military veterans has little effect on workforce quality in the U.S. federal government

For over a century, the U.S. federal government has biased its hiring procedures to increase the employment of military veterans. In a recent study, Tim Johnson examines the effect of these hiring procedures on the quality of the U.S. federal workforce. Contrary to both conventional wisdom and past research indicating that preferential hiring degrades workforce quality, he finds that veterans who benefit from preferential hiring reach quality benchmarks at rates comparable to other employees working in the same job circumstances

My Journey Through Cinematic Camera Movement

This thesis explains my journey to becoming a cinematographer and finding my cinematic style through both trial and error and classroom instruction at Stephen F. Austin State University, learning advanced camera and lighting techniques, and working on the 2022 summer feature as a Director of Photography (DP). I will begin with a little bit of history and then move into my influences, experiences, and preferences as it pertains to cinematography and camera movement