Standardized development of computer software. Part 2: Standards

This monograph contains standards for software development and engineering. The book sets forth rules for design, specification, coding, testing, documentation, and quality assurance audits of software; it also contains detailed outlines for the documentation to be produced

Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.Comment: PhD thesis, Mar 201

Investigating the Electrodeposition of Plutonium and Curium for Safeguarding the Electrorefiner

This research investigated the electrochemical deposition behavior of plutonium (Pu) and curium (Cm) for safeguarding the electrorefiner (ER) in a pyroprocessing facility. The main goal of this investigation was to evaluate the feasibility of using a safeguards concept called the neutron balance method to account for Pu in the ER. The neutron balance method relies on a known Pu/Cm ratio and measures neutrons from Cm-244 coming into and leaving a unit operation to track Pu. The application of the neutron balance approach for pyroprocessing facilities requires that Pu and Cm remain together in all extraction, product recovery, and waste streams. The electrochemical deposition experiments conducted, using a surrogate system of Tb and Gd, revealed that the Tb to Gd ratio was inconsistent between the electrolyte and the cathode deposit under different electrolyte concentrations. The Enhanced REFIN with Anodic Dissolution (ERAD) computer simulations of the same Tb/Gd surrogate system also found that the Tb to Gd ratio varied. ERAD simulations of a large-scale pilot facility also revealed that the Pu to Cm ratio was not constant between the electrolyte salt and the metal deposit when Pu co-deposition occurs. The safeguards assessment concluded that the neutron balance method was an insufficient safeguards approach for the eletrorefiner and working under the assumption that the Pu/Cm ratio was invariable resulted in the loss of significant quantities of special nuclear material (SNM) after processing only a few batches

An Experimental Study of Bias in the Rating Process: Effects of Rater and Ratee Sex and Job Type.

This study was designed to examine the influence of several potential sources of bias in the rating process. It specifically examined the potential effect of sex of rater and ratee, information about ratee characteristics, information about the job, and two rater personality factors on the way raters use performance information in coming to a final promotion decision. The study itself took place in a laboratory setting designed to simulate an assessment center. Raters were 320 business students at a large university. Pretesting was used to identify two jobs as recognizably male and female and two patterns of personality characteristics as either male or female . Male and female raters were then asked to judge promotability of ratees who were identified as either male or female, and who had either male or female characteristics. Candidates were being considered for either male or female jobs. Rater cognitive complexity, as measured by the Hidden Figures Test, and attitudes toward women, as measured by the Women as Managers Scale, were also examined. Results were examined in a 2 x 2 x 2 x 2 factorial design with two covariates. Variables were male vs. female rater, male vs. female ratee, male vs. female characteristics and male vs. female job. The rater personality characteristics were treated as covariates. Overall results indicated that a contrast effect was operating and that women having masculine characteristics were strongly preferred for all jobs, even over males having the same characteristics. Women with feminine characteristics were strongly rejected, when compared to males with the same characteristics. Results of regression analysis of characteristics judged as most important also supported these findings. Male and female raters shared the biases identified. There was little evidence that raters made use of job information or that the personality characteristics identified affected the rating process. Practical implications of this study are described and suggestions for future research are made