Shouldn’t a Model “Know” Its Own ID?: Banks Can Significantly Improve Their Model Discipline by Embedding One Critical Piece of Information

The unique IDs that firms assign to all important models typically appear in just three places: model documents, validation documents, and model inventory databases. Where the IDs do not, as a rule, appear is within the actual model source code. Incomplete model inventory information (including usage) is a chronic issue throughout the financial industry. Few firms can accurately answer such vexing questions as how many times each model in inventory was executed during the last year, which models exhibit significant seasonality, which models are used in each geographic region or legal entity, or whether any unvalidated models were used during the last year on any firm computer. This article will demonstrate that a root cause of model usage opacity is, unfortunately, that most models do not actually know who they are. This article will further explain how software-embedded model IDs can be leveraged to increase transparency and address some of the most difficult questions that may be posed about model usage.

SQL injection vulnerabilities have been predominant on database-driven web applications since almost one decade. Exploiting such vulnerabilities enables attackers to gain unauthorized access to the back-end databases by altering the original SQL statements through manipulating user input. Testing web applications for identifying SQL injection vulnerabilities before deployment is essential to get rid of them. However, checking such vulnerabilities by hand is very tedious, difficult, and time-consuming. Web vulnerability static analysis tools are software tools for automatically identifying the root cause of SQL injection vulnerabilities in web applications source code. In this paper, we test and evaluate three free/open source static analysis tools using eight web applications with numerous known vulnerabilities, primarily for false negative rates. The evaluation results were compared and analysed, and they indicate a need to improve the tools.


2019 ◽  
Vol 37 (11) ◽  
pp. 1409-1410 ◽  
Author(s):  
Joanna Emerson ◽  
Rachel Bacon ◽  
Alma Kent ◽  
Peter J. Neumann ◽  
Joshua T. Cohen

2021 ◽  
Author(s):  
soumya banerjee

Bayesian models are very important in modern data science. These models can be used to derive estimatesfor noisy and sparse data. This manuscript outlines the basics and derivations of a Bayesian linearregression model. Source code for performing Bayesian linear regression is also provided. I hope thisresource will enable broader understanding of the basics of Bayesian models.


Author(s):  
André Riboira ◽  
Rui Rodrigues ◽  
Rui Abreu ◽  
José Campos

Automated debugging techniques based on statistical analysis of historical test executions data have recently received considerable attention due to their diagnostic capabilities. However, the tools that materialize such techniques suffer from a common, rather important shortcoming: the lack of effective diagnostic reports’ visualizations. This limitation prevents the wide adoption of such tools, as it is difficult to understand the diagnostic reports yielded by them. To fill this gap, the authors propose a framework for integrating interactive visualizations of automatic debugging reports in a popular development environment (namely, the Eclipse integrated development environment). The framework, coined GZoltar, provides several important features to aid the developer’s efficiency to find the root cause of observed failures quickly, such as direct links to the source code editor. Furthermore, the authors report on the results of a user study conducted to assess GZoltar‘s effectiveness.


2019 ◽  
Vol 37 (11) ◽  
pp. 1411-1411
Author(s):  
Joanna Emerson ◽  
Rachel Bacon ◽  
Alma Kent ◽  
Peter J. Neumann ◽  
Joshua T. Cohen

Author(s):  
Martin J. Mahon ◽  
Patrick W. Keating ◽  
John T. McLaughlin

Coatings are applied to appliances, instruments and automobiles for a variety of reasons including corrosion protection and enhancement of market value. Automobile finishes are a highly complex blend of polymeric materials which have a definite impact on the eventual ability of a car to sell. Consumers report that the gloss of the finish is one of the major items they look for in an automobile.With the finish being such an important part of the automobile, there is a zero tolerance for paint defects by auto assembly plant management. Owing to the increased complexity of the paint matrix and its inability to be “forgiving” when foreign materials are introduced into a newly applied finish, the analysis of paint defects has taken on unparalleled importance. Scanning electron microscopy with its attendant x-ray analysis capability is the premier method of examining defects and attempting to identify their root cause.Defects are normally examined by cutting out a coupon sized portion of the autobody and viewing in an SEM at various angles.


2020 ◽  
Vol 51 (3) ◽  
pp. 807-820
Author(s):  
Lena G. Caesar ◽  
Marie Kerins

Purpose The purpose of this study was to investigate the relationship between oral language, literacy skills, age, and dialect density (DD) of African American children residing in two different geographical regions of the United States (East Coast and Midwest). Method Data were obtained from 64 African American school-age children between the ages of 7 and 12 years from two geographic regions. Children were assessed using a combination of standardized tests and narrative samples elicited from wordless picture books. Bivariate correlation and multiple regression analyses were used to determine relationships to and relative contributions of oral language, literacy, age, and geographic region to DD. Results Results of correlation analyses demonstrated a negative relationship between DD measures and children's literacy skills. Age-related findings between geographic regions indicated that the younger sample from the Midwest outscored the East Coast sample in reading comprehension and sentence complexity. Multiple regression analyses identified five variables (i.e., geographic region, age, mean length of utterance in morphemes, reading fluency, and phonological awareness) that accounted for 31% of the variance of children's DD—with geographic region emerging as the strongest predictor. Conclusions As in previous studies, the current study found an inverse relationship between DD and several literacy measures. Importantly, geographic region emerged as a strong predictor of DD. This finding highlights the need for a further study that goes beyond the mere description of relationships to comparing geographic regions and specifically focusing on racial composition, poverty, and school success measures through direct data collection.


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