Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest

Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain

Application of a Novel Method for Assessing Cumulative Risk Burden by County

The purpose of this study is to apply the Human Security Index (HSI) as a tool to detect social and economic cumulative risk burden at a county-level in the state of Texas. The HSI is an index comprising a network of three sub-components or “fabrics”; the Economic, Environmental, and Social Fabrics. We hypothesized that the HSI will be a useful instrument for identifying and analyzing socioeconomic conditions that contribute to cumulative risk burden in vulnerable counties. We expected to identify statistical associations between cumulative risk burden and (a) ethnic concentration and (b) geographic proximity to the Texas-Mexico border. Findings from this study indicate that the Texas-Mexico border region did not have consistently higher total or individual fabric scores as would be suggested by the high disease burden and low income in this region. While the Economic, Environmental, Social Fabrics (including the Health subfabric) were highly associated with Hispanic ethnic concentration, the overall HSI and the Crime subfabric were not. In addition, the Education, Health and Crime subfabrics were associated with African American racial composition, while Environment, Economic and Social Fabrics were not. Application of the HSI to Texas counties provides a fuller and more nuanced understanding of socioeconomic and environmental conditions, and increases awareness of the role played by environmental, economic, and social factors in observed health disparities by race/ethnicity and geographic region

Developing measures on the perceptions of the built environment for physical activity: a confirmatory analysis

Abstract Background Minimal validity evidence exists for scales assessing the built environment for physical activity. The purpose of this study was to assess the test-retest reliability and invariance of a three-factor model (Neighborhood Characteristics, Safety/Crime, and Access to Physical Activity Facilities) across gender, race, geographic location, and level of physical activity. Methods To assess measurement invariance, a random sample of 1,534 adults living in North Carolina or Mississippi completed a computer assisted telephone interview that included items examining perceptions of the neighborhood for physical activity. Construct level test-retest reliability data were collected from a purposeful sample of 106 participants who were administered the questionnaire twice, approximately two weeks apart. Fit indices, Cronbach's alpha, Mokken H and Spearman correlation coefficients (SCC) were used to evaluate configural and co/variance invarianc,e and intraclass correlation coefficients (ICC) were used to assess reliability. Results Construct test-retest reliability was strong (ICC 0.90 to 0.93). SCC for Neighborhood Characteristics and Crime/Safety were weak with Access (0.21 and 0.25), but strong between Crime/Safety and Neighborhood Characteristics (0.62). Acceptable fit and evidence of measurement invariance was found for gender, race (African American and White), geographic location, and level of physical activity. Fit indices consistently approached or were greater than 0.90 for goodness of fit index, normed fit index, and comparative fit index which is evidence of configural invariance. There was weak support of variance and covariance invariance for all groups that was indicative of factorial validity. Conclusions Support of the validity and reliability of the three-factor model across groups expands the possibilities for analysis to include latent variable modeling, and suggests these built environment constructs may be used in other settings and populations

Assessing and managing the potential for compliance default of applications submitted in terms of South Africa's environmental impact assessment regulations

In the environmental impact assessment (EIA) field, much attention is paid to the process leading up to the granting of an environmental authorization, but very little is given to what happens after the consent decision is granted. This study aimed to address this lack through the implementation of an EIA follow-up procedure in a region of the Eastern Cape Province, South Africa. Follow-up focused on assessing the amount of non-compliance with EIA consent conditions as well as the overall impact that projects had had on the environment. The results of this follow-up process were then used to develop a risk screening tool that could be used to screen out new EIA applications that were likely to require follow-up to control default or impact. Projects that showed the greatest amount of default were those submitted by local municipal proponents for basic infrastructure type activities such as sewage treatment works and low-income housing developments. Private companies that compete in the open market presented the lowest default risk. Default with consent conditions ranged between 0% and 100% with an average rate of default of 49%. The overall environmental impact for the majority (58%) of projects followed-up on was low (on a 5 point scale of low to high), with only 1% of projects scoring high on impact. The study found a moderate positive correlation between default and impact (rs = 0.48) although a significant percentage (39%) of projects scored high on default but low on impact. Follow-up appears to have had a positive effect on reducing the average amounts of default and to a lesser extent the degree of impact. The risk screening tool developed is promising with statistically significant matches between predicted and actual default and impact scores for three out of the four test samples.GeographyD. Phil. (Geography

Prevalence of Eating Disorder Risk and Body Image Perceptions of Collegiate Cheerleading Coaches

In Volume 4, Issue 1 of the JSMAHS you will find Professional research abstracts, as well as Under Graduate student research abstracts, case reports, and critically appraised topics. Thank you for viewing this 4th Annual OATA Special Edition

Good hands and green thumbs

More than forty years have passed since diplomatic ties were cut between the United States and Cuba. In that time, Cubans have survived a series of economic crises and Americans have survived a series of farm crises. Recent advances in reestablishing a Cuban/American trade relationship are offset by increasing limitations on U.S. travel to Cuba. It is time to change the nature of this dysfunctional relationship by building bridges of understanding. Permitting and promoting face-to-face educational exchanges can do this. In efforts to establish educational exchanges between sustainable agriculture enthusiasts in Iowa and Cuba, I spent an academic semester in Havana. In the process of laying the necessary groundwork I met two extraordinary farmers from two distinct backgrounds. One is from Iowa, one is from Cuba. They came face to face during the Iowa State University delegation\u27s visit to Cuba in May of 2000. Their encounter provoked me to look further into their lives to better understand the spirit they each emanate in their work. That spirit is what I was drawn to when I first set foot on Cuban soil. That spirit is what I felt when reading Paolo Freire\u27s and Jose Marti\u27s calls for educational and agrarian reforms. That spirit is what both farmers Gary and Miguel call the \u27spirit of a farmer.\u27 Herein lie their stories, the history of their countries, and visions for a future that promote educational exchanges and sustainable agriculture. These farmers\u27 stories and their countries are inextricably linked. The sustainability of their farms and their futures depend on one another. Their spirits can bring them both together

A predictive biogeography of selected alien plant invaders in South Africa

Five techniques were used to predict the potential biogeography of the four alien plant species, Acacia longifolia, Acacia mearnsii, Opuntia ficus-indica and Solanum sisymbrifolium. Prediction was based on five environmental factors, median annual rainfall, co-efficient of variation for rainfall, mean monthly maximum temperature for January, mean monthly minimum temperature for July and elevation. A geographical information system was used to manage the data and produce the predictive maps. The models were constructed with presence and absence data and then validated by means of an independent data set and chisquared tests. Of the five models used, three (the range, principal components analysis and discriminant function analysis) were linear while the other two (artificial neural networks and fuzzy logic) were non-linear. The two non-linear techniques were chosen as a plant's response to its environment is commonly assumed to be non-linear. However, these two techniques did not offer significant advantages over the linear methods. The principal components analysis was particularly useful in ascertaining the variables that were important in determining the distribution of each species. Artifacts on the predictive maps were also proved useful for this purpose. The techniques that produced the most statistically accurate validation results were the artificial neural networks (77% correct median prediction rate) and the discriminant function analysis (71% correct median prediction rate) while the techniques that performed the worst were the range and the fuzzy classification. The artificial neural network, discriminant function analysis and principal component analysis techniques all show great potential as predictive distribution models

Socioeconomic and Cultural County-level Factors Associated with Race/Ethnic Differences in Body Mass Index in 4th Grade Students in Texas

Purpose: To evaluate the relationship between county-level socioeconomic environment and the propensity to be overweight or obese by race/ethnic group in a sample of fourth grade children the Texas public school system. Methods: The data used come from the School Physical Activity and Nutrition (SPAN) surveillance study – a surveillance study of school-aged children in Texas. The outcome variable used was Body Mass Index (BMI) categorized as underweight/normal/healthy, overweight, and obese. Exposure variables were derived from county-level Hispanic and Black concentration and socioeconomic data using the Human Security Index (HSI) as a framework. We made comparisons between Non-Hispanic White, Black and Hispanic children enrolled in the fourth grade. We used bivariate and regression analysis using jackknife variance estimates. Results: Hispanic and Black children were more likely to be obese and overweight than non-Hispanic White children. Hispanic and Black children were more likely to live in counties with higher economic, educational and social stress than non-Hispanic White children. In the logistic regression analysis comparing the odds of overweight or obese to underweight/healthy/normal weight, both Hispanic and Black children continued to have a higher odds of overweight and obesity than non-Hispanic White children. In separate regression analyses for each race/ethnic group (ie, Hispanic, Black, and White students) the county-level educational and social stress variables had a significant association with overweight and obesity in Hispanic children only. Hispanic ethnic concentration was associated with the odds of being overweight but not obese, while percent Black was associated with obesity in Hispanic children. There were no significant associations between socioeconomic or ethnic concentration and overweight or obese in Black children. Discussion: The results from this study indicate that county-level effects on childhood obesity may be more than just socioeconomics and ethnic concentration. Future research should involve multiple levels of analysis, including census tract, school and home variables simultaneously, in order to understand how the environments children live in impact their risk for obesity and how these influences may vary by race/ethnicity

A leucine-rich repeat peptide derived from the Drosophila Toll receptor forms extended filaments with a β-sheet structure

AbstractLeucine-rich repeats (LRRs) are 22–28 amino acid-long sequence motifs found in a family of cytoplasmic, membrane and extracellular proteins. There is evidence that LRRs function in signal transduction, cellular adhesion and protein-protein interactions. Here we report unusual properties of a synthetic LRR peptide derived from the sequence of the Drosophila membrane receptor Toll. In neutral solution the peptide forms a gel revealed by electron microscopy to consist of extended filaments approximately 8 nm in thickness. As the gel forms, the circular dichroism spectrum of the peptide solution changes from one characteristic of random coil to one associated with β-sheet structures. Molecular modelling suggests that the peptides form an amphipathic structure with a predominantly apolar and charged surface. Based on these results, models for the gross structure of the peptide filaments and a possible molecular mechanism for cellular adhesion are proposed