Using Random Forests to Describe Equity in Higher Education: A Critical Quantitative Analysis of Utah’s Postsecondary Pipelines

The following work examines the Random Forest (RF) algorithm as a tool for predicting student outcomes and interrogating the equity of postsecondary education pipelines. The RF model, created using longitudinal data of 41,303 students from Utah\u27s 2008 high school graduation cohort, is compared to logistic and linear models, which are commonly used to predict college access and success. Substantially, this work finds High School GPA to be the best predictor of postsecondary GPA, whereas commonly used ACT and AP test scores are not nearly as important. Each model identified several demographic disparities in higher education access, most significantly the effects of individual-level economic disadvantage. District- and school-level factors such as the proportion of Low Income students and the proportion of Underrepresented Racial Minority (URM) students were important and negatively associated with postsecondary success. Methodologically, the RF model was able to capture non-linearity in the predictive power of school- and district-level variables, a key finding which was undetectable using linear models. The RF algorithm outperforms logistic models in prediction of student enrollment, performs similarly to linear models in prediction of postsecondary GPA, and excels both models in its descriptions of non-linear variable relationships. RF provides novel interpretations of data, challenges conclusions from linear models, and has enormous potential to further the literature around equity in postsecondary pipelines

The Maestro Attack: Orchestrating Malicious Flows with BGP

We present the Maestro Attack, a Link Flooding Attack (LFA) that leverages Border Gateway Protocol (BGP) engineering techniques to improve the flow density of botnet-sourced Distributed Denial of Service (DDoS) on transit links. Specific-prefix routes poisoned for certain Autonomous Systems (ASes) are advertised by a compromised network operator to channel bot-to-bot ows over a target link. Publicly available AS relationship data feeds a greedy heuristic that iteratively builds a poison set of ASes to perform the attack. Given a compromised BGP speaker with advantageous positioning relative to the target link in the Internet topology, an adversary can expect to enhance flow density by more than 30 percent. For a large botnet (e.g., Mirai), the bottom line result is augmenting the DDoS by more than a million additional infected hosts. Interestingly, the size of the adversary-controlled AS plays little role in this effect; attacks on large core links can be effected by small, resource-limited ASes. Link vulnerability is evaluated across several metrics, including BGP betweenness and botnet flow density, and we assess where an adversary must be positioned to execute the attack most successfully. Mitigations are presented for network operators seeking to insulate themselves from this attack

Utilizing Self-Determination Theory to Assist in Understanding College Students\u27 Motivation for Physical Activity

Obesity has become a national epidemic, (CDC, 2006; Desai, Miller, Staples, & Bravender, 2008), while causing life-threatening health conditions including cardiovascular disease, cancer, type 2 diabetes and other functional issues (Schoenborn & Strommel, 2011). It has been estimated that less than half of populations in industrialized countries are sufficiently physically active to prevent health issues (Sapkota, Bowles, Ham, & Kohl, 2005). The current study utilized the Self-Determination theory (SDT) by Deci and Ryan (1985, 2002) to help understand motivation, but more specifically exercise motivations. This study targeted basic psychological need (PNSE) and motivation regulations (BREQ-2) of a general population of college students. Correlations revealed that there were statistically significant correlations between achieving CDC physical activity recommendations and BMI, gender and four behavioral regulations (external, introjected, identified and intrinsic). These six variables developed a statistically significant logistic regression model (χ2= 28.92, df = 6, N = 3, p \u3c .001), predicting the correct group (achieved or not achieved) 74.7%. Additionally, there were not significant differences between psychological need and those who did and did not achieve CDC recommendations. Finally, there were statistically significant scores between four behavioral recommendations (external, introjected, identified and intrinsic) and those who achieved and did not achieve physical activity. Implications of these findings, directions for future research, limitations and strengths of the study were also discussed

Interdomain Route Leak Mitigation: A Pragmatic Approach

The Internet has grown to support many vital functions, but it is not administered by any central authority. Rather, the many smaller networks that make up the Internet - called Autonomous Systems (ASes) - independently manage their own distinct host address space and routing policy. Routers at the borders between ASes exchange information about how to reach remote IP prefixes with neighboring networks over the control plane with the Border Gateway Protocol (BGP). This inter-AS communication connects hosts across AS boundaries to build the illusion of one large, unified global network - the Internet. Unfortunately, BGP is a dated protocol that allows ASes to inject virtually any routing information into the control plane. The Internet’s decentralized administrative structure means that ASes lack visibility of the relationships and policies of other networks, and have little means of vetting the information they receive. Routes are global, connecting hosts around the world, but AS operators can only see routes exchanged between their own network and directly connected neighbor networks. This mismatch between global route scope and local network operator visibility gives rise to adverse routing events like route leaks, which occur when an AS advertises a route that should have been kept within its own network by mistake. In this work, we explore our thesis: that malicious and unintentional route leaks threaten Internet availability, but pragmatic solutions can mitigate their impact. Leaks effectively reroute traffic meant for the leak destination along the leak path. This diversion of flows onto unexpected paths can cause broad disruption for hosts attempting to reach the leak destination, as well as obstruct the normal traffic on the leak path. These events are usually due to misconfiguration and not malicious activity, but we show in our initial work that vrouting-capable adversaries can weaponize route leaks and fraudulent path advertisements to enhance data plane attacks on Internet infrastructure and services. Existing solutions like Internet Routing Registry (IRR) filtering have not succeeded in solving the route leak problem, as globally disruptive route leaks still periodically interrupt the normal functioning of the Internet. We examine one relatively new solution - Peerlocking or defensive AS PATH filtering - where ASes exchange toplogical information to secure their networks. Our measurements reveal that Peerlock is already deployed in defense of the largest ASes, but has found little purchase elsewhere. We conclude by introducing a novel leak defense system, Corelock, designed to provide Peerlock-like protection without the scalability concerns that have limited Peerlock’s scope. Corelock builds meaningful route leak filters from globally distributed route collectors and can be deployed without cooperation from other network

Social vulnerability and Lyme disease incidence: a regional analysis of the United States, 2000-2014

Background: Lyme disease (LD), which is highly preventable communicable illness, is the most commonly reported vector borne disease in the USA. The Social Vulnerability Index (SoVI) is a county level measure of SES and vulnerability to environmental hazards or disease outbreaks, but has not yet been used in the study of LD. The purpose of this study was to determine if a relationship existed between the SoVI and LD incidence at the national level and regional division level in the United States between 2000 and 2014. Methods: County level LD data were downloaded from the CDC. County level SoVI were downloaded from the HVRI at the University of South Carolina and the CDC. Data were sorted into regional divisions as per the US Census Bureau and condense into three time intervals, 2000-2004, 2005-2009, and 2010-2014. QGIS was utilized to visually represent the data. Logarithmic OLS regression models were computed to determine the predictive power of the SoVI in LD incidence rates. Results: LD incidence was greatest in the Northeastern and upper Midwestern regions of the USA.  The results of the regression analyses showed that SoVI exhibited a significant quadratic relationship with LD incidence rates at the national level. Conclusion: Our results showed that counties with the highest and lowest social vulnerability were at greatest risk for LD. The SoVI may be a useful risk assessment tool for public health practitioners within the context of LD control

Gene By Environment Interaction On Weight-Related Outcomes Over Time In Underserved African-American Adults

Obesity research in the area of prevention has become a national priority given the increasingly high prevalence rate of this condition among US adults, and subsequent health risks that are associated. The etiology of obesity is complex, so a more comprehensive understanding of the interaction between genetic predisposition and the social environment in regards to obesity in adults would advance our knowledge for future public health and prevention efforts. This study’s aim was to assess the impact of a gene by neighborhood social environment interactions on weight-related (i.e., waist circumference) and stress-related (i.e. cortisol) outcomes in underserved African-American adults. A bioecological framework was used in the present study to integrate factors, including social environmental factors (i.e. perceptions of safety from crime, neighborhood satisfaction, neighborhood social life, and collective efficacy) and genetic risk (Sympathetic Nervous System and Hypothalamic-Pituitary-Adrenal axis related genes) to better understand the gene by environment interactions on weigh-related and stress-related outcomes in adults. This study utilized participants from the Positive Action for Today’s Health (PATH) trial. Based on a dual risk model, it is hypothesized that those with the highest genetic risk and who experienced negative neighborhood environment conditions would have the worst outcomes (i.e. highest waist circumference and highest cortisol levels). There were no significant three-way interactions with gene by environment interactions predicting change over time. However, results did indicate three significant gene by environment interactions on weight related outcomes, all within the SNS pathway. These significant results included two interactions that support the dual risk model, which were the SNS genetic risk by neighborhood social life interaction (b=-0.108, t(618)=-2.018, p=0.04), and SNS genetic risk by informal social control (collective efficacy) interaction (b=-0.510, t(618)=-1.95, p=0.05) on waist circumference outcomes. Further, there was a significant SNS genetic risk by neighborhood satisfaction interaction (b=1.481, t(618)=2.233, p=0.02) on waist circumference outcomes, which did not match the dual risk hypothesis. For the secondary aims, however, there was only one SNS by social cohesion and trust interaction (b=0.59, p=0.02) on cortisol in the unexpected direction for the linear regression. Implications of these findings, limitations of the study and future directions are discussed

Disparities in First-to-Second Dose Measles-containing Vaccination Coverage: A Comparative Analysis of the Predictive Power of Three Economic Indices

While overall mortality from measles has decreased, it is still associated with significant global infant deaths. Studies indicate that a second dose of measles-containing vaccine (MCV) is necessary to produce sufficient immunity to measles, yet several developing countries are deficient of a two-dose schedule. This study examined the efficacy of three economic indices—the Human Development Index (HDI), the Inequality-adjusted Human Development Index (IHDI), and the Multidimensional Poverty Index (MPI)—in predicting first-to-second MCV dosage disparities. Country-level data for MCV coverage were downloaded from the World Health Organization (WHO). Briggsian logarithmic regression models of MCV dosage disparities were calculated to compare the predictive power of the HDI, IHDI, and MPI. The MPI explained the most variance in dosage disparities, F (1, 54) = 41.835, p \u3c 0.001, R2 = 0.437, b = 0.938, followed by the IDHI (R2 = 0.361, b = −0.935) and HDI (R2 = 0.354, b = −1.023). We suggest the MPI explained the greatest variance because it uses multiple indicators to determine poverty across three dimensions of human development. The MPI predicted larger disparities in more developing countries. Future efforts should be directed toward discovering and reducing barriers to second dose MCV administration in these countries