Undergraduate Researchers\u27 Attainment of Graduate Degrees

The existing literature suggests that faculty-student interactions have a positive effect on students’ pursuits to attain undergraduate and graduate degrees. However, some scholars argue that the type of interactions and the extent to which students benefit vary between student sub-populations. Understanding who engages in undergraduate research at urban research universities and who goes on to attain graduate degrees are essential to expanding the knowledgebase and policy-making at the institutional level. Investigating the efficacy of undergraduate research programs at urban institutions that have access to diverse populations will allow for analyses with different samples. The goal of this research was to create a dataset that allowed for the documentation of the demographic and academic makeup of a population of students that engaged in a university wide centralized undergraduate research intervention at an urban research university. The descriptive analysis included demographic and academic performance information, as well as timing and duration of engagement in undergraduate research. This study included a logistic regression analysis to examine differences in likelihood of graduate degree attainment, in relationship to race/ethnicity, financial need, timing, duration, and academic performance

Evaluating Machine Learning Techniques for Smart Home Device Classification

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, information leakage, and endangerment of senior leaders [1]. Recent research has shown that it is possible to model a subject’s pattern-of-life through data leakage from Bluetooth Low Energy (BLE) and Wi-Fi smart home devices [2]. A key step in establishing pattern-of-life is the identification of the device types within the smart home. Device type is defined as the functional purpose of the IoT device, e.g., camera, lock, and plug. This research hypothesizes that machine learning algorithms can be used to accurately perform classification of smart home devices. To test this hypothesis, a Smart Home Environment (SHE) is built using a variety of commercially-available BLE and Wi-Fi devices. SHE produces actual smart device traffic that is used to create a dataset for machine learning classification. Six device types are included in SHE: door sensors, locks, and temperature sensors using BLE, and smart bulbs, cameras, and smart plugs using Wi-Fi. In addition, a device classification pipeline (DCP) is designed to collect and preprocess the wireless traffic, extract features, and produce tuned models for testing. K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forests (RF) classifiers are built and tuned for experimental testing. During this experiment, the classifiers are tested on their ability to distinguish device types in a multiclass classification scheme. Classifier performance is evaluated using the Matthews correlation coefficient (MCC), mean recall, and mean precision metrics. Using all available features, the classifier with the best overall performance is the KNN classifier. The KNN classifier was able to identify BLE device types with an MCC of 0.55, a mean precision of 54%, and a mean recall of 64%, and Wi-Fi device types with an MCC of 0.71, a mean precision of 81%, and a mean recall of 81%. Experimental results provide support towards the hypothesis that machine learning can classify IoT device types to a high level of performance, but more work is necessary to build a more robust classifier

Morphology-dependent trends of galaxy age with environment in Abell 901/902 seen with COMBO-17

We investigate correlations between galaxy age and environment in the Abell 901/2 supercluster for separate morphologies. Using COMBO-17 data, we define a sample of 530 galaxies, complete at $M_V -5\log h<-18$ on an area of $3.5\times 3.5$ (Mpc/$h$)$^2$. We explore several age indicators including an extinction-corrected residual from the colour-magnitude relation (CMR). As a result, we find a clear trend of age with density for galaxies of all morphologies that include a spheroidal component, in the sense that galaxies in denser environments are older. This trend is not seen among Scd/Irr galaxies since they all have young ages. However, the trend among the other types is stronger for fainter galaxies. While we also see an expected age-morphology relation, we find no evidence for a morphology-density relation at fixed age.Comment: Accepted for publication in MNRAS (Letters

Tests of the Las Campanas Distant Cluster Survey from Confirmation Observations for the ESO Distant Cluster Survey

The ESO Distant Cluster Survey (EDisCS) is a photometric and spectroscopic study of the galaxy cluster population at two epochs, z~0.5 and z~0.8, drawn from the Las Campanas Distant Cluster Survey (LCDCS). We report results from the initial candidate confirmation stage of the program and use these results to probe the properties of the LCDCS. Of the 30 candidates targeted, we find statistically significant overdensities of red galaxies near 28. Of the ten additional candidates serendipitously observed within the fields of the targeted 30, we detect red galaxy overdensities near six. We test the robustness of the published LCDCS estimated redshifts to misidentification of the brighest cluster galaxy (BCG) in the survey data, and measure the spatial alignment of the published cluster coordinates, the peak red galaxy overdensity, and the brightest cluster galaxy. We conclude that for LCDCS clusters out to z~0.8, 1) the LCDCS coordinates agree with the centroid of the red galaxy overdensity to within 25'' (~150 h^{-1} kpc) for 34 out of 37 candidates with 3\sigma galaxy overdensities, 2) BCGs are typically coincident with the centroid of the red galaxy population to within a projected separation of 200 h^{-1} kpc (32 out of 34 confirmed candidates), 3) the red galaxy population is strongly concentrated, and 4) the misidentification of the BCG in the LCDCS causes a redshift error >0.1 in 15-20% of the LCDCS candidates. These findings together help explain the success of the surface brightness fluctuations detection method.Comment: 10 pages, 9 figures, accepted for publication in the November 10 issue of Ap