Understanding and Enriching Randomness Within Resource-Constrained Devices

Random Number Generators (RNG) find use throughout all applications of computing, from high level statistical modeling all the way down to essential security primitives. A significant amount of prior work has investigated this space, as a poorly performing generator can have significant impacts on algorithms that rely on it. However, recent explosive growth of the Internet of Things (IoT) has brought forth a class of devices for which common RNG algorithms may not provide an optimal solution. Furthermore, new hardware creates opportunities that have not yet been explored with these devices. in this Dissertation, we present research fostering deeper understanding of and enrichment of the state of randomness within the context of resource-constrained devices. First, we present an exploratory study into methods of generating random numbers on devices with sensors. We perform a data collection study across 37 android devices to determine how much random data is consumed, and which sensors are capable of producing sufficiently entropic data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs opportunistic collection of data from on-board sensors and applies a light-weight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology (NIST) statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead. Second, we explore the design, implementation, and efficacy of a Collaborative and Distributed Entropy Transfer protocol (CADET), which explores moving random number generation from an individual task to a collaborative one. Through the sharing of excess random data, devices that are unable to meet their own needs can be aided by contributions from other devices. We implement and test a proof-of-concept version of CADET on a testbed of 49 Raspberry Pi 3B single-board computers, which have been underclocked to emulate resource-constrained devices. Through this, we evaluate and demonstrate the efficacy and baseline performance of remote entropy protocols of this type, as well as highlight remaining research questions and challenges. Finally, we design and implement a system called RightNoise, which automatically profiles the RNG activity of a device by using techniques adapted from language modeling. First, by performing offline analysis, RightNoise is able to mine and reconstruct, in the context of a resource-constrained device, the structure of different activities from raw RNG access logs. After recovering these patterns, the device is able to profile its own behavior in real time. We give a thorough evaluation of the algorithms used in RightNoise and show that, with only five instances of each activity type per log, RightNoise is able to reconstruct the full set of activities with over 90\% accuracy. Furthermore, classification is very quick, with an average speed of 0.1 seconds per block. We finish this work by discussing real world application scenarios for RightNoise

Implications of the No Child Left Behind Legislation on Career and Technical Education

The passage of No Child Left Behind (NCLB) legislation in 2001 brought about a multitude of education reform policies for education institutions that made the future of Career and Technical Education’s (CTE) role in secondary educations unclear. These mandates forced educational leaders to emphasize student enrollment in tracks that prepare them for post-secondary educational opportunities that require a stronger background in academic courses. The standards-based mandates required by NCLB legislation make promotion of CTE courses more difficult because these courses are primarily elective courses and have caused educational agencies to look at what CTE programs provide in terms of meeting these requirements. The data presented in this research provides an in-depth look at the impact that NCLB had on CTE in Mississippi and how its students fared on state subject area tests (biology, algebra, and language arts) compared to students who did not enroll in CTE programs of study. A trend analysis of CTE student enrollment over the decade of NCLB implementation and adaptation for the state of Mississippi gives insight to the impact that a more specific emphasis on academics had on CTE enrollment. Also, a local school district’s biology subject area test score data is used to compare students enrolled in an agriculture program with students who were not enrolled to determine if a difference existed between student performances. Finally, a focus group dialogue with former students of CTE completers and noncompleters in the same district is discussed to determine the effect that participation or nonparticipation had on student postsecondary or career choices. Understanding the influence that an increased focus on academic courses had on CTE programs will enable school leaders and district planners to become better prepared as redesign models and career pathways begin to transform public education in the future. Educational organizations that use this research to embrace and promote CTE should see reductions in class size, dropout rates, and increased attendance, not to mention the performance-driven curriculum that reaches across CTE programs and conceptualizes the goals of CTE and academic programs alike

Where the Action Is: An Analysis of Partisan Change in House of Representatives Open Seat Elections, 2000-2014

Open seat House of Representatives elections are an area that has not received the same attention as seats with incumbents, despite open seats traditionally providing more interesting results. This research examines partisan change in open seat House races from 2000-2014 in order to determine whether previous research is still applicable in light of changing behavior of open seats in the 2000s. This research found that since 2004 partisan change has occurred more often with incumbents being defeated and not due to open seats. A logit model was used with partisan change as the dichotomous dependent variable, a unique approach to House elections. The model found that candidate spending was the most significant variable in explaining partisan change, while other variables such as district competitiveness, candidate quality, and unemployment were also significant. The model was then used to predict the 2014 House elections, correctly predicting roughly 75% of races. Finally two case studies were examined where the model failed to provide accurate predictions to determine improvements that could be made to future iterations of the mode

Labeling Paths with Convolutional Neural Networks

With the increasing development of autonomous vehicles, being able to detect driveable paths in arbitrary environments has become a prevalent problem in multiple industries. This project explores a technique which utilizes a discretized output map that is used to color an image based on the confidence that each block is a driveable path. This was done using a generalized convolutional neural network that was trained on a set of 3000 images taken from the perspective of a robot along with matching masks marking which portion of the image was a driveable path. The techniques used allowed for a labeling accuracy of over 95%

Liquefaction Mitigation of Three Projects in California

Ground displacements resulting from earthquake-induced soil liquefaction and dynamic densification can cause moderate to severe structural damage during and after an earthquake. Geotechnical construction methods of mitigating these potential ground displacements include mass excavation and replacement with engineered fill, ground improvement such as soil mixing, jet grouting, compaction piers, vibro compaction, vibro stone columns, and deep dynamic compaction, or deep foundations such as driven piles. The ground improvement methods rely on altering the soil properties to resist the seismically-induced shear stresses and soil grain redistribution while deep foundation methods bypass liquefiable soil deposits to found in deeper competent soil or rock. This paper presents an advancement in displacement ground improvement methods used to control soil liquefaction potential by driving highly compacted aggregate into the soil deposit. The ground improvement is accomplished by driving a pipe mandrel to displace the soil mass, backfilling the cavity with select aggregate, and compacting the aggregate in controlled lifts utilizing vertical, vibratory driven methods to further displace and densify the soil deposit while creating a dense Rammed Aggregate Pier®. Specifically the ground improvement method 1) reinforces the soil deposit to resist and re-distribute seismic shear stresses, 2) increases the density and horizontal stress of the surrounding soil, and 3) provides a gravel drain to enhance dissipation of seismicallyinduced excess pore water pressure in the soil. Several projects performed in California, in areas of high seismic activity, have been tested for the resulting shear reinforcement effects and increased density effects manifested by this advanced method of construction. These projects and their resulting field test results are presented and discussed

3R- Reach, Recruit, Reform: Working with the Grand Rapids Community to Meet the Volunteer Needs of the Heartside Gleaning Initiative

The purpose of this project is to address the volunteer needs of the Heartside Gleaning Initiative, a nonprofit organization founded by Grand Valley State University professor Lisa Sisson. The mission of the Heartside Gleaning Initiative is to “empower the Heartside community to become healthier through nutrition education and improving accessibility of healthy foods” (Heartside, 2014). Members of the Heartside Gleaning Initiative are currently working to give people living in the Heartside community of Grand Rapids access to fresh produce. Volunteers glean the produce from local farmers at the Fulton Street Farmers Market and then deliver it to shelters in the Heartside neighborhood. This work is also a part of a larger goal to fight the national issue of food insecurity, which affects millions of people living in the United States. For this project, our group chose to focus on volunteer recruitment. Volunteers are the backbone of the Heartside Gleaning Initiative and a necessary component for the work being done. We have begun to work with several Grand Rapids schools and local churches in the Heartside neighborhood to generate awareness about the initiative and to try and fill this need for volunteers. The organization specifically needs a core group of four to five volunteers who can consistently work with the initiative. Though we have generated interest among community members to volunteer for a weekend, we struggled to establish this core group of leaders. This proved to be our biggest challenge with the project, and finding a group of leaders will continue to be a task for the initiative in the future, though we have several suggestions that may help their efforts. The final goal for this project was to provide the Heartside Gleaning Initiative with recruitment materials. We recreated a pamphlet for the organization to give to potential volunteers. It includes information about the goals and the mission of the organization and contact information. It can be used to generate awareness and knowledge about the Heartside Gleaning Initiative. We also provided the organization with a list of the local churches and schools with interested members. We hope the initiative will be able to use these materials to continue to recruit a stable group of volunteers

Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology. Even the Android pseudo RNG (APRNG) merely builds atop the Linux RNG to produce random numbers. This paper presents an exploratory study into methods of generating random numbers on sensor-equipped mobile and Internet of Things devices. We first perform a data collection study across 37 Android devices to determine two things-how much random data is consumed by modern devices, and which sensors are capable of producing sufficiently random data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs collection of data from on-board sensors and combines them via a lightweight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead

Age but not BMI Predicts Accelerated Progression of KOA: Data from the Osteoarthritis Initiative

Background/Objectives: Knee osteoarthritis (KOA) accounts for about 35% of the arthritis burden among adults. Most adults with KOA have slowly-progressing, common knee osteoarthritis (CKOA); however, some individuals experience accelerated KOA (AKOA), rapid progression to end-stage disease within 48 months. This study analyzed individuals without radiographic evidence of KOA at baseline to determine which (baseline) characteristics were associated with progression to CKOA and/or AKOA status 48 months later.Methods: Data (n = 1,561) from the Osteoarthritis Initiative (OAI) were utilized. Multinomial logistic regression was employed to determine the magnitude of association between baseline risk factors and 48-month KOA status (AKOA and CKOA, compared to no KOA).Results: Older age (p = 0.032), greater baseline BMI (p < 0.001), female gender (p = 0.009), and greater baseline PASE score (p =0.036) were significantly associated with CKOA (11.9% of participants) and/or AKOA (3.5% of participants) at 48 months. Age, BMI, andPASE were all more strongly associated with greater risk of AKOA compared to risk of CKOA (Age: OR = 1.59 vs. 0.97; BMI: OR = 1.62vs. 1.28; PASE: OR = 1.21 vs. 1.08). Of these, only BMI was significantly associated with greater risk of both AKOA and CKOA.Conclusion: Certain factors impact the risk of AKOA and CKOA differently. Age did not increase the risk of CKOA, but among thosewith CKOA or AKOA, the proportion with AKOA increased with age. Thus, older age at onset is associated with more rapid KOA progression