<p> As Moore&rsquo;s Law slows and process scaling yields only small returns, computer architecture and design are poised to undergo a renaissance. This thesis brings the productivity of modern software tools to bear on the design of future energy-efficient hardware architectures. </p><p> In particular, it targets one of the most difficult design tasks in the hardware domain: Coherent hierarchies of on-chip caches. I have extended the capabilities of Chisel, a new hardware description language, by providing libraries for hardware developers to use to describe the configuration and behavior of such memory hierarchies, with a focus on the cache coherence protocols that work behind the scenes to preserve their abstraction of global shared memory. I discuss how the methods I provide enable productive and extensible memory hierarchy design by separating the concerns of different hierarchy components, and I explain how this forms the basis for a generative approach to agile hardware design. </p><p> This thesis describes a general framework for context-dependent parameterization of any hardware generator, defines a specific set of Chisel libraries for generating extensible cache-coherent memory hierarchies, and provides a methodology for decomposing high-level descriptions of cache coherence protocols into controller-localized, object-oriented transactions. </p><p> This methodology has been used to generate the memory hierarchies of a lineage of RISC-V chips fabricated as part of the ASPIRE Lab&rsquo;s investigations into application-specific processor design.</p>

<p>Recent reliance on social media platforms as major sources of news and information, both for journalists and the larger population and especially during times of crisis, motivate the need for better methods of identifying and tracking high-impact events in these social media streams. Social media's volume, velocity, and democratization of information (leading to limited quality controls) complicate rapid discovery of these events and one's ability to trust the content posted about these events. This dissertation addresses these complications in four stages, using Twitter as a model social platform. The first stage analyzes Twitter's response to major crises, specifically terrorist attacks in Western countries, showing these high-impact events do not significantly impact message or user volume. Instead, these events drive changes in Twitter's topic distribution, with conversation, retweets, and hashtags relevant to these events experiencing significant, rapid, and short-lived bursts in frequency. Furthermore, conversation participants tend to prefer information from local authorities/organizations/media over national or international sources, with accounts for local police or local newspapers often emerging as central in the networks of interaction. Building on these results, the second stage in this dissertation presents and evaluates a set of features that capture these topical bursts associated with crises by modeling bursts in frequency for individual tokens in the Twitter stream. The resulting streaming algorithm is capable of discovering notable moments across a series of major sports competitions using Twitter's public stream without relying on domain- or language-specific information or models. Furthermore, results demonstrate models trained on sporting competition data perform well when transferred to earthquake identification. This streaming algorithm is then extended in this dissertation's third stage to support real-time event tracking and summarization. This real-time algorithm leverages new distributed processing technology to operate at scale and is evaluated against a collection of other community-developed information retrieval systems, where it performs comparably. Further experiments also show this real-time burst detection algorithm can be integrated with these other information retrieval systems to increase overall performance. The final stage then investigates automated methods for evaluating credibility in social media streams by leveraging two existing data sets. These two data sets measure different types of credibility (veracity versus perception), and results show veracity is negatively correlated with the amount of disagreement in and length of a conversation, and perceptions of credibility are influenced by the amount of links to other pages, shared media about the event, and the number of verified users participating in the discussion. Contributions made across these four stages are then usable in the relatively new fields of computational journalism and crisis informatics, which seek to improve news gathering and crisis response by leveraging new technologies and data sources like machine learning and social media.

<p> Irrigation decision making is critical for crop producers in the Midwestern United States because of the high demand for water during the peak of growing season of corn and soybean fields. Agronomists try to use agricultural-related data to optimize irrigation decision making. The biggest obstacle is the gap of transforming data to usable information which producers can access and take corresponding actions regarding when to irrigate their fields.</p><p> We developed CornSoyWater (http://cornsoywater.unl.edu), a web-based app that can be used in a web browser of any desktop computers or mobile devices. The goal is to use state-of-the-art quantitative agronomic sciences and information technologies, and in-season real-time weather data with field-specific crop management information to predict crop development and growth, crop water use and soil water balance to aid producers&rsquo; irrigation decision making. </p><p> For practical use of the app, the corn crop model (Hybrid-Maize model) which runs inside of the app needed to be tested for its accuracy. We used a 5-year field dataset to test the performance of Hybrid-Maize model on estimating soil water balance near Mead, NE. We conducted a 2-year field experiment to test the performance of Hybrid-Maize model on maize growth and crop water use under a range of irrigation treatments including 100% (recharge top 30 cm soil to field capacity), 75% and 50% of the 100%, and 0% (rainfed) in Lincoln, Nebraska. The results showed that the Hybrid-Maize model simulated soil water balance well for the entire root zone, but underestimated the soil water balance at 0-30 cm and 60 cm to maximum rooting depth, respectively. For the fields at Mead, Hybrid-Maize model can reduce irrigation pumping by 93 mm during the season compared to actual irrigation scheduling by delaying the first irrigation and reducing the overall number of irrigation events. The Hybrid-Maize model performed well in a relatively wet year for biomass and grain yield simulation.</p><p> The test results indicated that producers can utilize this app for irrigation decision making. A business plan was proposed on how a startup can commercialize this type of agricultural-related apps or technologies to benefit producers.</p>

<p> Relative motion is an ecological mechanism with the power to change the stability and longevity of populations and predict large scale movement patterns in highly mobile species. This dissertation introduces relative motion as an ecological mechanism using simulations and experiments at varying levels of spatial complexity. Chapters 2 and 3 describe the interactions between population movement and one-dimensional habitat movement, while Chapters 4 and 5 focus on the interactions between individual movement and three-dimensional habitat movement. Chapters 2 and 4 lay out my model justification, model development, and simulation results, while the remaining two chapters describe case studies competing those models with data. In Chapter 2, I simulate populations chasing moving habitat using stochastic spatial spread models. Results from these simulations show that populations lose symmetry when the habitat begins to move and suggest that loss of symmetry increases extinction risk. Results also show that assisted migration can restore some of that lost symmetry, but the success of assisted migration is sensitive to the transplant location and habitat speed. In Chapter 3, I build on the simulations presented in Chapter 2 by investigating assisted migration as a method of restoring symmetry using <i> Tribolium</i> microcosm experiments. Experimental results show that assisted migration both restored symmetry to the moving populations under fast-moving habitat conditions and significantly reduced extinction risk compared to the controls. Chapter 4 describes a 3-dimensional Geographic Information System (GIS) to track multiple sources of relative motion in the environment at once, using rigid body mathematics to move individual components in their own direction. In Chapter 5, I apply this GIS to deconstruct the migratory paths of 22 Greater shearwater (<i>Puffinus gravis</i>) migrants and rank the relative contributions of solar, wind, temperature, humidity, and surface cues to the figure-8 shaped migratory paths observed in this species.</p>

<p> Corporate Social Responsibility (CSR) has become an integral component of many companies especially technology companies. A huge percentage of technology companies want to engage their employees through corporate social responsibility as well; leading to the evolution of a new kind term- &ldquo;Employee Community Engagement&rdquo;. With 80% job seekers preferring to work for companies that are socially responsible and employee engagement reducing the staff turnover, CSR and employee engagement has become something not just preferable, but a need of the hour to affect the bottom line of companies.</p><p> The current method of doing CSR and employee engagement for companies is to manage the process manually, using spreadsheets, emails and calls. This process is extremely time consuming, allows companies no analytics and insights into their engagement process. My main contributions include interviewing five companies to find the problems, building a technical solution and implement it with users and finally recording the results and trends.</p><p> To create a technology solution for this problem, I interviewed the head of community and government relations for five different companies ranging for 50 to 10,000 employees &amp; analyzed their requirements based on their answers. The top problems were analyzed to be too much work as overhead, no tracking of employee data, lack of employee engagement, less employee turn out and skill and cause matching.</p><p> I built an MVP in core PHP was build to solve these problems first for company heads, nonprofits as well as employee volunteers before moving on to solve for other issues to see if using a technology platform may help solve the problem. A smooth user registration and onboarding was created for each of the user types so that everyone in a company can be engaged easily on the platform, a twitter like networking community was created so that users can interact about their philanthropy, administrators were given traceability regarding the engagement levels of their employees, an opportunity to create events and send out invitations through the portal was allowed, the creation of specific events was allowed for a nonprofit and an opportunity for closed team events for employees was allowed after the creation of the proposal by a nonprofit.</p><p> Implementing this saw an increase in engagement of employees from 4% to an average of 25% with a shift in the usage of CSR for team and culture building by the company as well. With increase in usage, requirements for grant management and workplace donations surfaced as well which would be a future leap for the product to become an end to end corporate social responsibility solution.</p>

<p> Unauthorized data removal and modification from information systems represents a major and formidable threat in modern computing. Security researchers are engaged in a constant and escalating battle with the writers of malware and other methods of network intrusion to detect and mitigate this threat. Advanced malware behaviors include encryption of communications between the server and infected client machines as well as various strategies for resilience and obfuscation of infrastructure. These techniques evolve to use any and all available mechanisms. As the Internet has grown, DNS has been expanded and has been given security updates. This study analyzed the potential uses of DNSSEC as a covert channel by malware writers and operators. The study found that changing information regarding the Start of Authority (SOA) and resigning the zone can create a covert channel. The study provided a proof of concept for this previously undocumented covert channel that uses DNSSEC. </p>

<p> The rate at which humanity is producing visual data from both large-scale scientific imaging and consumer photography has been greatly accelerating in the past decade. This thesis is motivated by the hypothesis that this trend will necessarily change the face of observational science and the humanities, requiring the development of automated methods capable of distilling vast image collections to produce meaningful analyses. Such methods are needed to empower novel science both by improving throughput in traditionally quantitative disciplines and by developing new techniques to study culture through large scale image datasets.</p><p> When computer vision or machine learning in general is leveraged to aid academic inquiry, it is important to consider the impact of erroneous solutions produced by implicit ambiguity or model approximations. To that end, we argue for the importance of algorithms that are capable of generating multiple solutions and producing measures of confidence. In addition to providing solutions to a number of multi-disciplinary problems, this thesis develops techniques to address these overarching themes of confidence estimation and solution diversity. </p><p> This thesis investigates a diverse set of problems across a broad range of studies including glaciology, developmental psychology, architectural history, and demography to develop and adapt computer vision algorithms to solve these domain-specific applications. We begin by proposing vision techniques for automatically analyzing aerial radar imagery of polar ice sheets while simultaneously providing glaciologists with point-wise estimates of solution confidence. We then move to psychology, introducing novel recognition techniques to produce robust hand localizations and segmentations in egocentric video to empower psychologists studying child development with automated annotations of grasping behaviors integral to learning. We then investigate novel large-scale analysis for architectural history, leveraging tens of thousands of publicly available images to identify and track distinctive architectural elements. Finally, we show how rich estimates of demographic and geographic properties can be predicted from a single photograph.</p>

<p> Software is omnipresent from refrigerators to financial institutions. In addition to software that defines cyber system functionality, there is an increasing amount of digitized data on cyber systems. This increasing amount of easily available data has prompted a rise in attacks on cyber systems by globally organized attackers. The solution (which has been proposed by multiple authors) is to plan security into software products throughout all software development phases. This approach constitutes a change in the software development life cycle (SDLC) process. Acceptance and approval from all software development stakeholders is needed to make this type of cultural paradigm shift. A Delphi study into what would encourage software development stakeholders to accept the need for security during software development was performed. Results of the three-round Delphi study revealed education (formal and informal) would increase software development stakeholder understanding of the risks of insecure software and educate stakeholders on how to plan and write more secure software. The Delphi study also revealed that mitigation of time and resource constraints on software projects is needed to encourage software teams to embrace the need and efforts necessary to include security in all phases of the SDLC. </p>

<p> This thesis discusses the application of advanced digital signal and image processing techniques, particularly the technique known as super-resolution (SR), to enhance the imagery produced by cameras mounted on an airborne platform such as an unmanned aircraft system (UAS). SR is an image processing technology applicable to any digital, pixilated camera that is physically limited by construction to sample a scene with a discrete, <i><b>m x n</b></i> pixel array. The straightforward objective of SR is to utilize mathematics and signal processing to overcome this physical limitation of the <i><b> m x n</b></i> array and emulate the &ldquo;capabilities&rdquo; of a camera with a higher-density, <i><b>km x kn</b></i> (<i><b>k</b></i>><b> 1</b>) pixel array. The exact meaning of &ldquo;capabilities&rdquo;, in the preceding sentence, is application dependent.</p><p> SR is a well-studied field starting with the seminal 1984 paper by Huang and Tsai. Since that time, a multitude of papers, books, and software solutions have been written and published on the subject. However, although sharing many common aspects, the application to imaging systems on airborne platforms brings forth a number of unique challenges as well as opportunities that are neither currently addressed nor currently exploited by the state-of-the-art. These include wide field-of-view imagery, optical distortion, oblique viewing geometries, spectral variety from the visible band through the infrared, constant ego-motion, and availability of supplementary information from inertial measurement sensors. Our primary objective in this thesis is to extend the field of SR by addressing these areas. In our research experiments, we make significant use of both simulated imagery as well as real video collected from a number of flying platforms.</p>

<p> The research topic of this study is the impact of consumer security awareness on the adoption of the Internet of Things. The Internet of Things (IoT) is the emerging network of Internet connected smart devices. Several authors have predicted that adoption of the IoT will be hindered if security issues are not addressed. Other authors have noted that users often trade security and privacy for convenience. To better understand these two points of view, the main research question of this study is: to what extent does consumer security awareness impact adoption of the Internet of Things. To address the competing factors impacting adoption, the unified theory of acceptance and use of technology (UTAUT) will be used as the base model of this study and was extended to account for the construct of security awareness. A quantitative non-experimental correlational study was designed to measure the impact. The population of this study is U.S. adult consumers of Internet connected smart devices. The sample frame was selected from the SurveyMonkey&trade; voluntary audience panel. Multiple regression was used as the statistical analysis to perform hypothesis testing and attempt to answer the research questions. The findings of the study showed that although there is a statistically significant impact of security awareness on adoption of the IoT, it is not the dominant factor. Other factors, such as performance expectation and effort expectation prove to be better indicators of adoption of the IoT at this time. Several recommendations are given to improve future studies in this area. The results of this study provide business managers, IoT device manufacturers and service providers with valuable information on the relation between awareness of security risks and adoption of the IoT.</p>