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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Status: Published
Description
The purpose of this project is to analyze Jane Austen's unfinished novel Sanditon (1817) and its inclusion of a character of color. This thesis discusses Austen's mixed-race heiress, Miss Lambe, in the context of two other pieces of fiction that feature mixed-race heroines--the anonymously published The Woman of Colour (1808) and Mary Ann Sullivan's Owen Castle (1816). Scholarship on Austen's awareness of the Abolitionist movement and her sympathy for its politics has previously been published. I advance our conversations on the subject by discussing Austen's Miss Lambe as a mixed-race heiress in the context of gender, race, and ethnicity in late eighteenth- and early nineteenth-century novels. My thesis considers literary and historical treatments of people of color and provides a trans-Atlantic approach to female characters identified as mixed race.
Juxtaposing Sanditon, The Woman of Colour, and Owen Castle provides insight into how Austen was working within a set of established literary traditions, while creating ways to disrupt some of its problematic elements. This project looks at conventions of the mixed-race female characters in five ways. To begin, I discuss the mixed-race heroine and the compulsion to define her place of origin. Second, I consider the convention of describing mixed-race heiresses' rights to their inheritance. An analysis of the significance of naming mixed-race heiresses follows. I discuss literary conventions of the betrayal of mixed-race females. Lastly, I explore the common use of black maid figures in novels of this era to advance social critique against prejudice. Comparative analysis of Austen with other novels featuring mixed-race heroines in this era allows us to reach new understandings of Sanditon. Austen's unfinished last novel is shown to question the power of fortune, to undermine the orthodoxy of categorizing race and ethnicity, and to unsettle the hierarchy among characters of different races and ethnicities.
Juxtaposing Sanditon, The Woman of Colour, and Owen Castle provides insight into how Austen was working within a set of established literary traditions, while creating ways to disrupt some of its problematic elements. This project looks at conventions of the mixed-race female characters in five ways. To begin, I discuss the mixed-race heroine and the compulsion to define her place of origin. Second, I consider the convention of describing mixed-race heiresses' rights to their inheritance. An analysis of the significance of naming mixed-race heiresses follows. I discuss literary conventions of the betrayal of mixed-race females. Lastly, I explore the common use of black maid figures in novels of this era to advance social critique against prejudice. Comparative analysis of Austen with other novels featuring mixed-race heroines in this era allows us to reach new understandings of Sanditon. Austen's unfinished last novel is shown to question the power of fortune, to undermine the orthodoxy of categorizing race and ethnicity, and to unsettle the hierarchy among characters of different races and ethnicities.
ContributorsBaugh, Victoria (Author) / Looser, Devoney (Thesis advisor) / Justice, George (Committee member) / Wernimont, Jacqueline (Committee member) / Arizona State University (Publisher)
Created2017
Description
This research conceptualizes Gothic literature featuring undead characters produced and popularized by Britain in the early nineteenth century as educational texts. As an influx of new ideas at home and abroad disrupted the lives of the Romantics, not to mention the literal uprising of bodies in the French Revolution and the lost war with the North American colonies, British citizens dedicated themselves to preserving the relative safety of their shores from external and internal threats. I expand the definition of the “undead” to include any tangible, corporeal being once technically dead and now reanimated. In doing so, I invite a broader range of texts, and authors, into the conversation of Gothic literature and the genre’s continued legacy. My work reads male and female authors in dialogue with one another, both sexes working within common networks, rather than as creating separate or disparate traditions. The production of instructive undead bodies becomes particularly important to the development of British national identity and reveals a reliance on the maternal to educate and inform future citizens. The texts examined in this dissertation reveal the necessity of contemplating the histories and experiences of the past, of non-white voices, and of the female influence.
The texts range in publication date from 1805 to 1863 and thus demonstrate the continued used of the undead in the Gothic genre. An examination of the reanimated corpse in Romantic narrative demonstrates how authors utilized the undead as an educational tool both for the characters inside the text and the actual individuals reading the narrative. The undead offers a lens to look at the Gothic not regarding authorial gender or even a character’s gender, but rather in how the genre portrays bodies, and how those bodies interact with and instruct others. This dissertation’s perception of the undead as a powerful educational force in literature assists in the attempt to complete a more comprehensive analysis of Gothic, and therefore Romantic, literature.
ContributorsZarka, Emily (Author) / Lussier, Mark (Thesis advisor) / Looser, Devoney (Committee member) / Broglio, Ron (Committee member) / Arizona State University (Publisher)
Created2018
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
"The Problem of Hope: Literary Tragedy in Mid-Twentieth Century American Fiction" examines Arthur Miller's Death of a Salesman, Ralph Ellison's Invisible Man, and Sylvia Plath's The Bell Jar through the lens of tragedy. This thesis delves into how conflicts between internal and external identities can create a tragic individual, what kinds of success count toward achievement of the "American Dream," and whether the tragic "common man" is the socially normative one or the socially disenfranchised one. It raises a three-dimensional theoretical approach to American tragedy and, most importantly, considers the significance of tragic hope for American literature. This paper questions the construction of American identities across class, race, and gender according to social scripts. It seeks to uncover what forces these scripts exert on American cultural myths and rereads those myths through tragedy to explore Miller's idea of a noble common man. By moving from Miller to Ellison to Plath, this thesis traces the undercurrents of tragedy through some of the most identity-focused novels of mid-twentieth century American fiction to see how the overarching American narrative changed from 1940 to 1969 as the US underwent significant social changes domestically and image changes abroad. Ultimately, this paper concludes that tragedy in mid-twentieth century American fiction points toward a new idea of American success as a success that occurs beyond social scripts.
ContributorsMedeiros, Amy Marie (Author) / Holbo, Christine (Thesis director) / Sadowski-Smith, Claudia (Committee member) / Department of English (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This thesis examines contemporary cinematic adaptations of the Ovidian Pygmalion story. The films Blade Runner (1981), Lars and the Real Girl (2007), Ruby Sparks (2012), and Her (2013) are analyzed. This thesis seeks to understand why this particular myth is so resonant in today's popular culture and what this relevance reveals about modern society. The roles of female subjugation, sexualization, and relationship with technology will be major areas of concern. Research includes film criticism, Ovidian scholarship, and new advances in computer technology.
ContributorsStory, Sara Katherine (Author) / Corse, Taylor (Thesis director) / Ellis, Lawrence (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor)
Created2015-05
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
The Roller Derby Club at Arizona State University became a student organization in the fall of 2013. They became a practicing team known as the Derby Devils in the spring of 2014. This project documents the creation and development a collegiate roller derby team as they go from a student organization to an athletic team. Collegiate roller derby is still in its infant stages and therefore the purpose of this project is to provide a guide for future collegiate roller derby teams as well as other athletic teams.
ContributorsLee, Alisa Yulim (Author) / Looser, Devoney (Thesis director) / Hultsman, Wendy (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Department of Chemistry and Biochemistry (Contributor) / W. P. Carey School of Business (Contributor)
Created2014-12