Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- All Subjects: Machine Learning
- All Subjects: ethics
Description
Criminal Justice is a complex subject matter, and not everyone agrees on the way a criminal justice system ought to function. But one feature that is common to virtually all forms of proposed justice systems is that a true justice system treats people ethically. The question, then, is how a justice system can achieve this. This investigation analyzed two ethical theories, Kantianism and Utilitarianism, to determine which one would be better suited for guiding a criminal justice system on how to treat the people involved ethically. This investigation focused on applying the two theories to the U.S. Criminal Justice System in particular.
Kantianism is a duty-based moral theory in which actions have an intrinsic moral worth. This means certain actions are morally right and other are morally wrong, regardless of the intended or realized consequences. The theory relies on the categorical imperative to judge the morality of certain actions. It states that an action is moral if its maxim can be willed universal law and if it avoids treating people as merely a means. In contrast, Utilitarianism is a consequentialist theory which focuses on the consequences of an action in judging moral worth. In Utilitarianism, the morally correct action is the one which will maximize utility; that is to say, the morally right action is the one which will produce the greatest amount of happiness and minimize the amount of pain for the greatest number of people.
After applying these two theories to moral dilemmas facing the U.S. Criminal Justice System, including the appropriate collection of DNA evidence, the use of police deception, and the use of criminal punishments such as solitary confinement or the death penalty, it was clear that Kantianism was the ethical theory best suited for guiding the system in treating people ethically. This is because Kantianism’s focus on the intrinsic moral worth of an action rather than its consequences leaves less room for ambiguity than does Utilitarianism.
Kantianism is a duty-based moral theory in which actions have an intrinsic moral worth. This means certain actions are morally right and other are morally wrong, regardless of the intended or realized consequences. The theory relies on the categorical imperative to judge the morality of certain actions. It states that an action is moral if its maxim can be willed universal law and if it avoids treating people as merely a means. In contrast, Utilitarianism is a consequentialist theory which focuses on the consequences of an action in judging moral worth. In Utilitarianism, the morally correct action is the one which will maximize utility; that is to say, the morally right action is the one which will produce the greatest amount of happiness and minimize the amount of pain for the greatest number of people.
After applying these two theories to moral dilemmas facing the U.S. Criminal Justice System, including the appropriate collection of DNA evidence, the use of police deception, and the use of criminal punishments such as solitary confinement or the death penalty, it was clear that Kantianism was the ethical theory best suited for guiding the system in treating people ethically. This is because Kantianism’s focus on the intrinsic moral worth of an action rather than its consequences leaves less room for ambiguity than does Utilitarianism.
ContributorsMorett, Xavier Laakea (Author) / Manninen, Bertha (Thesis director) / Kimberly, Kobojek (Committee member) / School of Criminology and Criminal Justice (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
This paper will review past unethical studies conducted in the last 100 years on humans, including studies such as the WWII Concentration Camp studies on hypothermia and sterilization, Tuskegee Syphilis Study, and the case of Henrietta Lacks; Analyze why they were deemed unethical, the laws that emerged from these studies, and how it relates to contemporary technology, with a focus on the issues surrounding the development of an electronic wearable pregnancy monitor. The studies will include details of how they were conducted as well as what deemed them unethical and an explanation of why the results are unusable. Following the studies will be an explanation of the laws that were set into place following the studies with a lead into current technologies and how these technologies created a new set of ethics. The Google Mini, the wearable biosensor onesies for infants, and the intensive care unit at Banner Baywood will be described and so will their role in the development of an electronic wearable pregnancy monitor. The mini-meta analysis includes possible features of the monitor as well as a description of what the ethical consent form will look like. To conclude the paper, the importance of analyzing past unethical studies will help create a new ethical device that will make a point to go above and beyond to ensure the physical health of unborn children, in a way that is both ethical and significant.
ContributorsWallace, Sydney Sarah (Author) / Hall, Rick (Thesis director) / Kamenca, Andrea (Committee member) / Human Systems Engineering (Contributor) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
What's in a name? A person not a number is a multimedia eBook that will explore how the media treats coverage of sexual assault victims and challenges the traditional no-naming policy instilled in almost every professional newsroom. Historical context to no-naming policies, opinions from critics of the no-naming policy and legal information will be provided. This book serves to encourage journalists and editors to consider identifying victims after long, thoughtful discussions, to educate media consumers on the topic, to eradicate the societal stigma of rape, and to reflect the views of survivors so that they may feel more willing to share their stories. Identifying sexual assault victims conforms to the journalistic imperative to tell the truth as fully as possible and to inform the public as completely as possible. When the information is part of the public record and there are no legal limitations on its use, identifying sexual assault victims will have a positive impact in educating the public and eradicating the stigma associated with being the victim of sexual assault. This book proposes that through educated, thoughtful and truthful stories about sexual assault can spark careful conversations and help turn around the stigma our society has placed on victims. The full eBook, complete with photos, videos and other audio components, is available at https://alejandraarmstrong.atavist.com/whats-in-a-name-a-person-not-a-number.
ContributorsArmstrong, Alejandra Moya (Author) / Gilger, Kristin (Thesis director) / Petchel, Jacqueline (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Abstract. The term "sex trafficking" can mean many different things, depending on who uses it. To some, it may be synonymous with prostitution. To others, it may equate to slavery. And some may find that sex trafficking differs from both slavery and prostitution. But I find that the term "sex trafficking" is used improperly when referring to phenomena that may not entail the violation of rights of any individual involved. For this reason, various definitions of "sex trafficking" may inappropriately conflate sex trafficking with prostitution. In this essay, I argue against such a conflation through supporting a rights-based approach of defining "sex trafficking," in which every instance of true sex trafficking necessitates a violation of someone's rights. First, I begin by laying the foundation of my discussion with definitions and various government and non-government uses of the term "sex trafficking." Then, I argue for the rights-based approach. I proceed to explore how the rights-based approach relates to consent, force, coercion, deception, and competence. Then, I compile my findings, synthesize a definition, and elaborate on a few questions regarding my definition. Using the term "sex trafficking" correctly, as I argue, means that we necessarily use the term in a context of a violation of rights.
ContributorsMiller, Isaac Jonathan (Author) / de Marneffe, Peter (Thesis director) / McGregor, Joan (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Financial statements are one of the most important, if not the most important, documents for investors. These statements are prepared quarterly and yearly by the company accounting department, and are then audited in detail by a large external accounting firm. Investors use these documents to determine the value of the company, and trust that the company was truthful in its statements, and the auditing firm correctly audited the company's financial statements for any mistakes in their books and balances. Mistakes on a company's financial statements can be costly. However, financial fraud on the statements can be outright disastrous. Penalties for accounting fraud can include individual lifetime prison sentences, as well as company fines for billions of dollars. As students in the accounting major, it is our responsibility to ensure that financial statements are accurate and truthful to protect ourselves, other stakeholders, and the companies we work for. This ethics game takes the stories of Enron, WorldCom, and Lehman Brothers and uses them to help students identify financial fraud and how it can be prevented, as well as the consequences behind unethical decisions in financial reporting. The Enron scandal involved CEO Kenneth Lay and his predecessor Jeffery Skilling hiding losses in their financial statements with the help of their auditing firm, Arthur Andersen. Enron collapsed in 2002, and Lay was sentenced to 45 years in prison with his conspirator Skilling sentenced to 24 years in prison. In the WorldCom scandal, CEO Bernard "Bernie" Ebbers booked line costs as capital expenses (overstating WorldCom's assets), and created fraudulent accounts to inflate revenue and WorldCom's profit. Ebbers was sentenced to 25 years in prison and lost his title as WorldCom's Chief Executive Officer. Lehman Brothers took advantage of a loophole in accounting procedure Repo 105, that let the firm hide $50 billion in profits. No one at Lehman Brothers was sentenced to jail since the transaction was technically considered legal, but Lehman was the largest investment bank to fail and the only large financial institution that was not bailed out by the U.S. government.
ContributorsPanikkar, Manoj Madhuraj (Author) / Samuelson, Melissa (Thesis director) / Ahmad, Altaf (Committee member) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
I argue that a morally heavy, poorly researched, emotionally powerful piece of non-fiction media with complex subject matter shown to an ill-equipped audience is unethical. I then evaluate methods of avoiding unethical situations from the perspective of media creators. I conclude by calling for a strictly diligence based ratings board anchored in the professional guilds of the entertainment industry.
ContributorsBroderick, Nathan Andrew (Author) / Maday, Gregg (Thesis director) / Watson, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / School of Film, Dance and Theatre (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2014-12