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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
Description
This study asks the question: does gender-based discrimination exists within Arizona State University's Army Reserve Officer Training Corps (ROTC), and if so, what are the effects of such discrimination? Within this study, discrimination is defined as: the treatment or consideration of, or making a distinction in favor of or against, a person or thing based on the group, class, or category to which that person or thing belongs, rather than on individual merit. The researcher predicted that this study would show that gender-based discrimination operates within the masculine military culture of Army ROTC at ASU, resulting from women's hyper-visibility and evidenced by their lack of positive recognition and disbelief in having a voice in the program. These expectations were based on background research claiming that the token status of women in military roles causes them to be more heavily scrutinized, and they consequentially try to attain success by adapting to the masculine military culture by which they are constantly measured. For the purposes of this study, success is defined as: the attainment of wealth, favor, or eminence . This study relies on exploratory interviews and an online survey conducted with male and female Army ROTC cadets of all grade levels at Arizona State University. The interviews and survey collected demographic information and perspectives on individual experiences to establish an understanding of privilege and marginalization within the program. These results do support the prediction that women in Army ROTC at ASU face discrimination based on their unique visibility and lack of positive recognition and voice in the program. Likewise, the survey results indicate that race also has a significant impact on one's experience in Army ROTC, which is discussed later in this study in regard to needs for future research. ASU Army ROTC includes approximately 100 cadets, and approximately 30-40 of those cadets participated in this study. Additionally, the University of Arizona and the Northern Arizona University Army ROTC programs were invited to participate in this study and declined to do so, which would have offered a greater sample population. Nonetheless, the results of this research will be useful for analysis and further discussion of gender-equality in Army ROTC at Arizona State University.
ContributorsAllemang, Lindsey Ann (Author) / Wood, Reed (Thesis director) / Switzer, Heather (Committee member) / School of Politics and Global Studies (Contributor) / School of Social Transformation (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
As the U.S. reckons with the reality of sexual assault and harassment in the wake of the #MeToo movement, it is particularly important to consider sexual assault in the military, an institution that is a massive employer and the face of the U.S. abroad. Media coverage is a catalyst for change, and the nature and scope of coverage is indicative of public and political attitudes. This thesis uses both quantitative and qualitative data to analyze characteristics of military sexual assault cases that complicate media coverage and to identify strengths and weaknesses of the media's approach to such stories. On the quantitative side, it takes advantage of nearly 600 case reports of sexual assault from U.S. military bases in Japan that were categorized to identify themes such as disposition outcomes, alcohol involvement and victim participation in investigations. Qualitatively, this thesis includes interviews with military officials, victims' advocates, journalists and other stakeholders that help to create a more holistic understanding of how media cover military sexual assault. Notably, this thesis finds that a lack of public interest in the military, a lack of congruency between military and civilian systems, and a highly complex hierarchy that limits journalists' access to military sources and data all complicate coverage. Drawing from these conclusions, it recommends that the media avoid episodic reporting, focus on personalizing stories in an institutional context, embrace accountability journalism and dedicate resources to pursuing complex investigations. It also acknowledges the important role of non-traditional media in the future of information sharing on the topic of military sexual assault.
ContributorsArmstrong, Mia Anne (Author) / Warner, Carolyn (Thesis director) / Gilger, Kristin (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Politics and Global Studies (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
It goes without saying that marriage, and the concept of two people staying together for the rest of their lives, is not easy. In today's society, divorce is something that is becoming more and more prominent among people. However, despite the divorce phenomenon there are still success stories of couples who last and manage to stick together despite the odds. It is difficult to measure the "successfulness" of any marriage due to the fact that so many different elements comprise them. However, there are endless assessments available to be used as tools for attempting measurement of success. A majority of them are related to measuring relationship quality in terms of individual satisfaction by focusing on each individual's happiness within the relationship. Obviously, every marriage is different and there are many things that can impact a couple's' likeliness to stay together such as the general circumstances surrounding their union and each partner's willingness to persevere. For instance, there are a variety of different factors that influence the overall success of marriages within and surrounding the United States Military. Such as physical proximity, frequency of communication, and a mutual desire to make the relationship work. Cultivating a relationship in which one partner is a service member and one partner is a civilian is stressful for both people involved. Specifically, the intense stress couples experience associated with deployment can often cause severe problems such as depression and anxiety that may lead to divorce or mental health problems later on down the road. Stressors specifically related to the deployment cycle can contribute to depression among both service members and their spouses. Most of these families face unique stressors through the course of military service and deployments, including frequent relocations and recon�gurations of the family system, ambiguous loss and fear for a loved one's safety, and high levels of stress and/or dysfunction among family members (Flake, Davis, Johnson, & Middleton, 2009; Huebner, Mancini, Wilcox, Grass, & Grass, 2007) Separation , unpredictable duty hours, and single parenting (parenting while the veteran is away either being deployed or on training courses) are just a few of the stressors that face partners of veterans on a regular basis (Padden, Connors & Agazio, 2011). Dr. John Gottman, the executive director of the Relationship Research Institute. has conducted extensive research regarding marital stability and divorce prediction on thousands of couples over the last forty years of his career. Using video cameras, heart monitors, and other biofeedback equipment, he and his colleagues have screened interviewed and tracked what couples experience during moments of conflict and closeness. Over the span of the last forty years, Dr. Gottman has created a theory he calls "The Four Horsemen of the Apocalypse". In the New Testament, the Four Horsemen of the Apocalypse are a metaphor marking the beginning of end times. Dr. Gottman's Four Horsemen on the other hand, are a metaphor marking the beginning of the demise of a marriage. The horsemen include criticism, contempt, defensiveness, and stonewalling. They are communication styles among couples that Dr. Gottman says can predict the end of a relationship. This notion holds true especially in the implication of military relationships. Focusing on the predictors of divorce, and inspecting the elements of these relationships in which the military is a condition of the union, discoveries can be made as to what makes these military relationships more difficult. An examination through the lens of Dr. Gottman's horsemen of the circumstances surrounding these unions in which deployment physically separates the two partners demonstrates how deployment in and of itself can cause couples to encompass each of the horsemen and eventually push them towards divorce. Throughout the course of this paper, the different elements that embody each of the four horsemen will be examined and analyzed as they pertain to the deployment process. Upon completion of the examination of these different factors, it can be suggested that deployment in its nature becomes the harbinger of the apocalypse. By encompassing all the different aspects of the first four original horsemen, and pushing military couples towards the behaviors that lead in the direction of divorce, deployment in and of itself can be thought of as predecessor, or harbinger of the apocalypse.
ContributorsSerdy, Taylor B (Author) / Martin, Thomas (Thesis director) / Mowzoon, Nura (Committee member) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Based upon personal involvement from August 2010 to July 2014 as a Marine Option Midshipman within the ASU Naval Reserves Officer Training Corps (NROTC), being a student of leadership training within my degree plan, and gender difference research I conducted, this creative project addresses potential issues that reside within the ASU NROTC and the ways in which the program overall can be changed for the Marine Options in order to bring about proper success and organization. In order to officially become a Marine within the Unites States Marine Corps, it is necessary for Marine Option students to fulfill Officer Candidate School (OCS) at Quantico, Virginia. As the first female to go through OCS as a midshipman from the ASU NROTC, I found that there is an inadequate amount of preparation and training given in regards to the gender differences and what is to be expected for successful completion. I will offer a brief history regarding the NROTC across the Unites States and the ASU NROTC itself. These subjects will cover the program layouts as well as the leadership training that is required and provided within it and the ways in which this is conducted. I will then compare and contrast this to the leadership training given to me within my study of Leadership and Ethics regarding the transformational leadership, gender-based leadership, and coercive leadership. Finally, I end my thesis with a reflection of personal experiences taken away from these avenues and offer recommendations to better equip the ASU NROTC program in having successful retention and success of the female Marine Option midshipman.
ContributorsCamarena, Leonor Jimenez (Author) / Lucio, Joanna (Thesis director) / Warnicke, Margaretha (Committee member) / Barrett, The Honors College (Contributor) / School of Public Affairs (Contributor)
Created2014-12
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
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05