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- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
For as long as humans have been working, they have been looking for ways to get that work done better, faster, and more efficient. Over the course of human history, mankind has created innumerable spectacular inventions, all with the goal of making the economy and daily life more efficient. Today, innovations and technological advancements are happening at a pace like never seen before, and technology like automation and artificial intelligence are poised to once again fundamentally alter the way people live and work in society. Whether society is prepared or not, robots are coming to replace human labor, and they are coming fast. In many areas artificial intelligence has disrupted entire industries of the economy. As people continue to make advancements in artificial intelligence, more industries will be disturbed, more jobs will be lost, and entirely new industries and professions will be created in their wake. The future of the economy and society will be determined by how humans adapt to the rapid innovations that are taking place every single day. In this paper I will examine the extent to which automation will take the place of human labor in the future, project the potential effect of automation to future unemployment, and what individuals and society will need to do to adapt to keep pace with rapidly advancing technology. I will also look at the history of automation in the economy. For centuries humans have been advancing technology to make their everyday work more productive and efficient, and for centuries this has forced humans to adapt to the modern technology through things like training and education. The thesis will additionally examine the ways in which the U.S. education system will have to adapt to meet the demands of the advancing economy, and how job retraining programs must be modernized to prepare workers for the changing economy.
ContributorsCunningham, Reed P. (Author) / DeSerpa, Allan (Thesis director) / Haglin, Brett (Committee member) / School of International Letters and Cultures (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper explores how changing the color of chocolate can affect its perceived taste. While color psychology and its effects on food industry marketing are widely studied, this experiment focuses on blue, red, green, and purple striped chocolates. The study conducted for this paper focuses on these four colors based on their utilization in previously conducted experiments. Each color of chocolate involved 25 participants, for a total of 100 total individuals, who each taste tested one piece and immediately filled out a survey. The survey asked demographic questions, colored chocolate preferences, and questions ranking the chocolate's appeal. While the outcome showed that blue, green, red, and purple was indeed the order of appealing colors, the study results indicate the participants' color preferences did not affect their perceived taste of the chocolate they sampled. Rather, their preference was based on experiences they associated with the color of the chocolate they tasted.
ContributorsChan, Sydney (Author) / Gray, Nancy (Thesis director) / Giard, Jacques (Committee member) / 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
The purpose of this project is to raise awareness for children with social anxiety. As a book directed to children around the age of 12, it will give them a character they can relate to, so they can feel less alone. Throughout the story, the main character experiences symptoms of social anxiety and is subject to events that exacerbate those symptoms. Despite her challenges, the main character is able to effectively cope with her social anxiety through her own hard work, and help from her family members, teachers, and peers. The intent is to show children with social anxiety that, contrary to what their disorder makes them feel, they are special and have the capacity to develop skills that are relevant to their talents and interests, and overcome their fears. They should know that parents, teachers, and peers will be there to help and support them and will not judge them as harshly as they suspect. The supporting characters in this story show how a strong support base can influence the success of children with social anxiety. By the end of the story, the main character still has social anxiety, but has gained confidence and her symptoms are less severe. This illustrates that, although social anxiety cannot simply be overcome—that is, it doesn’t go away completely—it can be effectively managed with assistance from close others, and perseverance.
ContributorsDillard, Bethlehem (Author) / Lewis, Stephen (Thesis director) / Gaffney, Cynthia (Committee member) / School of Social and Behavioral Sciences (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
The current study investigated whether intermittent restraint stress (IRS) would impair fear extinction learning and lead to increased anxiety and depressive- like behaviors and then be attenuated when IRS ends and a post- stress rest period ensues for 6 weeks. Young adult, male Sprague Dawley rats underwent restraint stress using wire mesh (6hr/daily) for five days with two days off before restraint resumed for three weeks for a total of 23 restraint days. The groups consisted of control (CON) with no restraint other than food and water restriction yoked to the restrained groups, stress immediate (STR-IMM), which were restrained then fear conditioned soon after the end of the IRS paradigm, and stress given a rest for 6 weeks before fear conditioning commenced (STR-R6). Rats were fear conditioned by pairing a 20 second tone with a footshock, then given extinction training for two days (15 tone only on each day). On the first day of extinction, all groups discriminated well on the first trial, but then as trials progressed, STR-R6 discriminated between tone and context less than did CON. On the second day of extinction, STR- IMM froze more to context in the earlier trials than compared to STR-R6 and CON. As trials progressed STR-IMM and STR-R6 froze more to context than compared to CON. Together, CON discriminated between tone and context better than did STR-IMM and STR-R6. Sucrose preference, novelty suppressed feeding, and elevated plus maze was performed after fear extinction was completed. No statistical differences were observed among groups for sucrose preference or novelty suppressed feeding. For the elevated plus maze, STR-IMM entered the open arms and the sum of both open and closed arms fewer than did STR- R6 and CON. We interpret the findings to suggest that the stress groups displayed increased hypervigilance and anxiety with STR-R6 exhibiting a unique phenotype than that of STR-IMM and CON.
ContributorsShah, Vrishti Bimal (Author) / Conrad, Cheryl (Thesis director) / Newbern, Jason (Committee member) / Judd, Jessica (Committee member) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Pandora is a play exploring our relationship with gendered technology through the lens of artificial intelligence. Can women be subjective under patriarchy? Do robots who look like women have subjectivity? Hoping to create a better version of ourselves, The Engineer must navigate the loss of her creation, and Pandora must navigate their new world. The original premiere run was March 27-28, 2018, original cast: Caitlin Andelora, Rikki Tremblay, and Michael Tristano Jr.
ContributorsToye, Abigail Elizabeth (Author) / Linde, Jennifer (Thesis director) / Abele, Kelsey (Committee member) / Department of Information Systems (Contributor) / Economics Program in CLAS (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 purpose of this study was to examine whether positive affirmations can lower depressive symptoms amongst male and female Arizona State University (ASU) honors students. Male and female ASU honors students (20-22 years of age; N=40) were recruited from Barrett, the Honors College, through email and online newsletters. Students who had been previously or were at the time diagnosed with clinical depression were not permitted to participate in the study. Only 9 female and 14 males completed the entire study. Participants completed a pre- and post- test that each consisting of reading aloud questions and their answers from the Beck's Depression Inventory (BDI) while being video and audio recorded. Participants were given a list of 20 affirmations after the pre-test and were instructed to choose and read to themselves a new affirmation three times a day, 3 times a week for a total of 6 weeks. There was an average increase among all participants' BDI scores, but no significance was found in the improvement. Emotional responses were captured using the facial recognition software, Noldus FaceReader, and was used to observe whether there was emotional dissonance in the BDI answers. The correlation between the emotion "sad" and the answer chosen was found by using Pearson's r for each participants. There were only 2 total interviews that indicated a strong positive correlation and 1 interview that indicated strong negative correlation. All others were either moderate or minimal correlation, showing that the majority of participants' emotions may have not affected their answer choices. Results indicated there is no significant improvement when using affirmations to improve depressive symptoms and mood.
ContributorsChan, Angie (Co-author) / Duran, Jose (Co-author) / Chisum, Jack (Thesis director) / Hrncir, Micki (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05