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Meditation and Sleep Applications: Testing Piano Music Modality to Reduce Anxiety and Stress in College Students

Description

My research aims to determine the effectiveness of meditation and sleep applications (apps) on the reduction of anxiety and stress in college students, with a focus on sedative piano music. Results showed a significant reduction of stress and anxiety levels in college students when listening to sedative piano music versus…

My research aims to determine the effectiveness of meditation and sleep applications (apps) on the reduction of anxiety and stress in college students, with a focus on sedative piano music. Results showed a significant reduction of stress and anxiety levels in college students when listening to sedative piano music versus non-sedative piano music. Music along with other therapy modalities in meditation and sleep apps show promise in reducing students’ anxiety and stress and promoting their successes.

ContributorsPantha, Bidur (Author) / Brian, Jennifer (Thesis director) / Patten, Kristopher (Committee member) / School of Molecular Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Artificial Biasedness: How Inherent Biases in the Creation of Algorithms make Artificial Intelligence Facial Recognition Programs Racially Biased

Description

Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased…

Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased data is difficult to spot as the programming field is homogeneous and this issue reflects underlying societal biases. Facial recognition programs do not identify minorities at the same rate as their Caucasian counterparts leading to false positives in identifications and an increase of run-ins with the law. AI does not have the ability to role-reverse judge as a human does and therefore its use should be limited until a more equitable program is developed and thoroughly tested.

ContributorsGurtler, Charles William (Author) / Iheduru, Okechukwu (Thesis director) / Fette, Donald (Committee member) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

The Art of the Novella

Description

For this Creative Project, I decided to explore the elements that set novellas apart from other genres and then experiment writing in the form. In doing so, I took into account three main categories: Plot Structure, Character Development, Style/Format, and then used my findings to write 45 pages of a…

For this Creative Project, I decided to explore the elements that set novellas apart from other genres and then experiment writing in the form. In doing so, I took into account three main categories: Plot Structure, Character Development, Style/Format, and then used my findings to write 45 pages of a novella titled Emmy and Me.

ContributorsBingham, Roxanne Marie (Author) / Irish, Jennifer (Thesis director) / Danielson, Jonathan (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Art Education For Incarcerated Youth

Description

My project is designed to provide art education to incarcerated youth in Arizona. This project will address two current issues in Arizona; the underfunding of art programs and high rates of incarceration. As of 2021, there are no state-funded art programs in Arizona. Arizona is tied with Texas for the…

My project is designed to provide art education to incarcerated youth in Arizona. This project will address two current issues in Arizona; the underfunding of art programs and high rates of incarceration. As of 2021, there are no state-funded art programs in Arizona. Arizona is tied with Texas for the eighth highest rate of incarceration in the country. In Arizona, 750 out of every 100,000 people are incarcerated. This project is an art course for incarcerated youth. The project includes a packet detailing the course content and assignment details, a class syllabus, a course flyer, and a certificate of completion. The course is intended to be taught at the Adobe Mountain School facility. The course is designed so that it can be implemented in other facilities in the future. The class will be taught by volunteers with a background in studio art, design, or art education. Each student will receive a course packet that they can use to keep track of information and assignments. Instructors will use the course packet to teach the class. The course focuses on drawing with charcoal and oil pastel, which will build a foundation in drawing skills. The course covers a twelve-week semester. The course content packet includes a week-by-week breakdown of the teaching material and project descriptions. The course consists of two main projects and preparatory work. The preparatory work includes vocabulary terms, art concepts, drawing guides, brainstorming activities, and drawing activities. The two main prompts are designed for students to explore the materials and to encourage self-reflection. The class is curated so that students can create art in a low-risk, non-judgemental environment. The course will also focus on establishing problem-solving and critical thinking skills through engaging activities.

ContributorsSheppard, Eve (Author) / Cornelia, Wells (Thesis director) / Jennifer, Nelson (Committee member) / School of Art (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Using Neural Networks to Perform Colorwork on Images

Description

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a…

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

ContributorsLobo, Ian (Co-author) / Koleber, Keith (Co-author) / Markabawi, Jah (Co-author) / Masud, Abdullah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

The Verde River Watershed: Using Geospatial Web Applications for a Virtual Learning Experience

Description

The COVID-19 Pandemic has provided a challenge for educators to create virtual learning materials that are engaging and impactful during times of high stress and isolation. In this creative project, I explore the variety of virtual tools and web applications from Esri by creating a Story Map on the Verde…

The COVID-19 Pandemic has provided a challenge for educators to create virtual learning materials that are engaging and impactful during times of high stress and isolation. In this creative project, I explore the variety of virtual tools and web applications from Esri by creating a Story Map on the Verde River Watershed. This Story Map is intended for an audience of students in late middle school and early high school but can be a resource to teachers for a wider age range. The integration of interactive technology and virtual tools in educational practices is likely to continue past the immediate circumstances of the COVID-19 pandemic. The purpose of this Story Map is to showcase one of the many uses for geospatial web applications beyond the immediate realm of GIS.

ContributorsTueller, Margaret (Author) / Frazier, Amy (Thesis director) / Dorn, Ron (Committee member) / School of Geographical Sciences and Urban Planning (Contributor, Contributor, Contributor) / Division of Teacher Preparation (Contributor) / The Design School (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

System complexity reduction via feature selection

Description

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve…

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.

ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)

Created2011

A study of boosting based transfer learning for activity and gesture recognition

Description

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs…

Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.

ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)

Created2011

TaxiWorld: developing and evaluating solution methods for multi-agent planning domains

Description

TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all…

TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.

ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)

Created2011

Representing the language of the causal calculator in answer set programming

Description

Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language…

Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.

ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)

Created2011

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