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Description
CourseKarma is a web application that engages students in their own learning through peer-driven social networking. The influence of technology on students is advancing faster than the school system, and a major gap still lingers between traditional learning techniques and the fast-paced, online culture of today's generation. CourseKarma enriches the

CourseKarma is a web application that engages students in their own learning through peer-driven social networking. The influence of technology on students is advancing faster than the school system, and a major gap still lingers between traditional learning techniques and the fast-paced, online culture of today's generation. CourseKarma enriches the educational experience of today's student by creating a space for collaborative inquiry as well as illuminating the opportunities of self and group learning through online collaboration. The features of CourseKarma foster this student-driven environment. The main focus is on a news-feed and Question and Answer component that provides a space for students to share instant updates as well ask and answer questions of the community. The community can be as broad as the entire ASU student body, as specific as students in BIO155, or even more targeted via specific subjects and or skills. CourseKarma also provides reputation points, which are the sum of all of their votes received, identifying the individual's level and or ranking in each subject or class. This not only gamifies the usual day-to-day learning environment, but it also provides an in-depth analysis of the individual's skills, accomplishments, and knowledge. The community is also able to input and utilize course and professor descriptions/feedback. This will be in a review format providing the students an opportunity to share and give feedback on their experience as well as providing incoming students the opportunity to be prepared for their future classes. All of the student's contributions and collaborative activity within CourseKarma is displayed on their personal profile creating a timeline of their academic achievements. The application was created using modern web programming technologies such as AngualrJS, Javascript, jQuery, Bootstrap, HTML5, CSS3 for the styling and front-end development, Mustache.js for client side templating, and Firebase AngularFire as the back-end and NoSQL database. Other technologies such as Pivitol Tracker was used for project management and user story generation, as well as, Github for version control management and repository creation. Object-oreinted programming concepts were heavily present in the creation of the various data structures, as well as, a voting algorithm was used to manage voting of specific posts. Down the road, CourseKarma could even be a necessary add-on within LinkedIn or Facebook that provides a quick yet extremely in-depth look at an individuals' education, skills, and potential to learn \u2014 based all on their actual contribution to their academic community rather than just a text they wrote up.
ContributorsCho, Sungjae (Author) / Mayron, Liam (Thesis director) / Lobock, Alan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

ContributorsLi, Vincent (Author) / Turaga, Pavan (Thesis director) / Buman, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Compass portal features tools that help teachers, psychologists, behavioral specialists gain insights on students’ performance through activities they have completed.

ContributorsNallagula, Nithin Sagar (Co-author) / Shah, Neha (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexadnra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

COMPASS portal features tools that help teachers, psychologists, behavioral Specialists gain insights on students’ performance through activities they have completed.

ContributorsShah, Neha Manish (Co-author) / Nallagula, Nithin Sagar (Co-author) / Gary, Kevin (Thesis director) / Mehlhase, Alexandra (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their

The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their S3 buckets along with their speech to text transcription service. This type of application process can give admissions teams the opportunity to get to know who will potentially be attending their university and allows the applicants to express themselves to admissions teams in a new and unique way.
ContributorsStephan, Meagan (Co-author) / Pratt, Devan (Co-author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
The Tutoring Center Management System is a web-based application for ASU’s University Academic Success Programs (UASP) department, particularly the Math Tutoring Center. It is aimed at providing a user-friendly interface to track queue requests from students visiting the tutoring centers and convert that information into actionable data with the potential

The Tutoring Center Management System is a web-based application for ASU’s University Academic Success Programs (UASP) department, particularly the Math Tutoring Center. It is aimed at providing a user-friendly interface to track queue requests from students visiting the tutoring centers and convert that information into actionable data with the potential to live-track and assess the performance of each tutoring center and each tutor. Numerous UASP processes are streamlined to create an efficient and integrated workflow, such as tutor scheduling, tutor search, shift coverage requests, and analytics. The intended users of the application feature ASU students and the UASP staff, including tutors and supervisors.
ContributorsJain, Prakshal (Co-author) / Gulati, Sachit (Co-author) / Nakamura, Mutsumi (Thesis director) / Selgrad, Justin (Committee member) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
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Description
The purpose of this thesis is to accurately simulate the surface brightness in various spectral emission lines of the HH 901 jets in the Mystic Mountain Formation of the Carina Nebula. To accomplish this goal, we gathered relevant spectral emission line data for [Fe II] 12660 Å, Hα 6563 Å,

The purpose of this thesis is to accurately simulate the surface brightness in various spectral emission lines of the HH 901 jets in the Mystic Mountain Formation of the Carina Nebula. To accomplish this goal, we gathered relevant spectral emission line data for [Fe II] 12660 Å, Hα 6563 Å, and [S II] 6720 Å to compare with Hubble Space Telescope observations of the HH 901 jets presented in Reiter et al. (2016). We derived the emissivities for these lines from the spectral synthesis code Cloudy by Ferland et al. (2017). In addition, we used WENO simulations of density, temperature, and radiative cooling to model the jet. We found that the computed surface brightness values agreed with most of the observational surface brightness values. Thus, the 3D cylindrically symmetric simulations of surface brightness using the WENO code and Cloudy spectral emission models are accurate for jets like HH 901. After detailing these agreements, we discuss the next steps for the project, like adding an external ambient wind and performing the simulations in full 3D.
ContributorsMohan, Arun (Author) / Gardner, Carl (Thesis director) / Jones, Jeremiah (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force

If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
ContributorsMathews, Nicolle (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
Through the personal experience of volunteering at ASU Project Humanities, an organization that provides resources such as clothing and toiletries to the homeless population in Downtown Phoenix, I noticed efficiently serving the needs of the homeless population is an important endeavor, but the current processes for Phoenix nonprofits to collect

Through the personal experience of volunteering at ASU Project Humanities, an organization that provides resources such as clothing and toiletries to the homeless population in Downtown Phoenix, I noticed efficiently serving the needs of the homeless population is an important endeavor, but the current processes for Phoenix nonprofits to collect data are manual, ad-hoc, and inefficient. This leads to the research question: is it possible to improve this process of collecting statistics on client needs, tracking donations, and managing resources using technology? Background research includes an interview with ASU Project Humanities, articles by analysts, and related work including case studies of current technologies in the nonprofit community. Major findings include i) a lack of centralized communication in nonprofits collecting needs, tracking surplus donations, and sharing resources, ii) privacy assurance is important to homeless individuals, and iii) pre-existing databases and technological solutions have demonstrated that technology has the ability to make an impact in the nonprofit community. To improve the process, standardization, efficiency, and automation need to increase. As a result of my analysis, the thesis proposes a prototype solution which includes two parts: an inventory database and a web application with forms for user input and tables for the user to view. This solution addresses standardization by showing a consistent way of collecting data on need requests and surplus donations while guaranteeing privacy of homeless individuals. This centralized solution also increases efficiency by connecting different agencies that cater to these clients. Lastly, the solution demonstrates the ability for resources to be made available to each organization which can increase automation. In conclusion, this database and web application has the potential to improve nonprofit organizations’ networking capabilities, resource management, and resource distribution. The percentile of homeless individuals connected to these resources is expected to increase substantially with future live testing and large-scale implementation.
ContributorsKhurana, Baani Kaur (Author) / Bazzi, Rida (Thesis director) / Sankar, Lalitha (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
Learning a new language can be very challenging. One significant aspect of learning a language is learning how to have fluent verbal and written conversations with other people in that language. However, it can be difficult to find other people available with whom to practice conversations. Additionally, total beginners may

Learning a new language can be very challenging. One significant aspect of learning a language is learning how to have fluent verbal and written conversations with other people in that language. However, it can be difficult to find other people available with whom to practice conversations. Additionally, total beginners may feel uncomfortable and self-conscious when speaking the language with others. In this paper, I present Hana, a chatbot application powered by deep learning for practicing open-domain verbal and written conversations in a variety of different languages. Hana uses a pre-trained medium-sized instance of Microsoft's DialoGPT in order to generate English responses to user input translated into English. Google Cloud Platform's Translation API is used to handle translation to and from the language selected by the user. The chatbot is presented in the form of a browser-based web application, allowing users to interact with the chatbot in both a verbal or text-based manner. Overall, the chatbot is capable of having interesting open-domain conversations with the user in languages supported by the Google Cloud Translation API, but response generation can be delayed by several seconds, and the conversations and their translations do not necessarily take into account linguistic and cultural nuances associated with a given language.
ContributorsBudiman, Matthew Aaron (Author) / Venkateswara, Hemanth Kumar Demakethepalli (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12