Matching Items (36)
Filtering by
- All Subjects: Python
- All Subjects: ios
- Creators: Computer Science and Engineering Program
- Creators: Bazzi, Rida
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
This project explores how modern mobile technology can be used to provide support for domestic violence victims. The goal of the project is to create a proof-of-concept iOS mobile application that maintains a discreet safety front and provides domestic violence victims with resources and safety planning. The design and implementation are disguised as a hair salon app to maintain a low profile on the user’s phone. The HairHelp app features quick exit navigation, a secure database to store a user’s private and personal documents in case of emergency, and a checklist of safety planning measures. The steps taken in this project serve as the foundation for a larger project in the long term.
ContributorsShovkovy, Sophia (Author) / Balasooriya, Janaka (Thesis director) / Wilkey, Douglas (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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 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
When planning a road trip today, there are solutions that let the user know what comes along their route, but the user is often presented with too much information, which can overwhelm the user. They are provided suggestions all along the route, not just at those times when they would be needed. RoutePlanner simply takes all that information and only presents that data to the user, that they would need at a particular time. Gas station suggestions would show when the gas tank range is going to be hit soon, and restaurant suggestions would only be shown around lunch time. The iOS app takes in the users origin and destination and provides the user the route as given by GoogleMaps, and then various stop suggestions at their given time. Each route that is obtained, is broken down into a number of steps, which are basically a connection of coordinate points. These coordinate point collections are used to point to a location at a certain distance or duration away from the origin. Given a coordinate, we query the APIs for places of interest and move to the next stop, until the end of the route.
ContributorsDamania, Harsh Abhay (Author) / Balasooriya, Janaka (Thesis director) / Faucon, Christophe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
Description
As technology's influence pushes every industry to change, healthcare professionals must move to a more connected model. The nearly ubiquitous presence of smartphones presents a unique opportunity for physicians to collect and process data from their patients more frequently. The Mayo Clinic, in partnership with the Barrett Honors College, has designed and developed a prototype smartphone application targeting palliative care patients. The application collects symptom data from the patients and presents it to the doctors. This development project serves as a proof-of-concept for the application, and shows how such an application might look and function. Additionally, the project has revealed significant possibilities for the future of the application.
ContributorsGaney, David Howard (Author) / Balasooriya, Janaka (Thesis director) / Lipinski, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
The face of computing is constantly changing. Wearable computers in the form of glasses or watches are becoming more and more common. These devices have very small screens (measured in millimeters), and users often interact with them through voice input and audio feedback. Weather is one of the most regularly checked app category on smart devices, but weather results on these devices are often limited to raw data, canned responses, or sentence templates with numbers plugged in. The goal for this project was to build a system that could generate weather forecast text, which could then be read to a user through text-to-speech. By using methods in language generation, the system can generate weather forecast text in millions of different ways. This is all computed locally, and it covers every possible weather case. In order to generate natural weather forecast texts, the system retrieved raw weather data from a weather API and created the text through six methods: content determination, document structuring, sentence aggregation, lexical choice, referring expression generation, and text realization. Content determination is the process of deciding on what information to include in a computer generated text. The document structuring phase deals with the order and structure of the information. Sentence aggregation is the merging of similar sentences to improve readability and to reduce redundancy. Lexical choice is the process of putting words to concepts. Referring expression generation is the process of identifying objects, regions, time periods, and locations within a text. Finally text realization involves creating sentences with proper syntax, morphology, and orthography. Through these six stages, a system was developed that could generate unique weather forecast text from raw data accurately and efficiently. It was built for iOS devices with Apple's new programming language, Swift, and it will be ported to the Apple Watch when the API is fully opened to developers.
ContributorsJorgensen, Jacob Paul (Author) / Baral, Chitta (Thesis director) / Faucon, Christophe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (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 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
The purpose of this project was to program a Raspberry Pi to be able to play music from both local storage on the Pi and from internet radio stations such as Pandora. The Pi also needs to be able to play various types of file formats, such as mp3 and FLAC. Finally, the project is also to be driven by a mobile app running on a smartphone or tablet. To achieve this, a client server design was employed where the Raspberry Pi acts as the server and the mobile app is the client. The server functionality was achieved using a Python script that listens on a socket and calls various executables that handle the different formats of music being played. The client functionality was achieved by programming an Android app in Java that sends encoded commands to the server, which the server decodes and begins playing the music that command dictates. The designs for both the client and server are easily extensible and allow for any future modifications to the project to be easily made.
ContributorsStorto, Michael Olson (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
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 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
Description
Finding applications on Apple’s iOS device Home screen is a difficult task since applications are arranged in a disorganized grid of icons and small labels. By “jailbreaking” an iOS device, it is possible to install third party “tweaks” that modify the operating system to customize and fix annoying aspects of iOS. Current jailbreak tweaks exist that can launch applications differently than Apple’s stock Home screen, but they leave much to be desired in terms of functionality, usability, and aesthetics. HomeList is a watchOS-inspired tweak I created to add an easy to read, quick to navigate, and visually appealing list of applications integrated directly into the Home screen. Research into Apple’s private iOS frameworks was used to figure out how to perform tasks required by an app launcher as well as match iOS design aesthetics.
ContributorsBoxberger, Blake Palmer (Author) / Balasooriya, Janaka (Thesis director) / Faucon, Philippe Christophe (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
ContributorsPeterson, Austin (Author) / Yang, Yezhou (Thesis director) / Sadasivam, Aadhavan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05