Matching Items (20)
Filtering by
- All Subjects: Python
- Creators: Computer Science and Engineering Program
Description
An application called "Productivity Heatmap" was created with this project with the goal of allowing users to track how productive they are over the course of a day and week, input through scheduled prompts separated by 30 minutes to 4 hours, depending on preference. The result is a heat map colored according to a user's productivity at particular times of each day during the week. The aim is to allow a user to have a visualization on when he or she is best able to be productive, given that every individual has different habits and life patterns. This application was made completely in Google's Android Studio environment using Java and XML, with SQLite being used for database management. The application runs on any Android device, and was designed to be a balance of providing useful information to a user while maintaining an attractive and intuitive interface. This thesis explores the creation of a functional mobile application for mass distribution, with a particular set of end users in mind, namely college students. Many challenges in the form of learning a new development environment were encountered and overcome, as explained in the report. The application created is a core functionality proof-of-concept of a much larger personal project in creating a versatile and useful mobile application for student use. The principles covered are the creation of a mobile application, meeting requirements specified by others, and investigating the interest generated by such a concept. Beyond this thesis, testing will be done, and future enhancements will be made for mass-market consumption.
ContributorsWeser, Matthew Paul (Author) / Nelson, Brian (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
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
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 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
Description
The most important task for a beginning computer science student, in order for them to succeed in their future studies, is to learn to be able to understand code. One of the greatest indicators of student success in beginning programming courses is the ability to read code and predict its output, as this shows that the student truly understands what each line of code is doing. Yet few tools available to students today focus on helping students to improve their ability to read code. The goal of the random Python program generator is to give students a tool to practice this important skill.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
ContributorsDiLorenzo, Kaitlyn (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Next year, Arizona State University is launching a chatbot that will place their knowledge and services right into the palms of their students’ hands. Currently named Sunny, this virtual assistant will be able to answer questions regarding all aspects of college life, from orientation to housing, financial aid, schedules, intramurals, and more. Over the last semester, I have met with members of the Sunny development team to discuss their design and implementation plans. With their information plus a bit of outside research, I was able to combine several frameworks and technologies to build a prototype for Sunny. Prototypes allow developers to evaluate their designs early on, giving them ample time to make any necessary adjustments. I am confident that the Sunny development team will be able to learn from my basic implementation, from its triumphs and failures, to create the best possible chatbot for the students attending Arizona State University.
ContributorsGrossnickle, Brandon Michael (Co-author) / Grossnickle, Brandon (Co-author) / Balasooriya, Janaka (Thesis director) / Gray, Bobby (Committee member) / Longie, Joel (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
The goal of this research study was to empirically study the effects of a project based learning activity. The effectiveness of this study was benchmarked according to two results: the effectiveness in communicating the scope and impact of engineering, and the effectiveness in increasing interest in computer systems engineering (CSE). This research report presents an analysis of the effects of making engineering education socially relevant, interesting and accessible. High school students participated in a learning experience in which they designed flood evacuation systems that could warn a city of incoming floods. Both pre-assessments and post-assessments were implemented to capture students' awareness of engineering tasks and their interest levels in engineering tasks. Data on students' perceptions of specific engineering tasks were analyzed quantitatively through Wilcoxon signed-rank testing and determined that the program had significant positive effects on developing more accurate conceptions of engineering tasks. The results relating to student interest in CSE indicated that there was an increased level of interest in CSE engineering tasks after the program. There was a 14% increase in number of students who found engineering tasks interesting from 64% to 78%. However, as participants self-selected to participate in this learning experience, many students had positive perceptions of engineering tasks prior to engaging in the learning experience. This study was successful and met both of its primary goals of enhancing awareness and interest in engineering in this particular group of high school students.
ContributorsRidhwaan, Syed (Author) / Ganesh, Tirupalavanam (Thesis director) / Shrake, Scott (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The purpose of this project was to implement and analyze a new proposed rootkit that claims a greater level of stealth by hiding in cache. Today, the vast majority of embedded devices are powered by ARM processors. To protect their processors from attacks, ARM introduced a hardware security extension known as TrustZone. It provides an isolated execution environment within the embedded device that enables us to run various memory integrity and malware detection tools to identify possible breaches in security to the normal world. Although TrustZone provides this additional layer of security, it also adds another layer of complexity, and thus comes with its own set of vulnerabilities. This new rootkit identifies and exploits a cache incoherence in the ARM device as a result of TrustZone. The newly proposed rootkit, called CacheKit, takes advantage of this cache incoherence to avoid memory introspection from tools in secure world. We implement CacheKit on the i.MX53 development board, which features a single ARM Cortex A8 processor, to analyze the limitations and vulnerabilities described in the original paper. We set up the Linux environment on the computer to be able to cross-compile for the development board which will be running the FreeScale android 2.3.4 platform with a 2.6.33 Linux kernel. The project is implemented as a kernel module that once installed on the board can manipulate cache as desired to conceal the rootkit. The module exploits the fact that in TrustZone, the secure world does not have access to the normal world cache. First, a technique known as Cache-asRAM is used to ensure that the rootkit is loaded only into cache of the normal world where it can avoid detection from the secure world. Then, we employ the cache maintenance instructions and resisters provided in the cp15 coprocessor to keep the code persistent in cache. Furthermore, the cache lines are mapped to unused I/O address space so that if cache content is flushed to RAM for inspection, the data is simply lost. This ensures that even if the rootkit were to be flushed into memory, any trace of the malicious code would be lost. CacheKit prevents defenders from analyzing the code and destroys any forensic evidence. This provides attackers with a new and powerful tool that is excellent for certain scenarios that were previously thought to be secure. Finally, we determine the limitations of the prototype to determine possible areas for future growth and research into the security of networked embedded devices.
ContributorsGutierrez Barnett, Mauricio Antonio (Author) / Zhao, Ziming (Thesis director) / Doupe, Adam (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The Internet of Things has spread Wi-Fi connectivity to household and business devices everywhere. It is important that we understand IoT's risks and capabilities as its popularity continues to grow, and that we recognize new and exciting uses for it. In this project, the ESP8266 Wi-Fi controller, powered by a lithium battery, is used to transmit messages from a user's browser or mobile phone to an OLED display. The ESP8266 is a system on a chip (SOC) which boasts impressive features such as full TCP/IP stack, 1 MB of flash memory, and a 32-bit CPU. A web server is started on the ESP8266 which listens at a specific port and relays any strings from the client back to the display, acting as a simple notification system for a busy individual such as a professor. The difficulties with this project stemmed from the security protocol of Arizona State University's Wi-Fi network and from the limitations of the Wi-Fi chip itself. Several solutions are suggested, such as utilizing a personal cellular broadband router and polling a database for stored strings through a service such as Data.Sparkfun.com.
ContributorsKovatcheva, Simona Kamenova (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Agent Based modeling has been used in computer science to simulate complex phenomena. The introduction of Agent Based Models into the field of economics (Agent Based Computational Economics ACE) is not new, however work on making model environments simpler to design for individuals without a background in computer science or computer engineering is a constantly evolving topic. The issue is a trade off of how much is handled by the framework and how much control the modeler has, as well as what tools exist to allow the user to develop insights from the behavior of the model. The solutions looked at in this thesis are the construction of a simplified grammar for model construction, the design of an economic based library to assist in ACE modeling, and examples of how to construct interactive models.
ContributorsAnderson, Brandon David (Author) / Bazzi, Rida (Thesis director) / Kuminoff, Nicolai (Committee member) / Roberts, Nancy (Committee member) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05