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- Creators: Computer Science and Engineering Program
Description
Karate is a Japanese martial art that originated approximately a century ago, with heavy influence from Chinese martial arts at the time. Although it was originally created as a form of self-defense, many today practice it for sport. Organizations such as the World Karate Federation (WKF) and USA Karate establish rules for competitions as well as host tournaments for practitioners of all ages and skill levels to participate in. Dojos will often host small, local tournaments for their students to practice and sharpen their competition skills. Smaller tournaments often do not have the same tools and technologies that larger tournaments do. Sign-ups are typically done in-person and payments are cash-only, which can be inconvenient for those who are extremely busy or forgetful. Another issue with hosting local tournaments is that the software used to run the timer is a desktop application, called Karate Semaphore. In the case of technical difficulties, installing the software on another machine can be extremely time-consuming and delay the progression of the tournament. Not to mention, Karate Semaphore was created following the 2012 WKF rules—meaning it is currently out of date, as it does not contain any features supporting new rules.
For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.
For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.
ContributorsRuan, Shirley (Author) / Sarwat, Mohamed (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Multi-view learning, a subfield of machine learning that aims to improve model performance by training on multiple views of the data, has been studied extensively in the past decades. It is typically applied in contexts where the input features naturally form multiple groups or views. An example of a naturally multi-view context is a data set of websites, where each website is described not only by the text on the page, but also by the text of hyperlinks pointing to the page. More recently, various studies have demonstrated the initial success of applying multi-view learning on single-view data with multiple artificially constructed views. However, there lacks a systematic study regarding the effectiveness of such artificially constructed views. To bridge this gap, this thesis begins by providing a high-level overview of multi-view learning with the co-training algorithm. Co-training is a classic semi-supervised learning algorithm that takes advantage of both labelled and unlabelled examples in the data set for training. Then, the thesis presents a web-based tool developed in Python allowing users to experiment with and compare the performance of multiple view construction approaches on various data sets. The supported view construction approaches in the web-based tool include subsampling, Optimal Feature Set Partitioning, and the genetic algorithm. Finally, the thesis presents an empirical comparison of the performance of these approaches, not only against one another, but also against traditional single-view models. The findings show that a simple subsampling approach combined with co-training often outperforms both the other view construction approaches, as well as traditional single-view methods.
ContributorsAksoy, Kaan (Author) / Maciejewski, Ross (Thesis director) / He, Jingrui (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
The original version of Helix, the one I pitched when first deciding to make a video game
for my thesis, is an action-platformer, with the intent of metroidvania-style progression
and an interconnected world map.
The current version of Helix is a turn based role-playing game, with the intent of roguelike
gameplay and a dark fantasy theme. We will first be exploring the challenges that came
with programming my own game - not quite from scratch, but also without a prebuilt
engine - then transition into game design and how Helix has evolved from its original form
to what we see today.
for my thesis, is an action-platformer, with the intent of metroidvania-style progression
and an interconnected world map.
The current version of Helix is a turn based role-playing game, with the intent of roguelike
gameplay and a dark fantasy theme. We will first be exploring the challenges that came
with programming my own game - not quite from scratch, but also without a prebuilt
engine - then transition into game design and how Helix has evolved from its original form
to what we see today.
ContributorsDiscipulo, Isaiah K (Author) / Meuth, Ryan (Thesis director) / Kobayashi, Yoshihiro (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
RecyclePlus is an iOS mobile application that allows users to be knowledgeable in the realms of sustainability. It gives encourages users to be environmental responsible by providing them access to recycling information. In particular, it allows users to search up certain materials and learn about its recyclability and how to properly dispose of the material. Some searches will show locations of facilities near users that collect certain materials and dispose of the materials properly. This is a full stack software project that explores open source software and APIs, UI/UX design, and iOS development.
ContributorsTran, Nikki (Author) / Ganesh, Tirupalavanam (Thesis director) / Meuth, Ryan (Committee member) / Watts College of Public Service & Community Solut (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Debate provides a valuable educational opportunity for students to learn a variety of portable research and public speaking skills, but many of its goals are hindered by the lack of a dedicated software. Currently, the primary tool for research and presentation of evidence is paperlessdebate.com's Verbatim, which is built as a template for Microsoft Word. While functional, Verbatim suffers from several shortcomings; its reliance on Word means that it cannot be fully cross-platform, and it also means that it is difficult to streamline Verbatim's workflow for the particular needs of debaters. Thus, the goal of this project was to fill the need for a stand-alone, cross platform application that debaters (and coaches) can use to research and present evidence. The bulk of the project consisted of creating a specialized editor, including a variety of features catered towards usability in a range of debate contexts. Additionally, the software is integrated with a back end database such that it can also replace the mixture of storage solutions (such as Dropbox and Microsoft's OneDrive) that teams currently use to maintain and share their data. In order to make the software more extensible and to improve its accessibility, it is released as free open source software under the GNU General Public License v3.0. This paper describes the core features of the application and the motivation behind those features' implementations, and briefly includes a discussion of the companion mobile app for Android devices. It also reviews the technologies that were used to create the software's implementation.
ContributorsRedman, Colin (Author) / Symonds, Adam (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
DescriptionThe goal of this study is to equip administrators and instructors with a deeper understanding of the apparent cheating problem in Computer Science courses, with proposed solutions to lower academic dishonesty from the students’ perspective.
ContributorsAl Yasari, Farah (Co-author) / Alyasari, Farah (Co-author) / Tadayon-Navabi, Farideh (Thesis director) / Bazzi, Rida (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Mobile app development has been the fastest growing software development since the release of the iPhone in 2007 and the creation of the App Store in 2008. After the rise in popularity of the smartphone and mobile app development, businesses started to shift from browser-based software to mobile platforms. There are multiple reasons that any company would want to reach out in the mobile environment, including attracting new customers, creating a better experience, expanding their brand, being more accessible, and overall growth. Developers started to build new platforms that would make it easy for not only their own company but anyone else to develop new applications at a faster pace. Today there are three different ways to create a mobile application: a native app, a hybrid app, and a web app. At DriveTime, we spent time researching and developing possible options for how we could make a mobile application to expand our user involvement. Our conclusion was to use a hybrid application that would incorporate our already existing web app and relaunch it as a native app. I took over this project for DriveTime and planned to implement two new features for their mobile application. Using Apache Cordova, I dived into the Software Development Life Cycle to create and deploy a Rate and Review feature and Push Notification functionality. In the end, I was only able to implement the Rate and Review feature due to some unexpected delays in development. I assumed all roles of a standard Agile development team to understand all of the different aspects that go into planning, creating, and publishing software.
ContributorsAnderson, Jacob Scott (Author) / Balasooriya, Janaka (Thesis director) / Irwin, Don (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
SmartAid aims to target a small, yet relevant issue in a cost effective, easily replicable, and innovative manner. This paper outlines how to replicate the design and building process to create an intelligent first aid kit. SmartAid utilizes Alexa Voice Service technologies to provide a new and improved way to teach users about the different types of first aid kit items and how to treat minor injuries, step by step. Using Alexa and RaspberryPi, SmartAid was designed as an added attachment to first aid kits. Alexa Services were installed into a RaspberryPi to create a custom Amazon device, and from there, using the Alexa Interaction Model and the Lambda function services, SmartAid was developed. After the designing and coding of the application, a user guide was created to provide users with information on what items are included in the first aid kit, what types of injuries can be treated through first aid, and how to use SmartAid. The
application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.
application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.
ContributorsHasan, Bushra Anwara (Author) / Kobayashi, Yoshihiro (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have affected the field of disaster relief on social media as well. These bots cause problems such as preventing rescuers from determining credible calls for help, spreading fake news and other malicious content, and generating large amounts of content which burdens rescuers attempting to provide aid in the aftermath of disasters. To address these problems, this research seeks to detect bots participating in disaster event related discussions and increase the recall, or number of bots removed from the network, of Twitter bot detection methods. The removal of these bots will also prevent human users from accidentally interacting with these bot accounts and being manipulated by them. To accomplish this goal, an existing bot detection classification algorithm known as BoostOR was employed. BoostOR is an ensemble learning algorithm originally modeled to increase bot detection recall in a dataset and it has the possibility to solve the social media bot dilemma where there may be several different types of bots in the data. BoostOR was first introduced as an adjustment to existing ensemble classifiers to increase recall. However, after testing the BoostOR algorithm on unobserved datasets, results showed that BoostOR does not perform as expected. This study attempts to improve the BoostOR algorithm by comparing it with a baseline classification algorithm, AdaBoost, and then discussing the intentional differences between the two. Additionally, this study presents the main factors which contribute to the shortcomings of the BoostOR algorithm and proposes a solution to improve it. These recommendations should ensure that the BoostOR algorithm can be applied to new and unobserved datasets in the future.
ContributorsDavis, Matthew William (Author) / Liu, Huan (Thesis director) / Nazer, Tahora H. (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12