Matching Items (719)
Filtering by
- Creators: Computer Science and Engineering Program
Description
This project aims to identify particular traits, specifically off-field and non-gameplay, of sports narratives that elevate them to legendary, beloved storylines among the canon of sports history, focusing on the “big three” American sports of baseball, basketball, and football. This was accomplished through an analysis of existing literature on the topic of sports narratives, as well as three case studies of individual narratives from varied sports and points in history. Each study, each representing either a legendary, hate-watched, or forgotten narrative, was broken down into its background, relevant people, events, and contemporary media coverage, and lasting legacy. The various aspects of these studies were then compared and contrasted, with concepts from the literature review being included in the synthesis of the storylines. Ultimately, the presence of a clear protagonist-antagonist dynamic, balanced media coverage, high stakes, and perceived authenticity were determined to be crucial for a sports narrative to gain legendary status. In addition, the notion of authentic coverage was found to have been able to shift public perception of a narrative as well as “resurrect” forgotten storylines of the past.
ContributorsMitchell, Andrew (Author) / O'Flaherty, Katherine (Thesis director) / Boivin, Paola (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This thesis presents the design, development, and strategic decisions of Translatica, a
voice-preserving AI translation platform. In contrast to conventional translation tools
that prioritize textual accuracy at the expense of speaker identity, Translatica seeks
to amplify global communication by preserving the original speaker’s tone, emotion,
and personality throughout the translation pipeline.
The system integrates transcription, context-aware translation, and neural voice
replication to produce emotionally expressive, multilingual video outputs. Originating from a hackathon focused on educational technology, Translatica evolved into a
scalable solution seeking to bring accessible content for everyone. Its primary use
case began in education but now spans creators, enterprises, and institutions seeking
culturally resonant global communication.
This work analyzes current market trends, competitor limitations, and the rising
demand for speech-to-speech translation in education and media. It explores user
interface design decisions, business model strategies, and ethical considerations in
deploying voice synthesis technologies. Reflections from the development process
highlight the importance of clearly defined problem statements and collaborative
iteration in building impactful, user-first AI systems.
This thesis represents one component of a broader collaborative project developed
alongside two other engineers. While this paper focuses on the business strategy,
user interface design, and ethical implications of the platform, the complementary
theses address backend engineering, software infrastructure, and the training and
deployment of AI models powering Translatica’s translation pipeline.
ContributorsHsu, Jeffrey (Author) / Jhaj, Baaz (Co-author) / Ramani, Krishna (Co-author) / Osburn, Steven (Thesis director) / Zhu, Haolin (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This honors thesis explores the factors that shape the interest and engagement of college-aged women in STEM (Science, Technology, Engineering, and Mathematics), with a focus on their high school experiences. Despite ongoing efforts to promote gender diversity in STEM fields, women continue to be underrepresented. This study aims to understand the barriers women face while pursuing STEM careers, specifically by examining their motivations, challenges, and the opportunities available to them during high school. The research is driven by two primary questions: (1) What opportunities are available to women interested in STEM careers? and (2) What are the barriers of entry for women interested in STEM? To investigate these questions, the study integrates both a literature review and a survey-based methodology. Future studies in this area should focus on other factors such as race, socioeconomic background, and gender (outside of the binary), as this could affect the types of opportunities that are available. Additionally, any future studies should survey current high school students and possibly expand to middle school students.
ContributorsKopparapu, Siri (Author) / Alcantara, Christiane (Thesis director) / Chavez Echeagaray, Maria Elena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor)
Created2025-05
Description
The primary goal of this research is to investigate how network traffic and host-based information can be used in tandem to detect cyber attacks. The study investigates this approach by designing and launching several malicious attack simulations against a Linux virtual environment using the Metasploit/Meterpreter frameworks. System audit data and network packet captures collected from this attack sequence are then processed into graphical representations to be trained on a GNN-RNN FusionNet machine learning model. The model is able to predict an origin IP address as well as target processes and/or files affected by a cyber threat. Combining results from the model's prediction along with a BFS traversal of the system audit graph give security analysts and researchers greater insight into how a threat reaches a system, as well as the damages caused by the attack, in comparison to outdated NIDS/antivirus detection capabilities.
ContributorsJohnson, Ian (Author) / Xiao, Xusheng (Thesis director) / Baek, Jaejong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Aura is a modern productivity and mindfulness device that helps college students and young professionals manage stress, stay focused, and relax. It combines RFID technology with personalized audio playback, allowing users to tap RFID discs onto the device to instantly access curated playlists tailored for studying, relaxation, or focus, without having to use their phone. This solution closes the gap between technology and concentration, offering a distraction-free tool designed to integrate perfectly into the lives of a tech-savvy and overstimulated audience. Aura’s target market potential is college aged students to 35 year old young professionals in the Gen Z to young millennial age group of all genders. The individuals in these age groups tend to seek out more technological advancements and innovative products that assist in day to day tasks. Individuals in this demographic also want to grow personally and desire to pursue self development, something our Aura device offers. This market contains diverse individuals that are music enthusiasts, mindfulness practitioners, and individuals seeking innovative audio experiences. Primary stakeholders that are impacted include consumers that tend to be more technology inclined and individuals that value personalized music experiences or want to invest in personal development. Community members impacted by Aura include individuals seeking mindfulness tools, small business owners enhancing their customer environments, and anyone looking for a unique way to connect with music.
ContributorsInocencio, Claudine (Author) / Rumph, Emily (Co-author) / Canto, Chané (Thesis director) / Sisouvanh, Arreya (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2025-05
Description
This thesis project presents a standardized approach to implementing a Zero Trust (ZT) security model within embedded systems, addressing unique security challenges characteristic of resource-constrained environments. While ZT has gained significant traction in enterprise environments, its application to embedded systems design remains largely unexplored despite growing security threats to these increasingly connected and insecure devices. This paper systematically maps Zero Trust principles to embedded system components through SysML models, identifying practical implementation considerations that accommodate computational and energy limitations. This paper also proposes additional adjustments that adapt core ZT principles—including continuous verification, least privilege access, and micro-segmentation—for embedded contexts while maintaining operational performance. The proposed framework is assessed against operational scenarios targeting embedded systems. Implementation challenges and future research directions are provided to give practical context.
ContributorsChang, Ethan (Author) / Osburn, Steven (Thesis director) / Parwani, Rahul (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05
DescriptionDesigning and implementing components for a new SMS messaging strategy for Go Together Inc. Includes ensuring compliance with federal messaging regulations, a schoolwide notifications/announcements console, feedback reception for completed trips via automated messaging, and user segmentation and automated messaging.
ContributorsJaramillo, Evan (Author) / Baumann, Alicia (Thesis director) / Moore, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This applied thesis project presents an integrated Financial Market Prediction
System that provides cross-asset analysis through advanced machine learning techniques.
By seamlessly unifying predictive analytics across stocks, cryptocurrencies, forex,
commodities, and real estate markets, the system transcends traditional siloed approaches
to financial forecasting. Leveraging machine learning ensemble methods including
Gradient Boosting, Linear Regression and Random Forest algorithms. The architecture
incorporates a sophisticated constellation of over 25 technical indicators, asset-specific
feature engineering, and a recursive prediction methodology that dynamically calibrates
confidence intervals based on temporal distance and market volatility.
The system's elegant backtesting framework employs walk-forward validation
across multiple market regimes, delivering robust performance metrics while effectively
mitigating overfitting risks inherent in financial modeling. Through an intuitive yet
powerful interactive interface, users can effortlessly navigate complex market dynamics,
visualize probabilistic forecasts, and gain unprecedented insights into feature importance
hierarchies that drive market movements. Beyond mere prediction, the platform serves as
an educational cornerstone, seamlessly integrating a comprehensive financial knowledge
base with real-time analytical capabilities.
ContributorsTewary, Ganap Ashit (Author) / Menees, Jodi (Thesis director) / Srinivasan, Aravind (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
P.O.F.T.S. is an open-source fitness-tracking platform designed with transparent data privacy, a remarkable level of customization, and frequent user interaction in mind. Unlike other commercial fitness apps on the current market, P.O.F.T.S. gives the users total control over their data for a personalized fitness experience, including workout recommendations, social leaderboards, and devices synced with wearables.
P.O.F.T.S. (Personalized Open-Source Fitness Tracking Software)
ContributorsAlfred Vijay, Heinric (Author) / Mejari, Vikas (Co-author) / Byrne, Jared (Thesis director) / Howell, Travis (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05
Description
This thesis introduces an intelligent database system that harnesses Natural Language Processing (NLP) and Machine Learning (ML) to enable seamless querying and visualization of sports data. Centered on the English Premier League—the top tier of English football—the system empowers users to interact with complex datasets through simple natural language queries. These queries are automatically translated into structured SQL commands, eliminating the need for technical expertise and making data retrieval more accessible.
In addition to flexible querying, the system supports dynamic data visualization, presenting results in user-specified formats such as tables, charts, or graphs. By integrating NLP and ML, the system streamlines the end-to-end process of data access, analysis, and presentation. This not only enhances the usability of sports data for analysts, researchers, and enthusiasts but also promotes data-driven exploration and insight generation. The proposed system represents a step toward democratizing sports analytics by bridging the gap between natural language understanding and structured data querying, enabling richer, more intuitive interactions with complex information.
ContributorsMartinez, Sebastian (Author) / Gupta, Vivek (Thesis director) / Bryan, Chris (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05