Physical Sciences and Mathematics Commons^™

Try It Together - Qualitative Coding With Atlas.Ti, Danping Dong, Bryan Leow

AI for Research Week

This hands-on session introduces Atlas.ti, a well-established qualitative data analysis tool for analyzing your transcripts and textual data. The session will cover coding data, extracting insights, creating visualizations, and exploring the tool's latest AI features.

Try It Together: Transcribing Your Audio With Whisper Api, Bella Ratmelia

AI for Research Week

In this hands-on session, we will explore using the Whisper API to transcribe audio recordings from interviews, focus groups, and speeches. The session will delve into best practices and address common issues that may arise during the transcription process.

Memories Of Recipes In Twentieth-Century Irish Cookbooks, Gary Thompson

Dublin Gastronomy Symposium

This paper analyses and categorises the ways in which authors and their publishers have chosen to include the author’s culinary, food and personal memories within the texts of twenty twentieth century Irish Cookbooks. Cookbooks are subjects of culinary nostalgia with the reading of a recipe capable of triggering in the reader a memory of a meal enjoyed, a dish cooked in times past by a loved one, or recollections of the disgust felt for a food hated in childhood. Independent from the reader, the culinary memories of the author can be captured at the time of publication in the text …

Academic Search And Discovery Tools In The Age Of Ai And Large Language Models: An Overview Of The Space, Aaron Tay

AI for Research Week

In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?

In this talk, I will share my views on how the space has developed over two …

A Comparative Analysis Of Source Identification Algorithms, Pablo A. Curiel

Biology and Medicine Through Mathematics Conference

No abstract provided.

Securing The Internet Of Things At Scale, Steven L. Willoughby

Student Research Symposium

The world of the connected “Internet of Things” (IoT), including the "Industrial Internet of Things" (IIoT) is expanding to include more devices which observe and influence our daily lives, routines, locations, and even our state of health. But have the underlying protocols by which they communicate this data kept pace with the need to protect our privacy and security?

My talk will introduce my research into an approach to better secure this information flow using appropriate access controls without sacrificing performance. I will assess the historical challenges and simple access controls applied to IoT networking protocols and how they can …

A Novel Caching Algorithm For Efficient Fine-Grained Access Control In Database Management Systems, Anadi Shakya

Student Research Symposium

Fine-grained access Control (FGAC) in DBMS is vital for restricting user access to authorized data and enhancing security. FGAC policies govern how users are granted access to specific resources based on detailed criteria, ensuring security and privacy measures. Traditional methods struggle with scaling policies to thousands, causing delays in query responses. This paper introduces a novel caching algorithm designed to address this challenge by accelerating query processing and ensuring compliance with FGAC policies. In our approach, we create a circular hashmap and employ different replacement techniques to efficiently manage the cache, prioritizing entries that are visited more frequently. To evaluate …

Improving Tattle-Tale K-Deniability, Nicholas G.E. Morales

Student Research Symposium

Ensuring privacy for databases is an ongoing struggle. While the majority of work has focused on using access control lists to protect sensitive data these methods are vulnerable to inference attacks. A set of algorithms, referred to as Tattle-Tale, was developed that could protect sensitive data from being inferred however its runtime performance wasn’t suitable for production code. This set of algorithms contained two main subsets, Full Deniability and K-Deniability. My research focused on improving the runtime or utility of the K-Deniability algorithms. I investigated the runtime of the K-Deniability algorithms to identify what was slowing the process down. Aside …

Integration Of Agent Models And Meta Reinforcement Learning (Meta-Rl) Algorithms For Car Racing Experiment, Vidyavarshini Holenarasipur Jayashankar

Student Research Symposium

Introduction: Achieving optimal performance in 2D racing games presents unique challenges, requiring adaptive strategies and advanced learning algorithms. This research explores the integration of sophisticated agent models with Meta Reinforcement Learning (Meta-RL) techniques, specifically Model-Agnostic Meta-Learning (MAML) and Proximal Policy Optimization (PPO), to enhance decision-making and adaptability within these simulated environments. We hypothesize that this innovative approach will lead to marked improvements in game performance and learning efficiency.

Methods: In our experimental setup, we applied MAML for its rapid adaptation capabilities and PPO for optimizing the agents' policy decisions within a 2D racing game simulator. The objective was …

Story Of Your Lazy Function’S Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Laura Israel, Nicholas Coltharp

Student Research Symposium

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the push …

Systematic Comparison Of Reservoir Computing Frameworks, Nihar S. Koppolu, Christof Teuscher

Student Research Symposium

In this poster, we present a systematic evaluation and comparison of five Reservoir computing (RC) software simulation frameworks, namely reservoirpy, RcTorch, pyRCN, pytorch-esn, and ReservoirComputing.jl. RC is a specific machine learning approach that leverages fixed, nonlinear systems to map signals into higher dimensions. Its unique strength lies in training only the readout layer, which reduces the training complexity. RC excels in temporal signal processing and is also well suited for various physical implementations. The increasing interest in RC has led to the proliferation of various RC simulation frameworks. Our RC simulation framework evaluation focuses on a feature comparison, documentation quality, …

Behavioral Intention For Ai Usage In Higher Education, Isaac A. Odai, Elliot Wiley

Student Research Symposium

This study sought to further understand the cognitive factors that influence undergraduate students' behavioral intention to use generative AI. Generative AI's presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for …

Gpr-103 Personalized Pedagogy Through A Llm-Based Recommender System, Mourya Teja Kunuku, Bharath Y Yadla

C-Day Computing Showcase

The educational domain is undergoing transformation due to the incorporation of Artificial Intelligence (AI), Large Language Models (LLMs), and generative AI technologies, raising the need for educators to integrate cutting-edge technological advancements and methodologies into their teaching approaches. Pedagogical Design Patterns (PDPs) have become prominent for their role in sharing effective educational practices and narrowing the divide between academic research and actual teaching methods. Despite their potential, the lack of widely accessible resources and the scattered nature of publishing outlets pose significant barriers to the broad application of PDPS. To address this issue, we propose the application of large language …

Gc-104 Edai – Ai Enabled Teaching Robot For Informal Learning, Jennifer Bower, Shane Williams, Justice Fuller

C-Day Computing Showcase

We began our project by researching various popular, open-source AI tools that are available today. After we chose to focus on ChatGPT as our AI tool, we decided on cybersecurity as our subject matter. Next, we researched traditional cybersecurity training methods used by companies to train their employees on cybersecurity issues. Our project focused on determining whether or not open-source AI tools such as ChatGPT could replace traditional cybersecurity training tools and methods for companies.

Gc-101 Learning Resource Finder: A Web Scraping Tool For Educational Materials, Ashrith Kumar Devara

C-Day Computing Showcase

The Learning Resource Finder is a pioneering tool designed to alleviate the challenges associated with navigating the vast landscape of online educational content. Leveraging sophisticated web scraping techniques and API integrations, this tool empowers users to efficiently discover relevant learning materials tailored to their specific needs. Through a user-friendly Flask-based web interface, users initiate search queries, which are then encoded and utilized to fetch pertinent URLs from leading educational platforms such as JavaTPoint, W3Schools, Coursera, Udemy, and GeeksforGeeks, as well as Google search results. The core of the web scraping process lies in the meticulous extraction of URLs from HTML …

Gc-17 Crm For Ccse Department Of Ksu, Venkatesh Alla, Keerthi Nannapaneni, Vinay Kumar Rapolu

C-Day Computing Showcase

This project outlines the development of a bespoke Customer Relationship Management (CRM) system specifically designed for the College of Computer and Software Engineering (CCSE). The initiative aims to centralize customer information into a unified repository, thereby enhancing the confidentiality, management, and optimization of data and communication processes within the college. The CRM system will integrate features for detailed profiles, communication optimization, complex workflow management, document repository, and data migration to ensure efficiency and data integrity. It will also facilitate seamless interaction with Microsoft 365 and Outlook, supporting the college's operational needs and maintaining its commitment to excellence in education and …

Gc-22 Fabric Moderation Ticketing Mod For Minecraft, John Lambert, Kathy Nguyen, Hayden B Scarbrough, Dasiane Taplin

C-Day Computing Showcase

This project targets enhancing the KSU Esports program’s Minecraft server by implementing an in-game ticketing system. The system will enable players to report any instances of in-game incidents/issues seamlessly within the game environment. The integration with the KSU Minecraft Discord server will facilitate efficient communication between players and administrators. With a user-friendly interface and optimized resource usage, the system aims to streamline moderation processes.

Gmr-45 Cloud Based Bus Tracking And Ticketing System, Vyghni Sudha Kommineni, Damacharla Sravani

C-Day Computing Showcase

In many urban areas, public transportation systems frequently fail to fulfill passenger demand, causing aggravation owing to a lack of real-time information about bus locations, timetables and delays. Outdated ticketing processes, which are labor-intensive and prone to mistakes, compound the annoyance by failing to match current travelers' expectations. Furthermore, transportation operators encounter difficulties with fleet management, route optimisation, and issue response due to a lack of comprehensive data analytics. Reliable monitoring systems are required to prioritize passenger and bus safety, and achieving sustainability targets necessitates efficient operations to reduce fuel consumption and emissions. A scalable and customizable cloud-based bus tracking …

Gmr-118 Stress Detection By Wearable Devices: Integrating Multimodal Physiological Signals And Machine Learning Techniques, Pranita Subhash Shedage

C-Day Computing Showcase

In the medical field, the most common complaint of patients is “stress”. Stress can cause severe effects on the human body. For example, prolonged mental stress can cause serious health issues in long term such as hypertension, cardiovascular diseases, increased susceptibility to infections, and depression. These health issues can be prevented by early detection of stress and by taking preventive measures. The most common detecting stress was determined from the questionnaires or from the interactive sessions conducted to assess the people's affective state. However, this method is not highly reliable and can be biased depending on the person who is …

Gmr-59 Customer Segmentation For Marketing Campaigns Using K-Means Clustering, Jaswanthi Vellanki, Hrithik Singh Chandel, Vyghni Sudha Kommineni, Priyanka A Bagal, Rutvikkumar K Ramani

C-Day Computing Showcase

The objective of this project is to use K-means clustering an unsupervised machine learning algorithm to categorize customers based on characteristics such as demograp hics, purchasing history and interaction behavior. The purpose is to discover different client segments that can be targeted with specialized marketing techniques that improve marketing campaign efficiency and increase consumer satisfaction and engagement.

Gmr-23 Jamming Signal Detection Using Extreme Gradient Boosting (Xgboost) Algorithm, Keerthana Adamana, Christian K Sao

C-Day Computing Showcase

Radar jamming involves sending intentionally disruptive radio waves toward the target radar, which might over-saturate its receiver so it can’t receive anything or deceive it into interpreting false information. Machine learning (ML) techniques increased the capability to automatically learn the experience without being explicitly programmed. Machine learning models usually require a large, labeled sample to perform. Building a robust jamming detection model will be challenging due to the wide variability of jamming signals and less available labeled samples. In this project, we developed an eXtreme Gradient Boosting(XGBoost) algorithms for radar jamming signal classification and achieved superior performance compared with Random …

Gc-84 Owl Cyber Defense Systems, Randolph S Gilstrap, Christopher Dunbar, Stephanie Aguirre, Ryan M Leblanc, Justin Place

C-Day Computing Showcase

Owl Cyber Defense Systems is a fictitious (for now?) start-up offering comprehensive cybersecurity solutions for small and medium businesses. Our premier flagship product is an AI Chatbot that answers security related questions and provides vetted code and security settings to secure and harden a variety of systems. Starting with the initial concept, we methodically progressed through the business planning process, carefully considering technology usage and design. This comprehensive approach ultimately enabled us to develop a robust set of client offerings. We used a hybrid approach combining Agile Scrum and traditional Waterfall methodologies to complete the project. We utilized Jira Project …

Uc-27 Crm Proposal, Luis A Maiocchi Castro, Crystal Misko, Steven Damico, Ethan Groves, Maryah Outlaw

C-Day Computing Showcase

This project addresses concerns raised by a Sponsor regarding inefficiencies in managing CCSE capstone projects. Key pain points include organization, project management, and communication among stakeholders. The proposed solution involves implementing Customer Relationship Management (CRM) software. Upon gathering requirements from the Sponsor, including contact information capture, workflow management, document control, and customization capabilities, we evaluated several CRM platforms. A total of thirty-two CRMs were reviewed and Vtiger, OroCRM, and SuiteCRM were selected for testing. SuiteCRM was chosen for its comprehensive features and user-friendliness. The second step of the project involved testing SuiteCRM functionalities on a dedicated server, leading to its …

Uc-33 Big Munchin', Tabitha Reynolds, Steven M Amerson, David J Buck, Jacques P Gatipon, Aaliyah Mcelrath, Nick S Watson

C-Day Computing Showcase

Big Munchin' is a video game designed to help teach portioning skills and promote healthy eating habits. The overall metabolic health of individuals in America is comparatively low to other countries. Metabolic health is bolstered by several factors including exercise and a proper diet consisting of essential vitamins, proteins, and other important biomolecules.The increased cost of healthy nutrient-rich foods and a lack of proper nutritional education have hindered the overall metabolic health of modern Americans. Engaging individuals in learning more about nutritional health can be a difficult task made easier by an engaging experience that stays in the user’s minds. …

Uc-35 Ksu Ccse Crm, Alex Curran, Dj Mitchell, Christian Miller

C-Day Computing Showcase

Our task was to select, implement and customize a CRM solution for the College of Computing and Software Engineering to more effectively manage communication with industry partners and manage projects such as capstones, and C-Day. Our team selected SuiteCRM as our recommendation and have implemented an instance on a virtual machine provided by UITS. We have customized branding including using a KSU logo provided by the Office of Strategic Communications and Marketing, as well as customizations based on the official KSU color pallete. The process we used to select our CRM recommendation involved gathering requirements from our sponsor and comparing …

Uc-69 Interactive Training Game Suite, Garrett J Perry, Joscelyn Cauley, Issabella Du, Sergiu Ursu, Rahaf Kokash

C-Day Computing Showcase

It is now common knowledge that simple lectures are not the most effective way for the average person to learn and retain knowledge. The Core of Engineers at the Warner Robins Air Logistic Center have tasked us to transform their PowerPoint presentations into interactive training games to improve comprehension, interaction and retention while saving time and logistical resources compared to giving a traditional lecture. We been tasked with creating game modules covering STINFO or what is or is not considered classified information, Records Management, and the No FEAR Act detailing whistleblower rights and protocols. Our team has developed a log …

Uc-105 Model Un Crisis Software, Gregory Hicks, James Ritzi, Vincent K Kipchoge

C-Day Computing Showcase

Utilizing an Agile approach, this project develops a web-based solution, the Model UN Crisis Software, to streamline the management of crisis committees in Model UN conferences. The software is developed using Microsoft Visual Studio and Microsoft SQL Server Management Studio, adhering to the .NET framework and related conventions. It leverages Microsoft Azure SQL Database for back-end data storage and follows the ASP.NET core MVC framework, utilizing powerful .NET tools such as C# and Razor. The developed software provides comprehensive features to reduce the strain of hosting a crisis committee, such as directive and news management, user management, and a messaging …

Gpr-16 Attention Driven Framework For Detecting Mental Illness Causes From Social Media, Abm Adnan Azmee, Dinesh Chowdary Attota, Francis E Nweke

C-Day Computing Showcase

Mental health is a critical aspect of our overall well-being. Mental illness refers to conditions that impact an individual's psychological state, resulting in considerable distress, and limitations in functioning day-to-day tasks. Due to the progress of technology, social media has merged as the platform, for individuals to share their thoughts and emotions. The psychological state of individuals can be accessed with the help of data from these platforms. However, it is challenging for conventional machine learning models to analyze the diverse linguistic contexts of social media data. In this work, we propose a novel attention-driven deep framework to overcome these …

Gpr-18 Case Exploration: Automatic Keyword Matching Framework For Behavioral Health, Francis E Nweke, Abm Adnan Azmee

C-Day Computing Showcase

In this demonstration, we propose a framework for exploring, identifying, and matching repeated behavioral health keywords in first-responder reports to the current set of Behavioral Health Index Terms provided by subject matter experts (SMEs). The tool incorporates behavioral health-related keywords and has a Graphical User Interface (GUI) that allows non-technical users to explore and analyze 911 first-responder reports. We utilized an inverted index, best-matching (BM25), and plain-text searching algorithms to match keywords in first-responder reports. This tool provides a comprehensive approach to report analysis by identifying indicators of mental health disorders and taking into account the assessments of humanities and …

Gpr-63 Adaptive Attention Aware Fusion For Human-In-Loop Behavioral Health Detection, Martin Brown, Abm Adnan Azmee

C-Day Computing Showcase

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based …