Kennesaw State University

DigitalCommons@Kennesaw State University
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    19681 research outputs found

    ArtsKSU Presents: Band of Other Brothers

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    ArtsKSU Series, Friday Night Spotlight Series. ArtsKSU proudly presents Band of Other Brothers. Led by renowned saxophonist Jeff Coffin (Dave Matthews Band, Bela Fleck & The Flecktones), this all-star jazz quintet also features Nir Felder, guitar, Felix Pastorius, bass, Chris Walters, keyboard, and Jordan Perlson, drums.

    Passage Re-Ranking in Live QA NLP Pipelines with BERT

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    Passage ranking and document ranking are two common tasks in NLP. Many state of the art pipelines use BM25 to retrieve passages. The top results of this ranking are then re-ranked using a BERT transformer trained on the MS MARCO Passage data set. This system and variations have proved highly effective. In addition, questions and answers using BERT are also well explored topics. However, these systems are fundamentally limited by speed and resource consumption requirements. Given an arbitrary corpus and a collection of pre-trained models, we would like to prove that it is possible to create a live Question Answering machine without fine tuning for a particular topic. In particular, we employ a BERT re-ranker to find the first acceptable fit to pass to our QA transformer. This approach is fundamentally different from past research in that it is focused on first fit and not best fit. The goal of this research is to allow anyone to employ off the shelf components to create an effective, interactive question answering system

    Katharine Kosowski, soprano

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    Katharine Kosowski, soprano Judy Cole, piano

    Implementation of Natural Language Processing Language Models to Generate Executable Code

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    Our scientific literature is abundant with mathematical concepts firmly based on proofs. The Curry-Howard correspondence establishes a connection between mathematical proofs and computer programs, suggesting that we can extract computer programs from mathematical literature and vice versa. While mathematical rigor varies among fields and authors, those seeking to tackle the problem may be daunted by the layers of jargon and innumerable notations they may encounter. By analyzing Mathematics and physics-based academic literature through various natural language processing techniques, the analysis will generate an executable code required to test the provided equations and precisely describe the code

    Convolutional Autoencoder for Email Spam Detection

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    In this paper, I talk about a novel technique for email spam detection. Using the extremely adept pattern recognition abilities of Autoencoders, I designed a Convolutional Autoencoder network to analyze and classify emails as either ham (legitimate) or spam (illegitimate/scam) emails. With promising results, this type of model could help revolutionize email spam detection and tagging, making everyone’s inbox less crowded with emails they don’t want to read

    Towards Assessing Cybersecurity Posture of Manufacturing Companies: Review and Recommendations

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    With the continued changes in the way businesses work, cyber-attack targets are in a constant state of flux between organizations, individuals, as well as various aspects of the supply chain of interconnected goods and services. As one of the 16 critical infrastructure sectors, the manufacturing sector is known for complex integrated Information Systems (ISs) that are incorporated heavily into production operations. Many of these ISs are procured and supported by third parties, also referred to as interconnected entities in the supply chain. Disruptions to manufacturing companies would not only have significant financial losses but would also have economic and safety impacts on society. The vulnerabilities of interconnected companies created inherited exploitations in other interconnected companies. Cybersecurity practices need to be further enhanced to understand supply chain cybersecurity posture and manage the risks from lower-tier interconnected entities up to the top-level dependent organization. This paper will provide an overview of the Theory of Cybersecurity Footprint to emphasize the relationship among interconnected entities and the cybersecurity effects one organization can have on another regardless of size. This paper provides a literature review on the manufacturing industry with a recommendation for future developmental research using the Delphi method with a panel of experts to develop an index to measure cybersecurity posture based on interconnected entities from lower tiers and establish index weights specifically for the manufacturing industry

    Stories for a Winter\u27s Night

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    Stories for a Winter\u27s Night

    Calm Mid-Century Playlist

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    This illustration is a cover for a mid-century music playlist. Inspired by old jazz vinyl covers and Rene Gruau‘s fashion illustrations, it has a plain cream background with a thick pink border, mid-century styled typography, and an elegant woman in a turquoise dress. The woman is modeled off of Julie London, a prominent jazz singer from the 1940s to 1960s.


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    BODYTRAFFIC uses the creative spirit of its Los Angeles home as a backdrop for delivering performances that inspire audiences around the globe to simply love dance. Since its founding in 2007 by Artistic Director Tina Finkelman Berkett, the company has held fast to its mission of championing contemporary dance, educating audiences, and inciting positive change. Its goal is simple: get the world moving.

    Developing a Machine Learning Model to Categorize Mental Health Forums Using Scraping and Crawling in Python

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    Mental health forums serve as invaluable online communities where individuals struggling with mental health problems find solace, support, and valuable resources. These platforms offer a unique space where young people can openly discuss their struggles, seek guidance from moderators and fellow users, and receive vital assistance. Within these forums, it is not uncommon to encounter posts that contain severe content, indicating that the user is in acute distress and may be at risk of self-harm. Research conducted through inductive thematic analysis highlights that while forums cannot replace the role of a trained counselor or therapist, they fulfill a critical role in providing young people with essential, lower-level support requirements. Participants in these forums have consistently reported them to be supportive environments where they feel comfortable sharing their experiences, offering advice, and asking questions. This sense of community makes individuals feel less isolated and more connected to others who understand their struggles. Our current project uses the power of machine learning to enhance the functionality of these mental health forums. We aim to develop a sophisticated model capable of automatically categorizing posts and discussions enabling more efficient navigation and targeted assistance. To accomplish this, we used web scraping and crawling techniques to gather data from diverse mental health forums. This collected data will serve as the foundation for training our machine-learning model to categorize forum posts into relevant mental health topics. This project promises to provide a valuable tool for both forum users seeking specific information and mental health professionals looking to offer precise and targeted support. Ultimately, our project strives to bolster the effectiveness of these forums as vital resources in the journey toward better mental well-being


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    DigitalCommons@Kennesaw State University is based in United States
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