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Articles 184141 - 184170 of 193314
Full-Text Articles in Entire DC Network
Enhancing Environmental Health And Safety: Fine-Tuning Large Language Models For Domain-Specific Applications, Mohammad Adil Ansari
Enhancing Environmental Health And Safety: Fine-Tuning Large Language Models For Domain-Specific Applications, Mohammad Adil Ansari
Master's Projects
This study aims to simplify Environmental Health and Safety (EHS) by leveraging the power of Large Language Models (LLMs). In this research, we focus on fine-tuning three LLMs — LLaMA, Mistral, and Falcon — using PEFT techniques such as QLoRA and SFT, to address domain-specific needs such as safety compliance, incident reporting, and knowledge dissemination. Our research methodology involves fine-tuning each LLM model on a custom dataset compiled from various regulatory agencies, supplemented by targeted web scraping and manual collection of questionnaires to capture and enrich the models with the latest regulations and guidelines. This study aims to compare the …
Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu
Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu
Master's Projects
The primary objective of this project is to optimize the Llama language model to generate customized resumes containing domain-specific job descriptions and maintain the linguistic capabilities of the large language model. Building upon the prior research by Sumed Kale on Job Tailored Resume content generation using GPT-2, where he employed full fine-tuning of the model and demonstrated the capability of LLMs to generate resume content, it is evident that while effective, full fine-tuning has its limitations. Primarily, it is computationally expensive, which can pose constraints, especially for large models. Additionally, during the fine-tuning process, there is a risk of losing …
Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti
Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti
Master's Projects
This project introduces a "dynamic forecasting engine," designed to transform the way restaurants predict sales. The engine dynamically handles seasonal ARIMA_HoltWinter hybrid model, XGBoost, and LSTM algorithms to dynamically select the best forecasting method based on data volume, variety, and customer taste preferences delving upon the spice level categorical sales. This guide differs from traditional crystal ball approaches because it has the ability to improve over time as new data comes in terms of spice levels. It emphasizes the importance of dataset size in the selection of machine learning algorithms through complexity for large datasets and simplicity for smaller ones …
Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati
Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati
Master's Projects
With enormous amount of social media content, we can draw valuable insights. In this paper, we apply different Machine Learning and Deep Learning techniques on Instagram data to determine the techniques that work well to discover the engagement class of a social media post. Out of all the social media platforms, Instagram is growing rapidly not just in the number of users but also in terms of Advertisement and marketing surpassing YouTube’s advertisement revenue. The end goal of this paper is to propose a technique to predict the engagement class. We applied Random Forest (RF), Stacking Classifier, Extreme Gradient Boost …
Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi
Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi
Master's Projects
The combination of MapReduce (MR) & Kubernetes (K8s) strengths is not explored, and this study leverages the synergy between the two frameworks to meet the growing demands of data-intensive applications. First, this report elaborates on the existing literature work to understand the pros and cons of using MR and K8s, in what use cases these frameworks come to use, and investigates the effectiveness of research studies that explore the combination. This study aims to research the efficacy of the fusion of MR and K8s, considering these factors - application use case, infrastructure design, resource allocation, load balancing, and hypertuning parameters …
Satellite Handover Optimization Using Predicted Satellite-To-Base-Station Proximity, Pranathi Kunadi
Satellite Handover Optimization Using Predicted Satellite-To-Base-Station Proximity, Pranathi Kunadi
Master's Projects
Modern telecommunications heavily rely on Satellite communication networks to provide global coverage, especially in remote areas which link the whole world in a loop. Conventional handover algorithms methods rely on fixed and predefined rules and thresholds predefined statically to make a handover decision. However, these static handover algorithms may become inefficient under the changing conditions of the network. Therefore, it would be useful to measure the proximity order of satellites to the specific base station. Consequently, the assessed relative proximity helps in optimizing the handovers proactively inside related coverage areas. This results in the service quality and the delays in …
Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru
Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru
Master's Projects
The identification of bird species using deep learning techniques presents a novel approach in bioacoustics, by significantly advancing our understanding and enhancing our capabilities in bird species recognition from audio recordings. The value of audio over visual data for monitoring ecological patterns in birds can be highlighted with the deployment of automated recording devices in remote wildlife sensing, offering a more cost-effective, non-invasive, and practical solution. However, the methods of processing and classifying the audio remain challenging due to the complexity of bird audio, characterized by diverse vocalizations and imminent environmental noise, which poses difficult challenges to perform effective classification. …
Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare
Novel Approach To Music Analysis Using Apache Spark, Nidhi Zare
Master's Projects
Music is one of the most common source of entertainment. Every user has their own taste of music and prefer to listen music that adheres to their taste and mood. There are various categories, called as music genres in which music can be classified. This research project addresses the challenge in music genre classification by using various deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Very Deep Convolutional Networks (VGGNet), ResNet and others. The primary objective of this research is to enhance the accuracy of music genre classification using a distributed computing framework Apache Spark. …
Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare
Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare
Master's Projects
Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, …
Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre
Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre
Master's Projects
The quest for AI systems that can mirror the intricate aspects of human emotion and personality is crucial for enhancing their performance. This project delves into the capabilities of Large Language Models (LLMs) to mimic the Big Five personality traits in human-written essays by utilizing contextual prompts and fine-tuning methods. Diverging from traditional research in this domain, this project explores smaller, open-source LLMs, including LLaMA 2 7B chat, LLaMA 2 13B chat, and Vicuna v.15 13B, to assess their potential in personality prediction tasks, thereby making high-level personality emulation more accessible and practical for application integration. Through meticulous prompt engineering, …
Sudoku As A Proof Of Useful Work Protocol On The Blockchain, Abishek Padaki
Sudoku As A Proof Of Useful Work Protocol On The Blockchain, Abishek Padaki
Master's Projects
The Proof of Work (PoW) consensus used by many blockchain networks like Bitcoin has been criticized for its excessive energy consumption and lack of tangible utility beyond maintaining the network. This report proposes a Proof of Useful Work (uPoW) protocol as an alternative consensus mechanism that utilizes computational resources to solve intrinsically valuable problems. Specifically, it explores the implementation of uPoW on the SpartanGold blockchain test network, where miners must solve Sudoku puzzles to validate new blocks. The report examines the shortcomings of traditional Proof-of-Work protocols, such as their environmental impact and inefficient use of computing power. It then delves …
Ranking-Based Hashtag Recommendation With Collaborative And Content-Based Filtering, Fei Pan
Ranking-Based Hashtag Recommendation With Collaborative And Content-Based Filtering, Fei Pan
Master's Projects
The purpose of this project is recommending relevant hashtags for users using both Collaborative Filtering (CF) and Content-based filtering with Twitter dataset. The Twitter dataset was collected by leveraging Twitter API v2. After data preprocessing, 40,806 tweets posted by 278 users with 3,107 hashtags from 01/01/2022 to 04/30/2022 are used for model training and testing. For CF models, we will mainly focus on generating embeddings to learn about user and hashtag latent factors and finally predict a probability for unseen hashtags with most possibility will be ranked as topK items for corresponding users. In this project, Matrix Factorization (MF), Neural …
Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair
Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair
Master's Projects
No system has ever reached the levels of proliferation that the Internet now enjoys. It stands as the most widely spread distributed system across the globe; yet this evolution has given rise to an ever-growing wave of malintent that challenges every user and entity on the vast expanse of cyberspace. Malicious URLs loom large as vulnerabilities leaving users naked as they traverse online landscapes, but cybersecurity experts craft models with esoteric algorithms in a bid to stem this tide and shield users from cybercrime. However, peering into the decision-making corridors of these models holds key importance, it’s through understanding such …
Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina
Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina
Master's Projects
Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it …
Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing, Manikanta Sanjay Veera
Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing, Manikanta Sanjay Veera
Master's Projects
Serverless edge computing is an emerging technology that realizes the low latency and resource-efficient function calls for responsive computing. In cloud-based serverless computing, it is a common practice to cache sufficiently many function containers for future reuse to reduce the overhead of container initiation. In contrast, the capacity limitation of edge nodes poses a complex problem to the caching strategy in serverless edge computing of selecting an appropriate set of container caches based on the request distribution. Deep Reinforcement Learning (DRL) can play a crucial role in optimizing the caching decisions under dynamic request arrivals. In this paper, we propose …
Passive Sampler Derived Profiles And Mass Flows Of Perfluorinated Alkyl Substances (Pfass) Across The Fram Strait In The North Atlantic, Matthew Dunn, Simon Vojta, Thomas Soltwedel, Wilken-Jon Von Appen, Rainer Lohmann
Passive Sampler Derived Profiles And Mass Flows Of Perfluorinated Alkyl Substances (Pfass) Across The Fram Strait In The North Atlantic, Matthew Dunn, Simon Vojta, Thomas Soltwedel, Wilken-Jon Von Appen, Rainer Lohmann
Graduate School of Oceanography Faculty Publications
Per- and polyfluorinated alkyl substances (PFAS) are a family of pollutants of high concern due to their ubiquity and negative human health impacts. The long-range marine transport of PFAS was observed during year-long deployments of passive tube samplers in the Fram Strait across three depth transects. Time weighted average concentrations ranged from 2.4 to 360 pg L–1, and 10 different PFAS were regularly observed. PFAS profiles and concentrations were generally similar to those previously characterized for polycyclic aromatic hydrocarbons (PAHs) at these sites. The detection of several anionic PFAS in “old” water demonstrated that they are not perfect …
From Lay Apostles To Missionary Disciples: Father Thomas A. Judge, C.M., And The Future Of The Catholic Laity, William L. Portier Ph.D.
From Lay Apostles To Missionary Disciples: Father Thomas A. Judge, C.M., And The Future Of The Catholic Laity, William L. Portier Ph.D.
Vincentian Studies
In the early twentieth century, Father Thomas Judge founded the Cenacle, a lay movement consisting of the secular Blessed Trinity Missionary Institute and two religious communities, the Missionary Servants of the Most Blessed Trinity and the Missionary Servants of the Most Holy Trinity. Inspired by Vincent de Paul and the French School of spirituality, Father Judge envisioned the Cenacle as a group that would help return people to the Church. William Portier explains what a lay apostle was in Judge’s time and how we might interpret the term in ours. Pope Francis has called the Church to a “synodal journey,” …
Vincentian Formation In Africa For Missionary Preaching In The Footsteps Of Saint Vincent De Paul, Linus Umoren C.M.
Vincentian Formation In Africa For Missionary Preaching In The Footsteps Of Saint Vincent De Paul, Linus Umoren C.M.
Vincentian Studies
Vincent de Paul was deeply concerned with the proper preparation of the clergy so that the poor could be evangelized. Linus Umoren outlines Vincent's method of preaching and the challenges the Church faced in reforming the clergy during the Counter-Reformation. Many of today’s new priests, including Vincentians, are coming from Africa. It is therefore necessary to develop a formation program that is uniquely suited to the African cultural context. Because of colonialism, the Church in Africa still needs to develop its own theological perspective instead of the one that had been brought to it by Europeans. It also faces several …
Fifty Years Of 'Cut To Grow': How Changing Narratives Around Corporate Tax Policy Have Undermined Child And Family Well-Being, Reuven S. Avi-Yonah, Emily Divito, Niko Lusiani
Fifty Years Of 'Cut To Grow': How Changing Narratives Around Corporate Tax Policy Have Undermined Child And Family Well-Being, Reuven S. Avi-Yonah, Emily Divito, Niko Lusiani
Articles
What follows in this report is an assessment, though not exhaustive, of the central worldviews and set of assumptions driving key US corporate tax reform moments in history—and their consequences for the well-being of children and families in the US. Though political narratives of all kinds are never cleanly chronological (and this remains true of those pertaining to corporate taxation and well-being policies), we aim to build on existing understanding of how dominant narratives come to be and how they, to the extent that they do, drive corporate tax policy outcomes in the US. Looking back over the past 50 …
Corporate Taxpayers And Frivolous Arguments, Part 2, Reuven S. Avi-Yonah
Corporate Taxpayers And Frivolous Arguments, Part 2, Reuven S. Avi-Yonah
Articles
In my previous column, I discussed the Liberty Global cases and argued that the taxpayer’s positions were frivolous or lacked economic substance. I also argued that a corporate tax director who knows that a return position is frivolous or that a transaction has no economic substance should “just say no.” Until the day arrives that most tax directors take this view (I am not holding my breath), the IRS must rely on a combination of the uncertain tax position schedule and penalties under section 6662.
Answering The Right Questions In Rawat, Reuven S. Avi-Yonah
Answering The Right Questions In Rawat, Reuven S. Avi-Yonah
Articles
The Rawat case, which is currently before the D.C. Circuit, has generated a huge amount of commentary. The problem is that much of this commentary has focused on the partnership tax issues in the case and not on the international tax issue, which is the source of the relevant income. In my opinion, the international tax issue should be dispositive.
Should Digital Services Taxes Be Creditable?, Reuven S. Avi-Yonah
Should Digital Services Taxes Be Creditable?, Reuven S. Avi-Yonah
Articles
The impending collapse of pillar 1 of the OECD’s base erosion and profit-shifting 2.0 project means that many countries are likely to join Austria, France, Italy, Spain, and the United Kingdom in enacting digital services taxes. There is a moratorium on new DSTs that has been extended until the end of 2024, but any extension beyond that is unlikely because the elimination of DSTs was premised on pillar 1 coming into effect, and that cannot happen without U.S. ratification of the multilateral tax convention (MLC). It is safe to predict that there will not be 67 votes in the Senate …
Why The United States Needs A Gaar, Reuven S. Avi-Yonah
Why The United States Needs A Gaar, Reuven S. Avi-Yonah
Articles
The Internal Revenue Code of 1986, as amended, has over 1 million words and more than 5,600 pages. It is by far the longest and most complicated law in the U.S. code. The regulations add over 4 million words. Why?
A Fractured Supreme Court: Select Criminal Law And Procedure Cases From The Supreme Court’S 2023-24 Term, Eve Brensike Primus, Jordan Schuler
A Fractured Supreme Court: Select Criminal Law And Procedure Cases From The Supreme Court’S 2023-24 Term, Eve Brensike Primus, Jordan Schuler
Articles
In its 2023-2024 Term, the Supreme Court outlined the contours of when a former President of the United States would be immune from criminal prosecution; issued important decisions interpreting the scope of the Cruel and Unusual Punishments Clause, the Double Jeopardy Clause, the Confrontation Clause, and the Second Amendment right to bear arms; decided a number of important statutory interpretation cases; and continued to avoid Fourth Amendment issues, only addressing them in the context of a malicious prosecution charge. Perhaps more striking than the Court’s decisions in these cases, though, was the Justices’ lack of consensus. Of the 16 criminal …
Mitochondrial Dna Genetic Variation Of Feral And Managed Honey Bee, Apis Mellifera, Colonies From Oklahoma, Allen Szalanski
Mitochondrial Dna Genetic Variation Of Feral And Managed Honey Bee, Apis Mellifera, Colonies From Oklahoma, Allen Szalanski
Entomology and Plant Pathology Faculty Publications and Presentations
Understanding the genetic diversity of honey bees in North America provides valuable insight into the conservation, ecology, and evolution of this economically important insect. Here, we characterized the mitochondrial DNA (mtDNA) genetic variation in populations of the honey bee, Apis mellifera L., in Oklahoma by sequencing a portion of the mitochondrial cytochrome oxidase (COI-COII) intergenic region. The samples were primarily of feral origin (n = 164), as well as from 24 managed colonies. Samples were obtained from 50 of Oklahoma's 77 counties, which represented six distinct ecoregions. Of the 188 colonies sampled,19 distinct haplotypes were observed, which included: A (African) …
Mitochondrial Dna Variation In Honey Bee (Apis Mellifera) Colonies From Arkansas, U.S., Allen Szalanski
Mitochondrial Dna Variation In Honey Bee (Apis Mellifera) Colonies From Arkansas, U.S., Allen Szalanski
Entomology and Plant Pathology Faculty Publications and Presentations
This study characterized the mitochondrial DNA (mtDNA) genetic variation in Arkansas honey bee, Apis mellifera L., by sequencing a portion of the mitochondrial cytochrome oxidase (COI-COII) intergenic region). The samples were primarily of hobbyist-managed origin (n=180), as well as 32 feral colonies and two swarms. Of the 214 honey bee colonies and swarms sampled, 23 haplotypes were observed. The haplotypes were from the: A (African) (1.87%); C (South eastern European) (92.52%); M (Northern and Western European) (3.27%); and O (Near East and Middle East) lineages (2.34%). Six C lineage haplotypes were predominantly detected (n=189, 88.31%), all of which are common …
Characterization Of Curing Kinetics Of An Anisotropic Conductive Adhesive Using Rheology And Differential Scanning Calorimetry, Connor Kirkpatrick
Characterization Of Curing Kinetics Of An Anisotropic Conductive Adhesive Using Rheology And Differential Scanning Calorimetry, Connor Kirkpatrick
Theses
A kinetic study performed to further the understanding of a novel magnetically aligned anisotropic conductive adhesive (ACA) was performed using differential scanning calorimetry (DSC) and a rheometer, under both isothermal and non-isothermal conditions. Isothermal data was collected at 70, 80, and 90°C, whereas non-isothermal data was collected at 5, 10, 20, and 30°C/min, with results interpreted through the use of an autocatalytic model. For isothermal experiments, the rate of conversion was found to increase with increasing isothermal curing temperature, and this was reflected in the kinetic rate constant. Moreover, the rate constant determined by DSC was found to range from …
Advancing Elastomer Cutting Tool Development Through Material-Centric Cutting Mechanism Analysis, Shuhuan Zhang
Advancing Elastomer Cutting Tool Development Through Material-Centric Cutting Mechanism Analysis, Shuhuan Zhang
Theses
Cutting elastomers is a very common procedure that is used in a wide variety of applications, including rubber product manufacturing, food processing, and surgical process. To conserve energy and improve cutting accuracy, decreasing the cutting force and elastomer deformation is always of interest. Numerous studies have concentrated on methods for reducing cutting force and deformation, but only a few have explained the underlying mechanics of these cutting techniques. Without a deep understanding of elastomer cutting mechanics, it's difficult to improve or develop a new cutting method. To understand the cutting mechanics, the mechanical, tribological, and rheological properties of the elastomer …
Predictive Modeling In Healthcare, Ibrahim Essa Abdulla Ali Alattar
Predictive Modeling In Healthcare, Ibrahim Essa Abdulla Ali Alattar
Theses
Predictive modelling, especially the use of horizontal lines, has become an important tool in clinical practice to help make informed decisions and accurate predictions. This study focuses on the use of horizontal regression in clinical practice, evaluating its effectiveness in revealing patterns and improving the accuracy of predictions. This study introduces the process of developing a linear model, emphasizing the importance of preliminary data analysis, feature selection, and model evaluation. To make sure your model's predictions are accurate, consider key assumptions such as sampling, independence, and homoscedasticity. The main goal is to provide doctors with the knowledge and skills needed …