Digital Commons Network^™

Collective Memory, Culinary Continuity, And Solemn Repasts: Lagana, Itria And The History Of Pasta In Southern Italy, Anthony F. Buccini

Dublin Gastronomy Symposium

Though today it is communis opinio that the Arabs introduced pasta, especially dried pasta, to Sicily and from there it spread to the continent, there is no evidence to support this theory (Buccini 2013, 2015b, 2024). There is, however, ample evidence both textual and linguistic that this food has been known in southern Italy at least since classical times. Here I argue that an examination of holiday foods, especially those of what I call “solemn holidays,” provides further evidence that pasta has been an integral part of southern Italian cuisine for a very long time.

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …

Intelligent Solutions For Retroactive Anomaly Detection And Resolution With Log File Systems, Derek G. Rogers, Chanvo Nguyen, Abhay Sharma

SMU Data Science Review

This paper explores the intricate challenges log files pose from data science and machine learning perspectives. Drawing inspiration from existing methods, LAnoBERT, PULL, LLMs, and the breadth of recent research, this paper aims to push the boundaries of machine learning for log file systems. Our study comprehensively examines the unique challenges presented in our problem setup, delineates the limitations of existing methods, and introduces innovative solutions. These contributions are organized to offer valuable insights, predictions, and actionable recommendations tailored for Microsoft's engineers working on log data analysis.

Baseball Decision-Making: Optimizing At-Bat Simulations, Varun Gopal, Krithika Kondakindi, Nibhrat Lohia, Morgan Williams

SMU Data Science Review

Pitch selection in baseball plays a crucial role, involving pitchers, catchers, and batters working together. This practice, dating back to early baseball, has seen teams try various methods to gain an advantage. This research aims to use reinforcement learning and pitch-by-pitch Statcast data to improve batting strategies. It also builds on previous statistical work (sabermetrics) to make better choices in pitch selection and plate discipline. The dataset used, including over 700,000 pitches for each full season and 200,000 pitches for the COVID-shortened 2020 season, encompasses a wealth of crucial metrics including pitch release point, velocity, and launch angle. This study …

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …

Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava

SMU Data Science Review

Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for …

Building Effective Large Language Model Agents, Sydney Holder, Shreyash Taywade

SMU Data Science Review

The advancement of large language models (LLMs) has significantly expanded the influence of artificial intelligence across various sectors. This paper explores building LLM agents to power applications and examines what is necessary to build an efficient and helpful AI assistant. The research investigates the core components necessary to create specialized agents, facilitate collaboration in problem-solving, and improve human task performance. The development and application of tools designed to augment the capabilities of LLM agents are also explored. The paper addresses the potential risks of the unknowns, such as hallucinations, which can compromise the success of agent-based solutions within LLM applications. …

Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young

SMU Data Science Review

Abstract. “Growth strategies that are purpose-led, customer-centric, experience-driven, data/AI-enabled, and technology-scaled require new mindsets…” (Cornfield, 2021). What can we take from this? Business growth and customer experience are inextricably tied together. Therefore, thriving, as an organization, is dependent on reimagining enterprise operations through modern, scalable data and AI technologies. Our study aims to enhance support operations with emerging AI capabilities, including OpenAI’s LLM models, built on self-attention mechanism transformer architecture, and tailored for business needs through prompt engineering. Our research uses Markov Decision Process and the Q-learning algorithm to evaluate synthetically created support incidents. Through this set of methods, our …

Game Recommendation Analysis Using Steam Profiles And Reviews, Robert Blue, Luis Garcia, Jacob Turner

SMU Data Science Review

Smaller game studios are at a disadvantage when it comes to getting their product noticed by users. This study aims to provide insights on how recommendation engines work so that these smaller studios can have their games noticed on Steam. Steam is one of the largest video game distribution services and they have a recommendation engine which promotes games to its user base. This study utilized user information such as number of games played, the type of games, and the hours played and created recommendation engines to identify the qualities in the game that are driving recommendations.

Personalization: University Fundraising, Jasmine O'Neal, Akib Hossain, Yogesh Bhalerao

SMU Data Science Review

University fundraising campaigns, which are typically a multi-year endeavor, help institutions build a strong financial foundation to enable a unique student experience. At the heart of effective fundraising is having strong relationships and partnerships with certain audiences that provide the best donation opportunities. Generally, alumni networks and the local community makeup these audiences as strong fundraising bases. Therefore, universities have a significant responsibility in developing these relationships to gain deeper insights into their constituents' needs and interests. This enables them to tailor effective engagement strategies, thereby increasing the likelihood of meeting donation expectations. This research explores how machine learning techniques …

Expanding The Horizon: Blockchain Technology Beyond The Bounds Of Cryptocurrency, Hassan Azhar

SMU Data Science Review

Blockchain technology has extended beyond its initial role as the infrastructure for cryptocurrencies to transform various industries with its decentralized and transparent ledger system. This paper examines the broad spectrum of blockchain applications beyond cryptocurrency. It explores its potential to innovate and drive change across finance, supply chain management, healthcare, real estate, and voting systems. We review recent literature, detail specific use cases, and discuss blockchain's challenges and opportunities, aiming to provide a comprehensive overview of its transformative impact. Integrating emerging technologies, scalability, regulatory considerations, and energy consumption are critical challenges to its adoption. Our findings underscore the need for …

Leveraging Transformer Models For Genre Classification, Andreea C. Craus, Ben Berger, Yves Hughes, Hayley Horn

SMU Data Science Review

As the digital music landscape continues to expand, the need for effective methods to understand and contextualize the diverse genres of lyrical content becomes increasingly critical. This research focuses on the application of transformer models in the domain of music analysis, specifically in the task of lyric genre classification. By leveraging the advanced capabilities of transformer architectures, this project aims to capture intricate linguistic nuances within song lyrics, thereby enhancing the accuracy and efficiency of genre classification. The relevance of this project lies in its potential to contribute to the development of automated systems for music recommendation and genre-based playlist …

Toxic Love: Mandating Standards In Arkansas’S Domestic Violence Laws, Alia B. Reddell

Arkansas Law Review

This Comment addresses the current scheme of domestic violence statutes, highlighting the inadequate state of domestic violence remedies and the ineffective law enforcement guidelines currently in place; it argues that the Arkansas State Legislature is in need of reforming its domestic violence policies. This recommendation is three-fold and proposes that Arkansas should abandon its current discretionary regime and adopt: (1) mandatory arrest policies; (2) statutory post-arrest procedures; and (3) mandatory prosecutorial policies to effectively protect domestic violence survivors. Part I will discuss the various types of domestic violence arrest statutes. Further, this Part will briefly highlight the significance of an …

Privacy’S Commodification And The Limits Of Antitrust, Jeffrey L. Vagle

Arkansas Law Review

This Article argues that the buying and selling of personal data forms what Debra Satz calls a “noxious market,” and, thus, any regulation of information privacy should not accept or depend upon its commodification but should stand on its own. This Article proceeds in three parts. Part I first lays out the history and effects of data commodification, arguing that the market created by this commodification is noxious and undesirable. Part II examines the renewal of antitrust’s purpose as a regulatory tool, especially in the context of its use in the regulation of large technology firms. Finally, Part III argues …

To Err Is Human, To Restore Is (Usually) The Law: Present Entitlement In Restitution’S Discharge-For-Value Rule, Layne S. Keele

Arkansas Law Review

This Article argues that the Second Circuit’s present-entitlement holding and the concurrence’s setoff argument in Citibank v. Brigade Capital do not reflect the state of the law and risk introducing confusion into an already convoluted area of law. First, I will briefly review the district court’s decision in Citibank and its reception among scholars and the marketplace. Next, I will examine the Second Circuit’s opinion, as well as the concurrence and the addendum to the opinion. Finally, I will critique the “present entitlement” requirement that the court grafted onto the discharge-for-value defense. In this critique, I will argue that the …

Contents, Journal Editors

Arkansas Law Review

No abstract provided.

Arkansas Law Review - Volume 77 Issue 1, Journal Editors

Arkansas Law Review

No abstract provided.

The Future Of Open Access Publishing In International Higher Education, Melissa Whatley, Santiago Castiello-Gutiérrez Ph.D.

Critical Internationalization Studies Review

No abstract provided.

Lightweight Retrieval Augmented Generation (Rag) Evaluation Pipeline, Kelsey Kraus, Margaret Kroll

Defensive Publications Series

Retrieval Augmented Generation (RAG) pipelines reduce the frequency of Large Language Model (LLM) hallucinations by grounding the LLM context in knowledge base documents. Proposed herein is a lightweight, generalizable framework for evaluating RAG systems regarding both their document retrieval accuracy, as well as their answer-generation accuracy.

Equipping Support Teams With Tools That Combine Traditional Nlp With Llms, Hp Inc

Defensive Publications Series

Properly equipped support teams have tools at their disposal that help them to work on customer requests in an effective way. We present 3 tools to equip support teams properly. The tools presented combine traditional NLP with LLMs and their output is presented to users through comments in Jira.

A Process For The Preparation Of Trazodone And Its Hydrochloride, Msn Laboratories Private Limited, R&D Center ; Srinivasan Thirumalai Rajan & Gade Srinivas Reddy.

Defensive Publications Series

The present disclosure relates to a process for the preparation of Trazodone of formula-1 or its salts.

Formula-1.

The Virtual Content Viewing Configuration With 3 Resolution Levels Which Horizontally Curves Around The Field Of View Of The User., Punarjeewa Abeysekera

Defensive Publications Series

This paper describes a virtual content viewing configuration with 3 resolution levels which horizontally curves around the field of view of the user. The virtual content viewing configuration will contain 2 identical main projection configurations, one main configuration per each eye of the user. Each main projection configuration will consists of 2 identical horizontally oriented projection configurations, which will be stacked on top of each other. Each generally utilized horizontally oriented projection configuration will contain 2 identical small projection configurations. Each generally utilized small projection configuration will contain a large angled projection reflecting surface, a convex lens stack, 2 small …

Zero-Shot Superpixel Learning For Network Configuration Optimization, Eric S Liang, Claire Y Chour, Qihong Shao, Dave Zacks

Defensive Publications Series

Proposed herein are novel techniques that utilize zero-shot learning and superpixels in order to efficiently predict an optimized configuration profile for a given network. The techniques may be able to not only efficiently handle large amounts of data for the prediction, but also account for any dynamic network changes without the need of manual intervention. The techniques may perform well with dynamic unseen and/or out-of-training sample changes. Such techniques may be useful for efficiently creating network configurations that can improve network performance even if the network experiences structural changes.

Advanced Prediction Model For Variable Refresh Rate Compensation, Hsin Hsueh Li, Chien-Hui Wen

Defensive Publications Series

This publication describes enhancing image quality and delivering a smooth display experience using a variable reference rate (VRR) by using a compensation algorithm that reduces takt time in mobile device production lines.

Smart Mathematical Model For Variable Refresh Rate Prediction Algorithm, Hsin Hsueh Li, Chien-Hui Wen

Defensive Publications Series

This publication describes a smart mathematical model that involves compensating and calibrating the display to mitigate brightness and color differences when transitioning between different refresh rates for variable reference rate (VRR) compensation.

Process For The Preparation Of Pure Form Of 4-(4-{3-[4-Chloro-3-(Trifluoromethyl)Phenyl]Ureido} Phenoxy)N2-Methylpyridine-2-Carboxamide, Msn Laboratories Private Limited, R&D Center; Srinivasan Thirumalai Rajan; Ramakrishna M; Sathish Aithagani

Defensive Publications Series

A process for the preparation of 4-(4-{3-[4-chloro-3-(trifluoromethyl)phenyl] ureido}phenoxy)N²-methylpyridine-2-carboxamide compound of formula-I

Protectnic - Smartnic Ransomware Detection, Arnav Choudhury, Eason Liu, Anson Xu

Computer Science and Engineering Senior Theses

Ransomware, a form of malware that restricts access to data until a ransom is paid, accounts for 20% of all cyber crimes. Although companies and organizations often require their personnel to take training for awareness of such bad actors, social engineering is constantly evolving and ransomware slips through the cracks every year. In this paper, we suggest a system that would help detect ransomware using a Smart Network Interface Card (SmartNIC) which runs machine learning algorithms to detect ransomware before it enters the system. This relieves computers in the network of the burden of detecting malware, freeing CPU capacity to …

The Oneal Project: Negotiation Education For Library Workers, Courtney Fuson

Library Faculty Scholarship

Come and learn about a new open educational resource and community to support library workers as they develop their negotiation knowledge and skills. Designed to help all library workers learn negotiation best practices and strategies for both their personal and professional lives, this OER includes lectures, readings, assessments, and hand-on assignments. In addition to learning about this resource, attendees will learn about the community being created to provide support for library professionals responsible for negotiations and about key issues and trends uncovered during an interview research study. While the content developed around licensing is geared toward an academic audience, the …

April Sg Living Memoirs Interview, April Sg

Student Missionary Stories

April, a Southern alumnus, shared her time as a student missionary in Mali, West Africa, with Adventist Frontier Missions. She talked about how growing up in a close-knit Adventist family inspired her to serve and how her mission year challenged her in ways that strengthened her faith.

Mali is a country in West Africa with a rich history, known for its ancient cities like Timbuktu, which was once a center of trade and learning. The country is mostly covered by the Sahara Desert in the north, but the Niger River in the south brings water and supports farming. Mali is …