Digital Commons Network^™

Relational Database Schema To Support Research Profiling Studies, Natural Language Processing, And Bibliometric Analysis, Darnelle Melvin

Library Faculty Research

In this paper, a relational database schema is introduced that supports rapid prototyping, data preprocessing, and warehousing tasks associated with research profiling studies, natural language processing, and bibliometric analysis. Python scripts are leveraged for the seamless retrieval and processing of data from Semantic Scholar. This schema is tailored to efficiently analyze entities such as authors, their scientific papers, referenced papers, and cited papers. Adhering to the relational model, this schema offers a standardized approach to data storage and detailed information retrieval for scientific papers. Enhancing knowledge discovery in scientific databases, this schema provides researchers with a powerful platform for robust …

Design And Evaluation Of A Thai Speech Emotion Recognition Corpus With Ambiguous Annotations, Chompakorn Chaksangchaichot

Chulalongkorn University Theses and Dissertations (Chula ETD)

THAI-SER is the first large-scale Thai speech emotion recognition corpus, comprising 41.6 hours (27,854 utterances) from 100 recordings across diverse environments (Zoom and studio). The data includes both scripted and improvised speech by 200 professional actors (112 females, 88 males, aged 18–55), covering five emotions: neutral, angry, happy, sad, and frustrated. Utterances were labeled via crowdsourcing, with rigorous quality control ensuring a majority agreement score above 0.71. Annotation reliability, measured by Krippendorff’s alpha, reached 0.692 (above the 0.667 threshold), and human emotion recognition accuracy reached 0.772 after filtering. We also report benchmark results from models trained and evaluated on both …

Teaching Object-Oriented Design Through Interactive Uml: A Dual-Approach Framework For Code-Based Generation And Direct Diagram Manipulation, Moses Kayuni

Masters Theses & Specialist Projects

This thesis introduces an interactive educational framework for teaching object-oriented design through UML class diagrams. The framework implements a dual-approach methodology: code-based generation, where students write Java code that automatically transforms into UML diagrams, and direct diagram manipulation, where students build diagrams by interacting with highlighted terms in problem descriptions. This approach addresses common challenges in teaching UML, including cognitive load difficulties, visualization problems, and the disconnect between code implementation and visual design. Built on the Mermaid diagramming framework, the system features a React frontend for diagram creation and manipulation, and a Flask backend that handles some code parsing and …

Modeling Deductive Inference: A Historico-Philosophical Introduction To First-Order Logic, David J. Buller

Faculty Books & Book Chapters

This book is a companion text for lectures on first-order logic and its elementary metatheory (used in Intermediate Logic at Northern Illinois University). It covers the basic concepts of set theory necessary for a mathematical development of first-order logic; develops a formal language of first-order logic; presents a classical Tarskian semantics for the language and the “semantic” conception of logical consequence; presents a Gentzenian proof system and the “syntactic” conception of logical consequence; develops a partial decision procedure for logical consequence in the language; demonstrates applications of the formal system to modeling deductive inference expressed in natural language; and extends …

Intergenerational Classification Of Reddit Comments Based On Slang And Emoji Usage, James T. Dracup

West Chester University Master’s Theses

The rapid evolution of language, driven by technological advancements, has created notable cultural gaps between generations, particularly in how they communicate. This gap is most apparent in the growing use of slang and emojis among younger generations. This study aims to explore whether Reddit comments can be classified by generation based on the usage of slang and emojis, the frequency of their use across generations, and how such features (slang and emojis) might influence the meaning of traditional language. Using Reddit’s API, we collected comments from four generational subreddits and applied various machine learning models, Naïve Bayes, Neural Networks, and …

Fault Tree Analysis For Robust Design, Jonathan Degroff, Gene Jean-Win Hou

Mechanical & Aerospace Engineering Faculty Publications

The objective of this research is to incorporate system failure into a robust design formation and solution process. The system failure referred to here will be built using fault tree analysis (FTA), which will take all lower-level failure events into consideration. Two examples are investigated here. One will directly treat the probabilities of the basis events as design variables, The other will be formulated in five different models: deterministic design optimization, the reliability index-based, the “and” gate-based, the “or” gate-based and the “inhibit” gate-based robust design. Their corresponding optimization solutions will be compared with each other. The post-optimality analysis of …

Extraction Of Patient Subtypes Using Llm Generated Knowledge Graphs Integrated With A Transformer Architecture, Benjamin Holmes, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Extracting patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria) from unstructured medical notes is an ongoing challenge due to the variability of clinical language and the complex nature of patient conditions. We demonstrate a pipeline that combines named entity recognition (NER), transformer embeddings, guided dimensionality reduction, and LLM-mediated knowledge graph integration to enhance patient extraction. The approach begins with NER using the UMLS metathesaurus [1] to extract clinical terms, followed by transformation into vector embeddings using a biomedical transformer. These embeddings are augmented with structured knowledge graph representations generated through an LLM-driven …

A Comparative Analysis Of Data-Driven And Model-Based Neutrosophication Methods: Advancing True Neutrosophic Logic In Medical Data Transformation, Maikel Yelandi Leyva Vázquez, Lorenzo Cevallos-Torres, Omar Mar Cornelio, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic logic extends fuzzy logic by explicitly modeling indeterminacy (I), offering a robust framework for uncertainty representation. The transformation of crisp data into neutrosophic triplets {T, I, F}—known as neutrosophication—is crucial for applying neutrosophic models in real-world analysis. However, comparative evaluations of existing neutrosophication methods remain limited. This study presents a systematic comparison of five approaches: three model-based methods (Parabolic, Threshold Distance, Fuzzy Membership), one density-based method (Kernel Density Estimation), and a proposed data-driven K-Means clustering method integrating sigmoid membership functions. Using a medical dataset of 299 patients and six continuous clinical variables, we assessed statistical behavior, consistency, and alignment …

Bytes, Banter, And The Bible: An Interdisciplinary Account Of Objective Meaning, Cameron Bonin

Senior Honors Theses

The claim that the Bible has objective meaning is contested in a postmodern world. This claim can be more persuasively defended when it is addressed by insights from multiple disciplines. In particular, the field of computer science is apt to illuminate the concept of meaning through its reflection on the nature of languages and its concern with the accurate transmission of information. By synthesizing insights from the field of computer science, such as that of Claude Shannon, with Nicholas Wolterstorff’s use of speech-act theory, the concept of meaning can be understood more clearly. Consequently, this synthesis assists in answering questions …

Action This Day: The Mathematics And Machinations That Bested The German Enigma, Jonah Weinbaum

Dartmouth College Master’s Theses

This thesis presents a comprehensive and chronological overview of cryptographic techniques designed to break Enigma, beginning in 1932 and culminating in the creation of the Turing-Welchman Bombe. We discuss the mathematical theory and electromechanical implements used to decode one of history's greatest ciphers.

Reexamining the Bombe through the lens of modern group theory, we critique Alan Turing's estimation of the number of "stops" that the Bombe produces for various plaintext-ciphertext pairing structures. To address its limitations, we introduce a new framework for estimating the number of stops by extending John Dixon's theorem concerning the probability that uniformly distributed elements of …

Application Of Large Language Model Methods In Scientific And Technical Intelligence Practice, Bolin Hua, Yingze Wang

Journal of Scientific Information Research

[Purpose/significance]With the strong ability to process large-scale datasets and outstanding performance in various natural language processing tasks, large language models (LLMs) have excelled across multiple industries.Since scientific and technical intelligence primarily relies on textual data, LLMs are naturally well-suited for this field, ushering in a new wave of transformative changes. [Method /process]This article discusses the advantages of LLMs from five perspectives: low-dimensional dense vector representations of text, large-scale pre-trained models,fine-tuning and prompt learning, high-quality large-scale training data, and human alignment techniques. [Result/conclusion]LLMs have extensive applications in tasks such as intelligence identification, intelligence tracking, intelligence evaluation, and intelligence prediction, resulting in …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Evaluating Aspect-Based Sentiment Analysis In Healthcare Drug Reviews Across Machine Learning, Deep Neural Networks, And Transformer Models, Eun Soo Park

All Graduate Theses, Dissertations, and Other Capstone Projects

Sentiment analysis has become a critical area of research in Natural Language Processing (NLP), enabling insights from unstructured text. Within this field, Aspect-Based Sentiment Analysis (ABSA) plays a practical role in domains such as healthcare, where patients drug reviews often contain diverse opinions across multiple aspects, including overall comments, perceived benefits, and side effects. However, aspect-level classification remains challenging due to class imbalance, subtle sentiment expression, and the limitations of traditional models. This research investigates the performance of three modeling paradigms: traditional machine learning (SVM, SVC, and XGBoost), deep learning (CNN-BiLSTM), and transformer-based approaches (DistilBERT sentence-pair classification). Using the UCI …

Fake News Detection Using Machine Learning Models., Anne Higgins

ICT

This project investigates the use of machine learning to detect fake news, addressing the societal and political challenges posed by the widespread dissemination of false and misleading information. Using automated classification techniques, the project analyses news content to predict the likelihood of intentional deception. The methodology follows the CRISP-DM framework, encompassing data preparation, model development, and evaluation. By leveraging machine learning, the study aims to support organisations, governments, and digital platforms in mitigating misinformation, while also considering ethical, interpretability, and strategic implications. The findings provide actionable insights for enhancing content moderation and reducing the influence of disinformation in digital media …

Local-Neutrosophic Logic And Local-Neutrosophic Sets: Incorporating Locality With Applications, Florentin Smarandache, Takaaki Fujita

Branch Mathematics and Statistics Faculty and Staff Publications

The study of uncertainty has been a significant area of research, with concepts such as fuzzy sets [87], fuzzy graphs [51], and neutrosophic sets [58] receiving extensive attention. In Neutrosophic Logic, indeterminacy often arises from real-world complexities. This paper explores the concept of locality as a key factor in determining indeterminacy, building upon the framework introduced by F. Smarandache in [73]. Locality refers to processes constrained within a specific region, where an object or system is directly influenced by its immediate surroundings. In contrast, nonlocality involves effects that transcend spatial or temporal boundaries, where changes in one location have direct …

Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah

All Peer-Reviewed Publications

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in …

Destroy, Rebuild, Repeat: How To Break The Climate Disaster Cycle, Mark P. Nevitt

Faculty Articles

Climate change is fundamentally reshaping how we live, where we live, and whether we invest in or retreat from climate-exposed communities—but climate and disaster law is not changing with the climate. This legal latency is driven by antiquated statutes, doctrines, and policies that have not kept pace with the climate moment. Ex ante adaptation decisions governing where to live are life and death choices that shape ex post disaster response. Laws and policies should facilitate sound climate decisionmaking, but too often they frustrate individual and governmental decisions on whether to stay or retreat. In this Article, I argue that laws …

Automated Infinite Combos Detection System For Collectible Card Game, Amornpong Trakarnkulphun

Chulalongkorn University Theses and Dissertations (Chula ETD)

In Collectible Card Games, resources management is one of the most effective strategy. However, there are some combinations of cards which produce unlimited resources called infinite combos. Too-cheap infinite combos that can be played early in the game break game environment, because the player who executes the combo immediately wins. To solve the problem, game designers usually restrict a part of the combos from being played. Due to the large number of cards in the card pool, it is difficult to search for these combos and solve this problem before a release date of a card set. This research aims …

Large Language Model-Based Framework For Mental Health Support Conversation In Thai, Radchaneeporn Changpun

Chulalongkorn University Theses and Dissertations (Chula ETD)

As mental health support needs continue to rise in Thailand, access to professional care remains constrained. LLM-based chatbots present a scalable alternative, though most current systems are limited to single-turn, solution-focused interactions. This thesis introduces a Large Language Model-Based Framework for Mental Health Support Conversations in Thai, organizing dialogues around five fundamental mental health counseling stages: rapport building, problem identification, goal setting, working, and termination. Drawing from Person-Centered Therapy (PCT) and Acceptance and Commitment Therapy (ACT). The framework was assessed against three single-agent baseline models using LLM-simulated users, with positive user reaction rates evaluated by LLMs. Results demonstrated a 79.01% …

Advancing Voice Spoofing Detection In Thai : A Comprehensive Dataset And Performance Analysis On Speaking Styles And Channel Effects, Ticho Urai

Chulalongkorn University Theses and Dissertations (Chula ETD)

Voice authentication is increasingly used in applications such as banking and call center verification, but it faces serious risks from spoofed voices. Recent advances in text-to-speech (TTS) and voice cloning make it possible to generate highly natural fake speech, creating an urgent need for robust anti-spoofing systems. While most prior work focuses on English, little research addresses the Thai language. To fill this gap, we present the Chula Spoofed Speech (CSS) dataset, a large-scale Thai corpus containing 1.3M utterances of both bona fide and synthetic speech. The synthetic samples are generated using five state-of-the-art TTS systems from the same utterances …

Automating International Human Rights Adjudication, Veronika Fikfak, Laurence R. Helfer

Faculty Scholarship

International human rights courts and treaty bodies are increasingly turning to automated decision-making (“ADM”) technologies to expedite and enhance their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This article offers a comprehensive and balanced assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We argue in favor of using ADM to digitize documents and for internal case management purposes and to make straightforward recommendations regarding registration, inadmissibility, and the calculation of …

Exploring The Impact Of Generative Ai Chatgpt On Critical Thinking In Higher Education: Passive Ai-Directed Use Or Human-Ai Supported Collaboration?, Nesma Ragab Nasr, Chih-Hsiung Tu, Jennifer Werner, Tonia Bauer, Cherng-Jyh Yen, Laura Sujo-Montes

STEMPS Faculty Publications

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), …

Leveraging Gpt-4o For Automated Extraction Of Neural Projections From Scientific Literature, Rashmie Abeysinghe, Gorbachev Jowah, Licong Cui, Samden D Lhatoo, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to …

A Vascular Flora Of The Sacatar Trail Wilderness, Sierra Nevada, California, Kimberly Schaefer Pivetti

CGU Theses & Dissertations

The Sacatar Trail Wilderness in Inyo and Tulare counties, California, encompasses a bioregional transition zone in the southeastern Sierra Nevada. The ca. 233 km 2 (90 mi 2 ) study area, located 32 km (20 mi) northwest of the city of Ridgecrest, ranges from 930 to 2698 m (3051–8851 ft) in elevation. The highly diverse California Floristic Province, Great Basin, and Mojave Desert converge in the region including the Wilderness creating conditions to support a broad spectrum of vegetation assemblages. The Bureau of Land Management has prohibited vehicular activity in this area since its wilderness designation in 1994; consequently, recent …

Some Types Of Hyperneutrosophic Set (1): Bipolar, Pythagorean, Double-Valued, Interval-Valued Set, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

The Neutrosophic Set is a mathematical framework designed to manage uncertainty, characterized by three membership functions: truth (T), indeterminacy (I), and falsity (F). In recent years, extensions such as the Hyperneutrosophic Set and SuperHyperneutrosophic Set have been introduced to address more complex scenarios. This paper proposes new concepts by extending Bipolar Neutrosophic Sets, Interval-Valued Neutrosophic Sets, Pythagorean Neutrosophic Sets, and Double-Valued Neutrosophic Sets using the frameworks of Hyperneutrosophic and SuperHyperneutrosophic Sets. Additionally, a brief analysis of these extended concepts is presented.

Some Types Of Hyperneutrosophic Set (7): Type-M, Nonstationary, Subset-Valued, And Complex Refined, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundational advancements introduced in [26,39–43]. TheNeutrosophic Set provides a versatile mathematical framework for addressing uncertainty through its three membership functions: truth, indeterminacy, and falsity [84]. Extensions such as the Hyperneutrosophic Set and the SuperHyperneutrosophic Set have been recently proposed to address increasingly complex and multidimensional problems. Detailed formal definitions of these concepts can be found in [33]. In this paper, we extend the Type-𝑚, Nonstationary, Subset-Valued, and Complex Refined Neutrosophic Sets using the Hyperneutrosophic Set and the SuperHyperneutrosophic Set frameworks.

Some Types Of Hyperneutrosophic Set (6): Multineutrosophic Set And Refined Neutrosophic Set, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds on the foundational advancements introduced in [22, 29–32]. The Neutrosophic Set pro-vides a flexible mathematical framework for managing uncertainty by utilizing three membership functions: truth, indeterminacy, and falsity. Recent extensions, such as the HyperNeutrosophic Set and the SuperHy-perNeutrosophic Set, have been developed to address increasingly complex and multidimensional challenges. Comprehensive formal definitions of these concepts are provided in [26]. In this paper, we further extend various specialized classes of Neutrosophic Sets. Specifically, we explore extensions of the MultiNeutrosophic Set and the Refined Neutrosophic Set using HyperNeutrosophic Sets and 𝑛-SuperHyperNeutrosophic Sets, providing detailed analysis and examples.

Some Types Of Hyperneutrosophic Set (5): Support, Paraconsistent, Faillibilist, And Others, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This paper builds upon the foundational advancements introduced in [14, 25–27]. The Neutrosophic Set offers a versatile mathematical framework for addressing uncertainty through its three membership functions: truth, indeterminacy, and falsity. Extensions such as the Hyperneutrosophic Set and the SuperHyperneutrosophic Set have been recently proposed to tackle increasingly sophisticated and multidimensional problems. Detailed formal definitions of these concepts can be found in [20]. In this paper, we extend various specialized classes of Neutrosophic Sets—namely, the Support Neutrosophic Set, Neutrosophic Intuitionistic Set (distinct from the Intuitionistic Fuzzy Set), Neutrosophic Paraconsistent Set, Neutrosophic Faillibilist Set, Neutrosophic Paradoxist Set, Neutrosophic Pseudo-Paradoxist Set, Neutrosophic …

Neutrosophic Treesoft Expert Set And Forestsoft Set, Takaaki Fujita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Concepts such as Fuzzy Sets [28,57],Neutrosophic Sets [42,44], and Plithogenic Sets [48] have been extensively studied to address uncertainty, finding diverse applications across various fields. The Soft Set provides a framework that associates each parameter with subsets of a universal set, enabling flexible approximations [31]. The TreeSoft Set extends the Soft Set by introducing hierarchical, tree-structured parameters, allowing for multi-level data representation [53]. In this paper, we revisit the concept of the Neutrosophic TreeSoft Set, which has been discussed in other studies [8, 34]. Additionally, we propose and examine the Neutrosophic TreeSoft Expert Set by incorporating the framework of the …

A Critical Evaluation Of The Criticisms Against Neutrosophic Statistical Methods, Muhammad Aslam, Abdulrahman Alaita, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic statistical analysis has gained attention for incorporating the degree of indeterminacy when analyzing imprecise and interval data under uncertainty—an aspect often overlooked by classical statistics, fuzzy statistical analysis, and interval statistics. Recently, critical discussions have emerged regarding the use and applications of neutrosophic statistics, with some questioning its usefulness and validity. In this paper, we present a critical assessment of the existing literature, focusing on areas where misunderstandings and misinterpretations of neutrosophic statistical methods have occurred. We also examine flawed comparisons made between the results of neutrosophic statistics and interval statistics. Furthermore, substantial issues have been identified in the …