Digital Commons Network^™

Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad

C-Day Computing Showcase

Alzheimer’s disease is a growing public health issue due to its progressive nature and increasing prevalence. Large language models (LLMs) offer promising avenues for non-invasive cognitive assessment through natural language understanding. In this study, we evaluate DeepSeek’s general-purpose model V3 and reasoning-enhanced R1 variant—for identifying Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using transcripts derived from spontaneous speech. Two baseline prompting strategies (zero-shot, chain-of-thought ) were applied to both model types and an additional query (self-consistency prompting) was applied to assess better predictions. Accuracy was the primary performance metric. When positively identifying AD, the general-purpose DeepSeek V3 model produced …

Grm-050 Context-Aware Misinformation Detection Using Fine-Tuned Bert And Bilstm With Attention, Rakshak Gurung, Nino Tkabladze

C-Day Computing Showcase

Misinformation spreads fast, and 60% of consumers now question media reliability (Redline Digital, 2023). Manual verification is slow, and most systems still rely on binary real/fake classification, which overlooks nuanced types of misinformation. We propose a multi-class deep learning approach using a fine-tuned BERT model and a custom BiLSTM with attention to better detect categories like satire, conspiracy, and bias. Our models were trained on a balanced subset of the Fake News Corpus using nine distinct misinformation classes. By addressing both class imbalance and linguistic ambiguity, this system enhances contextual understanding and improves detection across varied news content. Our approach …

Grm-060 Abstractive Summarization Of Informal Text: Fine-Tuning Transformers On Reddit Discussions, Nong Ming, Arpana Challa, Sharon Edward John

C-Day Computing Showcase

In recent years, the rapid growth of social media platforms has led to an information overload, as a result, the ability to compress long and complex texts into short and precise summaries is essential, especially in online discussions and comment sections. Summarizing such content is difficult due to inconsistencies in sentence structure, slang, abbreviations, and the lack of formal grammar. State-of-the-art models such as BART and PEGASUS have shown promising results, but their performance on informal datasets remains lower compared to structured text benchmark. To address these challenges, we fine-tune BART and PEGASUS on the Reddit TIFU dataset, leveraging their …

Grm-042 Ihelp: A Care Partner Activation Program Mhealth System For Ad/Adrd Caregivers, Trisha Bhowmick

C-Day Computing Showcase

The iHelpCare platform is designed to offer a seamless and supportive experience for patients and caregivers through a clear and user-friendly interface. Users begin at the login page, where they can either sign in or create a new account. Once logged in, the home page provides access to essential services such as a 24/7 helpline, emergency visit coordination, emergency support, and a service directory. It also includes engagement tools like discussion forums, learning modules, and resource materials, along with community-focused features such as events, activities, and support groups. The personalized dashboard allows users to monitor health conditions, review patient history, …

Ur-112 Monarch: A Privacy-Focused Nlp Model For Emotional Pattern Detection, Tyler Clanton, Derick Burbano-Ramon

C-Day Computing Showcase

Introducing: Monarch — a privacy-focused deep learning model that interprets emotional patterns in text. Monarch is trained on large, lexicon-based datasets and uses fine-tuned NLP models (BERT) to identify patterns associated with sadness, worry, anger, and distress. It runs entirely offline with no data collection, making it ideal for private use. Monarch evaluates text and returns clear, readable probability scores across emotional categories, giving users insight into emotional trends. Monarch is interpretive, not diagnostic, displaying results based on scientifically backed linguistic patterns. Its potential use in schools could help flag early signs of distress, giving educators a chance to support …

Dictating The Divine: Revisiting Authorship, Intention, And Authority From Sacred Texts To Generative Ai, James Hutson, W. Travis Mcmaken

Faculty Scholarship

This article interrogates the historical practice of mediated authorship in religious texts to draw critical parallels with contemporary debates surrounding generative artificial intelligence (AI), specifically large language models (LLMs). By juxtaposing the mediated authorship of sacred texts, such as the Hebrew Bible and New Testament—where figures like the Apostle Paul dictated theological concepts to scribes who infused these directives with their interpretive insights—with the generative processes of LLMs, this research underscores the shared dynamics of co-constructed authorship across historical and technological contexts. Employing interdisciplinary methodologies from art history, textual studies, and reception theory, as well as theological and biblical studies, …

Learning Parent Strategies: A Trajectory-Based Approach To Analyzing Parent Child Interactions With Llms, Chelsea Joe

Computer Science Senior Theses

Recent advancements in large language models (LLMs) have demonstrated their ability to leverage its immense world knowledge to excel in traditional reinforcement learning tasks. Building on these capabilities, we introduce a framework that uses LLMs to learn from human behavior video data and generate insights that serve as guidelines for predicting how individuals are likely to act. Our framework focuses on parent- child dialogic reading, with an emphasis on understanding each parent’s unique parenting strategies. The system identifies meaningful interaction episodes within analyzed video data. The learning component of the framework utilizes a long-term memory system, which stores and retrieves …

Ai Foundations And Applications: Summary Of A Panel Discussion At Loyola University Chicago, George K. Thiruvathukal, Dmitry Dligach, Shilpika, Michael B. Burns, Joseph Vukov, Fraser Turner, Mary Usher

Computer Science: Faculty Publications and Other Works

This document summarizes the panel discussion titled "AI Foundations and Applications," held at Loyola University Chicago as part of the "Forum on Global Affairs: Artificial Intelligence in a Globalized World" series. The panel brought together interdisciplinary experts to discuss the foundational aspects of artificial intelligence (AI), its applications, ethical considerations, and implications for education and society.

Simplifying 3d Printing Using Natural Language Processing, Jared E. Rosenberger

Undergraduate Theses

3D printing is a crucial technology with many applications in different fields. To be able to use this technology to its full extent, expertise in computer aided design (CAD) technology and 3D modeling is required. Many people interested in 3D printing do not have this expertise and thus cannot build custom models, and are consequently forced to buy them instead. Natural language processing (NLP) is one tool that can vastly simplify 3D modeling for those lacking CAD experience. Using NLP, someone can simply dictate what they want to be able to print, and a computer can then build a 3D …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

A Neutrosophic Micro Vague Correlation Measure: Application To Multi-Criteria Decision Making Problems, Vargees Vahini T, Trinita Pricilla M

Neutrosophic Systems with Applications

Statistical methods called correlation measures are employed to express the degree of association or relationship between two variables. The correlation coefficient which may be computed in a variety of ways is the most often used kind of correlation measure in many disciplines including biology, psychology, economics, and finance. Knowing the correlations between variables is essential for analysis and decision-making. This paper aims to commence a novel type of correlation measure called Neutrosophic Micro vague Correlation Measure and Neutrosophic Micro Vague Weighted Correlation Measure. Further demonstrates the implementation of the Neutrosophic Micro Vague correlation measure in the MCDM Problem. By adopting …

Solving N-Players Continuous Differential Games Under Neutrosophic Environment, M. G. Brikaa

Neutrosophic Systems with Applications

Uncertainty plays a crucial role in decision-making problems, particularly in game theory. Various forms of uncertainty have been explored in the literature, including fuzzy, soft, rough, and interval-based approaches. Game theory has been extensively studied under these uncertainty models, with researchers addressing vagueness, and imprecision from multiple perspectives. More recently, neutrosophic sets have emerged as an alternative framework for handling uncertainty. Neutrosophic numbers effectively incorporate indeterminacy in decision-making by considering factors such as intuition, assumptions, judgment, behavior, evaluation, and preferences of decision-makers. This paper presents a novel approach to solving a new class of n-player continuous differential games within a …

Enhancing Smart City Management With Ai: Analyzing Key Criteria And Their Interrelationships Using Dematel Under Neutrosophic Numbers And Mabac For Optimal Development, Asmaa Elsayed, Mai Mohamed

Neutrosophic Systems with Applications

Purpose: This paper explores the transformative role of AI across various domains of smart cities, including urban mobility, energy management, public safety, healthcare, environmental monitoring, economic development, and data management. It aims to develop a novel decision-making framework that integrates AI technologies with a hybrid approach to analyze the interrelationships among smart city components.

Methodology: This paper employs a hybrid decision-making framework that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with single-valued trapezoidal neutrosophic numbers (STrNN). This approach is used to analyze the complex relationships among criteria and sub-criteria in smart city contexts, addressing uncertainty and incomplete information …

Unsupervised Contrastive Learning Based Clustering For Robust Emotion Classification, Nozaer Omar, Sanjay Kumar Madria

Miners Solving for Tomorrow Research Conference

No abstract provided.

Optimizing Llm X86 Assembly Code Comprehension Through Fine-Tuning, Darrin Michael Lea

LSU Master's Theses

Reverse engineering is a cybersecurity process that focuses on understanding the underlying functionality of software or malware. This is an arduous process that demands large amounts of time and effort from cybersecurity practitioners. Large Language Models (LLMs) offer a potential solution to this problem. LLMs have worked their way into various fields of cybersecurity in recent years, including incident response and malware classification. However, LLMs have historically struggled with low-level code comprehension: a necessary part of reverse engineering. While LLMs can generate code and explain its function on the surface level, they struggle to grasp the wider context. In this …

The Effect Of Nanomorphology As Quantified Via The K‑Index On The Drug Delivery Properties Of Isocyanate-Derived Aerogels, Stephen Y. Owusu, A. B. M. Shaheen Ud Doulah, Vaibhav A. Edlabadkar, Kamden J. George, Chariklia Sotiriou-Leventis

Miners Solving for Tomorrow Research Conference

No abstract provided.

Reframing Information Seeking In The Age Of Generative Ai: A Critical And Humanistic Approach, Joseph Kevin Sebastian

Library Faculty Research

Information-seeking has long been the subject of theoretical modeling, often drawing from cognitive, behavioral, computational, and even evolutionary perspectives to explain how individuals navigate, filter, and utilize information. Several dominant frameworks—Carol Kuhlthau’s Information Search Process, Marcia Bates’ Berrypicking Model, Peter Pirolli & Stuart Card’s Information Foraging Theory, Kiyohiko Nakamura’s Information Criteria framework, and Ian Ruthven’s Information Shaping Theory —have provided structured ways of understanding how people interact with information environments. However, while these frameworks offer valuable insights, they often operate within mechanistic or efficiency-driven paradigms, which risk overlooking the complex, embodied, and socioculturally situated nature of human information behaviors. These …

Mathematization In Missions, Jonathan Pawley

Mathematics Senior Capstone

This paper explores the implications of mathematization and the field of Christian missions.

2025- The Twenty-Ninth Annual Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the 29th Annual Symposium of Student Scholars, held on April 16-18, 2025. Includes abstracts from the presentations and posters.

Semantic Loss-Guided Data-Efficient Supervised Fine-Tuning For Safe Responses In Llms, Yuxiao Lu, Pradeep Varakantham, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corrective data. In this paper, we aim to take this problem and overcome limitations of requiring significant high-quality human data. Our method requires only a small set of unsafe responses to toxic prompts, easily obtained from the unsafe LLM itself. By employing a semantic cost combined with a negative Earth Mover Distance (EMD) …

Development And Evaluation Of The Da Vinci Ai Tutor: Enhancing Accessibility And Personalized Learning In Art History Education, James Hutson, Tiffani Barner

Faculty Scholarship

This study examines the implementation of the Da Vinci AI Tutor, an innovative artificial intelligence (AI)-based tutoring platform designed specifically for enhancing personalized and accessible learning in art history within higher education. Launched in Fall 2024 at a private liberal arts institution in the Midwest, the system integrates a conversational AI avatar modeled after Leonardo da Vinci, incorporating immersive virtual reality environments and multimodal interaction capabilities to engage students across undergraduate survey courses, advanced Renaissance classes, and graduate comprehensive exam preparations. Addressing significant gaps in existing humanities education research, the current study explores two primary research questions: (i) How AI-driven …

‬ ‭ Spiritual Geographies And Living Ritual:‬ ‭ Misali Island’S Caves In Swahili Religious Life In Zanzibar, Everett Gordon

Tanzania-Zanzibar: Coastal Ecology and Natural Resource Management

Caves along the East African Swahili coast are more than geological formations—they are spiritual landscapes where the boundaries between visible and invisible worlds collapse. This paper explores the ritual and religious significance of three caves on Misali Island, a small coral rag island off the coast of Pemba in the Zanzibar Archipelago. Drawing on vegetation surveys and three weeks of ethnographic fieldwork, including interviews with local fishermen, waganga (healers), and marine rangers, this study investigates how these caves serve as sites of prayer, healing, and spirit veneration. It argues that the practices performed within and around these caves exemplify the …

David B. Smith Chats With Monday 1.0, David B. Smith

Publications and Research

This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …

Navigating Generative Ai In Honors, Anne Dotter, Victoria M. Bryan

Journal of the National Collegiate Honors Council Online Archive

Considering the historical significance and pedagogical impact of educational technologies on teaching and learning in higher education, authors suggest that generative artificial intelligence creates more questions than solutions for honors practitioners. What might meaningful AI literacy look like throughout a multidisciplinary honors curriculum? To what extent will generative AI level educational inequities, or create new ones? If policing AI misuse is ultimately a losing battle, how might this understanding reshape assignment design, assessment practices, and even our definitions of academic dishonesty? Drawing on examples in current teaching practice, authors observe the two sides of generative AI—one holding powerful possibilities for …

Editor's Introduction, Amy Mecklenburg-Faenger

Journal of the National Collegiate Honors Council Online Archive

Editorial for Journal of the National Collegiate Honors Council (2025) 26(1), special issue on Forum on AI and Honors.

Teaching Ai Literacy Through Science Studies, Rhetoric, And Ethical Reasoning, Michael J. Klein, Philip L. Frana

Journal of the National Collegiate Honors Council Online Archive

Building on the idea of productive troublemaking, this essay presents a team-taught interdisciplinary honors course that integrates science and technology studies, rhetorical analysis, ethical reasoning, and artificial intelligence policy. Rather than framing AI as a threat, this course invites honors students to experiment with AI technologies and develop competencies by analyzing AI as a social and subjectivity-shaping phenomenon—writing policy briefs, producing rhetorical analyses of science fiction, and completing self-paced AI literacy modules. Honors education is uniquely positioned to model responsible and human-centered uses of intelligent systems, thereby cultivating graduates who can both use and critically interrogate the AI tools that …

Honoring Intellectual Risk-Taking: A Dialogue, Julie Bowman, Alexis Teagarden

Journal of the National Collegiate Honors Council Online Archive

Presented in the form of a Socratic dialogue, this piece considers what honors courses should strive to teach. Bowman, an experienced instructor of both non-honors and honors classes, notes that while universities’ honors colleges prize intellectual curiosity, her honors students might not. Teagarden, the other interlocutor and a writing program administrator, wonders whether curiosity is a sufficient end goal for honors or any teaching. The speakers turn to whether curiosity or courage is the more important virtue to instill, explore a classical difference between courage and audacity, and then discuss whether and how courage could be taught.

Generative Ai And The Honors Thesis: A Rhetorical Framework For Gai Policy, Pedagogy, And Equity, Sean Chadwick

Journal of the National Collegiate Honors Council Online Archive

This article offers a rhetorical framework for understanding how honors students engage with the capstone thesis following the emergence of generative AI (GAI) tools. Author reviews the nature of the honors thesis and analyzes some rhetorical models for thinking about GAI and literacy before offering a heuristic framework identifying four interdependent skill categories—writing, social, executive, and subject matter—that shape students’ thesis work. Drawing on findings from an interview study, this framework clarifies how GAI tools interface with existing thesis practices and pain points, allowing honors practitioners to better articulate our values, evaluate use cases, and craft GAI-informed policy. As a …

A Study Of Conceptual Primitive Elimination: Embedding Ingest Into Ptrans, Jamie C. Macbeth, Alexis Kilayko

Computer Science: Faculty Publications

In cognitive systems and cognitive linguistics, primitive decomposition systems attempt to explain cognitive phenomena by breaking things down into conceptual building blocks and provide rich and flexible representations for systems. A prime example is the Schank–Minsky Conceptual Dependency Trans-frames system, which maintains a commitment to keeping the number of primitives small and allowing them to be combined in complex ways in representing meaning, knowledge, and dynamic episodic memory. Motivated by the desire to keep the set of primitives small, this paper describes an effort to eliminate the Conceptual Dependency INGEST primitive and reconstitute its uses through combinations of the CD …

An Argument Against Gradual Type Systems In Programming Language Semantics, Natalie Lau

LMU Theses and Dissertations

In 2006, Jeremy Siek and Walid Taha formalized the concept of gradual type systems, which integrates static and dynamic typing in a single programming language. This allows the programmer to statically or dynamically type portions of their code at will, which offers more flexibility than languages that require all code to be statically typed. Despite the added convenience, gradual typing comes with its own set of tradeoffs, and researchers have been debating whether the drawbacks of integrating static and dynamic types outweigh its benefits. This thesis builds off of previous research to investigate the advantages and disadvantages of using a …