Digital Commons Network^™

Fictional Failures And Real-World Lessons: Ethical Speculation Through Design Fiction On Emotional Support Conversational Ai, Fayle Kollig, Jessica Pater, Fayika Farhat Nova, Casey Fiesler

Health Services and Informatics Research

Conversational artificial intelligence (CAI), which replicates human-to-human interaction as human-to-machine, is increasingly developed to address insufficient access to healthcare. In this paper, we use design fiction methods to speculate on ethical consequences of CAI that offers emotional support to complement or replace mental healthcare. Through a near-future news article about a fictional, failed CAI, we explore safety and privacy concerns associated with mismatches between what an emotional support CAI is advertised to do, what it technically can do, and how it is likely to be used. We pose the following questions to researchers, regulators, and developers: How might we jointly …

A Hybrid Deep Learning-Based Approach For Visual Field Test Forecasting, Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

OBJECTIVE: Longitudinal assessment of visual field (VF) testing is essential in glaucoma management. Conventional VF forecasting methods require numerous prior tests, while deep learning techniques have shown promising results with fewer tests. This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting.

DESIGN: A retrospective longitudinal study using deep learning-based VF forecasting models.

SUBJECTS AND CONTROLS: A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University.

METHODS: Three deep …

A Comprehensive Intelligent Traffic Monitoring System Based On A Novel Integration Of Neutrosophic Multi-Criteria Decision-Making Techniques, Mai Mohamed, Amira Salam, Rana Muhammad Zulqarnain, Muhammad Gulistan

Neutrosophic Systems with Applications

With the spread of road accidents and traffic congestion that costs countries and governments a lot of money in addition to the loss of human lives, and since traditional methods of monitoring traffic have not been as effective as desired, attention has been drawn to the search for more effective solutions to the problem of monitoring and regulating traffic. With the spread of technology and the Internet of Things, UAVs have emerged as a promising tool for monitoring traffic, as they can fly for a sufficient period and operate in difficult climatic conditions, in addition to their ability to monitor …

A Performance Comparison Between Two Speech-To-Asl-Gesture-Projection Translation Implementations, Alexandra Kashani Motlagh, Shikha Mehta, Ishfaq Ahmad, Addison Clark, Hansheng Lei

Informatics and Engineering Systems Faculty Publications

Millions of people with hearing disabilities use sign language for communication, creating a communication gap with those who are not fluent in ASL (American Sign Language). This paper aims to introduce an ASL interpreter system using a smart-glasses-based augmented reality system. We begin by introducing and comparing two models that translate spoken language into ASL poses. The first system translates spoken text to ASL Gloss, an intermediate representation, before generating ASL poses. The second system directly translates the text to ASL poses. Our analysis shows that using ASL Gloss as an intermediate step significantly improves the translation speed. We then …

Ontologies For Smart Agriculture: A Path Toward Explainable Ai-A Systematic Literature Review, Rima Grati, Najla Fattouch, Khouloud Boukadi

All Works

Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into …

Expanding The Horizons Of Nonagonal Neutrosophic Numbers As A Versatile Framework For Decision-Making And Scientific Applications In Covid-19, Muhammad Naveed Jafar, Kainat Muniba, Hamiden Abd El-Wahed Khalifa, Fahd Jarad

Neutrosophic Systems with Applications

In this article, the concept of nonagonal neutrosophic numbers has been introducing in the disjunctive frame of reference. We also proposed the dependency and independency of membership function in regards to nonagonal neutrosophic number. We also introduce a new score function and its computation also formulated in a distinct rational viewpoint. We developed the concept of weighted arithmetic averaging operator and weighted geometric averaging operator for nonagonal neutrosophic numbers. It will open new doors for MCDM and develop the concept with new approaches. Additionally, we analyze the effect of COVID-19 for different ages.

Leveraging Hypersoft Set To Optimize Livestock In The Era Of Unmanned Aerial Vehicles, Alaa Salem, Mona Mohamed, Nebojsa Bacanin, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Due to urbanization and industrialization, rapid global change and the potential loss of arable land, agricultural output must rise in production levels and harvest, distribute, and use resources more efficiently. It is believed that using technology on livestock would help meet the expanding population's demand for food. Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) integration in conventional farming has transformed operations, providing farmers with greater productivity, improved decision-making, and sustainability. We assume that there are enough UAVs to cover the entire pasture, and our goal is to identify the best UAVs. Accordingly, determining the best type of UAVs …

A Comprehensive Intelligent Traffic Monitoring System Based On A Novel Integration Of Neutrosophic Multi-Criteria Decision-Making Techniques, Mai Mohamed, Amira Salam, Rana Muhammad Zulqarnain, Muhammad Gulistan

Neutrosophic Systems with Applications

With the spread of road accidents and traffic congestion that costs countries and governments a lot of money in addition to the loss of human lives, and since traditional methods of monitoring traffic have not been as effective as desired, attention has been drawn to the search for more effective solutions to the problem of monitoring and regulating traffic. With the spread of technology and the Internet of Things, UAVs have emerged as a promising tool for monitoring traffic, as they can fly for a sufficient period and operate in difficult climatic conditions, in addition to their ability to monitor …

Nash Equilibrium Solutions For Continuous Static Games Under Neutrosophic Environment, M. G. Brikaa

Neutrosophic Systems with Applications

Neutrosophic set theory plays an important role in dealing with the impreciseness and inconsistency in data encountered in solving real life problems. This paper presents a novel approach to solving a new class of continuous static games within a neutrosophic framework. In the proposed methodology, the neutrosophic continuous static games are redefined into two separate crisp problems: the lower problem and the upper problem. The study further establishes the necessary conditions for determining equilibrium strategies in neutrosophic continuous static games. To demonstrate its effectiveness and practical applicability, the proposed method is validated through a numerical example.

Revisiting Bipolar Neutrosophic Graph And Interval-Valued Neutrosophic Graph, Takaaki Fujita

Neutrosophic Systems with Applications

Graph theory explores networks composed of nodes (vertices) and their connections (edges). A graph class is a collection of graphs that share common structural properties, defined by specific rules or constraints. This paper examines various models of uncertain graphs, including Fuzzy, Intuitionistic Fuzzy , Neutrosophic, Turiyam Neutrosophic, and Plithogenic Graphs. In particular, this study focuses on Bipolar Graphs and Interval-valued Graphs, analyzing them within the frameworks of Fuzzy, Neutrosophic, Turiyam Neutrosophic, and Plithogenic Graphs.

Assessing The Sustainable Blockchain-Metaverse-Iot Platform In The Healthcare Industry: An Intelligent Decision Support Model, Ibrahim M. Hezam, Ahmed M. Ali, Ibrahim A. Hameed, Karam Sallam, Mohamed Abdel-Basset

Neutrosophic Systems with Applications

Healthcare services must fulfill patients' desires for secure data sharing and high accessibility. Blockchain technology, through blockchain platforms (BPs), can overcome healthcare challenges. This study develops a decision-making methodology for selecting the best BP, by integrating blockchain with IoT and Metaverse, the proposed approach ensures data integrity, quality, privacy and security, secure data sharing, and interoperability. The decision-making methodology uses the multi-criteria decision-making (MCDM) methodology to handle conflicting criteria. Two MCDM methods are used in this study: CRiteria Importance Through Intercriteria Correlation (CRITIC) for weight computation, and Ranking of Alternatives with Weights of Criterion (RAWEC) for alternative ranking. To deal …

A Novel Neutrosophic Decision-Making Approach For Optimizing Metaverse Headphone Design: Balancing Technical Performance And User Emotional Needs, Mai Mohamed, Amira Salam, Karam Sallam, Bilal Arain

Neutrosophic Systems with Applications

The concept of the metaverse, which combines various technologies to create a wide range of virtual experiences, has gained significant popularity in recent years. To fully engage in these metaverse environments, users rely on access devices such as virtual reality (VR) headsets and smartphones for augmented reality (AR). These devices must be lightweight, compact, and user-friendly to ensure comfort and enhance customer satisfaction. There is a growing focus on innovating and designing products that not only meet technical requirements but also address the emotional needs of users, ultimately improving the overall experience. Selecting the ideal design for Metaverse headphones is …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Stereotyping In Language (Technologies): An Examination Of Racial And Gender Stereotypes In Natural Language And Language Models, Messi Lee

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation examines stereotyping across natural language and language technologies through three interconnected studies. The first chapter applies a contemporary model of race relations from social psychology to investigate America's racial framework within American English, revealing how language encodes hierarchical associations between racial/ethnic groups and attributes of superiority and Americanness. The second chapter extends this analysis to Large Language Models (LLMs), finding that these language technologies portray socially subordinate groups as more homogeneous compared to dominant groups. The third chapter investigates stereotyping in Vision Language Models (VLMs), showing that these language technologies generate more uniform representations for women than men …

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 …

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 …