Digital Commons Network^™

All We (And Llms) Need Is Fuzzy: An Argument, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Large Language Models (LLMs) like ChatGPT have spectacular successes -- but they also have surprising failures that an average person with common sense could easily avoid. It is therefore desirable to incorporate the imprecise ("fuzzy") common sense into LLMs. A natural question is: to what extent will this help? This way, we may avoid a few simple mistakes, but will it significantly improve the LLMs' performance? What portion of the gap between current LLMs and ideal perfect AI-based agents can be, in principle, covered by using fuzzy techniques? Judging by the fact that few researchers working on LLMs (and on …

Estimation And Model Misspecification For Recurrent Event Data With Covariates Under Measurement Errors, Ravinath Alahakoon, Gideon K.D. Zamba, Xuerong Meggie Wen, Akim Adekpedjou

Mathematics and Statistics Faculty Research & Creative Works

For subject i, we monitor an event that can occur multiple times over a random observation window [0, (Formula presented.)). At each recurrence, p concomitant variables, (Formula presented.), associated to the event recurrence are recorded—a subset ((Formula presented.)) of which is measured with errors. To circumvent the problem of bias and consistency associated with parameter estimation in the presence of measurement errors, we propose inference for corrected estimating equations with well-behaved roots under an additive measurement errors model. We show that estimation is essentially unbiased under the corrected profile likelihood for recurrent events, in comparison to biased estimations under a …

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals, Sunil Arora, John D. Hastings

Research & Publications

This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile …

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, Jiating Li, Yufeng Ge, Laila A. Puntel, Derek M. Heeren, Geng Bai, Guillermo R. Balboa, John A. Gamon, Timothy J. Arkebauer, Yeyin Shi

School of Natural Resources: Faculty Publications

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared …

Devising Optimized Maize Nitrogen Stress Indices In Complex Field Conditions From Uav Hyperspectral Imagery, J. Li, Y. Ge, J. Gamon, L. A. Puntel Et Al.

School of Natural Resources: Faculty Publications

No abstract provided.

Perspectivism And Wicked Problems: Patterns In The Discovery Process Of Leibniz, Bohr, And Turing, Ernesto Diaz

Natural Sciences and Mathematics | Faculty Authored Dissertations and Theses

This research investigates fundamental patterns in how transformative scientific knowledge emerges and becomes established, examining three pivotal cases: Leibniz's development of calculus, Bohr's formulation of complementarity, and Turing's conceptualisation of computation and artificial intelligence. The study introduces the o-é-c model (Ouverture Ontologique - Épistémè Socialisante - Connaissance Éclairante) to explain how individual insights become integrated into collective knowledge through structured phases of development. Drawing on Minsky's conception of knowledge as mental models for problem-solving and Longino's social epistemology, the research demonstrates how wicked problems—those that resist formulation within existing frameworks—catalyse the creation of new knowledge domains. The model reveals how …

Celebrating Faculty Scholarship 2025, Smith College Libraries

Celebrating Faculty Scholarship: Bibliographies

Scroll down to view the bibliography of selected scholarly and creative work produced by Smith College faculty and submitted by faculty for the 2025 celebration which took place in the Neilson Library Skyline Reading Room, Smith College on April 24, 2025.

Navigating Artificial Intelligence: How Traditional Midwestern Four-Year Higher Education Institution Distance Learning Programs Are Addressing Artificial Intelligence, Eric Samaritoni

Theses, Dissertations and Capstones

AI's rapid evolution and integration into society has had and will continue to influence how distance education programs teach their students profoundly. This study aimed to explore the opinions of higher education distance education Provost administrators, program managers, directors, department chairs, and other subject matter experts on the perceived impact of AI in the classroom. This study used a qualitative, phenomenological approach to examine how AI impacts students, faculty, program delivery, institutional policies, and university budgets. Semistructured interviews were conducted with 13 faculty members meeting the criteria to answer research questions based on their experience or observations. The study used …

Neural Congruency Contrastive Learning Framework Validation Using Artificially Created Eeg Data For Dyslexia Research, Jacqueline M. Torres

Theses and Dissertations

Analysis of electroencephalogram (EEG) recordings in children with dyslexia has been commonly used to explore the neural mechanisms underlying reading disorders. Yet, challenges such as low signal-to-noise ratio (SNR), high inter-subject and inter-trial variability, and the inherently multivariate nature of EEG signals hinder the isolation of neural components elicited during reading diagnostic tests. To mitigate these challenges, the neural-congruency analysis framework was recently proposed, leveraging traditional machine learning optimization methods to incorporate domain knowledge about the congruency of neural responses across participants (i.e., consistent neural responses among proficient readers). However, the application of deep learning techniques, specifically contrastive learning, remains …

The Influence Of Generative Artificial Intelligence On Leadership: An Exploration Of Technology Professionals' Perceptions Regarding Leadership, Adaptation, And Organizational Culture In The Digital Age, Paul Thomas Herdman

Theses, Dissertations and Capstones

The emergence of generative artificial intelligence (GenAI) has introduced new complexities for organizational leadership, requiring technology professionals to adapt in real time to evolving digital tools, strategic demands, and cultural dynamics. Although prior research has examined AI’s broad influence on business processes, few studies have explored how technology leaders experience and interpret the leadership challenges and opportunities arising from GenAI. This qualitative dissertation addressed that gap by investigating the perceptions of senior technology professionals regarding GenAI’s influence on leadership roles, competencies, decision making, and organizational culture. Using Lanigan’s (1977) phenomenological method of human science, this study explored the lived experiences …

The Implosion Of The Titan Submersible: A Stress–Strain Experiment, Gregory A. Dilisi, Steven J. Eppell, Richard A. Rarick

2025 Faculty Bibliography

No abstract provided.

Machine Learning-Driven Music Genre Recognition, Redeate Kidanue

All Undergraduate Theses and Capstone Projects

Music is a tool that has been integrated into society for thousands of years; it has influenced social aspects of life and has also aided in communication. Today we have various uses for music that go past our traditional uses for entertainment and self-expression. For example, music therapy has been seen to show improvements in patients with Alzheimer’s disease, depression, and PTSD. Additionally, music has played a role in political movements, demonstrating its emotional power. Social media relies heavily on the music industry as many social media posts include music either in the background, or as the forefront of posts. …

The Relationship Between Trait Empathy And The Involuntary Mimicry Of Emotion: An Electromyographical Study Of Facial Suppression, Madison E. Dawes, Karlie Souder, Eddie Broad, Htoo Aye, Sian Huai

Science University Research Symposium (SURS)

Abstract

Facial expressions are one of the main communication styles that signify emotion and meaning across cultures and individuals. Despite it being one of our main forms of communication, in many social situations, people are required to suppress or control their emotional expressions to act in a socially acceptable manner. However, research has shown that a person’s facial muscles continue to exhibit subtle muscle and move in expressivity even when attempting to suppress emotions from others. To support this, Dimberg et al. (2002) found that electromyography (EMG) technology can detect these subtle facial movements even if they may be invisible …

Modeling Trust And Deception In Multi-Agent Reinforcement Learning Using The Werewolf Game, Pathikkumar Dharmeshbhai Patel

Computer Science and Engineering Theses - Archive

This thesis explores the emergence of trust, deception, and adaptive strategy in multi-agent reinforcement learning (MARL) environments using the social deduction game Werewolf as a simulation framework. In this environment, agents operate with hidden roles, incomplete information, and the need to reason about others’ intentions- mirroring the complexities of real-world social interactions. We present and evaluate two agent architectures: Agent vA, a symbolic, heuristic-based agent with probabilistic trust modeling and scalable memory structures; and Agent vB, a modular Q-learning agent that learns phase-specific policies through reinforcement. Agent vA relies on symbolic reasoning, bounded belief updates, and generalizable heuristics, while Agent …

Centrality Algorithms For Weighted Homogeneous Multilayer Networks, Ayomide Ayowole-Obi

Computer Science and Engineering Theses - Archive

Applications need to be modeled for analyses. With the availability of many alternate data Models, choosing one needs to be done carefully by considering the complexity of data to be modeled and its analyses requirements. Social networks and other newer applications are primarily relationship-oriented and also have multiple types of entities and relationships. Although simple and attributed graphs have been used historically for modeling these applications, recently, multilayer networks (or MLNs) have been shown to be more effective in preserving the semantics of the application better and further provide flexibility of analyses. However, choice of MLN as a data model …

Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit, Solomon G. Dandekar

Computer Science and Engineering Theses - Archive

The increasing integration of technology into daily life has provided numerous benefits but also significant risks, particularly when exploited by malicious actors in cases of technology facilitated abuse (TFA). Per- petrators can misuse technology to monitor, control, and intimidate their partners, random strangers, etc. exacerbating cycles of abuse. From location tracking and cellphone surveillance to smart device manipula- tion, spyware, and doxing, digital tools have become powerful instruments for coercion and control. This research project investigates the role of technology in stalking and harassment by analyzing discussions on a relevant subreddit where victims share their experiences, strategies for coping, and …

A Deep Reinforcement Learning Framework For Sequential Art Creation, Asmin Pothula

Computer Science and Engineering Theses - Archive

Most computational art systems rely on generative models that produce a complete artwork in a single pass, without capturing the gradual, decision-driven process through which human artists construct visual pieces. Prior research in sequential, stroke-based image generation, including differentiable neural painters and model-based reinforcement learning agents, has explored step-by-step creation, but these systems typically aim to reconstruct the input image within the same visual representation space, closely matching brushstrokes, textures, or colors to the target. In contrast, this thesis investigates sequential art creation in a different artistic representation, where the final artwork does not share the same visual form as …

Scalable Approaches Towards Characterizing And Mitigating Emerging Phishing Scams, Sayak Saha Roy

Computer Science and Engineering Dissertations - Archive

Phishing scams are among the most dangerous and persistent forms of cybercrime, leveraging social engineering to exploit human behavior and obtain sensitive information, leading to widespread identity theft and data breaches. In the past year, these attacks have resulted in financial losses exceeding $10 billion in the United States alone. As phishing scams continue to evolve, they have not only expanded in scale but also grown in sophistication, spreading rapidly across social media and employing adversarial techniques to evade detection by anti-scam tools. The situation is further exacerbated by the availability of advanced phishing kits, and more recently, generative AI, …

Feel Bad To Discard A Fashion Product: How Ai Designers Influence Individuals' Sustainable Consumption, Ha Kyung Lee, Dooyoung Choi

Educational Leadership & Workforce Development Faculty Publications

This study explores how AI technology in fashion design influences consumers' sustainable consumption behaviors, focusing on emotional attachment to products. By comparing AI-generated and human-designed fashion items, the study examines how designer type impacts negative emotions about discarding products, mediated by emotional attachment. Results from two experimental studies reveal that designer type significantly affects negative emotions toward discarding human-designed items, but emotional attachment was not influenced by designer type in the first study. This lack of difference may be due to personal characteristics that moderate the effect. The second study found that individuals who perceive AI as human-like form stronger …

Meteorology, Weather And War In South East Asia: Malaya C. 1940-1960, Fiona Williamson

Research Collection College of Integrative Studies

This article interrogates the positioning of British colonial meteorology in Malaysia and Singapore from the 1940s to 1960. This period spanned a global conflict and an internecine war, effecting profound sociopolitical changes from which neither Malaysia nor Singapore would emerge the same. The meteorological services were essential to Britain's armed conflicts, providing vital weather information to the army, navy and, especially, the air forces, as well as supporting the aviation and shipping industry often in difficult and dangerous circumstances. This article argues that British military policy in South East Asia and the specific concerns of the colonial government in Malaya …

Confronting The Seaport Resilience Gap: Incentives, Frameworks, And Case Studies For Enhancing Resilience To Natural Hazards In Port Planning And Policy, Rosemarie Fusco

Open Access Dissertations

This dissertation investigates the critical issue of seaport resilience, focusing on the gaps in resilience planning and the role of incentives in motivating resilience investment. The research arc addresses the question: What incentivizes seaports to increase natural hazard resilience (NHR)? It is organized into three interconnected manuscripts, each examining distinct aspects of the problem.

The first manuscript critically examines the resilience planning approaches of Small Island Developing States (SIDS) as outlined in their National Adaptation Plans (NAPs), highlighting the limited consideration of seaports despite their critical role in economic and social connectivity. SIDS offer critical insight into seaport resilience due …

Environment And Intention Awareness For Navigation And Collaboration, Bhaskar Chandra Trivedi

Computer Science and Engineering Dissertations - Archive

Unmanned Aerial Systems (UAS) have become increasingly popular as versatile platforms for tasks such as surveillance, inspection, delivery, and maintenance. In many applications, UAS operate in environments frequented by people or containing sensitive infrastructure, which introduces physical risks in case of vehicle failure, as well as psychological and privacy concerns that may limit their acceptability. Ensuring safe and efficient operation thus requires that UAS consider these risks when planning navigation strategies. While prior information, such as city maps and building layouts, can partially inform risk assessment, such data is often incomplete, necessitating real-time augmentation of risk maps using sensor information. …

Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

Mathematics & Statistics Faculty Publications

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …

Perceptions Of Artificial Intelligence Use To Enhance Feedback For Preservice Teachers During Field Experiences, Betsy Schamber

Dissertations and Theses

University supervisors (USs) play a key role in providing feedback for preservice teachers (PSTs). Although artificial intelligence (AI)’s use in providing feedback in educational settings had been explored, its use for feedback for PSTs’ field experience remained unknown. The first case study herein explores PSTs’ perceptions of AI-assisted feedback for field experiences. Findings highlighted PSTs’ perceptions of AI as a catalyst for new idea generation. While AI provided a starting point, the ending output still needed to reflect PSTs’ personalities. Similarly, PSTs valued the human element of feedback, noting how USs’ lived experiences provided an added value. In K–12 classrooms, …

Artificial Intelligence In Society: Transformations And Ethical Paradigm, Yashoda Urmilla Itwaru

Selected Full-Text Master Theses 2021-

The recent advancement and adoption of AI (Artificial Intelligence) make it vital to study the effects it has on individuals and communities. Adopting AI into daily life changes interactions such as the decision-making process and task performance. AI algorithms can shape a user’s online experiences, influence their preferences, beliefs, and behavior. However, people raised concerns regarding privacy, autonomy, and manipulation. For these reasons, debates regarding the ethical aspects of AI arose.

AI provides society with more tools to complete tasks. AI can automate data intensive jobs and physical tasks. This brings a sense of job insecurity, which affects the mental …

Edge-Enhanced Yolo V8 Architecture For Accurate Kl Assessment In Knee Osteoarthritis Imaging, Meghana Arikilla

Selected Full-Text Master Theses 2021-

Knee Osteoarthritis (KOA) is a degenerative joint condition characterized by the progressive narrowing of joint space and structural deterioration. The structural degradation of the joint space is evaluated using the Kellgren–Lawrence (KL) grading system, and accurate classification across all grades, specifically in the early stages, remains a challenge owing to subtle radiographic differences. This study presents an automated KL-grade classification framework that integrates joint edge enhancement with deep learning to improve KOA grading using radiographic images.

Edge detection filters, namely Sobel, Scharr, and Canny, were applied to X-ray images to enhance the joint space boundaries and osteoarthritic features. These preprocessed …

A Proof Of Np-Completeness For The K-Means Clustering Algorithm, Brooke C. Feinberg

Scripps Senior Theses

The k-means clustering algorithm is one of the most widely used clustering techniques in data analysis and machine learning, yet its exact computational complexity remains subject to ongoing theoretical investiga- tion. This work establishes the NP-completeness of k-means by proving (1) it is NP-hard and (2) it lies in NP. To demonstrate NP-hardness, we construct a series of polynomial-time reductions from well-known NP-complete problems. Specifically, we reduce 3sat to Vertex Cover, and then reduce Vertex Cover to k-means, thereby establishing the computational hardness of the k-means clustering problem. We then prove k-means is in NP, and thus conclude it is …

Ally With Ai: An Icebreaker To Unlock Career Aspirations For Online Or Hybrid Organizational Behavior Cohorts, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Modern organizational behavior classroom, which are increasing in size, diversity, and complexity, are shifting to online and hybrid learning environments, challenging the use of traditional icebreaker activities. This paper introduces a 15-minute icebreaker designed to address these issues while integrating the principles of Kolb's experiential learning theory and fostering social capital through peerr engagement in an online setting. By leveraging the availability of generative AI, students prompt a template code to unlock their career aspirations and stimulate social connections among their classmates. This provides an innovative teaching model for meeting the foundational icebreaker goal while serving a suitable tool for …

Design And Implementation Of A Low-Cost Raspberry Pi And Ai-Based Intrusion Detection System For Surveillance, Metrine Nyaboke Osiemo

All Graduate Theses, Dissertations, and Other Capstone Projects

As security concerns continue to rise, there is a growing demand for affordable and intelligent surveillance solutions to ensure safety in homes, businesses, and other environments. Many individuals are embracing AI-driven technologies such as Closed-Circuit Television (CCTV), smart doorbells, and automated security systems to protect their properties. This project presents a design and implementation of a cost-effective AI-powered intrusion detection system utilizing Raspberry Pi 5 for home surveillance, with adaptability for broader applications. The system integrates a camera module and an LCD screen running on a Linux-based platform, with Python, and OpenCV as key software components. It employs dlib’s deep …