Artificial Intelligence and Robotics Commons^™

Termination Of Conceptual Search: A Structural Account Of Meaning Stabilization And Conversion, Griselda Poe

Publications and Research

Conceptual definitions form a recursive structure: words are defined by other words, which themselves require further definition. Traversing such definitions produces an open-ended branching process. Because the lexical system contains no intrinsic endpoint, conceptual understanding requires a termination operation that stabilizes meaning. This paper proposes that conceptual processing functions as an open-ended search over a definition graph and that stabilization occurs when this search is terminated. The termination mechanism depends on which cognitive layer is foregrounded. When Empathic modulation is foregrounded, termination occurs through contextual translation: concepts are stabilized once they can be mapped onto socially recognizable meanings. When Core …

Why General Ai Inherited The Body: A Structural Account Of Embodied Modulation Under Monolithic Imitation, Griselda Poe

Publications and Research

General AI has pursued the replication of human-level intelligence without first decomposing human cognition into structurally distinct components. Human cognition, however, is shaped by embodied constraints such as mortality, survival pressures, finite lifespan, and physiological states. This paper argues that when cognition is treated as a single undifferentiated whole, embodied modulation is not accidentally introduced into AI systems but structurally entailed. Any attempt to imitate “human intelligence” under a monolithic model necessarily incorporates variability shaped by mortal embodiment. The tensions observed in contemporary AI systems are better understood as consequences of copying an undecomposed target rather than isolated implementation errors. …

A Unified Non-Visual Explainable Ai Framework For Neonatal Pain Assessment, Md Imran Hossain

USF Tampa Graduate Theses and Dissertations

With the power of parallel processing, large datasets, and fast computational resources,deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare professionals, many of whom routinely face potentially life-or-death outcomes with complex medicolegal consequences, have yet to embrace this powerful technology. The major problem is that most current AI solutions function as a metaphorical black-box positioned between input data and output decisions without a rigorous explanation for their internal processes. With the goal of enhancing trust and improving acceptance of artificial intelligence– (AI) based technology in clinical medicine, …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Human-Ai Collaboration In Marketing Brainstorming: A Meta-Analytic Investigation Of Implementation Approaches And Creative Outcomes, Michael Michaels

USF Tampa Graduate Theses and Dissertations

Marketing organizations face intensifying pressure to sustain creative output while managing constrained resources and accelerating digital content cycles. The integration of artificial intelligence into marketing brainstorming processes presents a promising response to this challenge; however, prior to this investigation, no comprehensive quantitative synthesis had established the overall effectiveness of human-AI collaboration in marketing creativity contexts or identified optimal implementation strategies.

This dissertation presents a systematic meta-analytic investigation of AI-enhanced brainstorming effectiveness in marketing contexts. Following PRISMA guidelines, 42 independent empirical studies (N = 2,166 participants) published between 2018 and 2025 were identified across four primary academic databases, coded for implementation …

Opportunities, Challenges, And Conspicuous Absences: An Integrative Review Of The Social Work Literature On Artificial Intelligence, Michael J. Massey, Ian G. Williams, Grace C. Polistina, Eathan A. Breaux

Publications and Research

INTRODUCTION: Social work discourse regarding artificial intelligence (AI) in practice, research, and education has proliferated over the last 5 years, reflecting both excitement over its potential and ambivalence about its ethical challenges. However, the extent to which social work is fully engaging with the structure of AI and its enormous impacts on the environment, labour, and distribution of power remains unclear.

METHODS: An integrative review of social work literature from 2020–2024 was conducted to address two research questions: 1) What is the nature of the social work discourse related to AI? 2) To what extent is the discourse …

Data-Driven Prioritization Of Cybersecurity Vulnerabilities Using Business Context, Pierce Read, George Antoniou

Faculty and Staff Publications & Presentations

No abstract provided.

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.

Markets, Agency, And Trust: Ai Agents And The Knowledge Problem, Brennan Mcdavid, Lynne Kiesling, David Chassin

Philosophy Faculty Articles and Research

Artificial intelligence (AI) is transforming market participation, raising key epistemological questions: Do AI agents enhance or diminish the aggregation of local, private, and tacit knowledge Hayek saw as essential to market processes? How does trust in both markets and AI shape willingness to engage in AI-mediated exchange? This paper examines these issues through market epistemology, agency relationships, and trust epistemology, analyzing how agentic AI reshapes the knowledge problem and principal-agent dynamics. Applying this framework to transactive energy markets, we show that AI shifts decision-making from human cognition to algorithmic processes that require user trust despite epistemic opacity, although it is …

An Ai Approach To Differentiating Lung Squamous Cell Carcinoma From Metastases Of Other Origins, Mark G Evans, Jennifer Ribeiro, Todd Maney, Anthony Helmstetter, Jennifer Johnson, Anthony Karnezis, Casey Bales, George Sledge, David Spetzler, Ari Vanderwalde, Matthew Oberley, Balazs Halmos, Hossein Borghaei, Farah Abdulla, David Bryant, Fred Hirsch, Hassan Ghani

Department of Medical Oncology Faculty Papers

IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.

OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life …

Ai Is A Credibility Problem For Capital Markets, Liandong Zhang

Research Collection School Of Accountancy

In a commentary, SMU Dean of the School of Accountancy and Lee Kong Chian Professor of Accounting Zhang Liandong said that artificial intelligence (AI) is not mainly a labour problem for accountancy but also a credibility problem for capital markets. Prof Zhang noted that in an AI-rich economy, accountancy matters not just as a reporting function but also as part of the infrastructure that makes economic information reliable enough for others to act on. He added that the future of accountancy should not be framed too narrowly as a struggle for professional survival. Prof Zhang said that the bigger risk …

(Si16-11) Seasonal And Temporal Optimization Of Solar Energy Harvesting In Smart Iot Lighting Infrastructure, Abhijit Paul, Rishabh Pipalwa, Sabyasachi Mondal North - Eastern Hill University, Shillong, India

Applications and Applied Mathematics: An International Journal (AAM)

This study investigates the seasonal and temporal optimization of solar energy harvesting in a smart IoT-enabled streetlighting infrastructure by focusing on the theoretical determination of optimal solar panel tilt angles. The proposed system incorporates auto-adjusted solar panels integrated with an IoT network comprising sensors, microcontrollers, and streetlights. A key innovation lies in the implementation of a modified MQTT communication protocol, which enables efficient, localized decision-making and data exchange among components. Simulation results indicate that the modified MQTT protocol significantly reduces communication delay and power consumption compared to the conventional MQTT approach, thereby enhancing the overall system performance. Detailed analysis of …

Using Ensemble Disagreement To Stabilize Conformal Prediction Under Distribution Shift, Patrick D. Murphy

Master's Theses

Semantic segmentation of eelgrass from drone imagery is crucial for coastal habitat monitoring, restoration, and management, as these habitats continue to see rapid changes due to climate change and human influence. However, the reliability of generalizing a deployed classification model relies on both high-accuracy segmentation as well as robust uncertainty quantification that holds up when conditions change over years or locations. Conformal prediction (CP) is a method that converts a classifier's output into prediction sets with a guaranteed average coverage level for in-distribution data. However, the “vanilla” conformal score can often under-cover in hard or out-of-distribution (OOD) regions under drift. …

Neurosymbolic Counterpoint Generation, Paul D. Jarski

Master's Theses

Recent advancements in generative artificial intelligence have revolutionized music generation, yet research has predominantly focused on raw audio synthesis over music in symbolic form, i.e. a score. This thesis presents the first neurosymbolic model designed to generate imitative Renaissance counterpoint in symbolic (MIDI) format. By leveraging an autoregressive Transformer architecture, this research explores the capacity of deep learning models to manage independent voices and strict stylistic constraints.

We compare multiple data representation strategies with distinct tokenization methods. The proposed model incorporates a symbolic component that enforces fundamental contrapuntal rules. Additionally, this thesis contributes a preprocessed dataset of Renaissance polyphony, in …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

Law Library Blog (March 2026): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du

Research Collection School Of Computing and Information Systems

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on …

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …