Digital Commons Network^™

Ai-Powered Knowledge Engines As Research Infrastructure For Systematic Knowledge Discovery, Gary Welz

Publications and Research

This paper proposes knowledge engines as a framework for understanding how intelligent systems — both human and artificial — systematically discover, integrate, and generate knowledge. We argue that history’s greatest scientific minds functioned as knowledge engines, processing information through iterative cycles of ingestion, analysis, synthesis, and communication, guided by curiosity and willingness to challenge established beliefs.

We propose a taxonomy of nine integrated capabilities — ingestion, digestion, analysis, calculation, comparison, connection, association, analogy, and multimodal communication — that any serious knowledge engine must combine systematically. The argument is deliberately integrative: achieving ambitious research goals requires orchestrating all nine capabilities within …

Can An Ai System Be Creative? A Critical Perspective From Art And Engineering, Ivan Magrin-Chagnolleau

Presidential Fellows Articles and Research

This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy. Drawing on Margaret Boden’s foundational framework — both her three properties of creativity (novelty, surprise, and value) and her three types of creative processes (combinatorial, exploratory, and transformational) — the paper argues that AI systems are structurally incapable of creativity in …

One-For-All Community Search On Unseen Graphs, Mo Li, Zhaosong Zhao, Linlin Ding, Renata Borovica-Gajic, Zhongming Yao, Jianxin Li

Research outputs 2022 to 2026

Community search is a fundamental graph-based retrieval problem that aims to identify a query-dependent subgraph whose nodes exhibit strong internal connectivity. While recent learning-based methods improve retrieval effectiveness via graph representation learning, they follow a ''one-use-one-train'' paradigm that requires retraining or fine-tuning for each target graph, leading to high data dependency, high training costs, and limited generalization. To handle this, we propose OFA-CS, a ''one-for-all'' community search framework trained once on source datasets and directly deployed to arbitrary unseen graphs without retraining or fine-tuning, while preserving strong performance. Specifically, we introduce a Spectral-Aware Feature Alignment module to unify feature dimensionality …

Together//Apart Explorations In Choreorobotic Performance Ontologies, Kate Sicchio, Patrick J. Martin

Computer Science Faculty Publications

This chapter presents a practice-as-research approach to developing an improvisational choreorobotic performance. Our performance process motivated the creation of new human–robot interaction and live choreography technologies. These technologies were tested during the performance and evaluated by the audience through feedback on their perceptions about the coexistence of humans and machines in a shared space. Examining these results through both autonomous robotics and performance studies ontologies, we formulated a new analytical process in which choreographic practice informs design and robots inform performance.

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

Compressed Cinema As A Study In Llm Latent Spaces, Mallen Clifton

ELO (un)supervised 2026

In his article “Spec Acts” (2021), Matthew Kirschenbaum analyzes the AI-generated novel 1 the Road to develop his titular concept of the spec act, “the future in its multitudes collapsing into an actionable present.” With the proliferation of texts produced by generative AI and subsequent critical analyses of them, one element in particular calls for further theorization: “the future in its multitudes,” or more directly, the latent space. This echoes arguments by critics such as Antonio Somaini, who offered his own “Theory of Latent Spaces” last year. However, where Somaini’s attention is towards visual culture, I turn mine to the …

Observations On Recurrent Loss In The Neural Network Model Of A Partial Differential Equation: The Advection–Diffusion Equation, Jonah A. Reeger

Faculty Publications

A growing body of literature has been leveraging techniques of machine learning (ML) to build novel approaches to approximating the solutions to partial differential equations. Noticeably absent from the literature is a systematic exploration of the stability of the solutions generated by these ML approaches. Here, a recurrent network is introduced that matches precisely the evaluation of a multi-step method paired with a collocation method for approximating spatial derivatives in the advection–diffusion equation. This allows for two things: (1) the use of traditional tools for analyzing the stability of a numerical method for solving PDEs and (2) bringing to bear …

Assessing Flaws In Captcha Security Through Progress In Ai, Jaydon Stanislowski

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Protecting the internet from the threat of malicious bot activity is an important problem as AI tools become more powerful and commonplace over time. To that end, security measures are employed across websites in the form of CAPTCHAs, short challenges designed to identify and block fake web traffic. Yet, they become less effective over time as AI becomes more powerful, and thus more capable of solving them. This paper examines recent research on the threat to CAPTCHA security posed by current AI models and how this security can be reinforced over time, focusing primarily on Google’s reCAPTCHA v3.

Llm-As-A-Judge For Infection Prevention And Control And Antimicrobial Resistance Impact: Comparing Three Main Llms Vs. Human Experts' Assessment, Marcello Di Pumpo, Leonardo Villani, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Patrizia Laurenti, Vittorio Maio, Stefania Boccia, Walter Ricciardi

College of Population Health Faculty Papers

BACKGROUND: Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks.

METHODS: We performed a secondary analysis of an expert-annotated dataset of health messages. Three leading LLMs (ChatGPT, Claude, Gemini) independently evaluated the same messages using an adapted DISCERN tool across five domains: information reliability, quality, AMR impact, persuasiveness, and overall score. We utilized descriptive statistics, intra-rater reliability tests, and mixed-effects ordinal regression to analyze divergence …

Genedit: A Context-Aware Equity, Diversity And Inclusion Principles Integration Tool For Software Engineering Education, Chetan Arora, Ajanie Kodagoda Bammanna Arachchige, Jinchun Du, Muhammad Aamir Cheema, Aster Cosmos, Antonette Shibani, Vasudha Malhotra, Naeem Janjua, Afaq Shah

Research outputs 2022 to 2026

Equity, diversity and inclusion (EDI) is widely acknowledged as essential in software engineering (SE), yet day-to-day integration into teaching remains uncommon due to time pressures, low instructor confidence, and fragmented resources. We present GenEDIt, a purpose-built, LLM-backed chatbot that helps educators weave EDI into SE education artefacts without altering intended learning outcomes. Unlike generic chat interfaces, GenEDIt provides a tailored UI and workflow, and embeds retrieval over a vetted EDI knowledge base. The tool supports five educator-centred modes - (1) methods for integrating EDI into current activities; (2) examples/datasets; (3) EDI-integrated assessments and rubrics; (4) reflective prompts; and (5) rapid …

Decoding The Allosteric Grammar Of Protein Kinases: A Dual-Stream Framework Integrating Protein Language Models And Energy Landscape Frustration Analysis, Will Gatlin, Max Ludwick, Lucas Turano, Brandon Foley, Kamila Riedlova, Vít Škrhák, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence-based artificial intelligence (AI) models. We present a protein language model (PLM)-guided approach complemented by the energy landscape frustration analysis as a dual-stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine-tuned residue-level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type …

Expert Interview: "The Mirror Of Ai" In The Domain Of Scientifc Information Research, Taitian Mao, Yulai Bao, Jianxiang Wei, Peng Wu, Chuanming Yu, Gan Tang, Dongyan Wei, Yifei Ma, Wei Wang, Yu Ma

Journal of Scientific Information Research

Professor Mao Taitian and colleagues argues elucidates the adaptation logic, practical pathways, and prerequisites of intelligent agents to empower the high-quality development of scientific information. Professor Bao Yulai and colleagues advocate that integrating the perceptual elasticity of domain-specific large models with the cognitive rigidity of ontology can establish a new paradigm for intelligence services in complex scenarios. The synergy between the two can not only expand the theoretical boundaries of information science and serve national strategies, but also advance intelligence services from assisted analysis to intelligent decision-making. Professor Wei Jianxiang and colleagues point out that generative artificial intelligence has triggered …

Religious Bias In Llms Is Significantly Understudied, Sheryl Carty, Nancy Fulda, Walter Reade

Faculty Publications

In the earlier years of development of LLMs, it was relatively easy to prompt an LLM to respond with toxic or biased statements about religion. Subsequent improvements in frontier models addressed many of the issues of bias and toxicity in general, including against religion. At the same time, the adoption and usage of these models has grown exponentially. Small and implicit biases, therefore, have a magnified overall impact. In this paper, we (1) briefly review previous efforts to measure religious bias in LLMs, (2) show, by reviewing over 12,000 papers dealing with bias in LLMs, that religious bias has been …

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …

Heterogeneous Graph-Augmented Contrastive Learning For Extreme Multi-Class Fiqh Classification, Ali A. Jalil

Al-Bahir

• Background/Introduction: Fine-grained text classification in the field of Islamic Jurisprudence (Fiqh) is difficult because of the structural interdependence of the legal concepts and the extremely multi-class long-tail data distribution (667 classes with 5,979 samples, 52.2% of which contain less than 5 samples). The main problem with traditional flat classifiers is that they assume that target classes are independent and orthogonal output neurons which discards very important relational semantics.
• Objectives: This paper seeks to remediate this extreme imbalance and maintain structural taxonomy by modeling the structural space of classification label space itself as an object to be learned, while giving a …

Chi Meta-Project Ecosystem Overview - Spring 2026, David B. Smith

Publications and Research

This paper offers a high-level account of the Center for Holistic Integration’s (CHI) meta-project ecosystem as visualized in the included system map. CHI provides an organizational structure framed around persistent meta-projects that support and extend individual initiatives across curriculum, scholarly and applied research, infrastructure, artistic production, AI development, cultural inquiry, and external partnerships. Rather than presenting the map as a static inventory of projects, the paper examines how its core domains function as living systems through which knowledge, tools, documentation, participants, and collaborations can accumulate over time. It also considers how CHI-mediated connectivity, institutional integration, and external funding allow the …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in …

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and …

Benchmarking Current Progress In 3d Content Generation, Vuong Ho

Graduate Theses and Dissertations

In recent years, 3D generation has rapidly advanced with the development of powerful generative AI models capable of producing high-quality 3D content from various modalities, including text, images, and multi-view inputs. These advancements have significantly accelerated progress in applications such as gaming, virtual reality, robotics, and digital content creation. Despite this progress, there is still a lack of standardized and fair benchmarking protocols for evaluating 3D generation methods. Existing approaches are often assessed under inconsistent experimental settings, using different datasets, evaluation metrics, and processing pipelines. Such inconsistencies make reliable and objective comparisons difficult, limiting our understanding of the strengths and …

Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He

Research Collection School Of Computing and Information Systems

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web …

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr

LSU New Orleans Theses and Dissertations

This thesis asks how best to forecast real datacenter operational telemetry. Can pretrained foundation time-series forecasters do it zero-shot, or do trained classical baselines perform best? On a single-site benchmark of eight audited targets and four horizons (32 slices), the zero-shot foundation forecasters Moirai, Chronos, and TimesFM win 26 of 32 slices against multivariate classical baselines. However, the foundation wrappers operate per-channel on the target history, while classical baselines consume the full feature tensor. When the same classical models are retrained univariate, the foundation lead persists (27 of 32 slices) and a 3-seed rerun of a classical challenger reproduces it; …

A Machine-Learning-Based Systematic Framework For Modeling Compression And Recompression Indices For Florida Soils, Michael Morales

Doctoral Dissertations and Master's Theses

This thesis develops a machine-learning framework for estimating the compression index and the recompression index of Florida soils from routinely measured index properties, and reports two studies that build it. Consolidation settlement design requires both indices, and both are obtained from the incremental-loading oedometer test, which occupies a specimen for one to two weeks; the index tests that accompany it are complete within hours. Empirical correlations have filled that interval since the 1950s, but their coefficients are calibrated on specific soil populations and transfer poorly between regions. The first study analyzes 376 consolidation tests compiled for the Florida Department of …

Online Ppo-Based Multi-Hop Task Offloading Strategy For Vehicular Edge Computing, Wenzhu Zhang, Yuewei Bian, Fuli Xiong, Siqi Cai

Journal of System Simulation

Abstract: To address the problems of frequent communication link interruptions caused by dynamic network topologies and the sharp increase in computational complexity triggered by high-dimensional decision spaces in the vehicular edge computing (VEC) environment, a multi-hop task offloading strategy for VEC based on an online PPO algorithm was proposed. A multi-hop task offloading optimization model simultaneously considering link effective time, transmission rate, and computing resource constraints was constructed; a multi-hop A* path search algorithm integrating link stability and end-to-end delay was designed; an online offloading decision framework based on PPO was proposed, which transformed the 0-1 mixed integer nonlinear programming …

Research On Temporal Action Localization Methods For Cross-Modal Understanding, Jinwei Li, Xiaoyang Liu, Rusheng Ju

Journal of System Simulation

Abstract: To address the problems of insufficient localization accuracy and high model complexity in the temporal action localization (TAL) task for video-text cross-modal understanding, an anchor-free action transformer (AFAT) model was proposed. Based on the anchor-free framework, the local self-attention mechanism of Transformer was introduced to enhance the global modeling capability of temporal features. A multi-scale feature pyramid structure was combined to strengthen the representation of actions with different durations, and a lightweight predictor was adopted to reduce computational redundancy. Experimental results show that the average precision of this model on the THUMOS14 dataset is significantly improved compared with the …

Resilience Modeling Method For Combat System-Of-Systems Based On Hypernetwork And Game Theory, Yuxian Duan, Hanqiang Deng, Jiarui Zhang, Jian Huang, Shijia Zhang

Journal of System Simulation

Abstract: In view of the difficulties in dynamic reconfiguration and resilience evaluation faced by modern combat system-of-systems in a highly adversarial environment, and the deficiencies of existing studies in depicting high-order interaction relationships and cluster evolution mechanisms, this paper proposed a resilience modeling method for combat system-of-systems integrating hypernetwork and game theory, aiming to analyze the resilience mechanism of the system-of-systems in all dimensions from micro, mesoscopic, to macro levels. At the micro level, a high-order motif structure was introduced to represent the complex interaction modes among combat units, which overcame the information loss of traditional binary relationships in depicting …

Simulation Study Of Elevator Group Control Scheduling Based On Real-Time Occupancy Perception, Yiyong Han, Shiyu Wang, Yang Ye, Zhen Zhang, Fengque Pei, Minghai Yuan

Journal of System Simulation

Abstract: To address the conflict between car space allocation and peak passenger flow response efficiency in elevator group control scheduling, a multi-objective scheduling method based on proximal policy optimization (PPO) with real-time occupancy perception was proposed. A simulation environment considering car capacity constraints was constructed, and a reward-penalty mechanism with average passenger waiting time, system energy consumption, and car congestion as optimization objectives was designed. Based on this, state and action spaces were defined to form a PPO-based scheduling framework; a simulation platform integrating traffic flow visualization, policy scheduling, and performance evaluation was developed. Simulation results show that this method …

Adaptive Spatiotemporal Graph Neural Network-Based Net Load Forecasting For Residential Areas, Yongqi Yang, Cong Wang, Hongli Zhang, Ping Ma, Yue Meng, Tianhao Zhou

Journal of System Simulation

Abstract: To address the issue that traditional residential overall power load forecasting methods fail to fully consider the differences in users' electricity consumption habits, making it difficult to improve prediction accuracy, a residential net load forecasting method combining representation learning clustering and an AGCN-Transformer was proposed. The representation learning method was utilized to fully extract the latent features of users' net load data, and users were divided into different groups based on the similarity of electricity consumption behaviors; the net load data of the same group were aggregated, and a graph structure suitable for this task was constructed by comprehensively …

Evolution And Prospects Of Polarization Image Simulation Technology, Gengpeng Li, Wei Cai, Zhiyong Yang, Zhili Zhang, Xiaowei Wang

Journal of System Simulation

Abstract: Polarization image simulation technology is a key means to break through the bottleneck of polarization data acquisition and promote the development of polarization vision. This study systematically reviewed three evolutionary paradigms of this technology: Physical mechanism simulation, based on the polarization bidirectional reflectance distribution function and polarization ray tracing, strictly solves polarization light transmission, which has high interpretability and credibility, but it is computationally complex and lacks visual realism. Data-driven simulation, using models like neural radiance fields to learn polarization appearance from data, has high generation efficiency and visual fidelity but weaker physical consistency and interpretability. Physics-data fusion simulation …

Mbse Design And Approach-Phase Operational Simulation Of Bdsbas Airborne Receiver, Ruihua Liu, Tongwei Wang, Zan Ma

Journal of System Simulation

Abstract: In view of the problem that traditional document-based design methods are difficult to effectively capture the dynamic characteristics and internal interactions in navigation accuracy and integrity assurance required by BeiDou satellite-based augmentation system (BDSBAS) airborne receivers during the approach phase, which easily leads to designs deviating from actual requirements and affects system performance, a model-based systems engineering(MBSE) method was introduced. A multi-dimensional system architecture model encompassing system requirement analysis, behavior description, structure design, and parameter constraints was established. Taking BDSBAS as the object, a co-simulation method of system modeling language and MATLAB for required navigation performance (RNP) was proposed, …