Digital Commons Network^™

Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan (Cqap), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Developing Blockchain-Based Transparent E-Commerce Solutions For Danish Smes To Promote Sustainable Design Products, Somnath Mazumdar, Robert John Kauffman, Thomas Jensen, Raghava Rao Mukkamala, Jan Damsgaard

Research Collection School Of Computing and Information Systems

Typically, a firm's objectives include establishing consumer confidence, preserving its brand image, and developing a profitable business strategy. Consumers now place greater emphasis on the sustainability and transparency of their purchases. Given environmental and economic limitations, firms are often compelled to implement sustainable production methods. This is especially a struggle for small- and medium-sized enterprises (SMEs) with new technology, as it can increase their risk of failure. This has led to a problem for consumers, who must cross-check the sustainability-related claims of the firms they buy from. This is challenging because of limited process trace data and restricted enforcement capabilities. …

Dreamcs: Geometry-Aware Text-To-3d Generation With Unpaired 3d Reward Supervision, Xiandong Zou, Ruihao Xia, Hongsong Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation — leading to geometric artifacts, such as the Janus face problem and geometric incompleteness, due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the …

Real-Time Motion-Controllable Autoregressive Video Diffusion, Kesen Zhao, Jiaxin Shi, Beier Zhu, Junbao Zhou, Xiaolong Shen, Yuan Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the …

Teamwise: Exploring Virtually Embodied Ai Facilitation For Video-Based Team Onboarding, Venkata Akhila Rani Obilisetty, Mikkeline Elleby, Anthony Tang, April Yi Wang

Research Collection School Of Computing and Information Systems

AI-mediated facilitation has emerged as a scalable approach to supporting onboarding and coordination in newly formed remote teams, yet existing systems are predominantly text-based. To explore how video-based, virtually embodied AI facilitators shape team experiences, we present TeamWise, which joins video-based onboarding meetings as an on-screen avatar. TeamWise guides teams through a structured facilitation flow of low-stakes activities to foster rapport, mutual awareness, and shared identity. While the overall sequence of activities and facilitation goals is predefined, the facilitator’s turn-by-turn utterances are generated dynamically by an LLM in response to participant input. We conducted a formative study of TeamWise to …

Autologger: A Multi-Agent Framework For The End-To-End Automated Logging, Renyi Zhong, Yintong Huo, Wenwei Gu, Yichen Li, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose AutoLogger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. AutoLogger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on …

Context Engineering For Ai Agents In Open-Source Software, Seyedmoein Mohsenimofidi, Matthias Galster, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects they operate in. Vendors of popular agentic tools (e.g., Claude Code) recommend maintaining version-controlled Markdown files that describe aspects such as the project structure, code style, or building and testing. The content of these files is then automatically added to each prompt. Recently, AGENTS.md has emerged as a potential standard that …

Who Said Cve? How Vulnerability Identifiers Are Mentioned By Humans, Bots, And Agents In Pull Requests, Pien Rooijendijk, Christoph Treude, Mairieli Wessel

Research Collection School Of Computing and Information Systems

Vulnerability identifiers such as CVE, CWE, and GHSA are standardised references to known software security issues, yet their use in practice is not well understood. This paper compares vulnerability ID use in GitHub pull requests authored by autonomous agents, bots, and human developers. Using the AIDev pop dataset and an augmented set of pull requests from the same repositories, we analyse who mentions vulnerability identifiers and where they appear. Bots account for around 69.1% of all mentions, usually adding few identifiers in pull request descriptions, while human and agent mentions are rarer but span more locations. Qualitative analysis shows that …

Iron Deficiency And Depressive Symptoms: Effects Of Racial And Ethnic Disparity In Two Reproductive-Aged Us Women, Alexis Monkhouse

USF Tampa Graduate Theses and Dissertations

Problem: Reproductive- aged women are one of the populations most at risk for iron deficiency and depression in the United States. While many studies have looked at the relationship between these two illnesses, none have explored it from a biocultural framework, examining how race/ethnicity affects this relationship. Using data from NHANES, this thesis examined the relationship between iron status (total body iron) and depressive symptoms (PHQ-9) among reproductive-aged women in the U.S. and how race/ethnicity affects it. Methods. NHANES survey data from 2005-2010 were pulled. Demographic and laboratory data were used to determine eligibility, total body iron, and depressive scores. …

Behavioral, System, And Informational Cyberattacks: A Human-In-The-Loop Driving Simulator Experiment, Samuel Petkac

Psychology Theses & Dissertations

Advanced technologies such as sensors and AI/ML algorithms have enabled increasing levels of automated driving system that detects, responds, and even predicts changes in a driving environment supported by wireless connectivity to nearby vehicles and infrastructure. Such connected and automated vehicles (CAVs) can be particularly vulnerable to cyberattacks targeting not only infotainment systems but also firmware and other applications, critically compromising driver safety. As we anticipate a “mixed” traffic where vehicles with various levels of automated technologies share the road for the foreseeable future, it is urgent to systematically examine types of possible cyberattacks and control human behaviors in such …

Dissecting The Etiology Of Alcohol Use Disorder By An Integrative Heritable Component Approach, Ivy Garrenton

Computer Science Theses & Dissertations

Alcohol Use Disorder (AUD) is a pervasive condition characterized by complex interplay among genetic, phenotypic, and environmental factors. Although previous studies have identi fied genetic loci associated with alcohol consumption, these efforts have not captured the genetic heterogeneity and gene-environment interactions underlying AUD pathogenesis. To address this critical gap, we developed a novel statistical methodology that integrates phenotypic, genotypic, and environmental data through an environmentally modified Genetic Relationship Matrix (GRM) to derive AUD-related traits with enhanced heritability.

This approach demonstrated superior performance in both simulated and real-world datasets. Traits derived using the environmentally modified GRM exhibited significantly higher estimated heritability …

Personality Predictors Of Cybersecurity Vulnerability: Insights From Self-Reports And Stimulated Threat Scenarios, Saroja Roy Grandhi

Psychology Theses & Dissertations

In this cyber dependent and enabled era, understanding the role of human factors in digital security is essential. This study investigates the relationship between Big-Five personality traits and cybersecurity behaviors by examining both self-reported and stimulated behaviors in security threat scenarios. Participants completed validated questionnaires to report their personality traits, cybersecurity practices and engage in task-based stimulations to capture behaviors such as phishing detection, password creation, and response to security alerts. The study tested whether higher conscientiousness, openness, and agreeableness would be associated with stronger cybersecurity practices and smaller discrepancies between self-reported and observed behaviors. And, whether greater extraversion and …

Knowledge Distillation From A Large Vision-Language Model To Compact Students For Architectural Floor Plan Understanding, Kiran Silwal

Honors Theses

In this research, the use of a large vision-language model to train smaller, deployable models for architectural floor plan question answering is investigated. Reading a floor plan today requires either a human expert or a paid query to a proprietary model, and neither option is practical for real-estate platforms that must process thousands of units at scale. To address this problem, a knowledge distillation approach is employed in which a large teacher model (GPT-4.1-mini) generates labeled question-answer pairs from floor plan images, and smaller student models learn from those labels. The teacher produced 37,027 labeled pairs from 12,343 floor plan …

Algorithm Performance In The Search For Hamiltonian Cycles, Chance Davis

Honors Theses

The Hamiltonian cycle problem is ubiquitous in both computer science and graph theory: Given a connected graph, a solution would either confirm the existence of a cycle which visits each vertex only once or its nonexistence. The importance of this problem, as well as its difficulty, is described in the Clay Mathematics Institute’s Millenium Prize Problems and Karp’s 21 NP-complete problems. Despite its “hardness,” solutions to the Hamiltonian cycle problem are desired in logistics, electronic circuit design, and network routing, among other fields. In this work, we benchmark a promising exhaustive enumeration algorithm on various graphs, including ones derived from …

Evaluating Predictive Structure In Penny Stocks Using Machine Learning And Statistical Methods, Susom Hait

Honors Theses

Market prediction attempts have primarily focused on large-cap stocks due to their stability and market consistency. As such, studies that use time-series techniques to predict large-cap stocks have produced consistent results. Despite the success of large-cap predictions, penny stocks have remained unexplored in modern academia due to their high volatility, low liquidity, and structural instability. Regardless, unexplored market potential and technological advancements underscore the need for preliminary research into penny stock forecasting. This study aims to determine whether meaningful predictive structures exist in time-series penny stock data. This study utilizes an incremental approach. Various penny stocks were selected, pooled, and …

Synthesis And Photophysical Studies Of Coumarin-Quinoline Derivatives, Nishrenika Pokhrel

Honors Theses

A new family of oxime-functionalized coumarin-quinoline derivatives, 5a and 5b, has been synthesized through a five-step procedure in high yields (45-98%). Compound 5a is designed as the sensing probe, while 5b serves as a model system to support mechanistic understanding. The sensing approach is based on the reactivity of the oxime group, which can undergo phosphorylation in the presence of organophosphate nerve agents. Based on this behavior, it is hypothesized that 5a can react irreversibly with a nerve agent mimic. The structures were fully characterized by NMR, FTIR, and HRMS. Photophysical properties of 5a were studied in various solvents, …

Rethinking News Classification Through A Multi-Dimensional Framework, Luana De Jesus Ferreira

Honors Theses

This thesis proposes a multi-dimensional framework for news classification that evaluates articles across three independent dimensions: headline accuracy, language neutrality, and content reliability. These dimensions produce both a continuous reliability score and a five-tier interpretive scale, while additionally classifying articles by genre and topic. To operationalize this framework, a structured annotation protocol was developed and applied to a dataset of 373 news articles drawn from 79 outlets spanning a wide range of contemporary media ecosystem. A binary Logistic Regression classifier trained on the ISOT Fake News Dataset was then evaluated against this dataset to examine how a model trained on …

Deep Learning-Based Automated Pneumonia Detection From Chest X-Rays: A Comparative Study Of Custom Cnn And Transfer Learning Architectures, Ahmed Sajim

Honors Theses

Pneumonia is a leading global cause of mortality, claiming approximately 2.5 million lives an-nually and placing exceptional diagnostic pressure on radiologists in resource-limited settings. Manual interpretation of chest X-ray (CXR) images is time-consuming, subject to inter-observer variability, and limited by radiologist availability. This thesis presents a systematic investiga-tion into deep learning-based automated pneumonia detection comparing five convolutional neural network (CNN) architectures: a custom-designed 2D CNN and four pretrained transfer learning models—ResNet, DenseNet, MobileNet, and VGG19.

A targeted data augmentation pipeline addresses the severe class imbalance in the Kag-gle Chest X-Ray Pneumonia dataset, expanding the Normal class from 1,583 to 9,495 …

A Real Account Of Deep Fakes, Benjamin L.W Sobel

Michigan Law Review

Laws regulating pornographic deepfakes are written to prohibit “digital forgeries,” “false” images, or media “indistinguishable” from “authentic” recordings. Yet the typical anti-deepfake law covers materials that aren’t forgeries, aren’t false, and that reasonable observers can easily distinguish from authentic recordings. Though drafted as if they regulate statements of fact, anti-deepfake laws actually target certain outrageous depictions per se—and rightly so, because pornographic deepfakes cause harm irrespective of their truth or falsity. However, the inapposite language of facts results in statutes with crucial ambiguities. Moreover, because anti-deepfake laws ban outrageous depictions irrespective of the factual assertions they make, they differ fundamentally …

Generative Artificial Intelligence With A Human Touch: Building Hana, Conrad Johnson

Faculty Scholarship

This Essay examines how generative artificial intelligence (GenAI) can be integrated into legal education and public interest law practice in a way that meaningfully enhances — rather than diminishes — human judgment, professional responsibility, and access to justice. Drawing on the experience of Columbia Law School’s Lawyering in the Digital Age Clinic, the Essay situates GenAI within an experiential pedagogy that emphasizes competence, ethical awareness, and collaborative problem-solving. It argues that law students and lawyers must move beyond a passive or uncritical use of GenAI tools; toward a deeper understanding of how these systems operate, the risks they pose, and …

Federated Retrieval-Augmented Generation For Cybersecurity In Resource-Constrained Iot And Edge Environments: A Deployment-Oriented Scoping Review, Hangyu He, Yuan, Kai Wu, Wei Ni

Research outputs 2022 to 2026

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes …

Structural Silence: When Ai Infrastructure Fails Speakers Of Underrepresented Languages, Avijit Roy, Proma Roy

Publications and Research

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools—training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures—encodes a set of assumptions that systematically disadvantages speakers of underrepresented languages before a single model is trained. This paper examines those assumptions through the lens of Bengali, one of the world’s most widely spoken languages with roughly 285 million speakers (Ethnologue, 2025; International Communication and Leadership School, 2026), and the structural barriers that emerge when attempting to build AI-assisted educational tools for Bengali-speaking learners in low-connectivity …

Saturated Hierarchical Atomic Incremental Learning (Shail): A Behavioral Learning Perspective On Staged Mastery And Saturation, Ernest Fokoue

Articles

We introduce Saturated Hierarchical Atomic Incremental Learning (sHAIL), a learning paradigm in which complex tasks are approached through a sequence of simpler atomic subtasks, each mastered to saturation before progression. The central mechanism is a saturation criterion that detects when learning dynamics enter a plateau region, triggering consolidation and subsequent ascent to a higher level of task complexity. We develop a theoretical framework for sHAIL and show that it naturally gives rise to \emph{staircased convergence}: alternating phases of rapid improvement and genuine plateau. Within each level, classical convergence guarantees apply under standard smoothness conditions, while the hierarchical transitions are driven …

No Intelligence Without Statistics: The Invisible Backbone Of Artificial Intelligence, Ernest Fokoue

Articles

The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of statistics provides the indispensable foundation for machine learning and modern AI. We deconstruct AI into nine foundational pillars—Inference, Density Estimation, Sequential Learning, Generalization, Representation Learning, Interpretability, Causality, Optimization, and Unification—demonstrating that each is built upon century-old statistical principles. From the inferential frameworks of hypothesis testing and estimation that underpin model evaluation, to the …

Employing Effective Solution Methods For Caputo-Based Sequential Fractional Models, Eman A. A. Ziada, Mohamed F. Abouelenein, Hijaz Ahmad, Monica Botros

Mathematical Modelling and Numerical Simulation with Applications

This paper investigates a class of nonlinear sequential singular fractional differential equations (FDEs) involving Caputo derivatives. This type of equation has several key advantages that enhance its value, such as capturing memory and hereditary effects. Viscoelastic materials and anomalous diffusion, as well as biological systems, can take advantage of this feature. In addition, fractional derivatives possess a sequential structure that enables the implementation of multiscale processes and hierarchical memory responses. Moreover, it provides an effective and flexible framework for solving differential equations compared to classical differential equations. It can therefore be used to model complex systems in physics, biology, and …

Optimal Control Strategies For Infectious Diseases: A Numerical Simulation Approach, M. Abu Salek, Jannatun Nayeem, Romana Yesmin, M. Haider Ali Biswas, M. Humayun Kabir

Mathematical Modelling and Numerical Simulation with Applications

Various infectious diseases, such as Tuberculosis and Hepatitis B virus (HBV), caused by Mycobacterium bacteria and hepatitis B virus, respectively, pose serious threats worldwide. This paper examines the spread of these diseases using a mathematical model that incorporates control measures, including vaccination and awareness programs. We use a system of differential equations with control variables and apply Pontryagin's principle to determine optimal control strategies. The stability of the Disease Free Equilibrium (DFE) has been analysed and demonstrated to be both locally and globally asymptotically stable when the basic reproduction number remains below unity, thereby ensuring disease elimination. Furthermore, the Endemic …

Forest Fires Increase Vulnerability To Midwinter Rain-On-Snow Snowmelt In The Western Oregon Cascades, Sage C. Ebel, Kelly Gleason

Environmental Science and Management Faculty Publications and Presentations

Forest fires and rain-on-snow events in the seasonal snow zone of the Pacific Northwest are increasing in frequency and magnitude, yet the combined impacts of these events on snowpack and water resources remain poorly understood. We show that forest fires doubled midwinter snowmelt proportions in 2023 and 2024 compared to unburned reference sites in the western Oregon Cascades. Data from snow monitoring and micrometeorological stations installed across an elevational gradient revealed increased snowpack vulnerability during midwinter rain-on-snow events, where we observed more snowmelt per rain-on-snow event in the burned forest, indicating higher vulnerability of these snowpacks to rapid melt, increasing …

Exact Soliton Solutions Of The Nonlinear Time-Fractional Schrödinger Equation Via Atangana–Baleanu And M-Truncated Operators, Bahadır Kopçasiz, Fatma Nur Kaya Sağlam, Mehmet Şenol

Mathematical Modelling and Numerical Simulation with Applications

The main objective of this work is to obtain exact soliton solutions for a nonlinear time-fractional equation model describing wave profiles arising in various physical systems. To derive different wave structures associated with the considered model, two analytical techniques are employed: the extended G'\G^2-expansion method and the modified auxiliary equation (MAE) approach. A wave transformation is applied to reduce the nonlinear time-fractional equation to a nonlinear ordinary differential equation (NLODE) by means of the M-truncated and Atangana-Baleanu (AB) fractional operators. Several classes of solutions, including exponential, hyperbolic, and trigonometric wave forms, are obtained. Over and above the analytical results, graphical …

Projections Of The Characteristics Of Extreme Precipitation In East Africa Using Bias-Corrected Cmip6 Models, Exavery K. Makula

Tanzania Journal of Science

In the context of ongoing global climate change, the intensification of the hydrological cycle is expected to modify the frequency, magnitude, and spatial distribution of extreme precipitation events. Understanding how precipitation extremes will evolve under climate change is critical for East Africa (EA), a region highly vulnerable to hydroclimatic hazards and strongly dependent on rainfall-driven socioeconomic systems. The region experiences two main rainfall seasons, namely the long rains (March-May, MAM) and the short rains (October-December, OND). This study investigates future projections of the characteristics of extreme precipitation across the EA using an ensemble of biascorrected CMIP6 global climate models. Extreme …