Digital Commons Network^™

[Birds-Of-A-Feather] Understanding And Handling Software Plagiarism In The Age Of Generative Ai, Daniel S. Katz, Scott C. Edmunds

FORCE 2026

Computing and software have supported research since their inception, and continue to play a significant role in knowledge production. However, the means of communicating research methods and results were developed long before computing existed, and the research community lacks best practices for documenting computational research elements transparently, reproducibly, and reusably.
Publishers are now more accepting of the inclusion of software (typically, source code) associated with submitted manuscripts, and many want to support processes to vouch for the integrity of software just as they do for other content, such as ensuring that ethical and legal concerns such as authorship, plagiarism, copyrights …

Diamond Open Access Journals In India: Status, Sustainability And Challenges, Mallikarjun Dora, Kanagasabai K, Raj Kishor Kampa

FORCE 2026

Open-access publishing has transformed scholarly communication in recent decades. The share of OA articles between 2014 and 2024 increased by 26%, from a modest 14% in 2014 to 40% in 2024. OA journals have evolved over time and exist in various models based on funding mechanisms, including the Gold, Green, Hybrid, and Diamond models. While OA publishing emerged in response to scientific inequality, middle- and low-income countries face the barrier of high Article Processing Charges (APC), which hinder them from publishing in those journals (Alperin, 2022). The Article Processing Charges are part of the Gold and Hybrid OA models, where …

Can Ai Help In Systematic Reviews? A Comparative Study Of Manual, Ai-Assisted, And Ai-Dominant Workflows For Librarians And Researchers, Aster Zhao

FORCE 2026

Systematic reviews are essential to evidence-based research but are often time-consuming and labor-intensive, for researchers to conduct, and for librarians to participate in, or teach. The rapid advances in Generative AI and AI-powered research tools, have created a growing interest in whether these technologies can assist—or even automate—parts of the systematic review process. However, questions remain about their effectiveness, reproducibility, and potential bias. This study explores the role of GenAI tools in the early stages of a systematic review: from literature discovery and screening, up to the identification of included studies.

We propose a comparative case study of one systematic …

Using Ai-Assisted Programming To Develop Research Services Tools For Research Impact, Open Access Publishing & More, Gary Lee

FORCE 2026

Academic libraries play a vital role in scholarly communication, As research practices become more data‑driven and interdisciplinary, librarians can help scholars by creating and sharing flexible, customizable tools that align with local workflows and user needs.

AI Assisted programming (sometimes called “vibe coding”) offers a new way for librarians without extensive programming knowledge to develop such tools . This allows previously non-expert librarians to go from conceptual goals to working applications by rapid prototyping, experimentation, and roll-out, resulting in service innovation and improvement.

This presentation illustrates how librarians at HKUST have explored vibe coding with tools like GROK, POE, and …

[2026 Honorable Mention] Chumash Heritage National Marine Sanctuary, Daylyn R. Reese

Ethnic Studies Research Paper Award

This research offers unique insights into the creation and development of the Chumash Heritage National Marine Sanctuary (N.M.S.) with a focus on indigenous populations’ roles (primarily Chumash and Costanoan). In this episode, I analyze what makes this NMS different from every NMS precluding it, how it recognizes a new goal in conservation (thrivability), and the areas in which improvements still must be made to authentically include and honor indigenous voices.

The $K$-Total Bondage Number Of A Graph, Jean-Pierre Appel, Gabrielle Fischberg, Kyle Kelley, Nathan B. Shank, Eliel Sosis

Theory & Applications of Graphs

Let \(G=(V,E)\) be a connected, finite undirected graph. A set \(S \subseteq V\) is said to be a total dominating set of \(G\) if every vertex in \(V\) is adjacent to some vertex in \(S\). The total domination number, \(\gamma_{t}(G)\), is the minimum cardinality of a total dominating set in \(G\). We define the \(k\)-total bondage of $G$ to be the minimum number of edges to remove from \(G\) so that the resulting graph has a total domination number at least \(k\) more than \(\gamma_{t}(G)\). In this work we establish general properties of \(k\)-total bondage and find exact values for …

Hyper Face-Magic Graphs, Ross Belgram, Donald Mcginn

Theory & Applications of Graphs

For a planar graph G of order n, let F(G) be the set of all faces of G embedded into R 2 , including the exterior face. A bijective vertex labeling f : V (G) → {1, 2, ..., n} induces a face labeling f ∗ : F(G) → N defined by setting f ∗ (F) equal to the sum of all labels of the boundary vertices of F. The graph G is said to be hyper face-magic if there exists a vertex labeling whose induced face labeling is constant. In this paper, we state properties of hyper face-magic graphs …

Rfc - Request For Information - Waste Rock Dump Stormwater Runoff Collection System Substitution, Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Flip It And Reverse It: Probing Consistency Of Llm World Knowledge Under Logical Transformations, Rhianna Smith

Computer Science Senior Theses

When assessing the extent to which someone truly understands the topics they discuss, you might ask them a series of questions that examine their knowledge from different angles. As Large Language Models (LLMs) are increasingly integrated into real-world tasks, they are often required to reason through ambiguous and uncertain scenarios that rarely have a single correct answer. In these settings, their usefulness is contingent on more than accuracy alone. Rather, it depends on the strength of the reasoning that underlies their responses. This begs the question: how might we extend these types of tests to LLMs? We formalize this informal …

Fabrication Of Two-Dimensional Optoelectrical Devices, Holly Walworth

Honors Projects

Optoelectrical devices enable the exploration of interesting quantum states observed in two-dimensional (2D) condensed material systems. 2D materials, such as graphene and transition metal dichalcogenides, exhibit unique electronic properties, including the Fractional Quantum Anomalous Hall effect (FQAHE). The realization of the FQAHE offers a pathway toward topological quantum computing via non-Abelian anyons. Still, fabricating high-quality 2D optoelectrical devices remains a significant experimental challenge. Using mechanical exfoliation and stacking techniques within an inert argon environment, we attempted to fabricate optoelectrical devices using twisted molybdenum ditelluride (tMoTe2) and gallium telluride (GaTe). Three devices were successfully assembled; however, structural defects including hexagonal boron …

The Architecture Of Chemical Understanding: How Representation Selection, Ordering, And Feature Salience Shape Students' Conceptions In Chemistry, Isaiah Nelsen

USF Tampa Graduate Theses and Dissertations

Chemical representations play a central role in how students learn and reason aboutchemistry. While representations are routinely used in chemistry classrooms, students often struggle to extract relevant information, connect representations to underlying chemical principles, and apply this information to problem-solving. This dissertation investigates the role of representations in supporting students’ conceptual understanding and reasoning across general and organic chemistry through three qualitative studies.

The first study in this work explores how variation in chemical representation influences how students understand foundational chemistry principles. This study sought to characterize how second semester general chemistry students approach dipole–dipole interaction tasks with four distinct …

Recognizing And Rewarding Peer Review: Rethinking Research Assessment For Openness, Fairness, And Global Equity, Tung Tung Chan, Bernd Pulverer, Johan Rooryck

FORCE 2026

The Coalition for Advancing Research Assessment (CoARA) Working Group on Recognizing and Rewarding Peer Review has developed a comprehensive framework for reforming how scholarly review is valued within research careers. Our recommendations address a fundamental question: how can peer review, a critical yet often invisible scholarly contribution, be made visible, credited, and meaningfully integrated into research assessment?

Developed through a collaborative effort across 16 European organisations, the Working Group’s outputs offer targeted recommendations for four key stakeholder groups: research performing organizations, research funding bodies, publishers and editors, and individual researchers. These recommendations are structured across five key dimensions:
(1) Openness: …

Open Research Information - How To Support Publishers To Make Metadata Openly Availble, Bianca Kramer

FORCE 2026

Research information, or scholarly metadata, is important for decision making around strategic priorities, distribution of resources, and evaluation of researchers and institutions. It is also used to assess the effect of policies, and to find and assess research results. Open research information (free to access and free to (re)use) is increasingly valued for fair assessment and equitable decision making, and is also important in digital sovereignty.


The Barcelona Declaration on Open Research Information calls on organizations performing, funding and evaluating research to make openness of research information the default, work with services and systems that support and enable open research …

Biologically Informed Negative Samplingfor Antibody Chain Pairing Classification, Ishita Singh

Computer Science Senior Theses

Antibody heavy and light chain (H/L) pairing is fundamental to antigen recognition and stability. While single-cell sequencing preserves native pairing information, widely used bulk repertoire and spatial transcriptomics platforms do not, motivating the need for efficient ML methods to infer H/L pairing. Training a binary classifier for this task faces the methodological challenge of a lack of true biological negatives, since natural selection eliminates B cells with incompatible H/L pairs.

In this thesis, I introduce a biologically informed negative sampling strategy for H/L pairing classification, drawing on known V-gene biases in heavy and light chain pairing. Pseudo-negatives are constructed by …

Mimic: A Multimodal Dataset For Affective Incongruity In Video, Ethan M. Baird

Computer Science Senior Theses

This thesis investigates the computational challenges of constructing a database for affective incongruity, exploring the difficulties in automating the collection of contradictory affective states. Capturing these incongruities is essential for advancing Vision-Language Models (VLMs) and sentiment analysis, which struggle to interpret signals deviating from basic emotional archetypes. Curating non-congruent affect provides the data necessary for models to navigate complex social contexts, which is critical for applications like automated Audio Description (AD) for the visually impaired and nuanced Human-Computer Interaction (HCI). To capture these signals, two distinct methodologies were employed. The first utilized a tripartite decomposition of video data, isolating textual, …

A Global Internal Tide Modeling Framework For Improving Satellite Observations Of Fine-Scale Ocean Circulation, Badarvada Yadidya, Brian K. Arbic, Edward D. Zaron, Jay F. Shriver, Maarten C. Buijsman, Eric P. Chassignet, Loren Carrère, Michel Tchilibou

Faculty Publications

Small-scale oceanic eddies and filaments mediate the vertical exchange of heat and carbon within the global ocean. The Surface Water and Ocean Topography (SWOT) mission resolves these features through wide-swath interferometry, but internal tides often mask these observations. Non–phase-locked internal tides present special difficulty because they vary with the evolving ocean background. We show that this chaotic variability can be predicted. We use a data-assimilative ocean forecast model to resolve the mesoscale environment and separate tidal signals from the broader circulation. The model captures the organized structure of these incoherent waves in the independent SWOT measurements. Correcting for the total …

Llzk-Symex: Building A Circuit Verification Framework For The Llzk Zero-Knowledge Intermediate Language, Nicolás Iair Schaievitch

Computer Science Senior Theses

The rising adoption of ZK (Zero-Knowledge) technology has led to a plethora of DSLs (Domain Specific Languages) for facilitating the development of ZK circuits. At the same time, there has also been an increase in the number of compilation targets for these languages, with different cryptographic foundations and performance optimizations. LLZK, by Veridise, aims to unify the ecosystem by providing a single IR (Intermediate Representation) for the different frontend DSLs, that would then also allow choosing between different backends. Given the high stakes (particularly financial) that a lot of ZK circuits are under, together with their high level of complexity, …

Capturing Large Language Model Similarity Through Spectral Analysis, Ishan Verma Prasad

Computer Science Senior Theses

With the rapid development of open-sourced models on Huggingface, there is a strong need for a way to systematically determine the similarity between models. More strongly, for intellectual property and organization, we need a way to determine the "lineage" of models. We borrow principles from Heavy-Tailed Self-Regularization and Random Matrix Theory to provide an inference-free method to accomplish this. We cluster a corpus of several model families by their spectral fingerprints and demonstrate that each model family occupies a distinct region in weight space. This confirms prior ideas of training setups leaving artifacts on model weights and allows us to …

Reliability-Aware Mixture-Of-Agents For Robust Ai-Generated Image Detection, Jake Jump, Yu-Wing Tai

Computer Science Senior Theses

Detecting AI-generated images requires reasoning across multiple levels of evidence, ranging from low-level statistical artifacts to high-level semantic inconsistencies. Existing approaches typically rely on a single class of signals or emphasize complex multi-agent coordination, which limits robustness under distribution shifts and common image degradations. We propose a reliability-aware Mixture-of-Agents (MoA) framework that treats Vision-Language Model (VLM) agents and computational features as complementary experts and aggregates their predictions based on empirically calibrated reliability. Rather than relying on intricate inter-agent reasoning, our approach centers on structured aggregation: high-precision “anchor” agents drive predictions, while weaker but complementary signals are adaptively incorporated to resolve …

Dancing For The People: A Culturally Grounded Learning-To-Rank Framework For Powwow Dance Evaluation, Avery C. Sutherland

Computer Science Senior Theses

Powwow dance is a form of Indigenous expression that combines movement, storytelling, regalia, and community values. Within competition powwows, dancers are evaluated through a highly subjective judging process that often lacks standardized criteria, creating challenges for consistency, transparency, and fairness. This work investigates whether a learning-to-rank framework can model subjective powwow dance preferences while remaining grounded in the cultural context of the practice. To support this research, a new dataset was constructed consisting of 19 Women's Fancy Shawl dancer profiles and 236 pairwise preference labels collected from experienced members of the powwow community. Each dancer profile combines textual descriptions, images, …

Rscore - A Tool For Learning And Quantifying Software Infrastructure Resiliency, Selena Yujia Zhou

Computer Science Senior Theses

As LLMs take over writing code, infrastructure resilience has become the central challenge of software development, especially for gaming applications, where latency, availability, and scale demands are extreme. However, holistic software infrastructure is difficult to understand for casual developers because of the multiple layers of abstraction that make up an application, and the lack of structured information from proprietary companies. This thesis explores the creation of rscore, a tool that quantifies software infrastructure resiliency and subsequently educates developers about the architecture of any existing gaming application. The tool generates two graphical models of a game’s early versus current infrastructure and …

Holistic Strategies For Optimizing Municipal It Operations And Cost Efficiency, Joseph Yazdanpanahi

Certified Public Manager® Applied Research

Municipalities work to deliver secure and impactful IT (information technology) services while often working within tight budget constraints. To address this problem, this article offers an experience-based and comprehensive framework to optimize municipal IT operations. It emphasizes leveraging automation, streamlining hardware and software management, and forging strategic partnerships to drive cost savings and efficiency gains. Designed to equip municipal IT leaders with practical tools and insights, this article aims to help modernize IT processes.

Residential Ai Data Centers: Security, Privacy, And Governance Concerns, Alan Saquella

Publications

The concept of placing mini data centers and distributed AI computer nodes inside residential homes may appear innovative from an energy efficiency perspective, but it introduces significant security, privacy, governance, and liability concerns. What is effectively occurring is the expansion of commercial and potentially critical infrastructure into lightly protected residential environments.

Once a residence becomes part of a distributed computer grid supporting hyper-scalers, AI providers, or enterprise workloads, the home is no longer simply a private residence. It becomes a commercial technology asset, a potential cyber target, and even a physical target. A distributed network of thousands of residential nodes …

Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

Fairness-Aware And Efficient Federated Learning Frameworks For Heterogeneous Systems, Simin Javaherian

Doctoral Dissertations

Federated Learning (FL) enables decentralized clients to collaboratively train machine learning models without sharing raw data, making it a promising paradigm for privacy-preserving intelligence across large-scale, heterogeneous systems. However, practical FL environments face significant challenges arising from variations in client resources, participation patterns, client behavior, and data distributions. These challenges often lead to inefficiency, unbalanced contributions, and unfairness, ultimately degrading model performance and discouraging long-term client participation. This dissertation advances the state of FL by developing a unified suite of fairness-aware and efficiency-driven frameworks tailored for heterogeneous environments. We investigate fairness from multiple perspectives, including client selection, contribution weighting, and …

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

Regioselective Tellurination Reactions And Their Utilities, Itunu Comfort Olanrewaju

Masters Theses

This thesis provides a comprehensive study of regioselective tellurination and its applications by integrating experimental and computational techniques, with an aim of synthesizing libraries of organotellurium heterocycles. This study addresses existing challenges in regioselective tellurination, such as unpredictable regioselectivity and avoidance of toxic reagents by harnessing intramolecular Te…O coordination and electrophilic aromatic tellurination. This strategy enables the development of previously poorly accessible compounds including benzotellurazinones, 2-acylaminobenzotellurazoles, and previously unknown 2,3-dihydrobenzo [b] oxatelluranes, which were prepared by antiMarkovnikov anti-addition of TeCl4 to C-C triple bonds. Experimental studies were complemented by computational DFT analyses to provide mechanistic and energetic insight into the …

Disparities In The Identification Of Vulnerable Communities Due To Design Storm Methods: A Case Study In The Vermilion River Watershed, South Louisiana, Usa, Claire Orgeron

Masters Theses

Accurately identifying socially vulnerable populations at-risk of flooding is critical for equitable disaster mitigation and planning. This study investigates how the selection of design storm method, which is fundamental to developing flood risk maps used for this identification, influences which socially vulnerable populations are recognized as being at elevated flood risk. The most widely used design storm approach relies on National Oceanic and Atmospheric Administration (NOAA) Atlas 14 point-based rainfall frequency estimates. These estimates are typically applied spatially using areal reduction factors, thereby disregarding rainfall spatial variability in real storms. Stochastic Storm Transposition (SST), an alternative design storm approach that …

Scientific Crosstalk: Natural Language Processing, Praveshika Bhandari

Theses and Dissertations

While sentiment analysis has made significant strides in domains such as social media and personal correspondence, its application to formal scientific writings remains under-explored. The crosstalk between emotional expressions in personal and professional communications has also received limited attention despite its potential to reveal insights into the emotional drivers of scientific creativity. Our research introduces a computational framework designed to detect and quantify emotional expressions across various documents over time. Leveraging state-of-the-art transformer models fine-tuned on domain-specific corpora, the framework models emotional tone distribution. Integrating emotion analysis with knowledge graph modeling enables the exploration of emotional trends alongside key scientific …