Digital Commons Network^™

Towards Fair Sequential Resource Allocation: Algorithmic Designs, Interventions, And Evaluations, Ashwin Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis develops a comprehensive framework for fair sequential resource allocation in multi-agent systems where a centralized allocator coordinates actions under global feasibility constraints, while satisfying preferences of different agents. Ranging from ridesharing platforms and homelessness intervention programs to power grid management, such systems play a critical role in shaping access to essential resources. Yet, existing approaches to resource allocation often prioritize aggregate utility, leading to systematic inequities across individuals and groups, particularly in sequential settings where decisions unfold over time. To address this challenge, we introduce the Distributed Evaluation, Centralized Allocation (DECA) framework, which unifies a broad class of …

Establishing Convergence Thresholds For Pre-Trajectory Sampling With Batched Execution Across Random Quantum Circuits, Taylor L. Eskew, Jerome F. Gonthier, Taylor L. Patti, Andrew N. Jordan

Student Scholar Symposium Abstracts and Posters

A crucial aspect of validating quantum protocols is understanding the noise produced by quantum computing devices. Using simulations that can replicate this noise allows for a lower-cost alternative to hardware experiments. Stochastic, so-called "trajectory" methods are often used as a quadratically reduced approximation to density matrix simulations, but traditional implementations have limited sampling capacity and provide no error-based metadata. The Pre-Trajectory Sampling with Batched Execution (PTSBE) [Patti et al., 2025] algorithm provides a solution by combining fine-tuned, well-documented noise sampling with computational intermediate caching.

While the original work is effective on quantum error correction circuits, its performance on general circuits …

Gamification Of A Bimanual Coordination Task, Robby Johnson, Caeden Kidd, Johnathan Oestringer, Jaylin Pigeon, Brandon Woolman

Infinite Loop

Video games are a promising future for research and development. A new way to measure motor skills is to use robots to test individuals. This technology is incredible and has helped the medical field, but there must be a way to allow individuals to use this technology in a similar and affordable manner. Space Trash is a game that was developed with the intent to gamify the object hit detection that is used in the Kin Arm Robot with the intent to see if a person will show signs of Alzheimer’s. The goal is to not only be able to …

Business Financial Information Systems, Lirie Koraqi, James Jolovski Prof.

International Journal of Business and Technology

An information system is a combination of software, hardware, and telecommunication networks to collect useful data, especially in an organisation. Many businesses use information technology to complete and manage their operations, interact with their consumers, and stay ahead of their competition. Some companies today are completely built on information technology.

Well designed and implemented business information systems should provide the information management and outside parties need to make informed and timely decisions about the operating health of the company. Business considers the need to have information available to assess the profitability of a new product they are selling or their …

Understanding Medical Information And Emotional Support Needs In Mental Health Questions With Large Language Models, Chen Liu, William Yu Chung Wang, Gohar Khan

All Works

Purpose – This study seeks to bridge the gap between users’ multidimensional needs and the single-task capabilities of existing Mental Health Question Answering (MHQA) systems by tackling the underexplored challenge of jointly understanding medical informational needs and emotional support needs within complex consumer mental health inquiries. Design/methodology/approach – Grounded in Rhetorical Structure Theory (RST), the proposed Multi-Needs and Context Recognition (MNCR) framework decomposes mental health question understanding task into four interrelated subtasks: Medical Needs Recognition (MNR), Medical Needs-related Context Extraction (MNCE), Emotional Needs Recognition (ENR) and Emotional Needs-related Context Extraction (ENCE). A new benchmark dataset, MHQ-MedEmo, was constructed through multi-layered …

Bridging Machine Learning And Islamic Scholarship: A Study In Hadith Translation And Similarity Analysis, Asiyah R. Speight

Student Scholar Symposium Abstracts and Posters

Translation of Islamic religious texts poses unique challenges requiring both linguistic and theological expertise. This study explores the application of neural machine translation (NMT) models to Arabic-English hadith translation while analyzing semantic similarity patterns across different human translations. Using the complete Sahih Bukhari corpus (7,550 hadiths) as the primary dataset, we adopt a dual approach combining transfer learning and comprehensive neural network analysis to demonstrate the critical impact of corpus size on model performance.

First, we fine-tune a pre-trained MarianMT Arabic-English translation model on the full Sahih Bukhari corpus, comparing models trained on 40 hadiths versus 7,550 hadiths. Performance is …

Viability Of Widely Used Encryption Schemes In Drone Transmission, Emanuel Yasir Nelson

Cybersecurity Undergraduate Research Showcase

This paper presents throughout research on the security issues related to drone transmission. These topics were addressed and explained, in particular the aspects relating to cybersecurity, for utmost clarity. These include threats and vulnerabilities, drone transmission the impact of encryption on latency, and the details of the encryption methods AES-128, AES-256, and ChaCha20 that were used in the experiment described in the paper. Each encryption method performance was measured and outputted by the Python code developed and used in the experiment. Afterwards, the performance of each method was analyzed in relation to their decryption time, encryption time, end to end …

Experience From A Distance: Improving Transparency For The Multnomah Athletic Club, Matthew Penner

University Honors Theses

Portland is home to the largest and one of the most prestigious athletic clubs in the world: Multnomah Athletic Club. In many ways, it is the pinnacle of luxury and innovation, and over time, it finds any way to entice prospective members to pay the expensive upfront fee of $6000 and monthly membership fees exceeding $300. Due to the previous technological barrier, which was not being able to see the full extent of what amenities the club had to offer, the club faced major challenges in recruitment and marketing. Over two academic terms, a team of six computer science capstone …

Confidential, Attestable, And Efficient Inter-Cvm Communication With Arm Cca, Sina Abdollahi, Amir Al Sadi, Marios Kogias, Hamed Haddadi, David Kotz

Other Faculty Materials

Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation guarantees, existing CVM architectures lack first-class mechanisms for inter-CVM data sharing due to their disjoint memory model, making inter-CVM data exchange a performance bottleneck in compartmentalized or collaborative multi-CVM systems. Under this model, a CVM's accessible memory is either shared with the hypervisor or protected from both the hypervisor and all other CVMs. This design simplifies reasoning about memory ownership; however, it fundamentally precludes plaintext data sharing between CVMs because all inter-CVM communication must pass through hypervisor-accessible …

Pymelt-Px: A Python Script For Modeling Melting Of A Pyroxenite-Peridotite Bilithological Mantle, Ana L. Jimenez Bustos

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The study of oceanic crust formation is a fundamental building block in our understanding of the processes of planetary formation, necessitating an understanding of the melt generation processes that help form oceanic crust. To aid in this purpose, we present pyMeltPX: a bilithological pyroxenite-peridotite mantle modeling script. Although the mantle is primarily comprised of peridotite, pyroxenite is a minor but ubiquitous feature of the mantle and can contribute a disproportionate amount of melt to crustal generation in mid-ocean ridge and ocean island basalt settings. Our model, pyMeltPX, is a python coded, extensible tool based on the Excel calculator by Lambart …

Registration Is A Powerful Rotation-Invariance Learner For 3d Anomaly Detection, Yuyang Yu, Zhengwei Chen, Xuemiao Xu, Lei Zhang, Haoxin Yang, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity, particularly in capturing local geometric details and achieving rotation invariance. These limitations become more pronounced when registration fails, leading to unreliable detection results. We argue that point-cloud registration plays an essential role not only in aligning geometric structures but also in guiding feature extraction toward rotation-invariant and locally discriminative representations. To this end, we propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives …

Safe-Sora: Safe Text-To-Video Generation Via Graphical Watermarking, Zihan Su, Xuerui Qiu, Hongbin Xu, Tangyu Jiang, Jun-Hao Zhuang, Chun Yuan, Ming Li, Shengfeng He, Fei Yu

Research Collection School Of Computing and Information Systems

The explosive growth of generative video models has amplified the demand for reliable copyright preservation of AI-generated content. Despite its popularity in image synthesis, invisible generative watermarking remains largely underexplored in video generation. To address this gap, we propose Safe-Sora, the first framework to embed graphical watermarks directly into the video generation process. Motivated by the observation that watermarking performance is closely tied to the visual similarity between the watermark and cover content, we introduce a hierarchical coarse-to-fine adaptive matching mechanism. Specifically, the watermark image is divided into patches, each assigned to the most visually similar video frame, and further …

Jury-And-Judge Chain-Of-Thought For Uncovering Toxic Data In 3d Visual Grounding, Kaixiang Huang, Qifeng Zhang, Jin Wang, Jingru Yang, Yang Zhou, Huan Yu, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

3D Visual Grounding (3DVG) faces persistent challenges due to coarse scene-level observations and logically inconsistent annotations, which introduce ambiguities that compromise data quality and hinder effective model supervision. To address these challenges, we introduce Refer-Judge, a novel framework that harnesses the reasoning capabilities of Multimodal Large Language Models (MLLMs) to identify and mitigate toxic data. At the core of Refer-Judge is a Jury-and-Judge Chain-of-Thought paradigm, inspired by the deliberative process of the judicial system. This framework targets the root causes of annotation noise: jurors collaboratively assess 3DVG samples from diverse perspectives, providing structured, multi-faceted evaluations. Judges then consolidate these insights …

Cssa-Fusion: Channel Selective And Spatial Alignment Infrared-Visible Image Fusion, Zhen Li, Zhi Zeng, Zhongrui Xiao, Ming Wen, Zhiyuan Zhang, Yibin Tian

Research Collection School Of Computing and Information Systems

Infrared-visible image fusion aims to integrate complementary information from two modalities to generate images with enriched semantic content. However, existing methods often neglect two critical aspects: the design of a local–global feature enhancement architecture and spatial alignment. To address these challenges, we propose Channel Selective and Spatial Alignment Fusion (CSSA-Fusion), a novel framework composed of two synergistic modules. The first is a selective channel and redundancy suppression module, which introduces a dual-branch selective channel attention mechanism to jointly capture local saliency and global channel importance for enhanced feature representation, and an informativeness–redundancy separation strategy to suppress redundant information while preserving …

A Partition Cover Approach To Tokenization, Jia Peng Lim, Shawn Tan, Davin Choo, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Tokenization is the process of encoding strings into tokens of a fixed vocabulary size, and is widely utilized in Natural Language Processing applications. The leading tokenization algorithm today is Byte Pair Encoding (BPE), which formulates the tokenization problem as a compression problem and tackles it by performing sequences of merges. In this work, we formulate tokenization as an optimization objective, show that it is NP-hard via a simple reduction from vertex cover, and propose a polynomial-time greedy algorithm GreedTok. Our formulation naturally relaxes to the well-studied weighted maximum coverage problem which has a simple -approximation algorithm GreedWMC. Through empirical evaluations …

Sheetpedia: A 300k-Spreadsheet Corpus For Spreadsheet Intelligence And Llm Fine-Tuning, Zailong Tian, Zhuoheng Han, Houfeng Wang, Lizi Liao

Research Collection School Of Computing and Information Systems

Spreadsheets are widely used for data analysis and reporting, yet their complex structure and formula logic pose significant challenges for AI systems. We introduce Sheetpedia, a large-scale corpus of over 290,000 diverse spreadsheets (from 324,000+ workbooks) compiled from enterprise email archives and online forums. We detail a rigorous collection and preprocessing pipeline (integrating the Enron email spreadsheet archive and the Fuse web corpus, plus a new crawl of Excel forums) to standardize formats, filter languages, and remove duplicates. Sheetpedia provides extensive coverage of real formulas and annotations – addressing a gap left by prior table datasets (e.g. web tables used …

An Analysis Of Face Synthesis Methods And Their Influence On Human Perception, Maha Habib Almaimani

All Dissertations

Synthetic faces (e.g., computer-generated characters) have been increasingly utilized across various fields, including entertainment, healthcare, and education. Perceptual studies are often conducted to understand how synthetic faces are perceived by humans, aiming to enhance both quality and user experience in these domains. Over the years, numerous methods have been developed to create synthetic faces, ranging from traditional techniques such as image composites, Active Appearance Models, and 3D Morphable Models to more recent machine-learning-based frameworks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

Despite the growing adoption of synthetic face generation and the variety of algorithms available for their creation, …

From Static Prediction To Mindful Machines: A Paradigm Shift In Distributed Ai Systems, Rao Mikkilineni, W. Patrick Kelly

Barowsky School of Business | Faculty Scholarship

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore …

Nonlinear Phase Space Analysis For Anomaly Detection In Ros 2 Communications: Detecting Man-In-The-Middle Attacks In Simulated Environments, William L. Locklier

Graduate Theses and Dissertations (2019 - present)

Robot Operating System 2 (ROS 2) marks a significant advancement over its predecessor through the transition from a centralized to a decentralized architecture, integrating the Data Distribution Service (DDS) to support real-time, scalable communications. Despite these improvements, inherent vulnerabilities in the ROS 2 communication stack continue to leave these systems exposed to sophisticated network-based attacks. This study leveraged nonlinear phase space analysis (NLPSA) as an intrusion detection system (IDS) to detect man-in-the-middle (MitM) attack anomalies in ROS 2 traffic. Grounded in Takens’ embedding theorem, NLPSA reconstructs the phase space of communication features and compares the resulting structure against a baseline …

Adaptive Deep Learning In Physical Layer Applications, Ali Owfi

All Dissertations

Traditionally, signal processing models in communication systems have been designed based on solid foundations in statistics and information theory, often assuming linearity and optimizing for simplified models. However, real-world communication systems exhibit numerous imperfections and non-linearities that traditional linear models struggle to capture accurately. Deep Learning (DL)-based approaches, unconstrained by rigid mathematical models, have shown promise in optimizing system performance by accommodating specific hardware configurations and dynamic channel conditions as an alternative to the traditional methods. Despite all the recent research efforts on DL-based methods for physical layer applications, DL models have still not been widely applied to physical layer …

How Do Server-Based Architectures Compare To Serverless Architectures In Terms Of Development, Deployment Process, Scalability, And Cost-Effectiveness?, Natalio Fernandes Gomes

Honors Program Theses and Projects

Applications are tested in developers’ machines before deployment, since this is the standard practice that ensures the software is functioning as it should before deploying it to a standard practice that ensures the software is functioning as it should before deploying it to a specific environment. The deployment process consists of setting up the environmental requirements, infrastructure configuration on cloud or on-premises servers. Consequently, developers often run into the infamous "It works on my machine" problem. This issue is defined by the fact that applications function correctly in development environments but fail during deployment to production. Furthermore, scalability concerns and …

Privacy Preserving-Based Artificial Intelligence For Precision Agriculture, Partha P. Sengupta

Dissertations

The research work finds a solution to precision agriculture of cotton cultivation using artificial intelligence (AI) models. Two sets of model performance based on the application are selected namely a low resource and a high resource setting. This is because using drone surveys to capture images identifying the classes of stressed and unstressed cotton plantation requires limited model architecture and CPU based computation. Thus, traditional AI models were selected for low resource settings. Again, for high computation intensive models like transfer learning-convolution neural network (CNN) based architectures were grouped into high resource settings. There was another issue of class imbalance …

Performance Enhancement For Rufa: Rapid Urban Forest Assessment, Nicholas Tan

Master's Theses

Urban forests are crucial to the livability and resilience of cities, offering critical ecosystem benefits such as air quality enhancement, temperature regulation, and biodiversity. Managing said urban forests is essential to ensure their sustainability and adaptability to rapidly changing environmental and climate conditions. The Rapid Urban Forest Assessment (RUFA) tool was developed to address the need for a standardized approach to evaluating and comparing urban and community forestry programs. By analyzing and aggregating tree-specific data across California, such as canopy cover, tree counts, and diversity scores, RUFA assigns a comprehensive urban forestry score for each city. This score allows for …

Visionglow: Evaluating Minimal-Disruption Smart-Home Control In Apple Vision Pro, Hongxiao Zheng

Dartmouth College Master’s Theses

Smart-home control in mixed-reality environments like Apple Vision Pro often relies on disruptive, application-based paradigms, such as using a smartphone or a windowed virtual interface. These methods create a “mode switch” that imposes cognitive load and pulls users from their primary tasks. We present VisionGlow, a minimal-disruption spatial interaction technique for Vision Pro. VisionGlow represents devices as spatially-anchored “orbs.” To control a device, the user looks at its orb and performs a pinch gesture, which invokes a compact, contextual control panel. We conducted a within-subjects study (N=18) comparing VisionGlow against two baselines: the standard Apple Home app on a smartphone …

Proverag: Provenance-Driven Vulnerability Analysis With Automated Retrieval-Augmented Llms, Reza Fayyazi, Stella Hoyos Trueba, Michael Zuzak, Jay Yang

Institute for Informatics and Applied Technology Scholarship

In cybersecurity, security analysts constantly face the challenge of mitigating newly discovered vulnerabilities in real-time, with over 300,000 vulnerabilities identified since 1999. The sheer volume of known vulnerabilities complicates the detection of patterns for unknown threats. While LLMs can assist, they often hallucinate and lack alignment with recent threats. Over 40,000 vulnerabilities have been identified in 2024 alone, which are introduced after most popular LLMs’ (e.g., GPT-5) training data cutoff. This raises a major challenge of leveraging LLMs in cybersecurity, where accuracy and up-to-date information are paramount. Therefore, we aim to improve the adaptation of LLMs in vulnerability analysis by …

Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le

Research Collection School Of Computing and Information Systems

The nexus between data characteristics and parametric models is fundamental for developing effective and reliable artificial intelligence (AI) systems. Mismatches in data properties for model development may lead to deleterious effects on AI model performance in machine learning practice. This paper proposes a Reliable Data Split (RDS) procedure to learn how to select data points that will generalise the target domain adequately by employing prior knowledge of the data generative process. We introduce a reinforced selection strategy using deep reinforcement learning with diverse black box predictors in maximising ensemble rewards as the proxy of model performance potential while maintaining an …

Discriminative And Generative Video Modeling, Anh Pha Nguyen

Graduate Theses and Dissertations

Video modeling stands at the core of modern computer vision, enabling progress in domains such as surveillance, autonomous driving, and instructional assistance. Yet the complexity of spatiotemporal dynamics, multimodal integration, and the need for scalable and generalizable models present significant challenges. This dissertation addresses these issues from three complementary perspectives: discriminative modeling, multimodal (vision + language) alignment, and generative approaches, contributing new methods, datasets, and paradigms for advancing video understanding. In the discriminative setting, we propose a domain-adaptive framework for crowd counting that employs entropy minimization and adversarial learning to improve cross-domain generalization, and introduce a single-stage global association method …

Emotion Analysis And Neural Language Models For Classification, Andrew Mackey

Graduate Theses and Dissertations

Emotion analysis is a branch of artificial intelligence and natural language processing focused on recognizing emotions hidden throughout various forms of digital data, including text, images, and multi-modal representations. In this dissertation, we present four published and planned works that investigate different methodologies for natural language analysis tasks using deep learning techniques. The first published work we present considers the task of identifying fake news using various text and emotion representations. We demonstrate that emotion representations combined with word embedding techniques can improve the accuracy of fake news classification. Our second published work further investigates the fake news classification task …

Dancing With Logic: The Impact Of Integrating Dance In Teaching Introductory Computer Science Concepts On Student Understanding And Engagement, April Monk

Master's Theses

The purpose of this study was to investigate the effectiveness of dance-integrated pedagogical methods in enhancing the learning experiences of students in an introductory computer science course. In particular, the research aimed to measure the impact of creative movement on student comprehension, engagement, and perceptions of computer science. To guide this investigation, the study explored three research questions: Does integrating dance into lessons impact students’ comprehension of fundamental computer science concepts? How does dance integration affect student perceptions of both dance and computer science? And what elements of dance contribute to differences in comprehension between dance-integrated and traditional lessons? A …

A Learning‑Augmented Dynamic Programming Approach For Orienteering Problem With Time Windows, Guansheng Peng, Lining Xing, Fuyan Song Ma, Aldy Gunawan, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Recent years have witnessed a surge of interest in solving combinatorial optimization problems (COPs) using machine learning techniques. Motivated by this trend, we propose a learning-augmented exact approach for tackling an NP-hard COP, the Orienteering Problem with Time Windows, which aims to maximize the total score collected by visiting a subset of vertices in a graph within their time windows. Traditional exact algorithms rely heavily on domain expertise and meticulous design, making it hard to achieve further improvements. By leveraging deep learning models to learn effective relaxations of problem restrictions from data, our approach enables significant performance gains in an …