Digital Commons Network^™

Framework For Task Offloading In O-Ran Architecture For Heterogeneous Computing Applications, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Computational offloading transfers tasks from resource-constrained devices to more capable servers or cloud platforms, improving processing speed and user experience. Open radio access networks (O-RAN's) disaggregated architecture and open interfaces make it suitable for offloading delay-sensitive tasks, enhancing real-time application performance. This study focuses on task offloading in O-RAN, a reference network architecture. Although research on O-RAN is limited, existing work lacks a comprehensive approach to offloading, including offloading layer determination, node selection, and resource allocation based on task types and their latency needs. We propose a delay-aware task offloading framework within O-RAN to support diverse delay requirements, improving offloading …

Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observation of environments. However, these methods struggle in real-world applications because of the higher cost of panoramic RGB-D cameras. This paper studies a low-cost and practical VLN-CE setting, e.g., using monocular cameras of limited field of view, which means “Look Less” for visual observations and environment semantics. In this paper, we propose a ThinkMatter framework for monocular VLN-CE, where we motivate monocular robots to “Think More” by 1) generating novel views …

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics, Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu

Research Collection School Of Computing and Information Systems

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing "select-then-refine" pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Generative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we propose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. …

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

Volatility Modeling With An Application To Risk Parity Portfolios, Kenneth Hou

CMC Senior Theses

This thesis studies volatility modeling in the context of risk parity portfolio construction. I compare three risk parity portfolios that differ only in their underlying volatility model: a historical covariance baseline, a Bayesian stochastic volatility model, and a GRU–GARCH hybrid neural network. Using daily returns on Kenneth French’s five industry portfolios from January 2016 through December 2025, I construct monthly rebalanced portfolios under each model, with the SV and GRU forecasts embedded in hybrid covariance matrices that combine forecasted volatilities with rolling historical correlations. The results document a divergence between forecast accuracy and portfolio performance: the SV model is the …

Rank Rebalancing In Commodity And Foreign Exchange Markets, Prateek D. Vyas

CMC Senior Theses

This thesis empirically tests the rank-rebalancing mechanism of Stochastic Portfolio Theory (SPT) across commodity futures, foreign exchange futures, and equity ETFs. The Reverse Price-Weighted strategy (RPW) assigns, to each asset, the market weight of the asset at the opposite price rank, and generates an annualized excess return of 2.90% over the price-weighted (MKT) commodity benchmark, during the period of November 1977 to October 2025 (HAC t = 2.058, p = 0.040). The differential Sharpe ratio (dSharpe) of 0.245 is confirmed by a stationary block bootstrap, with a 𝑝-value of 0.015, and factor regressions controlling for carry, momentum, and value yield …

Microbial Community Structure In Global Soils, Matthew Jabro

CMC Senior Theses

Soil harbors the most diverse microbial communities on Earth, yet whether predictable community types exist across biomes and whether taxonomic composition encodes habitat of origin remain open questions at global scale. This thesis addresses both questions by applying unsupervised clustering and supervised classification to transformed 16S ribosomal RNA (rRNA) amplicon profiles from two independent datasets: the global topsoil survey of Bahram et al. (193 samples) and the Earth Microbiome Project (EMP) soil subset of Thompson et al. (2,209 samples). Application of a sample clustering method based on a mixture of Gaussian Graphical Models (MixGGM) identified 19 clusters in the topsoil …

Wiener-Granger Causality To Model Banking Sector Contagion, Riya Bhasin

CMC Senior Theses

In this thesis, we explore the main drivers behind the 2008 Global Financial Crisis and how the interconnectivity of banking sectors contributed to the worldwide spread of its effects. To understand how this became a global phenomenon, we apply linear Wiener-Granger Causality as a form of network analysis, building on pre-existing methodology in order to study continent banking indices. We find connections across locales on the continent and country level, but ones that are weak. Our modelling was insufficient to completely capture existing and well-known relationships accurately. This occurs due to limitations of the data and of the linear models’ …

Analysis Of Δ¹¹B As A Seawater Ph Proxy: Comparing Ocean Circulation Inverse Model Output With Marine Calcifier Geochemistry, Jesse I. Dong

CMC Senior Theses

Increasing anthropogenic carbon flux into the oceans decreases seawater pH, alters dissolved inorganic carbon speciation, and reduces biogenic calcification. The marine calcifiers— specifically corals and coralline algae—incorporate elements from surrounding seawater into their carbonate structures, which preserve past records of ocean carbon chemistry. In particular, boron in biogenic carbonate is a potentially valuable proxy for historical ocean pH across human timescales. Within seawater, boron primarily exists as boric acid B(OH)3 and borate ions B(OH)4 - , where higher pH favors the formation of borate ions. Borate ions preferentially incorporate the heavier ¹¹B isotope over 10B. On the other hand, if …

Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Coffee is a highly valued commodity and a widely consumed beverage, playing an important role in global trade. However, coffee farming landscapes are increasing transitioning into smaller-scale agricultural setups. This transformation highlights the critical need for accurate classification and mapping of coffee cropping systems (CS), especially in countries like Uganda, where dense vegetation and complex terrain present substantial challenges to traditional land survey methods. Moreover, understanding the spatial distribution of Robusta coffee (Coffea canephora) CS is essential for developing site-specific management strategies, guiding extension services, and informing evidence-based policy decisions. To address this gap, the present study aimed to enhance …

Impact Of Environmental Microparticles On Insect Olfaction, Steve B.S. Baleba, Danube K.N. Wandji, Yves H. Tchiechoua, Komi Mensah Agboka, Iman B. Hassaballa, Victor O. Omondi, Beatrice T. Nganso, Saliou Niassy, Souleymane Diallo, Merid N. Getahun

All Peer-Reviewed Publications

Terrestrial insects underpin key ecosystem services, including pollination, herbivory regulation, decomposition, nutrient cycling, and disease control. These functions depend on chemical communication that guides insects to food, mates, hosts, shelters, and oviposition sites while helping them avoid threats. Environmental microparticles, such as micro- and nanoplastics, tyre wear particles, soot, mineral dust, and agricultural residues, are now widespread across air, soil, vegetation, and indoor environments, exposing insects through contact, deposition, and ingestion. Growing evidence shows that these particles disrupt insect olfaction by adsorbing volatile compounds, blocking antennal sensilla, and interfering with receptor and neuronal processes. These disruptions impair foraging, mating, oviposition, …

Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz

Theses and Dissertations

This thesis develops and evaluates a deep learning-based prediction model capable of identifying intended limb movement from surfaced electromyography (sEMG) signals using sequence learning techniques. sEMG signals change over time due to multiple factors such as muscle fatigue or user variability. Traditional prosthetics control methods rely on static feature extraction, ignoring how signals change over time, thereby limiting their ability to capture the temporal changes of muscle activity. As a result, these approaches often lead to poor accuracy, robustness, and generalization. Limited experimental validation has been conducted on sequence-based machine learning approaches using temporal sEMG data from publicly available datasets …

Evaluating Hybrid Quantum-Classical Models For Image Classification In The Nisq Era, Utku Binkanat

Theses and Dissertations

This thesis presents a systematic empirical evaluation of quantum machine learning performance under noisy intermediate-scale quantum (NISQ) era constraints. Through 670 controlled experiments, it evaluated quantum kernel support vector machines and variational quantum classifiers against classical baselines on MNIST binary and multiclass classification tasks with systematic variation of problem difficulty, feature dimensionality (4, 8 qubits), and training set size (n ∈{100, 250, 400, 500, 2000, 4000}). Statistical rigor was ensured through five random seeds per condition and comprehensive significance testing. During the testing with binary datasets, classical methods (SVM, logistic regression, k-NN, neural networks) achieved 85.9% to 99.6% accuracy with …

A Pipeline For Creating Obfuscated Binary Samples To Train Ai-Powered Detection Models, Luka R.H. Wilmink

Theses and Dissertations

The analysis of binary files is a critical component of antivirus software and is one of the most important tools for incident response teams across the industry. In the field, malware is often obfuscated, a practice in which the compilation process is transformed with different techniques to hinder decompilation and reverse engineering. Artificial Intelligence and Machine Learning techniques can assist, but models need to be trained on well constructed datasets first. This paper outlines a pipeline for creating such a dataset and builds a proof-of-concept machine learning classification model. All associated data and code are supplied in the project GitHub …

Understanding Phishing Susceptibility Through Expert Consensus Using Digital Marketing Parallels And A Machine Learning-Based Implementation, Mansoor Ahmad

All Graduate Theses, Dissertations, and Other Capstone Projects

Phishing remains one of the most effective attack vectors for gaining unauthorized access to organizational systems, yet defenders often lack systematic methods to assess their exposure before an attack. This study develops a framework that uses machine learning to encode the collective expertise of cybersecurity practitioners into a portable phishing susceptibility assessment tool, with the goal to help security teams proactively identify patterns, prioritize awareness training, and strengthen detection controls. The study surveyed 27 practitioners with extensive experience in social engineering, red teaming, penetration testing, and threat analysis to identify which factors most influence phishing susceptibility. Practitioners provided quantitative ratings …

Teaching Effectiveness On Secondary Mathematics: Evidence From Pisa—Shanghai-China, Ting Shen

Psychological Science Faculty Research & Creative Works

Educational researchers and policymakers around the world have a strong interest in understanding the underlying reasons for the remarkable academic achievement of Chinese students in the Programme for International Student Assessment (PISA). Although teachers have a significant impact on student achievement, empirical evidence on teaching effectiveness in the Chinese education system has been scarce. This study uses the PISA 2012 Shanghai-China data and employs both multilevel models and quantile regression models to investigate effective teaching factors and their differential effects for students at different mathematics achievement levels. The results reveal the importance of cognitive activation and disciplinary climate as consistent, …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Impacts Of Management Practices And Soil Properties On Free-Living Nitrogen Fixation In Eastern South Dakota, Laura Galvan Nuevo

Electronic Theses and Dissertations

Free-living nitrogen (N) fixation (FLNF) may represent a sustainable pathway to supplement crop N demand in agroecosystems, yet edaphic and management factors regulating this process under field conditions remain poorly understood in the Northern Great Plains. Using 15N2 direct method to quantify potential FLNF (pFLNF) and baseline FLNF (bFLNF), this thesis investigates how crop rotations (2-, 3-, and 4-year systems), tillage management (conventional vs. no-till), cover cropping, and soil properties affect the potential of free-living diazotrophs to supply N via FLNF in a long-term no-till corn-based system in Southeastern South Dakota. A two-year field study evaluated pFLNF across four growing …

Ai Vs. Genai: Combating Llm Generated Prescription Fraud With Transformer Based Detection Models, Ankitha Vokkaleri Shankarappa

Selected Full-Text Master Theses 2021-

The rapid evolution of Large Language Models (LLMs) has introduced a sophisticated new vector for healthcare fraud: the generation of high-fidelity, synthetic medical prescriptions. Traditional fraud detection systems, which rely on rule-based engines and basic statistical anomalies, are increasingly ill-equipped to identify these AI-generated forgeries that mimic the structural and clinical logic of authentic records. This thesis presents a robust detection framework using Transformer-based architectures to distinguish between human-authored Medicare Part D prescriptions and fully synthetic records generated by GPT-4.

The research was conducted across two distinct phases: an initial pilot study using 4,000 samples and a rigorous validation stress …

Part Qm: Quantum Mechanics, Konstantin Likharev

Essential Graduate Physics

Includes: Introduction; 1D Wave Mechanics; Higher Dimensionality Effects; Bra-ket Formalism; Some Exactly Solvable Problems; Perturbation Theories; Open Quantum Systems; Multiparticle Systems; Introduction to Relativistic Quantum Mechanics; Making Sense of Quantum Mechanics

Part Cm: Classical Mechanics, Konstantin Likharev

Essential Graduate Physics

Includes: Review of Fundamentals; Lagrangian Formalism; A Few Simple Problems; Oscillations; From Oscillations to Waves; Rigid Body Motion; Deformations and Elasticity; Fluid Mechanics; Deterministic Chaos; A Bit More of Analytical Mechanics

Erosion Of Trust In Online Information, Tirth Desai

A with Honors Projects

Researching how AI spreads misinformation and impacts trust in information.

In Situ Observations Of Thermal Ions In Perturbed Ionospheres: Techniques And Results, Magdalina Louise Moses

Dartmouth College Ph.D Dissertations

Prediction and mitigation of space weather events are active research topics that require knowledge of the physics governing the ionosphere. Sounding rockets can be used to make in situ observations. The Lynch Rocket Lab created the Petite-Ion-Probe (PIP), a small retarding potential analyzer, to measure thermal ion parameters (i.e., ion density and temperature). A PIP's raw data consists of a series of measured anode currents as a function of screen bias voltages, called IV curves. PIPs can be integrated onto a sounding rocket’s main payload and/or be deployed from the rocket on small platforms called ``PIP-Bobs''. Note that as the …

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

Collaboration And Co-Management Ahead Of Permitting: Understanding How Actors And Their Interactions Lead To Non-Optimal Shoreline Projects, Juita-Elena (Wie) Yusuf, Mariana Saitgalina, Michelle Covi

School of Public Service Faculty Publications

Living shorelines are widely promoted as nature-based solutions to coastal erosion and wetland protection, yet hardened shoreline structures continue to dominate even in jurisdictions with explicit policy mandates prioritizing living shorelines. In this research, we examine why non-optimal shoreline modification outcomes persist in Virginia (USA) despite a regulatory framework designed to promote ecological alternatives. We use primary data from interviews with wetlands board members, marine contractors, and nonprofit organizations and findings from a secondary survey of shoreline property owners to analyze shoreline management as a multi-sector collaborative decision-making process. Findings show that shoreline outcomes are shaped less by individual regulatory …

Sea-Level Rise Adaptation And Collaboration In Polycentric Governance: A Comparison Of Three Regions, Francesca Vantaggiato, Mark Lubell, Matthew C. Nowlin, Juita-Elena (Wie) Yusuf, Majid Shafiee-Jood

School of Public Service Faculty Publications

Adaptation to sea-level rise confronts coastal communities worldwide with a new set of collective action problems that require collaboration. When does collaboration result in concrete action for adaptation? To address this question, we combine the Ecology of Games and Collaborative Governance frameworks using a comparative analysis of three coastal regions in the United States. We leverage original survey data from the San Francisco Bay Area in California (2018, N = 878), the Tri-County (Charleston) Area in South Carolina (2022, N = 152), and the Hampton Roads region in Virginia (2023, N = 153), three regions that differ in terms of …

Hyena: An Observing System Simulation Experiment For Hypothetical Energetic Neutral Atom Imagers, Joel Tibbetts, Amy Keesee, Matina Gkioulidou, Robert Demajistre, Viacheslav Merkin

Faculty Publications

Mesoscale plasma sheet flows are widely understood to be a critical component of the overall picture of energy, particle, and magnetic flux transport during periods of geomagnetic activity. Without simultaneous measurements with true global coverage, the short lifetime (~10 min) and localized spatial extents (~1–3 R_E in azimuth) of these structures make obtaining a global picture of their contributions to stormtime magnetosphere dynamics difficult to characterize. Energetic neutral atom (ENA) imaging can enable remote mapping of plasma energization in the magnetosphere; however, its line-of-sight integrated nature, coupled with the challenging-to-characterize attributes of the target physical phenomena, means that numerous …

Fixed Dune Grassland Mapping, Ronica Reyes, Madison Mayenschein, Jared Cruz

Environmental Science & Management Senior Capstones

Our  study applies Geographic Information Systems (GIS) and field-based data collection to map and assess a degraded fixed dune grassland parcel in Humboldt County, CA. Our project supports restoration planning led by Friends of the Dunes by identifying site features, habitat composition, and disturbance patterns at the site. Using GPS surveys, aerial imagery, and ArcGIS Pro, we mapped infrastructure, cement footprints, debris piles, vegetation types, and habitat boundaries. We mapped infrastructure such as concrete foundations and debris, which contribute to ongoing ecological degradation and the presence of invasive species. In addition, our spatial analysis highlights variability in disturbance. From this …

Healing Our Life Source: Riparian Eco-Cultural Restoration In Blue Lake Rancheria At School Creek, A Tributary Of Baduwa't In Northern California, Rae Mcgrath, Lily Moore, Keneti Jesus Pinones, Colin Rogers, Aberdeen Spade

Environmental Science & Management Senior Capstones

School Creek, a tributary in the Baduwa’t (mad river) Watershed, is a riparian corridor that is key habitat for Coho Salmon. This project is under the management of the Blue Lake Rancheria, located on the ancestral home of the Wiyot people, and was conducted by cal poly humboldt students. The restoration of School Creek is an ongoing project aimed at repairing the health of the riparian corridor, improving the creek’s channel structure, and creating a diverse community of culturally significant plant species. Previous restoration efforts have established plantings to further develop the riparian habitat and are surveyed within this report. …