Digital Commons Network^™

Adaptive Boundary-Aware Fact-Checker Placement For Misinformation Suppression In Social Networks, Mostafa Taghizade Firouzjaee, Ghazal Naderi, Ross Gore, Neda Moghim

School of Cybersecurity Faculty Publications

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news …

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 …

Pathways To Participation: Enhancing Inclusion For Individuals With Idd, Katie Koxlien

Occupational Therapy Capstones

Background: Youth with intellectual and developmental disabilities (IDD) continue to experience barriers to meaningful community participation despite policies promoting inclusive community-based services (Friedman, 2017). Sensory sensitivities, difficulty with transitions, and overstimulating environments frequently limit engagement and increase anxiety or behavioral responses when appropriate supports are not present (Bagatell et al., 2022; Dean et al., 2018; Tint et al., 2017). Research indicates that inclusion requires intentional programming that incorporates structured supports, consistent routines, and opportunities for meaningful engagement (Edwards et al., 2021; Hall, 2016).

Methods: The purpose of this scholarly project was to develop a week long sensory exploration camp for …

Socio-Spatial Patterns Of Suicide Mortality In The United States, Kushagra Tiwari, M. Amin Rahimian, Marie-Laure Charpignon, Philippe J. Giabbanelli, Praveen Kumar

VMASC Publications

Suicides cause more than 49,000 deaths per year in the United States, including 55% associated with the use of a firearm. Across states and counties in the US, suicide mortality exhibits substantial geographical and sociodemographic heterogeneity. However, the role of large-scale social networks in shaping this variation remains underexplored. To assess how both the risk of suicide mortality and the effect of firearm restriction policies propagate through inter-county social ties, we integrate data on county-level suicide mortality (2010–2022) and the Facebook Social Connectedness Index (SCI), a continuous measure of the strength of social ties between counties used to derive weighted …

"Women’S Language" In Supreme Court Oral Arguments, Carolina Nunez, Lucy Williams, David Wingate, Aerin Christensen, Anna Mae Walker

University of Colorado Law Review

No abstract provided.

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Auseinandersetzung: Heidegger's Black Notebooks And Intercultural Engagement, Steven Burik

Research Collection School of Social Sciences

Heidegger’s relation to Asian philosophy should be guided by the notion of Auseinandersetzung. Often translated as con-frontation, this term plays a pivotal role in understanding Heidegger’s engagements with Asian philosophy. Auseinandersetzung (and related terms) can help us realise the changed perspectives on our own thinking that we derive from engagements with culturally and/or historically other types of thinking. Heidegger warns us against facile endeavours of comparing and contrasting, which, as a form of comparative philosophy, he sees as a kind of dialectics unworthy of true thinking. Thinking through the term Auseinandersetzung allows us to reflect more meaningfully on comparative philosophy.

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 …

Family, Stability, Mentors & Money: Student Definitions Of Success, Arturo Caldera

CMC Senior Theses

This research takes a deep dive into the factors shaping marginalized communities’ educational experiences, particularly for low-income Latino students from migrant backgrounds navigating public schooling spaces. Across sixteen qualitative interviews, participant narratives reveal how a systemic lack of resources at the institutional, interpersonal, and structural levels undermines the quality of support students receive and shapes their academic trajectories. Grounded in student-centered practices and informed by scholarship in multicultural education, Chicanx/Latinx studies, feminism, and psychology, this study contributes to ongoing efforts to move beyond deficit-based frameworks and instead recognize the cultural wealth, knowledge, and resilience students bring from their homes and …

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 …

The Impact Of Asu 2023-08 On Reported Profitability Of Cryptocurrency-Exposed Firms, William C. Graff

CMC Senior Theses

This study investigates the performance of corporate cryptocurrency holdings in the context of recent accounting regulation changes. It specifically examines how adopting Accounting Standards Update (ASU) 2023-08 has affected firms with material cryptocurrency exposure. Using a panel of U.S. public firms from 2019–2025, I classified 138 companies as having material crypto exposure if cryptocurrency comprised more than 0.01 percent of total assets, which was manually calculated. Of these firms, 67 were exposed to the rule change (under GAAP), 66 were not, and 5 were excluded from final calculations due to a lack of relevant information.

Results showed statistically significant effects …

An Intuitionistic Fuzzy Hypergraph Model Enhanced ‎By Forest-Of-Walks Dynamics For Medical Image ‎Segmentation, Brinthaguru Thangaraj, Karthick Palanisamy

Mansoura Engineering Journal

The process of accurately segmenting Urolithiasis and pulmonary nodules from medical images presents significant difficulties because of three main factors which include nonuniform intensity distribution and the presence of weak object borders and the uncertainty caused by anatomical structure overlaps. The study introduces F-Zinhger as a solution to these problems through its application of the Forest of Walks technology. The method uses superpixels as fundamental elements which form a multi-scale hypergraph that establishes hyperedges through three criteria: intensity similarity, spatial proximity, and Z-intuitionistic fuzzy hesitation to capture complex contextual connections. The system incorporates a random walk-based label propagation system. The …

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, …

Pathways, Outputs And Impact Of Nih-Supported Bioinformatics And Genomics Graduate Trainees In Africa, Daudi Jjingo, Andrew Walakira, Suhaila Hashim, Cisse Cheickna, Ronald Galiwango, Caleb Kibet, Florence N. Kivunike, Gerald Mboowa, Fredrick Elishama Kakembo, Babajide Ayodele, Jean Baka Domelevo Entfellner, Santie De Villiers, Karen Wambui, Segun Fatumo, Tinashe Chikowore, John Mukisa, Alfred Ssekagiri, Nicholas Bbosa, Julius Mulindwa, Samuel Kyobe, Mike Nsubuga, Grace Kebirungi, Eric Katagirya, Savannah Mwesigwa, Ibra Lujumba, Rogers Kamulegeya, Samuel Kirimunda, Stephen Kanyerezi, Shahiid Kiyaga, Ivan Sserwadda, Davis Kiberu, Daniel Masiga

All Peer-Reviewed Publications

Global biomedical and health research is increasingly relying on genomic and computational approaches, largely driven by the increasing volumes of nucleic acid sequencing. Concurrently, epidemiological studies and clinical records are generating enormous amounts of data amenable to disease modeling, machine learning, and artificial intelligence techniques. Bioinformatics and data science expertise is therefore essential for improved population health. Accordingly, in 2012, the US National Institutes of Health (NIH) in partnership with the Wellcome Trust, and with support from the African Society for Human Genetics, initiated the H3Africa (Human Heredity and Health in Africa) consortium. One of its key goals was to …

Effects Of Temperature On The Life-History Traits Of Myzus Persicae And Its Efficiency In Transmitting Potato Virus Y (Pvy) In Potato Crops, Bonoukpoè M. Sokame, Henri E. Tonnang, Heidy Gamarra, Pablo Carhuapoma, Jan Kreuze, Leah R. Johnson, Ali Arab, Peter A. Armbruster, Oswaldo C. Villena

All Peer-Reviewed Publications

Aphids are highly sensitive to temperature changes and play a crucial role in transmitting plant viruses, accounting for the transmission of more than 50% of viruses that cause disease in crops. Among them, Myzus persicae is a major global pest, affecting over 400 plant species and transmitting more than 100 plant viruses, including potato virus Y (PVY), which poses a severe threat to potato crops. This study examines how temperature influences the life-history traits of M. persicae and its efficiency in transmitting PVY. Our research revealed that temperature significantly affects developmental duration, survival, and fecundity of M. persicae. The aphids …

Determining Knowledge Gaps In Natural Disaster Preparedness: An Exploratory Analysis Of Socioeconomic Variables And Response Quality, Michelle S. Repass

Theses and Dissertations

This exploratory study examines potential knowledge gaps in natural disaster preparedness across socioeconomic groups in Lee County, Alabama and identifies communication barriers affecting response behavior through the lens of the knowledge gap hypothesis. Survey data from 20 residents were analyzed using an objective socioeconomic status (OSES) score based on household income and highest level of education. Results indicate that while most respondents reported access to weather alerts and favorable behavioral responses that align with current guidelines from the National Weather Service, gaps are present in comprehension, response timing, and engagement in risky behavior that could delay protective action. Findings suggest …

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 …

Exploring Secondary Stem Teachers’ Experiences In Online Learning Environments: A Directed Content Analysis Using The Community Of Inquiry Framework, Tiffany Janese Lester

Theses and Dissertations

This study is a directed qualitative content analysis of 21 peer-reviewed studies published between 2020 and May 2025 on secondary STEM teaching in online environments. Using the Community of Inquiry framework, I examined how teaching, social, and cognitive presence shaped instructional strategies, teacher experiences, and reported challenges. Coding with deductive and inductive methods identified recurring themes: teachers adapted to digital platforms, navigated uneven levels of digital fluency, and implemented intentional strategies to maintain social interaction. The studies also revealed persistent equity gaps in access and participation alongside innovative approaches to course and pedagogical design. These themes demonstrate how secondary STEM …

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 …

Ai And Its Impacts On Modern Theatre, Micaiah J. Byrd

Theses and Dissertations

What do you think of when you type a question into a search engine, and the “AI Overview” pops up without your having to request it? What may happen if theatre students and theatre industry professionals have that same ability to type in a prompt into an AI generator, and it has the ability to produce an output of a theatrical design concept with that same level of ease. Is this even possible with the programs that we have now, or could it be anticipated that this could be possible in the future. Would this be a benefit or would …

Confidence In Action: A Qualitative Inquiry Into Teachers' Perceived Abilities In Implementing Instructional Technology, Latorri West

Theses and Dissertations

Education is evolving through the integration of academic content and technology to support student success. Despite significant investments in educational technology, challenges persist in effective implementation, particularly in rural contexts where resources and support may be limited. Using the Technological Pedagogical Content Knowledge (TPACK) framework, the researcher investigated teachers’ perceptions of implementing the i-Ready adaptive learning platform in a rural Georgia school district. i-Ready, an adaptive diagnostic and instructional tool for reading and mathematics used by more than 25% of U.S. schools, represents a widely adopted educational technology that requires specific technical, pedagogical, and content knowledge for successful implementation.

The …

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 …