Behind The Screen: Online Sex Buyer Networks And The Organized Criminal Promotion Of Exploitation, Ava Kamdem, Vanessa Bouché, Ben Silver, Nick Freeman, Justin Euteneier

Dignity: A Journal of Analysis of Exploitation and Violence

This study investigates the extent to which online sex buyer communities in the United States function as organized criminal networks pursuant to the United Nations Convention against Transnational Organized Crime’s definition of organized criminal groups. Drawing from theories of organized crime, social network analysis, and digital ecosystems, we conceptualize sex buyers not as isolated actors but as participants in coordinated, mutually reinforcing networks that share information to facilitate exploitation, mitigate risk, and sustain criminal behavior. To test this framework, we scraped over 1.2 million posts from a publicly accessible sex buyer forum, comprising 72,974 unique users and nearly 800,000 direct …

An Open-Source Evaluation Framework For Risc-V Co-Design-Based Decimal Arithmetic, Riaz Ul Haque Mian, Michiko Inoue

Research outputs 2022 to 2026

Hardware–software co-design is a balanced strategy for computationally intensive algorithms such as decimal computing. It can provide several Pareto points for the development of embedded systems in terms of hardware cost and performance. In this study, we propose an efficient and accurate evaluation framework for decimal computing. The framework was designed and developed for hardware–software co-design decimal arithmetic using the RISC-V ecosystem. New binary and decimal-oriented instructions supported by an accelerator were developed. The framework can perform cycle-accurate analysis for performance and assess hardware overhead for co-design-based decimal arithmetic. Unlike previous studies that focused primarily on implementing and evaluating individual …

Optimizing Few-Shot Learning In Pruned Large Language Models With Task-Specific Prompts, Danyal Aftab

Dissertations

Few-shot learning enables large language models to efficiently perform tasks given only a limited number of labeled examples. However, training these models entirely from scratch requires substantial computational resources, making it challenging for many organizations to fully leverage their potential. This thesis explores how structured pruning, task-specific prompting, and parameter-efficient fine-tuning can be combined to preserve few-shot learning capabilities in compressed LLMs, while also extending their utility to real-world recommendation systems.

In this research, we propose the Tailored LLM framework, which first reduces model size through structured pruning and then enhances few-shot learning performance using carefully designed prompts. We experiment …

Dynamics Of High-Growth Young Firms And The Role Of Venture Capitalists, Yoshiki Ando

Research Collection School Of Economics

Motivated by the substantial growth and upfront investments of venture capital (VC)-backed firms observed in administrative US Census data, this study develops a life-cycle firm dynamics model. In the model, startups choose the source of financing from VC, angel investors, or banks, depending on their growth potential, and invest in innovation. The calibrated model explains the life-cycle dynamics of firms with different sources of financing and suggests that venture capitalists' managerial advice accounts for around 22% of the growth in VC-backed firms. A counterfactual economy without VC financing would experience an aggregate consumption loss of around 0.46%.

Semiparametric Cointegrating Rank Selection For Curved Cross-Section Time Series, Peter C. B. Phillips

Research Collection School Of Economics

Cointegrating rank selection is studied in a function space reduced rank regression where the data are time series of cross-section curves. Consistent cointegrating rank estimation is developed using information criteria extended to curve time series environments. The asymptotic theory involves two-parameter Gaussian processes that generalise the standard limit processes involved in cointegrating regressions. Simulations provide evidence of the effectiveness of consistent rank selection by the BIC criterion and the tendency of AIC to overestimate order as in standard lag order selection in autoregression, as well as in reduced rank regression with multiple time series.

Marital Stability And Intrahousehold Inequality, Tomoki Fujii, Xirong Lin, Jacob Penglase

Research Collection School Of Economics

We study what predicts marital instability using a unique dataset from Japan with detailed measures of spouses’ consumption, savings, labor supply, satisfaction, and marriage-market conditions. We combine descriptive survival and event-study analyses with several econometric and machine-learning approaches, including Cox proportional-hazards models and DynForest, a method designed for survival data with time-varying covariates. Across these approaches, match quality, household resources, time use, stated satisfaction, and marriage-market conditions all contain predictive information. Our main contribution is to show that intrahousehold consumption allocation matters, highlighting information that is missed by standard demographic, time use, and income measures. We relate these findings to …

Automated Battery Management Systems For Electric Vehicles, Woonki Na, Yuanyuan Xie

Mineta Transportation Institute

Electric vehicle (EV) safety, efficiency, and lifetime are strongly influenced by how well battery cells are managed, and as EV adoption accelerates, improving battery performance has become critical to vehicle reliability, cost, and public trust. This report conducts a field study on the core technologies and challenges in Battery Management Systems (BMS), focusing on the classification of BMS circuit topologies (design/structures) and systematically reviewing different topologies of active cell balancing circuits (systems that move energy from stronger battery cells to weaker ones). The study provides a detailed analysis of the working principles, advantages, and disadvantages of various active balancing circuit …

Teacher Self-Efficacy And Its Relationship To Student Achievement: A Quantitative Analysis Using Map Growth Data, Joseph Todd Holzmann

Electronic Theses and Dissertations

One factor that has been widely studied is teacher self-efficacy. Teacher self-efficacy is often considered an indicator of teacher effectiveness and has been linked to a variety of positive educational outcomes. The purpose of this quantitative study was to examine the relationship between teacher self-efficacy and student achievement. Additionally, the study investigated whether teacher self-efficacy differed based on years of teaching experience, grade level taught, and subject area taught. The Teachers’ Sense of Efficacy Scale (TSES), developed byTschannen-Moran and Woolfolk Hoy, was used to measure teacher self-efficacy among 88 participants with corresponding student MAP Growth data available for analysis. Results …

The Impact Of Using History On Learning In Physics Laboratory Settings, Patrick W. Clouse

Dissertations

Integrating History of Science (HOS) into science courses has many benefits. Students demonstrate improved scientific content knowledge, develop their understanding of Nature of Science (NOS), and improve their attitudes towards science. However, barriers prevent instructors from effectively incorporating history into their teaching. The traditions and culture of science instruction, individual teacher attitudes and skills, existing institutional frameworks, and prevailing curricular materials all prevent HOS integration. Preservice teachers may never be exposed to HOS in their science or education courses, meaning they lack experience and knowledge surrounding the benefits of HOS. Therefore, alternative avenues for effective integration should be considered to …

Community Detection In Bipartite Networks Using Bipartite Stochastic Block Models With Node-Level Covariates, Geraldine Elaine Percival

Dissertations

In this age, monumental webs of data demands for perpetual cultivation of ways to untangle these webs of information. One of the many curiosities is how to systematically group entities. When clustering, one avenue to take is ascertaining the interconnectedness between the data points, and gauging their influence to each other. This perspective is programmed to model the relationships between the data presented as a network. Many of the methods being used today are algorithm-based, which may pose limitations in understanding and explaining the uncertainty revolving around the data. Hence, it is proposed to steer towards a model-based approach that …

The Impact Of Mentoring: Professional Growth, Reflection, And Retention In Secondary Science Education, Summer M. Landreth

All Dissertations

Teacher attrition threatens the stability of the U.S. education system, with science teachers experiencing particularly high turnover rates. While research has primarily focused on early-career teachers, less is known about the factors influencing mid-career and veteran science teachers’ decisions to remain in or leave the profession. This study explored mentoring student teachers as a potentially transformative experience for cooperating teachers, offering opportunities for professional development, identity development, and enhanced retention. Guided by Mezirow’s Transformative Learning Theory, the study examined how mentoring may function as a productive experience that prompts critical reflection, revitalizes pedagogy, and reconnects teachers with their professional purpose. …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Continuous Query For Top-K Maximal Sum Intervals Over Streaming Data, Zhongshuai Zhang, Xiaochun Yang, Baihua Zheng, Rui Zhu, Haomin Li, Bin Wang

Research Collection School Of Computing and Information Systems

The continuous identification of top-k maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small k values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, …

Dynamic Spectral Denoising With Global-Context Attention For Multi-Behavior Recommendation, Miaomiao Cai, Yunshan Ma, Fangqi Zhu, Junfeng Fang, Zhijie Zhang, Zhiyong Cheng, Xiang Wang, See-Kiong Ng

Research Collection School Of Computing and Information Systems

Multi-behavior recommendation improves target-behavior predic-tion by exploiting heterogeneous auxiliary feedback (e.g., view,collect, and cart), yet its robustness is often undermined by behavior-dependent noise and inconsistency. We argue that the key bottle-neck is not merely noisy behaviors, but a representation-level failurecaused by two coupled heterogeneities. First, intra-behavior rep-resentation entanglement arises when multi-hop propagationblends incidental signals with true preferences in the embeddingspace. This entanglement renders coarse spatial denoising inef-fective, since it cannot suppress noise without sacrificing weak-but-informative niche signals. Second, inter-behavior reliabilityheterogeneity complicates cross-behavior fusion, as the predic-tive value of auxiliary behaviors varies substantially across usersand contexts. Without reliability calibration, aggregation can …

Approximation And Learning-Based Algorithms For Influence Maximization In Multilayer Social Networks, Xueqin Chang, Ruize Liu, Qing Liu, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we introduce a hybrid propagation model that jointly captures layer-specific diffusion dynamics and probabilistic cross-layer propagation. Based on this model, we formally define the Mlim problem and establish its NP-hardness, monotonicity, and submodularity. To address the Mlim problem, we first propose a greedy baseline Mlim-Greedy, which achieves a (1-1/e) approximation. Since …

Audeter: A Large-Scale Dataset For Deepfake Audio Detection In Open Worlds, Qizhou Wang, Hanxun Huang, Guansong Pang, Sarah Erfani, Christopher Leckie

Research Collection School Of Computing and Information Systems

Speech synthesis systems can now produce highly realistic vocalisations that pose significant authenticity challenges. Despite substantial progress in deepfake detection models, their real-world effectiveness is often undermined by evolving distribution shifts between training and test data, driven by the complexity of human speech and the rapid evolution of synthesis systems. Existing datasets suffer from limited real speech diversity, insufficient coverage of recent synthesis systems, and heterogeneous mixtures of deepfake sources, which hinder systematic evaluation and open-world model training. To address these issues, we introduce AUDETER (AUdio DEepfake TEst Range), a large-scale and highly diverse deepfake audio dataset comprising over 4,500 …

Left: Learnable Fusion Of Tri-View Tokens For Unsupervised Time Series Anomaly Detection, Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

Research Collection School Of Computing and Information Systems

As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations. A key challenge in unsupervised TSAD is that many anomalies are too subtle to exhibit detectable deviation in any single view (e.g., time domain), and instead manifest as inconsistencies across multiple views like time, frequency, and a mixture of resolutions. However, most cross-view methods rely on feature or score fusion and do not enforce analysis–synthesis consistency, meaning the frequency branch is not required to reconstruct the time signal through an inverse transform, and …

Timeradar: A Domain-Rotatable Foundation Model For Time Series Anomaly Detection, Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang

Research Collection School Of Computing and Information Systems

Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstream tasks (\eg, forecasting). Consequently, they are often ineffective for inherently unsupervised downstream tasks—such as time series anomaly detection (TSAD), which aims to identify rare, irregular patterns. This limitation arises because such abnormal patterns can closely resemble the regular patterns when presented in the same time/frequency domain. To address this issue, we introduce TimeRadar, an innovative TSFM built in a fractional time–frequency domain to support generalist TSAD across diverse unseen datasets. Our key insight is that …

Task-Aligned Haze Removal With Semantic-Aware Fusion And Contrast Self-Correction, Jinbin Wang, Aiping Yang, Guosong Jiang, Wenlong Yu, Dongwei Ren, Qinghua Hu

Research Collection School Of Computing and Information Systems

Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real‐world scenarios. To address this issue, this work proposes a task‐aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low‐level restoration with high‐level detection objectives. The framework incorporates a Semantic‐Aware Multi‐Scale Fusion Module (SMFM) that embeds pixel‐level semantic knowledge into the dehazing process, enabling selective …

2026 August, Morehead State University. Office Of Communications & Marketing.

Morehead State Press Release Archive, 1961 to the Present

Press releases for August of 2026.

Cross-Layer Security And Responsive Evaluation Architectures For Quantum Resistance In Connected Vehicles, Geoff Twardokus

Theses

Connected vehicle (CV) technologies are emerging as a crucial technology for improving safety on future roadways. Wireless vehicle-to-everything (V2X) communication allows CVs to exchange information that provides enhanced awareness and supports better driving decisions, especially in high-risk or non-line-of-sight scenarios. However, V2X also exposes new surfaces to physical layer attacks and increasingly sophisticated, potentially quantum-capable threat actors. As human or autonomous CV drivers will make split-second safety-critical decisions based on the contents of V2X messages, ensuring their authenticity, as well as their reliable and timely delivery, is crucial. Otherwise, malicious attackers may interfere with safety services or spoof messages to …

Analysis Of Gwtc-3 With Multiple Quasicircular Next-Generationwaveform Models, Noah M. Manning

Theses

The interpretation of gravitational wave sources depends on the specific choice of model used to interpret signals. For example, previous analyses of GWTC-3 event candidates using the SEOBNRv4PHM, IMRPhenomXPHM, and NRSur7dq4 models have arrived at sometimes notably di!erent conclusions about event properties. Unfortunately, subtle analysis settings and code di!erences can also produce notable di!erences in event properties. Previous work on GWTC-3 has not yet used a consistent analysis framework to reassess all events with state-of- the-art models. In this work, we revisit GWTC-3, reassessing every event using three waveform models: two state-of-the-art models (SEOBNRv5PHM and IMRPhenomTPHM) and one older waveform …

Brief Insights: Learning, Leading, And Growing Together: Highlights From A Multilingual Learner And Mathematics Focused Research, Practice, Policy Partnership, Elvira G. Armas, Ed.D., Magaly Lavadenz, Ph.D., Kaivan Yuen, Ed.D., Miguel Valencia

Education and Policy Briefs

Building on a longstanding partnership, the Center for Equity for English Learners (CEEL) at Loyola Marymount University and Montebello Unified School District (MUSD) expanded their Research, Practice, Policy Partnership (RPPP) to examine Spanish academic oral language development among TK–5 Dual Language Immersion students engaged in Cognitively Guided Instruction (CGI) in mathematics. Teacher-researchers analyzed video samples of students explaining their mathematical reasoning using a rubric aligned with the California Spanish Language Development Standards.

Findings demonstrate meaningful growth in students’ Spanish academic oral language across the school year, particularly in their ability to support opinions and select language resources to communicate mathematical …

Hvi-Cidnet+: Beyond Extreme Darkness For Low-Light Image Enhancement, Kangbiao Shi, Xiaowen Ma, Yixu Feng, Tao Hu, Peng Wu, Guansong Pang, Qingsen Yan

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) aims to recover visually pleasing content and details from degraded low-light images. However, existing RGB-based methods often suffer from color bias and brightness artifacts due to inherent high color sensitivity. Although the HSV color space can decouple brightness and color, it introduces noticeable red and black noise artifacts. To address these challenges, we adopt the Horizontal/Vertical-Intensity (HVI) color space for LLIE, which is defined by the HV color map and learnable intensity. The former enforces small distances for red coordinates to alleviate red noise artifacts, while the latter adaptively compresses low-light regions to suppress black noise …

Continuous Authentication For Industrial System Access: Evaluating Bluetooth Low Energy Direction Finding And Channel Sounding For Tailgating And Relay Attacks, Mitchell Mennelle

LSU New Orleans Theses and Dissertations

In physical access control, authentication is often viewed as a one-time event, where, once an authorized user crosses a protected boundary, downstream systems assume the user remains physically present. Tailgating and relay attacks violate this assumption. In this thesis we propose a continuous authentication layer based on two Bluetooth Low Energy spatial signals. Angle of Arrival direction finding follows the trail of a worn credential to determine when an operator exits a work zone. Bluetooth Channel Sounding measures a physical property of the radio path and verifies distance during stationary periods. Limiting Relay Attacks with Event-Driven Distance Verification. A stream …

The Impact Of State Insulin Copayment Caps On Diabetics In The United States, Ryan C. Meyer

All Theses

Insulin is a life-saving medication for people with diabetes that helps regulate blood glucose levels throughout the body. A Type 1 diabetic cannot survive without insulin, and a Type 2 diabetic’s quality of life greatly diminishes without access and use of this drug. Currently, many diabetics skip, ration, or abstain from insulin due to financial barriers. As of June 2026, 29 states have enacted insulin copayment caps for state-regulated commercial health insurance plans to reduce the financial burden of insulin costs. This study examines the impact of these caps on all commercially insured diabetics in the United States, particularly on …

Electrokinetic Flow Instabilities In Non-Newtonian Fluids With Conductivity Gradients, Md Mainul Islam

All Theses

Electrokinetic instabilities (EKI) arise in electrically driven flows because the applied electric field interacts with spatial variations in electrical conductivity, generating electric body forces that can destabilize fluid interfaces. EKI can be beneficial by enhancing mixing in microchannels where diffusion is slow, but it can also be detrimental by disrupting stable sample transport, focusing, and separation. EKI has been widely studied in Newtonian fluids, and the electric Rayleigh number is known to govern the onset of this instability. However, very few studies have examined its behavior in non-Newtonian fluids, despite their prevalence in biological, chemical, and industrial applications. 

This thesis …

Insect Abundance And Bat Use Of Fields For Foraging In The Cumberland Plateau, Caroline M. Abramowitz

All Theses

Bat populations and their insect prey are declining, making it urgent to understand how habitat features and management support both groups. Early successional habitat (ESH), grasslands and shrublands with reduced vegetative clutter and abundant flowering resources, may benefit foraging bats and nocturnal insects. This study examined how landscape context, patch characteristics, microhabitat features, and management (mechanical mastication and prescribed burns) affected insects and bats across 29 managed fields in Big South Fork National River and Recreation Area (TN/KY) from 2024–2025. Flight-intercept traps collected 25,297 insects across five orders. Though results were order-specific, vegetative composition and height were the strongest predictors …

What Makes A Cozy Game Cozy? Looking Through The Lens Of Feminism And 4e Cognition, Mary Catherine M. Wilcox

All Theses

Why do some games feel cozy, and others miss the mark, even if they are tricked out in bright colors and festooned with bows? This thesis examines the emerging genre of “cozy games” to better understand why some titles successfully evoke comfort and relaxation while others fail, despite relying on conventionally cute aesthetics or farming-sim mechanics. Although cozy games have grown rapidly in popularity over the past decade, the genre remains under-studied and is often dismissed as frivolous, reflecting the broader historical devaluation of media associated with women. Accordingly, this study argues that understanding cozy games requires examining women’s relationship …

When The Best-Fit Model Is Not Best: The Glass Slipper Fallacy And Latent Growth Mixture Modelling, Jonathan L. Chia, Markus Wettstein, Andree Hartanto

Research Collection School of Social Sciences

Despite the use of latent growth mixture modelling (LGMM) to study longitudinal changes, existing practices may inadvertently impede this very investigation. Although subgroup trajectories may theoretically differ in their structure (e.g., some subgroups being linear, some curvilinear), the current convention advocates overreliance on the baseline model to derive subsequent profile trajectories, which may obscure these structural differences. In this article, we provide a brief description of extant LGMM practices, after which we explicate the pitfalls of the current approach. Finally, we provide a principled approach for LGMM research moving forward. Specifically, we recommend specifying a set of theoretically plausible models …