Digital Commons Network^™

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

A Rate-Dependent Coreset Selector For Continual Learning On Time-Varying Data Distributions, Zilin Luo, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

In this paper we review the concept of “phase” defined in Class-Incremental Learning (CIL), i.e., learning new classes while not forgetting old ones. Due to this design, classic CIL algorithms are mostly offline or can handle only intensive data distribution shifts across the phases. However, real-world data streams are often online, usually with uncertain or untraceable changes in their data distributions. To this end, we design the per-step distribution shifts by modeling the class sampling weights using bell-shaped curves. Such a design respects the rise-and-fall nature and presents realistic but underexplored challenges for CIL: 1) The data non-stationarity across steps …

General Test-Time Backdoor Detection In Split Neural Network-Based Vertical Federated Learning, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in …

Kpiroot+: An Efficient Integrated Framework For Anomaly Detection And Root Cause Analysis In Large-Scale Cloud Systems, Wenwei Gu, Renyi Zhong, Guangba Yu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their runtime status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

Fl-Cdf: Collaborative Defense Framework For Backdoor Mitigation In Federated Learning, Haiyan Zhang, Xinghua Li, Yinbin Miao, Shunjie Yuan, Mengyao Zhu, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module …

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 …

Towards Inclusive Digital Futures Of Cultural Heritage: Insights From A Critical Discourse Analysis Of Unesco Dialogues, Shiqing Huang, Keng Siau, Xiaoting Chen

Research Collection School Of Computing and Information Systems

Digital technologies are shaping many aspects of cultural heritage, but very little research has examined the implications of digital transformation. Drawing on concepts from Fairclough’s three-dimensional critical discourse analysis, this research examines the discourse using seven online dialogues (available on the UNESCO website) between 18 professionals who have different backgrounds and cultures to identify social practices related to the digital transformation of cultural heritage. We identify four digital transformation discourse types in professional dialogues: documentation, management, interpretation, and interaction. We also identify seven main groups: memory institutions including libraries, archives, and museums (LAMs), governments, international organizations, art and creative supporters, …

Cropcapsnet: Enhanced Capsule Network For Crop Disease Classification, Juan Qin, Linfan Deng, Cong Li, Junjie He, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The prevention and treatment of crop diseases are crucial for the development of smart agriculture. The classification of crop diseases based on deep learning for early disease monitoring and control has become the mainstream direction of research. This paper proposes a novel deep learning model called ”CropCapsNet”, which combines Squeeze-and-Excitation Inception (SE-Inception) module and has improved capsule structure for crop disease classification. The network first extracts shallow features of input samples through double-layer convolution, then uses SE-Inception to achieve deep multi-scale feature acquisition, and finally outputs classification results through an improved capsule structure. SE-Inception adds Squeeze-and-Excitation(SE) attention after each multi-scale …

Island-Based Evolutionary Computation With Diverse Surrogates And Adaptive Knowledge Transfer For High-Dimensional Data-Driven Optimization, Xianrong Zhang, Yuejiao Gong, Zhiguang Cao, Jun Zhang

Research Collection School Of Computing and Information Systems

In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantly when applied to large-scale optimization problems (LSOPs). To address the challenge, this paper proposes an offline DDEA named DSKT-DDEA. DSKT-DDEA leverages multiple islands that utilize different data to establish diverse surrogate models, fostering diverse subpopulations and mitigating the risk of premature convergence. In the intra-island optimization phase, a semi-supervised learning method is devised to fine-tune the surrogates. It not only facilitates …

Leveraging Disparate Wildland Fire Datasets To Address Longstanding Fire Management Questions, Kendra Fallon

Boise State University Theses and Dissertations

While wildfire season is perceived as occurring in the warm months, and expanding, wildfire management is a year-long process. Juxtaposed to wildfire seasons are the reactive seasons of mitigating for post-fire effects, namely first-order effects of potential hydrological impacts in burn footprints, and the proactive implementation of fuels treatments to modify fire behavior to lessen the reactive workload. In this dissertation, I focus on answering lingering questions that are critical to the management of the shoulder seasons of wildfire season. To assist in resource allocation and identification of post-hydrological risks, I present a model identifying the major drivers of soil …

Deep Reinforcement Learning For Adaptive Mesh Refinement: A Training Parameter Study, Antone Chacartegui

Boise State University Theses and Dissertations

Adaptive mesh refinement (AMR) enables efficient solution of partial differential equations by dynamically allocating computational resources to regions requiring higher resolution. It has been demonstrated that deep reinforcement learning agents could learn effective mesh adaptation policies. However, practical deployment requires a systematic understanding of how training parameters affect performance across diverse computational scenarios.

This thesis presents a parameter optimization study analyzing the relationship between deep reinforcement learning for adaptive mesh refinement (DRL-AMR) training parameters and model performance. We implement an independent DRL-AMR system with a discontinuous Galerkin solver and actor-critic (A2C) algorithm for one-dimensional linear advection. The study explores four …

Combinatorics Of Trees Via Derivatives Of Polynomial Functors In Homotopy Type Theory, Warren David Hatton

Boise State University Theses and Dissertations

Polynomial functors are, essentially, polynomials whose variables take values in sets, groupoids, or some other suitable category, rather than in numbers. In this thesis, we will explore three related aspects of polynomial functors over $\infty$-groupoids in the framework of homotopy type theory: first, polynomial functors provide a foundation for inductively defined data types as wellfounded trees; second, polynomial functors have a notion of derivative which, viewed in light of the connection to data types, yields a computational interpretation of differentiation; and third, suitably finite polynomial functors over (higher) groupoids yield categorified generating functions, and thus subsume the theory of combinatorial …

A Comparative Study Of Novel Methods For Variances, Taoreed Muritala

Boise State University Theses and Dissertations

Understanding variability is fundamental to knowledge advancement across various disciplines, including manufacturing, clinical research, biology and genetics. Population variances play a crucial role in processes such as quality control, treatment evaluation and the interpretation of biological mechanisms. However, statistical procedures for comparing variances across multiple populations remain less developed than those for comparing means. Classical omnibus tests, such as Bartlett's and Levene's, can detect overall variance heterogeneity but fail to identify which populations differ, thereby limiting their usefulness in multi-population analyses and increasing the risk of inflated family-wise error rates through repeated testing.

This thesis develops and evaluates three multiple …

Abelian Sandpile Models On Edge Periodic Graphs, Payton Lyons

Boise State University Theses and Dissertations

The abelian sandpile model is a Markov chain that is able to decompose any recurrent configuration into a basic alive divisor, some amount of sand placed on the sink vertex, and a series of vertex firings. In this paper we begin to explore some of the dynamics of this system when the edges of the graph are allowed to alternate between two different edge sets. This system will be framed in a similar fashion as the classical model, and then some basic numerical simulations were used to observe the threshold behavior of the system. We lay some of the initial …

An Intelligent Healthcare System For Rare Disease Diagnosis Utilizing Electronic Health Records Based On A Knowledge-Guided Multimodal Transformer Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Ankur Pandey

All Works

Rare diseases are a common problem with millions of patients globally, but their diagnosis is difficult because of varied clinical presentations, small sample size, and disparate biomedical data sources. Current diagnostic tools are not able to combine multimodal information effectively, which results in a timely or wrong diagnosis. To fill this gap, this paper suggests a smart multimodal healthcare framework integrating electronic health records (EHRs), genomic sequences, and medical imaging to improve the detection of rare diseases. The framework uses Swin Transformer to extract hierarchical visual features in radiographic scans, Med-BERT and Transformer-XL to learn semantic and long-term temporal relations …

Big Data Transfer Service Architecture For Cloud Data Centers: Problems, Methods, Applications, And Future Trends, Muhammad Umar Majigi, Ismaila Idris, Shafi’I Muhammad Abdulhamid, Richard A. Ikuesan

All Works

Data volume, velocity, and structure have significantly evolved over the years. The complex networking architectures of current infrastructures, and the development, and accessibility of cloud services to a diverse user base have introduced numerous challenges which have raised concerns regarding the quality-of-service performance in data processing for both service providers and customers. Key issues identified in the context of big data transfer services for cloud data centers include storage, big data transfer, service transfer architecture, data processing, bandwidth, and security, all of which demand extensive research. After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data …

A Hybrid Fog-Edge Computing Architecture For Real-Time Health Monitoring In Iomt Systems With Optimized Latency And Threat Resilience, Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira

All Works

The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing …

A Deep Learning Framework For Automated Breast Cancer Diagnosis Using Intelligent Segmentation And Classification, Ahed Abugabah

All Works

Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution …

Connecting The Dots: Iot, Sustainability, And Sdgs, Saadat M. Alhashmi, Islam Al-Qudah, Ibrahim Abaker Hashem, Belal Alsinglawi, Raiza Borreo, Hassan S․ Migdadi, Weisi Chen

All Works

Internet of Things (IoT) technologies can transform various sectors by converging with global sustainability goals. This paper systematically reviews how IoT supports fulfilling the United Nations Sustainable Development Goals (SDGs). This study initially identified publications that are most relevant to IoT and sustainability. Each publication was carefully examined and mapped to its corresponding SDG, methodology, context, and country. This work presents an opportunity to learn about country contributions, collaborations, and IoT and SDG research trends over the past decade. India, China, and the US were among the top contributors to the IoT and SDG literature, with India accounting for 68 …

Wildfire Smoke And Public Health: Comparing 2023 Canadian Wildfire Events With Hospital Admissions In Douglas County, Nebraska, Jeremy Poell

Capstone Experience: Master of Public Health

Wildfires are becoming increasingly common in Canada and the United States. Smoke produced from these fires creates a multitude of air pollution constituents that can cause breathing and other health issues for humans, particularly those with asthma and other respiratory conditions. Of these pollutants, PM2.5 (particulate matter that is 2.5 microns or smaller) is particularly problematic as these particles are inhaled deep into lung tissue, where they create inflammation and oxidative stress. Poor air quality can also trigger asthma and respiratory issues, leading to an increase in emergency department admissions for breathing treatments. The goal of this study is to …

Evaluation Of Graphite And Activated Carbon As Suitable Water Purification Media For In-Situ Resource Utilisation (Isru) On Mars, Ciaran Archenoul

The Plymouth Student Scientist

Eventual human missions to the red planet will require large-scale in-situ resource utilisation to meet their objectives. Perhaps the most critical element for developing a sustainable architecture is propellant production from the products of water electrolysis and atmospheric carbon dioxide. The purpose of this study was to assess how graphitic materials may be used as porous filters to remove solvated ions from Martian water prior to electrolysis, reducing the overall complexity of ISRU systems. The two materials under study were PG25 graphite and activated carbon powder.

A comprehensive three-technique characterisation of both materials was performed, using helium pycnometry, N_{2 …}

Vacuum-Assisted Resin Transfer Moulding (Vartm): Can Ultrasonic Vibration Decrease Fill Time, Increase Composite Properties, And Reduce Void Formation, Iulia D. Precup

The Plymouth Student Scientist

This study investigated the potential of ultrasonic vibration (28 kHz) to optimize the Vacuum-Assisted Resin Transfer Moulding (VARTM) process. The aim was to assess whether introducing ultrasonic vibration during the resin infusion phase could reduce fill time, improve mechanical properties, and minimize void formation in glass fibre-reinforced polyester composites. Two flexible tooling systems were compared: a reusable silicone membrane and a traditional vacuum bag. Composite plates were manufactured with and without ultrasonic vibration for both systems. Fill time was observed, and post-cured samples were subjected to mechanical testing for flexural modulus, flexural strength, and interlaminar shear strength (ILSS). Void content …

Analysis Of Organic Compounds Extracted From Recycled Polypropylene Via Gc-Fid/Ms And Pyr-Gc-Ms For Future Pharmaceutical Applications, India E. Hateley

The Plymouth Student Scientist

A mandatory target for 30% of plastic packaging to be recycled has been proposed by 2030, in order to mitigate the problem of plastic waste being incinerated or deposited in landfills. However, during the recycling process additives are added and with polypropylene being used for plastic packaging and surgical masks, these additives need to be analysed for the presence of contaminants. Examples include VOCs, PAHs, phthalates, and brominated diphenyl’s, all of which have health issues. Two samples of recycled polypropylene pellets were obtained from a national recycling company, along with a reference pellet purchased through Sigma-Aldrich. All pellets underwent ultrasound-assisted …

A Generational Analysis Of Psychological Motivators Influencing Seafarer Retention, Perisah Tahanci

The Plymouth Student Scientist

Seafarer retention is a growing concern amid increasing global demand for skilled maritime officers. This study investigates the generational differences in psychological motivators influencing seafarer retention, where ‘psychological motivators’ encompass the cognitive factors, including mental and physical wellbeing, work-life balance, compensation, working conditions, and external factors, that shape retention intentions. A cross-sectional survey (n=214) combined quantitative and qualitative analyses to systematically assess how the perceived importance and experience of these factors vary across Generation Z, Millennials, Generation X, and Baby Boomers.

The findings reveal that significant generational differences exist across most factors, particularly in work-life balance and mental wellbeing, in …

What Environmental Circumstances Generate Hazardous Wave Overtopping Conditions For Infrastructure Disruption: Dawlish (United Kingdom) Case Study, Zbigniew Tomasz Zielonka

The Plymouth Student Scientist

Coastal infrastructure faces increasing threats from wave overtopping due to climate change, rising sea levels, and intensifying storms. This research investigates the environmental conditions that drive hazardous wave overtopping using Dawlish, a vulnerable section of the UK’s railway, as a case study. Understanding these conditions is essential for improving knowledge, developing effective mitigation strategies, and enhancing the resilience of shoreline defences against future challenges. Using one-year observations from WireWall instruments, offshore wave buoy, and meteorological data, the research applies a Random Forest model to assess the influence of metocean conditions on overtopping. The results indicate that wave height, tidal level, …

Method Optimisation And Environmental Analysis Of Methanesulfonic Acid (Msa) In Rainwater By Ion Chromatography, Thomas Levett

The Plymouth Student Scientist

This research optimised a method of analysis for methanesulfonic acid (MSA) alongside common inorganic anions using ion chromatographic (IC) apparatus developed at the University of Plymouth. The aim was to assess whether MSA could be more easily and routinely monitored alongside common rainwater anions. The method’s analytical quality was evaluated through each development stage of the research and its limitations found. Historical rainwater samples collected at Penlee Point, Plymouth, UK (2015–2018) were also used for testing. The method successfully provided detectable ranges of MSA between 0.001 – 5 mg/L for F^-, Cl^-, NO_2-, Br …

Quantification Of Trace Metal(Loids) In The River Seaton, Cornwall, Uk And The Influence Of The South Caradon Mine On Ecological Risk, Bjorn Piñol

The Plymouth Student Scientist

Freshwater quality is critical to the health of aquatic ecosystems and human water security, yet anthropogenic activities, such as historic mining, continue to degrade aquatic systems. While extensive research has examined metal contamination in major rivers like the River Tamar, smaller tributaries affected by legacy mining remain understudied. This study investigates trace metal contamination and its controlling factors in the River Seaton, Cornwall, a small river influenced by historic mining from the South Caradon Mine. Water samples collected from four study sites along the river gradient and a reference site at Siblyback Lake were subjected to ICP-MS analysis and physico-chemical …

Scaling Wind Farm Layout Optimisation To Meet Uk Energy Demands, Grace Richardson

The Plymouth Student Scientist

This research aims to explore the Wind Farm Layout Optimisation Problem (WFLOP) through both single- and multi-objective optimisation, to show that it can be used to create farm layouts that provide enough power for all homes in the UK. We highlight the intricacies of the problem, including the application of different algorithms and models, and the challenges of scaling the models to achieve optimal results. We employ both a Hill Climbing Algorithm (HCA) and an evolutionary algorithm (NSGA-II) to obtain wind farm layouts, and compare these to the well-known results proposed by Mosetti et al. Moreover, we present a novel …

Collective Agency And Community Resilience Among Farmworkers In The Lower Rio Grande Valley, Angel Velazquez

Theses and Dissertations

This research assesses how farmworkers in the Lower Rio Grande Valley of Texas use everyday practices to build resilience and adapt to changes in their lives. I apply two interconnected theories, White’s (2017, 2019) framework of collective agency and community resilience and Castro and Sen’s (2022) framework of everyday adaptation actions, to study these practices. I conducted 19 semi-structured interviews with Latino farmworkers and professionals who work with local farmworker organizations in the Rio Grande Valley (RGV) of Texas. Interview transcripts were analyzed to determine if and how Latino farmworkers in the RGV can work towards community resilience and implement …

Determination Of An Effective Natural Boxelder Bug (Boisea Trivittata) Repellent, Reyna Chavez

Theses and Dissertations

This study investigates the use of ecofriendly repellant alternatives to pest management to control boxelder bugs (Boisea trivittata). This bug gathers by the thousands on warm surfaces of exterior suburban homes during early fall and late spring. I determined bug repellency to turmeric oil (Curcuma longa), catnip (Nepeta cataria), citronella (Cymbopogon nardus), and lavender (Lavandula angustifolia). The treatments consisted of essential oils containing 20% methanol. All treatments demonstrated repellency of Boxelder bugs. Citronella was the most effective and had the highest level of repellency range of 90-99%. Catnip was the least effective with a repellency range of 63-82%.