Computer Sciences Commons^™

Cyber-Informed Engineering Of Industrial Control Systems By Prioritization Of High Consequence Events, Conflict Clause Learning Technique, And Autotuning Mechanisms For The Real-Time Monitoring Of Critical Processes, Chidi Ugo Agbo

Boise State University Theses and Dissertations

Industrial Control Systems (ICS) are systems employed to supervise, regulate, and control industrial processes and critical infrastructure. These critical systems require robust measures to protect them against potential safety and security violations. Ensuring the safety and security of ICS is a significant challenge facing nations and states today, necessitating the development of robust, dependable, and resilient ICS. Traditional cybersecurity and engineering practices continue to adopt an inefficient approach that treats security as an add-on element during the system design and development. In response, this dissertation builds on a novel theoretical approach known as Cyber-Informed Engineering (CIE) that leverages safety and …

Social Network Analysis Of A Biomedical Research Co-Authorship Network, Amrina Ferdous

Boise State University Theses and Dissertations

A social network analysis (SNA) is a graph-based method for visualizing social networks. Our study uses the SNA to analyze co-authorship patterns in National Institutes of Health (NIH) - Centers of Biomedical Research Excellence (COBRE), Idaho Institutional Development Award (IDeA), Network of Biomedical Research Excellence (INBRE), and Biomedical Research Infrastructure Network (BRIN) grants between 2001 and 2022. It is of interest to us to analyze the growth and expansion of research over time to look at the social networks of research Hubs, the social networks of potential research Hubs in the near future, and the social networks of potential leaders …

Toward Privacy-Preserving Electronic Voting With Ballot-Spoiling Using Blockchain, Yoonsu Ra

Boise State University Theses and Dissertations

Several voting techniques have been presented in recent years to address the security and privacy concerns associated with electronic voting. Some of these systems have used blockchain technology to reduce the requirement for centralized authority trust while maintaining voter privacy and ballot confidentiality. Existing methods, however, neglect to comply with government standards and therefore are unsuitable for real-world election settings. They often overlook essential attributes such as ballot spoiling and voter-to-ballot unlinkability, dismissing them as conflicting features. In this thesis, we introduce ORBIT, a cryptographic voting scheme that complies with regulations outlined in the Idaho state law while achieving receipt-freeness …

Improving Security Of Order-Preserving Encryption And Its Applications In Data Outsourcing, Ning Shen

Boise State University Theses and Dissertations

Encryption is an important tool to protect data confidentiality and privacy. One important practice of computation primitives is encrypting data with Order-Preserving Encryption (OPE). Order-Preserving Encryption is an encryption algorithm that allows the ciphertexts to preserve the same order of plaintext and it is very useful for range queries in databases and other applications. However, OPE has certain security vulnerabilities, such as it may leak sensitive information other than the ordering. Currently, the application of OPE is also limited, which is primarily used in the encryption of outsourced databases.

Due to the OPE’s security concern and limited usage, this dissertation …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Clueless: Revolutionizing Sustainable Fashion And Combating Overconsumption, Tanya Ravichandran

Graphic Communication

“Clueless” revolutionizes sustainable fashion by combating wardrobe overconsumption and the industry’s carbon footprint, using AI to suggest personalized outfits from existing wardrobes tailored to weather and wear history. It enhances user engagement through features like outfit ‘shuffle’ and provides insights into wardrobe utilization and carbon impact.

It’s more than an app; it’s a step towards a greener wardrobe and a healthier planet.

Supporting Software Engineers With Large Language Model-Based Automation, Ting Zhang

Dissertations and Theses Collection (Open Access)

In recent years, software engineering (SE) has witnessed significant growth, leading to the creation and sharing of an abundance of software artifacts such as source code, bug reports, and pull requests. Analyzing these artifacts is crucial for comprehending the sentiments of software developers and automating various SE tasks, ultimately leading to more human-centered automated SE and enhancing software development efficiency. However, the diverse and unstructured nature of software text poses a significant challenge to this analysis. In response, researchers have investigated a variety of approaches, including the utilization of natural language processing techniques. The advent of large language models (LLMs), …

Towards Securing Smart Contracts Systematically, Duy Tai Nguyen

Dissertations and Theses Collection (Open Access)

Smart contracts are a groundbreaking technique that allows users to programmatically modify the state of the blockchain. They are essentially self-enforcing programs that are deployed and executed on top of the blockchain. In recent years, we have witnessed various smart contract incidents that led to substantial financial losses and even business closures. These incidents mainly arise from design flaws in Solidity, a dominant programming language for writing smart contracts, which complicates the process of detecting and repairing vulnerabilities. Furthermore, there is a growing interest in attacking smart contracts by the attackers. This thesis is dedicated to developing effective methods to …

Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble

All Dissertations

Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is …

Hpc-Enabled Fast And Configurable Dynamic Simulation, Analysis, And Learning For Complex Power System Adaptation And Control, Cong Wang

All Dissertations

This dissertation presents an HPC-enabled fast and configurable dynamic simulation, analysis, and learning framework for complex power system adaptation and control. Dynamic simulation for a large transmission system comprising thousands of buses and branches implies the latency of complicated numerical computations. However, faster-than-real-time execution is often required to provide timely support for power system planning and operation. The traditional approaches for speeding up the simulation demand extensive computing facilities such as CPU-based multi-core supercomputers, resulting in heavily resource-dependent solutions. In this work, by coupling the Message Passing Interface (MPI) protocol with an advanced heterogeneous programming environment, further acceleration can be …

Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon

All Dissertations

The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …

Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu

Graduate Theses and Dissertations

With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …

Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia

Graduate Theses and Dissertations

This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …

Learning To Search Feasible And Infeasible Regions Of Routing Problems With Flexible Neural K-Opt, Yining Ma, Zhiguang Cao, Yew Meng Chee

Research Collection School Of Computing and Information Systems

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored action factorization method and a customized recurrent dual-stream decoder. As a pioneering work to circumvent the pure feasibility masking scheme and enable the autonomous exploration of both feasible and infeasible regions, we then propose the Guided Infeasible Region Exploration (GIRE) scheme, which supplements the NeuOpt policy network with feasibility-related features and leverages reward shaping to steer reinforcement learning more effectively. Besides, we further equip NeuOpt with dynamic data augmentations during inference for more …

A Poisson-Based Distribution Learning Framework For Short-Term Prediction Of Food Delivery Demand Ranges, Jian Liang, Jintao Ke, Hai Wang, Hongbo Ye, Jinjun Tang

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic has caused a dramatic change in the demand composition of restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such as DoorDash, Grubhub, and Uber Eats—to a large extent. With massive amounts of data on customers, drivers, and merchants, OFD platforms can achieve higher efficiency with better strategic and operational decisions; these include dynamic pricing, order bundling and dispatching, and driver relocation. Some of these decisions, and especially proactive decisions in real time, rely on accurate and reliable short-term predictions of demand ranges or distributions. In this paper, we develop a Poisson-based distribution prediction (PDP) …

Distxplore: Distribution-Guided Testing For Evaluating And Enhancing Deep Learning Systems, Longtian Wang, Xiaofei Xie, Xiaoning Du, Meng Tian, Qing Guo, Zheng Yang, Chao Shen

Research Collection School Of Computing and Information Systems

Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distribution-aware methods. However, distribution-unaware testing lacks effectiveness by not explicitly considering the distribution of test cases and may generate redundant errors (within same distribution). Distribution-aware testing techniques primarily focus on generating test cases that follow the training distribution, missing out-of-distribution data that may also be valid and should be considered in the testing process. In this paper, we propose a novel …

Software Architecture In Practice: Challenges And Opportunities, Zhiyuan Wan, Yun Zhang, Xin Xia, Yi Jiang, David Lo

Research Collection School Of Computing and Information Systems

Software architecture has been an active research field for nearly four decades, in which previous studies make significant progress such as creating methods and techniques and building tools to support software architecture practice. Despite past efforts, we have little understanding of how practitioners perform software architecture related activities, and what challenges they face. Through interviews with 32 practitioners from 21 organizations across three continents, we identified challenges that practitioners face in software architecture practice during software development and maintenance. We reported on common software architecture activities at software requirements, design, construction and testing, and maintenance stages, as well as corresponding …

Monocular Depth Estimation For Glass Walls With Context: A New Dataset And Method, Yuan Liang, Bailin Deng, Wenxi Liu, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We …

Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield

Electronic Theses and Dissertations

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …

Explainable Artificial Intelligence: Approaching It From The Lowest Level, Ralf P. Riedel

Graduate Theses and Dissertations (2019 - present)

The increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their affects on network output or pruning model components after the often-extensive time-consuming training. It is postulated in this study that understanding of neural network can benefit from model structure simplification. In turn, it is shown that model simplification can benefit from investigating network node, the most fundamental unit of neural networks, evolving trends during training. Whereas studies on simplification of model structure …

Remote Side-Channel Disassembly On Field-Programmable Gate Arrays, Brandon R. Baggett

Graduate Theses and Dissertations (2019 - present)

Over the last two decades, side-channel vulnerabilities have shown to be a major threat to embedded devices. Most side-channel research has developed our understanding of the vulnerabilities to cryptographic devices due to their implementation and how we can protect them. However, side-channel leakage can yield useful information about many other processes that run on the device. One promising area that has received little attention is the side-channel leakage due to the execution of assembly instructions. There has been some work in this area that has demonstrated the idea’s potential, but so far, this research has assumed the adversary has physical …

A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu

Electronic Theses and Dissertations

Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning tasks. While they were evolved separately in diverse fields, current research has revealed some similarities and links between them. This work focuses on bridging the gap between GNNs and Transformers by offering a uniform framework that highlights their similarities and distinctions. We perform positional encodings and identify key properties that make the positional encodings node embeddings. We found that the properties of expressiveness, efficiency and interpretability were achieved in the process. We saw that it is possible to use positional encodings as node embeddings, which can be …

Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh

All Works

The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …

Developing Detection And Mapping Of Roads Within Various Forms Of Media Using Opencv, Jordan C. Lyle

Computer Science and Computer Engineering Undergraduate Honors Theses

OpenCV, and Computer Vision in general, has been a Computer Science topic that has interested me for a long time while completing my Bachelor’s degree at the University of Arkansas. As a result of this, I ended up choosing to utilize OpenCV in order to complete the task of detecting road-lines and mapping roads when given a wide variety of images. The purpose of my Honors research and this thesis is to detail the process of creating an algorithm to detect the road-lines such that the results are effective and instantaneous, as well as detail how Computer Vision can be …

Customer Churn Prediction Using Composite Deep Learning Technique, Asad Khattak, Zartashia Mehak, Hussain Ahmad, Muhammad Usama Asghar, Muhammad Zubair Asghar, Aurangzeb Khan

All Works

Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract …

Advanced Caching And Streaming For Large Scale Point Cloud Data Visualization On The Web, Pravin Poudel

All Graduate Theses and Dissertations, Fall 2023 to Present

Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving …

Deep Learning With Effective Hierarchical Attention Mechanisms In Perception Of Autonomous Vehicles, Qiuxiao Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Autonomous vehicles need to gather and understand information from their surroundings to drive safely. Just like how we look around and understand what's happening on the road, these vehicles need to see and make sense of dynamic objects like other cars, pedestrians, and cyclists, and static objects like crosswalks, road barriers, and stop lines.

In this dissertation, we aim to figure out better ways for computers to understand their surroundings in the 3D object detection task and map segmentation task. The 3D object detection task automatically spots objects in 3D (like cars or cyclists) and the map segmentation task automatically …

Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data, Kevin Vulcano

All Graduate Theses and Dissertations, Fall 2023 to Present

In the realm of safeguarding digital systems, the ability to detect anomalies in log sequences is paramount, with applications spanning cybersecurity, network surveillance, and financial transaction monitoring. This thesis presents AdvSVDD, a sophisticated deep learning model designed for sequence anomaly detection. Built upon the foundation of Deep Support Vector Data Description (Deep SVDD), AdvSVDD stands out by incorporating Adversarial Reweighted Learning (ARL) to enhance its performance, particularly when confronted with limited training data. By leveraging the Deep SVDD technique to map normal log sequences into a hypersphere and harnessing the amplification effects of Adversarial Reweighted Learning, AdvSVDD demonstrates remarkable efficacy …

Collaborative Task Completion For Simulated Hexapod Robots Using Reinforcement Learning, Tayler Don Baker

All Graduate Theses and Dissertations, Fall 2023 to Present

There is growing interest in developing autonomous systems capable of exhibiting collaborative behaviors. Using methods such as reinforcement learning is another way to train multiple robots for collaborative task completion. This study was able to successfully in simulation train multiple hexapod robots to push a target to a designated goal collaboratively. This required each robot to learn how find the target and push that target to a goal. This work suggests that using reinforcement learning for collaborative task completion for hexapod robots may simplify the complexity of the software and improve the decisions that they make.

Optimal Stopping Of Multi-Robot Exploration For Unknown, Bounded Environments, Trey D. Crowther

All Graduate Theses and Dissertations, Fall 2023 to Present

Limited resources and uncertainty pose a substantial problem for multi-robot exploration of unknown environments. This research paper looks to determine the optimal time to terminate robot exploration while maximizing information gathered. Whilst making this determination, the system's resources and capabilities must be taken into account. To see if our strategy works, we ran many simulations in varying environments. The results of this research are important for real-world uses like robot exploration, search and rescue missions, and automated surveillance. Determining when to stop exploring can help the system save resources, explore faster, and make better decisions.