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Some Studies On Information Set Decoding Algorithms And Universal Hash Functions, Sreyosi Bhattacharyya

Doctoral Theses

This thesis presents some studies on Information Set Decoding algorithms and Universal Hash Functions. In the context of Information Set Decoding (ISD) the thesis studies time/memory trade-off of ISD algorithms and in the context of universal hash functions, the thesis studies design and efficient implementations of polynomial hash functions defined over prime order fields. A cornerstone of ISD algorithms is the algorithm proposed by Stern and it introduced the meet-in-the-middle collision search approach to ISD algorithms. Though this algorithm is more efficient in terms of asymptotic time complex- ity than the preceding algorithms proposed by Prange, Lee and Brickell and …

Discovery Of Drug Transporter Inhibitors Tied To Long Noncoding Rna In Resistant Cancer Cells; A Computational Model -In Silico- Study, Mohanad Diab, Amel Hamdi, Feras Al-Obeidat, Wael Hafez, Ivan Cherrez-Ojeda, Muneir Gador, Gowhar Rashid, Sana F. Elkhazin, Mahmad Anwar Ibrahim, Tarek Farag Ismail, Samar Sami Alkafaas

All Works

Chemotherapeutic resistance is a major obstacle to chemotherapeutic failure. Cancer cell resistance involves several mechanisms, including epithelial-to-mesenchymal transition (EMT), signaling pathway bypass, drug efflux activation, and impairment of drug entry. P-glycoproteins (P-gp) are an efflux transporter that pumps chemotherapeutic drugs out of cancer cells, resulting in chemotherapeutic resistance. Several types of long noncoding RNA (lncRNAs) have been identified in resistant cancer cells, including ODRUL, MALAT1, and ANRIL. The high expression level of ODRUL is related to the induction of ATP-binding cassette (ABC) gene expression, resulting in the emergence of doxorubicin resistance in osteosarcoma. lncRNAs are observed to be regulators of …

Dsa-Api: Data Standardization Automation Using Ai-Powered Apis, Andrew Asher Turner

Master's Theses

The common factor with current implementations of Artificial Intelligence (AI) is data. Companies are constantly looking for new ways to analyze data, but it comes in various formats: Text, Comma Separated Value (CVE), JavaScript Object Notation (JSON), Extensible Markup Language (XML), and Excel. How can AI be adapted to standardize formats for data analysis, integration, and digestion efficiently? Published research acknowledged that Machine Learning (ML) and AI can provide an automated method to speed up this process and limit the human decision-making error. With the advancement in AI, Application Programming Interfaces (APIs) prompt the idea that they can take in …

Who Should Take Responsibility For Artificial Intelligence Actions And Outcomes? Perception Of Auditors As Users Of Ai Systems, Hanh Hoang Le

Doctoral Dissertations

As artificial intelligence (AI) systems become increasingly embedded in auditing processes, questions arise regarding how professional auditors perceive and allocate responsibility for AI-assisted decisions. This study investigates the effects of AI explainability and auditors’ perceived autonomy on perceived responsibility in the context of audit decision-making. Drawing on theories of moral responsibility and professional judgment, the study employs a 2x2 experimental design using hypothetical audit scenarios to manipulate levels of AI explainability and auditors’ autonomy. Hierarchical regression analysis reveals that perceived autonomy statistically significantly increases auditors’ perception of responsibility for AI-assisted decisionmaking, whereas AI explainability is not a significant predictor. Additionally, …

Blockchain-Enabled Master Data Management, Shakhawat Hossain

Theses and Dissertations

Master Data Management (MDM) is essential for maintaining data quality, accuracy, consistency, and governance within organizations. However, traditional centralized MDM systems continue to face challenges related to data integrity, security, and scalability. This research presents a blockchain-enabled MDM framework designed to overcome these limitations by leveraging blockchain’s decentralized, immutable, and secure architecture. The study aims to identify and address the shortcomings of conventional MDM practices, examine the applicability of blockchain technology in enhancing these systems, and develop a functional prototype to validate the proposed model. The framework incorporates decentralized review mechanisms that improve auditability and ensure trusted data verification by …

Transformer Decoder-Enhanced Swin Unetr For Multi-Organ Semantic Segmentation On Openkbp: Improving Radiotherapy Planning Accuracy, Zainab Adnan Jwad, Israa Hadi Ali

Karbala International Journal of Modern Science

Accurate segmentation of organs-at-risk (OARs) in head and neck CT scans is crucial for radiotherapy planning. The CNN-based decoder limitation of Swin UNETR hinders its capacity to process meaningful information from multiple organ positions essential for accurate medical segmentation. The proposed Transformer Decoder-enhanced Swin UNETR model targets the OpenKBP dataset multi-organ segmentation through its dedicated design for this purpose. The model utilizes transformers along with cross-attention approaches in its decoder to improve segmentation mask outputs through analysis of extensive global information. The model gets additional feature representation power through the addition of squeeze-and-excitation (SE) blocks linked with spatial attention mechanisms …

Computational Approach: 3d-Qsar, Molecular Docking, Molecular Dynamics Simulation Investigations, Drug- Like-Ness, And Dft Score Evaluation Of A Potential Novel And Retrosynthesis Of Some Tr-H Derivatives As Streptococcus Pneumoniae Drug., Belaidi Mustapha, Tchouar Noureddine, Djelilate Mohammed, Saleh Bufarwa, Dalal K. Thbayh

Karbala International Journal of Modern Science

Streptococcus pneumoniae is the main source of hospital-acquired pneumonia and meningococcal pneumonia in children, adults, and the elderly, especially the immunocompromised. Recently, it has attracted the attention of many research studies around the world as a potential target for remediation. TR-H are considered SP antibacterial agents, as they can inhibit. To describe their mode of action and to identify new antibacterial drugs against Streptococcus pneumoniae exhibiting the TR-H scaffold, the present work included 3D-QSAR, in- silico pharmacokinetic evaluation and molecular simulation modelling of TR-H. Using an atom-based method for quantitative structure-activity relationship (QSAR) study, a comprehensive 3D-QSAR model …

Dynamate: Leveraging Ai-Agents For Customized Research Workflows, Orlando A. Mendible-Barreto, Misael Díaz-Maldonado, Fernando J. Carmona Esteva, J. Emmanuel Torres, Ubaldo M. Córdova-Figueroa, Yamil J. Colón

Computer and Data Science Faculty Publications

No abstract provided.

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

Leveraging Artificial Intelligence In Education To Drive Cross-Sector Innovation, Brent Terwilliger, John Faraca

Publications

As artificial intelligence (AI) reshapes educational practices, particularly in technical fields such as uncrewed systems, robotics, and aviation/ aerospace, its integration raises promise and complexity. This exploratory study features an investigation of the impact AI tools adoption has on instruction, curriculum support, and workforce preparation, with a focus on online learning environments. Drawing from pilot survey data across aviation and aerospace education stakeholders and hands-on evaluation of AI video production platforms, findings reveal diverse applications, perceived benefits, and critical concerns, including ethical, pedagogical, and institutional challenges. Additionally, the analysis explored how AI-enabled education intersects with broader industry and government innovation …

Historical Perspectives In Volatility Forecasting Methods With Machine Learning, Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

All Faculty Open Access Publications

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to …

Book Review Of, Participatory Engineering Of Algorithmic Socialtechnologies: An Extended Book Review Of David G. Robinson's Voices Inthe Code, Rajesh Venkatachalapathy

Complex Systems Faculty Publications and Presentations

David G. Robinson’s Voices in the Code, reviewed here, approaches the issue of governance and regulation of algorithmic social technologies with a case study orientation that offers a grounding in the details, illuminating the fundamentally sociological and processual nature of modern governance issues that are mostly missed or left implicit in academic, regulatory, and legislative discussions. This important book urges us to design adaptive, malleable, and evolvable participatory organizational structures instead. Accepting the author’s invitation to carry the torch forward, this review builds on and extends the book’s arguments by presenting broad and transferable insights on regulation and governance …

Towards Multi-Modal Multi-Document Understanding Capabilities In Foundation Models, Chuhan Li

Computer Science Theses

Contemporary foundation models are predominantly evaluated on isolated documentor image-understanding tasks, thereby overlooking the inherent multimodal multi-document reasoning that characterizes scientific inquiry. To bridge this gap, M3SCIQA is introduced, aMulti-Modal,Multi-document Scientific Question Answering benchmark crafted to test foundation models in practical scientific research settings. A comprehensive evaluation of 18 leading foundation models shows a substantial performance gap between models and human experts. Detailed error analysis reveals persistent deficiencies in both scientific visual reasoning tasks and long-range retrieval. Addressing the former, SPACECUE offers a concise yet effective visual prompting that overlays grid coordinates and Semantic-SAM masks …

B-Spline Representations For Hyperspectral Inverse Rendering, Rachel Liang

Computer Science Theses

This work explores the use of a B-spline-based approach for hyperspectral inverse rendering from RGB images, experimenting on both spectral and geometric reconstruction. While the B-spline method is less accurate than brute-force optimization, it offers significant improvements in computational efficiency- reducing both runtime and memory usage.

Our experiments show that the B-spline representation can approximate smooth spectral data effectively but struggles with sharper spectral features unless more knots are introduced. Notably, wavelengths near the edges of the visible spectrum (around 400 nm and 700 nm) were less stable during optimization, reflecting lower convergence reliability. Despite these challenges, the final RGB …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Noise-Embedded Image Processing Based On Quantum Data Encodings, Yayu Mo

Multidisciplinary Studies Theses and Dissertations

Advancements in quantum information have significantly impacted the field of image processing, although challenges remain. Especially in the edge detection and image encoding area, distorted feature and noises would affect the further classification or super resolution tasks. In our work, we conduct researches on two stages to both evaluate the potential of Quantum-based Convolutional Structure in extracting distorted feature and further explore the effects of quantum noise channels on quantum image encodings.

In the first stage, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of …

First Annual Advances In Business Education Conference 2025 Proceedings, Kelsey Metz, Joshua Ray

Advances in Business Education (ABE) Conference Proceedings

Conference Overview: The First Annual Advances in Business Education (ABE) Conference was held on May 16, 2025, at Lincoln Memorial University in Harrogate, Tennessee. Hosted by the LMU School of Business, the ABE Conference was established to promote teaching excellence through innovation and collaboration in business education. With a focus on fostering meaningful dialogue among educators, researchers, and students, the conference welcomed participants from across disciplines and institutions. The event was structured around three key tracks:

Pedagogy & Teaching Excellence: Showcasing innovative teaching methods and strategies for enhancing student learning and engagement.

Business Research: Presenting research focused on advancing knowledge …

Promoting Collaboration And Multi-Directional Reliance By Sharing Mental Model Information For Effective Multi-Agent Teaming, Audrey L. Aldridge

Theses and Dissertations

Successful human-agent teaming requires teammates to form and maintain a shared or common understanding of several attributes regarding taskwork and teamwork. With enhanced information sharing, mental model development, and team functionality, teammates (human, autonomous) can learn to anticipate each others' behaviors, preferences, and needs as well as understand their capabilities and limitations. In designing a framework to support this type of cooperative teaming, there is a need to determine how sharing knowledge, mental models, and common understandings impacts teaming dynamics and performance. By incorporating each individual's understanding into a human-agent interface, this research enables better team coordination and performance through …

Optimizing Mars Terrain Segmentation With Weakly Supervised Learning: A Focus On Weighted Loss From Annotation Metadata, Malika Dutta

Theses and Dissertations

The study of planetary surfaces heavily depends upon space rovers that gather detailed images of terrain needed for analysis and navigation. Deep neural networks and other sophisticated machine learning techniques are necessary for autonomous navigation in challenging terrain. However, the inconsistent annotations by citizen scientists frequently hinder the performance of these models. This study seeks to optimize terrain segmentation to improve the autonomous capabilities of future Mars rovers by presenting a novel weakly supervised learning framework to handle noise and unreliability in datasets. Using factors like number of clicks, pixel accuracy, and annotator dependability, the method utilizes annotation metadata in …

Application Of Pu Learning In Detection Of Ddos Attacks, Gagana Sathya Narayana Prasad

Theses and Dissertations

The gcore radar 2024 says, the number of DDoS attacks has been increased by 46% in 12 months. Supervised and unsupervised techniques struggle detecting DDoS attacks due to the scarcity of labeled attack samples and an overwhelming presence of benign traffic. In contrast PU- Learning offers a promising solutions by dividing the data into positive and unlabeled data. This study explores the effectiveness of PU-learning in detecting DDoS attacks by comparing it with unsupervised methods. This method employs PU Bagging, Two Step method and auto-encoder based models to extract meaningful patters from network traffic data, utilizing CICDDoS2017 dataset for evaluation. …

Cyber Security Threat Recognition And Preparedness Of Undergraduate Students, Litany Hope Lineberry

Theses and Dissertations

Cybersecurity awareness and preparedness are critical competencies for individuals across academic disciplines and professional sectors. However, undergraduate students often lack sufficient knowledge and skills to recognize and mitigate cybersecurity threats. This dissertation examines cybersecurity threat recognition and preparedness among undergraduate students through a three-phase research approach. Study 1 explores faculty perspectives on students' cybersecurity awareness, identifying gaps in knowledge and preparedness across various fields of study. Study 2 investigates industry professionals' perceptions of new hires’ cybersecurity readiness, assessing the alignment between academic training and industry expectations. Study 3 evaluates the effectiveness of an online intervention designed to enhance students' cybersecurity …

Establishing A Baseline For Detecting Lotl Attacks In Windows Operating Systems, Ashlyn Martin Phillips

Theses and Dissertations

There has been an increasing realization of the rise in living off the land (LOTL) attacks where adversaries misuse legitimate system tools, particularly with state-sponsored actors targeting critical infrastructure in the United States. These attacks are difficult to detect because they allow attackers to remain present in a system without the user’s knowledge for an extended period. This thesis establishes an initial baseline specifically for Windows operating systems to measure normal system activity, focusing on CPU usage, memory utilization, and process activity. It particularly examines the use of PowerShell alongside other applications. The findings from this baseline are used to …

Zeroizing Trust In A Naïve Federated Zero Trust Environment, Keith E. Strandell

Theses and Dissertations

The position of the United States on the global stage is predicated on information dominance and the ability to project power through cooperative engagements with mission partners in both wartime and peacetime. Recent cyber-attacks highlighted the need for a more robust cybersecurity posture. As the United States progresses toward the adoption of Zero Trust, it is incumbent on the Department of Defense to assess the impact to the ability to share data across strategic partnerships while securing the data of both the United States and its partners. This paper proposes research into ensuring an environment rooted in Zero Trust and …

A Framework For Modular Knowledge Composition In Network Intrusion Detection Systems, Patrick L. Day

Theses and Dissertations

Autonomic Intrusion Detection Systems (AIDS) are sophisticated software systems designed to autonomously and adaptively identify and respond to security threats and intrusions in computer networks or systems. One of the fundamental challenges in intrusion detection research lies in the limited availability and scope of publicly available datasets. The proposed research aims to address data-related gaps with autonomic and traditional intrusion detection systems by describing a comprehensive approach to investigate the impact and potential of data augmentation. The goal is to explore various data augmentation techniques, assess their effectiveness in introducing variability, and evaluate their impact on the performance of neural-based …

From Data To Action: An Adaptable Crosstabs Template For Participatory Survey Data Analysis, Natalia Pinzon, Vikram Koundinya, William O'R Dowling, Ryan Galt

Journal of Extension

We present a practical and accessible template for quantitative survey data analysis designed for non-academic researchers in order to facilitate engagement from community collaborators. The template, created in Google Sheets, is mainly for computing cross-tabulations, but it also displays frequency distributions and p-values for determining statistical significance. The template allows collaborators to record their observations and questions, promoting an efficient yet interactive review process and fostering a democratic analysis environment. Based on our own experience using this template for a data party, we highlight its effectiveness in promoting collaborative data interpretation, decision-making, and the actionable use of survey findings.

Aerial Robotic Studies Of Volcanic Co2 Emissions, John Ericksen

Computer Science ETDs

Volcanic systems are inherently complex, involving dynamic interactions among magma flow, gas emissions, and atmospheric dispersion. This dissertation focuses on developing and analyzing autonomous UAS algorithms for efficiently surveying volcanic CO2 plumes, introducing several novel methods: the LoCUS algorithm, a swarm coordination and self-healing algorithm that supports gradient-based plume tracking, a transect-based technique that employs a 2D Gaussian fit to calculate CO2 plume flux, and the Sketch algorithm for rapid plume boundary tracing. By treating multiple UAS as a single scientific instrument, these methods leverage swarm algorithms to use in-situ data in ways impossible with individual drones. Validated through simulations …

The Confluence, Volume 4, Issue 1, Full Issue

The Confluence

No abstract provided.

Cybersecurity And Global Threats: A Comparative Analysis Of Estonia And Russia’S Policies, María Paula Morales Palacios

The Confluence

As digital technology continues to reshape the foundations of modern life, the question of how states respond to cyber threats has become increasingly urgent. This paper examines how political systems shape national cybersecurity policies by comparing Estonia and Russia, two countries facing similar external threats but governed by vastly different structures. Estonia’s democratic framework emphasizes transparency, citizen participation, and international cooperation, while Russia’s semi-authoritarian model centers on state sovereignty, centralized control, and strategic offensive capabilities. Drawing on key historical events, including the 2007 cyberattacks on Estonia and the 2016 attacks on Russian banks, the paper explores how each state’s political …

Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian

UNLV Theses, Dissertations, Professional Papers, and Capstones

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …

Exploring The Facets Of Responsible Ai: Interpretability, Biases, And Morality Of Large Language Models, Sean Xie

Dartmouth College Ph.D Dissertations

This thesis investigates critical aspects of responsible artificial intelligence (AI) — specifically model interpretability, bias detection and mitigation, and moral alignment in large language models (LLMs) — due to their pivotal role in the deployment of transparent, fair, and ethical AI systems. By addressing these dimensions of responsible AI, we hope to foster the increased trust and understanding necessary for wider AI adoption.

We begin by surveying the existing landscape of interpretability metrics and critically assess the effectiveness of interpretability methods designed to generate reliable explanations. Building upon this evaluation, we introduce novel model architectures and frameworks explicitly developed to …