Computer Sciences Commons^™

Eco-Assisted Ultrasonic Synthesis, Characterization, And Electrical Conductivity Study Of Polyaniline/ Silica@Fe3o4 Q1 Nanocomposites, Ali Salim Shaway, Kholoud Dham Khamkheem, Jawad Kadhim Abaies, Athra G. Sager

Karbala International Journal of Modern Science

وفي هذه الدراسة الصوتية، أصبحت السيليكا المُنتجة بأكسيد الحديد (Fe₃O₄) ومركبات نانوية قائمة على البولي أنيلين مُطعّم بنسبة 5-15% من السيليكا المُعزز للحديد (Fe₃O₄). وأكملت نهائيا ماء أنيلين والمركبات العلمية المتنوعة الارتباط باستخدام تقنيات FT-IR وUV-Vis وXRD وSEM وTEM. وأحدثت نتائج XRD أن حجم جسيمات البولينج أنيلين الناي (PANI) يبلغ 55.5. مكثف هذا الحجم إلى 45.2 عند تطعيم بولي أنيلين بنسبة 15% وزناً من السيليكا المُغلفة بأكسيد الحديد (Fe₃O₄) مُحضّرة بطريقة جيدة بشكل جيد، مما يميل إلى التمدد بشكل ثابت في تشكيل المركب المتباين المنظم. ضاقت النطاق البصري من 3.2 إلى 2.5 إلكترون فولت مع زيادة نسبة المطعّم، مما يشير …

Threat-Analysis Oriented Digital Twinning Of Ml-Powered Future Autonomous Weapon Systems, Thomas Neubert

Doctoral Dissertations and Master's Theses

Warfare is undergoing a rapid transformation with the integration of artificial intelligence (AI) and machine learning (ML) into autonomous weapon systems (AWS) for perception, decision support, and control. As these systems become more software-defined, their cyber attack surface expands across sensing, communications, autonomy logic, and human-machine interfaces. As human oversight diminishes, ensuring the cybersecurity, resilience, and reliability of these systems becomes critical to mission success. This thesis investigates how a digital twin-driven threat modeling framework that integrates system-centric analysis with adversary-informed methodologies can support structured cybersecurity vulnerability evaluation and defensive strategy development associated with ML-powered AWS. First, the study analyzes …

The Expanding Digital Border: Ai, Surveillance, And The Fight For Justice, James Chesser

Immigration and Human Rights Law Review

As artificial intelligence transforms the mechanisms of immigration control, the modern border has become a digital filter—one governed less by geography and more by code. This Article examines the legal, technical, and ethical implications of AI-driven systems now central to global border enforcement, including biometric surveillance, algorithmic risk scoring, and predictive profiling. It explores how states use these technologies not only to manage irregular migration, but to compete for global talent—constructing migration regimes that reward capital and compliance while eroding transparency, due process, and equality.

Through an international and comparative lens, the piece highlights the expansion of algorithmic decision-making across …

Adaptive Parallel Downloader For Large Genomic Datasets, Rasman Mubtasim Swargo

Miners Solving for Tomorrow Research Conference

Modern next-generation sequencing (NGS) projects routinely generate terabytes of data that researchers download from public repositories such as SRA and ENA. Existing download tools typically employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times under dynamic network conditions. We introduce FastBioDL, a parallel downloader for large biological datasets with an adaptive concurrency controller. FastBioDL frames downloading as an online optimization problem, using a utility function and gradient descent to adjust the number of concurrent socket streams during runtime. This approach maximizes throughput while minimizing resource overhead. Evaluations on public genomic datasets show that FastBioDL achieves up …

Introducing Gridtrees For Streaming, Scalable Hierarchical Data Visualization, Nathan Tibbetts

Miners Solving for Tomorrow Research Conference

File browsing in the consumer sphere has seen very little advancement in recent years, although attempts have been made to improve upon it. With the goal of multi-level visual file browsing in mind, we present a prototype GridTree, a dynamic, recursive, stable, spatial layout data structure based on subdividing grids, represented as a hierarchy of maps whose granularity increases with depth. We motivate this work with characteristics we have identified as important for viability of a multi-level file-browser, which have become our design goals. We touch on the underlying logic of a GridTree and identify its complexity. We briefly discuss …

Thermal Mirage: Towards Robust Thermal Perception Via Gan-Guided Diffusion, Nuzaer Omar

Miners Solving for Tomorrow Research Conference

Thermal object detection systems are critical for safety sensitive applications due to their reliability under adverse conditions. However, existing robustness evaluations in thermal domain primarily focuses on physical or sensor-level perturbations, overlooking vulnerabilities from semantically realistic scene and object manipulations. We introduce Thermal Mirage, a generative framework that leverages GAN-guided diffusion to expose weaknesses in thermal detectors through controlled object and context level perturbations. Our approach learns class-conditional thermal priors via a GAN and uses diffusion to transform object appearances into ambiguous or low-saliency patterns. Simultaneously, a context module degrades scene conditions by simulating harsher night environments. Integrated with a …

Robust Federated Learning With Strategic Adversaries, Manoj Twarakavi

Miners Solving for Tomorrow Research Conference

Federated Learning (FL) leverages the intelligence of untrusted distributed devices through collaborative training. This makes the training process susceptible to malicious behavior. Existing defense mechanisms largely consider an adversary who attacks a proactive FL server without any adaptability. However, they overlook the presence of a strategic adversary. To address this challenge, our work proposes a Robust Game-theoretic framework where the adversary is both strategic and is equipped with the capability of performing large-scale poisoning attacks.

Distributed Control Plane For Cross-Silo Federated Learning, Rabin Pandey

Miners Solving for Tomorrow Research Conference

Cross-silo federated learning (FL) trains shared models across geographically distributed institutions without centralizing raw data, but its reliance on wide-area networks makes round completion time highly sensitive to heterogeneous link conditions, congestion, and straggler clients. Existing SDN-based FL frameworks address this through centralized traffic engineering, an approach that breaks down when silos span independent administrative domains where no single entity can maintain complete, timely network knowledge. We replace the centralized control plane with a fully distributed overlay in which each silo gateway operates as an equal peer, continuously probing local links, exchanging EWMA-smoothed metrics via bounded gossip, and computing least-cost …

Robustness Of Fuzzy Artmap To Adversarial Attacks And Progressive Adversarial Training For Streaming Learning, Shane Cairns

Miners Solving for Tomorrow Research Conference

Incremental learners deployed on streaming data must remain robust to evolving adversarial perturbations, yet most adversarial-robustness studies assume offline multi-epoch training with repeated access to historical data. We investigate adversarial robustness in Fuzzy ARTMAP, a prototype-based Adaptive Resonance Theory model that supports single-pass learning without replay. We propose WB-Softmax, a differentiable relaxation that aggregates category-level activations into class-level scores for gradient-based attacks. WB-Softmax PGD achieves 89–100% attack success on vanilla models, exceeding transfer and query-based baselines. We then study adversarial training under true streaming constraints by comparing offline versus online adversarial example generation and standard versus selective updates. Offline adversarial …

Anti-Inflammatory And Insulin-Modulating Effects Of Tithonia-Curcuma-Moringa (Tcm) Formulation In Type 2 Diabetes: An Experimental Study In Mice, Rif’Atul Hawani Burhan, Nabila Shafa Yumna Salsabila, Devita Zulfa Pratama, La Tazkia Aulia Wibowo, Yulian Ervin Maulana, Moh Dliyauddin, Noviana Dwi Lestari, Agung Pramana Warih Marhendra, Aris Soewondo, Hideo Tsuboi, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Type 2 diabetes is a chronic metabolic disorder characterized by hyperglycemia and chronic inflammation. This study investigated the anti-inflammatory and insulin-modulating effects of Tithonia-Curcuma-Moringa (TCM) formulation in type 2 diabetes model. Male BALB/c mice were divided into six groups: normal control, type 2 diabetes control, metformin treatment, and three TCM formulation treatment groups with three different dose combinations. Type 2 diabetes was induced using a high-fat diet combined with streptozotocin injection, supported by sucrose administration. After 21 days of treatment, the spleen and pancreas were isolated for flow cytometry analysis. The results showed that the expression levels of …

Bridging The Cybersecurity Education Gap: The Role Of Open Educational Resources In Supporting Rural Cybersecurity Programs, Brittni Hardie

Theses and Dissertations

This study examines the intersection of cybersecurity education, open educational resources (OER), and rural higher education through a systematic review of current literature and an exploratory survey of rural community college faculty. The purpose of this research, consistent with the approved Institutional Review Board (IRB) protocol, was to understand how OER can be leveraged to design and deliver an affordable, high-quality System Security course within a rural higher-education environment. As cybersecurity workforce shortages continue to grow across the United States, rural institutions face persistent challenges in sustaining high-quality programs due to financial constraints, limited faculty capacity, and rapidly evolving curriculum …

Elucidating Complex H Behavior In Amorphous Oxide Semiconductors By Machine Learning, Lucas Ethington

Miners Solving for Tomorrow Research Conference

In this project, the disordered oxide structures will be mapped by a machine-learning algorithm (e.g., HDBSCAN) to identify characteristic behaviors of the proton across different material densities and defect types. This should help identify the under-coordinated, highly distorted, weakly-bonded, and dynamically unstable atoms to predict the most probable H locations. This fast and accurate prediction of energetically favorable H distribution will enable a reliable and fast screening of a large number of AOSs with variable cation and/or anion compositions. The approach will help find AOSs with suppressed numbers of M-OH defects (that form deep electron traps, limiting the number of …

Wheel-Spoke Encoding: A Product Quantization-Compatible Radial Encoding Scheme For Convex Polygonal Data, Maris Reinkemeyer

Miners Solving for Tomorrow Research Conference

Product quantization has historically been unapplied to GIS datasets, likely due to a mismatch between quantization’s input precondition of fixed, equal length vectors and GIS data’s inherent variability in the number of data points. Therefore, any quantization-compatible encoding method must operate independently of the data points within GIS records. The challenge is to balance the guarantee of producing fixed, equal length vectors with the preservation of semantic meaning present in the raw data. Here, we develop a radial polygon encoding method that achieves this balance while providing acceptable recall in a time complexity of O(nrv), where n is the number …

Dynamical Transition From A Two-Dimensional Soliton To A Rogue Wave In Quantum Droplets, Punit Sesha Sai Turlapati

Miners Solving for Tomorrow Research Conference

We investigate the nonequilibrium dynamics of two-dimensional quantum droplets: ultracold self-bound many-body states stabilized by the interplay of mean-field attractive interactions and repulsive quantum fluctuations. Flat-top ground state droplets are subject to an external potential, an attractive well and a repulsive barrier. Under the influence of the attractive well, we observe signatures of a Townes soliton formation, which for increasing strength of the well transitions into a two-dimensional rogue wave structure, a time-periodic highly localized configuration with amplitude three times larger than the background. The barrier instead favors a dynamical splitting of the droplet. We have developed a parallelized simulation …

Lay Summarization For Medical Patents, Manav Raja Vinotha

Miners Solving for Tomorrow Research Conference

Technical documents, such as medical patents, are often inaccessible to non-expert audiences due to specialized terminology. This paper presents a multi-agent system for automated lay summarization that optimizes both quality and computational efficiency through strategic task decomposition. The proposed pipeline utilizes four specialized stages: autonomous medical entity retrieval from the Unified Medical Language System (UMLS), context distillation into lay conceptualizations, and a structured writer-critic loop for iterative refinement. By distributing cognitive load across specialized agents, this architecture effectively leverages smaller, cost-effective language models while maintaining the performance of SOTA LLMs. Evaluated against single-agent baselines using an LLM as a Judge …

Analysis Of Autonomous Vehicle Survivability To 5g Communication Errors, Sydney Clark

Miners Solving for Tomorrow Research Conference

Autonomous vehicles rely on low-latency, high-reliability data exchange for real-time perception and control. Disruptions such as packet loss, latency variation, protocol-level errors, and malicious interference can pose significant safety risks to both passengers and surrounding environments. This project aims to evaluate, quantify, and predict the survivability of autonomous vehicle systems to communication errors, with focus on 5G network environments. The impact of these communication impairments on vehicle stability and control will be investigated through high-fidelity cyber-physical simulation of the vehicle and its surrounding environment. Experiments designed to capture varying network conditions will be used to assess a broad range of …

A Wearable Mxene-Based Sweat Sensor For Real-Time Monitoring Of Inflammatory Biomarkers, Ariel Pilger

Miners Solving for Tomorrow Research Conference

Many conventional biosensing approaches rely on invasive sampling or bulky benchtop instrumentation, limiting their use in continuous and portable applications. This project focuses on the development of wearable sweat-based biosensors that enable non-invasive, continuous, and portable monitoring of physical, chemical, and biological markers. The system will be designed to target markers present in sweat and transduce the biochemical interactions into measurable electrical signals. These signals will be processed through integrated electronics to produce clear, interpretable outputs for users and medical professionals. Supporting circuitry including filters, amplifiers, and an independent power supply will be implemented as necessary to ensure signal accuracy, …

Phase 5 - Post Implementation, Anna Pugerud

Distinguished Student Scholarship Collection

This paper presents a post‑implementation analysis and system design plan for digitizing animal health records within the Animal Health Services (AHS) department. The project addresses inefficiencies, data inaccuracies, and physical storage limitations associated with paper‑based documentation by proposing the adoption of a Software as a Service (SaaS) digital records solution. Through stakeholder interviews, observations, and document analysis, the project identifies functional, non‑functional, and system requirements necessary to support streamlined reporting, treatment tracking, and regulatory compliance. The analysis outlines feasibility across technical, economic, and organizational dimensions, demonstrating that the proposed system can reduce documentation time, improve response times, and lower operational …

A Real Account Of Deep Fakes, Benjamin L.W Sobel

Michigan Law Review

Laws regulating pornographic deepfakes are written to prohibit “digital forgeries,” “false” images, or media “indistinguishable” from “authentic” recordings. Yet the typical anti-deepfake law covers materials that aren’t forgeries, aren’t false, and that reasonable observers can easily distinguish from authentic recordings. Though drafted as if they regulate statements of fact, anti-deepfake laws actually target certain outrageous depictions per se—and rightly so, because pornographic deepfakes cause harm irrespective of their truth or falsity. However, the inapposite language of facts results in statutes with crucial ambiguities. Moreover, because anti-deepfake laws ban outrageous depictions irrespective of the factual assertions they make, they differ fundamentally …

A View Under The Hood: Duquesne Kline's Law And Computing Program, Wesley M. Oliver, Katherine L.W. Norton, Martin Mckown, David Horrigan

West Virginia Law Review

No abstract provided.

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

Behavioral, System, And Informational Cyberattacks: A Human-In-The-Loop Driving Simulator Experiment, Samuel Petkac

Psychology Theses & Dissertations

Advanced technologies such as sensors and AI/ML algorithms have enabled increasing levels of automated driving system that detects, responds, and even predicts changes in a driving environment supported by wireless connectivity to nearby vehicles and infrastructure. Such connected and automated vehicles (CAVs) can be particularly vulnerable to cyberattacks targeting not only infotainment systems but also firmware and other applications, critically compromising driver safety. As we anticipate a “mixed” traffic where vehicles with various levels of automated technologies share the road for the foreseeable future, it is urgent to systematically examine types of possible cyberattacks and control human behaviors in such …

The Energy And Environmental Footprint Of Ai, Michael P. Vandenbergh, Ethan I. Thorpe, Jonathan M. Gilligan

Michigan Journal of Environmental & Administrative Law

Artificial intelligence (AI) has the potential to create major economic and social benefits, but also to rapidly escalate electricity demand and its associated environmental impacts. Information availability has been a cornerstone of environmental law for half a century, and this Article argues that providing information to individual, corporate, and other users about the electricity demand and environmental impacts of AI can reduce those impacts without delaying development of the technology. Little is known about how different large language models (LLMs) compare on these metrics, though. To address whether users have access to the information necessary to address this shortcoming, the …

Match-A-Fit, Brianna Mendoza, Adan Diaz De Leon, Pedro Jacobo, Juan Marco Saca Dada

Posters - 2026

Welcome to Match-a-Fit! Match-a-Fit is an iOS application that allows the user to create a digital closet by uploading images of their clothing items. With AI, the program can generate outfits based on the digital closet, the time, and the occasion. Match-a-Fit’s purpose is designed to help users who struggle to get ready, run out of time, or can’t decide on an outfit, by easily generating outfit options based on the occasion.

Maddenlite, Sergio Pena

Presentations - 2026

Problem •“What If” scenarios impossible to test accurately •Commercial games rely on arcade physics •Spreadsheets lack visual engagement

Motivation •Passion for football analytics •Desire to simulate cross-era matchups •Apply math models to real-world sports data

Solution •Python based simulation engine using historical play-by-play data •Simulates outcomes based on probability

A.I.R.E., Laurene Robinson

Presentations - 2026

•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly

Harvest Scanner, Alexander Murphy

Posters - 2026

In current times, people can find themselves at the whims of markets and may be spending more than they realize or want to on regular, everyday goods. New tools can help users keep track of the goods they are paying for. Harvest Scanner was developed to scan and track local grocery prices from stores using their publicly available website information. It was developed in Python using PyQt5 for GUI. The database is stored as an SQL file with Python using SQLite engine. Users will be able to view local grocery prices in a database interface (GUI). There are many features …

A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson

Posters - 2026

Reverse engineering plays a vital role in cybersecurity by helping analysts examine unknown binaries, investigate malware, identify vulnerabilities, and better protect sensitive systems. However, once a program is compiled and stripped, the meaningful names that describe its behavior are lost, leaving behind generic function labels like FUN_00401a30. Analysts must then manually interpret decompiled code, trace call chains, and infer program behavior function by function, which is slow and mentally demanding on large binaries. To address this challenge, this project introduces A.I.R.E., a local Ghidra extension that extracts contextual evidence from stripped functions and uses a locally hosted language model to …

Topshelf, Ayden Jay Soliz

Posters - 2026

With so many great video games releasing each year, it becomes challenging to keep up with the latest. Players find it difficult to maintain an updated list of future games to play, and many existing online trackers have become too complicated to use. TopShelf is designed to be a simple video game backlogging website that will track games for the player. By connecting to an online video game database API, users can add/drop games from their personal list and enable tracking and receive emails for platform releasing. Gamers can leave all the tracking and updates responsibilities to TopShelf

Lego®-Based Cubesats For Space Outreach, Stefan Brandle, Joshua Brown, Evan Smith, Drew Nye, Sydney Reddy, Twidy Kwae, Kaiya Grzesiak, Matthew Roderer, John Hetz

Faculty-Led Student Projects in CSE

Taylor University Computer Science and Engineering, in collaboration with NearSpace Education, has initiated a multi-year effort to enhance space-awareness by offering K-12 students career-impacting opportunities to build and fly satellites that have a LEGO®-robotics-based payload. This effort is intended as an outreach in the spirit of the ThinSat project (2017-2021) supported by Virginia Space, Twiggs Space Lab, Orbital ATK, NearSpace Launch, and the NASA Wallops Flight Facility, among others. We hope to receive permission to name the satellite LEGO-SAT-0.