Computer Sciences Commons^™

Enhancing Security And Healthcare Through Continuous Monitoring On Wearable Devices, Sicong Chen

Dissertations - ALL

Wearable and mobile devices are becoming deeply embedded in daily life, supporting a growing range of tasks - from communication and entertainment to health monitoring and productivity. As people increasingly rely on these devices, it is essential to ensure both the protection of the sensitive data they store and the ability to derive meaningful insights that enhance users’ well-being. This dissertation investigates how wearable and mobile devices can be leveraged to provide robust and user-friendly solutions through continuous monitoring across two critical domains: security and mental health. The foundation of this dissertation is the development of a security infrastructure, as …

Online And Offline Learning For Embodied Ai In Autonomous Systems, Kun Wu

Dissertations - ALL

Embodied Artificial Intelligence (AI), which integrates physical embodiment with intelligent decision-making, is increasingly critical in advancing autonomous systems across diverse domains such as autonomous driving and robotic manipulation. This dissertation presents a comprehensive exploration of online and offline learning approaches for Embodied AI in autonomous systems, addressing both algorithmic innovations and dataset construction to overcome fundamental challenges in perception, decision-making, and control. Through five interconnected studies, we systematically advance the state of the art in deep reinforcement learning (DRL) and imitation learning for embodied control. First, we introduce CADRE, a cascade online DRL framework for vision-based autonomous urban driving that …

Grades Are Bugs, Jordan Freitas

Computer Science Faculty Works

This paper argues that grades are bugs in our educational system, undermining desired behaviors and outcomes. Grades were introduced into higher education for purposes directly at odds with the goals of inclusive pedagogy today, as well as the neuroscience of human motivation and learning. Students enter computer science programs from increasingly varied backgrounds and experiences, and face a rapidly evolving landscape of prospective career paths while higher education costs in the United States are ever increasing. Computer science educators have a responsibility to adapt and carefully re-examine typical approaches to all aspects of the learning environments we build and curricula …

Data-Efficient 3d Deep Learning, Minmin Yang

Dissertations - ALL

3D data, whether represented as point clouds, volumetric data or meshes, plays a critical role in domains such as autonomous driving, robotics, and medical imaging. However, the complexity of 3D data acquisition and the high cost of annotation often make it impractical to curate large, fully labeled 3D datasets. Furthermore, the unstructured nature of point clouds and the high dimensionality of volumetric data pose additional challenges for designing effective deep learning models. While existing architectures, like PointNet and DGCNN, have made significant progress in learning directly from raw 3D inputs, they typically rely on data-rich scenarios. This dissertation focuses on …

An Exploratory Analysis Of Automated Deception Detection For Mental Health Applications, Sayde Leya King

USF Tampa Graduate Theses and Dissertations

Deception in mental health settings can undermine therapeutic relationships, compromise treatment efficacy, and impact patient outcomes. Yet, research shows that mental health clinicians often perform no better than chance at detecting deceptive behavior in therapy. Automated deception detection, leveraging artificial intelligence (AI) and multimodal behavioral cues—such as eye gaze, body gestures, and facial expressions—offers a promising alternative. However, most existing research focuses on high-stakes legal contexts, limiting its applicability to mental health settings.

This dissertation addresses this gap by pursuing three key research objectives using a mixed-methods approach. First, we investigate mental health clinicians’ perspectives on AI-assisted deception detection through …

Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to …

Enhancement Of Phenolic, Flavonoid, And Biological Activi-Ties In Fermented Pea (Pisum Sativum) Extracts Via Frac-Tionation, Anastasia Fitria Devi, Euis Filailla, Setyani Budiari, Hani Mulyani, Nina Artanti

Karbala International Journal of Modern Science

In this study, peas were fermented for 24 h using either Rhizopus oligosporus or Aspergillus oryzae. The resulting fermented peas, along with unfermented peas, were extracted using either methanol or water. The methanol extracts showed greater improvements following fermentation compared to the water extracts, as shown by the IC₅₀ values, which progressed from >500 to ≤200 µg/mL, as determined via the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) method. Consequently, further fractionations focused on the methanol extracts of the fermented peas. Six bands were resolved on thin-layer chromatography plates for each extract. Their positions demonstrated significant effects (p < 0.05) on bioactivity. The bottom band (i.e., Band 1) exhibited approximately 70% inhibition, as determined via the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method and 35% inhibition according to the ABTS method, while the other bands displayed lower to negligible inhibition. Band 5 displayed 85–97% cytotoxicity, while Band 1 did not show any cytotoxicity. The total phenolic content (TPC) of Band 1 and the total flavonoid content (TFC) of Band 6 increased 4.4- and 8.9-fold, respectively, compared to those of the unfractionated extracts. Furthermore, a correlation analysis revealed a strong correlation between TFC and the three observed bioactivities. The different cultures significantly (p < 0.05) affected the antioxidant activity of either the fermented pea extracts or the resulting bands, as determined via the DPPH method. These results indicated that fermentation enhanced the antioxidant activity of peas. Subsequent fractionations separated and concentrated compounds from the fermented peas, improving the bioactivities, TPC, and TFC of the resulting fractions.

Synthesis Of Gold Nanoparticles Using Hordeum Vulgare Leaf Extract And Their Antibacterial Activity, Mohamed M. Sehree, Shakir Ghazi Gergees, Pakhshan A. Hassan

Karbala International Journal of Modern Science

Plant extracts and gold nanoparticles are promising alternatives for combating antibiotic resistance in light of the increasing bacterial resistance. Leaf extract of barley was used to synthesize gold nanoparticles (AuNPs). Barley gold nanoparticles (BL-AuNPs) were produced by adjusting some reaction parameters. These BL-AuNPs were characterized through employing the UV-visible spectroscopy technique, the scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDX). BL-AuNPs were tested for antibacterial efficacy against two strains of Gram-negative bacteria, clinically isolated and considered as multidrug-resistant pathogens, Acinetobacter baumannii and Salmonella typhi. The antimicrobial efficiency of the compounds was evaluated …

The Impact Of Microplastics On Water Quality, Heavy Metals, And Health Risks In Bioflocbased Tilapia Farming Systems, Dian Rizky Afriani, Deswati Deswati, Rahmiana Zein, Putri Ramadhani

Karbala International Journal of Modern Science

Along with microplastics, pollution of heavy metals, including iron (Fe), zinc (Zn), and copper (Cu), in freshwater ecosystems poses a serious environmental threat that can adversely affect human health. This study investigates the use of biofloc technology to reduce microplastic and heavy metal contamination while improving water quality. By utilizing microbial aggregates that capture microplastic and heavy metal particles through flocculation and biosorption processes, four experimental treatments were applied, i.e.: A (without biofloc and microplastics); B (with biofloc, without microplastics); C (with biofloc and low-density polyethylene microplastics); and D (with biofloc and high-density polyethylene microplastics). The results indicate that fish …

On Programmatic Aspects Of The Universality, Parameter, And Recursion Theorems Of Classical Computability, Vladimir A. Kulyukin

Computer Science Faculty and Staff Publications

The Universality, Parameter, and Recursion Theorems are three foundational results of classical computability theory. We show how these theorems can be programmatically illustrated and partially validated in Lisp. Our programs can be used as supplementary materials to texts on theoretical computer science, mathematical logic, or metamathematics.

Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related HPCs for multi-task classification, without incorporating …

Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …

Refining Participatory Design For Aac Users, Blade Frisch, Keith Vertanen

Michigan Tech Publications

Augmentative and alternative communication (AAC) is a field of research and practice that works with people who have a communication disability. One form AAC can take is a high-tech tool, such as a software-based communication system. Like all user interfaces, these systems must be designed and it is critical to include AAC users in the design process for their systems. A participatory design approach can include AAC users in the design process, but modifications may be necessary to make these methods more accessible. We present a two-part design process we are investigating for improving the participatory design for high-tech AAC …

Advancing Political Science With Machine Learning: A Gaussian Process Approach, Yehu Chen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The proliferation of data in recent decades including including survey, image and text data, has significantly transformed the landscape of political science research. Machine learning methods have played an instrumental role in analyzing these datasets, yet their application poses challenges in areas where political concepts are not directly measurable or the primary focus of inference is causality. In addition, the essence of machine learning algorithms being trained for prediction performance in a black-boxed manner, makes their outputs hardly interpretable and even unappreciated. This dissertation addresses these issues by proposing a novel methodological framework that employs Gaussian Process (GP) models, a …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

We propose an extension of renormalization into the domain of spiking neural networks, thereby providing a novel framework for coarse-graining neural networks without disrupting their critical properties. The proposed coarse-graining technique merges neurons and synaptic connections based on a graph-theoretic distance derived from synaptic weight strength and is configured to effectively prune the reservoir size while preserving the scale-free spiking dynamics indicative of criticality. Criticality in spiking neural networks may provide information-theoretic advantages by optimizing information processing and sensitivity to input. Using time-series prediction benchmarks, we demonstrate that networks operating at criticality exhibit up to 32% higher prediction accuracy before …

Enhancing Biosecurity In Tamper-Resistant Large Language Models With Quantum Gradient Descent, Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan

Computer and Data Science Faculty Publications

This paper introduces a tamper-resistant framework for large language models (LLMs) in medical applications, utilizing quantum gradient descent (QGD) to detect malicious parameter modifications in real time. Integrated into a LLaMA-based model, QGD monitors weight amplitude distributions, identifying adversarial fine-tuning anomalies. Tests on the MIMIC and eICU datasets show minimal performance impact (accuracy: 89.1 to 88.3 on MIMIC) while robustly detecting tampering. PubMedQA evaluations confirm preserved biomedical question-answering capabilities. Compared to baselines like selective unlearning and cryptographic fingerprinting, QGD offers superior sensitivity to subtle weight changes. This quantum-inspired approach ensures secure, reliable medical AI, extensible to other high-stakes domains.

Martingale Methods For Structural Change Detection And Feature Attribution In Dynamic Networks, Izhar Ali

Theses and Dissertations

Dynamic networks undergo structural changes when their generative process shifts at a change-point. We need to detect this change-point with minimal delay while identifying its underlying causes. This is an optimization problem of minimizing the expected detection delay while controlling the false alarm probability below a threshold---leading to three critical challenges: non-parametric detection without distributional assumptions, exact feature attribution, and early detection with rigorous false alarm control. We construct additive martingale statistics from multiple graph features using conformal prediction, providing false alarm guarantees via Ville's inequality. Our key theoretical contribution proves the Martingale-Shapley equivalence: each feature's martingale value equals its …

Multiscale Modeling Of Drug-Induced Liver Injury From Organ To Lobule, Alon Malka-Markovitz, Stelian Camara Dit Pinto, Mohammed Cherkaoui, Steven M Levine, Sharmila Anandasabapathy, Gagan K Sood, Sadhna Dhingra, Gao Yujia, John M Vierling, Nicolas R Gallo

Center for Medical Ethics and Health Policy Staff Publications

Drug-induced liver injury poses significant challenges in drug development and in clinical care. This study builds on prior work developing a Human Liver Virtual Twin by creating a Multiscale Computational Fluid Dynamics framework that integrates patient-specific anatomical data to predict acetaminophen-induced liver injury as a demonstration of its capability. The model bridges vascular, lobular, and cellular scales to simulate dynamic blood flow, drug transport, and injury mechanisms that accurately reflect clinically observed spatial heterogeneity. Results demonstrate accurate blood flow dynamics, predictions of hepatocellular damage, and a scalable framework for studying spatial heterogeneity applicable to other hepatic pathologies. This work establishes …

A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater

SMU Data Science Review

Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

Auroral Hemispheric Power Asymmetry During Geomagnetic Storms: A Multi-Database Investigation Using Ae And Supermag Indices, Melvin J. Reyes Lozada

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Melvin J. Reyes Lozada on June 20, 2025.

Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu

Journal of System Simulation

Abstract: To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second …

Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei

Journal of System Simulation

Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …

Incremental Planar Nearest Neighbor Queries With Optimal Query Time, John Iacono, Yakov Nekrich

Michigan Tech Publications

In this paper we show that two-dimensional nearest neighbor queries can be answered in optimal O(log n) time while supporting insertions in O(log1+ϵ n) time. No previous data structure was known that supports O(log n)-time queries and polylog-time insertions. In order to achieve logarithmic queries our data structure uses a new technique related to fractional cascading that leverages the inherent geometry of this problem. Our method can be also used in other semi-dynamic scenarios.

Convexity Helps Iterated Search In 3d, Peyman Afshani, Yakov Nekrich, Frank Staals

Michigan Tech Publications

Inspired by the classical fractional cascading technique [13, 14], we introduce new techniques to speed up the following type of iterated search in 3D: The input is a graph G with bounded degree together with a set Hv of 3D hyperplanes associated with every vertex of v of G. The goal is to store the input such that given a query point q ∈ R3 and a connected subgraph H ⊂ G, we can decide if q is below or above the lower envelope of Hv for every v ∈ H. We show that using linear space, it is possible …

Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng

Journal of System Simulation

Abstract: To eliminate the influence of parameter perturbations and external disturbances on the wheel angle tracking control performance of steer-by-wire (SbW) system, a fractional-order integral terminal sliding mode control scheme based on a finite-time disturbance observer is proposed. A sliding modebased second order finite-time disturbance observer (FDO) is designed to precisely estimate the total disturbance of the SbW system, and the estimated total disturbance is compensated into the system control input to reduce the wheel angle tracking error. A fractional-order fast integral terminal sliding mode control (FOFITSMC) scheme is designed to ensure fast convergence of the wheel angle tracking error …

Modeling And Simulation Of Hybrid Traffic Flow Considering The Inherent Dynamics Of Cacc Vehicular Platoons, Xiujian Yang, Jingjing Huang, Xi Wang

Journal of System Simulation

Abstract: To investigate the characteristics of single-lane mixed traffic flow with the presence of cooperative adaptive cruise control (CACC) vehicle platoons, a modeling approach based on cellular automata is proposed. This method distinguishes between the car-following strategies of human-driven vehicles and CACC vehicles, incorporating dynamic inter-vehicle spacing within the platoon and actual control behaviors to construct a mixed traffic flow model with inherent dynamic properties. The model enables an in-depth analysis of the influence of platoon features, such as geometric formation, carfollowing control strategies, and platoon size, on the characteristics of mixed traffic flow. It also allows us to study …

Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge

Journal of System Simulation

Abstract: In order to improve the visual obstacle avoidance ability of substation robots in complex environments, a robot visual obstacle avoidance method based on spatiotemporal networks is proposed. The method utilizes traditional image processing techniques to enhance road information and designs a lightweight deep convolutional neural network structure to extract road features from a spatial domain perspective; based on the spatial characteristics of the road, a long short-term memory network is introduced to mine the changes in the road from a temporal perspective, and a classification regression prediction structure is used to predict the robot's obstacle avoidance direction and angle; …

Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao

Journal of System Simulation

Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …