Computer Sciences Commons^™

Real-World Continuous Smartwatch-Based User Authentication, N. Al-Naffakh, N. Clarke, F. Li, P. Haskell-Dowland

Research outputs 2022 to 2026

User authentication is often regarded as the "gatekeeper"of cyber security. It has, however, long suffered from significant usability issues that have resulted in research focussing upon frictionless and transparent biometric approaches. Activity-based user authentication - a technique that authenticates a user by what they are physically doing at a specific point in time has attracted significant attention, particularly due to the increasing popularity of smartwatches. This research aims to overcome limitations in prior work by exploring the viability of the approach in real-world conditions. The study presents two principal experiments, one focused upon a constrained environment to provide a control …

Robust Contraction Decomposition For Minor-Free Graphs And Its Applications, Bandyapadhyay Sayan, William Lochet, Daniel Lokshtanov, Dániel Marx, Pranabendu Misra, Multiple Additional Authors

Computer Science Faculty Publications and Presentations

We prove a robust contraction decomposition theorem for H-minor-free graphs, which states that given an H-minor-free graph G and an integer p, one can partition in polynomial time the vertices of G into p sets Z₁,… ,Z_p such that tw(G/(Z_i ⧵ Z')) = O(p + |Z'|) for all i ∈ [p] and Z' ⊆ Z_i. Here, tw(⋅) denotes the treewidth of a graph and G/(Z_i ⧵ Z') denotes the graph obtained from G by contracting all edges with both endpoints in Z_i ⧵ Z'. Our result generalizes earlier results by Klein [SICOMP 2008] and Demaine et al. [STOC 2011] based …

Quantum Analysis Of Protein–Ligand Binding By Integrating Structural Resolution, Sequence Homology, And Ligand Properties, Don Roosan, Samira Samrose, Rubayat Khan, Saif Nirzhor, Brian Provencher

Computer and Data Science Faculty Publications

Predicting protein–ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning and quantum-enhanced modeling to investigate how crystal structure resolution, sequence similarity, and ligand properties jointly influence binding affinity. Using a curated “refined” dataset from PDBbind and an expanded general dataset, we first conduct correlation and regression analyses to quantify the relationships among binding affinity, ligand descriptors (e.g., molecular weight, logP), and protein structural metrics (resolution, R-factor). We observe moderate positive correlations …

Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid

Tanzania Journal of Engineering and Technology (TJET)

There are many uncertainties and complexities associated with maintaining Meter Gauge Railway (MGR) infrastructure, which calls for a methodical approach to decision-making. The development and application of a fuzzy-AHP-based decision support system (DSS) to select the optimal maintenance strategy for the MGR are presented in this study. The review covers research from 2013 to 2023 and focusses on the use of Multi-Criteria Decision Making (MCDM) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) techniques in railway infrastructure maintenance. To manage the inherent uncertainties and subjective judgements involved in maintenance decision-making, the Fuzzy-AHP methodology combines fuzzy logic with the Analytic Hierarchy Process (AHP). …

Energy Optimal Coverage Motion Trajectory Generation Using A Fourth-Order Motion Profile, Mathias Halinga

Tanzania Journal of Engineering and Technology (TJET)

Industrial machines are widely used in manufacturing sector to manufacture several products to meet customer demands. Most of these industries runs all the time throughout a day leading to high operating cost. To cut costs and satisfy customer demand for precise products, industrial machines’ motion generation is important in improving machine motion precision while using less energy. This study presents a coverage motion energy optimization which is generated by linear interpolation of each segment described by the fourth-order motion profile. The phase changes in the profile are attained with continuity of machine kinematic limits jerk, acceleration, and velocity, which are …

Analysis Of An Improved Reliability Dual-Buck Structured Three-Level Flying Capacitor Inverter, Almachius Kahwa Dr.

Tanzania Journal of Engineering and Technology (TJET)

With the increased demand for high-reliability power converters in the electric drive-train and propulsion systems, the efforts to design and analyze converters with high fault tolerance have become apparent. Among the emerging trends to improve the reliability of power converters is the incorporation of dual-buck (DB) structures in traditional converter topologies. Thus, this paper studies a single-phase dual-buck structured three-level flying capacitor (FC) inverter. The dual-buck flying capacitor (DBFC) inverter was constructed in such a way as to suppress the shoot-through problems that may occur because of the switching mismatch and gate driver delay, as exhibited in the traditional FC …

The Future Of Foreign Language Learning In The Age Of Artificial Intelligence: A Critical Analysis Of Trends, Challenges, And Opportunities, Ahmad Aljanadbah, Rashid Hamad Al Marri, Hamad Mubarak Almarri

All Works

The integration of Artificial Intelligence (AI) in foreign language education is transforming how languages are taught and learned. This study investigates the current trends, advantages, and challenges of AI applications in language acquisition. Employing a qualitative content analysis method, the research analyzes scholarly publications and practical innovations in AI-based tools, such as adaptive learning systems, intelligent chatbots, and automated writing feedback. The findings highlight AI's potential to enhance personalized instruction, boost learner engagement, and expand access to authentic language materials. Nonetheless, the study also reveals notable limitations, including concerns about data privacy, diminished human interaction, and ethical risks related to …

Towards Metrology 4.0 In Developing Countries’ Manufacturing Industries, Jailos Nzumile

Tanzania Journal of Engineering and Technology (TJET)

A systematic literature review was conducted to unveil the status of the digital transformation of metrology in developing countries, as they are lagging in utilising fourth industrial revolution (IR4.0) technologies to transform manufacturing industries. A PRISMA technique was employed using various keywords to identify, screen, and select the relevant literature. Forty publications were selected for the review, mainly discussing IR 4.0 technologies in metrological operations. The results indicate that the digital transformation of metrology has yet to be initiated in developing countries. However, the employment of IR4.0 technologies in advancing metrological operations in manufacturing industries is mostly discussed in the …

Towards Future Sustainable Infrastructure: The Role Of Technical Audit In Tanzania’S Public Works, George C. Haule

Tanzania Journal of Engineering and Technology (TJET)

This study aimed to investigate the vital role and impact of technical audits in promoting sustainable infrastructure development in Tanzania. The role and effects of technical audits in long-term infrastructure development were studied using a mixed-methods approach with both quantitative and qualitative parts. Data were collected through analysis of technical audit documentation, a semi-structured questionnaire, and stakeholder interviews. The study revealed the various dimensions of infrastructure investment projects, including initiation and planning, design, procurement of contractors and consultants, contract management, environment, health, and safety. The technical audit findings reported weaknesses or non-performance issues in infrastructure planning at the national level …

The Effectiveness Of Scenario-Based Cybersecurity Day Camps In Southern Rural Appalachia, Anna P. Rodgers-Stine, Tania Williams

Journal of Cybersecurity Education, Research and Practice

As the emphasis on cybersecurity instruction in the K12 environment continues to expand, furthering access to cybersecurity education is paramount across the United States. While designated cybersecurity courses are not available in many schools, the implementation of cybersecurity camps may help to bridge the gap and increase student interest in and awareness of cybersecurity as a field. From 2021 through 2024, cybersecurity day camps were held in a region in rural southern Appalachia with the goal of increasing student interest and access to cybersecurity topics. Through the creation and implementation of these camps, it was found that scenario-based cybersecurity day …

Advancements In Refreshable Braille Display Technology: A Comprehensive Survey, Maryam Etezad, Rajeev Joshi, Franceli L. Cibrian

Engineering Faculty Articles and Research

This paper provides a comprehensive mapping literature review of the advancements in refreshable Braille display (RBD) technology, which employs dynamically movable pins or dots to render Braille characters for individuals with blindness and visual impairment. This literature review, which includes 96 papers, aims to summarize the current evidence on six distinct types of RBDs: piezoelectric, electromagnetic, electroactive polymer (EAP), pneumatic, shape memory alloy (SMA), and microfluidic technologies. For each type, we discuss the underlying mechanisms, alongside the current trends and research opportunities. This comparative analysis aims to inform the selection of appropriate RBD technology based on specific user needs and …

Assessment Of Digital Solutions For Conformity Assessment Of Legally Controlled Measuring Instruments In Tanzania, Faraja Nyoni

Tanzania Journal of Engineering and Technology (TJET)

The advent of state-of-the-art digital technologies since 2011 has led to the digital transformation of legal metrology practices to ensure the trustworthiness of software-controlled measuring instruments globally. Despite the digital transformation in legal metrological practices, the conformity assessment of legally controlled measuring instruments is manually done (i.e., paper-based) in Tanzania. The paper-based conformity assessment of legally controlled measuring instruments is prone to error and lacks efficiency and effectiveness. This study aimed to assess digital solutions for improving conformity assessment through a comprehensive survey conducted across various regions in Tanzania, targeting a stratified sample of 51 respondents from organizations involved in …

Artificial Intelligence In Higher Education: A Case Study Of Faculty Teaching Methodologies At A Private University, Ellen Ramsey, George Antoniou, Matteo Peroni, Brent Muckridge, Raouf Ghattas, Philip L. Fazio, Wendy Wallberg, Saidi Porta, Gary Solomon, Mary Smith, Kristen Migliano, Karima Lanfranco, Lianfen Qian, David G. Wolf

Faculty and Staff Publications & Presentations

This qualitative case study investigates faculty perspectives on artificial intelligence (AI) integration within a private university context, examining pedagogical, administrative, and ethical implications. Data collected through semi-structured interviews with faculty across four disciplines revealed ambivalent yet cautiously optimistic attitudes. Participants acknowledged AI’s potential to enhance personalized learning and reduce bureaucratic burdens through automation. However, three critical barriers emerged: (1) insufficient institutional technological infrastructure, (2) lack of systematic faculty training programs, and (3) unresolved ethical dilemmas surrounding data privacy, algorithmic bias, and academic integrity. Notably, while faculty welcomed AI as a supplemental tool, they unanimously emphasized the irreplaceable role of human …

Developing Sky Plots Of Rocket Launches From Gps Scintillation Data, Ishaan Dey, Kshitija Deshpande

Beyond: Undergraduate Research Journal

Sky plots displaying GPS satellite and rocket launch trajectories are developed to determine the spatial correlation between satellites that display ionospheric scintillations and heavy thrust-producing rockets. The trajectories of three major Falcon Heavy and Artemis 1 rocket launches are used within this paper. Python code is utilized to compute and plot Ionospheric Pierce Point (IPP) coordinates which are then used to produce satellite trajectories from the receiver's point of view. Rocket latitude, longitude, and altitude data is integrated within the code to provide extensive detail into the location of the rocket in relation to GPS satellites that displayed scintillations from …

Refinement Or Disruption: Patterns Of Critical Inquiry In Computing Ethics Assignments, Vincent Barfield, Charles Wallace

Michigan Tech Publications

Ethical inquiry in computing has an evolving nature, where repeated critical questioning may lead to a series of reframings of the problem. Since many computing students have experience with agile project-based courses, an iterative approach can provide a familiar entry into ethics. Agile development, however, is typically oriented around satisfaction of customer goals, while ethical inquiry requires constant questioning that may cast those goals into doubt. Does the similarity between critical ethical inquiry and iterative development provide a helpful model or breed a false familiarity? To address this question, we examine student work in a computing ethics course, involving iterative …

Beyond The Hype: The Fundamental Challenges Of Machine Learning-Based Android Malware Detection In Cybersecurity, Guojun Liu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) algorithms have achieved remarkable success across various domains, including cybersecurity. Inspired by these advancements, the academic security community has explored numerous ML-based approaches for Android malware detection. While ML holds significant promise in this domain, its practical deployment faces substantial challenges, including data collection, feature selection, app representation across different models, performance instability across datasets, and inherent limitations of learning-based malware detection. These challenges can lead to overly optimistic detection results and weaken the reliability of malware detection frameworks.

Android malware detection has been extensively studied using both traditional ML and deep learning (DL) approaches. Although many …

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 …