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Revisiting Visualization Literacy: What Standardized Assessments Reveal And Conceal Across Instruments, Cultures, And Ai Systems, Saugat Pandey Apr 2026

Revisiting Visualization Literacy: What Standardized Assessments Reveal And Conceal Across Instruments, Cultures, And Ai Systems, Saugat Pandey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Data visualizations are now a primary medium for public communication, scientific reasoning, and decision-making. By transforming complex data into accessible graphical forms, visualizations are widely assumed to make information comprehensible to broad audiences. Yet the ability to accurately read, interpret, and critically evaluate a visualization, what researchers call visualization literacy, is neither uniform nor universal. It is a learned, multidimensional skill shaped by prior exposure, educational opportunity, and context. While the field has developed standardized instruments to measure visualization literacy, these tools were built under narrow assumptions: participants are typically paid, English-speaking, and recruited from Western online panels. When any …

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Research On The Accessibility Elements Of Rural Public Digital Cultural Services Based On Aism-Fmicmac, Lihui Peng, Banruo Gu, Chuang Hong, Xisheng Hu Apr 2026

Research On The Accessibility Elements Of Rural Public Digital Cultural Services Based On Aism-Fmicmac, Lihui Peng, Banruo Gu, Chuang Hong, Xisheng Hu

Journal of Scientific Information Research

[Purpose/significance] A structural analysis of the stratified dependencies among accessibility determinants in rural public digital cultural ecosystems enables identification of pivotal developmental nodes, thereby establishing theoretical foundations for optimizing service delivery mechanisms and achieving spatial equilibrium in digital cultural provision. [Method/process] This study applies grounded theory to conduct a comprehensive coding analysis of policy documents related to China's rural public digital cultural services. Through this process, five dimensions and fifteen sub-components of accessibility are identified. An index system for accessibility factors is then developed using the AISM approach. The identified elements are classified into three hierarchical levels. To further validate …

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Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu Apr 2026

Drug Risk Knowledge Discovery For Western Medicines Based On Knowledge Graph Link Prediction, Jianxiang Wei, Ma Hengyuan Ma, Yuehong Sun, Wenwen Du, Letian Hu

Journal of Scientific Information Research

[Purpose/significance] The risk information contained in drug instructions is usually incomplete, and some new adverse reactions can only be discovered in actual clinical use. This paper proposes an information organization and knowledge discovery method for pharmacovigilance, in order to timely and accurately identify missing risk knowledge in drug instructions. [Method/process] Drug instructions of 8 152 Western medicines are collected as the research data; On the basis of ontology construction, data annotation, and model training, the UIE model is used to jointly extract entity and relationship triplets from the research data; A new knowledge graph link prediction method CompGCN-RotatE, is proposed, …

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Ticking Toward Security: Practical Source Authentication For The Precision Time Protocol, Alexander Gebhard Apr 2026

Ticking Toward Security: Practical Source Authentication For The Precision Time Protocol, Alexander Gebhard

Dissertations (1934 -)

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 environments has greatly expanded the attack surface of critical infrastructure systems. Legacy industrial protocols originally intended for use only in isolated networks are now exposed across interconnected systems. This interconnectivity enables adversaries to target power grids, manufacturing plants, and other critical systems with increasing sophistication. Although vendors have retroactively introduced security extensions to industrial protocols such as EtherNet/IP and OPC Unified Architecture (OPC UA), the adequacy and practical feasibility of these security measures remain understudied. This dissertation advances the security of industrial protocols through three main contributions. …

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Isolation, Identification And Antibiotic-Resistance Profiling Of Bacteria Isolated From Mobile Phone Surfaces In Karbala., Kawkab Abdullah Alsaadi, Zahraa Raheem Murshidy, Dhuha Ali Hussein, Sura Abd Ali Kadhim, Kawakib Aboudi Hanoon Apr 2026

Isolation, Identification And Antibiotic-Resistance Profiling Of Bacteria Isolated From Mobile Phone Surfaces In Karbala., Kawkab Abdullah Alsaadi, Zahraa Raheem Murshidy, Dhuha Ali Hussein, Sura Abd Ali Kadhim, Kawakib Aboudi Hanoon

Karbala International Journal of Modern Science

Due to the diverse environments that mobile phones are exposed to via human handling they become reservoirs of microorganisms. The objective of the current study was to determine the level and character of bacterial contamination on mobile phones by identifying bacterial strains, and the degree of antimicrobial susceptibility. A cross-sectional study was conducted from April to July 2025 where phones belonging to 115 individuals were randomly chosen from various departments and units at Kerbala University and swabbed. The VITEK 2 automated system was used to identify bacterial taxa and test for antimicrobial susceptibility, and 50 of the swabs exhibited bacterial …

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The Intersection Of Visual Communications, User Experience, & Design Theory: A Case Study With The Dog Spaw, Jessica Miltner Apr 2026

The Intersection Of Visual Communications, User Experience, & Design Theory: A Case Study With The Dog Spaw, Jessica Miltner

Honors Projects

This project explored the intersection of visual communication, user experience, and design theory, focusing on researching design and UX principles and applying them in a real-world case study with The Dog Spaw from Celina, Ohio. The project resulted in a redesigned website that aims to influence user behavior by improving perceptions of trust, professionalism, brand consistency, and ease of use, and by examining how these perceptions, in turn, influence user action.

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Developing A Framework To Improve Phishing Response After Detection, Kevin Umba Apr 2026

Developing A Framework To Improve Phishing Response After Detection, Kevin Umba

Master's Theses (2009 -)

Phishing belongs to the most prevalent attack vectors in the cybersecurity industry. Academia, industry, and government resources offer a wealth of information on the subject. However, insights on phishing techniques and remediations are scattered across those sources. Most governmental and academic literature focuses on user awareness, policy guidelines, or phishing training. This leaves the security practitioners underserved. Furthermore, actionable information for security operations is often unstructured or vendor-specific. The goal of the thesis is to provide a preliminary framework to guide security professionals through indicators of compromise and remediation, using a vendor-neutral approach.

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Gopher: Efficient Dynamic Graph Pattern Mining Via Dag-Driven Execution, Yi Zhang, Yu Huang, Chaoqiang Liu, Haifeng Liu, Juntao Chen, Jingrui Yuan, Jianhui Yue, Xiaofei Liao, Hai Jin, Jingling Xue Apr 2026

Gopher: Efficient Dynamic Graph Pattern Mining Via Dag-Driven Execution, Yi Zhang, Yu Huang, Chaoqiang Liu, Haifeng Liu, Juntao Chen, Jingrui Yuan, Jianhui Yue, Xiaofei Liao, Hai Jin, Jingling Xue

Michigan Tech Publications

Graph pattern mining is essential for analyzing dynamic networks, where graphs evolve over time. To accommodate these changes, existing solutions update match sets incrementally, avoiding the need to re-mine the entire graph and achieving significant performance improvements. However, these methods suffer from inefficiencies due to redundant set intersection operations across subgraph instances, causing performance degradation. In this paper, we propose Gopher, a DAG-driven dynamic graph pattern mining system that leverages computation locality for enhanced performance. Gopher uses DAGs to represent set operations, identifying and merging common subexpressions at compile time. This reduces redundant computations during runtime. To maximize performance, we …

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Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir Apr 2026

Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir

All Works

Background: Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Westerncentric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. Objectives: To address this cultural bias problem, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Methods: Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes …

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Reminders App Execution, Alexander Gardner Apr 2026

Reminders App Execution, Alexander Gardner

Harrisburg University Other Works

This poster highlights the creation of an Android mobile application for reminders. This app is titled It's Time, Remind! Developed with the Flutter SDK and SQLite for the local database. Firebase Authentication was utilized for user account registration, login/ logout, and profile management. Users can additionally choose to use the app without an account. Users are able to view, create, and delete reminders and notes. Reminders also notify at specified times.

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The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman Apr 2026

The Role Of Human-Centered Artificial Intelligence In Crewed Mars Missions, Jordan Neuman

Student Publications and Presentations

The suggestion of implementing Artificial Intelligence (AI) in crewed missions to the Martian surface can revolutionize mission operations, psychological support, and ethical decision‑making under challenging conditions. This study is a structured, qualitative literature review of current AI research for the future of human space exploration. It uses publicly available peer‑reviewed journal articles and NASA/ESA technical reports on AI for Mars missions. Using Perplexity, the researcher first prompted the AI tool to identify recurring topics across these sources and generate themes. The researcher independently reviewed all sources to confirm and refine those themes. Three themes—mission autonomy, local resource utilization and life‑support, …

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Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak Apr 2026

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

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Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre Apr 2026

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

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Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu Apr 2026

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu

C-Day Computing Showcase

NeuroVision presents a unified framework for mapping electroencephalography (EEG) signals to language and visual meaning. Extracting semantic information from EEG remains a fundamental challenge due to its low signal-to-noise ratio, high dimensionality, and inter-subject variability. To address these challenges, we propose a multimodal representation learning framework that aligns EEG signals with both textual and visual embeddings through temporal modeling, spatial brain-region decomposition, and contrastive learning. The framework integrates self-supervised pretraining with supervised multimodal alignment to learn robust and transferable representations. Experimental results demonstrate BLEU-1 of 0.1106 and ROUGE-1 of 0.1493 for EEG-to-text generation, alongside a 52% improvement in retrieval performance …

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Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg Apr 2026

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

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Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra Apr 2026

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra

C-Day Computing Showcase

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that …

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Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed Apr 2026

Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed

C-Day Computing Showcase

Generative AI (GenAI) is increasingly used in pharmacy for drug information and decision support, yet accuracy remains variable. We systematically reviewed studies that reported full prompts and model responses to evaluate correctness across pharmacy‑relevant tasks. GenAI performed well on basic drug facts but was inconsistent for patient-specific recommendations and interaction checking, with occasional hallucinations. Findings support cautious, supplementary use in pharmacy practice and education.

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Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs Apr 2026

Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs

C-Day Computing Showcase

Automated dementia detection from speech offers a scalable approach to cognitive screening, but its reliability depends on rigorous experimental design. In this work, we reconstructed a Wav2Vec2-based dementia classification pipeline and identified critical methodological flaws, including speaker leakage, nondeterministic preprocessing, and invalid test partitions. We rebuilt the dataset using strict speaker-level separation, deterministic segmentation, and validation checks to ensure reproducibility. The corrected baseline achieved an accuracy of 44.74 percent and macro F1 score of 0.4439, reflecting a more realistic performance estimate than prior inflated results. This work establishes a scientifically valid foundation for evaluating augmentation strategies such as SpecAugment in …

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Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang Apr 2026

Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang

C-Day Computing Showcase

Temporal Graph Neural Networks (TGNNs) have reported near-perfect accuracy in Network Intrusion Detection (NID). However, this research reveals these results are often artifacts of dataset flaws rather than genuine model capability. Through a systematic audit of five benchmark datasets, we identify critical issues: node identity leakage, feature extraction artifacts, train/test contamination, and temporal sparsity. We demonstrate that models often learn to recognize specific attacker IP addresses instead of generalizing attack behavior. We propose a standardized evaluation framework featuring leakage-aware relabeling and attack-aware chronological splitting to provide a more reliable basis for future TGNN-NID research.

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Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi Apr 2026

Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi

C-Day Computing Showcase

This project is an interactive 2D educational boating safety game developed in Unity to teach students essential water navigation and life jacket safety practices in an engaging and immersive format. Designed in collaboration with a real-world sponsor, the game simulates a dynamic boating environment where players navigate obstacles, identify hazards, and make safety decisions under time constraints. The experience integrates instructional modules, guided character narration, and a final quiz phase that reinforces knowledge through immediate feedback, scoring, and achievement-based rewards. Players learn critical concepts such as proper life jacket fit, safe boating procedures, hazard identification, and shallow water awareness. The …

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Uc-164-215 Hootnest: Ai-Powered Ksu Student Assistant, Tabitha Washington, Aspen Steele Apr 2026

Uc-164-215 Hootnest: Ai-Powered Ksu Student Assistant, Tabitha Washington, Aspen Steele

C-Day Computing Showcase

HootNest helps prospective Kennesaw State students get clear, reliable answers about college life. It is designed for students who may not have easy access to counselors, mentors, or campus visits. The chatbot allows students to ask the chatbot anything they need to know.

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Ur-171-118 Aidflow: A Predictive Financial Aid Transparency System For Students, Chaathurya Nakkana Apr 2026

Ur-171-118 Aidflow: A Predictive Financial Aid Transparency System For Students, Chaathurya Nakkana

C-Day Computing Showcase

Students frequently experience delays and confusion regarding financial aid refunds due to unclear system statuses and lack of communication. This project introduces AidFlow, a predictive financial aid transparency system that translates complex financial data into clear explanations, predicts refund timelines, and provides actionable guidance. A rule-based model and system pipeline were developed to simulate real-world scenarios and improve student understanding and decision-making.

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Grm-153-198 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur- Rahman, Soarov Borty, Gowtham Ankolu Apr 2026

Grm-153-198 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur- Rahman, Soarov Borty, Gowtham Ankolu

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) are irreversible and degenerative neurological conditions that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

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Inc3vits Model: A Hybrid Architecture To Accelerate And Reduce Complexity For The Deepvariant Model For Variant Calling, Mustafa Al-Saffar, Sura Z. Al Rashid Apr 2026

Inc3vits Model: A Hybrid Architecture To Accelerate And Reduce Complexity For The Deepvariant Model For Variant Calling, Mustafa Al-Saffar, Sura Z. Al Rashid

Karbala International Journal of Modern Science

Deep learning has revolutionized genomic variant calling, yet the computational cost of current systems continues to limit scalability. We present a controlled efficiency study of DeepVariant-style pileup architectures under identical training and inference conditions, comparing architectural downsizing with a hybrid CNN–local attention design. Inc3ViTs pairs a streamlined InceptionV3 stem with a lightweight local attention head based on patch tokenization and windowed self-attention, enabling a direct comparison with a CNN-only reduced Inception baseline. Across whole-genome and whole-exome short-read datasets, Inc3ViTs reduces training time by ~40–50% and reduces inference runtime relative to the original DeepVariant. The CNN-only baseline indicates that most speedups …

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Designing, Integrating, And Evaluating Blockchain Assignments In The Undergraduate Computer Science Program, Karol Lejmbach Apr 2026

Designing, Integrating, And Evaluating Blockchain Assignments In The Undergraduate Computer Science Program, Karol Lejmbach

Dissertations (1934 -)

Blockchains as a technology rely heavily on a wide range of computer science topics. These topics include: algorithms, data structures, network communication, cryptography and many more. These topics align nicely with topics often covered in undergraduate computer science course. Blockchain technology offers an interesting opportunity for computer science educa- tors. Blockchains rely heavily on a wide range of computer science topics. These topics include: cryptography, networking, data structures, and algorithms. Many of these top- ics are explored in undergraduate computer science programs either in core or elective classes. This offers an opportunity for teaching students about emerging technologies, such as …

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Strategic Talent Management In Era Of Intelligence And Digitalization: Current Situation And Path Exploration, Chaoying Tang, Chunyuan Wang Apr 2026

Strategic Talent Management In Era Of Intelligence And Digitalization: Current Situation And Path Exploration, Chaoying Tang, Chunyuan Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The report of the 20th National Congress of the Communist Party of China emphasized “speed up efforts to build a contingent of personnel with expertise of strategic importance” to support China’s pursuit of high-level technological self-reliance and its goal of becoming a world-leading power in science and technology. The deep integration of digital and intelligent technologies is fundamentally transforming talent management paradigms and innovation processes, posing new demands on the knowledge structure, competency models, and management approaches of strategic talents. The study explores the concepts of strategic talent management in the digital intelligent era, establishes its logical framework, analyzes current …

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Northern Rainstorm Belt And Response Strategies, Jianping Huang, Xiaodan Guan, Xiaohuang Liu, Xiaohan Shen, Xiaojie Liu Apr 2026

Northern Rainstorm Belt And Response Strategies, Jianping Huang, Xiaodan Guan, Xiaohuang Liu, Xiaohan Shen, Xiaojie Liu

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the summer of 2025, the Northern Rainstorm Belt stretching from eastern Northwest China to the Northeast region exhibited a significant increase in summer precipitation and rainfall frequency over northern China. Cities in the north were frequently struck by extreme rainstorms, resulting in prominent disasters such as “floods in drylands and urban waterlogging”. Northern China spans a vast territory, serving not only as a major grain-producing area and a densely populated region but also as an ecological security barrier that connects the farming-pastoral ecotone and the Loess Plateau. Its stability has nationwide implications. Therefore, comprehensively enhancing flood control capacity, strengthening …

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Fairhiveframes-1k: A Public Fair Dataset Of 1265 Annotated Hive Frame Images With Preliminary Yolov8 And Yolov11 Baselines, Vladimir Kulyukin, Reagan Hill, Aleksey Kulyukin Apr 2026

Fairhiveframes-1k: A Public Fair Dataset Of 1265 Annotated Hive Frame Images With Preliminary Yolov8 And Yolov11 Baselines, Vladimir Kulyukin, Reagan Hill, Aleksey Kulyukin

Computer Science Faculty and Staff Publications

In precision apiculture, the portable digital camera is a cost-effective sensor for capturing hive images or videos used to quantify different colony variables. Openly accessible, well-annotated, interoperable cell-level image datasets are still the exception rather than the norm. This shortage constitutes a major barrier to AI-driven approaches aimed at automating image-based comb analysis. In this article, we present FAIRHiveFrames-1K, a publicly available dataset of 1265 annotated hive frame images (1920 × 1080 PNG) designed to facilitate research in AI-intensive image-based comb analysis automation. The dataset, derived from a 2013–2022 U.S. Department of Agriculture–Agricultural Research Service multi-sensor research reservoir, includes 124,669 …

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Phishing Restraint: University Simulated Phishing Campaigns, Alexander M. Abou Khir Apr 2026

Phishing Restraint: University Simulated Phishing Campaigns, Alexander M. Abou Khir

Cybersecurity Undergraduate Research Showcase

Universities face heightened vulnerability to phishing attacks due to their open information-sharing culture and diverse user populations. This study examines how phishing exploits human factors within campus environments and evaluates three major training strategies: embedded phishing, microlearning, and role-based instruction to understand their individual and combined effectiveness. I explored studies that implement these strategies in pairs and use the strategies alone, identified trends in susceptibility reduction, behavioral reinforcement, and contextual relevance. I suggest that, while each method independently improves user awareness, multiple approaches offer stronger, more adaptable protection by addressing both psychological triggers and role-specific risks. The paper contributes a …

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Hijacking The Prompt: A Survey Of Prompt Injection Attacks, Detection, And Defense In Large Language Models, Edward J. Griggs Apr 2026

Hijacking The Prompt: A Survey Of Prompt Injection Attacks, Detection, And Defense In Large Language Models, Edward J. Griggs

Cybersecurity Undergraduate Research Showcase

Prompt injection attacks, ranked the number-one vulnerability in AI systems by OWASP's 2025 Top 10 for Large Language Model Applications, remain largely unsolved, and this survey examines why. As large language models (LLMs) are deployed across enterprise workflows, agentic systems, and consumer tools, their fundamental inability to distinguish trusted instructions from untrusted user data has created a persistent and expanding attack surface. This paper presents a structured taxonomy of prompt injection attack vectors, including direct injection, indirect injection, multimodal attacks, tool and agent exploitation, hybrid chained techniques, and autonomous propagating threats. These vectors are mapped across five impact categories (data …

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