Digital Commons Network^™

Ai-Generated Phishing Attacks: Emerging Threats And Detection Strategies For Security Operations Centers, Bahzad Khalid

Journal of Cybersecurity Education, Research and Practice

Artificial intelligence (AI) is transforming cybersecurity by strengthening defensive capabilities while simultaneously enabling more sophisticated phishing attacks. AI generated phishing emails increasingly evade traditional detection methods through improved grammar, contextual relevance and realistic communication styles creating significant challenges for Security Operations Centers (SOCs). This study investigates the effectiveness of current email security systems in detecting AI generated phishing emails compared with traditional phishing emails. A mixed method experimental approach was employed using controlled Gmail and Outlook environments to evaluate the detection and classification of multiple phishing scenarios. The results demonstrate that several AI generated phishing emails successfully bypassed initial security …

A Narrative-Driven, Socratic Instructional Framework For Teaching Adversarial Cognition In Cybersecurity, Joshua K. Goossen

Journal of Cybersecurity Education, Research and Practice

The Socratic method has demonstrated, in studies across law, medicine, and nursing, that dialectic structured questioning improves expert reasoning. Cybersecurity currently teaches tools and techniques but lacks Socratic dialogic leaving graduates underprepared for novel scenarios requiring cross-domain reasoning. Because law, medicine and nursing cultivate similar reasoning as security there is principled reason to expect reasoning benefits could transfer to security students. This paper argues that the correspondence warrants investigation. Investigation requires a named, teachable reasoning target analogous to clinical reasoning and we specify that target as adversarial cognition, a framework integrating adversary features, system architecture, implementation logic, governance structure, human …

Reading, Writing, Speaking & Listening: Leveraging Ai Tools For Effective Pre-Service Teacher Education, Brittany E. Kiser, Kelly A. Burlison

College of Education Faculty Publications & Presentations

This interactive roundtable examines how AI tools can enhance pre-service teacher education and support multilingual learners in K–6 classrooms. Participants will explore practical applications for lesson planning, language scaffolding, differentiation, and assessment through real-world ESOL teaching experiences. Discussion will focus on aligning WIDA standards with content instruction and integrating evidence-based ESOL strategies across reading, writing, speaking, and listening. Attendees will leave with actionable approaches for the effective and ethical use of AI to support ELLs.

Buffeted By Change, Researchers Seek Continuity And Investment: Results From The Ithaka S+R Us Researcher Survey, Chelsea Mccracken, Dylan Ruediger, Ruby Macdougall

Copyright, Fair Use, Scholarly Communication, etc.

Executive summary

The research enterprise in the United States, established after the second world war, is experiencing massive disruption. US colleges and universities are grappling with federal and state disinvestment and intervention at a time when AI and other technologies are reshaping how research is conducted and its findings are communicated. As a result, researchers at US higher education institutions are operating in an unstable environment. Against this backdrop, the findings from our survey of 4,003 researchers, fielded in late 2025, demonstrate that they remain committed to carrying out essential work and that they are doing so in innovative ways—through …

Complementary Global–Local Feature Fusion And Ensemble Refinement For Facial-Expression Recognition On Fer2013, H. M. Shahzad, Hassan A. Ahmed

Business Faculty Publications

Facial-expression recognition (FER) on FER2013 remains challenging because of low-resolution images, class imbalance, and label ambiguity. This study presents a global–local feature-fusion framework that integrates complementary representations with validation-based ensemble refinement. A frozen DINOv2 ViT-Base captures global facial semantics, while EfficientNetB3 extracts complementary local texture features. Their fused representation is used for seven-class facial-expression classification. The classification head is first trained with targeted feature-space SMOTE, and the EfficientNetB3 branch is then partially fine-tuned. Five-view test-time augmentation (TTA) is further incorporated at inference, together with an independently trained ConvNeXt-Tiny branch to provide additional architectural diversity. Ensemble weights are selected using a …

Progress Towards An Analog Of The Darboux-Griffiths Converse Of Abel's Theorem In Characteristic P, John B. Little

Mathematics and Computer Science Department Faculty Scholarship

In this working paper, we report our recent efforts concerning a possible characteristic p analog of the so-called converse of Abel's theorem discussed by Darboux and Griffiths.  The characteristic zero version is used in one proof of the Lie-Wirtinger theorem on double translation manifolds and also in related aspects of the geometry of webs.  In informal terms, our results indicate that, apart from some isolated counterexamples, formal power series Abel relations over an algebraically closed field of characteristic p essentially only arise in the (generalized) Jacobians of algebraic curves.  The ultimate goal is to complete thevwork started in the author's …

Designing For Who Actually Shows Up: A Signal Framework For Online Adult Learners In Cybersecurity And Information Technology, Chad Whistle, Mayyada Al-Hammoshi

Journal of Cybersecurity Education, Research and Practice

Online adult learners pursuing cybersecurity and information technology credentials represent one of the fastest-growing student populations in American higher education, yet the frameworks institutions use to support their success were not designed for them. This population, disproportionately drawn from the 41.9 million Americans who hold some college credit but no credential, arrives workforce-embedded, time-constrained, and skeptical of institutional systems that previously failed to serve them. Existing persistence models grounded in traditional student integration theory inadequately account for the behavioral patterns, motivational structures, and credential expectations that define this learner. This paper proposes the SIGNAL Framework (Skills-based credential architecture, Integrated AI-informed …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

Navigating Text-To-Speech (Tts): Ethical Leadership In The Use Of Generative Ai For Extension, Xue A Dong, Paul A Hill

Journal of Extension

Text-to-speech (TTS) AI technology transforms written content into natural-sounding speech, offering a useful tool to enhance accessibility and inclusivity in Extension work. This article examines the role of TTS AI in bridging communication gaps, particularly for diverse and multilingual communities, and demonstrates the importance of ethical leadership in its adoption. By prioritizing diversity, equity, and inclusion, Extension professionals can leverage TTS AI to foster greater connection and engagement. Practical applications and examples are provided to guide the integration of TTS AI into programs. The article also offers recommendations for experimenting with innovative technologies to improve educational outcomes and increase the …

Interrater Reliability Of Software Optimized Movement Assessment With Traditional Methods And Video-Based Functional Movement Screen Scoring And Compensatory Movement Identification, Joshua Paul Verdillo, Nj Ermina, Tanya Mariel Capilla, Russel James Balane, Evriel Prince Apura, Daryl Reymon Apla-On, Ressyl Love Salvador

Philippine Journal of Physical Therapy

Introduction: The Functional Movement Screen (FMS) is a seven-part movement assessment used to identify injury risks caused by faulty biomechanics. There are three barriers in traditional FMS assessments that could affect the tool’s validity: the subjectivity of human scores that could cause bias, the need for in-person evaluations which limit access for remote patients, and the requirement of specialized training to use the tool, which makes it less accessible. This study investigates the effectiveness of SOMA, an AI-based web application that uses the MediaPipe framework to automatically assess (FMS) performances.

Methods: This study employs a quantitative, cross-sectional, comparative design to …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Radiological And Chemical Safety Assessment Of Drinking Water From Treatment Plants And Rivers In Kut City, Iraq, Ahmed A. Alswaty, Hadi D. Alattabi

Karbala International Journal of Modern Science

Climate change and increasing anthropogenic activities, particularly wastewater discharge into rivers, have raised pollution levels in the water sources of Kut City, necessitating an assessment of the radiological and chemical safety of drinking water at the city's treatment plants. Thirteen water samples were collected, comprising ten treated and three raw water samples from the source rivers. An HPGe detector was used to measure the radionuclides ²¹⁴Bi, ²¹⁴Pb, ²¹²Bi, ²¹²Pb, ⁴⁰K, and ¹³⁷Cs. All measured radionuclides were below the minimum detectable activity (MDA). ICP-OES analysis supported these findings, as U concentrations in most samples were …

Online Multidimensional Multiple Choice Knapsack Path Planner For Uavs, Manisha Wadhwa, Neelima Gupta, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) in safety–critical and time-sensitive applications such as disaster management, battlefield reconnaissance, urban surveillance and infrastructure inspection demands online path planning strategies. A fundamental requirement in such missions is generating flight paths that pass through some pre-specified waypoints while servicing dynamically requested spatio-temporal task points arriving randomly such as capturing aerial imagery. Existing UAV path planning approaches, including A* and its variants, meta-heuristic algorithms, and dynamic programming, find the shortest distance path from source to destination, but they are predominantly offline and assume complete prior environmental knowledge, thus, incur significantly higher computational cost …

Rent-A-Rag: Embedding-Space Watermarks For Auditing Third-Party Rag, Alexandr Goultiaev Tolstokorov, Kyriakos Mouratidis, Javad Dogani, Nikolaos Laoutaris

Research Collection School Of Computing and Information Systems

Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensation. Auditing this misuse is difficult because the operator is non-cooperative, answers are paraphrased by the generator, and one response may combine evidence from many providers. We propose DirBucket, a provider-side semantic watermarking and black-box auditing framework for document-level reuse in multi-provider RAG. DirBucket watermarks documents by meaningpreserving paraphrases whose embeddings are biased toward provider-bucket secret directions, enabling detection from black-box answers while preserving retrieval utility. On a …

Are We Stuck? Modeling And Detecting Deadlocks In Multi-Autonomous Vehicle Systems, Mingfei Cheng, Xiaofei Xie, Lili Quan, Yuan Zhou

Research Collection School Of Computing and Information Systems

Autonomous driving system (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) prior to deployment. As ADSs are increasingly deployed in multi-AV traffic environments, it becomes crucial to assess their cooperative performance, particularly with respect to deadlock, a fundamental liveness issue in concurrent systems that can lead to traffic congestion and prolonged stalling. However, the analysis and testing of ADSs’ cooperative capabilities with respect to deadlock remain largely underexplored. In this work, we present the first systematic study of deadlock in multi-AV systems. We formalize deadlock in autonomous driving using a time-indexed wait-for relation grounded …

Compiling Code Llms Into Lightweight Executables, Jieke Shi, Junda He, Zhou Yang, Chengran Yang, Mykhailo Klymenko, Thong (James) Hoang, Sherry (Xiwei) Xu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have produced many cloud-based tools for software engineering tasks such as code suggestion. Although effective, cloud deployment raises concerns over privacy, latency, and reliance on network connectivity. Running LLMs locally on personal devices such as laptops would address these issues, because it enables offline use and reduces response time. However, local deployment is challenging, since commodity devices lack high-performance accelerators such as GPUs and are constrained by limited memory and compute capacity, which makes it …

User-First Approach To Designing A Library Ai Chatbot, Samantha Seah, Zhenyan Li

Research Collection Library

At SMU Libraries, after a year of experimentation, user testing, and preparation, we have just launched an AI-assisted chatbot to handle Tier 1 questions – will the library be open this weekend, where can I find printers, what workshops does the library offer, etc. While this can be thought of as a technology-forward project, we hope to illustrate how the human elements were essential to elevate the project. Throughout this journey, while learning the ins and outs of new technologies, we placed more emphasis on user-centred service design and we will show how other libraries can do so as well. …

Sex-Based Disparities In Artificial Intelligence For Cardiovascular Disease: A Scoping Review, Maurgan Lee, Tarek Atasi, Michael Mclellan, Yodit Beru, Jason Booza

Population, Patient, Physician and Professionalism Projects

No abstract provided.

State-Aware Fuzzing Of Javascript Engines With Llm-Guided Instrumentation, Wai Kin Wong, Dongwei Xiao, Cheuk Tung Lai, Ping Fan Ke, Shuai Wang

Research Collection School Of Computing and Information Systems

The security of the modern web depends on the correctness of JavaScript (JS) engines, yet these complex systems remain vulnerable to high-impact bugs. A critical limitation of state-of-the-art fuzzers is the coverage plateau: once a fuzzer saturates the control-flow graph, edge coverage loses its ability to guide discovery. Because complex engine behaviors, such as JIT optimization tiers and hidden class transitions, often share identical edge coverage, standard coverage metrics are blind to the distinct internal states required to trigger deep errors. To bridge this gap, we present StateLens, a framework that employs Large Language Models (LLM) to automate the discovery …

Don’T Sweat Interaction Trees Proof-Guided Local Variable Lifting For Interaction Trees, Yiming Lin, Ian Kariniemi, Yao Li

Computer Science Faculty Publications and Presentations

Verifying existing software is hard: Programs are developed in languages not amenable to verification, involve complicated optimizations that obscure the underlying logic, and are gigantic in size. In this paper, we propose a way to ease this pain via a simplification framework that employs interaction trees as a language-agnostic interface. We show that local variable lifting, the technique underlying AutoCorres for the Simpl language, can be generalized to interaction trees via an implementation in Rocq. A key challenge with simplifying interaction trees is that they are highly dynamic structures and we would like our approach to work with mostly uninterpreted …

Chemnetworks: New Capabilities For High-Throughput, Real-Time Chemical Graph Construction And Analysis, Daniel J. Pope, Jackson Elowitt, Bo Zhang, Manish Parashar, Aurora E. Clark

Michigan Tech Publications

A major revision of the ChemNetworks software (originally published in the Journal of Computational Chemistry, 2014, 35, 495–505) is presented. While the original ChemNetworks provided foundational graph construction capabilities for chemical systems, it was limited to simple distance and 3-body angular edge criteria, was not designed for high-performance computing environments or real-time operation alongside running simulations. This release addresses these limitations through three core contributions. First, a recursive Z-matrix-based search algorithm is introduced that enables chemically intuitive, arbitrarily descriptive three-dimensional structure searches, supporting geometric, energetic, and logical criteria. Second, the DataSpaces data staging framework is incorporated as an optional I/O …

Digitizing Land Records For A More Inclusive Future, Md Sharif Hasan Rafi '30

Annual Student Research Poster Session

This project supported the Land Data Digitization for Inclusive Growth Project in Bangladesh by transforming paper-based land records into structured and accessible digital information. The work involved data entry, verification, quality control, standardization, and metadata management using a web-based system. More than 12,500 land records were processed, with 98% passing quality checks. The experience demonstrates how accurate and standardized digital land data can improve transparency, accessibility, government planning, and inclusive land administration while reducing errors and disputes.

Mutation-Based Multi-Agent Test Case Update, Dawei Tian, Jiakun Liu, Yun Peng, Yichen Zhang, Jianlei Chi, Jun Sun, Xiaohong Su

Research Collection School Of Computing and Information Systems

Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test update approaches leverage LLMs to refine test cases via execution feedback and exact-matching context retrieval, prioritizing executability and line coverage but suffering three limitations: (1) neglecting test assertion adequacy, weakening fault detection; (2) relying on coarse line coverage instead of specific uncovered lines/branches; (3) using exact-matching retrieval, which fails for LLM hallucinated queries. To address these, we propose MuMuTestUp, a mutation-guided multi-agent framework with three …

Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair, Qinyan Zhou, Peixin Zhang, Jun Sun, Haonan Zhang, Dongxia Wang

Research Collection School Of Computing and Information Systems

While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explainable framework for overrefusal testing and repair in a black-box setting, where only model inputs and outputs are accessible and internal safety mechanisms remain opaque. DDOR applies delta debugging to localize minimal refusal-triggering fragments (mRTFs) that provide phrase-level, explainable evidence for why a refusal occurs. Conditioned on these mRTFs, DDOR generates diverse, context-rich prompts and performs multi-oracle validation to filter intrinsically …

Characterizing And Repairing Obsolete Android Gui Tests Under Ui Evolution, Shiwen Song, Yiheng Xiong, Wenbo Guo, Manqi Sun, Jiaolong Kong, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Graphical user interface (GUI) tests are widely used in regression testing of mobile applications (apps) tovalidate app behavior from the user’s perspective. However, frequent app evolution, such as UI redesignsand feature updates, often renders existing GUI tests obsolete, even when underlying functionality remainsunchanged. Automatically repairing such tests is critical for maintaining test suites and reducing substantialmanual effort. Despite the practical importance, there is still a lack of a systematic understanding of thecharacteristics of obsolete GUI tests and a publicly available benchmark to support their study. To fill thisgap, we construct a benchmark comprising 736 obsolete GUI tests collected from 36 …

Probguard: Proactive Runtime Monitoring For Llm Agent Safety Via Probabilistic Prediction, Haoyu Wang, Christopher M. Poskitt, Jiali Wei, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents increasingly operate across domains such as robotics, virtual assistants, and web automation. However, their stochastic decision-making introduces safety risks that are difficult to anticipate during execution. Existing runtime monitoring frameworks, such as AgentSpec, primarily rely on reactive safety rules that detect violations only when unsafe behavior is imminent or has already occurred, limiting their ability to handle long-horizon dependencies. We present ProbGuard, a proactive runtime monitoring framework for LLM agents that anticipates safety violations through probabilistic risk prediction. ProbGuard abstracts agent executions into symbolic states and learns a Discrete-Time Markov Chain (DTMC) from execution traces …

Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems, Yulong Lyu, Ruiqi Hong, Jiawan Wang, Jun Sun, Lei Bu

Research Collection School Of Computing and Information Systems

As autonomous driving systems (ADS) are increasingly deployed in real-world environments, discovering diverse unsafe driving scenarios remains a fundamental yet difficult problem. Existing scenario generation and testing approaches often rely on black-box exploration or externally-observed heuristic feedback, which struggle to effectively guide the search toward high-risk scenarios induced by complex decision-making behaviors. A key difficulty stems from the fact that unsafe behaviors in ADS often arise from internal decision-making logic, which can induce structured and discontinuous responses that are hard to effectively explore using purely black-box guidance. Consequently, current tools tend to repeatedly discover a narrow set of similar unsafe …

Chatgpt, Can You Generate Multiple-Choice Questions For An Undergraduate Computing Course? An Evaluation Of Its Effectiveness Using Classical Test Theory, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Educators are increasingly adopting large language models such as ChatGPT to generate multiple-choice questions (MCQs), yet empirical evidence on the quality of these items in computing education assessments remains limited. This study compares 30 ChatGPT-generated and 30 manually crafted items from an undergraduate software design course, and evaluates item difficulty, discrimination, and distractor quality using Classical Test Theory. We triangulated two evaluations using within-subjects and between-subjects designs. Our quantitative findings show that all three metrics in both evaluations point in the same direction. ChatGPT-generated items are easier, less discriminating, and exhibit poorer distractor quality than manually crafted items. Distractor quality …

Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc

Journal of Global Hospitality and Tourism

This study investigates consumer responses to low-anthropomorphic service robots in restaurant front of-house roles using the AIDUA (Artificially Intelligent Device Use Acceptance). Data from 1,268  participants were analysed using PLS-SEM. The results revealed that social impact and  anthropomorphism significantly influenced both performance and effort expectancy, while hedonic  motivation influenced only performance expectancy. Performance expectancy strongly influenced  emotions, which in turn significantly influenced both the willingness to use service robots and objections  to their use. However, effort expectancy did not significantly influence emotions. The findings validate the  AIDUA model in this context and offer practical insights for robot design and implementation.

Domain Knowledge-Informed Machine Learning Model To Study Weather-Induced Flight Delays, Gurvir Bawa

Doctoral Dissertations and Master's Theses

Weather-induced Traffic Management Initiatives (TMIs) occur rarely relative to normal air traffic operations. This scarcity produces severe class imbalance that limits predictive models of flight delay. To address it, this dissertation leverages a physics- and domain- knowledge informed Variational Autoencoder (VAE), conditioned on airport identity, that generates physically plausible synthetic weather events for underrepresented TMI categories. The fidelity of the model is assessed with a calibrated discrepancy measure disclosed along- side the results rather than described in free-form terms. A multitask Graph Neural Network (GNN) is then trained on the augmented dataset to jointly classify airport-level TMI status and predict …