Computer Sciences Commons^™

Editor's Introduction, Amy Mecklenburg-Faenger

Journal of the National Collegiate Honors Council Online Archive

Editorial for Journal of the National Collegiate Honors Council (2025) 26(1), special issue on Forum on AI and Honors.

Ai Responsibilization: Shifting The Burden Of Academic Integrity, Daniel A. Cryer

Journal of the National Collegiate Honors Council Online Archive

Because AI text generators like ChatGPT give students unprece-dented power to outsource their work, concerns about academic integrity are escalating among instructors. This essay suggests that the proliferation of generative artificial intelligence in teaching and learning dramatically shifts the burden of academic integrity, typically shared between teachers and students, onto students. The concept of responsibilization, a defining feature of neoliberal societies in which individuals become responsible for costs and tasks once shouldered collectively, is a useful lens through which to view this new reality. Rather than policing students’ work, educators should recognize the new responsibilities conferred onto students by learning …

Pull Up A Chair, Deep Blue: Ai In Honors Education, Betsy Greenleaf Yarrison

Journal of the National Collegiate Honors Council Online Archive

Generative AI is the latest in a succession of technologies that allow us to do with machines what we used to have to do by hand. This essay argues that the elements of teaching in honors that can be automated probably should be and that it is the role of honors faculty to teach students how to distinguish superior thought from mediocre thought, good questions from great ones, and solid supporting evidence from weak or biased counterparts. Large learning models (LLMs) only collate what is already known and cannot teach thinking. As an information delivery system, AI can narrow the …

Teaching Ai Literacy Through Science Studies, Rhetoric, And Ethical Reasoning, Michael J. Klein, Philip L. Frana

Journal of the National Collegiate Honors Council Online Archive

Building on the idea of productive troublemaking, this essay presents a team-taught interdisciplinary honors course that integrates science and technology studies, rhetorical analysis, ethical reasoning, and artificial intelligence policy. Rather than framing AI as a threat, this course invites honors students to experiment with AI technologies and develop competencies by analyzing AI as a social and subjectivity-shaping phenomenon—writing policy briefs, producing rhetorical analyses of science fiction, and completing self-paced AI literacy modules. Honors education is uniquely positioned to model responsible and human-centered uses of intelligent systems, thereby cultivating graduates who can both use and critically interrogate the AI tools that …

News From The Front: How To Win The Ai War, Christine Haverington

Journal of the National Collegiate Honors Council Online Archive

While artificial intelligence is currently and justifiably a hot topic among scholars and university administrators, students are way ahead of the curve in terms of its use and application. Calling for educators to stop trying to catch AI “cheaters,” this essay provides evidence from honors and other classroom observations, student research on peer and faculty usage and attitudes, course evaluations, and external sources to demonstrate how and why generative AI can be creatively and effectively incorporated into teaching. Toward this end, practical pedagogical strategies are shared describing teaching modalities and innovative curricular design, avoiding the cognitive degradation of students, and …

Another “Tone Test” Moment: Authenticity, Ai, And The Admission Essay, Peter Tschirhart

Journal of the National Collegiate Honors Council Online Archive

Debates about “authenticity” are not new but cyclical, and insights from music history and performance studies can illuminate how we evaluate student work in an age of machine collaboration. At the turn of the twentieth century, Edison’s “tone tests” blurred the line between human and machine by staging performances in which audiences were challenged to distinguish live singers from phonographic recordings. These events inaugurated a century-long debate about authenticity in music, one that resonates strongly today as educators confront new challenges posed by large language models (LLMs). Drawing on Auslander’s (2023) account of liveness, authenticity in writing—like authenticity in music—can …

Honoring Intellectual Risk-Taking: A Dialogue, Julie Bowman, Alexis Teagarden

Journal of the National Collegiate Honors Council Online Archive

Presented in the form of a Socratic dialogue, this piece considers what honors courses should strive to teach. Bowman, an experienced instructor of both non-honors and honors classes, notes that while universities’ honors colleges prize intellectual curiosity, her honors students might not. Teagarden, the other interlocutor and a writing program administrator, wonders whether curiosity is a sufficient end goal for honors or any teaching. The speakers turn to whether curiosity or courage is the more important virtue to instill, explore a classical difference between courage and audacity, and then discuss whether and how courage could be taught.

Generative Ai And The Honors Thesis: A Rhetorical Framework For Gai Policy, Pedagogy, And Equity, Sean Chadwick

Journal of the National Collegiate Honors Council Online Archive

This article offers a rhetorical framework for understanding how honors students engage with the capstone thesis following the emergence of generative AI (GAI) tools. Author reviews the nature of the honors thesis and analyzes some rhetorical models for thinking about GAI and literacy before offering a heuristic framework identifying four interdependent skill categories—writing, social, executive, and subject matter—that shape students’ thesis work. Drawing on findings from an interview study, this framework clarifies how GAI tools interface with existing thesis practices and pain points, allowing honors practitioners to better articulate our values, evaluate use cases, and craft GAI-informed policy. As a …

A Study Of Conceptual Primitive Elimination: Embedding Ingest Into Ptrans, Jamie C. Macbeth, Alexis Kilayko

Computer Science: Faculty Publications

In cognitive systems and cognitive linguistics, primitive decomposition systems attempt to explain cognitive phenomena by breaking things down into conceptual building blocks and provide rich and flexible representations for systems. A prime example is the Schank–Minsky Conceptual Dependency Trans-frames system, which maintains a commitment to keeping the number of primitives small and allowing them to be combined in complex ways in representing meaning, knowledge, and dynamic episodic memory. Motivated by the desire to keep the set of primitives small, this paper describes an effort to eliminate the Conceptual Dependency INGEST primitive and reconstitute its uses through combinations of the CD …

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

Evaluating Open-Source Machine Learning Ransomware Detection Techniques, Sydney Steckart

Master's Theses (2009 -)

Ransomware remains one of the most disruptive and damaging form of cyber threat affecting both larger and smaller organizations. It is becoming increasingly more important to find tools to mitigate this threat, and one route that is much more prevalent is using machine learning techniques for detection. This work explores open-source work geared towards ransomware detection with the aid of artificial intelligence as a means to provide a cost-effective alternative for organizations that may not have the funding to purchase the commercial solutions. Many tools and malware repositories were investigated, and one was further analyzed with a dataset specifically catered …

Evaluating The Effectiveness Of Llm-Generated Phishing Campaigns, Nathan Sniegowski

Master's Theses (2009 -)

This paper investigates the effectiveness and security implications of Large Language Models (LLMs) in phishing campaigns. Existing research has explored using LLMs and AI for enterprise security tools and automating phishing email processes. However, few studies evaluated the effectiveness of fine-tuned LLMs in generating phishing email content and measuring real-world user interaction. This research used LLMs to produce the body content of phishing emails with phishing links manually inserted post-generation. The researcher performed three core experiments: (1) evaluating the success rate of jailbreaking three commercial LLMs to generate phishing content, (2) analyzing ChatGPT-4o mini’s phishing emails based on institution-specific context …

One-For-All: Towards Universal Domain Translation With A Single Stylegan, Yong Du, Jiahui Zhan, Xinzhe Li, Junyu Dong, Sheng Chen, Ming-Hsuan Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P …

A Formal Simulation Model For Discrete Rate Simulation, Thomas J. Tracey

Electrical & Computer Engineering Theses & Dissertations

Simulation is an essential tool for virtualizing systems by creating a representative model of real or hypothetical systems and observing how they change over time. Two predominant simulation paradigms include Discrete Event Simulation (DES) and Continuous Simulation, which both have their strengths and weaknesses. DES does not handle continuous state variables, while continuous simulation handles continuous state variables but encounters errors where these variables have discrete changes in their behavior. This difficulty between the two predominant simulation paradigms prompted the creation of a new simulation paradigm to cover this gap: Discrete Rate Simulation (DRS). DRS as a simulation paradigm focuses …

Mashed Potato Gravy Boat And Cream Cheese Fish: Modifying A 3d Printer To Print With Unconventional Materials, Aahanaa Tibrewal

Mathematics, Statistics, and Computer Science Honors Projects

3D printing is growing beyond plastics into fields like food and construction, bringing rapid additive manufacturing to various industries and consumers. However, high costs and the need for specialized knowledge limit access for many. My project aimed to modify a low-cost 3D printer to print with paste-like materials using commonly available parts and simple processes. I tested the modification with clay, mashed potatoes, and cream cheese, and found that it successfully worked with all three. This modification has three key benefits: it allows users to print with unconventional materials, helps researchers create low-cost proof of concepts, and contributes to the …

Assessing Readiness For Transformation From Rulebased To Ai-Based Chatbot In Uae Healthcare: A Case Study Of A Rehabilitation Hospital In Abu Dhabi, Mubarak Alketbi

Theses

This study investigates the readiness for transforming rule-based chatbots to AI-based chatbots in UAE healthcare, examining a rehabilitation hospital in Abu Dhabi through quantitative research involving healthcare professionals (N=96) and technical analysis. Findings revealed positive perceptions of the current system alongside enhancement opportunities through AI capabilities, with perceived usefulness strongly correlating with behavioural intention, high service quality ratings for empathy and responsiveness, midcareer professionals demonstrating the highest AI acceptance levels, and system integration identified as the highest priority implementation area.

The research contributes to healthcare technology transformation knowledge in the UAE by providing a structured implementation framework addressing technical requirements, …

Enmob: Unveil The Behavior With Multi-Flow Analysis Of Encrypted App Traffic, Mengmeng Ge, Ruitao Feng, Likun Liu, Xiangzhan Yu, Sachidananda Vinay, Xiaofei Xie, Yang Liu

Research Collection School Of Computing and Information Systems

In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users’ privacy. However, this encryption, while fortifying privacy, introduces challenges to security by hindering the effective management of network applications within encrypted data streams. Conventional detection methods for encrypted application traffic, relying heavily on statistical metrics like payload, packet size, and distribution, are constrained to single traffic flows, often yielding results of limited specificity. To address this limitation, our paper introduces an innovative approach that elucidates the multi-flow nature of application behavior traffic …

Democratic Training Against Universal Adversarial Perturbations, Bing Sun, Jun Sun, Wei Zhao

Research Collection School Of Computing and Information Systems

Despite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an inputagnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation deceives the model on a range of clean inputs without requiring input-specific optimization, which makes it particularly threatening. In this work, we observe that universal adversarial perturbations usually lead to abnormal entropy spectrum in hidden layers, which suggests that the prediction is dominated by a small number of “feature” in such cases (rather than democratically by many …

Towards Understanding Why Fixmatch Generalizes Better Than Supervised Learning, Jingyang Li, Jiachun Pan, Vincent Tan, Kim-Chuan Toh, Pan Zhou

Research Collection School Of Computing and Information Systems

Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize better than SL on DNNs. In this work, we present the first theoretical justification for the enhanced test accuracy observed in FixMatch-like SSL applied to DNNs by taking convolutional neural networks (CNNs) on classification tasks as an example. Our theoretical analysis reveals that the semantic feature learning processes in FixMatch and SL are rather different. In particular, FixMatch …

Building Trustable Methods For Group Recommendations: Advancing Fairness And Robustness Across Domains, Siva Likitha Valluru

Theses and Dissertations

On the internet, where the number of available choices for information is exponentially growing, there is a need to prioritize and deliver relevant results to users efficiently, on demand. Recommendation systems (RSs) address that need by searching through and filtering large amounts of dynamically generated information and providing users with recommendations tailored to them. These systems have primarily focused on (1) single-user models, where recommendations are tailored towards a specific individual, or (2) single-item models, where items are recommended based on a broader appeal to users and similarities in item metadata, in the past. In many real-world scenarios, however, recommendation …

Characterising Reproducibility Debt In Scientific Software: A Systematic Literature Review, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin

Research Collection School Of Computing and Information Systems

Context: In scientific software, the inability to reproduce results is often due to technical issues and challenges in recreating the full computational workflow from the original analysis. We conceptualise this problem as Reproducibility Debt (RpD). Much research has been performed to propose solutions to tackle these issues across various computational science disciplines. It is essential to identify and accumulate existing knowledge on reproducibility issues and state-of-the-art solutions so as to provide researchers and practitioners with information that enables further research activities and RpD management in practice. Objective: In the context of scientific software, we aim to characterise RpD by providing …

Pearl: Towards Permutation-Resilient Llms, Liang Chen, Li Shen, Yang Deng, Xiaoyan Zhao, Bin Liang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

The in-context learning (ICL) capability of large language models (LLMs) enables them to perform challenging tasks using provided demonstrations. However, ICL is highly sensitive to the ordering of demonstrations, leading to instability in predictions. This paper shows that this vulnerability can be exploited to design a natural attack - difficult for model providers to detect - that achieves nearly 80% success rate on LLaMA-3 by simply permuting the demonstrations. Existing mitigation methods primarily rely on post-processing and fail to enhance the model's inherent robustness to input permutations, raising concerns about safety and reliability of LLMs. To address this issue, we …

On Unraveling Student Resilience And Academic Performance In Higher Education, Aldy Gunawan, Ee-Peng Lim, Audrey Tedja Widjaja, William Tov, James Foo, Lieven Lode E. Demeester

Research Collection School Of Computing and Information Systems

The transition period from pre-tertiary to higher education levels is critical. We explore the role of resilience by conducting a survey to investigate students’ resilience and the relationship with overall academic performance, learning experience, and well-being. This effort is part of an initiative to develop strategies for better student engagement in the academic program, enhance their resilience, and prepare them for a competitive job market. We conclude that (i) high-resilience students are associated with better life satisfaction and are likely to perform well academically, (ii) a favorable learning environment supports students to study and perform well in the university, and …

Configx: Modular Configuration For Evolutionary Algorithms Via Multitask Reinforcement Learning, Hongshu Guo, Zeyuan Ma, Jiacheng Chen, Yining Ma, Zhiguang Cao, Xinglin Zhang, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enhancing their performance and adaptability across various BBO instances. However, they are often tailored to a specific EA, which limits their generalizability and necessitates retraining or redesigns for different EAs and optimization problems. To address this limitation, we introduce ConfigX, a new paradigm of the MetaBBO framework that is capable of learning a universal configuration agent (model) for boosting diverse EAs. To achieve so, our ConfigX first leverages a novel modularization system that enables the flexible combination of …

Bootstrapping Language Models With Dpo Implicit Rewards, Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin

Research Collection School Of Computing and Information Systems

Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used …

Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …

Towards Real-World Unsupervised Anomaly Detection For Images, Zhonghang Liu

Dissertations and Theses Collection (Open Access)

In the era of big data, data quality plays a critical role in computer vision, where the reliability and purity of training images are essential for optimal performance. When training models such as image classifiers and object detectors, the quality of the training data directly influences the success of the model. In other words, if the training dataset is contaminated, the model’s performance might accordingly decrease.

To address this challenge, unsupervised anomaly detection (UAD) has become an attractive research area. By automatically removing these anomalous data points, UAD can help improve the accuracy and robustness of machine learning models in …

Temporal Relational Graph Convolutional Networks For Financial Applications, Brindha Priyadarshini Jeyaraman

Dissertations and Theses Collection (Open Access)

The financial industry operates within a highly dynamic and interconnected ecosystem, presenting unique challenges for predictive modeling and decision-making. Accurately forecasting financial performance, assessing credit risk, detecting fraud, and ensuring compliance require methodologies that can capture complex temporal, relational, and contextual dependencies within financial data. This thesis investigates the use of Temporal Relational Graph Convolutional Networks (TRGCNs) combined with financial knowledge graphs (FKGs) to address these challenges and enable advanced analytics in the financial domain. We introduce FintechKG, a financial knowledge graph constructed through a threedimensional information extraction process, incorporating entities, temporal dimensions, and domain-specific financial relationships. A TRGCN-based framework …

Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel

Faculty Publications

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …

Ask An Hci Research Ethicist: A Recurring Column On Research Ethics Challenges, Casey Fiesler, Jessica Vitak, Michael Zimmer

Computer Science Faculty Research and Publications

Created in 2016, the SIGCHI Research Ethics Committee advises SIGCHI conferences and communities on ethical issues that arise in the course of conducting research. The committee also provides guidance and feedback on research ethics issues that arise during the peer review process; as a result, we have a broad sense of novel and persisting open questions within our community. Through this recurring column, we will continue this work by raising awareness and increasing discussion of ethics in the context of conducting HCI research.