Artificial Intelligence and Robotics Commons^™

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 …

The Impact Of Ai Usage On Employee Work Outcomes: The Mediating Roles Of Personal Control And Job Insecurity And The Moderating Role Of Ai Trust, Tiantian Wang

Dissertations and Theses Collection (Open Access)

The widespread application of artificial intelligence (AI) technology in the workplace offers significant potential for process optimization andperformance improvement. However, the psychological mechanisms throughwhich AI usage affects employee outcomes remain underexplored. To address this gap, the present study investigated a sample of 170 employees froma media company in China, utilizing a three-wave longitudinal survey design. Specifically, this study examined how AI usage influenced employee creativity and task performance improvement through two mediatingmechanisms: the enhancement of personal control in problem-solving and the elicitation of job insecurity. Furthermore, the moderating role of trust in AI inthe relationship between AI usage and job …

Ai And Prompt Engineering For Library Discovery Services, James Day

Publications

We have seen the rise of generative artificial intelligence in the form of Large Language Models (LLMs) to provide answers to users’ queries. Services such as ChatGPT, Copilot, and Gemini have quickly become accepted and adopted in the research process. Now library vendors are adding artificial intelligence (AI) to their discovery services to allow for natural language queries to produce generative results. However, the AI model used for discovery services differs from normal LLMs in a significant way that has several positive benefits, but it affects how prompts are written. Library discovery services use a model called Retrieval- Augmented Generation …

Emerging Technologies In Beluga Research: Potential And Possibilities, Alejandro Zuniga-Schettino

Posters - 2025

Beluga whale face increasing threats in the Arctic, demanding effective research for conservation. Transitional methods going on field trips to collect short videos in excel, going on field trips to collect short videos, and having to rewatch the video are often time- consuming labor intensive, and limited in scope. This poster explores how engineering and AI can improve research. Engineering can provide robust tools like autonous underwater vehicles with advanced sensors for data collection in challenging environments. These technology offer an enhanced understanding of belugas behavior and ecology

Association Of Ai Derived Biomechanics And Hand Grip Strength, Theophile Nsabimana

Posters - 2025

Biomechanical analysis offers a way of better understanding the mechanism of a person's movement pattern or functional decline. Usually, motion analysis is costly and requires the purchase of a lot of equipment and software. This makes the technology out of reach of students, educators and researchers in austere settings.

Fortunately, artificial intelligence has brought affordability to motion analysis and created a whole new method of analyzing functional performance. OpenCap is an application which was produced by Stanford University and is hailed as being a future replacement to higher costing systems. Gait analysis provides an indication of a person's walking symmetry …

Applying Software Engineering Black-Box Methods For Testing Machine Learning Models, Timothy Elvira

Doctoral Dissertations and Master's Theses

This dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system's code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. …

Framework For Integrating Industry Knowledge Into A Large Language Model To Assist Construction Cost Estimation, Prashnna Ghimire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The construction industry generates a large amount of data across projects produced by digital devices, tools, and methods, and this volume is rapidly increasing. However, the industry lags behind in adopting data-driven technologies. On the other hand, the rapid advancement of generative AI (GenAI) in recent years, especially state-of-the-art large language models (LLMs), shows great potential and has been increasingly adopted in many industries; however, the construction industry is behind in adoption. While academic studies have proposed various machine learning applications for construction, industry implementation has lagged due to a disconnect between these proof-of-concept developments and practical industry needs. Also, …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

The Role Of Artificial Intelligence In Workforce Learning And Development: A Systematic Review, Mildred V. Jones

Educational Leadership & Workforce Development Theses & Dissertations

The purpose of this study is to investigate how artificial intelligence (AI) is currently employed in workforce learning and development. The study examined the types of AI employed and the affordances realized for organizations and employees. A PRISMA systematic review methodology was utilized to address the overarching problem statement and answer the three questions guiding the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta Analysis for Protocols was used to direct each phase of the research. In addition, the Preferred Reporting Items for Systematic Reviews and Meta Analysis was used to conduct the article selection process. …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo

Research Collection School Of Computing and Information Systems

Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …

Frame-Voyager: Learning To Query Frames For Video Large Language Models, Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin, Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, Hao Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leading to sub-optimal performance. In this paper, we propose Frame-Voyager that learns to query informative frame combinations, based on the given textual queries in the task. To train Frame-Voyager, we introduce a new data collection and labeling pipeline, by …

Does Chatgpt-Permitted Assessments Help Students Generate Better Answers And Learn More?, Michelle L. F. Cheong, Yun-Chen Chen

Research Collection School Of Computing and Information Systems

We discuss our methodology and implementation of ChatGPT-permitted assessments for a university-level spreadsheets modelling module. Through our quantitative data analysis, our students rated ChatGPT’s answers to be incorrect on average and thus will not help them generate better answers directly, representing low “Perceived usefulness” (PU), while they rated ChatGPT 3.5 with relatively high “Perceived ease of use” (PE). They gave a good “Behavioural intention” (BI) rating indicating that they were motivated to use it in future as they could still learn more about this module by using ChatGPT 3.5. We found that both PU and PE affected BI positively, with …

Alphaedit: Null-Space Constrained Knowledge Editing For Language Models, Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Large language models (LLMs) often exhibit hallucinations, producing incorrector outdated knowledge. Hence, model editing methods have emerged to enabletargeted knowledge updates. To achieve this, a prevailing paradigm is the locatingthen-editing approach, which first locates influential parameters and then edits themby introducing a perturbation. While effective, current studies have demonstrated that this perturbation inevitably disrupt the originally preserved knowledge within LLMs, especially in sequential editing scenarios. To address this, we introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters. We theoretically prove that this projection ensures the output …

Predictive Modelling For Vessel Traffic Flow: A Comprehensive Survey From Statistics To Ai, Deshan. Chen, Chen. Huang, Tengze. Fan, Hoong Chuin Lau, Xinping. Yan

Research Collection School Of Computing and Information Systems

Recognizing the specific complexities of vessel traffic flow, this comprehensive survey exclusively addresses the predictive modelling in maritime transportation, tracing the evolution from conventional statistical approaches to modern artificial intelligence (AI) techniques. The survey examines a broad range of predictive targets, including vessel volume, trajectories, velocities, destinations and traffic patterns. Through bibliometric analysis utilizing Citespace, the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed. Our analysis indicates a clear trend towards AI-based models, highlighting their increasing dominance in enhancing predictive accuracy and efficiency. Additionally, we highlight persistent challenges, such as …

On Generalization Across Environments In Multi-Objective Reinforcement Learning, Jayden Jing Xiang Teoh, Pradeep Varakantham, Peter Vamplew

Research Collection School Of Computing and Information Systems

No abstract provided.

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 …

Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li

Research Collection School Of Computing and Information Systems

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into …

A Selective Vehicle Routing Problem For The Bloodmobile System, Aldy Gunawan, Samuel Alan Darmasaputra, Sy Hoang Do, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Mobile blood collection has the advantage of greater reach compared to blood drives at fixed donation sites and is preferable for individuals with limited time or means of transportation. Bloodmobiles are widely used in healthcare logistics to increase the number of donors and donation frequency and to better match blood demand with collection. Bloodmobiles are stationed at predetermined locations, while shuttles are assigned to visit these locations to collect the donated blood. This problem is formulated as the Selective Vehicle Routing Problem under the Bloodmobile System (SVRP-BM). This research extends the Selective Vehicle Routing Problem with Integrated Tours problem (SVRPwIT) …

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 …

Capo: Cooperative Plan Optimization For Efficient Embodied Multi-Agent Cooperation, Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees Snoek, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where discussion and cooperative plan are crucial. To solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance the cooperation efficiency of LLM-based embodied agents. Inspired by human cooperation schemes, CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan 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 …

Dual Operation Aggregation Graph Neural Networks For Solving Flexible Job-Shop Scheduling Problem With Reinforcement Learning, Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang

Research Collection School Of Computing and Information Systems

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods …

Graph-Assisted Offline-Online Deep Reinforcement Learning For Dynamic Workflow Scheduling, Yifan Yang, Gang Chen, Hui Ma, Cong Zhang, Zhiguang Cao, Mengjie Zhang

Research Collection School Of Computing and Information Systems

Dynamic workflow scheduling (DWS) in cloud computing presents substantial challenges due to heterogeneous machine configurations, unpredictable workflow arrivals/patterns, and constantly evolving environments. However, existing research often assumes homogeneous setups and static conditions, limiting flexibility and adaptability in real-world scenarios. In this paper, we propose a novel Graph assisted Offline-Online Deep Reinforcement Learning (GOODRL) approach to building an effective and efficient scheduling agent for DWS. Our approach features three key innovations: (1) a task-specific graph representation and a Graph Attention Actor Network that enable the agent to dynamically assign focused tasks to heterogeneous machines while explicitly considering the future impact of …

Neural Multi-Objective Combinatorial Optimization Via Graph-Image Multimodal Fusion, Jinbiao Chen, Jiahai Wang, Zhiguang Cao, Yaoxin Wu

Research Collection School Of Computing and Information Systems

Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve …