Chapman University
Innovative Soil Classification Approach For Achieving Global Biodiversity Framework Utilizing Integrated Data Fusion Of Emit And Multispectral Satellite Observations: Case Study Of Imam Turki Bin Abdullah Royal Reserve, Kingdom Of Saudi Arabia, Hesham Morgan, Ali Elgendy, Surendra Maharjan, Wenzhao Li, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Soil classification is essential for sustainable land management, ecological conservation, and combating desertification, particularly in arid and semi-arid regions. This study integrates hyperspectral data from the Earth Surface Mineral Dust Source Investigation (EMIT) and multispectral imagery from Sentinel-2 to achieve accurate soil classification for the Imam Turki bin Abdullah Royal Reserve (ITBA) in Saudi Arabia. Using advanced Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), the study highlights the power of data fusion in addressing the limitations of standalone remote sensing methods. The integration of hyperspectral and multispectral data combines the spectral richness of hyperspectral imaging with the spatial …
The Role Of Artificial Intelligence In Workforce Learning And Development: A Systematic Review,
2025
Old Dominion University
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. …
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,
2025
Singapore Management University
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 …
Machine Learning Methods For Hypervelocity Fragment Flyout Characterization,
2025
Embry-Riddle Aeronautical University
Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen
Doctoral Dissertations and Master's Theses
Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …
Ai And Prompt Engineering For Library Discovery Services,
2025
Embry-Riddle Aeronautical University
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 …
David B. Smith Chats With Monday 1.0,
2025
CUNY New York City College of Technology
David B. Smith Chats With Monday 1.0, David B. Smith
Publications and Research
This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …
On Generalization Across Environments In Multi-Objective Reinforcement Learning,
2025
Singapore Management University
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,
2025
Singapore Management University
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 …
Capo: Cooperative Plan Optimization For Efficient Embodied Multi-Agent Cooperation,
2025
Singapore Management University
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 …
Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems,
2025
Singapore Management University
Rethinking Light Decoder-Based Solvers For Vehicle Routing Problems, Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu
Research Collection School Of Computing and Information Systems
Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals …
Rethinking Neural Multi-Objective Combinatorial Optimization Via Neat Weight Embedding,
2025
Singapore Management University
Rethinking Neural Multi-Objective Combinatorial Optimization Via Neat Weight Embedding, Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong
Research Collection School Of Computing and Information Systems
Recent decomposition-based neural multi-objective combinatorial optimization (MOCO) methods struggle to achieve desirable performance. Even equipped with complex learning techniques, they often suffer from significant optimality gaps in weight-specific subproblems. To address this challenge, we propose a neat weight embedding method to learn weight-specific representations, which captures weight-instance interaction for the subproblems and was overlooked by most current methods. We demonstrate the potentials of our method in two instantiations. First, we introduce a succinct addition model to learn weight-specific node embeddings, which surpassed most existing neural methods. Second, we design an enhanced conditional attention model to simultaneously learn the weight embedding …
Alphaedit: Null-Space Constrained Knowledge Editing For Language Models,
2025
Singapore Management University
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 …
Humanist Copyright,
2025
Columbia Law School
Humanist Copyright, Jane C. Ginsburg
Faculty Scholarship
This exploration of the role of authorship in copyright law proceeds in three parts: historical, doctrinal, and predictive. First, I will review the development of author-focused property rights in the pre-copyright regimes of printing privileges and in early Anglo-American copyright law through the 1909 U.S. Copyright Act. Second, I will analyze the extent to which the present U.S. copyright law does (and does not) honor human authorship. Finally, I will consider the potential responses of copyright law to the claims of proprietary rights in AI-generated outputs. I will explain why the humanist orientation of U.S. copyright law validates the position …
Applying Software Engineering Black-Box Methods For Testing Machine Learning Models,
2025
Embry-Riddle Aeronautical University
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. …
Frame-Voyager: Learning To Query Frames For Video Large Language Models,
2025
Singapore Management University
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?,
2025
Singapore Management University
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 …
Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems,
2025
Singapore Management University
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,
2025
Singapore Management University
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,
2025
Singapore Management University
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 …
Configx: Modular Configuration For Evolutionary Algorithms Via Multitask Reinforcement Learning,
2025
Singapore Management University
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 …
