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Emotion Regulation Flexibility And Well-Being In College Athletes In Aesthetic Sports, Kaitlyn D. Chamberlain Dec 2025

Emotion Regulation Flexibility And Well-Being In College Athletes In Aesthetic Sports, Kaitlyn D. Chamberlain

Graduate Theses and Dissertations

This study examined emotion regulation flexibility and well-being (including psychological well-being, physical well-being, perceived sport performance and worth of sport participation) in athletes in aesthetic sports. This population experiences high environmental demands and constraints and thus low control over their environment. Collegiate athletes within aesthetic sports and female athletes experience specific social factors and demands that threaten their well-being, compared to those experienced by male athletes and athletes within non-aesthetic sports. Consistent with the transactional theory of stress and coping, individuals with less power to regulate their environment will require greater ability to regulate themselves in accordance with their changing …

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Free Radical Reactions Of Electron Rich Alkenes, David A. May Jr Dec 2025

Free Radical Reactions Of Electron Rich Alkenes, David A. May Jr

Graduate Theses and Dissertations

Radical reactions have known for over a century but have only been thought of as useful synthetic strategies for the last 70 years. During this period, much work has been done to develop theoretical explanations for their reactivity and modes of termination, resulting in them becoming a fundamental type of organic reaction in academia and industry. Previous studies by the McIntosh group have shown that a radical [1,3]-Rearrangement occurs when N-substituted pyridines are heated. Here, the scope of this new reaction is expanded to include polycyclic pyridines including neocuproine (2,9-dimethylphenanthroline) and 2-methylquinoline while also expanding the range of reactions available …

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Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano Dec 2025

Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano

Graduate Theses and Dissertations

In this dissertation, we explore the potential of machine learning and deep learning techniques to enhance the performance and robustness of applications across two major domains. By addressing the challenges within these fields, we demonstrate that we can leverage learning algorithms to obtain substantial improvements in accuracy and robustness. First, we tackle a problem in the field of predictive health maintenance. We propose a novel auto encoder and neural network based methodology to predict failure times in complex aviation systems to learn to distinguish between normal and abnormal operational behavior, and use this information to inform the neural network to …

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Agricultural Practices’ Impact On Soil Health Indicators In Mid-South U.S. Crop Production Systems, Katherine Suzanne French Dec 2025

Agricultural Practices’ Impact On Soil Health Indicators In Mid-South U.S. Crop Production Systems, Katherine Suzanne French

Graduate Theses and Dissertations

Soil health and regenerative agriculture are concepts gaining popularity across global agriculture systems. The effect of sustainable farming practices such as nutrient management, cover crops, adoption of no-tillage, and residue retention on crop yield, as well as environmental resilience, is increasingly being studied. This research aimed to evaluate the impacts of (i) cover cropping and nutrient management, and of (ii) soil sampling depth and timing on indicators of soil health in various mid-southern irrigated row crop systems. Soil health indicators like soil organic matter (SOM), carbon dioxide respiration (CDR), beta-glucosidase enzyme activity (BG), permanganate oxidizable carbon (POXC), and the soil …

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Design And Testing Of A Gallium Nitride Power Amplifier For High-Temperature Radar Applications, Walker Landry Harbison Dec 2025

Design And Testing Of A Gallium Nitride Power Amplifier For High-Temperature Radar Applications, Walker Landry Harbison

Graduate Theses and Dissertations

This thesis offers the design, fabrication, and evaluation of a gallium nitride (GaN) power amplifier integrated circuit (IC) intended for high-temperature radar applications. Radar systems are used in many different applications, such as defense, aerospace, and weather. For their function, these systems require high power, efficiency, and reliability under a wide range of operating conditions. Taking advantage of the material properties of GaN, including its high breakdown voltage, wide bandgap, and thermal conductivity bolstered by the use of silicon carbide in the substrate, this work focuses on examining amplifier performance in both ambient and elevated temperature conditions. The PA was …

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Error Reduction Methodology And Data Simulation For Interval Data, Ranik Christopher Jelinek Dec 2025

Error Reduction Methodology And Data Simulation For Interval Data, Ranik Christopher Jelinek

Undergraduate Honors Capstone Projects

Chronic kidney disease (CKD) is a progressive condition affecting hundreds of millions of individuals worldwide. However, clinical datasets often record continuous laboratory measurements as categorical intervals rather than precise numerical values. This interval-censored structure presents methodological challenges for standard regression-based classifiers. This study compares three strategies for handling interval-valued predictors prior to fitting a logistic LASSO model: (1) midpoint imputation, which replaces each interval with its arithmetic center; (2) ordinal encoding, which maps intervals to integer ranks; and (3) a Monte Carlo simulation approach, which repeatedly samples uniformly from each observed interval and averages predictions across replications. Using a 10-fold …

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Golden Eagle Predation Of Greater Sage-Grouse On Diamond Mountain Plateau, Victoria E. Thorpe Dec 2025

Golden Eagle Predation Of Greater Sage-Grouse On Diamond Mountain Plateau, Victoria E. Thorpe

All Graduate Theses and Dissertations, Fall 2023 to Present

Predation and the perceived risk of predation can have major impacts on prey populations. Golden Eagles are considered one of the main predators of adult greater sage-grouse. Sage-grouse populations have been declining for decades throughout the West and wildlife managers are concerned about the impacts of golden eagles on sage-grouse. We investigated the nonconsumptive effects of golden eagles on sage-grouse by conducting observational surveys for golden eagles, sage-grouse, and other predators at sage-grouse lek sites near Vernal, Utah during the sage-grouse breeding season from 2022–2024. Additionally, we examined the prevalence of sage-grouse in golden eagle diet using DNA metabarcoding of …

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Developing Standardized Testing Datasets For Benchmarking Automated Quality Control Algorithm Performance With Aquatic Sensor Data, Ehsan Kahrizi Dec 2025

Developing Standardized Testing Datasets For Benchmarking Automated Quality Control Algorithm Performance With Aquatic Sensor Data, Ehsan Kahrizi

All Graduate Theses and Dissertations, Fall 2023 to Present

Advances in water monitoring technologies have led to a large increase in the amount of data collected from rivers, lakes, and other water systems. However, ensuring that these data are accurate and reliable remains a major challenge. Traditional data quality checks are done manually by a technician, which can be slow, inconsistent, and not practical for real-time monitoring. This research addresses these challenges by developing standardized datasets for testing computer-based methods that automatically detect and correct errors in water data. Using information from the Logan River Observatory in northern Utah, we created a step-by-step process to identify, categorize, and label …

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The Effects Of Green Orientation And Technological Agility On Sustainable Competitive Advantage Under Environmental Uncertainty: Organizational Agility As A Pathway, Sahilali Saiyed, Vimal Kumar, Abdul Waaje, Adi Prasetyo Tedjakusuma Dec 2025

The Effects Of Green Orientation And Technological Agility On Sustainable Competitive Advantage Under Environmental Uncertainty: Organizational Agility As A Pathway, Sahilali Saiyed, Vimal Kumar, Abdul Waaje, Adi Prasetyo Tedjakusuma

Michigan Tech Publications

Despite the strengthened efforts on sustainability and technological activities by firms, the existing literature lacks a clear picture regarding how green orientation (GO) and technological agility (TAG) can be transformed into sustainable competitive advantage (SCA) and at what time the conversion process may be most efficient. This paper conceptualizes organizational agility (OGA) based on the resource-based view (RBV) and dynamic capabilities theory (DCT) and hypothesizes that GO and TAG indirectly affect SCA with the mediating impact of environmental uncertainty (ELU). A moderated-mediation model was tested based on a sample of 200 managers (HR, marketing, accounting/finance, quality control, R&D) of auto-parts …

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Can Better Transportation Infrastructure Help Bridge The Gender Mobility Gap?: Analysis Of The Dhaka Metro Rail, Prottoy A. Akbar, Tomoki Fujii, Arpita Khanna, Abu S. Shonchoy Dec 2025

Can Better Transportation Infrastructure Help Bridge The Gender Mobility Gap?: Analysis Of The Dhaka Metro Rail, Prottoy A. Akbar, Tomoki Fujii, Arpita Khanna, Abu S. Shonchoy

Research Collection School Of Economics

Inadequate transportation infrastructure is a pressing challenge in rapidly expanding urban centers globally. However, the differential impacts of infrastructure improvements for women versus men remain inadequately understood and underexplored. This study addresses this knowledge gap by documenting gender-specific travel patterns and perceptions of safety in Dhaka, Bangladesh. Specifically, we analyze how the introduction of Dhaka’s first mass rapid transit (MRT) line has altered modes of transportation, the frequency of travel, and safetyrelated concerns among women. Our empirical findings highlight that the new MRT line has notably influenced women’s travel behaviors, particularly by alleviating safety concerns that previously discouraged regular travel.

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Rethinking Singer: Toward A Valid Argument For Helping The Global Poor, Joshua Luczak Dec 2025

Rethinking Singer: Toward A Valid Argument For Helping The Global Poor, Joshua Luczak

Research Collection College of Integrative Studies

For more than 50 years, Peter Singer has argued that we are required to donate to aid agencies. While many commentators have rejected one or more of his premises, no one appears to have challenged the argument’s validity, and it is often assumed to be valid even by critics. This article demonstrates that Singer’s common-sense morality arguments for donating to aid agencies are invalid. It then reconstructs a valid version of the argument consistent with Singer’s broader work, but shows that this version carries significant costs. The paper concludes that although we have a moral obligation to help those less …

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Reliability Analysis Of In-Person And Virtual Goniometric Measurements For Select Shoulder And Forearm Motions, Autumn Whitson, Tracy Cook, Lisa Middleton, Casey E. Humphrey, Aaron D. Sciascia Dec 2025

Reliability Analysis Of In-Person And Virtual Goniometric Measurements For Select Shoulder And Forearm Motions, Autumn Whitson, Tracy Cook, Lisa Middleton, Casey E. Humphrey, Aaron D. Sciascia

EKU Faculty and Staff Scholarship

Background Previous research on upper extremity range of motion has compared in-person to virtual measures for sagittal plane motions (flexion/extension) showing good-excellent reliability. Since upper extremity evaluation includes motion in all planes, it is important to assess whether transverse plane motion (rotation, supination, pronation) can be reliably measured during a virtual assessment. Purpose To evaluate the reliability (test/re-test inter-rater and intra-rater) of goniometric measurements of shoulder internal rotation and forearm pronation/supination obtained in-person and virtually. Study Design Observational cohort, Reliability study Methods Subjects 18-60 years of age with no upper extremity injuries were recruited for range of motion (ROM) testing …

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Jury-And-Judge Chain-Of-Thought For Uncovering Toxic Data In 3d Visual Grounding, Kaixiang Huang, Qifeng Zhang, Jin Wang, Jingru Yang, Yang Zhou, Huan Yu, Guodong Lu, Shengfeng He Dec 2025

Jury-And-Judge Chain-Of-Thought For Uncovering Toxic Data In 3d Visual Grounding, Kaixiang Huang, Qifeng Zhang, Jin Wang, Jingru Yang, Yang Zhou, Huan Yu, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

3D Visual Grounding (3DVG) faces persistent challenges due to coarse scene-level observations and logically inconsistent annotations, which introduce ambiguities that compromise data quality and hinder effective model supervision. To address these challenges, we introduce Refer-Judge, a novel framework that harnesses the reasoning capabilities of Multimodal Large Language Models (MLLMs) to identify and mitigate toxic data. At the core of Refer-Judge is a Jury-and-Judge Chain-of-Thought paradigm, inspired by the deliberative process of the judicial system. This framework targets the root causes of annotation noise: jurors collaboratively assess 3DVG samples from diverse perspectives, providing structured, multi-faceted evaluations. Judges then consolidate these insights …

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Cropcapsnet: Enhanced Capsule Network For Crop Disease Classification, Juan Qin, Linfan Deng, Cong Li, Junjie He, Haibo Pen, Zhaoxia Wang Dec 2025

Cropcapsnet: Enhanced Capsule Network For Crop Disease Classification, Juan Qin, Linfan Deng, Cong Li, Junjie He, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The prevention and treatment of crop diseases are crucial for the development of smart agriculture. The classification of crop diseases based on deep learning for early disease monitoring and control has become the mainstream direction of research. This paper proposes a novel deep learning model called ”CropCapsNet”, which combines Squeeze-and-Excitation Inception (SE-Inception) module and has improved capsule structure for crop disease classification. The network first extracts shallow features of input samples through double-layer convolution, then uses SE-Inception to achieve deep multi-scale feature acquisition, and finally outputs classification results through an improved capsule structure. SE-Inception adds Squeeze-and-Excitation(SE) attention after each multi-scale …

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Sopo: Text-To-Motion Generation Using Semi-Online Preference Optimization, Xiaofeng Tan, Hongsong Wang, Xin Geng, Pan Zhou Dec 2025

Sopo: Text-To-Motion Generation Using Semi-Online Preference Optimization, Xiaofeng Tan, Hongsong Wang, Xin Geng, Pan Zhou

Research Collection School Of Computing and Information Systems

Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor highquality, human-preferred motions—a critical yet largely unexplored problem. In this work, we theoretically investigate the DPO under both online and offline settings, and reveal their respective limitation: overfitting in offline DPO, and biased sampling in online DPO. Building on our theoretical insights, we introduce Semi-online Preference Optimization (SoPo), a DPO-based method for training text-to-motion models using “semi-online” data pair, consisting of unpreferred motion from online distribution and preferred motion in …

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Hybrid-Balance Gflownet For Solving Vehicle Routing Problems, Ni Zhang, Zhiguang Cao Dec 2025

Hybrid-Balance Gflownet For Solving Vehicle Routing Problems, Ni Zhang, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Existing GFlowNet-based methods for vehicle routing problems (VRPs) typically employ Trajectory Balance (TB) to achieve global optimization but often neglect important aspects of local optimization. While Detailed Balance (DB) addresses local optimization more effectively, it alone falls short in solving VRPs, which inherently require holistic trajectory optimization. To address these limitations, we introduce the Hybrid-Balance GFlowNet (HBG) framework, which uniquely integrates TB and DB in a principled and adaptive manner by aligning their intrinsically complementary strengths. Additionally, we propose a specialized inference strategy for depot-centric scenarios like the Capacitated Vehicle Routing Problem (CVRP), leveraging the depot node's greater flexibility in …

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Uniteformer: Unifying Node And Edge Modalities In Transformers For Vehicle Routing Problem, Dian Meng, Zhiguang Cao, Jie Gao, Yaoxin Wu, Yaqing Hou Dec 2025

Uniteformer: Unifying Node And Edge Modalities In Transformers For Vehicle Routing Problem, Dian Meng, Zhiguang Cao, Jie Gao, Yaoxin Wu, Yaqing Hou

Research Collection School Of Computing and Information Systems

Neural solvers for the Vehicle Routing Problem (VRP) have typically relied on either node or edge inputs, limiting their flexibility and generalization in real-world scenarios. We propose UniteFormer, a unified neural solver that supports node-only, edge-only, and hybrid input types through a single model trained via joint edge-node modalities. UniteFormer introduces: (1) a mixed encoder that integrates graph convolutional networks and attention mechanisms to collaboratively process node and edge features, capturing cross-modal interactions between them; and (2) a parallel decoder enhanced with query mapping and a feed-forward layer for improved representation. The model is trained with REINFORCE by randomly sampling …

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Multi-Task Vehicle Routing Solver Via Mixture Of Specialized Experts Under State-Decomposable Mdp, Yuxin Pan, Zhiguang Cao, Chengyang Gu, Liu Liu, Peilin Zhao, Yize Chen, Fangzhen Lin Dec 2025

Multi-Task Vehicle Routing Solver Via Mixture Of Specialized Experts Under State-Decomposable Mdp, Yuxin Pan, Zhiguang Cao, Chengyang Gu, Liu Liu, Peilin Zhao, Yize Chen, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutilize the compositional structure of VRP variants, each derivable from a common set of basis VRP variants. This critical oversight causes unified solvers to miss out the potential benefits of basis solvers, each specialized for a basis VRP variant. To overcome this limitation, we propose a framework that enables unified solvers to perceive the shared-component nature across VRP variants by proactively reusing basis solvers, while mitigating the exponential growth of trained neural solvers. Specifically, we introduce a State-Decomposable MDP …

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Learning Memory-Enhanced Improvement Heuristics For Flexible Job Shop Scheduling, Jiaqi Wang, Zhiguang Cao, Peng Zhao, Rui Cao, Yubin Xiao, Yuan Jiang, You Zhou Dec 2025

Learning Memory-Enhanced Improvement Heuristics For Flexible Job Shop Scheduling, Jiaqi Wang, Zhiguang Cao, Peng Zhao, Rui Cao, Yubin Xiao, Yuan Jiang, You Zhou

Research Collection School Of Computing and Information Systems

The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are more effective in approaching optimality. However, the flexible machine allocation in FJSP poses significant challenges to the application of this framework, including …

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Mando-Llm: Heterogeneous Graph Transformers With Large Language Models For Smart Contract Vulnerability Detection, Nhat Minh Nguyen, Huu Hoang Nguyen, Long Le Thanh, Zahra Ahmadi, Thanh Nam Doan, Daoyuan Wu, Lingxiao Jiang Dec 2025

Mando-Llm: Heterogeneous Graph Transformers With Large Language Models For Smart Contract Vulnerability Detection, Nhat Minh Nguyen, Huu Hoang Nguyen, Long Le Thanh, Zahra Ahmadi, Thanh Nam Doan, Daoyuan Wu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Detecting vulnerabilities in smart contracts is vital for the security and reliability of decentralized apps. To facilitate vulnerability detection, contract codes, including bug patterns, are represented as heterogeneous graphs with various nodes and edges, like control-flow and function-call graphs. However, existing graph learning techniques struggle with large, complex graphs. This paper presents MANDO-LLM, a novel framework that combines heterogeneous graph transformers (HGTs) with large language models (LLMs) for detecting vulnerabilities in smart contracts represented as heterogeneous contract graphs built upon control-flow and call graphs. MANDO-LLM uses LLMs to capture code features from control-flow and call data, customizes HGTs to learn …

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Stableguard: Towards Unified Copyright Protection And Tamper Localization In Latent Diffusion Models, Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He Dec 2025

Stableguard: Towards Unified Copyright Protection And Tamper Localization In Latent Diffusion Models, Haoxin Yang, Bangzhen Liu, Xuemiao Xu, Cheng Xu, Yuyang Yu, Zikai Huang, Yi Wang, Shengfeng He

Research Collection School Of Computing and Information Systems

The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling …

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Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar Dec 2025

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

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Misodice: Multi-Agent Imitation From Mixed-Quality Demonstrations, The Viet Bui, Tien Mai, Hong Thanh Nguyen Dec 2025

Misodice: Multi-Agent Imitation From Mixed-Quality Demonstrations, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

We study offline imitation learning (IL) in cooperative multi-agent settings, where demonstrations have unlabeled mixed quality - containing both expert and suboptimal trajectories. Our proposed solution is structured in two stages: trajectory labeling and multi-agent imitation learning, designed jointly to enable effective learning from heterogeneous, unlabeled data. In the first stage, we combine advances in large language models and preference-based reinforcement learning to construct a progressive labeling pipeline that distinguishes expert-quality trajectories. In the second stage, we introduce MisoDICE, a novel multi-agent IL algorithm that leverages these labels to learn robust policies while addressing the computational complexity of large joint …

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Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen Dec 2025

Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific …

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Sempo: Lightweight Foundation Models For Time Series Forecasting, Hui He, Kun Yi, Yuanchi Ma, Qi Zhang, Zhengdong Niu, Guansong Pang Dec 2025

Sempo: Lightweight Foundation Models For Time Series Forecasting, Hui He, Kun Yi, Yuanchi Ma, Qi Zhang, Zhengdong Niu, Guansong Pang

Research Collection School Of Computing and Information Systems

The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across diverse downstream forecasting tasks, existing time series FMs possess massive network architectures and require substantial pre-training on large-scale datasets, which significantly hinders their deployment in resource-constrained environments. In response to this growing tension between versatility and affordability, we propose SEMPO, a novel lightweight foundation model that requires pretraining on relatively small-scale data, yet exhibits strong general time series forecasting. Concretely, SEMPO comprises two key modules: 1) energy-aware SpEctral decomposition module, that substantially improves the utilization of pre-training …

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A Rate-Dependent Coreset Selector For Continual Learning On Time-Varying Data Distributions, Zilin Luo, Zichen Tian, Yaoyao Liu, Qianru Sun Dec 2025

A Rate-Dependent Coreset Selector For Continual Learning On Time-Varying Data Distributions, Zilin Luo, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

In this paper we review the concept of “phase” defined in Class-Incremental Learning (CIL), i.e., learning new classes while not forgetting old ones. Due to this design, classic CIL algorithms are mostly offline or can handle only intensive data distribution shifts across the phases. However, real-world data streams are often online, usually with uncertain or untraceable changes in their data distributions. To this end, we design the per-step distribution shifts by modeling the class sampling weights using bell-shaped curves. Such a design respects the rise-and-fall nature and presents realistic but underexplored challenges for CIL: 1) The data non-stationarity across steps …

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Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le Dec 2025

Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le

Research Collection School Of Computing and Information Systems

The nexus between data characteristics and parametric models is fundamental for developing effective and reliable artificial intelligence (AI) systems. Mismatches in data properties for model development may lead to deleterious effects on AI model performance in machine learning practice. This paper proposes a Reliable Data Split (RDS) procedure to learn how to select data points that will generalise the target domain adequately by employing prior knowledge of the data generative process. We introduce a reinforced selection strategy using deep reinforcement learning with diverse black box predictors in maximising ensemble rewards as the proxy of model performance potential while maintaining an …

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Digital Communications Between Firms And Investors: Impact Of Explanatory Responses On Investor Engagement In Online Financial Q&A, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang Dec 2025

Digital Communications Between Firms And Investors: Impact Of Explanatory Responses On Investor Engagement In Online Financial Q&A, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang

Research Collection School Of Computing and Information Systems

The emerging trend of digital communications between firms and investors through online question-and-answer (Q&A) platforms is recognized as a vital strategy for managing investor relations, contributing to enhanced market efficiency and information transparency through increased information exchange. Potential investors can seek responses from firm managers to address their information needs, thereby mitigating market uncertainties. To provide foundational insights, we conduct a survey of investors to assess their awareness, usage, and perceptions of firm-investor Q&A platforms. In the subsequent empirical study, we specifically focus on the substance of managers’ responses, which are primarily aimed at clarifying firm events or information. In …

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Imprisonment When An Offender Cannot Pay A Fine, Benjamin Joshua Ong Dec 2025

Imprisonment When An Offender Cannot Pay A Fine, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

According to a common-law rule in place since the 1993 case of Low Meng Chay v Public Prosecutor [1993] 1 SLR(R) 46, if the court is minded to impose a fine but the offender will clearly be unable to pay a fine, the offender should be sentenced to imprisonment instead (as opposed to a fine coupled with a default imprisonment term). While one can understand why the courts may apply this practice, the practice obscures the crucial distinction between: (a) being sentenced to a fine, then imprisoned in default of payment (which, it is submitted, is the correct course of …

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Copyright Ownership And Duration Of Ai-Authored Works, Cheng Lim Saw Dec 2025

Copyright Ownership And Duration Of Ai-Authored Works, Cheng Lim Saw

Research Collection Yong Pung How School Of Law

On the assumption that Parliament has endorsed the notion of AI authorship and the prospect that copyright may well subsist in works created autonomously by the AI itself, this essay further explores allied issues surrounding the ownership and duration of copyright in AI-authored works.

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