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Effect Of Dietary Tryptophan Supplementation And Heat Stress On Pasture Broiler Welfare And Performance, Jaelen Matthew Cherry Aug 2025

Effect Of Dietary Tryptophan Supplementation And Heat Stress On Pasture Broiler Welfare And Performance, Jaelen Matthew Cherry

Graduate Theses and Dissertations

Pasture-raised broiler chickens face temperature extremes that can threaten welfare. In the study, we evaluated whether a 0.26 % tryptophan-supplemented diet (T) improved heat stress (HS) resilience in Ross-708 broilers raised on pasture. At D14, birds began the T or remained on the control diet (C) and were moved outdoors. On D20 and 41, half of each group underwent six-hour HS; the remainder stayed under thermoneutral (TN) conditions. Weekly body weight, feed intake on D20 and D41 were recorded, and fear was measured using the Tonic Immobility (TI) test. Neither diet nor HS affected body weight. However, tryptophan supplementation resulted …

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Strain Induced Quantum Emitters In Monolayer Wse₂: Fabrication And Characterization Of Dimple Dot Devices, Garrett Maxwell Kitterman Aug 2025

Strain Induced Quantum Emitters In Monolayer Wse₂: Fabrication And Characterization Of Dimple Dot Devices, Garrett Maxwell Kitterman

Graduate Theses and Dissertations

Strain induced defects in monolayer tungsten diselenide (WSe₂) give rise to localized exciton funnels, offering a promising approach for the development of tunable quantum emitters. The quantum devices fabricated for this thesis used a modified dimple dot geometry, in which nano indentation creates a strain profile on the monolayer WSe₂. This allows for the funneling of excitons to a confined region. These strain induced funnels enable localized emission from single excitons in WSe₂. Device fabrication was carried out through mechanical exfoliation, dry transfer, and electron beam lithography of Cr/Au gates on a SiO₂ substrate. These devices were then characterized using …

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Cooling Climate Across Last Interglacial High Stands On San Salvador And Great Inagua, The Bahamas, Ian Winkelstern, Sierra Petersen, H. Allen Curran, Cecilie Phillips, Alex Quizon, Bosiljka Glumac, David Griffing Aug 2025

Cooling Climate Across Last Interglacial High Stands On San Salvador And Great Inagua, The Bahamas, Ian Winkelstern, Sierra Petersen, H. Allen Curran, Cecilie Phillips, Alex Quizon, Bosiljka Glumac, David Griffing

Geosciences: Faculty Publications

The last interglacial (LIG) is the last time global climate was about as warm as today, with global sea-levels several metres higher. The LIG probably had a re- duced latitudinal temperature gradient, with warmer poles and cooler tropics than today. Well-constrained records from low latitudes can test this overall model. We used bivalve shells sampled from six localities thought to expose the LIG age Cockburn Town Member of the Grotto Beach Formation on both San Salvador and Great Inagua Islands, The Bahamas. Previous work described two LIG depositional intervals: older ‘Reef I’ and younger ‘Reef II’, separated by a disconformity. …

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Kinematic Representations Of Viscoelastic Postseismic Deformation, John Loveless, Brendan J. Meade Aug 2025

Kinematic Representations Of Viscoelastic Postseismic Deformation, John Loveless, Brendan J. Meade

Geosciences: Faculty Publications

Abstract Following large earthquakes, viscoelastic stress relaxation may contribute to postseismic deformation observed at Earth's surface. Mechanical representations of viscoelastic deformation require a constitutive relationship for the lower crust/upper mantle material where stresses are diffused and, for non‐linear rheologies, knowledge of absolute stress level. Here, we describe a kinematic approach to representing
geodetically observed postseismic motions that does not require an assumed viscoelastic rheology. The core idea is to use observed surface motions to constrain time‐dependent displacement boundary conditions applied at the base of the elastic upper crust by viscoelastic motions in the lower crust/upper mantle, approximating
these displacements as …

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Understanding The Experiences And Perceptions Of Faculty Supporting Undergraduate Students With Mental Health Challenges, Jenna Ranee Parbati Jamieson Aug 2025

Understanding The Experiences And Perceptions Of Faculty Supporting Undergraduate Students With Mental Health Challenges, Jenna Ranee Parbati Jamieson

Dissertations

College is a big adjustment for students, and it doesn't come without its challenges, including being in a new place with new responsibilities. This is a stressful time that presents new obstacles and frustrations that can lead to increased stress, anxiety, depression, and mental health challenges that may cause faculty to engage and support undergraduate students with mental health challenges. As students adjust and transition to college, it is vital for university faculty to feel confident and comfortable supporting students from diverse backgrounds with varying levels of distress. By interviewing faculty, a better understanding of their lived experiences and perceptions …

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Too Big To Fail? Using Microsatellites To Examine The Population Genetics Of Flathead Catfish (Pylodictis Olivaris) In Missouri Impoundments, Aaron Krolow Aug 2025

Too Big To Fail? Using Microsatellites To Examine The Population Genetics Of Flathead Catfish (Pylodictis Olivaris) In Missouri Impoundments, Aaron Krolow

Dissertations

Flathead Catfish (Pylodictis olivaris) is a popular sportfish managed for recreational and commercial uses. Currently, managers in Missouri have implemented various reservoir-specific bag and length limits for P. olivaris to create sustainable populations of quality-sized individuals available for anglers to capture. In conjunction with field studies to estimate growth rates and size/age distributions, I developed and scored 15 microsatellite markers to estimate the effective population (Ne) and population structure within 10 Missouri reservoirs. I assessed the power of the markers to get acceptably precise estimates of Ne using NEoGen assuming life-history parameters (e.g., age at maturity, longevity and fecundity) estimated …

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Difficulty In American Dramatic Literature, David Turkel Aug 2025

Difficulty In American Dramatic Literature, David Turkel

Dissertations

Speaking broadly, this dissertation explores the marginalization of American drama within the field of literary studies. In attempting to account for this marginalization, theater scholars have tended to invoke the genre’s supposed lack of “difficulty,” meaning its failure to pose meaningful interpretive challenges to its readers. However, this dissertation takes an alternative view of the matter, contending that if American drama has indeed been marginalized, it is not because literary studies departments have regarded it as intrinsically “simpler” than other literary genres; in fact, it is theater scholars who have been more likely to read the genre as simple, or …

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Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances Mcq Generation And Distractor Quality, Yimiao Qiu, Yang Deng, Quanming Yao, Zhimeng Zhang, Zhiang Dong, Chang Yao, Jingyuan Chen Aug 2025

Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances Mcq Generation And Distractor Quality, Yimiao Qiu, Yang Deng, Quanming Yao, Zhimeng Zhang, Zhiang Dong, Chang Yao, Jingyuan Chen

Research Collection School Of Computing and Information Systems

Generating high-quality Multiple Choice Questions (MCQs) remains challenging for educational tools due to the need for contextual relevance and plausible distractors. Existing methods still struggle with these dual requirements, leading to questions that lack depth and distractors that are either too obvious or irrelevant. In this paper, we propose BiFlow, a novel framework that integrates bidirectional reasoning perspectives: teacher reasoning generates contextually relevant questions and plausible distractors, while student reasoning evaluates question clarity and the misleading nature of the distractors. To further enhance reasoning, we introduce PathFinder, a mechanism that employs breadth-first search and Chainof-Thought (CoT) strategies to explore diverse …

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Non-Homophilic Graph Pre-Training And Prompt Learning, Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang Aug 2025

Non-Homophilic Graph Pre-Training And Prompt Learning, Xingtong Yu, Jie Zhang, Yuan Fang, Renhe Jiang

Research Collection School Of Computing and Information Systems

Graphs are ubiquitous for modeling complex relationships between objects across various fields. Graph neural networks (GNNs) have become a mainstream technique for graph-based applications, but their performance heavily relies on abundant labeled data. To reduce labeling requirement, pre-training and prompt learning has become a popular alternative. However, most existing prompt methods do not distinguish between homophilic and heterophilic characteristics in graphs. In particular, many real-world graphs are non-homophilic-neither strictly nor uniformly homophilic-as they exhibit varying homophilic and heterophilic patterns across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. …

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Coleclip: Open-Domain Continual Learning Via Joint Task Prompt And Vocabulary Learning, Yukun Li, Guansong Pang, Wei Suo, Chenchen Chen, Yuling Xi, Lingqiao Liu, Hao Chen, Guoqiang Liang, Peng Wang Aug 2025

Coleclip: Open-Domain Continual Learning Via Joint Task Prompt And Vocabulary Learning, Yukun Li, Guansong Pang, Wei Suo, Chenchen Chen, Yuling Xi, Lingqiao Liu, Hao Chen, Guoqiang Liang, Peng Wang

Research Collection School Of Computing and Information Systems

This article investigates the problem of continual learning (CL) of vision-language models (VLMs) in open domains, where models are required to perform continual updating and inference on a stream of datasets from diverse seen and unseen domains with novel classes. Such a capability is crucial for various applications in open environments, e.g., AI assistants, autonomous driving systems, and robotics. Current CL studies mostly focus on closed-set scenarios in a single domain with known classes. Large pretrained VLMs such as CLIP have showcased exceptional zero-shot recognition capabilities, and several recent studies have leveraged the unique characteristics of VLMs to mitigate catastrophic …

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Analysis Of Extended Producer Responsibility In E-Waste Management: Policy Drivers And Challenges In Singapore, Aldy Gunawan, Aidan Marc Wong, Tasaporn Visawameteekul, Minh Phuong Huynh, Linh Chi Tran Aug 2025

Analysis Of Extended Producer Responsibility In E-Waste Management: Policy Drivers And Challenges In Singapore, Aldy Gunawan, Aidan Marc Wong, Tasaporn Visawameteekul, Minh Phuong Huynh, Linh Chi Tran

Research Collection School Of Computing and Information Systems

This paper examines the role of the Extended Producer Responsibility (EPR) scheme in electronic waste (e-waste) management in Singapore. It investigates the policy drivers and challenges of e-waste management, using data from an online survey to explore the attitudes and behaviors of young consumers, with a particular focus on youth. We employ the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) frameworks to develop a model that examines the relationships among attitudes, perceived norms, awareness, and perceived convenience in relation to EPR awareness and perception. The findings highlight the need for customized policies tailored to different …

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Optimizing Group Utility In Itinerary Planning: A Strategic And Crowd-Aware Approach, Junhua Liu, Aldy Gunawan, Kristin L. Wood, Kwan Hui Lim Aug 2025

Optimizing Group Utility In Itinerary Planning: A Strategic And Crowd-Aware Approach, Junhua Liu, Aldy Gunawan, Kristin L. Wood, Kwan Hui Lim

Research Collection School Of Computing and Information Systems

Itinerary recommendation is a complex sequence prediction problem with numerous practical applications. The task becomes significantly more challenging when optimizing multiple factors simultaneously, such as user queuing times, crowd levels, attraction popularity, walking durations, and operating hours. These factors, combined with the dynamic and unpredictable nature of visitor flow, introduce substantial complexities, particularly when accounting for collective user behavior. Existing solutions often adopt a single-user perspective, overlooking critical challenges arising from natural crowd dynamics. For example, the Selfish Routing problem illustrates how individual decision-making can lead to suboptimal outcomes for the group as a whole. To address these challenges, we …

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Explainable Multimodal Sentiment Analysis Of Social Media Visual Content For Child Safety, Yee Sen Tan, Zhaoxia Wang Aug 2025

Explainable Multimodal Sentiment Analysis Of Social Media Visual Content For Child Safety, Yee Sen Tan, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Ensuring the safety and well-being of children is increasingly important, especially in a world where visual content is pervasive. This paper proposes a novel multimodal, multilingual, and multiclass sentiment analysis method for social media content, aimed at improving content moderation for child safety. Our approach integrates textual, visual, and audio data from videos, categorizing sentiment into four levels: positive, slightly negative, negative, and strongly negative, enabling granular detection of harmful content. To enhance explainability and trust, we also leverage interpretable mechanisms to analyze the contributions of each modality. Evaluation of our method demonstrates strong generalization across diverse video types, and …

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Information-Theoretic Detection Of Unusual Source Code Changes, Adriano Torres, Markus Wagner, Christoph Treude, Sebastian Baltes Aug 2025

Information-Theoretic Detection Of Unusual Source Code Changes, Adriano Torres, Markus Wagner, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

The code base of software projects evolves essentially through inserting and removing information to and from the source code. We can measure this evolution via the elements of information—tokens, words, nodes—of the respective representation of the code. In this work, we approach the measurement of the information content of the source code of open-source projects from an information-theoretic standpoint. Our focus is on the entropy of two fundamental representations of code: tokens and abstract syntax tree nodes, from which we derive definitions of textual and structural entropy. We proceed with an empirical assessment where we evaluate the evolution patterns of …

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Leveraging Reviewer Experience In Code Review Comment Generation, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet Aug 2025

Leveraging Reviewer Experience In Code Review Comment Generation, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet

Research Collection School Of Computing and Information Systems

Modern code review is a ubiquitous software quality assurance process aimed at identifying and resolving potential issues (e.g., functional, evolvability) within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained various deep learning based language models to imitate human reviewers in providing natural language code reviews for submitted code. Formally, this automation task is known as code review comment generation. Prior work has demonstrated improvements in code review comment generation by leveraging machine learning techniques and neural models, such as transfer learning and …

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Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang Aug 2025

Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics--such as edge-based distances--are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, …

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Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola Aug 2025

Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola

Research Collection School Of Computing and Information Systems

Autonomous rehabilitation support solutions, such as virtual coaches, should provide real-time feedback to improve motor function and maintain patient engagement. However, fully annotated dataset collection for real-time exercise assessment is time-consuming and costly, posing a barrier to evaluating proposed methods. In this work, we present a novel framework that learns a frame-level classifier using weakly annotated videos for real-time assessment of compensatory motions in stroke rehabilitation exercises by generating pseudo-labels at a frame level. We consider three approaches: 1) a baseline approach that uses a source dataset to train a frame-level classifier, 2) a transfer learning approach that uses target …

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Use Of A Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework For Low-Resource Settings: Development And Validation Study, Shao Wei Sean Lam, Min Hun Lee, Michael Dorosan, Samuel Altonji, Hiang Khoon Tan, Walter T. Lee Aug 2025

Use Of A Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework For Low-Resource Settings: Development And Validation Study, Shao Wei Sean Lam, Min Hun Lee, Michael Dorosan, Samuel Altonji, Hiang Khoon Tan, Walter T. Lee

Research Collection School Of Computing and Information Systems

Background: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, which are essential for accurate triage of at-risk patients.Objective: We introduce a preliminary AI-based screening framework to address this challenge for the triaging of at-risk patients in low-resource settings. This formative research addresses multiple challenges common in high-dimensional FNS videos: (1) selecting clear, informative images; (2) deriving regions within frames that show an anatomical landmark of interest; and (3) classifying patients into referral grades based on the FNS video …

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Reimagining Education With Ai, Margherita Pagani, Steven M. Miller, Jerry Wind Aug 2025

Reimagining Education With Ai, Margherita Pagani, Steven M. Miller, Jerry Wind

Research Collection School Of Computing and Information Systems

This chapter examines AI’s transformative potential in education, focusing on Generative AI (GenAI) and Large Language Models (LLMs) while at the same time emphasizing the importance of grounding and guiding AI efforts with learning science and education research findings. It synthesizes analyses and expert recommendations, highlighting opportunities like personalized learning and enhanced teacher productivity, alongside challenges such as over-reliance on AI. Practical steps for instructors include adopting a question-first approach, utilizing AI for personalized feedback, designing AI-enhanced learning experiences, fostering critical thinking, and ensuring ethical AI use. The chapter concludes with strategic recommendations for leveraging AI to sustainably improve educational …

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Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He Aug 2025

Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He

Research Collection School Of Computing and Information Systems

Monocular 3D human pose estimation remains a challenging task due to inherent depth ambiguities and occlusions. Compared to traditional methods based on Transformers or Convolutional Neural Networks (CNNs), recent diffusionbased approaches have shown superior performance, leveraging their probabilistic nature and high-fidelity generation capabilities. However, these methods often fail to account for the spatial and temporal correlations across predicted frames, resulting in limited temporal consistency and inferior accuracy in predicted 3D pose sequences. To address these shortcomings, this paper proposes StarPose, an autoregressive diffusion framework that effectively incorporates historical 3D pose predictions and spatialtemporal physical guidance to significantly enhance both the …

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Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li Aug 2025

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

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Ai-Assisted Risk Assessment In Generative Ai Governance, Wu Jiaqi Young, Fiona Fui-Hoon Nah Aug 2025

Ai-Assisted Risk Assessment In Generative Ai Governance, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Effective risk assessment is paramount for responsible generative AI (GenAI) deployment. Traditional governance approaches that rely on manual reviews are inadequate given the scale and velocity of GenAI outputs. A risk-based approach incorporating real-time monitoring and governance is paramount. In this research, we examine how the efficacy of suggestive versus supportive explanations for AI’s risk assessment of GenAI outputs is moderated by user domain expertise and AI’s risk assessment in determining user acceptance. We hypothesize that cognitive involvement increases with AI’s risk assessment, with higher risks triggering more critical evaluation. By drawing on the elaboration likelihood model, we hypothesize that …

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Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang Aug 2025

Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures …

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Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang Aug 2025

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

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Llm2rec: Large Language Models Are Powerful Embedding Models For Sequential Recommendation, Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat‑Seng Chua Aug 2025

Llm2rec: Large Language Models Are Powerful Embedding Models For Sequential Recommendation, Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model …

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Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher Aug 2025

Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher

Research Collection School Of Computing and Information Systems

The dissemination of fake news by social media users is a key factor in the escalation of misinformation. Research suggests that social media networks are becoming increasingly homophilic, which leads to an overreliance on social media friends that contributes to the spread of fake news. However, little is known about how social media mindfulness can reduce the sharing of fake news. To investigate this research question, we conceptualized a social media mindfulness construct and developed the social media mindfulness scale. We also hypothesize that social media mindfulness lowers overreliance on friends’ knowledge, which increases skepticism about social media news that …

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Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang Aug 2025

Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

A code context model comprises source code elements and their relations relevant to a programming task. The capture and use of code context models in software tools can benefit software development practices, such as code navigation and search. Prior research has explored approaches that leverage either the structural information of code or interaction histories of developers with integrated development environments to automate the construction of code context models. However, these approaches primarily capture shallow syntactic and lexical features of code elements, with limited ability to capture contextual and structural dependencies among neighboring code elements. In this paper, we propose GNNContext, …

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Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim Aug 2025

Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client’s actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and …

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Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic Aug 2025

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

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Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu Aug 2025

Memotune: A Measure And Moment-Driven Fine-Tuning Framework For Quantized Large Language Models, Yun Zhang, Xue Geng, Lizi Liao, Jintong Sun, Minghe Yu, Ge Yu

Research Collection School Of Computing and Information Systems

Quantizing large language models (LLMs) is essential for reducing memory and computational costs in natural language processing. Existing methods combine quantization with parameter-efficient fine-tuning but often fail to meet practical performance requirements. This paper introduces MeMoTune, a novel fine-tuning framework for quantized LLMs. By employing a measure and moment approach within a low-rank approximation framework in probability measure space, MeMoTune optimizes the objective function for superior fine-tuning results. The update process is further refined through scaled gradient, enhancing convergence efficiency and noise robustness. Experiments on tasks like text generation, summarization, and understanding show MeMoTune significantly outperforms state-of-the-art methods, e.g. fine-tuning …

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