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Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning, Brahmanage Janaka Chathuranga Thilakarathna, Akshat Kumar Jul 2026

Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning, Brahmanage Janaka Chathuranga Thilakarathna, Akshat Kumar

Research Collection School Of Computing and Information Systems

Sequential decision-making using Markov Decision Process underpins many real-world applications. Both model-based and model-free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often conflicting objectives, that can lead to unstable min–max, adversarial optimization. A promising alternative is safety reachability analysis, which precomputes a forward-invariant safe state–action set, ensuring that an agent starting inside this set remains safe indefinitely. Yet, most reachability-based methods address only hard safety constraints, and little work extends reachability to cumulative cost constraints. To address this, first, we define a safety-conditioned reachability set that decouples reward maximization …

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Scattered Hypothesis Generation For Open-Ended Event Forecasting, He Chang, Zhulin Tao, Lifang Yang, Xianglin Huang, Yunshan Ma Jul 2026

Scattered Hypothesis Generation For Open-Ended Event Forecasting, He Chang, Zhulin Tao, Lifang Yang, Xianglin Huang, Yunshan Ma

Research Collection School Of Computing and Information Systems

Despite the importance of open-ended event forecasting for risk management, current LLM-based methods predominantly target only the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. To bridge this gap, we advance open-ended event forecasting from pinpoint forecasting to scatter forecasting by introducing the proxy task of hypothesis generation. This paradigm aims to generate an inclusive and diverse set of hypotheses that broadly cover the space of plausible future events. To this end, we propose SCATTER, a reinforcement learning framework that jointly optimizes inclusiveness and diversity of the hypothesis. Specifically, we design a novel hybrid reward that consists of …

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Mab-Dqa: Addressing Query Aspect Importance In Document Question Answering With Multi-Armed Bandits, Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang, Jinhui Tang Jul 2026

Mab-Dqa: Addressing Query Aspect Importance In Document Question Answering With Multi-Armed Bandits, Yixin Xiang, Yunshan Ma, Xiaoyu Du, Yibing Chen, Yanxin Zhang, Jinhui Tang

Research Collection School Of Computing and Information Systems

Document Question Answering (DQA) involves generating answers from a document based on a user’s query, representing a key task in document understanding. This task requires interpreting visual layouts, which has prompted recent studies to adopt multimodal Retrieval-Augmented Generation (RAG) that processes page images for answer generation. However, in multimodal RAG, visual DQA struggles to utilize a large number of images effectively, as the retrieval stage often retains only a few candidate pages (e.g., Top-4), causing informative but less visually salient content to be overlooked in favor of common yet low-information pages. To address this issue, we propose a Multi-Armed Bandit–based …

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Co-Matching: Towards Human–Model Collaborative Legal Case Matching, Chen Huang, Xinwei Yang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua Jul 2026

Co-Matching: Towards Human–Model Collaborative Legal Case Matching, Chen Huang, Xinwei Yang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent efforts have aimed to improve AI models in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into models. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matching. To address this, we propose a collaborative matching framework called Co-Matching, which encourages both the model and the legal practitioner to participate in the matching process, integrating tacit knowledge. Unlike existing methods that rely solely on the model, Co-Matching allows both the legal practitioner and the model to …

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Using Individual Vs. Group Evaluation To Incentivize Effort For Heterogeneous Groups, Prasart Jongjaroenkamol Jul 2026

Using Individual Vs. Group Evaluation To Incentivize Effort For Heterogeneous Groups, Prasart Jongjaroenkamol

Research Collection School Of Accountancy

This study uses an analytical model to examine how individual and group performance evaluations affect motivation among agents with different abilities. While group evaluations encourage collaboration, they also introduce uncertainty. The results reveal a non-monotonic relationship between the weight assigned to group evaluations and motivation: motivation increases at moderate weights but decreases when the weight is too low or high. The study also identifies the optimal weighting between evaluation types to maximize motivation and examines factors influencing these allocations. These insights demonstrate how evaluation design shapes effort incentives and inform performance measurement and assessment practices in organizational and educational contexts.

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The Due Diligence That Investors In Family Businesses Must Not Skip, Yuanto Kusnadi, Kenneth T. Goh Jul 2026

The Due Diligence That Investors In Family Businesses Must Not Skip, Yuanto Kusnadi, Kenneth T. Goh

Research Collection School Of Accountancy

In a commentary, SMU Associate Professor of Accounting (Education) Yuanto Kusnadi and Kenneth Goh, Director of Private Wealth Management at UOB Kay Hian, opined that minority shareholders should understand the specific risks highlighted in company filings. They noted that the greatest risk of investing in a family-controlled company lies not only in its business performance, but also in how controlling families exercise their influence. While Singapore's regulatory framework provides safeguards for minority shareholders, these protections are not foolproof. However, they require significant transactions to be disclosed and give minority shareholders a voice, provided they review the circulars and exercise their …

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Personal Goal Facilitation, Burnout And Work Engagement: The Role Of Self-Discrepancy, Jacinth Jia Xin Tan, Bek Wuay Tang Jul 2026

Personal Goal Facilitation, Burnout And Work Engagement: The Role Of Self-Discrepancy, Jacinth Jia Xin Tan, Bek Wuay Tang

Research Collection School of Social Sciences

Employees who perceive greater personal goal facilitation through work (PGFW) experience lower burnout and higher work engagement. This research examined whether self-discrepancies and emotional experiences explain these relationships across two cross-sectional studies involving a nationally representative sample of working adults (Study 1; N = 889) and a sample of public school teachers (Study 2; N = 228) in Singapore. Participants listed personal goals and rated how much their jobs facilitated these goals, alongside measures of self-discrepancy, emotions, burnout, and engagement. Across both samples, higher PGFW predicted lower burnout and greater engagement. These associations were consistently explained by lower ideal self-discrepancy …

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Global Prevalence Of Internet Gaming Disorder: An Umbrella Review Of Meta-Analytic Evidence And Implications For Child And Adolescent Psychiatry, Elisabeth C. S. Poon, Xun Ci Soh, Trina J. H. Poh, Andre C. S. Tan, Andree Hartanto Jul 2026

Global Prevalence Of Internet Gaming Disorder: An Umbrella Review Of Meta-Analytic Evidence And Implications For Child And Adolescent Psychiatry, Elisabeth C. S. Poon, Xun Ci Soh, Trina J. H. Poh, Andre C. S. Tan, Andree Hartanto

Research Collection School of Social Sciences

Background/Objectives: Internet gaming has become a widespread global activity, raising concerns about the prevalence of Internet Gaming Disorder (IGD). However, existing meta-analyses report highly variable prevalence estimates. This umbrella review aims to clarify the prevalence and variability of IGD by synthesizing evidence from meta-analyses to provide a comprehensive overview of IGD prevalence across populations, age groups, and regions. Methods: Following PRISMA guidelines, systematic searches were carried out across five published literature databases and two supplementary search sources. Title and abstract screening, followed by full-text eligibility assessment, were conducted independently by three authors. The same three authors then performed data extraction …

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A 10-Minute Walk From The Mrt. But For Whom?, Paulin Tay Straughan, Yi Wen (Chen Yiwen) Tan, Wensi Lim Jul 2026

A 10-Minute Walk From The Mrt. But For Whom?, Paulin Tay Straughan, Yi Wen (Chen Yiwen) Tan, Wensi Lim

Research Collection School of Social Sciences

They highlighted that as people age, mobility naturally declines, adding that the convenient shorthand such as a “10-minute walk” to define accessibility is ultimately a social construct – one that must be grounded in lived realities and evidence. They noted that ageing in place is not just the ability to remain at home as one transitions into later life – it is about living in an environment that fosters attachment, continuity and well-being, which requires both the built and social environments to be supportive. Responses must be guided by solid evidence – through ongoing conversations with those whose needs we …

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Learning By Writing: Exploring Authentic Legal Learning Through Case Summaries, Ee-Ing Ong, Wei Yang Quek, Duan Ning, Magdeleine Lew Jul 2026

Learning By Writing: Exploring Authentic Legal Learning Through Case Summaries, Ee-Ing Ong, Wei Yang Quek, Duan Ning, Magdeleine Lew

Research Collection Yong Pung How School Of Law

We use authentic learning as a pedagogical framework in a collaboration between our law school and the national Supreme Court of a Southeast Asian country, which facilitates law students’ development of their legal analytical and writing skills, and helps them better bridge the gap between existing legal curricula and the needs of legal practice. Akin to a writing apprenticeship, students write summaries on selected Supreme Court judgments, with their output reviewed by faculty as well as judicial law clerks from the court. The results are published on the court’s website and circulated to other stakeholders. In the post-exercise survey, participating …

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Robust Generator Maintenance Schedule For Frequency-Secure Power Systems, Yang Yang, Qiuzhuang Sun, Jimmy Chih-Hsien Peng, Loon Ching Tang, Zhisheng Ye Jul 2026

Robust Generator Maintenance Schedule For Frequency-Secure Power Systems, Yang Yang, Qiuzhuang Sun, Jimmy Chih-Hsien Peng, Loon Ching Tang, Zhisheng Ye

Research Collection College of Integrative Studies

Problem definition: Normal operations of a power system require that alternating current frequency be maintained at a nominal value, for example, 50 Hz, whereas severe deviation from this value due to power deficiencies can cause cascading generator trips. Maintaining the frequency requires adequate inertia and frequency regulation reserve, which are primarily provided by online generators. In daily operations, generators due for preventive maintenance must be taken offline, and thus an improper maintenance schedule could jeopardize frequency security, as exemplified by the recent Texas power blackout. However, this natural nexus between frequency security and maintenance has been over-looked largely in the …

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Online Planning Of Power Flows For Power Systems Against Bushfires Using Spatial Context, Jianyu Xu, Qiuzhuang Sun, Yang Yang, Huadong Mo, Daoyi Dong Jul 2026

Online Planning Of Power Flows For Power Systems Against Bushfires Using Spatial Context, Jianyu Xu, Qiuzhuang Sun, Yang Yang, Huadong Mo, Daoyi Dong

Research Collection College of Integrative Studies

A power station or transmission line can be affected due to bushfires, increasing operation costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems under bushfires. We develop a model to capture the stochastic nature of bushfire spread based on Moore’s neighborhood model and propose an online optimization modeling framework to sequentially plan power flows in the electricity network. Our framework assumes that bushfire spread is non-stationary over time and that the spread and containment probabilities are unknown. To address these challenges, we develop a contextual online learning algorithm that treats the …

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Purifai: Detecting And Fixing Search-Induced Distortions In Web-Augmented Llms, Guoqing Wang, Zhao Zhang, Zeyu Sun, Xiaofei Xie, Yizhou Chen, Yanchao Tan, Dan Hao Jul 2026

Purifai: Detecting And Fixing Search-Induced Distortions In Web-Augmented Llms, Guoqing Wang, Zhao Zhang, Zeyu Sun, Xiaofei Xie, Yizhou Chen, Yanchao Tan, Dan Hao

Research Collection School Of Computing and Information Systems

As Large Language Models (LLMs) increasingly serve as interfaces for proprietary data (e.g., enterprise knowledge bases, legal statutes), ensuring their fidelity to trusted internal information is paramount. While integrating real-time web search can enhance model utility, it introduces a critical vulnerability: the ingestion of conflicting, misleading, or hallucinated content from the open web can override the model's adherence to its verified internal knowledge. We define this failure mode as search-induced distortion, a significant risk in high-stakes domains where the internal knowledge base serves as the absolute ground truth.To address this challenge, we present PurifAI, a proactive, model-agnostic, cache-level purification system …

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Verbalizing Lightgcn: Direct Learning Of Textual Representations From User-Item Interaction Graph Via Llms, Manh-Khanh Ngo Huu, Hady Wirawan Lauw Jul 2026

Verbalizing Lightgcn: Direct Learning Of Textual Representations From User-Item Interaction Graph Via Llms, Manh-Khanh Ngo Huu, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In this work, we propose VerbaLightGCN, a novel LLM-based recommendation framework that integrates the semantic understanding of LLMs with user-item interaction modeling. Traditional collaborative filtering (CF) models typically embed user and item IDs into a latent space to capture interaction signals. However, pretrained LLMs cannot natively interpret these learned embeddings. To bridge this gap, VerbaLightGCN adopts a CF-as-text paradigm, in which collaborative signals are encoded in textual form and directly learned from the user–item interaction graph, and are then combined with semantic information to construct user and item profiles that function as latent embeddings. Inspired by LightGCN, our method retains …

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Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji Jul 2026

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji

Research Collection School Of Computing and Information Systems

Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear …

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Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu Jul 2026

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

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Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua Jul 2026

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items.CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and(2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficultto generalize to …

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Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang Jul 2026

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

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Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar Jul 2026

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

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Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves Jul 2026

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment–uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that …

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Variational Speculative Decoding: Rethinking Draft Training From Token Likelihood To Sequence Acceptance, Xiandong Zou, Jianshu Li, Jing Huang, Pan Zhou Jul 2026

Variational Speculative Decoding: Rethinking Draft Training From Token Likelihood To Sequence Acceptance, Xiandong Zou, Jianshu Li, Jing Huang, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variational inference over latent proposals (draft paths). VSD maximizes the marginal probability of target-model acceptance, yielding an ELBO that promotes high-quality latent proposals while minimizing divergence from the target distribution. To enhance quality and reduce variance, we incorporate a path-level utility and optimize via an Expectation-Maximization procedure. The E-step draws MCMC samples from an oracle-filtered posterior, while the M-step maximizes weighted likelihood using …

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Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen Jul 2026

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Research Collection School Of Computing and Information Systems

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and …

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Itimo: An Llm-Empowered Synthesis Dataset For Travel Itinerary Modification, Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang, Hua Ma, Zhu Sun Jul 2026

Itimo: An Llm-Empowered Synthesis Dataset For Travel Itinerary Modification, Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang, Hua Ma, Zhu Sun

Research Collection School Of Computing and Information Systems

Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of shape need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of shape need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: …

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Bridging Llm Embeddings And Vae Parameters For Disentangled Recommendation, Nhu-Thuat Tran, Hady Wirawan Lauw Jul 2026

Bridging Llm Embeddings And Vae Parameters For Disentangled Recommendation, Nhu-Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Disentangled recommendation within the Variational Autoencoder (VAE) framework aims to capture multiple user interests. While effective, these VAEs are fundamentally constrained by their reliance on interaction data alone, lacking the rich external semantic knowledge needed to properly structure and separate latent interests. Meanwhile, Large Language Models (LLMs) excel at deriving profound user preference signals from textual data. Prevailing methods for integrating LLMs into recommendation, however, either focus on single-interest modeling or perform a shallow fusion by aligning LLM and VAE representation spaces. Thus, they fail to fundamentally shape the VAE's latent space for multi-interest learning, hindering recommendation performance. To bridge …

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Offshore Philanthropy: A Critical Look At Family Offices Using Hybrid Trusts, Hang Wu Tang Jul 2026

Offshore Philanthropy: A Critical Look At Family Offices Using Hybrid Trusts, Hang Wu Tang

Research Collection Yong Pung How School Of Law

Family offices manage substantial wealth and often undertake philanthropic initiatives as part of their broader objectives. These offices may assume a variety of legal forms, ranging from corporations, partnerships, trusts administered by private trust companies or a combination of these forms. This article is concerned with family offices which use trusts administered by private trust companies in offshore jurisdictions. In response to competition for global wealth, several offshore jurisdictions have enacted trust laws that relax traditional doctrinal constraints to accommodate the needs of family offices. These include the creation of discretionary trusts for hybrid purposes in perpetuity ie trusts that …

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Genai In Qualitative Data Analysis: Framework-Guided Prompt Engineering In Library Research Practice, Debby R. Wegener Jun 2026

Genai In Qualitative Data Analysis: Framework-Guided Prompt Engineering In Library Research Practice, Debby R. Wegener

FORCE 2026

As Generative AI (GenAI) tools become increasingly integrated across the research lifecycle, researchers need practical, reproducible methods for the responsible use of these technologies. This presentation will demonstrate a systematic approach to using GenAI for qualitative data analysis through a case study of thematic coding in a library website usability study at the Singapore Institute of Technology.

Drawing on prompt engineering frameworks like CLEAR, this session will illustrate how structured prompts can maintain academic rigour and enhance the reliability of GenAI-assisted analysis. The presentation will walk through the complete workflow, that is, from initial data preparation and tool selection to …

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Using Ai-Assisted Programming To Develop Research Services Tools For Research Impact, Open Access Publishing & More, Gary Lee Jun 2026

Using Ai-Assisted Programming To Develop Research Services Tools For Research Impact, Open Access Publishing & More, Gary Lee

FORCE 2026

Academic libraries play a vital role in scholarly communication, As research practices become more data‑driven and interdisciplinary, librarians can help scholars by creating and sharing flexible, customizable tools that align with local workflows and user needs.

AI Assisted programming (sometimes called “vibe coding”) offers a new way for librarians without extensive programming knowledge to develop such tools . This allows previously non-expert librarians to go from conceptual goals to working applications by rapid prototyping, experimentation, and roll-out, resulting in service innovation and improvement.

This presentation illustrates how librarians at HKUST have explored vibe coding with tools like GROK, POE, and …

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Semantic Search For Electronic Theses And Dissertations, Marcelo Garcia Jun 2026

Semantic Search For Electronic Theses And Dissertations, Marcelo Garcia

FORCE 2026

No abstract provided.

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Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma Jun 2026

Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and …

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Emotional Support Through Ai: Venting To Artificial Intelligence Or A Perceived Human May Offer Comparable Emotional Well-Being Benefits, Meilan Hu, Jerlyn Q. H. Ho, Claire Ng, Shermaine S. M. Wong, Andree Hartanto Jun 2026

Emotional Support Through Ai: Venting To Artificial Intelligence Or A Perceived Human May Offer Comparable Emotional Well-Being Benefits, Meilan Hu, Jerlyn Q. H. Ho, Claire Ng, Shermaine S. M. Wong, Andree Hartanto

Research Collection School of Social Sciences

Artificial Intelligence (AI) chatbots are increasingly being explored as sources of informal emotional support, with emerging evidence suggesting that venting to these systems can reduce negative affect. Yet, it remains unclear whether such benefits depend on the responder's perceived identity. Given that emotional relief from venting often hinges on perceived authenticity and emotional validation, this study investigates whether the emotional well-being benefits of venting differ when users believe they are interacting with an AI chatbot versus a human, even when responses are content-matched. In a pre-registered experiment ( N = 279), participants were randomly assigned to either an AI-assisted venting …

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