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Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam Jul 2025

Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam

Research Collection School Of Computing and Information Systems

Background/Introduction: Obesity is projected to affect more than half of the global population by 2035, posing significant health and economic challenges. While lifestyle modification is considered a cornerstone of weight management, its effectiveness often relies on substantial support systems. Purpose: This study aimed to evaluate the effectiveness of a 12-week, standalone Temporal Self-Regulation Theory (TST)-based weight loss mobile application, which integrates self-regulation techniques, food logging, and dietary nudging, in promoting weight loss among young adults with excess body weight. Methods: A two-arm, parallel-group, 1:1 randomized controlled trial was conducted, adhering to the CONSORT-Outcomes 2022 Extension guidelines. Participants completed a face-to-face …

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Cracking Aegis: An Adversarial Llm-Based Game For Raising Awareness Of Vulnerabilities In Privacy Protection, Jiaying Fu, Yiyang Lu, Zehua Yang, Fiona Fui-Hoon Nah, Ray Lc Jul 2025

Cracking Aegis: An Adversarial Llm-Based Game For Raising Awareness Of Vulnerabilities In Privacy Protection, Jiaying Fu, Yiyang Lu, Zehua Yang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited. To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis. Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and extract sensitive information from an AI agent, Aegis. A user study (n=22) revealed that players employed diverse deceptive linguistic strategies, including storytelling and emotional rapport, to manipulate Aegis. After playing, players reported connecting in-game scenarios with real-world privacy vulnerabilities, such as phishing and impersonation, and …

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Political Ecologies Of Storage For The 21st Century, Sayd Randle, Matthew Archer Jul 2025

Political Ecologies Of Storage For The 21st Century, Sayd Randle, Matthew Archer

Research Collection College of Integrative Studies

New resource storage arrangements are proliferating rapidly both in terms of physical infrastructures-for the storage of things like "clean" energy, nuclear waste, carbon dioxide, fresh water, and data-and as part of a set of discursive moves that reinforce a vision of a near future world in which problems of climate change mitigation and adaptation in particular (but also issues like energy security, water security, industry growth, etc.) are solved through eco-modernist techno-fixes. This Symposium sketches the contours of a framework we term political ecologies of storage. In doing so, we treat storage as both a potent imaginary and a concrete …

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Artificial Insights Or Historical Fidelity? Crafting An Ethical Framework For The Use Of Genai In The Restoration, Reconstruction And Recreation Of Movable Cultural Heritage, David Ocón, Chunzhi Yin, Jose Luna Jul 2025

Artificial Insights Or Historical Fidelity? Crafting An Ethical Framework For The Use Of Genai In The Restoration, Reconstruction And Recreation Of Movable Cultural Heritage, David Ocón, Chunzhi Yin, Jose Luna

Research Collection School of Social Sciences

This article explores the ethical considerations surrounding using Generative Artificial Intelligence (GenAI) in preserving movable cultural heritage, focusing specifically on its application in restoration, reconstruction, and recreation. While GenAI offers innovative methods for preserving and recreating cultural heritage, it also presents significant ethical challenges. The article reviews current studies on the role of GenAI in heritage preservation alongside relevant ethical guidelines and proposes a tailored ethical framework for its application in movable heritage. The framework addresses several critical ethical concerns, including cultural integrity and sensitivity, accuracy and authenticity, intellectual property rights, sustainability and social impact, and governance and ethical accountability. …

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Sensor Lag Adjustments On A Mobile Meteorological Cart In Tropical Environments: A Case Study From An Urban Park In Singapore, Moshe Eliezer Mandelmilch, Sin Kang Yik, Beatrice H. Ho, Graces N. Y. Ching, Peter J. Crank, Winston T. L. Chow Jul 2025

Sensor Lag Adjustments On A Mobile Meteorological Cart In Tropical Environments: A Case Study From An Urban Park In Singapore, Moshe Eliezer Mandelmilch, Sin Kang Yik, Beatrice H. Ho, Graces N. Y. Ching, Peter J. Crank, Winston T. L. Chow

Research Collection College of Integrative Studies

This study adds to the growing literature on mobile platforms by examining the performance of the Singapore MaRTy (SMaRTy) in conducting mobile micrometeorological measurements along a designated route in an urban park in Singapore, reflecting a pedestrian walking experience. The main objective of the study was to calculate the sensor lag for the mobile climate measurements: Air Temperature (TA), and Relative Humidity (RH) of SMaRTy along a designated route in an urban park in Singapore based on data from fixed stations (HOBO sensors) in two significant synoptic meteorological periods during the Northeast and Southwest monsoons. Statistically significant regression models were …

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Do Supplier Ceo's National Cultural Origins Affect Supplier-Customer Relationships?, Peng Liang, Hasan Cavusoglu, Nan Hu Jul 2025

Do Supplier Ceo's National Cultural Origins Affect Supplier-Customer Relationships?, Peng Liang, Hasan Cavusoglu, Nan Hu

Research Collection School Of Computing and Information Systems

This study investigates how the national cultural origins of supplier chief executive officers (CEOs), as characterized by Hofstede’s cross-cultural dimensions, influence the duration of supplier–customer relationships. By analyzing the cultural origins of supplier CEOs from 20 countries over a 25-year period, we find that supplier CEOs with high long-term orientation (LTO) and high uncertainty avoidance (UNA) are associated with longer lasting supplier–customer relationships, while those with high individualism (IND) are associated with shorter relationship durations. These findings are robust to several alternative explanations of customer and supplier CEO variables. To address potential endogeneity—specifically, the concern that CEOs with certain cultural …

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Unbounded Multi-Hop Proxy Re-Encryption With Hra Security: An Lwe-Based Optimization, Xiaohan Wan, Yang Wang, Haiyang Xue, Mingqiang Wang Jul 2025

Unbounded Multi-Hop Proxy Re-Encryption With Hra Security: An Lwe-Based Optimization, Xiaohan Wan, Yang Wang, Haiyang Xue, Mingqiang Wang

Research Collection School Of Computing and Information Systems

Proxy re-encryption (PRE) schemes enable a semi-honest proxy to transform a ciphertext of one user i to another user j while preserving the privacy of the underlying message. Multi-hop PRE schemes allow a legal ciphertext to undergo multiple transformations, but for lattice-based multi-hop PREs, the number of transformations is typically bounded due to the increase of error terms. Recently, Zhao et al. (ESORICS 2024) introduced a lattice-based unbounded multi-hop (homomorphic) PRE scheme that supports an unbounded number of hops. Nevertheless, their scheme only achieves the selective CPA security. In contrast, Fuchsbauer et al. (PKC 2019) proposed a generic framework for …

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An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng Jul 2025

An Incentive Mechanism For Privacy Preserved Data Trading With Verifiable Data Disturbance, Man Zhang, Xinghua Li, Bin Luo, Yanbing Ren, Yinbin Miao, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer's (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs's local disturbance's privacy and verifiability with …

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Collaboration With Dynamic Open Ad Hoc Team Via Team State Modelling, Jing Sun, Cong Zhang, Zhiguang Cao Jul 2025

Collaboration With Dynamic Open Ad Hoc Team Via Team State Modelling, Jing Sun, Cong Zhang, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Open ad hoc teamwork presents the challenging problem of designing an autonomous agent that can rapidly adapt to collaborate with teammates without prior coordination in an open environment. Existing methods primarily rely on fixed, predefined teammate types, overlooking the fact that teammates may change dynamically. To address this limitation, we propose a novel reinforcement learning approach, the Open Online Teammate Adaptation Framework (Open-OTAF), which enables a controlled agent to collaborate with dynamic teammates in open ad hoc environments. To achieve this, the controlled agent employs a dual teamwork situation inference model to capture the current teamwork state, facilitating decision-making under …

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Ts-Diff: Two-Stage Diffusion Model For Low-Light Raw Image Enhancement, Yi Li, Zhiyuan Zhang, Jiangnan Xia, Jianghan Cheng, Qilong Wu, Junwei Li Jul 2025

Ts-Diff: Two-Stage Diffusion Model For Low-Light Raw Image Enhancement, Yi Li, Zhiyuan Zhang, Jiangnan Xia, Jianghan Cheng, Qilong Wu, Junwei Li

Research Collection School Of Computing and Information Systems

This paper presents a novel Two-Stage Diffusion Model (TS-Diff) for enhancing extremely low-light RAW images. In the pre-training stage, TS-Diff synthesizes noisy images by constructing multiple virtual cameras based on a noise space. Camera Feature Integration (CFI) modules are then designed to enable the model to learn generalizable features across diverse virtual cameras. During the aligning stage, CFIs are averaged to create a target-specific CFIT, which is fine-tuned using a small amount of real RAW data to adapt to the noise characteristics of specific cameras. A structural reparameterization technique further simplifies CFIT for efficient deployment. To address color shifts during …

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Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang Jul 2025

Rustmap: Towards Project-Scale C-To-Rust Migration Via Program Analysis And Llm, Xuemeng Cai, Jiakun Liu, Xiping Huang, Yijun Yu, Haitao Wu, Chunmiao Li, Bo Wang, Imam Nur Bani Yusuf, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C’s high performance. Existing automated translation tools, such as C2Rust, may rely too much on syntactic, template-based translation and generate unsafe Rust code that is hard for human developers to read, maintain, or even compile. More semantic-aware translation that produces safer, idiomatic, and runnable Rust code is much needed. This paper introduces a novel dependency-guided and large language model (LLM)-based C-to-Rust translation approach, RustMap, based on three key ideas: (1) Utilize LLM’s capabilities to produce idiomatic Rust code from given small pieces of …

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Quantum Technologies In Decentralisation, Paul Robert Griffin, Rudy Raymond, Tsuyoshi Idé Jul 2025

Quantum Technologies In Decentralisation, Paul Robert Griffin, Rudy Raymond, Tsuyoshi Idé

Research Collection School Of Computing and Information Systems

Quantum technologies, rooted in the manipulation of quantum information, are revolutionizing computing and networking domains. Their impact on blockchains and decentralized systems is twofold. While much attention has been given to the potential of quantum computing to attack blockchains, these advanced technologies also offer avenues for strengthening and optimizing them. This chapter delves into the intricacies of quantum technologies, from the foundational concepts of qubits, quantum gates, and quantum networks to their implications for blockchains. We explore both the vulnerabilities of blockchains in a quantum-dominant era and the promising solutions quantum technologies provide, culminating in a use case examining their …

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The B2biers System: A Content-Based Perspective On Maximizing Influence And Subscription In Social Networks, Konstantinos Theocharidis, Hady Wirawan Lauw Jul 2025

The B2biers System: A Content-Based Perspective On Maximizing Influence And Subscription In Social Networks, Konstantinos Theocharidis, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

The popular problem of Influence Maximization (IM) asks for the k users who can maximize the influence of a fixed post in a social network. In contrast, the problem of Content- Aware Influence Maximization (CAIM) asks for the k features to form a viral tunable post in a social network starting its diffusion from a fixed set of initial adopters. CAIM paves the way for a number of novel problems to be studied that altogether can lead to the development of a system that would be valuable for advertisers who manage social network pages. This holds since features (brands) in …

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Evaluating Chatgpt To Answer Multi-Modal Exercises In Computer Science Education, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan Jul 2025

Evaluating Chatgpt To Answer Multi-Modal Exercises In Computer Science Education, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan

Research Collection School Of Computing and Information Systems

This study investigates ChatGPT-4o's ability to answer multi-modal assessment exercises in computer science (CS) courses. While the use of large language models (LLMs) to answer text-based exercises are extensively researched, their ability to answer exercises involving artifacts of other modalities remains underexplored. To close this gap, we evaluate ChatGPT-4o's answers to 120 multi-modal CS exercises in programming, software design, human-computer interaction, statistical analysis, process analysis, and simulation. The multi-modal artifacts in these exercises include class diagrams, sequence diagrams, user interface images, analytical charts, workflow diagrams and object-flow diagrams. Our comparisons to the expected answers of these exercises show that ChatGPT-4o …

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Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong Jul 2025

Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing broad aspects of daily life. Despite their remarkable performance, LLMs exhibit a fundamental limitation: hallucination—the tendency to produce misleading outputs that appear plausible. This inherent unreliability poses significant risks, particularly in high-stakes domains where trustworthiness is essential. On the other hand, Formal Methods (FMs), which share foundations with symbolic AI, provide mathematically rigorous techniques for modeling, specifying, reasoning, and verifying the correctness of systems. These methods have been widely employed in mission-critical domains such as aerospace, defense, and cybersecurity. However, the broader adoption of FMs remains constrained …

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An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi Jul 2025

An On-The-Fly Synthesis Framework For Ltl Over Finite Traces, Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Vardi

Research Collection School Of Computing and Information Systems

We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to formula size in the worst case. In this case, the synthesis cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components …

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Unified Neural Backdoor Removal With Only Few Clean Samples Through Unlearning And Relearning, Nay Myat Min, Long H. Pham, Jun Sun Jul 2025

Unified Neural Backdoor Removal With Only Few Clean Samples Through Unlearning And Relearning, Nay Myat Min, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Deep neural networks have achieved remarkable success across various applications; however, their vulnerability to backdoor attacks poses severe security risks—especially in situations where only a limited set of clean samples is available for defense. In this work, we address this critical challenge by proposing ULRL (UnLearn and ReLearn for backdoor removal), a novel two-phase approach for comprehensive backdoor removal. Our method first employs an unlearning phase, in which the network’s loss is intentionally maximized on a small clean dataset to expose neurons that are excessively sensitive to backdoor triggers. Subsequently, in the relearning phase, these suspicious neurons are recalibrated using …

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Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim Jul 2025

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

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Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Mohammad Aliannejadi, Yang Deng Jul 2025

Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Mohammad Aliannejadi, Yang Deng

Research Collection School Of Computing and Information Systems

Query understanding in CIS involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. LLM enhance this process by interpreting nuanced language and adapting dynamically, improving the relevance and precision of search results in real-time. In this tutorial, we explore advanced techniques to enhance query understanding in LLM-based CIS systems. We delve into LLM-driven methods for developing robust evaluation metrics to assess query understanding quality in multi-turn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We also discuss key challenges in integrating …

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Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng Jul 2025

Sanitizable Cross-Domain Access Control With Policy-Driven Dynamic Authorization, Jianfei Sun, Guowen Xu, Hongwei Li, Tianwei Zhang, Cong Wu, Xuehuan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The increasing demand for secure and efficient data sharing has underscored the importance of developing robust cryptographic schemes. However, many existing endeavors have overlooked the following critical issues: (1) unauthorized access resulting from malicious information leakage by senders; (2) absence of constraints on write and read permissions for participants; (3) and inflexibility of strategies to dynamically designate ciphertexts to multiple recipients. In this paper, we present SCPA, a cross-domain access control scheme imbued with sanitization features and propelled by policy-driven dynamic authorization, tailored for cloud-based data sharing. This scheme not only facilitates access controls, including regulations for no-read and no-write …

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Action Dubber: Timing Audible Actions Via Inflectional Flow, Wenlong Wan, Weiying Zheng, Tianyi Xiang, Guiqing Li, Shengfeng He Jul 2025

Action Dubber: Timing Audible Actions Via Inflectional Flow, Wenlong Wan, Weiying Zheng, Tianyi Xiang, Guiqing Li, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the task of Audible Action Temporal Localization, which aims to identify the spatiotemporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose T A2Net, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio …

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Unambiguous Granularity Distillation For Asymmetric Image Retrieval, Hongrui Zhang, Yi Xie, Haoquan Zhang, Cheng Xu, Xuandi Luo, Donglei Chen, Xuemiao Xu, Huaidong Zhang, Pheng Ann Heng, Shengfeng He Jul 2025

Unambiguous Granularity Distillation For Asymmetric Image Retrieval, Hongrui Zhang, Yi Xie, Haoquan Zhang, Cheng Xu, Xuandi Luo, Donglei Chen, Xuemiao Xu, Huaidong Zhang, Pheng Ann Heng, Shengfeng He

Research Collection School Of Computing and Information Systems

Previous asymmetric image retrieval methods based on knowledge distillation have primarily focused on aligning the global features of two networks to transfer global semantic information from the gallery network to the query network. However, these methods often fail to effectively transfer local semantic information, limiting the fine-grained alignment of feature representation spaces between the two networks. To overcome this limitation, we propose a novel approach called Layered-Granularity Localized Distillation (GranDist). GranDist constructs layered feature representations that balance the richness of contextual information with the granularity of local features. As we progress through the layers, the contextual information becomes more detailed, …

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Leakage-Resilient Easily Deployable And Efficiently Searchable Encryption (Edese), Jiaming Yuan, Yingjiu Li, Jun Li, Daoyuan Wu, Jianting Ning, Yangguang Tian, Robert H. Deng Jul 2025

Leakage-Resilient Easily Deployable And Efficiently Searchable Encryption (Edese), Jiaming Yuan, Yingjiu Li, Jun Li, Daoyuan Wu, Jianting Ning, Yangguang Tian, Robert H. Deng

Research Collection School Of Computing and Information Systems

Easily Deployable and Efficiently Searchable Encryption (EDESE) is a cryptographic primitive designed for practical searchable applications, offering efficient search and easy deployment. However, it remains vulnerable to Leakage-Abuse attacks, allowing adversaries to exploit keyword-matching processes to extract sensitive information. To address these vulnerabilities, we introduce Leakage-Resilient EDESE (LR-EDESE) with k-indistinguishability and controlled leakage functions. We then propose Volume Leakage-Resilient EDESE (VLR-EDESE), a new scheme to protect against both query and document volume leakage. Our experimental results demonstrate that at k = 5000 (maximum security setting), VLR-EDESE incurs an overhead of 63× compared to the baseline EDESE without leakage protection, outperforming …

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Llm-Based Multi-Agent Systems For Software Engineering: Literature Review, Vision And The Road Ahead, Junda He, Christoph Treude, David Lo Jul 2025

Llm-Based Multi-Agent Systems For Software Engineering: Literature Review, Vision And The Road Ahead, Junda He, Christoph Treude, David Lo

Research Collection School Of Computing and Information Systems

Integrating Large Language Models (LLMs) into autonomous agents marks a significant shift in the research landscape by offering cognitive abilities that are competitive with human planning and reasoning. This paper explores the transformative potential of integrating Large Language Models into Multi-Agent (LMA) systems for addressing complex challenges in software engineering (SE). By leveraging the collaborative and specialized abilities of multiple agents, LMA systems enable autonomous problem-solving, improve robustness, and provide scalable solutions for managing the complexity of real-world software projects. In this paper, we conduct a systematic review of recent primary studies to map the current landscape of LMA applications …

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Surrogate Learning In Meta-Black-Box Optimization: A Preliminary Study, Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao, Yue-Jiao Gong Jul 2025

Surrogate Learning In Meta-Black-Box Optimization: A Preliminary Study, Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configure low-level optimizers. However, existing MetaBBO approaches potentially consume massive function evaluations to train their meta-level policies. Inspired by the recent trend of using surrogate models for cost-friendly evaluation of expensive optimization problems, in this paper, we propose a novel MetaBBO framework which combines surrogate learning process and reinforcement learning-aided Differential Evolution algorithm, namely Surr-RLDE, to address the intensive function evaluation in MetaBBO. Surr-RLDE comprises two learning stages: surrogate learning and policy learning. In surrogate learning, we train a Kolmogorov-Arnold Networks …

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A Mixed-Curvature Based Pre-Training Paradigm For Multi-Task Vehicle Routing Solver, Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew-Soon Ong Jul 2025

A Mixed-Curvature Based Pre-Training Paradigm For Multi-Task Vehicle Routing Solver, Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew-Soon Ong

Research Collection School Of Computing and Information Systems

Solving various types of vehicle routing problems (VRPs) using a unified neural solver has garnered significant attentions in recent years. Despite their effectiveness, existing neural multi-task solvers often fail to account for the geometric structures inherent in different tasks, which may result in suboptimal performance. To address this limitation, we propose a curvature-aware pre-training framework. Specifically, we leverage mixed-curvature spaces during the feature fusion stage, encouraging the model to capture the underlying geometric properties of each instance. Through extensive experiments, we evaluate the proposed pre-training strategy on existing neural multi-task solvers across a variety of testing scenarios. The results demonstrate …

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Meta-Black-Box-Optimization Through Offline Q-Function Learning, Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong Jul 2025

Meta-Black-Box-Optimization Through Offline Q-Function Learning, Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong

Research Collection School Of Computing and Information Systems

Recent progress in Meta-Black-Box-Optimization (MetaBBO) has demonstrated that using RL to learn a meta-level policy for dynamic algorithm configuration (DAC) over an optimization task distribution could significantly enhance the performance of the low-level BBO algorithm. However, the online learning paradigms in existing works makes the efficiency of MetaBBO problematic. To address this, we propose an offline learning-based MetaBBO framework in this paper, termed Q-Mamba, to attain both effectiveness and efficiency in MetaBBO. Specifically, we first transform DAC task into long-sequence decision process. This allows us further introduce an effective Q-function decomposition mechanism to reduce the learning difficulty within the intricate …

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Shield: Multi-Task Multi-Distribution Vehicle Routing Solver With Sparsity And Hierarchy, Yong Liang Goh, Zhiguang Cao, Yining Ma, Jianan Zhou, Mohammed Haroon Dupty, Wee Sun Lee Jul 2025

Shield: Multi-Task Multi-Distribution Vehicle Routing Solver With Sparsity And Hierarchy, Yong Liang Goh, Zhiguang Cao, Yining Ma, Jianan Zhou, Mohammed Haroon Dupty, Wee Sun Lee

Research Collection School Of Computing and Information Systems

Recent advances toward foundation models for routing problems have shown great potential of a unified deep model for various VRP variants. However, they overlook the complex real-world customer distributions. In this work, we advance the Multi-Task VRP (MTVRP) setting to the more realistic yet challenging Multi-Task Multi-Distribution VRP (MTMDVRP) setting, and introduce SHIELD, a novel model that leverages both sparsity and hierarchy principles. Building on a deeper decoder architecture, we first incorporate the Mixture-of-Depths (MoD) technique to enforce sparsity. This improves both efficiency and generalization by allowing the model to dynamically select nodes to use or skip each decoder layer, …

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Hdifftg: A Lightweight Hybrid Diffusion-Transformer-Gcn Architecture For 3d Human Pose Estimation, Yajie Fu, Chaorui Huang, Junwei Li, Hui Kong, Yibin Tian, Huakang Li, Zhiyuan Zhang Jul 2025

Hdifftg: A Lightweight Hybrid Diffusion-Transformer-Gcn Architecture For 3d Human Pose Estimation, Yajie Fu, Chaorui Huang, Junwei Li, Hui Kong, Yibin Tian, Huakang Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

We propose HDiffTG, a novel 3D Human Pose Estimation (3DHPE) method that integrates Transformer, Graph Convolutional Network (GCN), and diffusion model into a unified framework. HDiffTG leverages the strengths of these techniques to significantly improve pose estimation accuracy and robustness while maintaining a lightweight design. The Transformer captures global spatiotemporal dependencies, the GCN models local skeletal structures, and the diffusion model provides step-by-step optimization for fine-tuning, achieving a complementary balance between global and local features. This integration enhances the model’s ability to handle pose estimation under occlusions and in complex scenarios. Furthermore, we introduce lightweight optimizations to the integrated model …

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O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen Jul 2025

O-Mapl: Offline Multi-Agent Preference Learning, The Viet Bui, Tien Mai, Hong Thanh Nguyen

Research Collection School Of Computing and Information Systems

Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL). The large joint state-action spaces and intricate inter-agent interactions in MARL make inferring the joint reward function especially challenging. While prior studies in single-agent settings have explored ways to recover reward functions and expert policies from human preference feedback, such studies in MARL remain limited. Existing methods typically combine two separate stages, supervised reward learning, and standard MARL algorithms, leading to unstable training processes. In this work, we exploit the inherent connection between reward functions and Q functions in cooperative MARL to …

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