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Articles 361 - 390 of 9003

Full-Text Articles in Computer Sciences

International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua Nov 2025

International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate …

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Security Modelling For Cyber-Physical Systems: A Systematic Literature Review, Shao Fei Huang, Christopher M. Poskitt, Lwin Khin Shar Nov 2025

Security Modelling For Cyber-Physical Systems: A Systematic Literature Review, Shao Fei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for CPS is an important mechanism to systematically identify and assess vulnerabilities, threats, and risks throughout system life cycles, and to ultimately ensure system resilience, safety, and reliability. This survey delves into state-of-the-art research on CPS security modelling, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, …

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Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang Nov 2025

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …

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Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen Nov 2025

Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth’s surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera’s look angles. Second, the time windows of each target vary during different observation cycles, …

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Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li Nov 2025

Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large …

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Exploring Autonomous Agents: A Closer Look At Why They Fail When Completing Tasks, Ruofan Lu, Yichen Li, Yintong Huo Nov 2025

Exploring Autonomous Agents: A Closer Look At Why They Fail When Completing Tasks, Ruofan Lu, Yichen Li, Yintong Huo

Research Collection School Of Computing and Information Systems

Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-source agent frameworks combined with two LLM backbones, observing a task completion rate of approximately 50%. Through in-depth failure analysis, we develop a three-tier taxonomy of failure causes aligned with task phases, highlighting planning errors, task …

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Why Stop At One Error? Benchmarking Llms As Data Science Code Debuggers For Multi-Hop And Multi-Bug Errors, Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng Nov 2025

Why Stop At One Error? Benchmarking Llms As Data Science Code Debuggers For Multi-Hop And Multi-Bug Errors, Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng

Research Collection School Of Computing and Information Systems

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs’ capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data Science Debugging Benchmark, the first benchmark for systematic evaluation of LLMs on multi-hop error tracing and multi-bug detection in data science code debugging. DSDBench adapts datasets from existing data science task benchmarks, such as DABench and MatPlotBench, featuring realistic data science debugging tasks with automatically synthesized multi-hop, multi-bug code snippets. …

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Predict Social Economic Outcomes By Transferred Knowledge With Satellite Imagery, Yang Tang, Shih-Fen Cheng, Yunqiang Zhu, Yichen Yang, Zhiqiang Zou Nov 2025

Predict Social Economic Outcomes By Transferred Knowledge With Satellite Imagery, Yang Tang, Shih-Fen Cheng, Yunqiang Zhu, Yichen Yang, Zhiqiang Zou

Research Collection School Of Computing and Information Systems

Traditional deep learning methods and econometric models have played a crucial role in the field of data mining, particularly in the prediction of socioeconomic outcomes. However, socio-economic information is unable to be directly extracted from remote sensing data. So, in this paper, we propose a method to leverage transfer learning to predict socioeconomic indicators (outcomes) through satellite imagery. Specifically, we use road network types as a proxy for socioeconomic factors, which is more effective and stable than using nightlight. We have extracted eleven distinct road topological features to generate reasonable road network types. Given the unique characteristics of road networks, …

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Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He Oct 2025

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

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Stroke2sketch: Harnessing Stroke Attributes For Training-Free Sketch Generation, Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu, Shengfeng He Oct 2025

Stroke2sketch: Harnessing Stroke Attributes For Training-Free Sketch Generation, Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu, Shengfeng He

Research Collection School Of Computing and Information Systems

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semanticfocused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely …

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Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim Oct 2025

Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between images and recipes tends to ignore subtle, recipe-specific details that are not visually apparent but are crucial for recipe retrieval. Specifically, the representations are biased to capture the dominant visual elements, resulting in difficulty in ranking similar recipes with subtle differences in use of ingredients and cooking methods. …

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Diffusionmat: Alpha Matting As Deterministic Sequential Refinement Learning, Yangyang Xu, Shengfeng He, Wenqi Shao, Yong Du, Kwan-Yee K. Wong, Yu Qiao, Jun Yu, Ping Luo Oct 2025

Diffusionmat: Alpha Matting As Deterministic Sequential Refinement Learning, Yangyang Xu, Shengfeng He, Wenqi Shao, Yong Du, Kwan-Yee K. Wong, Yu Qiao, Jun Yu, Ping Luo

Research Collection School Of Computing and Information Systems

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a deterministic sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring …

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Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau Oct 2025

Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of …

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Polyqent: A Polynomial Quantified Entailment Solver, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Milad Saadat, Maximilian Seeliger, Dorde Zikelic Oct 2025

Polyqent: A Polynomial Quantified Entailment Solver, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Milad Saadat, Maximilian Seeliger, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Polynomial quantified entailments with existentially and universally quantified variables arise in many problems of verification and program analysis. We present PolyQEnt which is a tool for solving polynomial quantified entailments in which variables on both sides of the implication are real valued or unbounded integers. Our tool provides a unified framework for polynomial quantified entailment problems that arise in several papers in the literature. Our experimental evaluation over a wide range of benchmarks shows the applicability of the tool as well as its benefits as opposed to simply using existing SMT solvers to solve such constraints.

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Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel Oct 2025

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

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Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li Oct 2025

Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li

Research Collection School Of Computing and Information Systems

In recent years, applying deep models to automatically learn construction heuristics for vehicle routing problems has achieved remarkable advancements. However, they are less effective in searching solutions due to two primary limitations: relying on deterministic probability distributions and overlooking the strategic advantage of prioritizing nearby unvisited nodes during the route construction process, resulting in suboptimal policies In this paper, we propose a novel lightweight population-based policy optimization (LPPO) framework that learns a diverse population of solution strategies through the utilization of innovative perturbation factors, in order to facilitate search exploration. Moreover, we design a localized attention synthesis (LAS) network to …

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Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong Oct 2025

Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong

Research Collection School Of Computing and Information Systems

The classical shadows protocol, introduced by Huang et al (2020 Nat. Phys. 16 1050), makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of M observables with failure probability δ using only O ( log ⁡ ( M / δ ) ) measurements. In their analysis, Huang et al used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker (2023 Proc. 36th Conf. on Learning Theory …

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Probabilistic Prototype Calibration Of Vision-Language Models For Generalized Few-Shot Semantic Segmentation, Jie Liu, Jiayi Shen, Pan Zhou, Jan-Jakob Sonke, Stratis Gavves Oct 2025

Probabilistic Prototype Calibration Of Vision-Language Models For Generalized Few-Shot Semantic Segmentation, Jie Liu, Jiayi Shen, Pan Zhou, Jan-Jakob Sonke, Stratis Gavves

Research Collection School Of Computing and Information Systems

Generalized Few-Shot Semantic Segmentation (GFSS) aims to extend a segmentation model to novel classes with only a few annotated examples while maintaining performance on base classes. Recently, pretrained vision-language models (VLMs) such as CLIP have been leveraged in GFSS to improve generalization on novel classes through multi-modal prototypes learning. However, existing prototype-based methods are inherently deterministic, limiting the adaptability of learned prototypes to diverse samples, particularly for novel classes with scarce annotations. To address this, we propose FewCLIP, a probabilistic prototype calibration framework over multi-modal prototypes from the pretrained CLIP, thus providing more adaptive prototype learning for GFSS. Specifically, FewCLIP …

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Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang Oct 2025

Visual-Enhanced Multimodal Framework For Flexible Job Shop Scheduling Problem, Peng Zhao, Zhiguang Cao, Di Wang, Wen Song, Wei Pang, You Zhou, Yuan Jiang

Research Collection School Of Computing and Information Systems

Multimodal models leverage complementary information across modalities to enrich feature representations. While visual information shows potential in representing structure for some combinatorial optimization problems (COPs), its application to complex scheduling like the Flexible Job Shop Scheduling Problem (FJSP) remains underexplored. Current learning-based FJSP solvers predominantly rely on handcrafted state features. This dependence can lead to inconsistencies and may not fully capture the problem's intricate dynamics. Crucially, these methods overlook visual modalities. Visual representations offer a distinct advantage by inherently capturing the global topological structure and complex resource interactions within the FJSP state. Unlike localized handcrafted features, this holistic, structural view …

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Genwardrobe: A Fully Generative System For Travel Fashion Wardrobe Construction, Peng Jin, Yilin Wen, Mingzhe Yu, Yunshan Ma, Rong Zheng, Jin‑Tu Fan, Chong Wah Ngo Oct 2025

Genwardrobe: A Fully Generative System For Travel Fashion Wardrobe Construction, Peng Jin, Yilin Wen, Mingzhe Yu, Yunshan Ma, Rong Zheng, Jin‑Tu Fan, Chong Wah Ngo

Research Collection School Of Computing and Information Systems

With the increasing demand for outfit planning in real-world travel scenarios, the need for constructing a travel fashion wardrobe, a series of outfits tailored to a user's personalization and destination-specific context over a short travel period, has grown significantly. However, existing systems or works often focus on isolated factors and rely on retrieval-based methods, with insufficient utilization of generative models, limiting their adaptability to real-world travel scenarios. To address this issue, this study introduces GenWardrobe, a fully generative system for travel fashion wardrobe construction. GenWardrobe consists of three key modules: user query analysis, fashion knowledge retrieval via retrieval-augmented generation and …

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Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo Oct 2025

Contrastrepair: Enhancing Conversation-Based Automated Program Repair Via Contrastive Test Case Pairs, Jiaolong Kong, Xiaofei Xie, Mingfei Cheng, Shangqing Liu, Xiaoning Du, Qi Guo

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recentstrides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR,especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-drivenAPR is contingent on the quality of the feedback information. In this article, we propose ContrastRepair, anovel conversation-based APR approach that augments conversation-driven APR by providing LLMs withcontrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback tothe LLM. Our key insight is to minimize the difference between the generated passing test and the …

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A Data-Driven Framework For Optimal Retail Store Location, Ming Hui Tan Oct 2025

A Data-Driven Framework For Optimal Retail Store Location, Ming Hui Tan

Dissertations and Theses Collection (Open Access)

This study develops a data-driven framework for optimal retail store location planning that integrates road network analysis, mobility data and optimization techniques. By addressing the limitations of traditional approaches that rely on outdated census data and manual site selection, this research offers a scalable and adaptable solution for retail expansion in diverse urban environments. Chapters 1 and 2 establish the foundational context and theoretical underpinnings of this research. Chapter 1 introduces the research problem and motivation, highlighting the limitations of existing approaches and defining three key research objectives: automating candidate site identification, improving footfall estimation, and developing a scalable multi-site …

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Harnessing Se Community Knowledge For Developer-Centric Code Intelligence, Chengran Yang Oct 2025

Harnessing Se Community Knowledge For Developer-Centric Code Intelligence, Chengran Yang

Dissertations and Theses Collection (Open Access)

The integration of Large Language Models (LLMs), particularly those tailored for programming tasks—referred to as code LLMs—has created novel opportunities to enhance developer productivity. These advanced models automate routine and repetitive coding tasks, such as code generation and debugging, and enable faster prototyping and more efficient problem-solving. Despite these remarkable advantages, the current generation of code LLMs exhibits notable limitations that impact their practical effectiveness in real-world software engineering scenarios. These models frequently produce code that is inefficient or suboptimal in runtime performance, demonstrate opaque reasoning processes, and struggle to adapt effectively to diverse developer contexts and specific requirements. Moreover, …

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Information Provision And Search Frictions: Evidence From The Taxi Industry In Singapore, Sumit Agarwal, Shih-Fen Cheng, Jussi Keppo, Long Wang, Yang Yang Oct 2025

Information Provision And Search Frictions: Evidence From The Taxi Industry In Singapore, Sumit Agarwal, Shih-Fen Cheng, Jussi Keppo, Long Wang, Yang Yang

Research Collection School Of Computing and Information Systems

Search frictions and misallocation are common in decentralized transportation markets. Using novel trip-level data of taxis in Singapore, this paper examines the impactof real-time demand information at airport terminals on search frictions. The information reduces taxi supply misallocation, increasing deadheading speed by 16.3% and decreasing deadheading time by 10.77%, benefiting both passengers and drivers. It raises daily earnings by $3.70 USD and adds 6.2 minutes of operational time per airport-trip taxi. Spatial spillovers are primarily observed among drivers in adjacentdistricts. Taxis from the Budget Terminal and drivers with fewer prior airport pickups benefit more from this information.

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A System Framework To Symbolically Explore Intel Tdx Module Execution, Pansilu Pitigalaarachchillage, Xuhua Ding Oct 2025

A System Framework To Symbolically Explore Intel Tdx Module Execution, Pansilu Pitigalaarachchillage, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present TDXplorer, the first dynamic symbolic analysis system for Intel's TDX Module, the software trusted computing base of TDX. Without using TDX hardware, an analyzer function on top of TDXplorer can not only apply dynamic analysis to control and instrument the TDX Module's execution, but also carry out symbolic execution for path exploration as well as security and functionality reasoning. The two types of analysis are seamlessly integrated in a way that symbolic execution is conducted directly upon the TDX Module's binary code and runtime states, which are shaped by using dynamic analysis techniques. We implement TDXplorer on Linux …

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Parameter-Efficient Variational Autoencoder For Multimodal Multi-Interest Recommendation, Nhu Thuat Tran, Hady Wirawan Lauw Oct 2025

Parameter-Efficient Variational Autoencoder For Multimodal Multi-Interest Recommendation, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning user preferences in recommendation systems is enriched by multimodal features, such as textual and visual content, and amplified by multi-interest modeling with Variational AutoEncoders (VAEs). However, prior efforts are limited by single modality focus and cumbersome, parameter-heavy architecture designs. To address these limitations, we introduce an innovative solution that blends the semantic richness of multimodal data with the representational power of multi-representation VAEs. Drawing inspiration from Mixture of Experts (MoE), we cast each VAE as an expert tailored to a specific modality, then fuse them via a novel parameter-merging function into a lean, unified model. This approach efficiently captures …

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Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang Oct 2025

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

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Ivycross: A Privacy-Preserving And Concurrency Control Framework For Blockchain Interoperability, Ming Li, Jian Weng, Jia-Si Weng, Yi Li, Yongdong Wu, Dingcheng Li, Guowen Xu, Deng, Robert H. Oct 2025

Ivycross: A Privacy-Preserving And Concurrency Control Framework For Blockchain Interoperability, Ming Li, Jian Weng, Jia-Si Weng, Yi Li, Yongdong Wu, Dingcheng Li, Guowen Xu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design …

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Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin Oct 2025

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

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Cookingdiffusion: Cooking Procedural Image Generation With Stable Diffusion, Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, Xiang Wang Oct 2025

Cookingdiffusion: Cooking Procedural Image Generation With Stable Diffusion, Yuan Wang, Bin Zhu, Yanbin Hao, Chong-Wah Ngo, Yi Tan, Xiang Wang

Research Collection School Of Computing and Information Systems

Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional inputs like cooking styles, ingredients, and recipes are utilized. However, a yet-unexplored challenge is generating a sequence of procedural images based on cooking steps from a recipe. This could enhance the cooking experience with visual guidance and possibly lead to an intelligent cooking simulation system. To fill this gap, we introduce a novel task called cooking procedural image generation. This task is inherently demanding, as it strives to create photo-realistic images that align with cooking steps while …

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