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Relation Prediction In Knowledge Graphs: A Self-Organizing Neural Network Approach, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan
Relation Prediction In Knowledge Graphs: A Self-Organizing Neural Network Approach, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Knowledge graphs (KGs) in specialized domains frequently suffer from incomplete information. While current relation prediction methods for KG completion typically rely on neural network-based representation learning, we present KG2ART---a novel self-organizing neural network that employs a fundamentally different approach. Instead of learning distributed representations, KG2ART encodes relation triples of knowledge graphs explicitly and performs parallel inference over the graph structure through bidirectional interactions between bottom-up activations and top-down pattern matching. Our comprehensive evaluation across five diverse KGs (Nations, UMLS, Kinship, CoDEx-M, and a jet engine technical KG) demonstrates that KG2ART consistently outperforms state-of-the-art baselines (TuckER, ComplEX, RESCAL, ConvE, CompGCN) in …
Guest Editorial: When Multimedia Meets Food: Multimedia Computing For Food Data Analysis And Applications, Weiqing Min, Shuqiang Jiang, Petia Radeva, Vladimir Pavlovic, Chong-Wah Ngo, Kiyoharu Aizawa, Wanqing Li
Guest Editorial: When Multimedia Meets Food: Multimedia Computing For Food Data Analysis And Applications, Weiqing Min, Shuqiang Jiang, Petia Radeva, Vladimir Pavlovic, Chong-Wah Ngo, Kiyoharu Aizawa, Wanqing Li
Research Collection School Of Computing and Information Systems
Food is central in our life for its fundamental role in our survival, health, mood and culture. The deployment of various networks (e.g., IoT and mobile networks), devices (e.g., hyperspectral imaging devices, electronic nose/tongue), databases (e.g., nutrition tables and food compositional databases), recipe-sharing websites (e.g., Yummly and Meishijie) and social media (e.g., Twitter and Weibo) has generated unprecedented volumes of multi-modal food data. Such multi-source multi-modal food data provides new perspectives to analyze and understand food consumption via multimedia computing. Riding on the wave of AI, food-oriented multimedia computing integrates AI, multimedia technology and food science to enable a wide …
Creating Talking Points For Client Advisers At Banks To Promote Sustainable Investing, Wewe Zi Yi, Pradeep Varakantham, Alan Megargel
Creating Talking Points For Client Advisers At Banks To Promote Sustainable Investing, Wewe Zi Yi, Pradeep Varakantham, Alan Megargel
Research Collection School Of Computing and Information Systems
Environmental, social and governance (ESG) factors have become key nonfinancial factors for investors to evaluate companies with respect to understanding material risks and growth opportunities. While not mandatory, companies are providing ESG reports that outline progress in different ESG metrics (six broad metrics and 15 specific ones). Client advisers (CAs) read these reports to identify key metrics of interest to investors. Given the number of companies and investment products, however, it is not feasible for CAs to read all the reports, which can sometimes run into tens or hundreds of pages). The authors have developed multiple frameworks building on leading …
Prompting An Embodied Ai Agent: How Embodiment And Multimodal Signaling Affects Prompting Behaviour, Tianyi Zhang, Colin Au Yeung, Emily Aurelia, Yuki Onishi, Neil Chulpongsatorn, Jiannan Li, Anthony Tang
Prompting An Embodied Ai Agent: How Embodiment And Multimodal Signaling Affects Prompting Behaviour, Tianyi Zhang, Colin Au Yeung, Emily Aurelia, Yuki Onishi, Neil Chulpongsatorn, Jiannan Li, Anthony Tang
Research Collection School Of Computing and Information Systems
Current voice agents wait for a user to complete their verbal instruction before responding; yet, this is misaligned with how humans engage in everyday conversational interaction, where interlocutors use multimodal signaling (e.g. nodding, grunting, or looking at referred to objects) to ensure conversational grounding. We designed an embodied VR agent that exhibits multimodal signaling behaviors in response to situated prompts, by turning its head, or by visually highlighting objects being discussed or referred to. We explore how people prompt this agent to design and manipulate the objects in a VR scene. Through a Wizard of Oz study, we found that …
Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu
Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu
Research Collection School Of Computing and Information Systems
Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …
Integrating Path Selection For Symbolic Execution And Variable Selection For Constraint Solving, Shunkai Zhu, Jun Sun, Jingyi Wang, Zhenbang Chen, Peng Cheng
Integrating Path Selection For Symbolic Execution And Variable Selection For Constraint Solving, Shunkai Zhu, Jun Sun, Jingyi Wang, Zhenbang Chen, Peng Cheng
Research Collection School Of Computing and Information Systems
Symbolic execution is a powerful technique that can accurately synthesize program inputs for program testing through constraint solving. Applying symbolic execution effectively means that we must solve two searching problems efficiently. One is to search through the many program paths and the other is, given a particular path condition, to search through the numerous variable assignments to identify one satisfying solution. With few exceptions, existing symbolic execution engines treat constraint solvers as black boxes. As a result, the two searches are completely separated, which results in much redundancy (i.e., the same variable assignments may be tried for solving many program …
Tensorjsfuzz: Effective Testing Of Web-Based Deep Learning Frameworks Via Input-Constraint Extraction, Lili Quan, Xiaofei Xie, Qianyu Guo, Lingxiao Jiang, Sen Chen, Junjie Wang, Xiaohong Li
Tensorjsfuzz: Effective Testing Of Web-Based Deep Learning Frameworks Via Input-Constraint Extraction, Lili Quan, Xiaofei Xie, Qianyu Guo, Lingxiao Jiang, Sen Chen, Junjie Wang, Xiaohong Li
Research Collection School Of Computing and Information Systems
The 2025 ACM Web Conference (WWW '25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its logo, featuring the Sydney Harbour Bridge, symbolizes the core "connecting" function of the Web. Formerly known as the International World Wide Web Conference (WWW), this event originated at CERN in 1994 and has long served as the premier venue for presenting and discussing research, development, standards, and applications related to the Web.The 2025 ACM Web Conference (WWW'25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its …
Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li
Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …
Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan
Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan
Research Collection School Of Computing and Information Systems
Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration …
Sans: Efficient Densest Subgraph Discovery Over Relational Graphs Without Materialization, Yudong Niu, Yuchen Li, Jiaxin Jiang, Laks V. S. Lakshmanan
Sans: Efficient Densest Subgraph Discovery Over Relational Graphs Without Materialization, Yudong Niu, Yuchen Li, Jiaxin Jiang, Laks V. S. Lakshmanan
Research Collection School Of Computing and Information Systems
How can we efficiently identify the densest subgraph over relational graphs? Existing dense subgraph discovery (DSD) approaches assume that a relational graph H is already derived from a heterogeneous data source and they focus on efficient discovery of the densest subgraph on the materialized H. Unfortunately, materializing relational graphs can be resource-intensive, which thus limits the practical usefulness of existing algorithms over large datasets. To mitigate this, we propose a novel Summary-bAsed deNsest Subgraph discovery (SANS) system. Our unique summary-based peeling algorithm forms the core of SANS. Following the peeling paradigm, it utilizes summaries of each node's neighborhood to efficiently …
Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang
Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang
Research Collection School Of Computing and Information Systems
We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations. Despite promising results, these approaches encounter practical limitations due to lengthy optimizations and significant memory consumption. In this work, we introduce Gamba, an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: (1) Efficient Backbone Design: introducing a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length, thereby accommodating a substantial number …
Building Bridges Across Papua New Guinea’S Digital Divide In Growing The Ict Industry, Marc Cheong, Sankwi Abuzo, Hideaki Hata, Priscilla Kevin, Winifred Kula, Benson Mirou, Christoph Treude, Dong Wang, Raula Gaikovina Kula
Building Bridges Across Papua New Guinea’S Digital Divide In Growing The Ict Industry, Marc Cheong, Sankwi Abuzo, Hideaki Hata, Priscilla Kevin, Winifred Kula, Benson Mirou, Christoph Treude, Dong Wang, Raula Gaikovina Kula
Research Collection School Of Computing and Information Systems
Papua New Guinea (PNG) is an emerging tech society with an opportunity to overcome geographic and social boundaries, in order to engage with the global market. However, the current tech landscape, dominated by Big Tech in Silicon Valley and other multinational companies in the Global North, tends to overlook the requirements of emerging economies such as PNG. This is becoming more obvious as issues such as algorithmic bias (in tech product deployments) and the digital divide (as in the case of non-affordable commercial software) are affecting PNG users. The Open Source Software (OSS) movement, based on extant research, is seen …
Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin
Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin
Research Collection School Of Computing and Information Systems
Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior in the global partition phase, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based …
“Ronaldo’S A Poser!”: How The Use Of Generative Ai Shapes Debates In Online Forums, Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Fui-Hoon Nah, Ray Lc
“Ronaldo’S A Poser!”: How The Use Of Generative Ai Shapes Debates In Online Forums, Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Fui-Hoon Nah, Ray Lc
Research Collection School Of Computing and Information Systems
Online debates can enhance critical thinking but may escalate into hostile attacks. As humans are increasingly reliant on Generative AI (GenAI) in writing tasks, we need to understand how people utilize GenAI in online debates. To examine the patterns of writing behavior while making arguments with GenAI, we created an online forum for soccer fans to engage in turn-based and free debates in a post format with the assistance of ChatGPT, arguing on the topic of "Messi vs Ronaldo". After 13 sessions of two-part study and semi-structured interviews with 39 participants, we conducted content and thematic analyses to integrate insights …
Iot In Sustainability And Iot In The Ai And Metaverse Age, Yuzhou Qian, Keng Siau
Iot In Sustainability And Iot In The Ai And Metaverse Age, Yuzhou Qian, Keng Siau
Research Collection School Of Computing and Information Systems
The Internet of Things (IoT) is a modern technology that has gained large popularity and is still developing. Connecting heterogeneous devices, such as phones, vehicles, and household appliances, IoT has brought convenience to our lives. Further, IoT plays a significant role in enhancing environmental sustainability. It provides timely data about different devices and enables users and managers to directly control the objects. IoT can optimize the existing energy systems and promote the usage of renewable technologies. In this paper, we discuss how IoT supports green initiatives (i.e., how it is applied in different sectors), how it can be "green" itself …
Flexfl: Flexible And Effective Fault Localization With Open-Source Large Language Models, Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang, Ming Liang, David Lo
Flexfl: Flexible And Effective Fault Localization With Open-Source Large Language Models, Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang, Ming Liang, David Lo
Research Collection School Of Computing and Information Systems
Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, …
Sigscope: Detecting And Understanding Off‑Chain Message Signing‑Related Vulnerabilities In Decentralized Applications, Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan
Sigscope: Detecting And Understanding Off‑Chain Message Signing‑Related Vulnerabilities In Decentralized Applications, Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan
Research Collection School Of Computing and Information Systems
In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks.This paper presents the first systematic study to uncover and characterize the security issues in off-chain message signing schemes and the DApps built atop them. We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. We evaluate …
Real-Time Rectifying Flight Control Misconfiguration Using Intelligent Agent, Ruidong Han, Shangzhi Xu, Juanru Li, Elisa Bertino, David Lo, Jianfeng Ma, Siqi Ma
Real-Time Rectifying Flight Control Misconfiguration Using Intelligent Agent, Ruidong Han, Shangzhi Xu, Juanru Li, Elisa Bertino, David Lo, Jianfeng Ma, Siqi Ma
Research Collection School Of Computing and Information Systems
Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem—misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because (1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; (2) the response to each configuration varies under different flying scenarios.In this article, we propose and implement a novel rectification …
Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-Awareness In Computing, Tadashi Okoshi, Zexiong Gao, Yi Zhen Tan, Takumi Karasawa, Takeshi Miki, Wataru Sasaki, Rajesh Krishna Balan
Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-Awareness In Computing, Tadashi Okoshi, Zexiong Gao, Yi Zhen Tan, Takumi Karasawa, Takeshi Miki, Wataru Sasaki, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In Affective computing, recognizing users’ emotions accurately is the basis of affective human–computer interaction. Understanding users’ interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users’ interoception challenging. This study aims to determine other forms of data that can explain users’ interoceptive or similar states in their real-world lives and propose a novel hypothetical concept “cyberoception,” a new sense (1) which …
Hierarchical Frameworks For Scaling-Up Multi-Agent Coordination, Minghong Geng
Hierarchical Frameworks For Scaling-Up Multi-Agent Coordination, Minghong Geng
Research Collection School Of Computing and Information Systems
Multi-agent reinforcement learning has emerged as a powerful framework for developing collaborative behaviors in autonomous systems. However, existing MARL methods often struggle with scalability in terms of both the number of agents and decision-making horizons. My research focuses on developing hierarchicalapproaches to scale up MARL systems through two complementary directions: structural scaling by increasing the number of coordinated agents and temporal scaling by extending planning horizons. My initial work introduced HiSOMA, a hierarchical framework integrating self-organizing neural networks with MARL forlong-horizon planning, and MOSMAC, a benchmark for evaluating MARL methods on multi-objective MARL scenarios. Building on these foundations, my recent …
Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan
Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art …
Toward Better Comprehension Of Breaking Changes In The Npm Ecosystem, Dezhen Kong, Jiakun Liu, Lingfeng Bao, David Lo
Toward Better Comprehension Of Breaking Changes In The Npm Ecosystem, Dezhen Kong, Jiakun Liu, Lingfeng Bao, David Lo
Research Collection School Of Computing and Information Systems
Code evolution is prevalent in software ecosystems, which can provide many benefits, such as new features, bug fixes, security patches, while still introducing breaking changes that make downstream projects fail to work. Breaking changes cause a lot of effort to both downstream and upstream developers: downstream developers need to adapt to breaking changes and upstream developers are responsible for identifying and documenting them. In the NPM ecosystem, characterized by frequent code changes and a high tolerance for making breaking changes, the effort is larger.For better comprehension of breaking changes in the NPM ecosystem and to enhance breaking change detection tools, …
Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham
Eduqate: Generating Adaptive Curricula Through Rmabs In Education Settings, Sidney Tio, Dexun Li, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in developing such tools is in exploring how mastery can be achieved across a diverse yet related range of content in an efficient manner. While Reinforcement Learning and Multi-armed Bandits have shown promise in educational settings, existing works often assume the independence of learning content, neglecting the prevalent interdependencies between such content. In response, we introduce Education Network Restless Multi-armed Bandits (EdNetRMABs), utilizing a network to represent the relationships between interdependent arms. Subsequently, we propose …
Does Chatgpt-Permitted Assessments Help Students Generate Better Answers And Learn More?, Michelle L. F. Cheong, Yun-Chen Chen
Does Chatgpt-Permitted Assessments Help Students Generate Better Answers And Learn More?, Michelle L. F. Cheong, Yun-Chen Chen
Research Collection School Of Computing and Information Systems
We discuss our methodology and implementation of ChatGPT-permitted assessments for a university-level spreadsheets modelling module. Through our quantitative data analysis, our students rated ChatGPT’s answers to be incorrect on average and thus will not help them generate better answers directly, representing low “Perceived usefulness” (PU), while they rated ChatGPT 3.5 with relatively high “Perceived ease of use” (PE). They gave a good “Behavioural intention” (BI) rating indicating that they were motivated to use it in future as they could still learn more about this module by using ChatGPT 3.5. We found that both PU and PE affected BI positively, with …
Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li
Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li
Research Collection School Of Computing and Information Systems
This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into …
Sita: Structurally Imperceptible And Transferable Adversarial Attacks For Stylized Image Generation, Jingdan Kang, Haoxin Yang, Yan Cai, Huaidong Zhang, Xuemiao Xu, Yong Du, Shengfeng He
Sita: Structurally Imperceptible And Transferable Adversarial Attacks For Stylized Image Generation, Jingdan Kang, Haoxin Yang, Yan Cai, Huaidong Zhang, Xuemiao Xu, Yong Du, Shengfeng He
Research Collection School Of Computing and Information Systems
Image generation technology has brought significant advancements across various fields but has also raised concerns about data misuse and potential rights infringements, particularly with respect to creating visual artworks. Current methods aimed at safeguarding artworks often employ adversarial attacks. However, these methods face challenges such as poor transferability, high computational costs, and the introduction of noticeable noise, which compromises the aesthetic quality of the original artwork. To address these limitations, we propose a Structurally Imperceptible and Transferable Adversarial (SITA) attacks. SITA leverages a CLIP-based destylization loss, which decouples and disrupts the robust style representation of the image. This disruption hinders …
Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo
Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …
Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo
Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo
Research Collection School Of Computing and Information Systems
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …
Nash Bargaining Strategy In Autonomous Decision Making For Multi-Ship Collision Avoidance Based On Route Exchange, Yang Wang, Qiangsheng Ye, Hoong Chuin Lau, Tengfei Wang, Bing Wu
Nash Bargaining Strategy In Autonomous Decision Making For Multi-Ship Collision Avoidance Based On Route Exchange, Yang Wang, Qiangsheng Ye, Hoong Chuin Lau, Tengfei Wang, Bing Wu
Research Collection School Of Computing and Information Systems
A novel scheme is proposed for the distributed multi-ship collision avoidance (CA) problem with consideration of the autonomous, dynamic nature of the real circumstance. All the ships in the envisioned scenarios can share their decisions or intentions through route exchange, allowing them to make subsequent decisions based on the route planning in each iteration. By leveraging route exchange, the multi-ship CA problem involves iterations for negotiation, and is regarded as a staged cooperative game under conditions of complete information. The concept of closest spatio-temporal distance (CSTD) is introduced to more accurately assess collision risk between ships. A coordinated CA mechanism …
Characterising Reproducibility Debt In Scientific Software: A Systematic Literature Review, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin
Characterising Reproducibility Debt In Scientific Software: A Systematic Literature Review, Zara Hassan, Christoph Treude, Michael Norrish, Graham Williams, Alex Potanin
Research Collection School Of Computing and Information Systems
Context: In scientific software, the inability to reproduce results is often due to technical issues and challenges in recreating the full computational workflow from the original analysis. We conceptualise this problem as Reproducibility Debt (RpD). Much research has been performed to propose solutions to tackle these issues across various computational science disciplines. It is essential to identify and accumulate existing knowledge on reproducibility issues and state-of-the-art solutions so as to provide researchers and practitioners with information that enables further research activities and RpD management in practice. Objective: In the context of scientific software, we aim to characterise RpD by providing …