Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3436)
- Software Engineering (2136)
- Artificial Intelligence and Robotics (1648)
- Information Security (1051)
- Numerical Analysis and Scientific Computing (1024)
-
- Graphics and Human Computer Interfaces (916)
- Engineering (857)
- Social and Behavioral Sciences (661)
- Business (625)
- Theory and Algorithms (493)
- Computer Engineering (431)
- Operations Research, Systems Engineering and Industrial Engineering (399)
- Programming Languages and Compilers (379)
- OS and Networks (322)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (207)
- Transportation (185)
- Medicine and Health Sciences (177)
- Education (164)
- Management Information Systems (164)
- Data Storage Systems (160)
- E-Commerce (146)
- Health Information Technology (107)
- International and Area Studies (107)
- Asian Studies (106)
- Technology and Innovation (100)
- Digital Communications and Networking (96)
- Keyword
-
- Deep learning (122)
- Machine learning (121)
- Social media (74)
- Artificial intelligence (70)
- Reinforcement learning (69)
-
- Data mining (64)
- Privacy (61)
- Cloud computing (58)
- Deep Learning (55)
- Empirical study (54)
- Security (53)
- Optimization (52)
- Visualization (51)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Online learning (48)
- Task analysis (48)
- Anomaly detection (47)
- Singapore (47)
- Twitter (46)
- Feature extraction (45)
- Blockchain (44)
- Collaboration (44)
- Semantics (43)
- Large Language Models (42)
- Access control (41)
- Algorithms (40)
- Android (39)
- Machine Learning (38)
- Publication Year
- File Type
Articles 571 - 600 of 8458
Full-Text Articles in Computer Sciences
Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu
Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu
Research Collection School Of Computing and Information Systems
As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …
Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma
Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma
Research Collection School Of Computing and Information Systems
Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …
Efficient And Green Large Language Models For Software Engineering: Literature Review, Vision, And The Road Ahead, Jieke Shi, Zhou Yang, David Lo
Efficient And Green Large Language Models For Software Engineering: Literature Review, Vision, And The Road Ahead, Jieke Shi, Zhou Yang, David Lo
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This article aims to redirect the focus of the research community toward the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance …
Deepvec: State-Vector Aware Test Case Selection For Enhancing Recurrent Neural Network, Zhonghao Jiang, Meng Yan, Li Huang, Weifeng Sun, Chao Liu, Song Sun, David Lo
Deepvec: State-Vector Aware Test Case Selection For Enhancing Recurrent Neural Network, Zhonghao Jiang, Meng Yan, Li Huang, Weifeng Sun, Chao Liu, Song Sun, David Lo
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test …
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …
Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg
Building Narratives And Probing Concepts: Preparing Materials For Co-Design With Autistic Livestreamers, Terrance Mok, Tyson Hartley, Anthony Tang, Adam Mccrimmon, Lora Oehlberg
Research Collection School Of Computing and Information Systems
Based on ten semi-structured interviews with autistic Twitch streamers, we introduce a series of scenario-based design narratives coupled with technology design concepts as a starting point for co-design discussion about autistic streaming. This work builds on prior thematic analysis of the unique intersection between autism and livestreaming. Our user-centered scenarios highlight the needs, goals, and challenges of autistic individuals in livestreaming contexts. By using evocative narratives, the scenarios serve to facilitate empathy and deeper engagement with the needs of autistic users, and help facilitate and support co-creative dialogues and discussions about new technology designs. We contribute this starting point for …
Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun
Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …
Enhancing Sub-Optimal Trajectory Stitching: Spatial Composition Rvs For Offline Rl, Sheng Zang, Zhiguang Cao, Bo An, Senthilnath Jayavelu, Xiaoli Li
Enhancing Sub-Optimal Trajectory Stitching: Spatial Composition Rvs For Offline Rl, Sheng Zang, Zhiguang Cao, Bo An, Senthilnath Jayavelu, Xiaoli Li
Research Collection School Of Computing and Information Systems
Reinforcement learning via supervised learning (RvS) has been known as a burgeoning paradigm for offline reinforcement learning (RL). While return-conditioned RvS (RvS-R) predominates across a wide range of datasets pertaining to the offline RL tasks, recent findings suggest that goal-conditioned RvS (RvS-G) outperforms in specific sub-optimal datasets where trajectory stitching is crucial for achieving optimal performance. However, the underlying reasons for this superiority remain insufficiently explored. In this paper, employing didactic experiments and theoretical analysis, we reveal that the proficiency of RvS-G in stitching trajectories arises from its adeptness in generalizing to unknown goals during evaluation. Building on this insight, …
Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al
Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al
Research Collection School Of Computing and Information Systems
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside …
Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun
Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun
Research Collection School Of Computing and Information Systems
Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FixDrive, a framework that analyses driving records from near-misses or law …
Decictor: Towards Evaluating The Robustness Of Decision-Making In Autonomous Driving Systems, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Junjie Wang, Guozhu Meng, Kairui Yang
Decictor: Towards Evaluating The Robustness Of Decision-Making In Autonomous Driving Systems, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Junjie Wang, Guozhu Meng, Kairui Yang
Research Collection School Of Computing and Information Systems
Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, particularly the ADS's ability to make optimal decisions and produce optimal paths for autonomous vehicles (AVs), is also vital to ensure the intelligence and reduce risks of AVs. Currently, there is little work dedicated to assessing the robustness of ADSs' path-planning decisions (PPDs), i.e., whether an ADS can maintain the optimal PPD after an insignificant change in the environment. The key challenges include the lack of clear oracles for assessing PPD optimality and the difficulty in …
Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua
Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently …
Oscar: Object Status And Contextual Awareness For Recipes To Support Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington
Oscar: Object Status And Contextual Awareness For Recipes To Support Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington
Research Collection School Of Computing and Information Systems
Following recipes while cooking is an important but difficult task for visually impaired individuals. We developed OSCAR (Object Status Context Awareness for Recipes), a novel approach that provides recipe progress tracking and context-aware feedback on the completion of cooking tasks through tracking object statuses. OSCAR leverages both Large-Language Models (LLMs) and Vision-Language Models (VLMs) to manipulate recipe steps, extract object status information, align visual frames with object status, and provide cooking progress tracking log. We evaluated OSCAR’s recipe following functionality using 173 YouTube cooking videos and 12 real-world non-visual cooking videos to demonstrate OSCAR’s capability to track cooking steps and …
Seaexam And Seabench: Benchmarking Llms With Local Multilingual Questions In Southeast Asia, Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing
Seaexam And Seabench: Benchmarking Llms With Local Multilingual Questions In Southeast Asia, Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing
Research Collection School Of Computing and Information Systems
This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evalu ate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real world scenarios from SEA regions. SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses sub jects such as local history and literature. In contrast, SeaBench is crafted around multi turn, open-ended tasks that reflect daily inter actions within SEA communities. Our evalua tions demonstrate that SeaExam and SeaBench more effectively discern LLM performance on …
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. …
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 …
Rotation-Adaptive Point Cloud Domain Generalization Via Intricate Orientation Learning, Bangzhen Liu, Chenxi Zheng, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Shengfeng He
Rotation-Adaptive Point Cloud Domain Generalization Via Intricate Orientation Learning, Bangzhen Liu, Chenxi Zheng, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Shengfeng He
Research Collection School Of Computing and Information Systems
The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ …
“I Can Run At Night!”: Using Augmented Reality To Support Nighttime Guided Running For Low-Vision Runners, Yuki Abe, Keisuke Matsushima, Kotaro Hara, Daisuke Sakamoto, Tetsuo Ono
“I Can Run At Night!”: Using Augmented Reality To Support Nighttime Guided Running For Low-Vision Runners, Yuki Abe, Keisuke Matsushima, Kotaro Hara, Daisuke Sakamoto, Tetsuo Ono
Research Collection School Of Computing and Information Systems
Dark environment challenges low-vision (LV) individuals to engage in running by following sighted guide—a Caller-style guided running—due to insufficient illumination, because it prevents them from using their residual vision to follow the guide and be aware about their environment. We design, develop, and evaluate RunSight, an augmented reality (AR)-based assistive tool to support LV individuals to run at night. RunSight combines see-through HMD and image processing to enhance one’s visual awareness of the surrounding environment (e.g., potential hazard) and visualize the guide’s position with AR-based visualization. To demonstrate RunSight’s efficacy, we conducted a user study with 8 LV runners. The …
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 …
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, …
Multiobjective Linear Ensembles For Robust And Sparse Training Of Few-Bit Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Simone Milanesi, Hoong Chuin Lau, Neil Yorke-Smith
Multiobjective Linear Ensembles For Robust And Sparse Training Of Few-Bit Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Simone Milanesi, Hoong Chuin Lau, Neil Yorke-Smith
Research Collection School Of Computing and Information Systems
Training neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear programming solvers, for instance, has the potential to exactly train an NN while avoiding computing-intensive training and hyperparameter tuning and simultaneously training and sparsifying the network. We study the case of few-bit discrete-valued neural networks, both binarized neural networks (BNNs) whose values are restricted to ±1 and integer-valued neural networks (INNs) whose values lie in the range {−P,…,P}. Few-bit NNs receive increasing recognition because of their lightweight architecture and ability to run on low-power devices: for …
Enriching Automatic Test Case Generation By Extracting Relevant Test Inputs From Bug Reports, Wendkuuni C. Ouedraogo, Laura Plein, Kader Kabore, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyande
Enriching Automatic Test Case Generation By Extracting Relevant Test Inputs From Bug Reports, Wendkuuni C. Ouedraogo, Laura Plein, Kader Kabore, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyande
Research Collection School Of Computing and Information Systems
The quality of software is closely tied to the effectiveness of the tests it undergoes. Manual test writing, though crucial for bug detection, is time-consuming, which has driven significant research into automated test case generation. However, current methods often struggle to generate relevant inputs, limiting the effectiveness of the tests produced. To address this, we introduce BRMiner, a novel approach that leverages Large Language Models (LLMs) in combination with traditional techniques to extract relevant inputs from bug reports, thereby enhancing automated test generation tools. In this study, we evaluate BRMiner using the Defects4J benchmark and test generation tools such as …
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 …
Disambiguart: A Neural-Based Inference Model For Knowledge Graph Disambiguation, Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan
Disambiguart: A Neural-Based Inference Model For Knowledge Graph Disambiguation, Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
One main challenge in constructing a knowledge graph (KG) is to deal with ambiguity. Specifically, an entity in the graph can be assigned with multiple meanings while two or more entities considered to have different meanings may actually be the same. Assigning an entity with the correct meaning may involve re-evaluation of its relevant contexts. This costly operation typically involves searching for other similar entities within the KG such that the context can be determined. In this paper, a new model called DisambiguART is proposed leveraging multi-channel matching and inference in a self-organizing neural network for sense disambiguation in knowledge …
Enhancing Deliberativeness: Evaluating The Impact Of Multimodal Reflection Nudges, Shun Yi Yeo, Zhuoqun Jiang, Anthony Tang, Simon Tangi Perrault
Enhancing Deliberativeness: Evaluating The Impact Of Multimodal Reflection Nudges, Shun Yi Yeo, Zhuoqun Jiang, Anthony Tang, Simon Tangi Perrault
Research Collection School Of Computing and Information Systems
Nudging participants with text-based reflective nudges enhances deliberation quality on online deliberation platforms. The effectiveness of multimodal reflective nudges, however, remains largely unexplored. Given the multi-sensory nature of human perception, incorporating diverse modalities into self-reflection mechanisms has the potential to better support various reflective styles. This paper explores how presenting reflective nudges of different types (direct: persona and indirect: storytelling) in different modalities (text, image, video and audio) affects deliberation quality. We conducted two user studies with 20 and 200 participants respectively. The first study identifies the preferred modality for each type of reflective nudges, revealing that text is most …
Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Yang Deng, Mohammad Aliannejadi
Query Understanding In Llm-Based Conversational Information Seeking, Yifei Yuan, Zahra Abbasiantaeb, Yang Deng, Mohammad Aliannejadi
Research Collection School Of Computing and Information Systems
Query understanding in Conversational Information Seeking (CIS) involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. Large Language Models (LLMs) 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 multiturn interactions, strategies for building more interactive systems, and applications like proactive query management and query reformulation. We …
Acccred: Improved Accountable Anonymous Credentials With Dynamic Triple-Hiding Committees, Sijiang Xie, Rui Shi, Yang Yang, Huiqin Xie, Yingjiu Li, Robert H. Deng
Acccred: Improved Accountable Anonymous Credentials With Dynamic Triple-Hiding Committees, Sijiang Xie, Rui Shi, Yang Yang, Huiqin Xie, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
Accountable anonymous credentials protect user privacy while holding the accountability of ill-intentioned individuals, which is a critical feature for applications such as online payments and other financial services. Existing accountable anonymous credentials rely on a public committee of trustworthy members who are assumed not to collude and are well protected to perform privacy revocation. However, this assumption is unsound in blockchain-based cryptocurrency systems because the selected committees may involve nodes with significant stakes, and public nodes serving as committee members are vulnerable against targeted attacks from high-computing power adversaries. In this paper, we propose an improved accountable anonymous credential called …
Shipnavisim: Data-Driven Simulation For Real-World Maritime Navigation, Quang Anh Pham, Janaka Chathuranga Brahmanage, Akshat Kumar
Shipnavisim: Data-Driven Simulation For Real-World Maritime Navigation, Quang Anh Pham, Janaka Chathuranga Brahmanage, Akshat Kumar
Research Collection School Of Computing and Information Systems
Maritime traffic management in busy ports faces growing challenges due to increased vessel traffic and complex waterway interactions. Strategies such as e-navigation by the International Maritime Organization aim to enhance navigation safety through traffic digitization. Maritime traffic simulation is essential for these systems, offering a virtual environment to model, analyze, and optimize traffic flows. Unlike road traffic, there are few simulators for maritime traffic, and they often lack realism and multi-ship interactions. In this paper, we (a) present ShipNaviSim, a data-driven maritime traffic simulator that utilizes a large-scale dataset over 2 years and electronic navigation charts to model vessel movements …
Few-Shot Learning On Graphs: From Meta-Learning To Llm-Empowered Pre-Training And Beyond, Yuan Fang, Yuxia Wu, Xingtong Yu, Shirui Pan
Few-Shot Learning On Graphs: From Meta-Learning To Llm-Empowered Pre-Training And Beyond, Yuan Fang, Yuxia Wu, Xingtong Yu, Shirui Pan
Research Collection School Of Computing and Information Systems
Graph representation learning has become central to many graph-based tasks, driving advancements in various domains such as web search, recommendation systems, and social network analysis. Traditionally, these methods rely on end-to-end supervised learning paradigms that require abundant labeled data, which can be costly and difficult to obtain. To address this limitation, few-shot learning on graphs has emerged as a promising approach, allowing models to generalize with minimal supervision and overcome data scarcity in real-world applications. This tutorial offers an in-depth exploration of recent advancements in few-shot learning for graphs, providing a comparative analysis of state-of-the-art methods and identifying future research …