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A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao
A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao
Research Collection School Of Computing and Information Systems
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …
Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng
Pcqpr : Proactive Conversational Question Planning With Reflection, Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Cheng
Research Collection School Of Computing and Information Systems
In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …
Ultra-High Resolution Image Segmentation Via Locality-Aware Context Fusion And Alternating Local Enhancement, Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu
Ultra-High Resolution Image Segmentation Via Locality-Aware Context Fusion And Alternating Local Enhancement, Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu
Research Collection School Of Computing and Information Systems
Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement …
Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe
Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe
Research Collection School Of Computing and Information Systems
Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …
Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma
Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma
Research Collection School Of Computing and Information Systems
The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …
Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun
Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun
Research Collection School Of Computing and Information Systems
Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …
Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua
Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …
Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang
Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang
Research Collection School Of Computing and Information Systems
Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …
Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang
Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, …
Pvp-Ssd: Point-Voxel Fusion With Partitioned Point Cloud Sampling For Anchor-Free Single-Stage Small 3d Object Detection, Xinlin Wu, Yibin Tian, Yin Pan, Zhiyuan Zhang, Xuesong Wu, Ruisheng Wang, Zhi Zeng
Pvp-Ssd: Point-Voxel Fusion With Partitioned Point Cloud Sampling For Anchor-Free Single-Stage Small 3d Object Detection, Xinlin Wu, Yibin Tian, Yin Pan, Zhiyuan Zhang, Xuesong Wu, Ruisheng Wang, Zhi Zeng
Research Collection School Of Computing and Information Systems
Single-stage object detection from 3D point clouds in autonomous driving faces significant challenges, particularly in accurately detecting small objects. To address this issue, we propose a novel method called Point-Voxel dual-branch feature extraction with Partitioned point cloud sampling for anchor-free Single-Stage Detection of 3D objects (PVP-SSD). The network comprises two branches: a point branch and a voxel branch. In the point branch, a partitioned point cloud sampling strategy leverages axial features to divide the point cloud. Then, it assigns different sampling weights to various segments to enhance the sampling accuracy. Additionally, a local feature enhancement module explicitly calculates the correlation …
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Research Collection School Of Computing and Information Systems
With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …
Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng
Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng
Research Collection School Of Computing and Information Systems
Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel Efficient Cascaded Multiscale Adaptive (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a …
Calibrated One-Class Classification For Unsupervised Time Series Anomaly Detection, Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang
Calibrated One-Class Classification For Unsupervised Time Series Anomaly Detection, Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang
Research Collection School Of Computing and Information Systems
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty …
Gradualreality: Enhancing Physical Object Interaction In Virtual Reality Via Interaction State-Aware Blending, Hyuna Seo, Juheon Yi, Rajesh Krishna Balan, Youngki Lee
Gradualreality: Enhancing Physical Object Interaction In Virtual Reality Via Interaction State-Aware Blending, Hyuna Seo, Juheon Yi, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
We present GradualReality, a novel interface enabling a Cross Reality experience that includes gradual interaction with physical objects in a virtual environment and supports both presence and usability. Daily Cross Reality interaction is challenging as the user’s physical object interaction state is continuously changing over time, causing their attention to frequently shift between the virtual and physical worlds. As such, presence in the virtual environment and seamless usability for interacting with physical objects should be maintained at a high level. To address this issue, we present an Interaction State-Aware Blending approach that (i) balances immersion and interaction capability and (ii) …
D2sr: Decentralized Detection, De-Synchronization, And Recovery Of Lidar Interference, Darshana Rathnayake, Hemanth Sabbella, Meera Radhakrishnan, Archan Misra
D2sr: Decentralized Detection, De-Synchronization, And Recovery Of Lidar Interference, Darshana Rathnayake, Hemanth Sabbella, Meera Radhakrishnan, Archan Misra
Research Collection School Of Computing and Information Systems
We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR’s active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple …
Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Research Collection School Of Computing and Information Systems
Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …
Motif Graph Neural Network, Xuexin Chen, Ruicui Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
Motif Graph Neural Network, Xuexin Chen, Ruicui Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
Research Collection School Of Computing and Information Systems
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order …
Foss: Towards Fine-Grained Unknown Class Detection Against The Open-Set Attack Spectrum With Variable Legitimate Traffic, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Jiongchi Yu, Fan Zhang, Rui Zhang, Binbin Chen, Xiangyang Luo, Ming Hu, Wenrui Ma
Foss: Towards Fine-Grained Unknown Class Detection Against The Open-Set Attack Spectrum With Variable Legitimate Traffic, Ziming Zhao, Zhaoxuan Li, Xiaofei Xie, Jiongchi Yu, Fan Zhang, Rui Zhang, Binbin Chen, Xiangyang Luo, Ming Hu, Wenrui Ma
Research Collection School Of Computing and Information Systems
Anomaly-based network intrusion detection systems (NIDSs) are essential for ensuring cybersecurity. However, the security communities realize some limitations when they put most existing proposals into practice. The challenges are mainly concerned with (i) fine-grained unknown attack detection and (ii) ever-changing legitimate traffic adaptation. To tackle these problem, we present three key design norms. The core idea is to construct a model to split the data distribution hyperplane and leverage the concept of isolation, as well as advance the incremental model update. We utilize the isolation tree as the backbone to design our model, named FOSS, to echo back three norms. …
Ocapo: Fine-Grained Occupancy-Aware, Empirically-Driven Pdc Control In Open-Plan, Shared Workspaces, Ravi Anuradha, Dulaj Sanjaya Weerakoon, Archan Misra
Ocapo: Fine-Grained Occupancy-Aware, Empirically-Driven Pdc Control In Open-Plan, Shared Workspaces, Ravi Anuradha, Dulaj Sanjaya Weerakoon, Archan Misra
Research Collection School Of Computing and Information Systems
Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle …
Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui
Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui
Research Collection School Of Computing and Information Systems
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time …
Brushbuds: Toothbrushing Tracking Using Earphone Imus, Qiang Yang, Yang Liu, Jake Stuchbury-Wass, Kayla-Jade Butkow, Dong Ma, Cecilia Mascolo
Brushbuds: Toothbrushing Tracking Using Earphone Imus, Qiang Yang, Yang Liu, Jake Stuchbury-Wass, Kayla-Jade Butkow, Dong Ma, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
Inadequate toothbrushing habits are a leading cause of oral health problems such as tooth decay. Many individuals are uncertain if they are brushing effectively or over-focusing on specific areas. While high-end electric toothbrushes can address these concerns, manual toothbrushes remain widely used due to their simplicity and affordability. In this paper, we introduce BrushBuds, an earphone-based toothbrushing monitoring system aimed at tracking brushing areas, which leverages the ubiquitous presence of earphones to enhance manual toothbrushing. BrushBuds utilizes Inertial Measurement Units (IMUs) in earphones to detect subtle head movements incurred by toothbrushing. By capturing distinct motion patterns specific to brushing for …
Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw
Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This article presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces …
Kpiroot: Efficient Monitoring Metric-Based Root Cause Localization In Large-Scale Cloud Systems, Wenwei Gu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu
Kpiroot: Efficient Monitoring Metric-Based Root Cause Localization In Large-Scale Cloud Systems, Wenwei Gu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu
Research Collection School Of Computing and Information Systems
To ensure the reliability of cloud systems, their run-time status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …
Constrained Assortment Optimization Under The Cross-Nested Logit Model, Cuong Le, Tien Mai
Constrained Assortment Optimization Under The Cross-Nested Logit Model, Cuong Le, Tien Mai
Research Collection School Of Computing and Information Systems
We study the assortment optimization problem under general linear constraints, where the customer choice behavior is captured by the cross-nested logit model. In this problem, there is a set of products organized into multiple subsets (or nests), where each product can belong to more than one nest. The aim is to find an assortment to offer to customers so that the expected revenue is maximized. We show that, under the cross-nested logit model, the unconstrained assortment problem is NP-hard even when there are only two nests, and the problem is generally NP-hard to approximate to any constant factors. To tackle …
Stagedvulbert: Multi-Granular Vulnerability Detection With A Novel Pre-Trained Code Model, Yuan Jiang, Yujian Zhang, Xiaohong Su, Christoph Treude, Tiantian Wang
Stagedvulbert: Multi-Granular Vulnerability Detection With A Novel Pre-Trained Code Model, Yuan Jiang, Yujian Zhang, Xiaohong Su, Christoph Treude, Tiantian Wang
Research Collection School Of Computing and Information Systems
The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed …
Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto
Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their “black-box” nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to …
Retrofitting A Legacy Cutlery Washing Machine Using Computer Vision, Hua Leong Fwa
Retrofitting A Legacy Cutlery Washing Machine Using Computer Vision, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed andutilization. With the existing operational constraints of …
Predicting The Limits: Tailoring Unnoticeable Hand Redirection Offsets In Virtual Reality To Individuals' Perceptual Boundaries, Martin Feick, Kora Persephone Regitz, Lukas Gehrke, André Zenner, Anthony Tang, Tobias Patrick Jungbluth, Maurice Rekrut, Antonio Krüger
Predicting The Limits: Tailoring Unnoticeable Hand Redirection Offsets In Virtual Reality To Individuals' Perceptual Boundaries, Martin Feick, Kora Persephone Regitz, Lukas Gehrke, André Zenner, Anthony Tang, Tobias Patrick Jungbluth, Maurice Rekrut, Antonio Krüger
Research Collection School Of Computing and Information Systems
Many illusion and interaction techniques in Virtual Reality (VR) rely on Hand Redirection (HR), which has proved to be effective as long as the introduced offsets between the position of the real and virtual hand do not noticeably disturb the user experience. Yet calibrating HR offsets is a tedious and time-consuming process involving psychophysical experimentation, and the resulting thresholds are known to be affected by many variables—limiting HR’s practical utility. As a result, there is a clear need for alternative methods that allow tailoring HR to the perceptual boundaries of individual users. We conducted an experiment with 18 participants combining …
Themis: Automatic And Efficient Deep Learning System Testing With Strong Fault Detection Capability, Dong Huang, Tsz On Li, Xiaofei Xie, Heming Cui
Themis: Automatic And Efficient Deep Learning System Testing With Strong Fault Detection Capability, Dong Huang, Tsz On Li, Xiaofei Xie, Heming Cui
Research Collection School Of Computing and Information Systems
Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepXplore) detect such faults by generating perturbed inputs to explore data flows that induce faults. Since a DLS often has infinitely many data flows, existing techniques require developers to manually specify a set of activation values in a DLS’s neurons for exploring fault-inducing data flows. Unfortunately, recent studies show that such manual effort is tedious and can detect only a tiny proportion …