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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 …
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 …
Video Editing For Video Retrieval, Bin Zhu, Kevin Flanagan, Adriano Fragomeni, Michael Wray, Dima Damen
Video Editing For Video Retrieval, Bin Zhu, Kevin Flanagan, Adriano Fragomeni, Michael Wray, Dima Damen
Research Collection School Of Computing and Information Systems
Though pre-training vision-language models have demonstrated significant benefits in boosting video-text retrieval performance from large-scale web videos, fine-tuning still plays a critical role with manually annotated clips with start and end times, which requires considerable human effort. To address this issue, we explore an alternative cheaper source of annotations, single timestamps, for video-text retrieval. We initialise clips from timestamps in a heuristic way to warm up a retrieval model. Then a video clip editing method is proposed to refine the initial rough boundaries to improve retrieval performance. A student-teacher network is introduced for video clip editing: the teacher model is …
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 …
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 …
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 …
Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft
Unraveling The Dynamics Of Stable And Curious Audiences In Web Systems, Rodrigo Alves, Antoine Ledent, Renato Assunção, Pedro Vaz-De-Melo, Marius Kloft
Research Collection School Of Computing and Information Systems
We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the …
Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti
Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti
Research Collection School Of Computing and Information Systems
We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in terms of time complexity but also has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation costs scale economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering …
Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui
Neuron Sensitivity Guided Test Case Selection, Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing, Xiaofei Xie, Junjie Chen, Heming Cui
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNNs) have been widely deployed in software to address various tasks (e.g., autonomous driving, medical diagnosis). However, they can also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal and repair incorrect behaviors in DNNs, developers often collect rich, unlabeled datasets from the natural world and label them to test DNN models. However, properly labeling a large number of datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity Guided Test Case Selection, which can reduce the labeling time by selecting valuable test …
Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen
Imbalanced Graph Classification With Multi-Scale Oversampling Graph Neural Networks, Rongrong Ma, Guansong Pang, Ling Chen
Research Collection School Of Computing and Information Systems
One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - …
Sound And Complete Witnesses For Template-Based Verification Of Ltl Properties On Polynomial Programs, Krishnendu Chatterjee, Amir Goharshady, Ehsan Goharshady, Mehrdad Karrabi, Dorde Zikelic
Sound And Complete Witnesses For Template-Based Verification Of Ltl Properties On Polynomial Programs, Krishnendu Chatterjee, Amir Goharshady, Ehsan Goharshady, Mehrdad Karrabi, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both …
Text-Driven Video Prediction, Xue Song, Jingjing Chen, Bin Zhu, Yu-Gang Jiang
Text-Driven Video Prediction, Xue Song, Jingjing Chen, Bin Zhu, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption …
Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw
Aligning Human And Computational Coherence Evaluations, Jia Peng Lim, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous work presents a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mining topic representations at a large scale while seeking to mitigate bias from sampling, enabling the investigation of widely used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the …
Probing Effects Of Contextual Bias On Number Magnitude Estimation, Xuehao Du, Ping Ji, Wei Qin, Lei Wang, Yunshi Lan
Probing Effects Of Contextual Bias On Number Magnitude Estimation, Xuehao Du, Ping Ji, Wei Qin, Lei Wang, Yunshi Lan
Research Collection School Of Computing and Information Systems
The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network's accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of- distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods …
Ft2ra: A Fine-Tuning-Inspired Approach To Retrieval-Augmented Code Completion, Qi Guo, Shangqing Liu, Xiaofei Xie, Ze Tang Tang
Ft2ra: A Fine-Tuning-Inspired Approach To Retrieval-Augmented Code Completion, Qi Guo, Shangqing Liu, Xiaofei Xie, Ze Tang Tang
Research Collection School Of Computing and Information Systems
The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for …
Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen
Bugs In Pods: Understanding Bugs In Container Runtime Systems, Jiongchi Yu, Xiaofei Xie, Ceng Zhang, Sen Chen
Research Collection School Of Computing and Information Systems
Container Runtime Systems (CRSs), which form the foundational infrastructure of container clouds, are critically important due to their impact on the quality of container cloud implementations. However, a comprehensive understanding of the quality issues present in CRS implementations remains lacking. To bridge this gap, we conduct the first comprehensive empirical study of CRS bugs. Specifically, we gather 429 bugs from 8,271 commits across dominant CRS projects, including runc, gvisor, containerd, and cri-o. Through manual analysis, we develop taxonomies of CRS bug symptoms and root causes, comprising 16 and 13 categories, respectively. Furthermore, we evaluate the capability of popular testing approaches, …
How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie
How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie
Research Collection School Of Computing and Information Systems
Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of research. Unlike traditional program analysis-based generators, this text-based approach is more generalized and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its e ectiveness and potential challenges. To …
An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude
An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude
Research Collection School Of Computing and Information Systems
Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …
Efficient And Secure Federated Learning Against Backdoor Attacks, Yinbin Miao, Rongpeng Xie, Xinghua Li, Zhiquan Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Efficient And Secure Federated Learning Against Backdoor Attacks, Yinbin Miao, Rongpeng Xie, Xinghua Li, Zhiquan Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, …
Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang
Meta-Learning For Multi-Family Android Malware Classification, Yao Li, Dawei Yuan, Tao Zhang, Haipeng Cai, David Lo, Cuiyun Gao, Xiapu Luo, He Jiang
Research Collection School Of Computing and Information Systems
With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious …
Fdi : Attack Neural Code Generation Systems Through User Feedback Channel, Zhensu Sun, Xiaoning Du, Xiapu Luo, Fu Song, David Lo, Li Li
Fdi : Attack Neural Code Generation Systems Through User Feedback Channel, Zhensu Sun, Xiaoning Du, Xiapu Luo, Fu Song, David Lo, Li Li
Research Collection School Of Computing and Information Systems
Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a pre-trained neural model and make it available to general users as a service (e.g., through remote APIs) and incorporate a feedback mechanism to extensively collect and utilize the users' reaction to the generated code, i.e., user feedback. However, the security implications of such feedback have not yet been explored. With a systematic study of current feedback mechanisms, we find that feedback makes these systems vulnerable to feedback data injection (FDI) attacks. We discuss the methodology of FDI …
Adavis: Adaptive And Explainable Visualization Recommendation For Tabular Data, Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang
Adavis: Adaptive And Explainable Visualization Recommendation For Tabular Data, Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang
Research Collection School Of Computing and Information Systems
Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable …
Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo
Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo
Research Collection School Of Computing and Information Systems
The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as “ghost commits”. The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the …
Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou
Genixer : Empowering Multimodal Large Language Models As A Powerful Data Generator, Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou
Research Collection School Of Computing and Information Systems
Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction template design, (iii) empowering MLLMs, and (iv) data generation and filtering. Additionally, we outline two modes of data generation: task-agnostic and task-specific, enabling controllable output. We demonstrate that a synthetic VQA-like dataset trained with LLaVA1.5 enhances performance on 10 …
Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond
Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond
Research Collection School Of Computing and Information Systems
Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical …
Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng
Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng
Research Collection School Of Computing and Information Systems
Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause …
Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Research Collection School Of Computing and Information Systems
HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu
Granular3d: Delving Into Multi-Granularity 3d Scene Graph Prediction, Kaixiang Huang, Jingru Yang, Jin Wang, Shengfeng He, Zhan Wang, Haiyan He, Qifeng Zhang, Guodong Lu
Research Collection School Of Computing and Information Systems
This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby …
Certified Continual Learning For Neural Network Regression, Hong Long Pham, Jun Sun
Certified Continual Learning For Neural Network Regression, Hong Long Pham, Jun Sun
Research Collection School Of Computing and Information Systems
On the one hand, there has been considerable progress on neural network verification in recent years, which makes certifying neural networks a possibility. On the other hand, neural network in practice are often re-trained over time to cope with new data distribution or for solving different tasks (a.k.a. continual learning). Once re-trained, the verified correctness of the neural network is likely broken, particularly in the presence of the phenomenon known as catastrophic forgetting. In this work, we propose an approach called certified continual learning which improves existing continual learning methods by preserving, as long as possible, the established correctness properties …
Low/No-Code And Traditional Code Integration In Digital Banking, Kim Siang Yeo, Alan @ Ali Madjelisi Megargel
Low/No-Code And Traditional Code Integration In Digital Banking, Kim Siang Yeo, Alan @ Ali Madjelisi Megargel
Research Collection School Of Computing and Information Systems
This paper seeks to combine the merits of Low/No-Code Programming (LNCP) with Traditional Programming (TP) systems to achieve true “agility” when creating banking infrastructure. While it is easy to fall prey to Shiny Object Syndrome in today’s dynamic and fast-paced banking technology world, it is not easy to pick out the right technology for today and tomorrow’s financial industry. Instead, LNCPs allow us to hedge all bets by equally lowering the technical entry barriers for each technology. The added integration of TP, when needed, also rounds out the faults related to sole LNCP use and provides any bank with a …