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Full-Text Articles in Physical Sciences and Mathematics
Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo
Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo
Research Collection School Of Computing and Information Systems
Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …
Adaptive Aggregation Networks For Class-Incremental Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun
Adaptive Aggregation Networks For Class-Incremental Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets) in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two …
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Research Collection School Of Computing and Information Systems
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by …
Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Research Collection School Of Computing and Information Systems
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results …
Neighbourhood Structure Preserving Cross-Modal Embedding For Video Hyperlinking, Yanbin Hao, Chong-Wah Ngo, Benoit Huet
Neighbourhood Structure Preserving Cross-Modal Embedding For Video Hyperlinking, Yanbin Hao, Chong-Wah Ngo, Benoit Huet
Research Collection School Of Computing and Information Systems
Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed.& x00A0;The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data …
Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia
Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia
Research Collection School Of Computing and Information Systems
Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …
Enhancing Automated Program Repair With Deductive Verification, Xuan-Bach D. Le, Quang Loc Le, David Lo, Claire Le Goues
Enhancing Automated Program Repair With Deductive Verification, Xuan-Bach D. Le, Quang Loc Le, David Lo, Claire Le Goues
Research Collection School Of Computing and Information Systems
Automated program repair (APR) is a challenging process of detecting bugs, localizing buggy code, generating fix candidates and validating the fixes. Effectiveness of program repair methods relies on the generated fix candidates, and the methods used to traverse the space of generated candidates to search for the best ones. Existing approaches generate fix candidates based on either syntactic searches over source code or semantic analysis of specification, e.g., test cases. In this paper, we propose to combine both syntactic and semantic fix candidates to enhance the search space of APR, and provide a function to effectively traverse the search space. …
Empirical Study On Synthesis Engines For Semantics-Based Program Repair, Le Dinh Xuan Bach, David Lo, Claire Le Goues
Empirical Study On Synthesis Engines For Semantics-Based Program Repair, Le Dinh Xuan Bach, David Lo, Claire Le Goues
Research Collection School Of Computing and Information Systems
Automatic Program Repair (APR) is an emerging and rapidly growing research area, with many techniques proposed to repair defective software. One notable state-of-the-art line of APR approaches is known as semantics-based techniques, e.g., Angelix, which extract semantics constraints, i.e., specifications, via symbolic execution and test suites, and then generate repairs conforming to these constraints using program synthesis. The repair capability of such approaches-expressive power, output quality, and scalability-naturally depends on the underlying synthesis technique. However, despite recent advances in program synthesis, not much attention has been paid to assess, compare, or leverage the variety of available synthesis engine capabilities in …