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Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao Oct 2023

Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects …

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Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei Oct 2023

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

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Rigid: Recurrent Gan Inversion And Editing Of Real Face Videos, Yangyang Xu, Shengfeng He, Kwan-Yee K. Wong, Pingluo Luo Oct 2023

Rigid: Recurrent Gan Inversion And Editing Of Real Face Videos, Yangyang Xu, Shengfeng He, Kwan-Yee K. Wong, Pingluo Luo

Research Collection School Of Computing and Information Systems

GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired inconsistent results over time. In this paper, we propose a unified recurrent framework, named Recurrent vIdeo GAN Inversion and eDiting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos. Our approach models the temporal relations between current and previous frames from three aspects. To enable a faithful real video reconstruction, we first maximize the inversion fidelity and consistency by learning a temporal compensated latent code. Second, we observe …

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Using The Typescript Compiler To Fix Erroneous Node.Js Snippets, Brittany Reid, Christoph Treude, Markus Wagner Oct 2023

Using The Typescript Compiler To Fix Erroneous Node.Js Snippets, Brittany Reid, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Most online code snippets do not run. This means that developers looking to reuse code from online sources must manually find and fix errors. We present an approach for automatically evaluating and correcting errors in Node.js code snippets: Node Code Correction (NCC). NCC leverages the ability of the TypeScript compiler to generate errors and inform code corrections through the combination of TypeScript’s builtin codefixes, our own targeted fixes, and deletion of erroneous lines. Compared to existing approaches using linters, our findings suggest that NCC is capable of detecting a larger number of errors per snippet and more error types, and …

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Stprivacy: Spatio-Temporal Privacy-Preserving Action Recognition, Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, Shuicheng Yan Oct 2023

Stprivacy: Spatio-Temporal Privacy-Preserving Action Recognition, Ming Li, Xiangyu Xu, Hehe Fan, Pan Zhou, Jun Liu, Jia-Wei Liu, Jiahe Li, Jussi Keppo, Mike Zheng Shou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks. First, they may compromise temporal dynamics in input videos, which are critical for accurate action recognition. Second, they are vulnerable to practical attacking scenarios where attackers probe for privacy from an entire video rather than individual frames. To address these issues, we propose a novel framework STPrivacy to perform video-level PPAR. For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i.e., sparsification …

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Understanding The Effect Of Counterfactual Explanations On Trust And Reliance On Ai For Human-Ai Collaborative Clinical Decision Making, Min Hun Lee, Chong Jun Chew Oct 2023

Understanding The Effect Of Counterfactual Explanations On Trust And Reliance On Ai For Human-Ai Collaborative Clinical Decision Making, Min Hun Lee, Chong Jun Chew

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of achieving human AI complementary performance. In this work, we utilized salient feature explanations along with what-if, counterfactual explanations to make humans review AI suggestions more analytically to reduce overreliance on AI and explored the effect of these explanations on trust and reliance on AI during clinical decision-making. We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality …

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Boosting Adversarial Training In Safety-Critical Systems Through Boundary Data Selection, Yifan Jia, Christopher M. Poskitt, Peixin Zhang, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay Oct 2023

Boosting Adversarial Training In Safety-Critical Systems Through Boundary Data Selection, Yifan Jia, Christopher M. Poskitt, Peixin Zhang, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay

Research Collection School Of Computing and Information Systems

AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve the models' robustness against them. Adversarial training is one approach to improve their robustness: it works by augmenting the training data with adversarial examples. This, unfortunately, comes with the cost of increased computational overhead and extended training times. In this work, we balance the need for additional adversarial data with the goal of minimizing the training …

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Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro Oct 2023

Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach …

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Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw Oct 2023

Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters.To fill the gap, …

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Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura Oct 2023

Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-The-shelf VR HMD setups rely on mid-Air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design …

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Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang Oct 2023

Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out the crux is the less effective training for the “joint hard samples”, which have high confidence prediction on different wrong labels, implying that the 2D and 3D models do not collaborate well. To this end, our proposed invariant training strategy, called INVJOINT, does not only emphasize the training more on the hard …

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Anomaly Detection Under Distribution Shift, Tri Cao, Jiawen Zhu, Guansong Pang Oct 2023

Anomaly Detection Under Distribution Shift, Tri Cao, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing AD studies assume that the training and test data are drawn from the same data distribution, but the test data can have large distribution shifts arising in many real-world applications due to different natural variations such as new lighting conditions, object poses, or background appearances, rendering existing AD methods ineffective in such cases. In this paper, we consider the problem of anomaly detection under distribution shift and establish performance benchmarks …

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Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun Oct 2023

Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun

Research Collection School Of Computing and Information Systems

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as events with timestamps. Timed automata are an extension of finite-state automata with a set of clocks evolving linearly and that can be tested or reset, making this formalism able to reason on systems involving concurrency and timing constraints. In this paper, we summarize a recent line of works using timed automata as the input formalism, in which we assume that the attacker has access (only) to the system execution time. First, we address the following execution-time opacity problem: given a timed …

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Designing A Human-Centered Intelligent System To Monitor & Explain Abnormal Patterns Of Older Adults, Min Hun Lee, Daniel P. Siewiorek, Alexandre Bernardino Oct 2023

Designing A Human-Centered Intelligent System To Monitor & Explain Abnormal Patterns Of Older Adults, Min Hun Lee, Daniel P. Siewiorek, Alexandre Bernardino

Research Collection School Of Computing and Information Systems

Older adult care technologies are increasingly explored to support the independent living of older adults by monitoring their abnormal activities and informing caregivers to provide intervention if necessary. However, the adoption of these technologies remains challenging due to several factors (e.g. lack of usability). In this work, we present a human-centered, intelligent system for older adult care. Our proposed designs of the system were created based on the findings from a focus group session with caregivers. This system monitors the abnormal activities of an older adult using wireless motion sensors and machine learning models. In addition, unlike previous work that …

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Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu Oct 2023

Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu

Research Collection School Of Computing and Information Systems

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …

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Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein Oct 2023

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …

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Ciri: Curricular Inactivation For Residue-Aware One-Shot Video Inpainting, Weiying Zheng, Cheng Xu, Xuemiao Xu, Wenxi Liu, Shengfeng He Oct 2023

Ciri: Curricular Inactivation For Residue-Aware One-Shot Video Inpainting, Weiying Zheng, Cheng Xu, Xuemiao Xu, Wenxi Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Video inpainting aims at filling in missing regions of a video. However, when dealing with dynamic scenes with camera or object movements, annotating the inpainting target becomes laborious and impractical. In this paper, we resolve the one-shot video inpainting problem in which only one annotated first frame is provided. A naive solution is to propagate the initial target to the other frames with techniques like object tracking. In this context, the main obstacles are the unreliable propagation and the partially inpainted artifacts due to the inaccurate mask. For the former problem, we propose curricular inactivation to replace the hard masking …

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Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu Oct 2023

Evlog: Identifying Anomalous Logs Over Software Evolution, Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, Michael Lyu

Research Collection School Of Computing and Information Systems

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving …

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Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic Oct 2023

Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider bidding games, a class of two-player zerosum graph games. The game proceeds as follows. Both players have bounded budgets. A token is placed on a vertex of a graph, in each turn the players simultaneously submit bids, and the higher bidder moves the token, where we break bidding ties in favor of Player 1. Player 1 wins the game iff the token visits a designated target vertex. Weconsider, for the first time, poorman discrete-bidding in which the granularity of the bids is restricted and the higher bid is paid to the bank. Previous work either did not impose …

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Log-Based Anomaly Detection Based On Evt Theory With Feedback, Jinyang Liu, Junjie Huang, Yintong Huo, Zhihan Jiang, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Minzhi Yan, R. Michael Lyu Sep 2023

Log-Based Anomaly Detection Based On Evt Theory With Feedback, Jinyang Liu, Junjie Huang, Yintong Huo, Zhihan Jiang, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Minzhi Yan, R. Michael Lyu

Research Collection School Of Computing and Information Systems

System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable …

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K-St: A Formal Executable Semantics Of The Structured Text Language For Plcs, Kun Wang, Jingyi Wang, Christopher M. Poskitt, Xiangxiang Chen, Jun Sun, Peng Cheng Sep 2023

K-St: A Formal Executable Semantics Of The Structured Text Language For Plcs, Kun Wang, Jingyi Wang, Christopher M. Poskitt, Xiangxiang Chen, Jun Sun, Peng Cheng

Research Collection School Of Computing and Information Systems

Programmable Logic Controllers (PLCs) are responsible for automating process control in many industrial systems (e.g. in manufacturing and public infrastructure), and thus it is critical to ensure that they operate correctly and safely. The majority of PLCs are programmed in languages such as Structured Text (ST). However, a lack of formal semantics makes it difficult to ascertain the correctness of their translators and compilers, which vary from vendor-to-vendor. In this work, we develop K-ST, a formal executable semantics for ST in the K framework. Defined with respect to the IEC 61131-3 standard and PLC vendor manuals, K-ST is a high-level …

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Generative Model-Based Testing On Decision-Making Policies, Zhuo Li, Xiongfei Wu, Derui Zhu, Mingfei Cheng, Siyuan Chen, Fuyuan Zhang, Xiaofei Xie, Lei Ma, Jianjun Zhao Sep 2023

Generative Model-Based Testing On Decision-Making Policies, Zhuo Li, Xiongfei Wu, Derui Zhu, Mingfei Cheng, Siyuan Chen, Fuyuan Zhang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging.In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different …

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Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen Sep 2023

Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

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Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun Sep 2023

Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun

Research Collection School Of Computing and Information Systems

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied, compared with identifying individual discrimination. In this work, we propose TestSGD, an interpretable testing approach which systematically identifies and measures hidden (which we call ‘subtle’) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes. Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set which categorizes the input space into two groups. Alongside, TestSGD …

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Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert Sep 2023

Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert

Research Collection School Of Computing and Information Systems

Android malware detection has been an active area of research. In the past decade, several machine learning-based approaches based on different types of features that may characterize Android malware behaviors have been proposed. The usually-analyzed features include API usages and sequences at various abstraction levels (e.g., class and package), extracted using static or dynamic analysis. Additionally, features that characterize permission uses, native API calls and reflection have also been analyzed. Initial works used conventional classifiers such as Random Forest to learn on those features. In recent years, deep learning-based classifiers such as Recurrent Neural Network have been explored. Considering various …

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Automated Question Title Reformulation By Mining Modifcation Logs From Stack Overflow, Ke Liu, Xiang Chen, Chunyang Chen, Xiaofei Xie, Zhanqi Cui Sep 2023

Automated Question Title Reformulation By Mining Modifcation Logs From Stack Overflow, Ke Liu, Xiang Chen, Chunyang Chen, Xiaofei Xie, Zhanqi Cui

Research Collection School Of Computing and Information Systems

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach QETRA motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we …

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Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves Sep 2023

Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves

Research Collection School Of Computing and Information Systems

We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in …

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Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw Sep 2023

Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …

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Revisiting The Identification Of The Co-Evolution Of Production And Test Code, Weifeng Sun, Meng Yan, Zhongxin Liu, Xin Xia, Yan Lei, David Lo Sep 2023

Revisiting The Identification Of The Co-Evolution Of Production And Test Code, Weifeng Sun, Meng Yan, Zhongxin Liu, Xin Xia, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

Many software processes advocate that the test code should co-evolve with the production code. Prior work usually studies such co-evolution based on production-test co-evolution samples mined from software repositories. A production-test co-evolution sample refers to a pair of a test code change and a production code change where the test code change triggers or is triggered by the production code change. The quality of the mined samples is critical to the reliability of research conclusions. Existing studies mined production-test co-evolution samples based on the following assumption: if a test class and its associated production class change together in one commit, …

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When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau Sep 2023

When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Peer-to-Peer (P2P) logistics platforms, unlike traditional last-mile logistics providers, do not have dedicated delivery resources (both vehicles and drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P2P logistics platform where new orders arrive dynamically and the platform operator needs to generate a list of recommended orders to the crowdsourced drivers. We formulate this problem as a Dynamic Order Recommendations Problem (DORP). …

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