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Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw Nov 2021

Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct …

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Finding A Needle In A Haystack: Automatic Mining Of Silent Vulnerability Fixes, Jiayuan Zhou, Michael Pacheco, Zhiyuan Wan, Xin Xia, David Lo, Yuan Wang, Ahmed E. Hassan Nov 2021

Finding A Needle In A Haystack: Automatic Mining Of Silent Vulnerability Fixes, Jiayuan Zhou, Michael Pacheco, Zhiyuan Wan, Xin Xia, David Lo, Yuan Wang, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Following the coordinated vulnerability disclosure model, a vulnerability in open source software (OSS) is suggested to be fixed “silently”, without disclosing the fix until the vulnerability is disclosed. Yet, it is crucial for OSS users to be aware of vulnerability fixes as early as possible, as once a vulnerability fix is pushed to the source code repository, a malicious party could probe for the corresponding vulnerability to exploit it. In practice, OSS users often rely on the vulnerability disclosure information from security advisories (e.g., National Vulnerability Database) to sense vulnerability fixes. However, the time between the availability of a vulnerability …

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Data Analytics And Audit Quality, Ru Gao, Sterling Huang, Rencheng Wang Nov 2021

Data Analytics And Audit Quality, Ru Gao, Sterling Huang, Rencheng Wang

Research Collection School Of Accountancy

We examine the impact of data analytics on audit quality. Using hand-collected information on data analytics skills of employees of audit firms, we find that human capital investment in data analytics improves audit quality. The analytics capability of both frontline and back-office employees contributes to audit quality. The effect is more pronounced for audit clients with complex business operations, for clients with complicated accounting estimates, and for clients that are more digitalized. Taken together, our findings suggest that an audit office’s analytics capability represents an important office attribute that can affect audit quality.

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Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu Nov 2021

Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu

Research Collection School Of Computing and Information Systems

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the …

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Mediate, Not Litigate, To Resolve Disputes, Aloysius Goh, Terence Quek Nov 2021

Mediate, Not Litigate, To Resolve Disputes, Aloysius Goh, Terence Quek

Asian Management Insights

Mediation is essentially a facilitated negotiation process. When businesses have a dispute they cannot resolve on their own, they seek a neutral third party to help confidentially. This mediator has no power to impose a binding outcome on the parties. Rather, his or her responsibility is to facilitate the negotiation and use problem-solving skills to guide the parties towards a mutually agreed solution through a face saving and pragmatic process. In many jurisdictions, including Singapore, parties are required to attempt mediation first before courts hear the matter. Refusing to engage in mediation without proper reason may lead to cost sanctions …

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Patchnet: Hierarchical Deep Learning-Based Stable Patch Identification For The Linux Kernel, Thong Hoang, Julia Lawall, Yuan Tian, Richard J. Oentaryo, David Lo Nov 2021

Patchnet: Hierarchical Deep Learning-Based Stable Patch Identification For The Linux Kernel, Thong Hoang, Julia Lawall, Yuan Tian, Richard J. Oentaryo, David Lo

Research Collection School Of Computing and Information Systems

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical …

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Does Active Service Intervention Drive More Complaints On Social Media? The Roles Of Service Quality And Awareness, Shujing Sun, Yang Gao, Huaxia Rui Nov 2021

Does Active Service Intervention Drive More Complaints On Social Media? The Roles Of Service Quality And Awareness, Shujing Sun, Yang Gao, Huaxia Rui

Research Collection School Of Computing and Information Systems

Despite many advantages of social media as a customer service channel, there is a concern that active service intervention encourages excessive service complaints. Our paper casts doubt on this misconception by examining the dynamics between social media customer complaints and brand service interventions. We find service interventions indeed cause more complaints, yet this increase is driven by service awareness rather than chronic complaining. Due to the publicity and connectivity of social media, customers learn about the new service channel by observing customer service delivery to others – a mechanism that is unique to social media customer service and does not …

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On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic Nov 2021

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in LexRSM not existing even for simple terminating programs. Our contributions are twofold: First, we introduce a generalization of LexRSMs which allows for some components …

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Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua Nov 2021

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

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Leap: Leakage-Abuse Attack On Efficiently Deployable, Efficiently Searchable Encryption With Partially Known Dataset, Jianting Ning, Xinyi Huang, Geong Sen Poh, Jiaming Yuan, Yingjiu Li, Jian Weng, Robert H. Deng Nov 2021

Leap: Leakage-Abuse Attack On Efficiently Deployable, Efficiently Searchable Encryption With Partially Known Dataset, Jianting Ning, Xinyi Huang, Geong Sen Poh, Jiaming Yuan, Yingjiu Li, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

Searchable Encryption (SE) enables private queries on encrypted documents. Most existing SE schemes focus on constructing industrialready, practical solutions at the expense of information leakages that are considered acceptable. In particular, ShadowCrypt utilizes a cryptographic approach named “efficiently deployable, efficiently searchable encryption” (EDESE) that reveals the encrypted dataset and the query tokens among other information. However, recent attacks showed that such leakages can be exploited to (partially) recover the underlying keywords of query tokens under certain assumptions on the attacker’s background knowledge. We continue this line of work by presenting LEAP, a new leakageabuse attack on EDESE schemes that can …

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Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li Nov 2021

Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Online chatrooms are gaining popularity as a communication channel between widely distributed developers of Open Source Software (OSS) projects. Most discussion threads in chatrooms follow a Q&A format, with some developers (askers) raising an initial question and others (respondents) joining in to provide answers. These discussion threads are embedded with rich information that can satisfy the diverse needs of various OSS stakeholders. However, retrieving information from threads is challenging as it requires a thread-level analysis to understand the context. Moreover, the chat data is transient and unstructured, consisting of entangled informal conversations. In this paper, we address this challenge by …

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Automating User Notice Generation For Smart Contract Functions, Xing Hu, Zhipeng Gao, Xin Xia, David Lo, Xiaohu Yang Nov 2021

Automating User Notice Generation For Smart Contract Functions, Xing Hu, Zhipeng Gao, Xin Xia, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Smart contracts have obtained much attention and are crucial for automatic financial and business transactions. For end-users who have never seen the source code, they can read the user notice shown in end-user client to understand what a transaction does of a smart contract function. However, due to time constraints or lack of motivation, user notice is often missing during the development of smart contracts. For endusers who lack the information of the user notices, there is no easy way for them to check the code semantics of the smart contracts. Thus, in this paper, we propose a new approach …

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Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan Nov 2021

Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate …

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Sofi: Reflection-Augmented Fuzzing For Javascript Engines, Xiaoyu He, Xiaofei Xie, Yuekang Li, Jianwen Sun, Feng Li, Wei Zou, Yang Liu, Lei Yu, Jianhua Zhou, Wenchang Shi, Wei Huo Nov 2021

Sofi: Reflection-Augmented Fuzzing For Javascript Engines, Xiaoyu He, Xiaofei Xie, Yuekang Li, Jianwen Sun, Feng Li, Wei Zou, Yang Liu, Lei Yu, Jianhua Zhou, Wenchang Shi, Wei Huo

Research Collection School Of Computing and Information Systems

JavaScript engines have been shown prone to security vulnerabilities, which can lead to serious consequences due to their popularity. Fuzzing is an effective testing technique to discover vulnerabilities. The main challenge of fuzzing JavaScript engines is to generate syntactically and semantically valid inputs such that deep functionalities can be explored. However, due to the dynamic nature of JavaScript and the special features of different engines, it is quite challenging to generate semantically meaningful test inputs.We observed that state-of-the-art semantic-aware JavaScript fuzzers usually require manually written rules to analyze the semantics for a JavaScript engine, which is labor-intensive, incomplete and engine-specific. …

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Towards A Calibrated Trust-Based Approach To The Use Of Facial Recognition Technology, Gary Kok Yew Chan Nov 2021

Towards A Calibrated Trust-Based Approach To The Use Of Facial Recognition Technology, Gary Kok Yew Chan

Research Collection Yong Pung How School Of Law

The use of facial recognition technology has given rise to much debate relating to issues concerning privacy infringements, bias and inaccuracies of data and outputs, possibilities of covert use, the lack of data security and the problem of function creep. Certain states and jurisdictions have called for bans and moratoria on the use of facial recognition technology. This paper argues that a blanket ban on facial recognition technology would be overly precautionary without fully considering the wide range of uses and benefits of the innovation. To promote its acceptance, trust in facial recognition technology should be developed in a calibrated …

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Generating Music With Sentiments, Chunhui Bao Nov 2021

Generating Music With Sentiments, Chunhui Bao

Dissertations and Theses Collection (Open Access)

In this thesis, I focus on the music generation conditional on human sentiments such as positive and negative. As there are no existing large-scale music datasets annotated with sentiment labels, generating high-quality music conditioned on sentiments is hard. I thus build a new dataset consisting of the triplets of lyric, melody and sentiment, without requiring any manual annotations. I utilize an automated sentiment recognition model (based on the BERT trained on Edmonds Dance dataset) to "label'' the music according to the sentiments recognized from its lyrics. I then train the model of generating sentimental music and call the method Sentimental …

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Code-Switching Patterns Differentially Shape Cognitive Control: Testing The Predictions Of The Adaptive Control Hypothesis, Giliaine Ng, Hwajin Yang Nov 2021

Code-Switching Patterns Differentially Shape Cognitive Control: Testing The Predictions Of The Adaptive Control Hypothesis, Giliaine Ng, Hwajin Yang

Research Collection School of Social Sciences

Bilinguals engage in qualitatively different code-switching patterns (alternation, insertion, and congruent lexicalization) to different degrees, according to their engagement in different types of interactional contexts (single-language context, dual-language context, and dense code-switching context). Drawing on the adaptive control hypothesis, we examined whether bilinguals’ code-switching patterns would differentially shape multiple aspects of cognitive control (interference control, salient cue detection, and opportunistic planning). We found that a dense code-switching context, which predominantly involves insertion and congruent lexicalization, was positively associated with verbal opportunistic planning but negatively associated with interference control and salient cue detection. In contrast, a dual-language context, which predominantly involves …

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Older Adult Employment Status And Well-Being: A Longitudinal Bidirectional Analysis, Jonathan L. Chia, Andree Hartanto Nov 2021

Older Adult Employment Status And Well-Being: A Longitudinal Bidirectional Analysis, Jonathan L. Chia, Andree Hartanto

Research Collection School of Social Sciences

Mixed findings in the literature on the effects of older adult employment on well-being and the reciprocal influence of well-being on employment suggest the need for more careful methodology in teasing out this relationship. Moreover, as previous research has shown that different domains of well-being relate to constructs differently, more nuanced definitions of well-being may be appropriate. The present study examined the longitudinal bidirectional associations of employment and different domains of well-being, controlling for stable within-person variables. The present study sampled older adults from the Midlife Development in the US study at three timepoints on employment status and well-being, specifically …

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Development And Psychometric Evaluation Of The Anticipated Food Scarcity Scale (Afss), Michal Folwarczny, Norman P. Li, Valdimar Sigurdsson, Lynn K. L. Tan, Tobias Otterbring Nov 2021

Development And Psychometric Evaluation Of The Anticipated Food Scarcity Scale (Afss), Michal Folwarczny, Norman P. Li, Valdimar Sigurdsson, Lynn K. L. Tan, Tobias Otterbring

Research Collection School of Social Sciences

Mass media extensively inform societies about events threatening the global food supply (e.g., pandemics or Brexit). Consumers exposed to such communication may perceive food resources as becoming scarcer. In line with an evolutionary account, these perceptions can shift decision-making in domains such as food preferences or prosociality. However, existing literature has solely focused on actual and past food insecurity experiences threatening mostly low-income families, thus neglecting the future-oriented perceptions among the general population. This paper broadens the food insecurity research scope by developing a new construct-anticipated food scarcity (AFS)-which is defined as the perception that food resources are becoming less …

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The "Silent Teacher": Learning By Teaching Via Writing A Verbatim Teaching Script, Kagen Y. L. Lim, Sarah Shi Hui Wong, Stephen Wee Hun Lim Nov 2021

The "Silent Teacher": Learning By Teaching Via Writing A Verbatim Teaching Script, Kagen Y. L. Lim, Sarah Shi Hui Wong, Stephen Wee Hun Lim

Research Collection School of Social Sciences

Learning by teaching others is a potent educational strategy, but its implementation is typically cumbersome. This study (N = 108) investigated “silent teaching”—writing a verbatim teaching script—as a convenient approach for independent learning, while assessing whether the teaching benefit is a production benefit. Learners studied a science text on the Doppler effect using one of three learning methods: (1) generating and studying their own notes (restudying control), (2) preparing to teach and then verbally teaching (verbal teaching), or (3) preparing to teach and then writing a verbatim teaching script (silent teaching). On a conceptual knowledge retention test 1 week later, …

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Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall Nov 2021

Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall

Research Collection Lee Kong Chian School Of Business

There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have substance, but there is a need is to articulate the mechanisms by which AI may actually do both, and in the process, transform work and business organizations alike. We use economic studies showing past transformations automation wrought on the structure of employment and skills (such as the favouring of nonroutine skills) to articulate a ground for discussion. We then use case evidence of AI and automation to show how AI is …

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Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu Nov 2021

Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu

Research Collection Lee Kong Chian School Of Business

Stock return prediction has been a hot topic in both research and industry given its potential for large financial gain. The return signal, apart from its inherent volatility and complexity, is often accompanied by a multitude of noises, such as other stocks’ performance, macroeconomic factors and financial news, etc. To better characterize these factors, we propose a new model that consists of two levels of sequence: an NLP-based module to capture the sequential nature of words and sentences in the financial news, and a time-series-based module to exploit the sequential nature of adjacent observations in the stock price. In this …

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Health Insurance And Subjective Well-Being: Evidence From Two Healthcare Reforms In The United States, Seonghoon Kim Nov 2021

Health Insurance And Subjective Well-Being: Evidence From Two Healthcare Reforms In The United States, Seonghoon Kim

Research Collection School Of Economics

We study the role of access to health insurance coverage as a determinant of individuals' subjective well-being (SWB) by analyzing large-scale healthcare reforms in the United States. Using data from the Behavioral Risk Factor Surveillance System and Panel Study of Income Dynamics, we find that the 2006 Massachusetts reform and 2014 Affordable Care Act Medicaid expansion improved the overall life satisfaction of Massachusetts residents and low-income adults in Medicaid expansion states, respectively. The results are robust to various sensitivity and falsification tests. Our findings imply that access to health insurance plays an important role in improving SWB. Without considering psychological …

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Exploiting Reasoning Chains For Multi-Hop Science Question Answering, Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam Nov 2021

Exploiting Reasoning Chains For Multi-Hop Science Question Answering, Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam

Research Collection School Of Computing and Information Systems

We propose a novel Chain Guided Retriever reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to …

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Building Legal Datasets, Jerrold Soh Nov 2021

Building Legal Datasets, Jerrold Soh

Research Collection Yong Pung How School Of Law

Data-centric AI calls for better, not just bigger, datasets. As data protection laws with extra-territorial reach proliferate worldwide, ensuring datasets are legal is an increasingly crucial yet overlooked component of “better”. To help dataset builders become more willing and able to navigate this complex legal space, this paper reviews key legal obligations surrounding ML datasets, examines the practical impact of data laws on ML pipelines, and offers a framework for building legal datasets.

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Cs-Light: Camera Sensing Based Occupancy-Aware Robust Smart Building Lighting Control, Anuradha Ravi, Kasun Pramuditha Gamlath, Siyan Hu, Archan Misra Nov 2021

Cs-Light: Camera Sensing Based Occupancy-Aware Robust Smart Building Lighting Control, Anuradha Ravi, Kasun Pramuditha Gamlath, Siyan Hu, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …

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Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun Nov 2021

Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional pseudo-labeling operation heavily relies on the initial model trained by using a handful of labeled data and may produce many noisy labeled samples. We propose to solve the problem with three steps: firstly, cherry-picking searches valuable samples from pseudo-labeled data by using a soft weighting network; and then, cross-teaching allows the classifiers to teach mutually for rejecting more noisy …

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Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn Nov 2021

Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn

Research Collection School Of Computing and Information Systems

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian …

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Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo Nov 2021

Incbl: Incremental Bug Localization, Zhou Yang, Jieke Shi, Wang Shaowei, David Lo

Research Collection School Of Computing and Information Systems

Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on …

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Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability, Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai Nov 2021

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability, Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Research Collection School Of Computing and Information Systems

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics, including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate these metrics using the interpretability scores of rules. We manually annotate all possible …

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