Digital Commons Network^™

Towards Low-Resource Rumor Detection: Unified Contrastive Transfer With Propagation Structure, Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng

Research Collection School Of Computing and Information Systems

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a simple yet effective framework with unified contrastive transfer learning, to detect rumors by adapting the features learned from well-resourced rumor data to that …

Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …

W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …

Beyond A Joke: Dead Code Elimination Can Delete Live Code, Haoxin Tu, Lingxiao Jiang, Debin Gao, He Jiang

Research Collection School Of Computing and Information Systems

Dead Code Elimination (DCE) is a fundamental compiler optimization technique that removes dead code (e.g., unreachable or reachable but whose results are unused) in the program to produce smaller or faster executables. However, since compiler optimizations are typically aggressively performed and there are complex relationships/interplay among a vast number of compiler optimizations (including DCE), it is not known whether DCE is indeed correctly performed and will only delete dead code in practice. In this study, we open a new research problem to investigate: can DCE happen to erroneously delete live code? To tackle this problem, we design a new approach …

Efficacy Of Digital Mental Health Interventions For Ptsd Symptoms: A Systematic Review Of Meta-Analyses, Yue Qi Germaine Tng, Xun Ci Soh, Xun Ci Soh, Andree Hartanto, Andree Hartanto

Research Collection School of Social Sciences

Background: The present systematic review aimed to synthesize the results of meta-analyses which examine the effects of digital mental health interventions (DMHIs) on post-traumatic stress disorder (PTSD) symptoms, and investigate whether intervention characteristics (i.e., technique, timeframe, and therapeutic guidance) and methodological characteristics including outcome measures and sample inclusion criteria (age, gender, socioeconomic status, country, comorbidity) moderate the efficacy of digital interventions. Methods: A systematic search of various sources (ECSCOhost PsycInfo, PubMed, Web of Science, Scopus, EBSCOhost ERIC, Google Scholar, ProQuest Dissertations & Theses) including five peer-reviewed journals was conducted to identify relevant meta-analyses up to December 2023, and 11 meta-analyses …

Research On Futures, Profit Hedging, And Enterprise Price Risk Management In The Bulk Commodity Industry Chain — A Case Study Of Hot-Rolled Coil Industry Chain, Shengxi Liu

Dissertations and Theses Collection (Open Access)

Traditional hedging strategies usually center around a single product, commonly involving companies hedging against the price of their endproducts or essential production materials. At present, traditional hedgingstrategies are no longer sufficient to address the production and management requirements of physical enterprises. There is a need to develop hedgingstrategies aligning with the significant changes in both domestic andinternational markets.

The thesis presents a new profit hedging model based on product manufacturing processes within the industry chain. This method involves hedging production profits by buying (selling) end products and selling(buying) raw materials according to the proportional relationships in the production process on …

Can Organizational Focus On Responsible Ai Lead To Improved Ai Adoption By Employees?, Seema Chokshi

Dissertations and Theses Collection (Open Access)

The duality inherent in Artificial Intelligence technology entails that while AI has the potential to bring about transformative benefits to organizations, unintended consequences of AI applications could lead to biased and discriminatory outcomes, which could have negative consequences for the organization and society in general. Concerns about such unintended consequences are an impediment to AI adoption where unwilling employees and practitioners often fear ethical breaches, thereby, negatively impacting their engagement with AI driven applications. In response to these concerns various organizations and regulatory bodies have developed governing frameworks broadly known as Responsible AI standards, that set guidelines to design, …

Learning To Outsmart A Crisis: A Strategic Management View On Managing Aviation Crises With Machine Learning, Kok Yew Tan

Dissertations and Theses Collection (Open Access)

In strategic management literature, a crisis is public, unexpected, interferes with the norm, has negative impact on organizations, and generates widespread negative perceptions among stakeholders. Among crises, aviation crises are complex and present multiple vulnerabilities due to their scale, and the involvement of numerous stakeholders. With the high visibility of aviation crises due to the potential for mass injuries, deaths, and collateral damage, it could be seen as a full spectrum crisis – one of the worst among crises. The magnitude of each aviation crisis decides and allows the world to assess the efficacy of an airlines or aviation firm’s …

When I Know Your Taste: Retail Customers’ Environmental Preferences And Firm Pollution, Mengjie Yang

Dissertations and Theses Collection (Open Access)

I examine whether and how firms incorporate retail customers’ environmental preferences into their pollution decisions. Leveraging the staggered revelations of firms’ environmental negative news and the granularity of household grocery shopping records, I quantify local customers’ heterogeneous environmental preferences based on the extent of product sales declines following the news events. In line with the conjecture that firms factor in rewards and penalties from customers and strategically reduce their pollution, I find a significant improvement in air quality near event firms’ facilities located in markets where local customers reveal the strongest environmental preferences. This effect is more pronounced when news …

Sequential Recommendation: From Representation Learning To Reasoning, Lei Wang

Dissertations and Theses Collection (Open Access)

The recommender system is a crucial component of today's online services. It helps users navigate through an overwhelmingly large number of items and discovering those that interest them. Unlike general recommender systems, which recommend items based on the user's overall preferences, sequential recommender systems consider the order of user-item interactions. Sequential recommendations aim to predict the next item a user will interact with, given a sequence of previously interacted items, while considering the short-term and long-term dependencies among items.

In this thesis, we focus on sequential recommendation methods: from representation learning to large language model (LLM)-based reasoning. On the one …

The Important Role Of System Dynamics Investigation On Business Model, Industry And Performance Management, Lina Gozali, Teuku Yuri M. Zagloel, Togar Mangihut Simatupang, Wahyudi Sutopo, Aldy Gunawan, Yun-Chia Liang, Bernardo Nugroho Yahya, Jose Arturo Garza-Reyes, Agustinus Purna Irawan, Yuliani Suseno

Research Collection School Of Computing and Information Systems

Purpose: This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in …

Unleashing The Power Of Clippy In Real-World Rust Projects, Chunmiao Li, Yijun Yu, Haitao Wu, Luca Carlig, Shijie Nie, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The error messages generated by the Rust compiler (rustc) are useful for developers to identify and diagnose suspicious code segments. Complementing the compiler, linters can also play an important role in promoting the adherence to certain coding style conventions and best practices. Prominent linters utilized in the Rust ecosystem include Clippy [1] and Rustfmt [2]. Among them, the Rust community particularly emphasizes on the importance of heeding the warnings provided by Clippy to mitigate common errors and promote the adoption of idiomatic conventions. Clippy provides a set of more than 600 lints in addition to the built-in rustc lints. These …

Bidirectional Paper-Repository Tracing In Software Engineering, Daniel Garijo, Miguel Arroyo, Esteban González Guardia, Christoph Treude, Nicola Tarocco

Research Collection School Of Computing and Information Systems

While computer science papers frequently include their associated code repositories, establishing a clear link between papers and their corresponding implementations may be challenging due to the number of code repositories used in research publications. In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. We have used our approach to analyze more than 14000 PDF and Latex files in the Software Engineering category of Arxiv, generating a dataset of more than 1400 paper-code implementations and assessing current citation practices on it.

Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude

Research Collection School Of Computing and Information Systems

Deep learning techniques applied to program analysis tasks such as code classification, summarization, and bug detection have seen widespread interest. Traditional approaches, however, treat programming source code as natural language text, which may neglect significant structural or semantic details. Additionally, most current methods of representing source code focus solely on the code, without considering beneficial additional context. This paper explores the integration of static analysis and additional context such as bug reports and design patterns into source code representations for deep learning models. We use the Abstract Syntax Tree-based Neural Network (ASTNN) method and augment it with additional context information …

Assessing Ai Detectors In Identifying Ai-Generated Code: Implications For Education, Wei Hung Pan, Ming Jie Chok, Jonathan Leong Shan Wong, Yung Xin Shin, Yeong Shian Poon, Zhou Yang, Chun Yong Chong, David Lo, Mei Kuan Lim

Research Collection School Of Computing and Information Systems

Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct.In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were …

Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of …

Coca: Improving And Explaining Graph Neural Network-Based Vulnerability Detection Systems, Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, Wei Liu

Research Collection School Of Computing and Information Systems

Recently, Graph Neural Network (GNN)-based vulnerability detection systems have achieved remarkable success. However, the lack of explainability poses a critical challenge to deploy black-box models in security-related domains. For this reason, several approaches have been proposed to explain the decision logic of the detection model by providing a set of crucial statements positively contributing to its predictions. Unfortunately, due to the weakly-robust detection models and suboptimal explanation strategy, they have the danger of revealing spurious correlations and redundancy issue.In this paper, we propose Coca, a general framework aiming to 1) enhance the robustness of existing GNN-based vulnerability detection models to …

Ppt4j: Patch Presence Test For Java Binaries, Zhiyuan Pan, Xing Hu, Xin Xia, Xian Zhan, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results.In this paper, we propose a new patch presence test framework named Ppt4J …

Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang

Research Collection School Of Computing and Information Systems

Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …

Filter-Based Stance Network For Rumor Verification, Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Rumor verification on social media aims to identify the truth value of a rumor, which is important to decreasethe detrimental public effects. A rumor might arouse heated discussions and replies, conveying differentstances of users that could be helpful in identifying the rumor. Thus, several works have been proposedto verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignorethat such a stance sequence could be decomposed into controversies with different intensities, which could beused to cluster the stance sequences with the same consensus. In addition, the existing stance extractors fail toconsider both the impact …

When Mothers Do It All: Gender-Role Norms And Women’S Fertility Intentions In Post-Industrial Societies, Sinn Won Han, Ohjae Gowen, Mary C. Brinton

Research Collection School of Social Sciences

Post-industrial countries with high rates of female labour force participation have generally had low fertility rates, but recent studies demonstrate that this is no longer the case. This has generated increased attention to how greater gender equality in the private sphere of the household may contribute to a positive relationship between women’s employment rates and fertility. Building on recent scholarship demonstrating the multidimensionality of gender-role attitudes, we argue that conversely, the prevalence of a gender-role ideology that supports women’s employment but places greater priority on their role as caregivers may depress the higher-order fertility intentions of working mothers. Using data …

Implementation And Evaluation Of Ai-Based Citizen Question-Answer Recommender (Acqar) To Enhance Citizen Service Delivery In Singapore Public Sector: A Case Study, Hui Shan Lee

Dissertations and Theses Collection (Open Access)

Government agencies prioritize citizen service delivery to foster trust with the public. Technological advancements, particularly in Artificial Intelligence (AI), hold promise for improving service provision and aligning government operations with citizens' needs. Yet the inherent inflexibility of Service Level Agreements (SLAs) often overlooks the nuances of human emotions and the varied nature of citizen inquiries, exacerbated by a lack of tools to guide appropriate responses. This dissertation aims to address the gaps of overlook of human emotions and non-support for appropriate responses, by exploring the following questions: (1) Can a predictive model incorporating both numeric and textual data effectively forecast …

From The Editors: Mobilizing New Sources Of Data: Opportunities And Recommendations, Denis A. Gregoire, Anne L. J. Ter Wal, Laura M. Little, Sekou Bermiss, Reddi Kotha, Marc Gruber

Research Collection Lee Kong Chian School Of Business

In June 2008, the U.S.-based website Glassdoor.com began posting anonymous company reviews and salary data from current and former employees of various organizations. Doing so not only brought to the world information that had hitherto been restricted to private circles, it spontaneously prompted some organizations to alter their workplace practices (Dineen & Allen, 2016; Dube & Zhu, 2021). At the same time, Glassdoor’s very activities gave rise to a completely new source of data for exploring a wealth of management and organizational phenomena (e.g., Bermiss & McDonald, 2018; Rhee, 2024). As this example illustrates, new data sources can not only …

Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.

Unveiling Memorization In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

The availability of large-scale datasets, advanced architectures, and powerful computational resources have led to effective code models that automate diverse software engineering activities. The datasets usually consist of billions of lines of code from both open-source and private repositories. A code model memorizes and produces source code verbatim, which potentially contains vulnerabilities, sensitive information, or code with strict licenses, leading to potential security and privacy issues.This paper investigates an important problem: to what extent do code models memorize their training data? We conduct an empirical study to explore memorization in large pre-trained code models. Our study highlights that simply extracting …

Constitutional Supremacy, Inherent Powers, And Orders That Damages Or Costs Be Paid In Instalments: Tan Meow Hiang V Ong Kay Yong [2023] Sghc 286, Benjamin Joshua Ong

Research Collection Yong Pung How School Of Law

The General Division of the High Court of Singapore stated (obiter) in Tan Meow Hiang v Ong Kay Yong that it does not have a general power to order that damages or costs be paid in instalments. It reasoned that any such power must have been impliedly restricted by legislation. So instalment orders can only be made (a) by certain subordinate courts; (b) by the General Division on appeal; (c) by the General Division at first instance in personal injury cases. In response, this note aims to advance the conversation on the law on instalment orders and what the Constitution …

Classifying Source Code: How Far Can Compressor-Based Classifiers Go?, Zhou Yang

Research Collection School Of Computing and Information Systems

Pre-trained language models of code, which are built upon large-scale datasets, millions of trainable parameters, and high computational resources cost, have achieved phenomenal success. Recently, researchers have proposed a compressor-based classifier (Cbc); it trains no parameters but is found to outperform BERT. We conduct the first empirical study to explore whether this lightweight alternative can accurately classify source code. Our study is more than applying Cbc to code-related tasks. We first identify an issue that the original implementation overestimates Cbc. After correction, Cbc's performance on defect prediction drops from 80.7% to 63.0%, which is still comparable to CodeBERT (63.7%). We …

Creative And Correct: Requesting Diverse Code Solutions From Ai, Scott Blyth, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

AI foundation models have the capability to produce a wide array of responses to a single prompt, a feature that is highly beneficial in software engineering to generate diverse code solutions. However, this advantage introduces a significant trade-off between diversity and correctness. In software engineering tasks, diversity is key to exploring design spaces and fostering creativity, but the practical value of these solutions is heavily dependent on their correctness. Our study systematically investigates this trade-off using experiments with HumanEval tasks, exploring various parameter settings and prompting strategies. We assess the diversity of code solutions using similarity metrics from the code …

Large Language Model For Vulnerability Detection: Emerging Results And Future Directions, Xin Zhou, Ting Zhang, David Lo

Research Collection School Of Computing and Information Systems

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.

Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which …