Digital Commons Network^™

Unlocking Markets: A Multilingual Benchmark To Cross-Market Question Answering, Yifei Yuan, Yang Deng, Anders Sogaard, Mohammad Alliannejadi

Research Collection School Of Computing and Information Systems

Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. Wepropose a novel task of Multilingual Crossmarket Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a largescale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: …

Ask-Before-Plan : Proactive Language Agents For Real-World Planning, Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs …

Politically Connected Firms And The Cash Flow Sensitivity Of Cash: International Evidence, Yuanto Kusnadi

Research Collection School Of Accountancy

This paper examines the role of political connections on the cash flow sensitivity of cash using a sample of international firms. The finding reveals that the cash flow sensitivity of cash is lower for politically connected firms than their non-connected counterparts, which is supportive of the precautionary motive of cash holdings. Further analyses reveal that the result is driven by political connections that are made through the directors of the firms and with members of parliament, as well as by differences in country-level institutional infrastructure. Overall, the empirical results corroborate that of previous studies on the relevance of the precautionary …

Discretionary Dissemination On Twitter, Richard M. Crowley, Wenli Huang, Hai Lu

Research Collection School Of Accountancy

The study provides large-scale descriptive evidence on the timing and nature of corporate financial tweeting. Using an unsupervised machine learning approach to analyze 24 million tweets posted by S&P 1500 firms from 2012 to 2020, we find that firms are more likely to tweet financial information around significantly negative or positive news events, such as earnings announcements and the filing of financial statements. This convex U-shaped relation between the likelihood of posting financial tweets and the materiality of accounting events becomes stronger over time. Whereas research based on early samples concludes that firms are less likely to disseminate financial information …

Media Use, Interpersonal Communication, And Personal Relevance As External And Internal Representations Of Climate Change, Sonny Rosenthal, Pengya Ai

Research Collection College of Integrative Studies

Personal relevance is a key driver of individual climate action. It is also related to media use and interpersonal communication, which the current study examines from two perspectives. First, individuals may find climate change personally relevant because they experience it vicariously through the media and other information sources. Second, they may engage with climate change information because the issue is personally relevant. This study tested these models using structural equation modeling of online survey data from representative samples in Singapore (n = 1,997) and the United States (n = 2,009). Findings supported both models, albeit the first one more strongly. …

What Have Corporations Got To Do With It? A Political Economy Approach To Organizations And Climate Change, Annika Rieger

Research Collection School of Social Sciences

Ecological issues are often seen as only one of the many “problems” societies face today, even though they go deep and affect everything – from physical disruption and displacement to perhaps more subtle long-term changes in temperature, flora, and fauna that alter the face of the planet and the norms of everyday life. As a result of this view, the study of the environment has been siloed into the realm of “environmental sciences” with a few “environmental fill-in-the-social-science-blank” subfields scattered about. This is not to say that only “environmental” problems are important, but that across all disciplines and subfields, greater …

A Dynamic Urban Digital Twin Integrating Longitudinal Thermal Imagery For Microclimate Studies, Vasantha Ramani, Marcel Ignatius, Joie Lim, Filip Biljecki, Clayton Miller

Research Collection College of Integrative Studies

Recently, the concept of a digital twin for the built environment has received significant attention due to its potential benefits to urban planners, engineers, and designers. The development of tools that aid in integrating real-world physical systems with digital capabilities is essential for advancing digital twin technology. In this work, we present one such digital twin tool that integrates the longitudinal thermal envelope data of buildings on the campus of the National University of Singapore with a virtual 3D model. Thermal images of the buildings were captured using a neighborhood-scale infrared observatory for a few months. The temperature profile of …

Optimal Har Inference, Liyu Dou

Research Collection School Of Economics

This paper addresses the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference for a scalar parameter of interest, under the assumption of a known upper bound on data persistence. Finite-sample optimal tests are derived within the Gaussian location model, revealing that robustness-efficiency tradeoffs are primarily determined by the maximal persistence. With a suitable adjustment to the critical value, the equal-weighted cosine (EWC) test emerges as nearly optimal, wherein the long-run variance is estimated through projections onto q type II cosines. This approach establishes a direct link between the choice of q and persistence assumptions, accompanied by adjustments to the …

Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task’s labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether …

Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …

From Data To Application: Harnessing Big Spatial Data And Spatially Explicit Machine Learning Model For Landslide Susceptibility Prediction And Mapping, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Recent advancements in information and communication technology have significantly enhanced access to extensive geospatial data, presenting a valuable opportunity to leverage big spatial data for improved modeling and predictive capabilities in natural disaster risk assessment. This paper explores the integration of a comprehensive dataset comprising historical landslide events and various geo-environmental variables within a spatially explicit machine learning framework. The study empirically demonstrates that incorporating big spatial data allows a more nuanced understanding of local variations and spatial dependencies. Ultimately, this empirical assessment produces more accurate landslide risk predictions than traditional baseline models. Using Italy’s expansive Valtellina Valley as a …

Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra

Research Collection School Of Computing and Information Systems

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in humancomputer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES’s highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within …

Revisiting The Conflict-Resolving Problem From A Semantic Perspective, Jinhao Dong, Jun Sun, Yun Lin, Yedi Zhang, Murong Ma, Jin Song Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Collaborative software development significantly enhances development productivity by enabling multiple contributors to work concurrently on different branches. Despite these advantages, such collaboration often increases the likelihood of causing conflicts. Resolving these conflicts brings huge challenges, primarily due to the necessity of comprehending the differences between conflicting versions. Researchers have explored various automatic conflict resolution techniques, including unstructured, structured, and learning-based approaches. However, these techniques are mostly heuristic-based or black-box in nature, which means they do not attempt to solve the root cause of the conflicts, i.e., the existence of different program behaviors exhibited by the conflicting versions.In this work, we …

A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …

Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …

Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts—self-executed and immutability—ensure the trustworthiness, transparency and efficiency of DeFi applications and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as “DeFi …

Self-Supervised Learning For Time Series Analysis : Taxonomy, Progress, And Prospects, Zhang Kexin, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Guansong Pang, Guansong Pang, Pan Shirui

Research Collection School Of Computing and Information Systems

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time …

Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji

Research Collection School Of Computing and Information Systems

Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these issues with a self-adaptive fine-grained multi-modal data augmentation framework for semi-supervised MCR, focusing on enriching training data from labeled datasets and tapping into the untapped potential of unlabeled data. Regarding the former issue, we first leverage text coreference resolution datasets and diffusion models,to perform fine-grained text-to-image generation with aligned text entities and image bounding boxes. We then introduce a self-adaptive selection …

An Empirical Study To Evaluate Aigc Detectors On Code Content, Jian Wang, Shangqing Liu, Xiaofei Xie, Yi Li

Research Collection School Of Computing and Information Systems

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with Large Language Models (LLMs), like ChatGPT, emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of LLMs, especially in security and safetycritical domains, such as academic integrity and answering questions on Stack Overflow, poses significant concerns. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by LLMs remains unexplored. To fill this gap, in this paper, we present an empirical study evaluating …

Direct Range Proofs For Paillier Cryptosystem And Their Applications, Zhikang Xie, Mengling Liu, Haiyang Xue, Man Ho Au, Robert H. Deng, Siu-Ming Yiu

Research Collection School Of Computing and Information Systems

The Paillier cryptosystem is renowned for its applications in electronic voting, threshold ECDSA, multi-party computation, and more, largely due to its additive homomorphism. In these applications, range proofs for the Paillier cryptosystem are crucial for maintaining security, because of the mismatch between the message space in the Paillier system and the operation space in application scenarios. In this paper, we present novel range proofs for the Paillier cryptosystem, specifically aimed at optimizing those for both Paillier plaintext and affine operation. We interpret encryptions and affine operations as commitments over integers, as opposed to solely over ZN. Consequently, we propose direct …

A Survey Of Protocol Fuzzing, Xiaohan Zhang, Cen Zhang, Xinghua Li, Zhengjie Du, Bing Mao, Yeting Li, Pan Li

Research Collection School Of Computing and Information Systems

Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to uncovering these vulnerabilities within protocol implementations. However, there still lacks a systematic overview of protocol fuzzing for answering the essential questions such as what the unique challenges are, how existing works solve them, and so on. To bridge this gap, we conducted a comprehensive investigation of related works from both academia and industry. Our study includes a detailed summary of the specific challenges in protocol fuzzing and provides a systematic categorization …

What Is The Fallacy Of Approximation?, Matthew Hammerton, Sovan Patra

Research Collection School of Social Sciences

Many philosophers appeal to the “fallacy of approximation”, or “problem of second best”. However, despite the pervasiveness of such appeals, there has been only a single attempt to provide a systematic account of what the fallacy is. We identify the shortcomings of this account and propose a better one in its place. Our account not only captures all the contexts in which approximation-based reasoning occurs but also systematically explains the several different ways in which it can be in error.

Daily Stress, Problematic Social Media Use, And Executive Functions: A Daily Diary Approach, Wee Qin Ng

Dissertations and Theses Collection (Open Access)

A growing body of research suggests that daily stress may be a risk factor for problematic social media use. However, findings concerning the relation between stress and social media use are mixed, suggesting the presence of moderators that may buffer the relation between stress and problematic social media use. Further, previous studies have relied on either cross-sectional designs or longitudinal designs that may not fully capture daily experiences of stressors and social media use. To overcome these limitations, the present study utilized a 7-day daily diary study to investigate the within-individual associations between daily stress and problematic social media use. …

Influence Of Artificial Intelligence (Ai) On Decision-Making For Market-Entry Strategies In Emerging Economies, Tejas Deshpande

Dissertations and Theses Collection (Open Access)

International firms with growth-oriented business models face a complex array of factors when planning to enter emerging markets. These markets are characterized by dynamic socio-economic and geopolitical conditions, often resulting in limited market intelligence and a fragmented understanding of the business ecosystem. To succeed, firms must align their short-term objectives and long-term strategic goals with the specific characteristics of these target markets.

Decision-making in such environments is fraught with uncertainty and is critical in determining the success or failure of market-entry strategies. While business leaders rely on their cognition and heuristics to navigate these challenges, the complexity and volume of …

Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun

Research Collection School Of Computing and Information Systems

Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …

Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu

Research Collection School Of Computing and Information Systems

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …

Collaborative Cross-Modal Fusion With Large Language Model For Recommendation, Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang

Research Collection School Of Computing and Information Systems

Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, …

Onerestore : A Universal Restoration Framework For Composite Degradation, Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow. Despite this reality, existing restoration methods typically target isolated degradation types, thereby falling short in environments where multiple degrading factors coexist. To bridge this gap, our study proposes a versatile imaging model that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios. In this context, we propose OneRestore, a novel transformer-based framework designed for adaptive, controllable scene restoration. The proposed framework leverages a unique cross-attention mechanism, merging degraded scene descriptors with image features, …

An Empirical Study Of Api Misuses Of Data-Centric Libraries, Akalanda Galappaththi, Sarah Nadi, Christoph Treude

Research Collection School Of Computing and Information Systems

Developers rely on third-party library Application Programming Interfaces (APIs) when developing software. However, libraries typically come with assumptions and API usage constraints, whose violation results in API misuse. API misuses may result in crashes or incorrect behavior. Even though API misuse is a well-studied area, a recent study of API misuse of deep learning libraries showed that the nature of these misuses and their symptoms are different from misuses of traditional libraries, and as a result highlighted potential shortcomings of current misuse detection tools. We speculate that these observations may not be limited to deep learning API misuses but may …

An Empirical Study Of Automatic Program Repair Techniques For Injection Vulnerabilities, Tingwei Zhu, Tongtong Xu, Kui Liu, Jiayuan Zhou, Xing Hu, Xin Xia, Tian Zhang, David Lo

Research Collection School Of Computing and Information Systems

Injection vulnerabilities are among the most serious and dangerous security defects, as they can be exploited by attackers to inject malicious inputs and carry out cybercrimes. Timely fixing of injection vulnerabilities is crucial. However, manual repairs of injection vulnerabilities often require specialized knowledge and are prone to errors, posing a challenge and a heavy burden on developers. In recent years, Automated Program Repair (APR) techniques have shown promising momentum in automatically fixing general defects. Yet, there has been no research on how APR techniques perform in repairing injection vulnerabilities. Therefore, in this paper, we conduct an empirical study. We first …