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Articles 1951 - 1980 of 10510
Full-Text Articles in Entire DC Network
Towards Llm-Based Fact Verification On News Claims With A Hierarchical Step-By-Step Prompting Method, Xuan Zhang, Wei Gao
Towards Llm-Based Fact Verification On News Claims With A Hierarchical Step-By-Step Prompting Method, Xuan Zhang, Wei Gao
Research Collection School Of Computing and Information Systems
While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still underexplored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questionsanswering steps progressively. Experiment results on two public misinformation datasets show that …
From Mindset To Action: Accountants As Stewards For Sustainability, Yvonne Chan
From Mindset To Action: Accountants As Stewards For Sustainability, Yvonne Chan
Asian Management Insights
They need to start preparing for this new role now.
Pursuing Profit And Sustainability In The Age Of Climate Change, Tracy Xie
Pursuing Profit And Sustainability In The Age Of Climate Change, Tracy Xie
Asian Management Insights
Building dual-agenda innovation capabilities is the way to go.
Building Urban Sustainability With Quality Elites, Havovi Joshi
Building Urban Sustainability With Quality Elites, Havovi Joshi
Asian Management Insights
Do elites contribute to a society more than they extract from it? In many countries where populism has surged in tandem with declining trust in elites in the political and other spheres, the answer.
The Impact Of Generative Ai In The Financial Sector [El Impacto De La Inteligencia Artificial Generativa En El Sector Financiero], Nydia Remolina Leon
The Impact Of Generative Ai In The Financial Sector [El Impacto De La Inteligencia Artificial Generativa En El Sector Financiero], Nydia Remolina Leon
Research Collection Yong Pung How School Of Law
No abstract provided.
Tail Risk Hedging: The Search For Cheap Options, Poh Ling Neo, Chyng Wen Tee
Tail Risk Hedging: The Search For Cheap Options, Poh Ling Neo, Chyng Wen Tee
Research Collection Lee Kong Chian School Of Business
The authors find that a simple heuristic of sorting liquid equity options by dollar price to construct a portfolio of cheap put options leads to a surprisingly robust hedge for tail risk – the superior performance holds even when compared against more advanced empirical strategies. Further investigation reveals the asymmetry in market correlation under different market conditions as the mechanism of this robust hedging performance. The cheap options selected by the heuristic comprises of stocks with diverse firm characteristics. The correlation spike accompanying tail risk events leads to the majority of these put options moving into-the-money (ITM), thus compensating the …
Delivering Healthcare To The Underserved, Edward Booty
Delivering Healthcare To The Underserved, Edward Booty
Asian Management Insights
Non-profits, governments, and businesses need to come together and use a data-driven approach to improve local basic healthcare access.
Leading By Example And Giving Back To Society, N.R. Narayana Murthy, Havovi Joshi
Leading By Example And Giving Back To Society, N.R. Narayana Murthy, Havovi Joshi
Asian Management Insights
N.R. Narayana Murthy, the founder and former Chairman of Infosys, a global provider of next-generation digital services and consulting, speaks with Havovi Joshi about the Indian growth story.
Cgt-Gan: Clip-Guided Text Gan For Image Captioning, Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He
Cgt-Gan: Clip-Guided Text Gan For Image Captioning, Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He
Research Collection School Of Computing and Information Systems
The large-scale visual-language pre-trained model, Contrastive Language-Image Pre-training (CLIP), has significantly improved image captioning for scenarios without human-annotated image-caption pairs. Recent advanced CLIP-based image captioning without human annotations follows a text-only training paradigm, i.e., reconstructing text from shared embedding space. Nevertheless, these approaches are limited by the training/inference gap or huge storage requirements for text embeddings. Given that it is trivial to obtain images in the real world, we propose CLIP-guided text GAN (CgT-GAN), which incorporates images into the training process to enable the model to "see" real visual modality. Particularly, we use adversarial training to teach CgT-GAN to mimic …
A Survey On Aspect-Based Sentiment Analysis: Tasks, Methods, And Challenges, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam
A Survey On Aspect-Based Sentiment Analysis: Tasks, Methods, And Challenges, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam
Research Collection School Of Computing and Information Systems
As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their …
Rethinking Conversational Agents In The Era Of Large Language Models: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua
Rethinking Conversational Agents In The Era Of Large Language Models: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Conversational systems are designed to offer human users social support or functional services through natural language interactions. Typical conversation researches mainly focus on the response-ability of the system, such as dialogue context understanding and response generation. In the era of large language models (LLMs), LLM-augmented conversational systems showcase exceptional capabilities of responding to user queries for different language tasks. However, as LLMs are trained to follow users' instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent's awareness …
Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam
Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam
Research Collection School Of Computing and Information Systems
Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is …
Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
As one of the core video semantic understanding tasks, Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine-grained spatial scene perception and the insufficiently modeling of video temporality. Towards this end, this work explores a novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal …
Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang
Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang
Research Collection School Of Computing and Information Systems
Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, …
An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le
An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le
Research Collection School Of Computing and Information Systems
In this work, we experiment an idealistic approach for smart contract correctness verification and enforcement, based on the assumption that developers are either desired or required to provide a correctness specification due to the importance of smart contracts and the fact that they are immutable after deployment. We design a static verification system with a specification language which supports fully compositional verification (with the help of function specifications, contract invariants, loop invariants and call invariants). Our approach has been implemented in a tool named iContract which automatically proves the correctness of a smart contract statically or checks the unverified part …
Typesqueezer: When Static Recovery Of Function Signatures For Binary Executables Meets Dynamic Analysis, Ziyi Lin, Jinku Li, Bowen Li, Haoyu Ma, Debin Gao, Jianfeng Ma
Typesqueezer: When Static Recovery Of Function Signatures For Binary Executables Meets Dynamic Analysis, Ziyi Lin, Jinku Li, Bowen Li, Haoyu Ma, Debin Gao, Jianfeng Ma
Research Collection School Of Computing and Information Systems
Control-Flow Integrity (CFI) is considered a promising solutionin thwarting advanced code-reuse attacks. While the problem ofbackward-edge protection in CFI is nearly closed, effective forward-edge protection is still a major challenge. The keystone of protecting the forward edge is to resolve indirect call targets, which although can be done quite accurately using type-based solutionsgiven the program source code, it faces difficulties when carriedout at the binary level. Since the actual type information is unavailable in COTS binaries, type-based indirect call target matching typically resorts to approximate function signatures inferredusing the arity and argument width of indirect callsites and calltargets. Doing so …
Opportunities For Spatial Database Research In The Context Of Preference Queries, Kyriakos Mouratidis
Opportunities For Spatial Database Research In The Context Of Preference Queries, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
This is the outline of the keynote speech at LocalRec@ACM SIGSPATIAL 2023. The main objective of the talk is to point out opportunities for spatial database researchers in the area of preference-based querying. We will commence with an overview of the standard queries for multi-objective decision making, and demonstrate their direct connection to recommendations and to market analysis. In this context, there is a number of specific decision criteria, and user preferences are represented as vectors with as many dimensions. We will demonstrate how and why this type of preferences are natural to actual applications and practical for the support …
Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang
Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, …
Faire: Repairing Fairness Of Neural Networks Via Neuron Condition Synthesis, Tianlin Li, Xiaofei Xie, Jian Wang, Qing Guo, Aishan Liu, Lei Ma, Yang Liu
Faire: Repairing Fairness Of Neural Networks Via Neuron Condition Synthesis, Tianlin Li, Xiaofei Xie, Jian Wang, Qing Guo, Aishan Liu, Lei Ma, Yang Liu
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNNs) have achieved tremendous success in many applications, while it has been demonstrated that DNNs can exhibit some undesirable behaviors on concerns such as robustness, privacy, and other trustworthiness issues. Among them, fairness (i.e., non-discrimination) is one important property, especially when they are applied to some sensitive applications (e.g., finance and employment). However, DNNs easily learn spurious correlations between protected attributes (e.g., age, gender, race) and the classification task and develop discriminatory behaviors if the training data is imbalanced. Such discriminatory decisions in sensitive applications would introduce severe social impacts. To expose potential discrimination problems in DNNs …
Laf: Labeling-Free Model Selection For Automated Deep Neural Network Reusing, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon
Laf: Labeling-Free Model Selection For Automated Deep Neural Network Reusing, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon
Research Collection School Of Computing and Information Systems
pplying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models, all of them are complex and costly. Therefore, reusing the open-sourced pre-trained model is a practical way to bypass this hurdle for developers. Given a specific task, developers can collect massive pre-trained deep neural networks from public sources for reusing. However, testing the performance (e.g., accuracy and robustness) of multiple deep neural networks (DNNs) and recommending which model should be …
Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li
Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li
Research Collection School Of Computing and Information Systems
It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement …
Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen
Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen
Research Collection School Of Computing and Information Systems
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and …
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities, Ngan H. Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities, Ngan H. Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in …
Pro-Cap: Leveraging A Frozen Vision-Language Model For Hateful Meme Detection, Rui Cao, Ming Shan Hee, Adriel Kuek, Wen Haw Chong, Roy Ka-Wei Lee, Jing Jiang
Pro-Cap: Leveraging A Frozen Vision-Language Model For Hateful Meme Detection, Rui Cao, Ming Shan Hee, Adriel Kuek, Wen Haw Chong, Roy Ka-Wei Lee, Jing Jiang
Research Collection School Of Computing and Information Systems
Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot …
Effective And Efficient Semantic Representations And Their Applications, Chong Cher Chia
Effective And Efficient Semantic Representations And Their Applications, Chong Cher Chia
Dissertations and Theses Collection (Open Access)
The proliferation of affordable and compact digital storage has also led to the creation of enormous databases of information, and much attention has been focused on the problem of processing unorganized and unstructured information into some form from which additional value can be extracted. Contemporary approaches to this problem virtually necessitate the use of complex models running on computational systems due to the sheer volume of information to be processed. While it is possible for the model to be fed the actual data as input, typically a representation of the data is used instead. These representations are therefore of interest, …
Decomposability And Strategy-Proofness In Multidimensional Models, Shurojit Chatterji, Huaxia Zeng
Decomposability And Strategy-Proofness In Multidimensional Models, Shurojit Chatterji, Huaxia Zeng
Research Collection School Of Economics
We introduce the notion of a multidimensional hybrid preference domain on a (finite) set of alternatives that is a Cartesian product of finitely many components. We demonstrate that in a model of public goods provision, multidimensional hybrid preferences arise naturally through assembling marginal preferences under the condition of semi-separability - a weakening of separability. The main result shows that under a suitable “richness” condition, every strategy-proof rule on this domain can be decomposed into component-wise strategy-proof rules, and more importantly every domain of preferences that reconciles decomposability of rules with strategy-proofness must be a multidimensional hybrid domain.
How Tiktok “Locks In” Its Users With Reference To What Happened In The Homeland Version Douyin Application, Yang Yang, Li Zi'ang, Zhigen Zhao, Sabrina (Zi Tong) Peng, Liyu Dou, Changyu Li, Yinqi Wang
How Tiktok “Locks In” Its Users With Reference To What Happened In The Homeland Version Douyin Application, Yang Yang, Li Zi'ang, Zhigen Zhao, Sabrina (Zi Tong) Peng, Liyu Dou, Changyu Li, Yinqi Wang
Research Collection School Of Economics
In October 2022, Douyin, the homeland version of TikTok, introduced a new rule requiring all its users to promote only Douyin-approved products during their live-streaming events and in their non-live short-videos. This requirement essentially restricts its users to monetize their content strictly through Douyin-approved products, as promoting products has become the most important source of income for Douyin users. This requirement essentially creates a lock-in effect.
Autolog: A Log Sequence Synthesis Framework For Anomaly Detection, Yintong Huo, Yichen Li, Yuxin Su, Pinjia He, Zifan Xie, R. Michael Lyu
Autolog: A Log Sequence Synthesis Framework For Anomaly Detection, Yintong Huo, Yichen Li, Yuxin Su, Pinjia He, Zifan Xie, R. Michael Lyu
Research Collection School Of Computing and Information Systems
The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets.While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to …
Riding The Decarbonisation Wave: Bhp And Its Lng Dual-Fuelled Vessels, Shantanu Bhattacharya, Flocy Joseph, Mahima Rao-Kachroo
Riding The Decarbonisation Wave: Bhp And Its Lng Dual-Fuelled Vessels, Shantanu Bhattacharya, Flocy Joseph, Mahima Rao-Kachroo
Asian Management Insights
What does it take to decarbonise charter-related shipping?
Verifytl: Secure And Verifiable Collaborative Transfer Learning, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Wei Zheng, Kim-Kwang Raymond Choo, Robert H. Deng
Verifytl: Secure And Verifiable Collaborative Transfer Learning, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Wei Zheng, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer learning techniques only focus on one-way transfer which may not benefit the source domain. In addition, there is the risk of a malicious adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper, we construct a secure and Verif iable collaborative T ransfer L earning scheme, VerifyTL, …