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Law-Aware Autonomous Driving, Yang Sun Nov 2024

Law-Aware Autonomous Driving, Yang Sun

Dissertations and Theses Collection (Open Access)

Autonomous driving systems (ADSs) necessitate comprehensive testing prior to deployment in Autonomous Vehicles (AVs). High-fidelity simulators are crucial for this testing, as they can replicate a wide range of scenarios, including those that are difficult or dangerous to recreate in real-world conditions. While previous approaches have demonstrated that test cases can be generated automatically, they often focus on weak oracles (e.g., reaching the destination without collisions) and fail to assess whether the journey was conducted safely and in compliance with some complex property specifications such as traffic laws. In this dissertation, beyond assessing basic properties like energy consumption and proximity …

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An Empirical Test Of Auction Efficiency: Evidence From Mbs Auctions Of The Federal Reserve, Pietro Bonadi, Ali Hortacsu, Zhaogang Song Nov 2024

An Empirical Test Of Auction Efficiency: Evidence From Mbs Auctions Of The Federal Reserve, Pietro Bonadi, Ali Hortacsu, Zhaogang Song

Research Collection Lee Kong Chian School Of Business

PurposeWe estimate the marginal cost curve of each dealer in each auction, based on structural models of the multiunit discriminatory-price auction.Design/methodology/approachAuction theory has ambiguous implications regarding the relative performance of three formats of multiunit auctions: uniform-price, discriminatory-price and Vickrey auctions. We evaluate the performance of these three auction formats using bid-level data of the Federal Reserve’s purchase auctions of agency MBS from June 2014 through November 2014.FindingsOur results suggest that neither uniform-price nor Vickrey auctions outperform discriminatory-price auctions in terms of the total expenditure. However, Vickrey auctions outperform discriminatory-price auctions in terms of efficiency, with the efficiency gain around 0.74% …

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Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai Nov 2024

Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai

Research Collection Lee Kong Chian School Of Business

As of June 2024, the U.S. Food and Drug Administration (FDA) has approved 950 medical artificial intelligence (AI) devices. The current regulatory framework freezes AI algorithms after approval, requiring new submissions for updates to ensure compliance with Good Machine Learning Practices (GMLP). This approach imposes a significant administrative burden, while hindering the ability of AI algorithms to learn from new data. To address these challenges, the FDA has explored a novel pathway known as Predetermined Change Control Plans (PCCP), allowing developers to outline future changes during initial submissions and exempting approved changes from regulatory review. Yet, the impact of this …

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Unlocking Markets: A Multilingual Benchmark To Cross-Market Question Answering, Yifei Yuan, Yang Deng, Anders Sogaard, Mohammad Alliannejadi Nov 2024

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: …

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Ask-Before-Plan : Proactive Language Agents For Real-World Planning, Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua Nov 2024

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 …

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Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe Nov 2024

Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe

Research Collection School Of Computing and Information Systems

Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …

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Scoping Software Engineering For Ai: The Tse Perspective, Sebastián Uchitel, Marsha Chechik, Massimiliano Di Penta, Bram Adams, Nazareno Aguirre, Gabriele Bavota, Domenico Bianculli, Kelly Blincoe, Ana Cavalcanti, Yvonne Dittrich, Filomena Ferrucci, Rashina Hoda, Liguo Huang, David Lo, Et Al. Nov 2024

Scoping Software Engineering For Ai: The Tse Perspective, Sebastián Uchitel, Marsha Chechik, Massimiliano Di Penta, Bram Adams, Nazareno Aguirre, Gabriele Bavota, Domenico Bianculli, Kelly Blincoe, Ana Cavalcanti, Yvonne Dittrich, Filomena Ferrucci, Rashina Hoda, Liguo Huang, David Lo, Et Al.

Research Collection School Of Computing and Information Systems

In recent years, important advances in Artificial Intelligence (AI), and, in particular, in Machine Learning (ML), including Deep Learning (DL) and Large Language Models (LLMs), have caused a substantial increase of submissions to all Software Engineering (SE) venues (conferences and journals) related to SE with and for AI. They are commonly referred to as AI for SE and SE for AI.

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Media Use, Interpersonal Communication, And Personal Relevance As External And Internal Representations Of Climate Change, Sonny Rosenthal, Pengya Ai Nov 2024

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. …

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Ai And Data Science For Public Policy, Kenneth Benoit Nov 2024

Ai And Data Science For Public Policy, Kenneth Benoit

Research Collection School of Social Sciences

Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need …

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The Epistemic Role Of Ai Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers, Rand Hirmiz Nov 2024

The Epistemic Role Of Ai Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers, Rand Hirmiz

Research Collection School of Social Sciences

Despite the importance of discussions over the epistemic role that artificially intelligent decision support systems ought to play, there is currently a lack of these discussions in both the AI literature and the epistemology literature. My goal in this paper is to rectify this by proposing an account of the epistemic role of AI decision support systems in medicine and discussing what this epistemic role means with regard to how these systems ought to be utilized. In particular, I argue that AI decision support systems are not epistemic superiors, inferiors, or peers. Instead, I recommend that they be classified in …

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Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra Nov 2024

Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra

Research Collection School Of Computing and Information Systems

Intelligent virtual agents are used to accomplish complex multi-modal tasks such as human instruction comprehension in mixed-reality environments by increasingly adopting richer, energy-intensive sensors and processing pipelines. In such applications, the context for activating sensors and processing blocks required to accomplish a given task instance is usually manifested via multiple sensing modes. Based on this observation, we introduce a novel Commit-and-Switch ( CAS ) paradigm that simultaneously seeks to reduce both sensing and processing energy. In CAS , we first commit to a low-energy computational pipeline with a subset of available sensors. Then, the task context estimated by this pipeline …

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Optimal Har Inference, Liyu Dou Nov 2024

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 …

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Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao Nov 2024

Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao

Research Collection School Of Computing and Information Systems

Target-driven recommendation dialogues present unique challenges in dialogue management due to the necessity of anticipating user interactions for successful conversations. Current methods face significant limitations: (I) inadequate capabilities for conversation anticipation, (II) computational inefficiencies due to costly simulations, and (III) neglect of valuable past dialogue experiences. To address these limitations, we propose a new framework, Experiential Policy Learning (EPL), for enhancing such dialogues. EPL embodies the principle of Learning From Experience, facilitating anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. To demonstrate its flexibility, we introduce Tree-structured EPL (T-EPL) …

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Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang Nov 2024

Context-Aware Adapter Tuning For Few-Shot Relation Learning In Knowledge Graphs, Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates …

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Dc-Instruct : An Effective Framework For Generative Multi-Intent Spoken Language Understanding, Bowen Xing, Lizi Liao, Minlie Huang Nov 2024

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 …

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Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang Nov 2024

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 …

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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 Nov 2024

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 …

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Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra Nov 2024

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 …

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Revisiting The Conflict-Resolving Problem From A Semantic Perspective, Jinhao Dong, Jun Sun, Yun Lin, Yedi Zhang, Murong Ma, Jin Song Dong, Dan Hao Nov 2024

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 …

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Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang Nov 2024

Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang

Research Collection School Of Computing and Information Systems

Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even …

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Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra Nov 2024

Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Continuous tracking of eye movement dynamics plays a significant role in developing a broad spectrum of human-centered applications, such as cognitive skills (visual attention and working memory) modeling, human-machine interaction, biometric user authentication, and foveated rendering. Recently neuromorphic cameras have garnered significant interest in the eye-tracking research community, owing to their sub-microsecond latency in capturing intensity changes resulting from eye movements. Nevertheless, the existing approaches for event-based eye tracking suffer from several limitations: dependence on RGB frames, label sparsity, and training on datasets collected in controlled lab environments that do not adequately reflect real-world scenarios. To address these limitations, in …

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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 Nov 2024

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 …

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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 Nov 2024

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 …

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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 Nov 2024

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 …

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Getting On Board With Incentivising Esg, Shai Ganu Nov 2024

Getting On Board With Incentivising Esg, Shai Ganu

Asian Management Insights

What gets rewarded gets measured and done. Shai Ganu, Managing Director and Global Leader of the Executive Compensation and Board Advisory business at WTW, discusses the increasing adoption of Environmental, Social, and Governance (ESG) metrics in structuring executive compensation. He explores how incorporating ESG goals into incentive plans drives accountability and aligns leadership performance with long-term sustainability objectives.

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Addressing Governance Challenges Of Digitalisation And Sustainability: The Case Of Central Bank Digital Currency, Heng Wang Nov 2024

Addressing Governance Challenges Of Digitalisation And Sustainability: The Case Of Central Bank Digital Currency, Heng Wang

Research Collection Yong Pung How School Of Law

Digitalisation and environmental sustainability are widely discussed topics. However, their nexus remains underexplored and can pose significant challenges for governments and industries alike. The environmental implications of digitalisation are becoming increasingly pertinent with the advent of central bank digital currencies (CBDCs) and their inherent energy consumption and production of e-waste. On the other hand, digitalisation could potentially support sustainability efforts. This begs the question of how systems of governance, such as regulatory frameworks and internal organisational governance, should harmonise digitalisation and sustainability goals. Such harmonisation entails ensuring that digitalisation processes are environmentally responsible while exploring how the application and features …

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The Promise And Perils Of Smart (City) Bots As Educational Tools, Thomas Menkhoff, Siew Ning Kan, Shaohui Foong Oct 2024

The Promise And Perils Of Smart (City) Bots As Educational Tools, Thomas Menkhoff, Siew Ning Kan, Shaohui Foong

Research Collection Lee Kong Chian School Of Business

Written from an educator’s perspective, the paper aims to examine the increasing use of chatbot technology capable of answering user queries in various fields such as commerce, public service delivery, urban areas, higher education etc. by simulating human conversation through text messages or voice commands. We analyse why smart bots have become popular technologies in the context smart cities; shed light on different types of bots (chatbots, large language models such as ChatGPT, flying bots); and explain how we use bots as teaching tool in an introductory AI for business course. A key argument putting forward is that smart bots …

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Demystifying And Extracting Fault-Indicating Information From Logs For Failure Diagnosis, Junjie Huang, Zhihan Jiang, Jinyang Liu, Yintong Huo, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Hui Dong, Zengyin Yang, Michael R. Lyu Oct 2024

Demystifying And Extracting Fault-Indicating Information From Logs For Failure Diagnosis, Junjie Huang, Zhihan Jiang, Jinyang Liu, Yintong Huo, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Hui Dong, Zengyin Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Logs are imperative in the maintenance of online service systems, which often encompass important information for effective failure mitigation. While existing anomaly detection methodologies facilitate the identification of anomalous logs within extensive runtime data, manual investigation of log messages by engineers remains essential to comprehend faults, which is labor-intensive and error-prone. Upon examining the log-based troubleshooting practices at CloudA 1, we find that engineers typically prioritize two categories of log information for diagnosis. These include fault-indicating descriptions, which record abnormal system events, and fault-indicating parameters, which specify the associated entities. Motivated by this finding, we propose an approach to automatically …

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Impact Of Industrial Park Platforms On Sme Financing: Evidence From The Ld Park, Zhendong Liu Oct 2024

Impact Of Industrial Park Platforms On Sme Financing: Evidence From The Ld Park, Zhendong Liu

Dissertations and Theses Collection (Open Access)

Small and medium-sized enterprises (SMEs) are essential to the Chinese economy. However, when seeking financing, SMEs often face stricter financing conditions and higher financing costs due to information asymmetry in the market, commonly referred to as the problem of “difficult and expensive financing.” This issue severely limits the growth potential of SMEs. This study explores the positive role that industrial parks play as a bridge between banks and SMEs in alleviating the “difficult and expensive financing” problem. The study surveyed 54 industrial parks and collected 268 valid questionnaires from banks, industrial parks, and SMEs. The data analysis yielded the following …

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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 Oct 2024

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 …

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