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Discriminative Reasoning With Sparse Event Representation For Document-Level Event-Event Relation Extraction, Changsen Yuan, Heyan Huang, Yixin Cao, Yonggang Wen Jul 2023

Discriminative Reasoning With Sparse Event Representation For Document-Level Event-Event Relation Extraction, Changsen Yuan, Heyan Huang, Yixin Cao, Yonggang Wen

Research Collection School Of Computing and Information Systems

Document-level Event-Event Relation Extraction (DERE) aims to extract relations between events in a document. It challenges conventional sentence-level task (SERE) with difficult long-text understanding. In this paper, we propose a novel DERE model (SENDIR) for better document-level reasoning. Different from existing works that build an event graph via linguistic tools, SENDIR does not require any prior knowledge. The basic idea is to discriminate event pairs in the same sentence or span multiple sentences by assuming their different information density: 1) low density in the document suggests sparse attention to skip irrelevant information. Our module 1 designs various types of attention …

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Modularized Zero-Shot Vqa With Pre-Trained Models, Rui Cao, Jing Jiang Jul 2023

Modularized Zero-Shot Vqa With Pre-Trained Models, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models (PTMs) show great zero-shot capabilities. In this paper, we study how to leverage them for zero-shot visual question answering (VQA).Our approach is motivated by a few observations. First, VQA questions often require multiple steps of reasoning, which is still a capability that most PTMs lack. Second, different steps in VQA reasoning chains require different skills such as object detection and relational reasoning, but a single PTM may not possess all these skills. Third, recent work on zero-shot VQA does not explicitly consider multi-step reasoning chains, which makes them less interpretable compared with a decomposition-based approach. We propose …

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Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley Jul 2023

Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley

Research Collection School Of Computing and Information Systems

Blockchain-based assets are commonly believed to attract new investors. To investigate this claim, we compared investor preferences for Web2 and Web3 domain name auctions by analyzing daily auction patterns in Namecheap and OpenSea. Our results indicate that Web3 platforms may attract extreme investors with low or high values as a niche market. We found a significantly higher number of bids per auction, higher average bid prices, and greater price spreads on OpenSea, but a significantly lower number of unique bidders per auction. Our findings highlight the importance of considering the auction platform's characteristics and asset context when evaluating bid patterns …

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Large-Scale Correlation Analysis Of Automated Metrics For Topic Models, Jia Peng Lim, Hady Wirawan Lauw Jul 2023

Large-Scale Correlation Analysis Of Automated Metrics For Topic Models, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Automated coherence metrics constitute an important and popular way to evaluate topic models. Previous works present a mixed picture of their presumed correlation with human judgement. In this paper, we conduct a large-scale correlation analysis of coherence metrics. We propose a novel sampling approach to mine topics for the purpose of metric evaluation, and conduct the analysis via three large corpora showing that certain automated coherence metrics are correlated. Moreover, we extend the analysis to measure topical differences between corpora. Lastly, we examine the reliability of human judgement by conducting an extensive user study, which is designed as an amalgamation …

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Generative Ai And Chatgpt Impact On Technostress Of Teachers, Xuenan Huo, Keng Siau Jul 2023

Generative Ai And Chatgpt Impact On Technostress Of Teachers, Xuenan Huo, Keng Siau

Research Collection School Of Computing and Information Systems

Generative AI, such as ChatGPT, is a disruptive technology with significant impacts on education. While it has the potential to transform the delivery and accessibility of education, it can also undermine educational effectiveness by facilitating academic honesty and creating technostress for educators. This study aims to (i) evaluate the extent to which Generative AI, such as ChatGPT, brings technostress to teachers and (ii) how Generative AI changes teachers' professional identities and the technostress that results from such changes. We hypothesize that techno-eustress and techno-distress are determined by three types of self-discrepancies concerning professional identity construction: actual-ought, actual-ideal, and ought-ideal discrepancies. …

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Marketing In Metaverse: Sales-Avatar Visual Features On Customer Trust, Yuxin Liu, Keng Siau Jul 2023

Marketing In Metaverse: Sales-Avatar Visual Features On Customer Trust, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The metaverse wave is sweeping across the world. Virtual shopping promises a bright future and has the potential to provide customers with an immersive and realistic shopping experience anytime and anywhere. Salesavatars are critical in customer trust development in virtual shopping in the metaverse by directly interacting with customers. Considering the virtual characteristics of the metaverse and the anonymity of salesavatars, this research investigates the effect of salesavatars' genders and ages on customers' trust in the metaverse through the lens of first impression formation. Two dimensions of facial first impression, perceived facial trustworthiness and perceived facial dominance, will be studied …

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Strategy‑Aware Bundle Recommender System, Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, Tat‑Seng Chua Jul 2023

Strategy‑Aware Bundle Recommender System, Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation.Towards this end, we develop a …

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Multi-Target Backdoor Attacks For Code Pre-Trained Models, Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, Yang Liu Jul 2023

Multi-Target Backdoor Attacks For Code Pre-Trained Models, Yanzhou Li, Shangqing Liu, Kangjie Chen, Xiaofei Xie, Tianwei Zhang, Yang Liu

Research Collection School Of Computing and Information Systems

Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks. Moreover, the majority of attacks for pre-trained models are designed for understanding tasks. In this paper, we propose task-agnostic backdoor attacks for code pre-trained models. Our backdoored model is pre-trained with two learning strategies (i.e., Poisoned Seq2Seq learning and token representation learning) to support the multi-target attack of downstream code understanding and generation tasks. During the deployment phase, the implanted backdoors in the victim …

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Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang Jul 2023

Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

A smart contract is a piece of application-layer code running on blockchain ledgers and it provides programmatic logic via transaction-based execution of pre-defined functions. Smart contract functions are by default invokable by any party. To safeguard them, the mainstream smart contract language, i.e., Solidity of the popular Ethereum blockchain, proposed a unique language-level keyword called “modifier,” which allows developers to define custom function access control policies beyond the traditional “protected” and “private” modifiers in classic programming languages.In this paper, we aim to conduct a large-scale security analysis of the modifiers used in real-world Ethereum smart contracts. To achieve this, we …

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Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi Jul 2023

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

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18 Million Links In Commit Messages: Purpose, Evolution, And Decay, Tao Xiao, Sebastian Baltes, Hideaki Hata, Christoph Treude, Raula Kula, Takashi Ishio, Kenichi Matsumoto Jul 2023

18 Million Links In Commit Messages: Purpose, Evolution, And Decay, Tao Xiao, Sebastian Baltes, Hideaki Hata, Christoph Treude, Raula Kula, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Commit messages contain diverse and valuable types of knowledge in all aspects of software maintenance and evolution. Links are an example of such knowledge. Previous work on “9.6 million links in source code comments” showed that links are prone to decay, become outdated, and lack bidirectional traceability. We conducted a large-scale study of 18,201,165 links from commits in 23,110 GitHub repositories to investigate whether they suffer the same fate. Results show that referencing external resources is prevalent and that the most frequent domains other than github.com are the external domains of Stack Overflow and Google Code. Similarly, links serve as …

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Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua Jul 2023

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT’s uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the …

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Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic Jul 2023

Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple examples of MDPs, strategies for safety objectives over distributions can require infinite memory and randomization.In light of this, we present a novel overapproximation approach to synthesize strategies in an MDP, such that a safety objective over the distributions is met. More precisely, we develop a new framework for template-based …

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Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam Jul 2023

Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Research Collection School Of Computing and Information Systems

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity …

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Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing Jul 2023

Peerda: Data Augmentation Via Modeling Peer Relation For Span Identification Tasks, Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing

Research Collection School Of Computing and Information Systems

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are …

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Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam Jul 2023

Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam

Research Collection School Of Computing and Information Systems

Product question answering (PQA), aiming to automatically provide instant responses to customer’s questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing …

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Goal Awareness For Conversational Ai: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua Jul 2023

Goal Awareness For Conversational Ai: 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 envisioned to provide social support or functional service to human users via natural language interactions. Conventional conversation researches mainly focus on the responseability of the system, such as dialogue context understanding and response generation, but overlooks the design of an essential property in intelligent conversations, i.e., goal awareness. The awareness of goals means the state of not only being responsive to the users but also aware of the target conversational goal and capable of leading the conversation towards the goal, which is a significant step towards higher-level intelligence and artificial consciousness. It can not only largely improve …

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Augmenting Fake Content Detection In Online Platforms: A Domain Adaptive Transfer Learning Via Adversarial Training Approach, Ka Chung Ng, Ping Fan Ke, Mike K. P. So, Kar Yan Tam Jul 2023

Augmenting Fake Content Detection In Online Platforms: A Domain Adaptive Transfer Learning Via Adversarial Training Approach, Ka Chung Ng, Ping Fan Ke, Mike K. P. So, Kar Yan Tam

Research Collection School Of Computing and Information Systems

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news …

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Vehicle Rebalancing In A Shared Micromobility System With Rider Crowdsourcing, Ziliang Jin, Yulan Wang, Yun Fong Lim, Kai Pan, Zuo-Jun Max Shen Jul 2023

Vehicle Rebalancing In A Shared Micromobility System With Rider Crowdsourcing, Ziliang Jin, Yulan Wang, Yun Fong Lim, Kai Pan, Zuo-Jun Max Shen

Research Collection Lee Kong Chian School Of Business

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Since customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a twostage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the …

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Business Schools Should Be Schools Of Management: An Evolutionary Perspective, Kai Peters, Howard Thomas Jul 2023

Business Schools Should Be Schools Of Management: An Evolutionary Perspective, Kai Peters, Howard Thomas

Research Collection Lee Kong Chian School Of Business

This chapter looks at the original driving forces which led to the creation of institutions, particularly in the US, including the vision of their founders which would support our perspective in favour of schools of management. It looks at the years following the Second World War and how the Ford and Carnegie ‘Foundation Reports’ as well as the Cold War led to further evolution away from a school of management to a business school mission. The chapter looks at the period roughly from 1970 to 2000 during which US business school funding, which had been largely provided by the foundations, …

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Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh Jul 2023

Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh

Research Collection Lee Kong Chian School Of Business

This study examined business communication practices with chatbots among various Small and Medium Enterprise (SME) stakeholders in Singapore, including business owners/employees, customers, and developers. Through qualitative interviews and chatbot transcript analysis, we investigated two research questions: (1) How do the expectations of SME stakeholders compare to the conversational design of SME chatbots? and (2) What are the business reasons for SMEs to add human-like features to their chatbots? Our findings revealed that functionality is more crucial than anthropomorphic characteristics, such as personality and name. Stakeholders preferred chatbots that explicitly identified themselves as machines to set appropriate expectations. Customers prioritized efficiency, …

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Managing The Creative Frontier Of Generative Ai: The Novelty-Usefulness Tradeoff, Anirban. Mukherjee, Hannah H. Chang Jul 2023

Managing The Creative Frontier Of Generative Ai: The Novelty-Usefulness Tradeoff, Anirban. Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

In this paper, drawing inspiration from the human creativity literature, we explore the optimal balance between novelty and usefulness in generative Artificial Intelligence (AI) systems. We posit that overemphasizing either aspect can lead to limitations such as hallucinations and memorization. Hallucinations, characterized by AI responses containing random inaccuracies or falsehoods, emerge when models prioritize novelty over usefulness. Memorization, where AI models reproduce content from their training data, results from an excessive focus on usefulness, potentially limiting creativity. To address these challenges, we propose a framework that includes domain-specific analysis, data and transfer learning, user preferences and customization, custom evaluation metrics, …

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Seeking Better Sharpe Ratio Via Bayesian Optimization, Peng Liu Jul 2023

Seeking Better Sharpe Ratio Via Bayesian Optimization, Peng Liu

Research Collection Lee Kong Chian School Of Business

Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signals. Simultaneously optimizing all these parameters to seek a high Sharpe ratio is a daunting and time-consuming task, partly because of the unknown mechanism determining the Sharpe ratio. This article proposes using Bayesian optimization to systematically search for the optimal parameter configuration that leads to a high Sharpe ratio. The author shows that the proposed intelligent search …

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Air Pollution, Regulations On Emission And Firms' Social Responsibility, Jun Myung Song Jun 2023

Air Pollution, Regulations On Emission And Firms' Social Responsibility, Jun Myung Song

Sim Kee Boon Institute for Financial Economics

This paper examines whether firms adjust their strategy in emission when air pollution is severe. Considering high PM 2.5 as severe air pollution across 65 countries, I show that firms from countries with severe air pollution have low emission score, suggesting that they put less effort in reducing emission. This is because if they improve emission strategy, firm performance deteriorates. However, such relationship disappears when the government’s environmental stringency is strong, suggesting that government’s intervention is crucial for sustainable environment. This paper concludes with analysis on the factors which can mediate the negative impact of air pollution on firms’ emission …

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Scanet: Self-Paced Semi-Curricular Attention Network For Non-Homogeneous Image Dehazing, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Ren Wenqi Jun 2023

Scanet: Self-Paced Semi-Curricular Attention Network For Non-Homogeneous Image Dehazing, Yu Guo, Yuan Gao, Ryan Wen Liu, Yuxu Lu, Jingxiang Qu, Shengfeng He, Ren Wenqi

Research Collection School Of Computing and Information Systems

The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details. Existing homogeneous dehazing methods struggle to handle the non-uniform distribution of haze in a robust manner. The crucial challenge of non-homogeneous dehazing is to effectively extract the non-uniform distribution features and reconstruct the details of hazy areas with high quality. In this paper, we propose a novel self-paced semi-curricular attention network, called SCANet, for non-homogeneous image dehazing that focuses on enhancing haze-occluded regions. Our approach consists of an attention generator network and a scene re-construction network. We use the luminance …

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Optimization Of Station-Skip In A Cyclic Express Subway Service, Jingfeng Yang, Hai Wang, Jiangang Jin Jun 2023

Optimization Of Station-Skip In A Cyclic Express Subway Service, Jingfeng Yang, Hai Wang, Jiangang Jin

Research Collection School Of Computing and Information Systems

With rapid population growth and increasing demand for urban mobility, metropolitan areas such as Singapore, Tokyo, and Shanghai are increasingly dependent on public transport systems. Various strategies are proposed to improve the service quality and capacity of bus and subway systems. Express trains—i.e., trains that skip certain stations—are commonly used because they can travel at higher speeds, potentially reduce travel time, and serve more passengers. In this paper, we study cyclic express subway service (CESS), in which express trains provide routine transport service with cyclic (periodic) station-skip patterns that can be used in daily service. We propose an exact Mixed …

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Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra Jun 2023

Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to "critical" portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision …

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Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao Jun 2023

Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios …

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Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang Jun 2023

Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang

Research Collection School Of Computing and Information Systems

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closedset classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of openset samples, our model leverages both class-wise and pixelwise …

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Imitating Opponent To Win: Adversarial Policy Imitation Learning In Two-Player Competitive Games, The Viet Bui, Tien Mai, Thanh H. Nguyen Jun 2023

Imitating Opponent To Win: Adversarial Policy Imitation Learning In Two-Player Competitive Games, The Viet Bui, Tien Mai, Thanh H. Nguyen

Research Collection School Of Computing and Information Systems

Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studies, adversarial policies are directly trained based on experiences of interacting with the victim agent. There is a key shortcoming of this approach --- knowledge derived from historical interactions may not be properly generalized to unexplored policy regions of the victim agent, making the trained adversarial policy significantly less effective. In this work, we design a new effective adversarial policy learning algorithm that overcomes …

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