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Articles 841 - 870 of 1897

Full-Text Articles in Artificial Intelligence and Robotics

Duties For Datasets, Jerrold Tsin Howe Soh Dec 2023

Duties For Datasets, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

Machine learning (ML) systems are increasingly being deployed in contexts, such as law, medicine and finance, where system errors present serious and foreseeable risks. As ML system behaviour is largely determined by their training inputs, should dataset providers owe duties of care to victims? Using the ImageNet dataset and the Generative Pre-trained Transformer (GPT) models as case studies, this chapter argues that the conventional approach of centralising duties on system providers alone yields insufficient safeguards. Dataset-specific duties should also be considered to incentivise precaution in the preparation of crucial ML input. The chapter analyses how dataset duties may be encompassed …

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The Persuasive Effect Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee Dec 2023

The Persuasive Effect Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee

Research Collection Lee Kong Chian School Of Business

Artificial intelligence (AI) technology seeks to emulate humans. One aspect is AI-synthesized voices, used in voice assistants (such as Amazon Alexa, Apple Siri, and Google Assistant) to assistive technologies (such as voiceover narration in product videos). For example, there are currently more than 3.25 billion voice assistants; a number that is expected to touch about 8 billion by next year (i.e., 2023) (Statista 2022). With the extensive availability and enhanced accuracy of AI-synthesized voices, consumer research is starting to examine the impact of AI-synthesized voices on consumer information processing and decision making. The extant literature, however, is relatively limited because …

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The Use Of Deception In Dementia-Care Robots: Should Robots Tell "White Lies" To Limit Emotional Distress?, Samuel R. Cox, Grace Cheong, Wei Tsang Ooi Dec 2023

The Use Of Deception In Dementia-Care Robots: Should Robots Tell "White Lies" To Limit Emotional Distress?, Samuel R. Cox, Grace Cheong, Wei Tsang Ooi

ROSA Journal Articles and Publications

With projections of ageing populations and increasing rates of dementia, there is need for professional caregivers. Assistive robots have been proposed as a solution to this, as they can assist people both physically and socially. However, caregivers often need to use acts of deception (such as misdirection or white lies) in order to ensure necessary care is provided while limiting negative impacts on the cared-for such as emotional distress or loss of dignity. We discuss such use of deception, and contextualise their use within robotics.

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Forecasting Traffic Speed During Daytime From Google Street View Images Using Deep Learning, Junfeng Jiao, Huihai Wang Dec 2023

Forecasting Traffic Speed During Daytime From Google Street View Images Using Deep Learning, Junfeng Jiao, Huihai Wang

Research Collection College of Integrative Studies

Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. …

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Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin Dec 2023

Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study …

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Reinforced Target-Driven Conversational Promotion, Huy Quang Dao, Lizi Liao, Dung D. Le, Yuxiang Nie Dec 2023

Reinforced Target-Driven Conversational Promotion, Huy Quang Dao, Lizi Liao, Dung D. Le, Yuxiang Nie

Research Collection School Of Computing and Information Systems

The ability to proactively engage with users towards pitching products is highly desired for conversational assistants. However, existing conversational recommendation methods overemphasize on acquiring user preferences while ignore the strategic planning for nudging users towards accepting a designated item. Hence, these methods fail to promote specified items with engaging responses. In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion. RTCP integrates short-term and long-term planning via a balanced gating mechanism. Inside which, the dialogue actions are predicted via a knowledge-integrated multi-head attention and guided via reinforcement learning rewards. RTCP then employs action-guided prefix tuning …

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End-To-End Task-Oriented Dialogue: A Survey Of Tasks, Methods, And Future Directions, Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li Dec 2023

End-To-End Task-Oriented Dialogue: A Survey Of Tasks, Methods, And Future Directions, Libo Qin, Wenbo Pan, Qiguang Chen, Lizi Liao, Zhou Yu, Yue Zhang, Wanxiang Che, Min Li

Research Collection School Of Computing and Information Systems

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this …

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Clusterprompt: Cluster Semantic Enhanced Prompt Learning For New Intent Discovery, Jinggui Liang, Lizi Liao Dec 2023

Clusterprompt: Cluster Semantic Enhanced Prompt Learning For New Intent Discovery, Jinggui Liang, Lizi Liao

Research Collection School Of Computing and Information Systems

The discovery of new intent categories from user utterances is a crucial task in expanding agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and properly transfer knowledge from existing intents to new intents. However, previous methods laid too much emphasis on relations among utterances or clusters for transfer learning, while paying less attention to the usage of semantics. As a result, these methods suffer from in-domain over-fitting and often generate meaningless new intent clusters due to data distortion. In this paper, we present a novel approach called Cluster Semantic Enhanced Prompt Learning (CsePL) for …

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A Reliable And Secure Mobile Cyber-Physical Digital Microfluidic Biochip For Intelligent Healthcare, Yinan Yao, Decheng Qiu, Huangda Liu, Zhongliao Yang, Ximeng Liu, Yang Yang, Chen Dong Dec 2023

A Reliable And Secure Mobile Cyber-Physical Digital Microfluidic Biochip For Intelligent Healthcare, Yinan Yao, Decheng Qiu, Huangda Liu, Zhongliao Yang, Ximeng Liu, Yang Yang, Chen Dong

Research Collection School Of Computing and Information Systems

Digital microfluidic, as an emerging and potential technology, diversifies the biochemical applications platform, such as protein dilution sewage detection. At present, a vast majority of universal cyberphysical digital microfluidic biochips (DMFBs) transmit data through wires via personal computers and microcontrollers (like Arduino), consequently, susceptible to various security threats and with the popularity of wireless devices, losing competitiveness gradually. On the premise that security be ensured first and foremost, calls for wireless portable, safe, and economical DMFBs are imperative to expand their application fields, engage more users, and cater to the trend of future wireless communication. To this end, a new …

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Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …

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Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath …

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Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …

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Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring Via Constructing The Optimal Subgraph Of Demonstrations And Prompts, Jiashu Pu, Ling Cheng, Lu Fan, Tangjie Lv, Rongsheng Zhang Dec 2023

Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring Via Constructing The Optimal Subgraph Of Demonstrations And Prompts, Jiashu Pu, Ling Cheng, Lu Fan, Tangjie Lv, Rongsheng Zhang

Research Collection School Of Computing and Information Systems

The use of modern Large Language Models (LLMs) as chatbots still has some problems such as hallucinations and lack of empathy. Identifying these issues can help improve chatbot performance. The community has been continually iterating on reference-free dialogue evaluation methods based on large language models (LLMs) that can be readily applied. However, many of these LLM-based metrics require selecting specific datasets and developing specialized training tasks for different evaluation dimensions (e.g., coherence, informative). The developing step can be time-consuming and may need to be repeated for new evaluation dimensions. To enable efficient and flexible adaptation to diverse needs of dialogue …

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Novus Ex Machina: Realise Your Organisation’S Creative Potential With Ai, Adam Tatarynowicz, Utz Claassen Nov 2023

Novus Ex Machina: Realise Your Organisation’S Creative Potential With Ai, Adam Tatarynowicz, Utz Claassen

Asian Management Insights

Innovation managers must learn how to harness AI’s transformative potential.

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Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink Nov 2023

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

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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 Nov 2023

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 …

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The Impact Of Generative Ai In The Financial Sector [El Impacto De La Inteligencia Artificial Generativa En El Sector Financiero], Nydia Remolina Leon Nov 2023

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.

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Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li Oct 2023

Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li

Research Collection School Of Computing and Information Systems

Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense …

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Voucher Abuse Detection With Prompt-Based Fine-Tuning On Graph Neural Networks, Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao Oct 2023

Voucher Abuse Detection With Prompt-Based Fine-Tuning On Graph Neural Networks, Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao

Research Collection School Of Computing and Information Systems

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task …

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Understanding The Effect Of Counterfactual Explanations On Trust And Reliance On Ai For Human-Ai Collaborative Clinical Decision Making, Min Hun Lee, Chong Jun Chew Oct 2023

Understanding The Effect Of Counterfactual Explanations On Trust And Reliance On Ai For Human-Ai Collaborative Clinical Decision Making, Min Hun Lee, Chong Jun Chew

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of achieving human AI complementary performance. In this work, we utilized salient feature explanations along with what-if, counterfactual explanations to make humans review AI suggestions more analytically to reduce overreliance on AI and explored the effect of these explanations on trust and reliance on AI during clinical decision-making. We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality …

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Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei Oct 2023

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

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Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro Oct 2023

Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach …

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Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw Oct 2023

Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters.To fill the gap, …

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Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro Oct 2023

Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Semantic segmentation models classify pixels into a set of known ("in-distribution") visual classes. When deployed in an open world, the reliability of these models depends on their ability to not only classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., …

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Feature Prediction Diffusion Model For Video Anomaly Detection, Cheng Yan, Shiyu Zhang, Yang Liu, Guansong Pang, Wenjun Wang Oct 2023

Feature Prediction Diffusion Model For Video Anomaly Detection, Cheng Yan, Shiyu Zhang, Yang Liu, Guansong Pang, Wenjun Wang

Research Collection School Of Computing and Information Systems

Anomaly detection in the video is an important research area and a challenging task in real applications. Due to the unavailability of large-scale annotated anomaly events, most existing video anomaly detection (VAD) methods focus on learning the distribution of normal samples to detect the substantially deviated samples as anomalies. To well learn the distribution of normal motion and appearance, many auxiliary networks are employed to extract foreground object or action information. These high-level semantic features effectively filter the noise from the background to decrease its influence on detection models. However, the capability of these extra semantic models heavily affects the …

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Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao Oct 2023

Hallucination Detection: Robustly Discerning Reliable Answers In Large Language Models, Yuyuan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects …

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Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein Oct 2023

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …

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Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic Oct 2023

Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider bidding games, a class of two-player zerosum graph games. The game proceeds as follows. Both players have bounded budgets. A token is placed on a vertex of a graph, in each turn the players simultaneously submit bids, and the higher bidder moves the token, where we break bidding ties in favor of Player 1. Player 1 wins the game iff the token visits a designated target vertex. Weconsider, for the first time, poorman discrete-bidding in which the granularity of the bids is restricted and the higher bid is paid to the bank. Previous work either did not impose …

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Icl-D3ie: In-Context Learning With Diverse Demonstrations Updating For Document Information Extraction, Jiabang He, Lei Wang, Yi Hu, Ning Liu, Hui Liu, Xing Xu, Heng Tao Shen Oct 2023

Icl-D3ie: In-Context Learning With Diverse Demonstrations Updating For Document Information Extraction, Jiabang He, Lei Wang, Yi Hu, Ning Liu, Hui Liu, Xing Xu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

arge language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the …

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Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi Sep 2023

Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional …

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