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Make The U In Uda Matter: Invariant Consistency Learning For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Hanwang Zhang Dec 2023

Make The U In Uda Matter: Invariant Consistency Learning For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) that do not generalize to the target domain. Unfortunately, even enriched with additional unsupervised target domains, existing Unsupervised DA (UDA) methods still suffer from it. This is because the source domain supervision only considers the target domain samples as auxiliary data (e.g., by pseudo-labeling), yet the inherent distribution in the target domain—where the valuable de-correlation clues hide—is disregarded. We propose to make the U in UDA matter by giving equal status to the two domains. Specifically, we learn an …

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Peer Learning In An Undergraduate Linear Algebra Course - A Social Network Analysis, Manoj Thulasidas, Kyong Jin Shim, Jonathan Teo Dec 2023

Peer Learning In An Undergraduate Linear Algebra Course - A Social Network Analysis, Manoj Thulasidas, Kyong Jin Shim, Jonathan Teo

Research Collection School Of Computing and Information Systems

This study employs Social Network Analysis (SNA) to explore peer learning behaviors among undergraduate Linear Algebra students. By examining the relational dynamics within the classroom, SNA unveils patterns of interaction, information flow, and collaboration among students. Our analysis identifies the prevalence and evolution of peer learning, and how it influences the students' academic performance. It also unveils the attributes of the students who engage in peer helping and the formation of small communities through such interactions. The findings of the study can provide valuable insights for educators aiming to enhance peer learning and improve educational practices in Linear Algebra and …

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Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang Dec 2023

Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Accurate detection of semantic code clones has many applications in software engineering but is challenging because of lexical, syntactic, or structural dissimilarities in code. CodeBERT, a popular deep neural network based pre-trained code model, can detect code clones with a high accuracy. However, its performance on unseen data is reported to be lower. A challenge is to interpret CodeBERT's clone detection behavior and isolate the causes of mispredictions. In this paper, we evaluate CodeBERT and interpret its clone detection behavior on the SemanticCloneBench dataset focusing on Java and Python clone pairs. We introduce the use of a black-box model interpretation …

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Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth Dec 2023

Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth

Electronic Theses, Projects, and Dissertations

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural …

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Emotional Health And Climate-Change-Related Stressor Extraction From Social Media: A Case Study Using Hurricane Harvey, Thanh Bui, Andrea Hannah, Sanjay Madria, Rosemary Nabaweesi, Eugene Levin, Michael Wilson, Long Nguyen Dec 2023

Emotional Health And Climate-Change-Related Stressor Extraction From Social Media: A Case Study Using Hurricane Harvey, Thanh Bui, Andrea Hannah, Sanjay Madria, Rosemary Nabaweesi, Eugene Levin, Michael Wilson, Long Nguyen

Computer Science Faculty Research & Creative Works

Climate change has led to a variety of disasters that have caused damage to infrastructure and the economy with societal impacts to human living. Understanding people's emotions and stressors during disaster times will enable preparation strategies for mitigating further consequences. in this paper, we mine emotions and stressors encountered by people and shared on Twitter during Hurricane Harvey in 2017 as a showcase. in this work, we acquired a dataset of tweets from Twitter on Hurricane Harvey from 20 August 2017 to 30 August 2017. the dataset consists of around 400,000 tweets and is available on Kaggle. Next, a BERT-Based …

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Prompting And Evaluating Large Language Models For Proactive Dialogues: Clarification, Target-Guided, And Non-Collaboration, Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, Tat-Seng Chua Dec 2023

Prompting And Evaluating Large Language Models For Proactive Dialogues: Clarification, Target-Guided, And Non-Collaboration, Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users' unreasonable requests, both of which are considered as key aspects of a conversational agent's proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three key aspects of proactive dialogues: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of …

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Depwignn: A Depth-Wise Graph Neural Network For Multi-Hop Spatial Reasoning In Text, Shuaiyi Li, Yang Deng, Wai Lam Dec 2023

Depwignn: A Depth-Wise Graph Neural Network For Multi-Hop Spatial Reasoning In Text, Shuaiyi Li, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlook the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic structures. However, classical GNNs face challenges in handling multi-hop spatial reasoning due to the over-smoothing issue, i.e., the performance decreases substantially as the number of graph layers increases. To cope with these challenges, we propose a novel Depth-Wise Graph Neural Network (DepWiGNN). Specifically, we design a novel node memory scheme and aggregate the …

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Unifying Text, Tables, And Images For Multimodal Question Answering, Haohao Luo, Ying Shen, Yang Deng Dec 2023

Unifying Text, Tables, And Images For Multimodal Question Answering, Haohao Luo, Ying Shen, Yang Deng

Research Collection School Of Computing and Information Systems

Multimodal question answering (MMQA), which aims to derive the answer from multiple knowledge modalities (e.g., text, tables, and images), has received increasing attention due to its board applications. Current approaches to MMQA often rely on single-modal or bi-modal QA models, which limits their ability to effectively integrate information across all modalities and leverage the power of pre-trained language models. To address these limitations, we propose a novel framework called UniMMQA, which unifies three different input modalities into a text-to-text format by employing position-enhanced table linearization and diversified image captioning techniques. Additionally, we enhance cross-modal reasoning by incorporating a multimodal rationale …

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Customer Cybersecurity And Supplier Cost Management Strategy, Xu Yang, Peng Liang, Nan Hu, Fujing Xue Dec 2023

Customer Cybersecurity And Supplier Cost Management Strategy, Xu Yang, Peng Liang, Nan Hu, Fujing Xue

Research Collection School Of Computing and Information Systems

In this paper, we explore the spillover effect of customer firms’ data breaches on their upstream supplier firms’ cost management strategies, proxied by cost stickiness. Our primary analyses suggest that data breaches suffered by customer firms are associated with a decrease in cost stickiness among supplier firms. Furthermore, the reductions in supplier cost stickiness are stronger if suppliers are managed by CEOs from national cultural groups with high uncertainty avoidance, low long-term orientations, and/or low individualism. In sum, the findings contribute to both Information Systems (IS) and Operations Management (OM) disciplines in terms of data breach, cost management strategy, and …

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Self-Supervised Pseudo Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro Dec 2023

Self-Supervised Pseudo Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer …

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Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng Dec 2023

Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng

Research Collection School Of Computing and Information Systems

Unprecedented demand for data science professionals in the industry has led to many educational institutions launching new data science courses. It is however imperative that students of data science programmes learn through execution of real-world, authentic projects on top of acquiring foundational knowledge on the basics of data science. In the process of working on authentic, real-world projects, students not only create new knowledge but also learn to solve open, sophisticated, and ill-structured problems in an inter-disciplinary fashion. In this paper, we detailed our approach to design a data science curriculum premised on learners solving authentic data science problems sourced …

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Llm4vis: Explainable Visualization Recommendation Using Chatgpt, Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang Dec 2023

Llm4vis: Explainable Visualization Recommendation Using Chatgpt, Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang

Research Collection School Of Computing and Information Systems

Data visualization is a powerful tool for exploring and communicating insights in various domains. To automate visualization choice for datasets, a task known as visualization recommendation has been proposed. Various machine-learning-based approaches have been developed for this purpose, but they often require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results. To address this research gap, we propose LLM4Vis, a novel ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples. Our approach involves feature description, demonstration example selection, explanation generation, demonstration example construction, and inference steps. To …

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Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw Dec 2023

Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. …

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Large Language Model Is Not A Good Few-Shot Information Extractor, But A Good Reranker For Hard Samples!, Yubo Ma, Yixin Cao, Yongchin Hong, Aixin Sun Dec 2023

Large Language Model Is Not A Good Few-Shot Information Extractor, But A Good Reranker For Hard Samples!, Yubo Ma, Yixin Cao, Yongchin Hong, Aixin Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have made remarkable strides in various tasks. However, whether they are competitive few-shot solvers for information extraction (IE) tasks and surpass fine-tuned small Pre-trained Language Models (SLMs) remains an open problem. This paper aims to provide a thorough answer to this problem, and moreover, to explore an approach towards effective and economical IE systems that combine the strengths of LLMs and SLMs. Through extensive experiments on nine datasets across four IE tasks, we show that LLMs are not effective few-shot information extractors in general, given their unsatisfactory performance in most settings and the high latency and …

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Robust Prompt Optimization For Large Language Models Against Distribution Shifts, Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua Dec 2023

Robust Prompt Optimization For Large Language Models Against Distribution Shifts, Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of the task prompt, leading to research on automatic prompt optimization using labeled task data. We reveal that these prompt optimization techniques are vulnerable to distribution shifts such as subpopulation shifts, which are common for LLMs in real-world scenarios such as customer reviews analysis. In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled …

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Molca: Molecular Graph-Language Modeling With Cross-Modal Projector And Uni-Modal Adapter, Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua Dec 2023

Molca: Molecular Graph-Language Modeling With Cross-Modal Projector And Uni-Modal Adapter, Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception — a critical ability of human professionals in comprehending molecules’ topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter. MolCA enables an LM (i.e., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector. Specifically, the cross-modal projector is implemented as a QFormer to connect a graph encoder’s representation space and an LM’s text space. Further, MolCA employs a uni-modal adapter (i.e., LoRA) for the LM’s efficient …

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Ensemble-Based Deep Reinforcement Learning For Vehicle Routing Problems Under Distribution Shift, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang Dec 2023

Ensemble-Based Deep Reinforcement Learning For Vehicle Routing Problems Under Distribution Shift, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jie Zhang

Research Collection School Of Computing and Information Systems

While performing favourably on the independent and identically distributed (i.i.d.) instances, most of the existing neural methods for vehicle routing problems (VRPs) struggle to generalize in the presence of a distribution shift. To tackle this issue, we propose an ensemble-based deep reinforcement learning method for VRPs, which learns a group of diverse sub-policies to cope with various instance distributions. In particular, to prevent convergence of the parameters to the same one, we enforce diversity across sub-policies by leveraging Bootstrap with random initialization. Moreover, we also explicitly pursue inequality between sub-policies by exploiting regularization terms during training to further enhance diversity. …

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Neural Multi-Objective Combinatorial Optimization With Diversity Enhancement, Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, Jiahai Wang Dec 2023

Neural Multi-Objective Combinatorial Optimization With Diversity Enhancement, Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, Jiahai Wang

Research Collection School Of Computing and Information Systems

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more …

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Deepaco: Neural-Enhanced Ant Systems For Combinatorial Optimization, Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li Dec 2023

Deepaco: Neural-Enhanced Ant Systems For Combinatorial Optimization, Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li

Research Collection School Of Computing and Information Systems

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework leveraging deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization …

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Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek Dec 2023

Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different …

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A Poisson-Based Distribution Learning Framework For Short-Term Prediction Of Food Delivery Demand Ranges, Jian Liang, Jintao Ke, Hai Wang, Hongbo Ye, Jinjun Tang Dec 2023

A Poisson-Based Distribution Learning Framework For Short-Term Prediction Of Food Delivery Demand Ranges, Jian Liang, Jintao Ke, Hai Wang, Hongbo Ye, Jinjun Tang

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic has caused a dramatic change in the demand composition of restaurants and, at the same time, catalyzed on-demand food delivery (OFD) services—such as DoorDash, Grubhub, and Uber Eats—to a large extent. With massive amounts of data on customers, drivers, and merchants, OFD platforms can achieve higher efficiency with better strategic and operational decisions; these include dynamic pricing, order bundling and dispatching, and driver relocation. Some of these decisions, and especially proactive decisions in real time, rely on accurate and reliable short-term predictions of demand ranges or distributions. In this paper, we develop a Poisson-based distribution prediction (PDP) …

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Disentangling Transformer Language Models As Superposed Topic Models, Jia Peng Lim, Hady Wirawan Lauw Dec 2023

Disentangling Transformer Language Models As Superposed Topic Models, Jia Peng Lim, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Topic Modelling is an established research area where the quality of a given topic is measured using coherence metrics. Often, we infer topics from Neural Topic Models (NTM) by interpreting their decoder weights, consisting of top-activated words projected from individual neurons. Transformer-based Language Models (TLM) similarly consist of decoder weights. However, due to its hypothesised superposition properties, the final logits originating from the residual path are considered uninterpretable. Therefore, we posit that we can interpret TLM as superposed NTM by proposing a novel weight-based, model-agnostic and corpus-agnostic approach to search and disentangle decoder-only TLM, potentially mapping individual neurons to multiple …

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Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun Dec 2023

Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B …

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Explorelah: Personalised And Smart Trip Planner For Mobile Tourism, Aldy Gunawan, Siu Loon Hoe, Xun Yi Lim, Linh Chi Tran, Dang Viet Anh Nguyen Dec 2023

Explorelah: Personalised And Smart Trip Planner For Mobile Tourism, Aldy Gunawan, Siu Loon Hoe, Xun Yi Lim, Linh Chi Tran, Dang Viet Anh Nguyen

Research Collection School Of Computing and Information Systems

Various recommender systems for mobile tourism have been developed over the years. However, most of these recommender systems tend to overwhelm users with too much information and may not be personalised to user preferences. In this paper, we introduce ExploreLah, a personalised and smart trip planner for exploring Point of Interests (POIs) in Singapore. The user preferences are categorised into five groups: shopping, art & culture, outdoor activity, adventure, and nightlife. The problem is considered as the Team Orienteering Problem with Time Windows. The algorithm is developed to generate itineraries. Simulated experiments using test cases were performed to evaluate and …

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Exploring Students' Adoption Of Chatgpt As A Mentor For Undergraduate Computing Projects: Pls-Sem Analysis, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky Dec 2023

Exploring Students' Adoption Of Chatgpt As A Mentor For Undergraduate Computing Projects: Pls-Sem Analysis, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

As computing projects increasingly become a core component of undergraduate courses, effective mentorship is crucial for supporting students' learning and development. Our study examines the adoption of ChatGPT as a mentor for undergraduate computing projects. It explores the impact of ChatGPT mentorship, specifically, skills development, and mentor responsiveness, i.e., ChatGPT's responsiveness to students' needs and requests. We utilize PLS-SEM to investigate the interrelationships between different factors and develop a model that captures their contribution to the effectiveness of ChatGPT as a mentor. The findings suggest that mentor responsiveness and technical/design support are key factors for the adoption of AI tools …

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M2-Cnn: A Macro-Micro Model For Taxi Demand Prediction, Shih-Fen Cheng, Prabod Manuranga Rathnayaka Mudiyanselage Dec 2023

M2-Cnn: A Macro-Micro Model For Taxi Demand Prediction, Shih-Fen Cheng, Prabod Manuranga Rathnayaka Mudiyanselage

Research Collection School Of Computing and Information Systems

In this paper, we introduce a macro-micro model for predicting taxi demands. Our model is a composite deep learning model that integrates multiple views. Our network design specifically incorporates the spatial and temporal dependency of taxi or ride-hailing demand, unlike previous papers that also utilize deep learning models. In addition, we propose a hybrid of Long Short-Term Memory Networks and Temporal Convolutional Networks that incorporates real world time series with long sequences. Finally, we introduce a microscopic component that attempts to extract insights revealed by roaming vacant taxis. In our study, we demonstrate that our approach is competitive against a …

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Interoperability In Blockchain: A Survey, Kunpeng Ren, Nhut-Minh Ho, Dumitrel Loghin, Thanh-Toan Nguyen, Beng Chin Ooi, Quang-Trung Ta, Feida Zhu Dec 2023

Interoperability In Blockchain: A Survey, Kunpeng Ren, Nhut-Minh Ho, Dumitrel Loghin, Thanh-Toan Nguyen, Beng Chin Ooi, Quang-Trung Ta, Feida Zhu

Research Collection School Of Computing and Information Systems

This paper presents a systematic and comprehensive survey on blockchain interoperability, where interoperability is defined as the ability of blockchains to flexibly transfer assets, share data, and invoke smart contracts across a mix of public, private, and consortium blockchains without any changes to the underlying blockchain systems. Analyzing the vast landscape of both research papers and industry projects, we classify the existing works into five categories, namely, (1) sidechains, (2) notary schemes, (3) hashed time lock contracts (HTLC), (4) relays, and (5) blockchain agnostic protocols. We analyze the existing works under a taxonomy that consists of system and safety characteristics, …

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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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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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Security Compliance And Work-Issued Mobile Devices: Out Of Sight, Out Of Mind?, Kent Marett, Shan Xiao, Sumin Kim Dec 2023

Security Compliance And Work-Issued Mobile Devices: Out Of Sight, Out Of Mind?, Kent Marett, Shan Xiao, Sumin Kim

Business and Information Technology Faculty Research & Creative Works

For security, economic, and efficiency reasons, many businesses supply mobile devices to employees to use both in the workplace and remotely, accompanied by policies governing their appropriate use. Extant research has shown that work-issued mobile devices can disrupt employees' perceptions of work-life balance (WLB) and, indeed, WLB can impact employees' job satisfaction and performance. The global COVID-19 pandemic meant that more employees than usual performed their work remotely, but this situation may have not fit the preferred WLB for some. Did this encroachment mean that appropriate use policies were forgotten? We conducted two rounds of surveys, one pre-pandemic and the …

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