Digital Commons Network^™

Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao

Research Collection School Of Computing and Information Systems

Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models?To tackle …

Large Language Model Powered Agents For Information Retrieval, An Zhang, Yang Deng, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like …

Towards Human-Centered Proactive Conversational Agents, Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …

Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …

Application Of An Improved Harmony Search Algorithm On Electric Vehicle Routing Problems, Vanny Minanda, Yun-Chia Liang, Angela H. L. Chen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP’s constraints, the EVRP requires vehicles to visit a charging station before the battery level …

Esem: To Harden Process Synchronization For Servers, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Process synchronization primitives lubricate server computing involving a group of processes as they ensure those processes to properly coordinate their executions for a common purpose such as provisioning a web service. A malfunctioned synchronization due to attacks causes friction among processes and leads to unexpected, and often hard-to-detect, application transaction errors. Unfortunately, synchronization primitives are not naturally protected by existing hardware-assisted isolation techniques e.g., SGX, because their process-oriented isolation conflicts with the primitive's demand for cross-process operations.This paper introduces the Enclave-Semaphore service (ESem) which shelters application semaphores and their operations against kernel-privileged attacks. ESem encapsulates all semaphores in the platform …

Fedstem-Adl: A Federated Spatial-Temporal Episodic Memory Model For Adl Prediction, Doudou Wu, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines …

Adaptive Stabilization Based On Machine Learning For Column Generation, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Eberhard Andrew, Guangquan Zhang

Research Collection School Of Computing and Information Systems

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. …

On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open-source software (OSS) projects. In this regard, we first investigate …

Privacy-Preserving Arbitrary Geometric Range Query In Mobile Internet Of Vehicles, Yinbin Miao, Lin Song, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. …

Prioritising Github Priority Labels, James Caddy, Christoph Treude

Research Collection School Of Computing and Information Systems

Communities on GitHub often use issue labels as a way of triaging issues by assigning them priority ratings based on how urgently they should be addressed. The labels used are determined by the repository contributors and notstandardisedbyGitHub.Thismakes it difficult for priority-related reasoning across repositories for both researchers and contributors. Previous work shows interest in how issues are labelled and what the consequences for those labels are. For instance, some previous work has used clustering models and natural language processing to categorise labels without a particular emphasis on priority. With this publication, we introduce a unique data set of 812 manually …

Configurable Mirror Descent : Towards A Unification Of Decision Making, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold’em poker, and mixed cooperative and competitive, e.g., football, are ubiquitous in the real world. Although various methods have been proposed to address the specific decision-making categories, these methods typically evolve independently and cannot generalize to other categories. Therefore, a fundamental question for decision-making is: Can we develop a single algorithm to tackle ALL categories of decision-making problems? There are several main challenges to address this question: i) different decision-making categories involve different numbers of agents and different relationships between agents, ii) different categories have different …

Augmenting Decision With Hypothesis In Reinforcement Learning, Minh Quang Nguyen, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling efficiency. However, our study shows it suffers from low exploitation in early training period and bias sensitiveness. To address these issues, we propose to augment the decision-making process with hypothesis, a weak form of environment description. Our approach relies on prompting the learning agent with accurate hypotheses, and designing a ready-to-adapt policy through incremental learning. We propose the ALH algorithm, showing detailed analyses on a typical learning scheme and a diverse set of Mujoco benchmarks. Our algorithm produces a significant improvement over value-based learning algorithms and other strong baselines. …

Unified Training Of Universal Time Series Forecasting Transformers, Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Research Collection School Of Computing and Information Systems

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel …

An Exploratory Study Of Conventional Machine Learning And Large Language Models For Sentiment Analysis, Cui Zou, Jingyuan Cai, Langtao Chen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Sentiment analysis is the use of natural language processing to identify affective states and determine people’s opinions in various analytical applications such as customer reviews and social media analyses. Large language models (LLMs) such as GPT-4o demonstrate impressive performance in text generation tasks. Despite numerous studies in the extant literature, few have compared the performance of conventional machine learning models with LLMs for sentiment analysis. This study aims to fill this gap by conducting an evaluation of these models using a balanced dataset of 2,000 IMDb movie reviews. Our study shows that GPT-4o achieves the highest performance, while GPT-3.5 and …

A Computational Aesthetic Design Science Study On Online Video Based On Triple-Dimensional Multimodal Analysis, Zhangguang Kang, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Computational video aesthetic prediction refers to using models that automatically evaluate the features of videos to produce their aesthetic scores. Current video aesthetic prediction models are designed based on bimodal frameworks. To address their limitations, we developed the Triple-Dimensional Multimodal Temporal Video Aesthetic neural network (TMTVA-net) model. The Long Short-Term Memory (LSTM) forms the conceptual foundation for the design framework. In the multimodal transformer layer, we employed two distinct transformers: the multimodal transformer and the feature transformer, enabling the acquisition of modality-specific patterns and representational features uniquely adapted to each modality. The fusion layer has also been redesigned to compute …

Understanding And Fighting Scams: Media, Language, Appeals And Effects, Shuhua Zhou, Xiao Fan Liu, Fiona Fui-Hoon Nah, S. Harrison, X. Zhang, S. Zhen, D. Yeung, J. Hsiao, R. Lc, A. Chan, X. Wang, C. Jiang, F. Lin, J. Li, A. Wong, L. Chan, B. George, P. Li

Research Collection School Of Computing and Information Systems

Scams are fraudulent activities aiming to deceive individuals into relinquishing money, property, or rights, and they have proliferated in the context of widespread misinformation and disinformation. In this paper, we propose strategies and a research plan to address key questions about the exploitation of new communication technologies by scammers, the prevalence and nature of different scam types, and the language characteristics and appeals used in scamming content. We aim to develop a comprehensive taxonomy of scams and identify factors that contribute to their persuasiveness. Additionally, we propose the use of advanced technologies, including artificial intelligence, physiological measures, and brain mapping, …

Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …

Double Issuer-Hiding Attribute-Based Credentials From Tag-Based Aggregatable Mercurial Signatures, Rui Shi, Yang Yang, Yingjiu Li, Huamin Feng, Guozhen Shi, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Attribute-based anonymous credentials offer users fine-grained access control in a privacy-preserving manner. However, in such schemes obtaining a user's credentials requires knowledge of the issuer's public key, which obviously reveals the issuer's identity that must be hidden from users in certain scenarios. Moreover, verifying a user's credentials also requires the knowledge of issuer's public key, which may infer the user's private information from their choice of issuer. In this article, we introduce the notion of double issuer-hiding attribute-based credentials ( DIHAC ) to tackle these two problems. In our model, a central authority can issue public-key credentials for a group …

Incorporating Intrinsic Structures Into Entity Matching And Representation Learning, Ween Jiann Lee

Dissertations and Theses Collection (Open Access)

The proliferation of internet-connected devices and online services has generated vast amounts of user-generated content in various formats, such as text, visual, and spatial information. Despite the potential of advanced deep learning techniques, challenges such as fragmentation, lack of cohesive structure, and the inability to capture intrinsic data structures persist, affecting data amalgamation and quality. Our research addresses these challenges by enhancing entity matching and representation learning across graph, semi-ordered, and spatial data. These advancements have significant implications for applications in transportation, recommendation systems, and urban planning.

In entity matching, we introduce Robust BiPoly-Matching and Semi-Ordered Bidirectional Poly-Matching. Matching records …

Creating And Delivering Audio Descriptions For Videos, Rosiana Natalie

Dissertations and Theses Collection (Open Access)

Despite anti-discrimination regulations mandating the provision of audio descriptions (ADs), the majority of online video content remains inaccessible to blind and low-vision (BLV) individuals. This is because these ADs are either absent or fail to adequately address the diverse and unique needs of the audience. Traditionally, content creators have relied on professionals to author ADs. However, this gold standard may not be accessible for some content creators because this method is still costly and has a long turnaround time. Moreover, when ADs are available, they tend to be static and unalterable, failing to cater to the unique preferences of BLV …

Essays On Artificial Intelligence (Ai) In Management, Bowen Zhou

Dissertations and Theses Collection (Open Access)

This dissertation comprises three essays that investigate the transformative potential of Artificial Intelligence (AI) in business.

Chapter 1 investigates the fundamental issue of how integrating AI within R&D activities influences a firm’s market value. We developed an "AI Index" using patent data and textual analysis. Interestingly, empirical results indicate a negative correlation between AI integration and market value. However, this does not suggest that AI is an unviable avenue for exploration. Further analysis of the boundary conditions reveals that complementary assets are crucial for successful commercialisation, highlighting that while AI adoption is costly, these assets significantly enhance its market value. …

Decentralized Consensus And Governance For Collaborative Intelligence, Huiwen Liu

Dissertations and Theses Collection (Open Access)

The data economy today is becoming increasingly collaborative in nature. Take business intelligence, for example. To unleash the full potential of big data, it is essential to integrate multi-source data depicting entities from a multi-faceted and multi-modal perspective, which, not surprisingly, is not achievable by any company alone. In collaborative intelligence, there are two core issues, namely "trust" and "incentive". The core mechanisms to solve these two problems are consensus and tokenization separately.

To solve the trust problem more effectively, we propose a systematic consensus evaluation framework to investigate whether existing consensus algorithms can do so. After a lot of …

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Preregistration And Registration As A New Method For Transparency Of External Validation In Artificial Intelligence And Machine Learning Applications To Address Overfitting, Underspecification, And Shortcut Learning, Marilyn Elaine Gartley

Theses and Dissertations

Statistics has been defined as the study of how information should be employed to reflect on and give guidance for action in a practical situation involving uncertainty. The essence of uncertainty is that there is more than one possible outcome, and the actual outcome is unknown in advance; it is indeterminate. The goal of statistical methods is inference: namely, statistical inference—reaching conclusions about populations or deriving scientific insights from data which are collected from a representative sample of that population through providing a mathematical understanding of inference, quantifying the degree of support that data offer for assertions of knowledge, as …

Enhancing The Security In High-Speed Networks Using P4 Programmable Switches, Ali Alsabeh

Theses and Dissertations

Network security has become increasingly essential in today's networks due to the growth of various network applications, such as Machine Learning (ML) and Fifth-Generation (5G) Networks. One fundamental Internet protocol is the Domain Name System (DNS), which maps domain names to Internet Protocol (IP) addresses. Despite its importance, DNS traffic is often forwarded without being analyzed, making it a center of ever-evolving attacks. Traditionally, defense strategies are implemented on fixed-function security middleboxes that are costly, proprietary, and hard to manage. Alternatively, defenses implemented in software use general-purpose servers (e.g., the control plane of a Software-Defined Networking (SDN) network) that cannot …

Multi-Scale Deep Representation Learning In Synthetic Biology, Xiaoyi Liu

Theses and Dissertations

Synthetic biology advances and combines the expertise of engineers and biologists, bridging the gap between engineering and natural life. Synthetic biology has been generally categorized into two broad branches by developing new biological components, networks, and systems to reprogram organisms. The first branch involves using synthetic molecules to mimic natural biological functions. The second branch focuses on assembling natural biological components in novel ways, aiming to produce systems with unique, practical functions. Thus, the de novo engineering of biological modules and synthetic pathways is used in related practical bioengineering applications, such as drug-targeting strategies and microbial product manufacturing. Therefore, synthetic …

Attribute-Based Fine-Grained Access Control Using Verifiable Credentials, Srinivasa Dumpa

Student Theses

In the era of digital transformation, ensuring secure and privacy-preserving access control mechanisms is of paramount importance. Traditional identity-based access control systems often fall short in providing granular control and user autonomy over digital identities. This thesis presents a novel approach to access control by leveraging the power of verifiable credentials and attribute-based access control. The proposed system introduces a decentralized and user-centric framework that enables fine-grained access control based on specific attributes encapsulated within verifiable credentials. These tamper-evident digital credentials, stored in a user's digital wallet, contain a rich set of attributes that can be selectively disclosed to grant …

A Feasibility-Preserved Quantum Approximate Solver For The Capacitated Vehicle Routing Problem, Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito, Nobuyoshi Asai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions increases exponentially with the number of customers, finding high-quality solutions remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum–classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems, such as …