Digital Commons Network^™

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

Automated Methods For Estimating Blood Alcohol Concentration Level From Facial Cues, Ensiyeh Keshtkaran

Theses: Doctorates and Masters

This thesis investigates different approaches for detecting alcohol intoxication in drivers by analysing facial video data. Tackling this issue necessitates the creation of a novel dataset to overcome the limitations of existing datasets. The dataset constructed in this study is the first to include RGB video recordings of individual faces at varying levels of alcohol intoxication during simulated driving, featuring 60 participants with BAC levels ranging from 0 to 0.165 g/100ml. The constructed dataset not only supports this thesis, but also offers the broader scientific community a valuable resource for further study and development.

Building on this, this thesis presents …

Toward Embodied Navigation Through Vision And Language, Muraleekrishna Gopinathan

Theses: Doctorates and Masters

Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and predicting the layout of the environment for better planning.

First, we …

Machine Learning For Computer-Aided Diagnostics From Complex Medical Images, Afsah Saleem

Theses: Doctorates and Masters

Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorptiometry (DXA). Among these, lateral spine DXA scans, commonly used for osteoporosis screening, offer a cost-effective and low-radiation opportunity for opportunistic CVD risk …

The Rise Of Women In Mining - How To Improve Inclusion And Diversity Through Organisational Programs, Ashley Mcgrath

Theses: Doctorates and Masters

The inclusion and diversity (I&D) of women has become a priority for organisations due to the positive association with business performance and rising expectations of employees and shareholders for fair outcomes for all genders. Improving the I&D of women is also being driven by the evolving Australian legal framework which has heightened the responsibility of organisations regarding psychological safety and the prevention of sexual harassment and sex-based discrimination. Companies operating in male dominated industries in Western Australia (WA) are under particular pressure due to lagging performance in addressing equity issues at a state level. Existing research correlates the I&D of …

Embodied Ai For Challenging Rearrangement Tasks In The Context Of Service And Assistive Robots, Mariia Khan

Theses: Doctorates and Masters

Embodied AI explores intelligent agents that learn through interaction with their environment, aiming to replicate human-like learning processes. Achieving this requires agents capable of understanding a scene via various sensors, reasoning about their actions, and reacting accordingly. These abilities are necessary for service domestic robots to assist humans in their day-to-day activities. Embodied AI tasks can include but are not limited to: visual exploration, visual navigation, instruction following and embodied question answering, which typically consider static (unchanging) environments, where objects do not move over time. This thesis addresses one of the most challenging Embodied AI tasks – visual room rearrangement, …

Anomaly Detection Of Seasonal Vessel Activity, Travis Rybicki

Theses: Doctorates and Masters

Monitoring maritime traffic is essential for ensuring the safety of vessels, safeguarding transported goods or persons, and preventing illicit or hazardous activity at sea. Increasingly, researchers have explored data-driven approaches to model expected vessel behaviour and detect deviations or anomalies. These anomalies—such as course deviations, unauthorised area entries, or unexpected operational patterns—can indicate emergencies, regulatory breaches, or unlawful intent. Data broadcast by vessels provides a valuable resource for such analyses; however, the inherent complexity and context-dependency of maritime behaviour present persistent modelling challenges. One critical yet underutilised factor in this context is seasonality. For certain vessel types, for example, fishing …

Financial Named Entity Recognition: How Far Can Llm Go?, Yi-Te Lu, Yintong Huo

Research Collection School Of Computing and Information Systems

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight …

An Assessment Of Vegetable Production Constraints, Trait Preferences And Willingness To Adopt Sustainable Intensification Options In Kenya And Uganda, Rose N. Okoma, Evanson R. Omuse, Daniel M. Mutyambai, Dennis Beesigamukama, Marius F. Murongo, Sevgan Subramanian, Frank Chidawanyika

All Peer-Reviewed Publications

Global food production systems are under pressure due to population increase, limited farmland, biotic and abiotic constrains, and ongoing climate change. Sustainable intensification is needed to increase agricultural productivity with minimal adverse environmental and social impacts. Vegetable-integrated push pull (VIPP) technology coupled with black soldier fly (BSF) frass offer such opportunities to smallholder farmers. However, farmers’ vegetable preferences and willingness to adopt these innovations remain unknown and are variable across various geographic scales. Focus group discussions (FGDs) and in-person interviews with smallholder farmers were conducted to assess vegetable production constraints and select vegetables to be integrated into VIPP coupled with …

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

The Gender Wage Gap In An Online Labor Market: The Cost Of Interruptions, Abi Adams, Kotaro Hara, Kristy Milland, Chris Callison-Burch

Research Collection School Of Computing and Information Systems

This paper analyses gender differences in working patterns and wages on Amazon Mechanical Turk, a popular online labour platform. Using information on 2 million tasks, we find no gender differences in task selection nor experience. Nonetheless, women earn 20% less per hour on average. Gender differences in working patterns are a significant driver of this wage gap. Women are more likely to interrupt their working time on the platform with consequences for their task completion speed. A follow-up survey shows that the gender differences in working patterns and hourly wages are concentrated amongst workers with children.

Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji

Research Collection School Of Computing and Information Systems

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …

Improving The Performance Of Multi-Stakeholder Partnerships For Sustainable Development In Coastal Areas : Sweden (Hanö Bay) As A Case Study, Jennie Larsson

World Maritime University Ph.D. Dissertations

Coastal areas are vital for both ecosystems and human societies. Comprising diverse terrestrial, freshwater, and marine ecosystems, coastal areas provide us with essential resources and services. However, these areas are under threat from human activities and climate change, necessitating new governance structures to ensure their sustainable management and conservation. This research investigates how to improve the performance of local multi-stakeholder partnerships (M-SPs) in coastal areas, promoted as key mechanisms for achieving sustainable development goals. By drawing on stakeholder theory and using Pattberg & Widerberg’s (2014) analytical framework with nine building blocks for successful M-SPs as a foundation, the study examined …

Hydrogen-Enriched Lng As A Mid-Term Solution To Mitigate Greenhouse Gas Emissions From Shipping, Kang-Ki Lee

World Maritime University Ph.D. Dissertations

In light of the International Maritime Organization’s (IMO) ambitious Initial GHG Strategy, the rise in international shipping’s carbon dioxide emissions by 2 percent in 2022 compared to 2019 poses a formidable challenge. This increase underscores the pressing need to address the limited availability of green hydrogen, prompting the exploration of mid-term solutions to bridge this critical gap. The IMO’s 2023 Strategy on Reduction of GHG Emissions from Ships attempted to tackle this challenge head-on, recognizing the urgency of reducing the industry’s substantial carbon footprint. While hydrogen offers a simple solution as a zero-carbon fuel, LNG remains the most widespread alternative …

Charting A Sustainable Course Beyond Carbon : A Critical Carbon-Water-Energy Assessment Of Net-Zero Shipping Scenarios With Synthetic Fuels And Ghc Offsetting, Alexandre Bellot

World Maritime University Ph.D. Dissertations

This thesis extends the scope of IMO maritime decarbonization studies by incorporating cradle-to-grave approaches. This study advocates for an approach incorporating cradle-to-grave resource efficiency and carbon neutrality, assessing cumulative energy demand and net freshwater withdrawal as key sustainability metrics. The thesis specifically addresses the question of the sustainability of decarbonization strategies centred on substituting fossil fuels with synthetic alternatives and offsetting the remaining carbon footprint. Advanced ship energy modelling: The thesis first introduces a ship energy model capable of evaluating ships’ fuel consumption with various propulsion and energy generation options. The model simulates energy transfers across a concept ship’s systems, …

Safety And Sustainability In The Domestic Ferry Sector : A Pci Framework For Esg-Aligned Maritime Governance, Mirza Zeeshan Baig

World Maritime University Ph.D. Dissertations

The domestic ferry sector is a significant component of maritime transportation. It strengthens social ties, supports economic growth and connects communities. Despites its importance, this industry faces ongoing challenges. These include fragmented governance, operational adequacies, safety risks and environmental concerns. The study deals with these issues by combining a systems-based approach with governance and change management theories. It examines the interplay between human, technical and organizational aspects. The study integrates the rule-based and rights-based maritime governance along with the Lewin’s three stage change management model (Unfreeze, Change, and Refreeze).

At the core of the study is the development of a …