Computer Sciences Commons^™

Forging The Future, Kenneth Benoit

Asian Management Insights

How AI is rewriting the rules of knowledge, expertise, and practice.

Strategy And Stewardship In An Uncertain World, Havovi Joshi

Asian Management Insights

This issue, as we continue to celebrate Singapore Management University’s (SMU) 25th anniversary, we explore leadership in higher education, the rise of artificial intelligence (AI), ethical stewardship, and other challenges, highlighting how they intersect in our increasingly complex, fast-changing world.

Binary Document Filtering For Retrieval-Augmented Generation, Sreyan Saha

Master’s Dissertations

Retrieval-Augmented Generation (RAG) has become a popular technique to enhance Large Language Models (LLMs) with access to external information sources. However, the success of RAG systems critically depends on the relevance and quality of the retrieved documents. In particular, supplying irrelevant or noisy context can lead to degraded downstream generation quality. To address this, our project focuses on improving the document filtering stage in a RAG pipeline through binary relevance classification — deciding whether a retrieved document is suitable to include in the final context window based on its usefulness in directly answering the user query. We explore a wide …

Nexusgs: Sparse View Synthesis With Epipolar Depth Priors In 3d Gaussian Splatting, Yulong Zheng, Zicheng Jiang, Shengfeng He, Yandu Sun, Junyu Dong, Huaidong Zhang, Yong Du

Research Collection School Of Computing and Information Systems

Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations. Exploiting the inherent epipolar geometry of 3DGS, our method introduces a novel point cloud densification strategy that initializes 3DGS with a dense point cloud, reducing randomness in point placement while preventing over-smoothing and overfitting. Specifically, …

Modfinity: Unsupervised Domain Adaptation With Multimodal Information Flow Intertwining, Shanglin Liu, Jianming Lv, Jingdan Kang, Huaidong Zhang, Zequan Liang, Shengfeng He

Research Collection School Of Computing and Information Systems

Multimodal unsupervised domain adaptation leverages unlabeled data in the target domain to enhance multimodal systems continuously. While current state-of-the-art methods encourage interaction between sub-models of different modalities through pseudo-labeling and feature-level exchange, varying sample quality across modalities can lead to the propagation of inaccurate information, resulting in error accumulation. To address this, we propose Modal-Affinity Multimodal Domain Adaptation (MODfinity), a method that dynamically manages multimodal information flow through fine-grained control over teacher model selection, guiding information intertwining at both feature and label levels. By treating labels as an independent modality, MODfinity enables balanced performance assessment across modalities, employing a novel …

Towards Uncertainty Aware Task Delegation And Human-Ai Collaborative Decision-Making, Min Hun Lee, Martyn Zhe Yu Tok

Research Collection School Of Computing and Information Systems

Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. In this paper, we investigate the utility of exploring distance-based uncertainty scores for task delegation to AI and describe how these scores can be visualized through embedding representations for human-AI decision-making. After developing an AI-based system for physical stroke rehabilitation assessment, we conducted a study with 19 health professionals and 10 students in medicine/health to understand the effect of exploring distance-based uncertainty scores on users’ reliance on AI. Our findings showed that distance-based uncertainty scores outperformed traditional …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …

Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …

Adapting Large Language Models For Parameter-Efficient Log Anomaly Detection, Ying Fu Lim, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems. Although Large Language Models (LLMs) have shown tremendous success in various fields, the use of LLMs in enabling the detection of log anomalies is largely unexplored. This work aims to fill this gap. Due to the prohibitive costs involved in fully fine-tuning LLMs,we explore the use of parameter-efficient fine-tuning techniques (PEFTs) for adapting LLMs to LAD.To have an in-depth exploration of the potential of LLM-driven LAD, we present a comprehensive investigation of leveraging two of the most …

Spos: An Attestation Solution For The Detection And Mitigation Of Point Of Sale Malware, Damian Singh Dhesi

Master's Theses

Securing 95% of card present transactions, accounting for billions of transactions a year, has made EMV the premier protocol for card-based payment. Created by and named after Europay, Mastercard, and Visa, the EMV protocol provides multiple solutions to resolve security concerns with the outdated, swipe-based, magnetic stripe payment. Such solutions are Chip and PIN which provides a more secure transaction at a significant time cost and EMV contactless which provides improved security to Chip and PIN at greater ease of use with its quick, tap-to-pay based payment. However, regardless of how secure the EMV protocol makes the card side of …

A Decision Support System For Conference Session Selection Using Natural Language Processing, Tillman E. Erb

Master's Theses

Conference attendees are faced with selecting from hundreds to thousands of presentations and sessions in pursuit of new findings and methods relevant to their area of interest, an overwhelming amount of information from which to clearly make a decision. To address this, we developed a decision support system leveraging natural language processing (NLP) techniques such as semantic matching. By creating and matching embeddings of conference presentation abstracts and titles, the application provides improved query matching compared to keyword searching. We introduce Session Scout, a novel conference decision support system built upon a semantic retrieval framework. Session Scout is designed to …

Filling Gaps In Scientific Data Sets Using Physics Informed Neural Networks: A Case Study In Velocity Fields, Ellen Saunders

Master's Theses

Gaps in scientific data sets are a persistent issue for researchers in a variety of fields, and while nothing makes up for missing out on real data, well-simulated synthetic data can be a useful tool. In the world of image processing, machine learning techniques have become quite sophisticated at taking an image with a missing component and filling in that space with something believable. The aim of this thesis is to take machine learning techniques similar to what gets used in image processing and repurpose them to infill gaps in scientific data sets in a realistic manner. This thesis compares …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Opening The Black Box With Regal: A Novel Explainable Ai Approach To Uncover Key Predictors In Search And Rescue Success, Brandon Hyunjun Kim

Master's Theses

The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL …

Contract Quality Feature Extraction Using Llm, Aaron C. Washington

Theses and Dissertations

This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.

Specialization Or Diversification? Creators’ Strategies On User-Generated Content Platforms, Ziwei Ye

Theses and Dissertations in Business Administration

Recent advancements in digital platforms have reshaped content creation and distribution. User-generated content (UGC), created and shared by internet users, is transforming entertainment, communication, and information sharing. The rise of UGC has fueled the growth of the "creator economy"—an ecosystem of creators, users, and advertisers facilitated by platforms such as YouTube and TikTok. While prior research has primarily explored how UGC platforms incentivize content quantity and quality, this study advances the literature by examining how creators' content strategies influence consumer attention and how platform mechanisms shape this relationship, offering new insights into the interplay between creator behavior and platform design. …

Efficient Blending Of Large Language Models, Sandeep Chatterjee

Master’s Dissertations

Due to the limited capabilities of single Large Language Models (LLMs), multiple LLMs can be employed in tandem for better reliability of answers. Blending refers to combining the strengths of various LLMs to make use of their complementary capabilities for generating high-quality responses. It is a non-trivial problem, and the task becomes even more difficult when aiming for minimal latency and supervising the blending components. The standard framework, LLM- Blender, approaches this in three stages: response generation, candidate selection via ranking, and response fusion through summarization. However, this pipeline faces two critical limita- tions—high latency due to repeated ranking steps, …

Geometry Based Uav Trajectory Planning For Mixed User Traffic In Mm Wave Communication, Sk Abid Hasan

Master’s Dissertations

Unmanned aerial vehicle (UAV) assisted communication is a revolutionary technology that has been recently presented as a potential candidate for beyond fifth-generation millimeter wave (mmWave) communications. Although mmWaves can o↵er a notably high data rate, their high penetration and propagation losses mean that line of sight (LoS) is necessary for e↵ective communication. Due to the presence of obstacles and user mobility, UAV trajectory planning plays a crucial role in improving system performance. In this work, we propose a novel computational geometry-based trajectory planning scheme by considering the user mobility, the priority of the delay sensitive ultra-reliable low-latency communications (URLLC) and …

Word Level Attack For Text Ranking, Tanmay Karmakar

Master’s Dissertations

Neural Ranking Models (NRMs) have become state-of-the-art in information retrieval, demonstrating remarkable effectiveness across various search and ranking tasks. However, their increasing deployment in real-world systems raises critical concerns about their robustness and susceptibility to adversarial attacks. This project investigates the fragility of modern NRMs by proposing and evaluating a document perturbation method based on targeted, single-word perturbation. Our approach strategically identifies an influential word depending on the query to be substituted or added in the document. We have done experiments on benchmark datasets to assess the impact of these minimal perturbations on ranking performance. Our findings reveal that even …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Alibaba: Building Advanced Intellectual Property Governance For E-Commerce Marketplaces, Liang Chen, Sin Mei Cheah, Can Huang, Guoqiao Liu

Asian Management Insights

How the global e-commerce powerhouse harnessed artificial intelligence (AI) to balance innovation and intellectual property (IP) rights protection.

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Exploiting Compiler-Introduced Vulnerabilities In C: A Cross-Compiler And Cross-Architecture Analysis Of Undefined Behavior, Erik Mccutchen

Master's Theses

Compilers are a critical component in generating secure software across engineering disciplines. However, languages like C that permit undefined behavior introduce a fundamental tension between the compiler’s interpretation of undefined behavior and the security of the generated code. This tension can result in security vulnerabilities that, from the programmer's perspective, are ``created'' by the compiler. The widespread use of these languages, combined with the complexity of modern optimizations and limited developer visibility into compiler behavior, makes these vulnerabilities both pervasive and difficult to detect.

Building on prior work, this thesis refines a dataset of C code snippets that exhibit Compiler-Introduced …

Minifying Deep Denoising Networks With Knowledge Distillation, Antonio L. Rozzi

Master's Theses

Hearing loss is a prevalent condition, affecting hundreds of millions globally, with a higher incidence among older adults. While hearing aids are the standard treatment, the majority of those who could benefit from hearing aids choose not to wear them, attributing this decision in large part to their inability to perform well in conversations in large groups and in noisy situations. To date, no denoising systems on commercial hearing aids are able to improve speech intelligibility. Recent advances in artificial intelligence research have shown that large deep-learning models can in fact improve speech intelligibility by removing background noise from audio. …

Enhancing Energy Consumption Forecasting For Electric Vehicle Charging Stations With Time Series Dense Encoder (Tide), Amril Nazir, Abdul Khalique Shaikh, Aftab Ahmed Khan, Abdul Salam Shah, Nadia Khalique

All Works

The increasing adoption of electric vehicles has led to the installation of charging stations in various locations in major cities worldwide. This study focuses on energy consumption forecasting for Boulder, Nevada, United States electric vehicle charging stations. Efficient management of energy resources at these charging points is crucial for optimizing resource utilization and reducing charging time. While existing literature has focused on energy consumption prediction in smart homes and grids, the significance of electric charging points in smart cities must be considered. The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the …

Adapting Teaching And Learning With Existing Generative Ai By Higher Education Students: Comparative Study Of Zayed University And King Abdulaziz University, Dina Tbaishat, Ghada Amoudi, Maha Elfadel

All Works

This study examines the role of higher education students’ perceptions in adapting Generative AI (GenAI) tools for teaching and learning, with a particular focus on the factors that influence student satisfaction and engagement. A comparative approach is adopted, exploring student experiences at Zayed University (ZU) in the UAE and King Abdulaziz University (KAU) in Saudi Arabia. The principal variables of interest, including Expected Benefits (EB), University Support (US), Ethical Awareness (EA), and Technology Self-Efficacy (TSE), are examined, with particular attention to their direct and mediated influences through Behavioral Intention (BI) on student satisfaction (SS). Data were collected through surveys and …

Glacial Lakes Segmentation Using Multispectral Remote Sensing Data And Deep Learning Models, Debankan Das

Master’s Dissertations

The identification and delineation of glacial lakes through segmentation is crucial for tracking glacial changes and evaluating potential dangers from sudden flood events (GLOFs). These floods can severely impact populated areas and man-made structures downstream. Recent advances in high-quality satellite imagery have sparked increased attention toward using advanced machine learning methods, particularly deep learning, to enable precise and automated glacial lake detection. In this study, we explore the effectiveness of deep learning-based pointwise semantic segmentation for glacial lake mapping using multisource remote sensing imagery, including both optical and synthetic aperture radar (SAR) data. We experiment with a novel stack combination …

Computerized Diagnostic Decision Support Systems-Isabel Pro Versus Chatgpt-4 Part Ii, Joe M Bridges, Xiaoqian Jiang, Michael Ige, Oluwatoniloba Toyobo

Faculty, Staff and Student Publications

Objective: Does a Tree-of-Thought prompt and reconsideration of Isabel Pro's differential improve ChatGPT-4's accuracy; does increasing expert panel size improve ChatGPT-4's accuracy; does ChatGPT-4 produce consistent outputs in sequential requests; what is the frequency of fabricated references?

Materials and methods: Isabel Pro, a computerized diagnostic decision support system, and ChatGPT-4, a large language model. Using 201 cases from the New England Journal of Medicine, each system produced a differential diagnosis ranked by likelihood. Statistics were Mean Reciprocal Rank, Recall at Rank, Average Rank, Number of Correct Diagnoses, and Rank Improvement. For reproducibility, the study compared the initial expert panel run …

Digital Resurrection Of Thonis-Heracleion: Technological Advances In Underwater Archaeology And A Speculative Ai-Driven Reconstruction Methodology, James Hutson, Passent Chahine

Faculty Scholarship

This article synthesizes past archaeological research on the submerged Egyptian city of Thonis-Heracleion, critically reviewing excavations and technological interventions deployed since its rediscovery by Franck Goddio and the IEASM team. Situated approximately 10 meters beneath Aboukir Bay near Alexandria, the city represents a significant nexus of Greek and Egyptian cultural heritage, vividly documented in classical sources such as Herodotus and Strabo. Prior excavations have recovered temple complexes, colossal statues, ritual artifacts, and an extensive array of ancient shipwrecks, mapping only a fraction of the extensive site. These investigations utilized pioneering geophysical methods, including multibeam sonar, side-scan sonar, and photogrammetry, establishing …

Coda: A Digital System For Generation Of Piano Practice Exercises From Symbolic Music Notation, Annie Tang

Computer Science Senior Theses

Effective practice remains one of the greatest challenges in music education, yet cur- rent digital music tools primarily support only passage engagement or surface-level feedback, failing to provide proactive guidance for overcoming technical challenges within piano repertoire. This thesis presents Coda, a digital system that generates custom piano practice exercises from symbolic music notation based on established piano pedagogy principles that have historically been taught orally.

Unlike existing tools that only provide a viewable symbolic music notation display or only an interface to take notes and record a practice session, Coda uses rule- based algorithmic transformations rooted in pedagogical logic …