Digital Commons Network^™

On Learning Informative Trajectory Embeddings For Imitation, Classification And Regression, Zichang Ge, Changyu Chen, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

In real-world sequential decision making tasks like autonomousdriving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitation, classification,and clustering. For example, self-driving cars must replicate humandriving behaviors, while robots and healthcare systems benefitfrom modeling decision sequences, whether or not they come fromexpert data. Existing trajectory encoding methods often focus onspecific tasks or rely on reward signals, limiting their ability togeneralize across domains and tasks.Inspired by the success of embedding models like CLIP andBERT in static domains, we propose a novel method for embeddingstate-action trajectories into a latent space that captures the skillsand competencies in the …

Exploring The Potential Of Large Language Models For Heterophilic Graphs, Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have presented significant opportunities to enhance various machine learning applications, including graph neural networks (GNNs). By leveraging the vast open-world knowledge within LLMs, we can more effectively interpret and utilize textual data to better characterize heterophilic graphs, where neighboring nodes often have different labels. However, existing approaches for heterophilic graphs overlook the rich textual data associated with nodes, which could unlock deeper insights into their heterophilic contexts. In this work, we explore the potential of LLMs for modeling heterophilic graphs and propose a novel two-stage framework: LLM-enhanced edge discriminator and LLM-guided edge reweighting. In the first …

“Ronaldo’S A Poser!”: How The Use Of Generative Ai Shapes Debates In Online Forums, Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Online debates can enhance critical thinking but may escalate into hostile attacks. As humans are increasingly reliant on Generative AI (GenAI) in writing tasks, we need to understand how people utilize GenAI in online debates. To examine the patterns of writing behavior while making arguments with GenAI, we created an online forum for soccer fans to engage in turn-based and free debates in a post format with the assistance of ChatGPT, arguing on the topic of "Messi vs Ronaldo". After 13 sessions of two-part study and semi-structured interviews with 39 participants, we conducted content and thematic analyses to integrate insights …

Iot In Sustainability And Iot In The Ai And Metaverse Age, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is a modern technology that has gained large popularity and is still developing. Connecting heterogeneous devices, such as phones, vehicles, and household appliances, IoT has brought convenience to our lives. Further, IoT plays a significant role in enhancing environmental sustainability. It provides timely data about different devices and enables users and managers to directly control the objects. IoT can optimize the existing energy systems and promote the usage of renewable technologies. In this paper, we discuss how IoT supports green initiatives (i.e., how it is applied in different sectors), how it can be "green" itself …

Flexfl: Flexible And Effective Fault Localization With Open-Source Large Language Models, Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang, Ming Liang, David Lo

Research Collection School Of Computing and Information Systems

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, …

Sigscope: Detecting And Understanding Off‑Chain Message Signing‑Related Vulnerabilities In Decentralized Applications, Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan

Research Collection School Of Computing and Information Systems

In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks.This paper presents the first systematic study to uncover and characterize the security issues in off-chain message signing schemes and the DApps built atop them. We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. We evaluate …

Real-Time Rectifying Flight Control Misconfiguration Using Intelligent Agent, Ruidong Han, Shangzhi Xu, Juanru Li, Elisa Bertino, David Lo, Jianfeng Ma, Siqi Ma

Research Collection School Of Computing and Information Systems

Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem—misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because (1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; (2) the response to each configuration varies under different flying scenarios.In this article, we propose and implement a novel rectification …

Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-Awareness In Computing, Tadashi Okoshi, Zexiong Gao, Yi Zhen Tan, Takumi Karasawa, Takeshi Miki, Wataru Sasaki, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In Affective computing, recognizing users’ emotions accurately is the basis of affective human–computer interaction. Understanding users’ interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users’ interoception challenging. This study aims to determine other forms of data that can explain users’ interoceptive or similar states in their real-world lives and propose a novel hypothetical concept “cyberoception,” a new sense (1) which …

Hierarchical Frameworks For Scaling-Up Multi-Agent Coordination, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning has emerged as a powerful framework for developing collaborative behaviors in autonomous systems. However, existing MARL methods often struggle with scalability in terms of both the number of agents and decision-making horizons. My research focuses on developing hierarchicalapproaches to scale up MARL systems through two complementary directions: structural scaling by increasing the number of coordinated agents and temporal scaling by extending planning horizons. My initial work introduced HiSOMA, a hierarchical framework integrating self-organizing neural networks with MARL forlong-horizon planning, and MOSMAC, a benchmark for evaluating MARL methods on multi-objective MARL scenarios. Building on these foundations, my recent …

Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art …

Toward Better Comprehension Of Breaking Changes In The Npm Ecosystem, Dezhen Kong, Jiakun Liu, Lingfeng Bao, David Lo

Research Collection School Of Computing and Information Systems

Code evolution is prevalent in software ecosystems, which can provide many benefits, such as new features, bug fixes, security patches, while still introducing breaking changes that make downstream projects fail to work. Breaking changes cause a lot of effort to both downstream and upstream developers: downstream developers need to adapt to breaking changes and upstream developers are responsible for identifying and documenting them. In the NPM ecosystem, characterized by frequent code changes and a high tolerance for making breaking changes, the effort is larger.For better comprehension of breaking changes in the NPM ecosystem and to enhance breaking change detection tools, …

Seaexam And Seabench: Benchmarking Llms With Local Multilingual Questions In Southeast Asia, Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing

Research Collection School Of Computing and Information Systems

This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evalu ate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real world scenarios from SEA regions. SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses sub jects such as local history and literature. In contrast, SeaBench is crafted around multi turn, open-ended tasks that reflect daily inter actions within SEA communities. Our evalua tions demonstrate that SeaExam and SeaBench more effectively discern LLM performance on …

Using Machine Learning To Detect Vault (Anti-Forensic) Apps, Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

Research outputs 2022 to 2026

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault …

Improving Home Security Through User Centered Device Positioning, Lalith Nadipalli

Theses and Dissertations

In today’s world, where technology is advancing rapidly and security threats are becoming more complex, the need for effective home safety measures is more critical than ever. Homeowners are increasingly turning to a variety of smart devices, such as smoke detectors, carbon monoxide detectors, and security cameras, to protect their living spaces against potential dangers like burglary, fire, and environmental hazards. These devices offer essential protection, acting as both early warning systems and visual surveillance tools. However, their effectiveness largely hinges on how well they are placed within the home. Proper placement of these safety devices ensures that they provide …

Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions, Tan-Hanh Pham

Theses and Dissertations

Deep learning has revolutionized numerous fields by enhancing precision, automation, and decision-making capabilities. This dissertation explores its applications in agriculture and medical image processing, introducing novel methodologies to improve accuracy and efficiency in these domains. These fields hold critical societal importance -- agriculture underpins global food security and sustainability, while medical imaging drives advancements in diagnostics and personalized healthcare, both benefiting significantly from data-driven innovations. In agriculture, deep learning is applied to precision spray systems through droplet analysis. Specifically, a generative model is designed to create synthetic droplet images, addressing the challenge of limited training samples, which are expensive …

Cross-Dataset Fairness Evaluation Of Transformer-Based Sentiment Models, Sara Zuiran

Theses and Dissertations

With the growing exploration of Natural Language Processing (NLP) systems in decision-making environments, it is essential to evaluate technical and ethical aspects of the dataset and the NLP model to improve fairness. To assess fairness, the thesis examines demographic imbalances in sentiment classification models by evaluating transformer-based models fine-tuned on the Stanford Sentiment Treebank version 2 dataset (SST-2) against the demographically annotated Comprehensive Assessment of Language Model dataset (CALM). This work identifies performance disparities in sentiment prediction across demographic groups by examining sensitive attributes such as gender and race. The study evaluates both the RoBERTa and MentalBERT transformer models using …

Bridging The Gap: Enhancing Devops Security Through Comprehensive Threat Modeling, Ashutosh Jagdish Sonar

Theses and Dissertations

Today, security is an essential component of software development, especially in DevOps environments where rapid and continuous product release cycles are common. Systems are vulnerable to new attacks because traditional security approaches often cannot keep up with the pace of change. The threat modeling approaches used in DevOps are examined in this thesis, along with their advantages, disadvantages, and suitability for use in current software development processes. Well-known frameworks including STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege), Attack Trees, LINDDUN (Linking, Identifying, Non-Repudiation, Detecting, Data Disclosure, Unawareness, and Non-Compliance.), Practical Threat Analysis (PTA), …

The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed

Research Collection School of Social Sciences

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 …

Analytics Insights From Text: Machine Learning, Ai, And Sentiment Analysis On Beige Books, Charlie Smith

Graduate Theses and Dissertations (2019 - present)

Business analytics is about drawing actionable insights from data. These distinct but connected essays represent a novel approach to explore how natural language processing (NLP) advances and machine learning can transform unstructured text data into actionable conclusions. Essay 1 provides a broad framework. Essay 2 strengthens the sentiment analysis with the most recent artificial intelligence methodologies for capturing nuanced sentiment in complex texts. Essay 3 applies those insights to forecast recessions using topics that can be readily interpreted and applied.

The research demonstrates how these methodologies can be applied to enhance understanding of the same dataset, Beige Books. Published by …

Comparative Analysis Of Regression And Random Forest Models For Player Performance Prediction In The Mls, Joshua Clement Madeti

Senior Honors Theses

Advanced technology and analytics have transformed the world and have benefited several industries throughout, the sport industry being one of them. Data is constantly generated during sports and requires post-game or post-season analysis which is crucial to team and player success. In this paper, the researcher will focus on the impact of analytics on soccer and soccer players. With over three billion active fans, soccer is the most famous sport in the world yet, when it comes to analytics, it is lagging. The thesis includes a comparative study of multiple linear regression and random forest regression to explore whether these …

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

Fairness And Robustness In Decentralized Federated Learning, Kaichuang Zhang

Theses and Dissertations

Federated Learning (FL) has emerged as a privacy-preserving paradigm that allows multiple clients to collaboratively train a machine learning model without sharing raw data. However, traditional FL relies on a central server for model aggregation, which introduces a single point of failure and makes the system vulnerable to server-side attacks or breakdowns. To address these limitations, Decentralized Federated Learning (DFL) has been proposed, eliminating the need for a central server and enhancing system resilience. Despite these advantages, DFL faces critical challenges related to fairness and robustness, especially under non-i.i.d. data distributions and adversarial conditions. In this thesis, we propose a …

Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee

Chemical Engineering Undergraduate Honors Theses

This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …

Understanding The Impact Of Ransomware On Biotechnology, Tswvyim Vang

Electronic Theses, Projects, and Dissertations

Biotechnology encompasses the use of research on both biology and technology to create products that can advance in areas such as healthcare and agriculture. Because of the technology and products that arise from the use of biotechnology, the industry is especially targeted by cyber-attacks. A prominent type of cyber-attack that is utilized by cyber criminals on biotechnology is ransomware. The goal of this project is to determine the impact of ransomware on biotechnology. The research questions posed are: Q1) What are real examples of ransomware attacks that have occurred on biotechnology companies and what patterns can be identified by these …

On Bed Posture Recognition Using Deep Learning With Pressure Sensors, Farheen Akhter Ms

Electronic Theses, Projects, and Dissertations

In healthcare applications such as disease prevention, sleep quality evaluation, and patient monitoring, bed posture recognition is essential. Using pressure sensor arrays placed on top of or embedded in mattresses, this study investigates the application of deep learning models for non-invasive posture classification. Although they have been widely employed, traditional machine learning approaches like support vector machines (SVM) and k-nearest neighbors (KNN) sometimes struggle with feature extraction and real-time performance necessitating considerable processing resources. I implemented a model using conventional approaches to get over these restrictions, then fine-tuned it using the following deep learning architectures for bed posture recognition: ResNet-50, …

Artificial Intelligence In Modern Game Development, Karan Raval

ART 108: Introduction to Games Studies

Artificial intelligence has transformed the landscape of game development in ways early pioneers could only imagine. In my project I explore four major phases of AI evolution: classical AI methods, the rise of machine learning, the advent of transformer architectures, and a spirited debate about whether transformers truly think. To illustrate these shifts I use examples like MYCIN, which helped doctors decide treatments, and Deep Blue, which beat a world chess champion. You’ve probably lost track of time exploring an open world that adapts to your choices, right? That sense of immersion is powered by AI behind the scenes. From …

Learning Through Online Participation: From Breakout Rooms To Discord, Makayla Moster

All Dissertations

For software development teams, teamwork is an essential part of their day-to-day lives. However, due to the aftermath of the COVID-19 pandemic, more companies have allowed employees to have more hybrid and remote work options than before. As more companies are adopting hybrid and remote workstyles, we need to ensure that we are preparing the next batch of young software developers to conduct teamwork in remote and hybrid settings. In this dissertation, I address the tools students use for teamwork and how to improve their teamwork inside and outside of the classroom. I present my research on improving student experiences …

A System And Method For Measuring Spatially Varying Surface Appearances With A Study Of Feathers, Jessica Baron-Lis

All Dissertations

Real-world materials, particularly biological structures such as feathers exhibit complex appearances that vary spatially across their surfaces. The field of computer graphics provides a means of understanding such surfaces through material modeling which uses both analytical models and data acquired from light-surface interactions. There are many efforts within the past decade in measuring materials for graphics, but common limitations in these works include not accounting for spatially varying properties and reliance on neural networks and synthetic datasets.

Feathers from modern birds present diverse appearances due to how light interacts with their unique hierarchical microstructures. Variations in those structures lead to …

A Systemic Approach To Maximize Heterogeneous System Performance, Thomas L. Randall

All Dissertations

Continuous increases in high performance computing (HPC) throughput have served as catalysts for industry and scientific advancement in countless manners that have fundamentally shaped our modern world. Our demands on compute resources continue to scale, but the limitations of Ahmdal’s law and Dennard scaling have proven increasingly difficult to overcome when approached solely through hardware or software design. Furthermore, many HPC applications fail to utilize the collective system’s performance, even on the most advanced supercomputers.

However, the resurgence of AI in the industry has promoted an explosion of hardware and software codesign that have fueled massive improvements in GPU design …

Towards Explainable And Robust Nlp: Neutrosophic Probability Augmentation In Text Classification, Nabil M. Abdel-Aziz, Mahmoud Ibrahim, Khalid A. Eldrandaly

Neutrosophic Systems with Applications

The rapid growth of textual data necessitates advanced text classification models. However, traditional methods struggle with ambiguity and uncertainty in natural language, reducing classification reliability. To address this, we integrate neutrosophic logic, which explicitly models truth, indeterminacy, and falsity, into a DistilBERT-based text classification framework. Additionally, we employ data augmentation using synonym replacement to enhance generalization. Our approach is evaluated on the AG News dataset, classifying articles into four categories: World, Sports, Business, and Science/Technology. By incorporating neutrosophic attributes, the proposed framework assesses text quality, mitigates uncertainty, and improves robustness against ambiguous inputs. Experimental results demonstrate an accuracy of 94.10%, …