Digital Commons Network^™

Leveraging Blockchain Technology To Revamp The Vehicle Electrification Journey: Perspectives Of Accountability And Economic Circularity, Eva Escobar Brown

Electronic Theses, Projects, and Dissertations

The automotive industry is undergoing a significant transition accelerated by global emission regulations for a phase out of internal combustion engines (ICEs) and a transition toward the adoption of electric vehicles (EVs). While regulatory measures and incentivized adoption for EVs presents opportunities for reducing emissions and promoting sustainability, it also poses complex challenges. The EV industry faces potential production challenges, particularly in the sourcing, manufacturing, and lifecycle management of critical minerals and raw materials for electric vehicle batteries (EVBs). With a heavy reliance on a steady and diversified supply of critical minerals such as lithium, cobalt and rare earth elements, …

Ethical Imperatives In Ai-Driven Educational Assessment: Framework And Implications, Ming Soon Tristan Lim

Dissertations and Theses Collection (Open Access)

This dissertation embarks on an extensive exploration of the ethical challenges emerging from the integration of AI in educational assessments. It uncovers the complex interplay between AI and the ethical imperatives these technologies pose within educational assessments.

Amidst the rapid development of AI-enabled educational technologies, such as Ubiquitous, Adaptive, and Immersive technologies, this research identifies a notable gap in literature specifically concerning the ethical imperatives and implications of AI in educational assessments. Addressing this gap, the dissertation has three primary objectives: to comprehend and analyze the underpinning educational technologies driving assessments, to elucidate the intricate relationship between AI, ethics, and …

Enabling And Optimizing Multi-Modal Sense-Making For Human-Ai Interaction Tasks, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage

Dissertations and Theses Collection (Open Access)

The rapid pace of adoption of mixed-reality in tandem with advances in NLP and computer vision have opened up unprecedented opportunities for more naturalistic interaction interfaces which underpin Human-AI collaborative applications such as spatial computing and interactive conversational agents. One notable example is the emergence of interactive virtual assistants, which facilitate more natural communication of instructions and queries through modalities like voice and text. This trend is driving the development of innovative ubiquitous, mixed-reality computing applications. Such interactive, natural communication is also critical to support advances in human-robot interactive co-working, across a variety of industrial, commercial and home environments. Conventional …

Enabling Criticality-Aware Optimized Machine Perception At The Edge, Ila Nitin Gokarn

Dissertations and Theses Collection (Open Access)

Cyber-physical systems and applications have fundamentally changed people and processes in the way they interact with the physical world, ushering in the fourth industrial revolution. Supported by a variety of sensors, hardware platforms, artificial intelligence and machine learning models, and systems frameworks, CPS applications aim to automate and ease the burden of repetitive, laborious, or unsafe tasks borne by humans. Machine visual perception, encompassing tasks such as object detection, object tracking and activity analysis, is a key technical enabler of such CPS applications. Efficient execution of such machine vision perception tasks on resource-constrained edge devices, especially in terms of ensuring …

Improving The Performance Of Wi-Fi Indoor Localization In Both Dense And Unknown Environments, Quang Truong Hai

Dissertations and Theses Collection (Open Access)

Indoor localization is important for various pervasive applications, garnering considerable research attention over recent decades. Despite numerous proposed solutions, the practical application of these methods in real-world environments with high applicability remains challenging. One compelling use case for building owners is the ability to track individuals as they navigate through the building, whether for security, customer analytics, space utilization planning, or other management purposes. However, this task becomes exceedingly difficult in environments with hundreds or thousands of people in motion. Conversely, the need to track oneself’s location is also meaningful from the perspective of individuals traversing in crowded spaces. These …

Satellite Image Analysis And Sidewalk Classification Using Deep Learning Models, Subeksha Khanal

Master's Theses

Lack of sidewalk pavement can contribute to pedestrian fatalities and injuries in the USA. Although many researchers have conducted research on sidewalk detection, there are not many publicly available datasets that we would work on for sidewalk classification. In this study, I conducted classification tasks using pretrained CNN models including VGG16 and ResNet50. I extended these models by adding custom layers at the top of pretrained layers, employing various techniques to improve the classification accuracy.

The dataset comprises 4,731 images of sidewalk based on occlusion levels, including images where the sidewalk is visible from overhead and instances where sidewalk is …

Legislative Recommendations On Biometric Security And Privacy Jurisprudence, Joshua M. Morrow

All Student Scholarship

This project focuses on the prevalence of biometrics today, their various applications, and the biometric laws and legislations in place in the United States (U.S.) and Maine. Due to various threats and vulnerabilities imposing risk on collecting and using peoples’ biometric data, sufficient cyber protections related to citizens’ privacy rights, ethical control, and security of personally identifiable information (PII) must become necessary components of contemporary biometric laws and legislation. Without such explicit cyber protections, citizens participate in and comply with various technical domains and entities, such as private companies and governmental agencies, with minimal awareness or comprehension that their sensitive …

Policy-Based Remote User Authentication From Multi-Biometrics, Yangguang Tian, Yingjiu Li, Robert H. Deng, Guomin Yang, Nan Li

Research Collection School Of Computing and Information Systems

In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user's authentication request if and only if the user's multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies.

Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski

Theses and Dissertations

Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …

Vaid: Indexing View Designs In Visual Analytics System, Lu Ying, Aoyu Wu, Haotian Li, Zikun Deng, Ji Lan, Jiang Wu, Yong Wang, Huamin Qu, Dazhen Deng, Yingcai Wu

Research Collection School Of Computing and Information Systems

Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up …

Compositional Policy Learning In Stochastic Control Systems With Formal Guarantees, Dorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SPECTRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided …

Social Balance On Networks: Local Minima And Best-Edge Dynamics, Krishnendu Chatterjee, Jakub Svoboda, Dorde Zikelic, Andreas Pavlogiannis, Josef Tkadlec

Research Collection School Of Computing and Information Systems

Structural balance theory is an established framework for studying social relationships of friendship and enmity. These relationships are modeled by a signed network whose energy potential measures the level of imbalance, while stochastic dynamics drives the network toward a state of minimum energy that captures social balance. It is known that this energy landscape has local minima that can trap socially aware dynamics, preventing it from reaching balance. Here we first study the robustness and attractor properties of these local minima. We show that a stochastic process can reach them from an abundance of initial states and that some local …

Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-training and fine-tuning paradigm in Policy-Space Response Oracles (PSRO) to improve scalability in solving large-scale PEGs. However, these methods primarily focus on specific PEGs with fixed initial conditions that may vary substantially in real-world scenarios, which significantly hinders the applicability of the traditional methods. To address this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs. Our contributions are threefold: …

Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas

Research Collection School Of Computing and Information Systems

In this article, we describe our experience in developing an undergraduate Linear Algebra course tailored to highlight its relevance and applicability in Computer Science. Over the course of three years, the course transitioned from a traditional direct-instruction format to a flipped-classroom design, resulting in positive student learning outcomes. This article covers the course design philosophy, its syllabus, learning objectives, and the incorporation of both quantitative and qualitative student feedback in shaping the course. Furthermore, the article shares the insights gleaned from our experience, which can serve as best practices for instructors aiming to deliver a successful Linear Algebra course for …

The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo

Research Collection School Of Computing and Information Systems

This article presents the design and implementation of the Grader, a grading assistant application deployed for a Web Application Development course at our school. The Grader is equipped to handle various logistical aspects of lab tests, including file management, consistent application of rubrics, and auto-grading of questions with test cases. Additionally, it incorporates heuristic rules to detect cheating attempts. We anticipate that the Grader will find widespread utility in programming courses where lab tests serve as summative assessments. Developed within the same programming environment taught in the class, the Grader also serves as a pedagogical tool, demonstrating to students a …

Exploring Diffusion Time-Steps For Unsupervised Representation Learning, Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I-Chao Chang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Model (DM) in unsupervised learning of the modular attributes. We build a theoretical framework that connects the diffusion time-steps and the hidden attributes, which serves as an effective inductive bias for unsupervised learning. Specifically, the forward diffusion process incrementally adds Gaussian noise to samples at each time-step, which essentially collapses different samples into similar ones by losing attributes, e.g., fine-grained attributes such as texture are lost with less noise added (i.e., early time-steps), while coarse-grained ones such …

Regret-Based Defense In Adversarial Reinforcement Learning, Roman Belaire, Pradeep Varakantham, Thanh Hong Nguyen, David Lo

Research Collection School Of Computing and Information Systems

Deep Reinforcement Learning (DRL) policies are vulnerable to adversarial noise in observations, which can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about traffic signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) can be fatal. Leading existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on (a) regularization approaches that make expected value objectives robust by adding adversarial loss terms; or (b) employing "maximin'' (i.e., maximizing the minimum value) notions of robustness. While regularization approaches are adept at reducing the probability …

Multiple Continuous Top-K Queries Over Data Stream, Rui Zhu, Yujin Jia, Xiaochun Yang, Baihua Zheng, Bin Wang, Chuanyu Zong

Research Collection School Of Computing and Information Systems

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Hjg: An Effective Hierarchical Joint Graph For Anns In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Ruiyao Ma, Baihua Zheng, Jingwen Zhao

Research Collection School Of Computing and Information Systems

Owing to the widespread deployment of smartphones and networked devices, massive amount of data in different types are generated every day, including numeric data, locations, text data, images, etc. Nearest neighbour search in multi-metric spaces has attracted much attention, as it can accommodate any type of data and support search on flexible combinations of multiple metrics. However, most existing methods focus on single metric queries, failing to answer multi-metric queries efficiently due to the complex metric combinations. In this paper, for the first time, we study the approximate nearest neighbour search (ANNS) in multi-metric spaces, and propose HJG, a hierarchical …

Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models, Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang

Research Collection School Of Computing and Information Systems

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models …

Scaling Up Cooperative Multi-Agent Reinforcement Learning Systems, Minghong Geng

Research Collection School Of Computing and Information Systems

Cooperative multi-agent reinforcement learning methods aim to learn effective collaborative behaviours of multiple agents performing complex tasks. However, existing MARL methods are commonly proposed for fairly small-scale multi-agent benchmark problems, wherein both the number of agents and the length of the time horizons are typically restricted. My initial work investigates hierarchical controls of multi-agent systems, where a unified overarching framework coordinates multiple smaller multi-agent subsystems, tackling complex, long-horizon tasks that involve multiple objectives. Addressing another critical need in the field, my research introduces a comprehensive benchmark for evaluating MARL methods in long-horizon, multi-agent, and multi-objective scenarios. This benchmark aims to …

Explaining Sequences Of Actions In Multi-Agent Deep Reinforcement Learning Models, Phyo Wai Khaing, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper introduces a method to explain MADRL agents’ behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents’ sequences of actions as memory episodes wherein an aggregating memory retrieval can generalize them into a concise abstract representation of collective strategies. To assess the effectiveness of our method, we applied it to explain the actions of QMIX MADRL agents playing a StarCraft Multi-agent Challenge (SMAC) video game. A user study on the perceived explainability of the extracted strategies indicates that our method can provide comprehensible explanations at various levels of …

Learning Nighttime Semantic Segmentation The Hard Way, Wenxi Liu, Jiaxin Cai, Qi Li, Chenyang Liao, Jingjing Cao, Shengfeng He, Yuanlong Yu

Research Collection School Of Computing and Information Systems

Nighttime semantic segmentation is an important but challenging research problem for autonomous driving. The major challenges lie in the small objects or regions from the under-/over-exposed areas or suffer from motion blur caused by the camera deployed on moving vehicles. To resolve this, we propose a novel hard- class-aware module that bridges the main network for full-class segmentation and the hard-class network for segmenting aforementioned hard-class objects. In specific, it exploits the shared focus of hard-class objects from the dual-stream network, enabling the contextual information flow to guide the model to concentrate on the pixels that are hard to classify. …

Term Importance For Transformer-Based Qa Retrieval : A Case Study Of Stackexchange, Bryan Zhi Yang Tan, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Question-answering (QA) retrieval is the task of retrieving the most relevant answer to a given question from a collection of answers. Various approaches to QA retrieval have been developed recently. One successful and popular model is Contextualized Late Interaction over BERT (ColBERT), a transformer-based approach that adopts a query-document scoring mechanism that retains the granularity of transformer matching, whilst improving on efficiency. However, one key limitation is that it requires further fine-tuning for new query or collection types. In this work, we explore and propose several non-parametric retrieval augmentation methods based on explicit signals of term importance that improve over …

Baffle : Hiding Backdoors In Offline Reinforcement Learning Datasets, Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Kecen Li, Arunesh Sinha, Bowen Xu, Xinwen Hou, David Lo, Tianhao Wang

Research Collection School Of Computing and Information Systems

Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given …

Factored Mdp Based Moving Target Defense With Dynamic Threat Modeling, Megha Bose, Praveen Paruchuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of …

Rule-Guided Counterfactual Explainable Recommendation, Yinwei Wei, Xiaoyang Qu, Xiang Wang, Yunshan Ma, Liqiang Nie, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

To empower the trust of current recommender systems, the counterfactual explanation (CE) method is adopted to generate the counterfactual instance for each input and take their changes causing the different outcomes as the explanation. Although promising results have been achieved by existing CE-based methods, we propose to generate the attribute-oriented counterfactual explanation. Different from them, we aim to generate the counterfactual instance by performing the intervention on the attributes, and then build an attribute-oriented counterfactual explainable recommender system. Considering the correlation and categorical values of attributes, how to efficiently generate the reliable counterfactual instances on the attributes challenges us. To …

Learning To Generate Explainable Stock Predictions Using Self‑Reflective Large Language Models, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. However, the task of stock prediction remains challenging for LLMs, as it requires the ability to weigh the varying impacts of chaotic social texts on stock prices. The problem gets progressively harder with the introduction of the explanation component, which requires LLMs to explain verbally why certain factors are more important …

Discovering Personalized Characteristic Communities In Attributed Graphs, Yudong Niu, Yuchen Li, Panagiotis Karras, Yanhao Wang, Zhao Li

Research Collection School Of Computing and Information Systems

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of …

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …