Digital Commons Network^™

Intent Visualization In Human-Agent Teams, Rahul Tushar Mehta

Theses and Dissertations

Despite advances in autonomous systems, effective collaboration between humans and intelligent agents remains a significant challenge, particularly in shared-control scenarios. This study investigates how intent visualization affects human-agent collaboration in telecollaboration scenarios, examining its impact on team performance, operator trust, and workload. Using a custom simulation environment and Wizard-of-Oz methodology, we conducted an experiment with 13 participants who completed exploration tasks under two conditions: a baseline interface and an enhanced interface with intent visualization. Results showed that while intent visualization did not significantly improve objective performance metrics, it led to a 22.6\% increase in explicit disagreements between operators and the …

Populations Digitally Excluded From Education: Issues, Factors, Contributions And Actions For Policy, Practice And Research In A Post-Pandemic Era, Don Passey, Jean Gabin Ntebutse, Manal Yazbak Abu Ahmad, Janet Cochrane, Simon Collin, Asmaa Ganayem, Elizabeth Langran, Sadaqat Mulla, Ma. Mercedes T. Rodrigo, Toshinori Saito, Miri Shonfeld, Saunand Somasi

Department of Information Systems & Computer Science Faculty Publications

This conceptual paper draws on a wide range of research and policy literature, providing a contemporary view of issues, factors and practices that affect education for digitally excluded populations. Concern for how education for digitally excluded populations can be supported is focal to this paper, with different sections offering key related perspectives. From an analysis of issues, factors and practices, actions for policy, practice and research are identified. Given a key finding that power issues can have major effects on plans, implementation processes and outcomes when addressing needs of education for digitally excluded populations, the paper concludes by offering frameworks …

Phantom Jam Sybil Attack In Connected Vehicular Networks, Ahmed Ali Elamin Mohamed

Masters Theses and Doctoral Dissertations

Vehicular Ad-hoc Networks (VANETs) are vulnerable to Sybil attacks, mostly due to the lack of encryption in BSMs. In VANETs, multiple digital certificates (pseudonyms) are assigned to each vehicle to ensure their privacy. However, malicious nodes can exploit these pseudonyms to create ghost vehicles, inducing fake traffic jams and disturbance to other vehicles which may lead to accidents. In this work, we have developed the first sophisticated sybil attack, in which an attacker uses legitimate pseudonyms to create multiple ghost vehicles. These ghost vehicles transmit realistic kinematic data, using trajectory formulas and road maps. Additionally, the ghost vehicles randomly simulate …

Proof Of Concept: Simulating Drone Tracking In A Border Security Context, Jose Ruben Espinoza

Theses and Dissertations

Worldwide availability of drone technology has risen to unprecedented levels within the past century due to its commercial availability. While there has been various positive applications of such technology, it has additionally found usage within security critical contexts. Specifically, there have been reports of illegal drug smuggling along the Mexico-United States border in which quadrocopter based drones have been used. Within our research we aim to showcase, as a proof of concept, that autonomous drone technology can be leveraged within a defensive approach via the usage of reinforcement learning and object detection for security critical contexts. To promote the importance …

Detecting Anomalies In Dynamic Attributed Graphs: An Unsupervised Learning Approach, Austin Hamilton

Electronic Theses and Dissertations

Dynamic attributed graphs, which evolve over time and hold node-specific attributes, are essential in fields like social network analysis, where anomalous node detection is a growing area. Vehicular social networks (VSNs), a subset of these graphs, are ad hoc networks in which vehicles exchange data with one another and with infrastructure. In this dynamic context, identifying anomalous nodes is challenging but crucial for maintaining trust within the network. This work presents an unsupervised deep learning approach for anomalous node detection in VSNs. This model achieved an accuracy of 71% while detecting synthetic anomalies in a simulated network based on real-world …

A Comparative Study Of Patterns, Causes, And Impacts Of Data Breaches Across Geographical Regions And Time Frames, Bhavish Balsara

Electronic Theses, Projects, and Dissertations

The rise of digital technologies and interconnected systems has made data breaches a growing global concern. This culmination project explores the patterns, causes, and impacts of data breaches across various countries with varying levels of economic development and cybersecurity infrastructure from 2020 to 2023. This research aims to provide insights into the global landscape of data breaches and how they have evolved in recent years. The research questions are: (Q1) How do data breach patterns differ between countries with different levels of economic development and cybersecurity infrastructure? (Q2) What patterns and trends can be identified in data breaches when analyzing …

Container Runtime Vulnerability Mitigation Using User Namespace Isolation, Alexander Edsell

Electronic Theses, Projects, and Dissertations

Although containers have revolutionized application deployment by allowing for rapid and consistent deployment, their growing adoption has also raised significant security concerns. Each container is an isolated instance of an operating system that comes pre-packaged with the users desired applications. With multiple containers running on a host machine, an adversary can potentially break out of the container into the host machine. This project investigates the effectiveness of user namespace isolation as a security mechanism to mitigate container escape vulnerabilities that target the container’s runtime.

The research questions are: Question 1, does user namespace isolation mitigate container runtime vulnerabilities that target …

The Significance Of Continuous User Authentication On Mobile Devices, Mikayla Lawrence

Electronic Theses, Projects, and Dissertations

With the constant evolution of technology specifically on mobile devices, keeping personal and sensitive information safe has become increasingly vital. Continuous user authentication marks a major step forward in mobile security because it provides ongoing verification of user identity beyond the initial log in. This research explores the significance of continuous user authentication systems across mobile devices through literature-based analysis. The following research questions are addressed: (Q1) How effective are continuous user authentication methods in mitigating mobile device threats? (Q2) What are the vulnerabilities associated with continuous user authentication systems on mobile devices? (Q3) How do different continuous user authentication …

Exploiting Randomness In Secret Sharing, Cailyn Bass

All Theses

Shamir's (k,n)-threshold scheme is a method for sharing a secret among n participants such that any group of k or more participants can recover the secret. Additionally, any group of participants with size less than k should learn nothing about the secret. The scheme works by distributing a share to each participant, where each share is a linear combination of the secret and k-1 random symbols. This allows any group of k or more participants to solve a linear system to compute the secret. Any group of less than k participants does not have enough to determine anything about the …

Correlation And Causation Analysis For Cross-Sectional And Panel Data, Barry Nuqoba

Dissertations and Theses Collection (Open Access)

This dissertation investigates how data, algorithms, and expert knowledge can be harnessed to better understand human behavior and enhance well-being. It emphasizes the critical importance of interdisciplinary collaboration to bridge knowledge gaps and foster insights that support preventive care, causal theory advancement, and policy development.

The first study, part of the SHINESeniors project, shed light on the potential usefulness of passive, unobtrusive sensors for detecting nocturia and poor sleep quality, symptoms commonly observed in chronic diseases, thereby enabling live-alone older adults to age in place. Utilizing machine learning techniques on sensor-derived features, the study can identify nocturia and poor sleep …

Causality Analysis For Neural Network Security, Bing Sun

Dissertations and Theses Collection (Open Access)

While neural networks are demonstrating excellent performance in a wide range of applications, there has been a growing concern on their reliability and dependability.Similar to traditional decision-making programs, neural networks inevitably have defects that need to be identified and mitigated at times. Neural networks are usually inherently black-boxes and do not provide explanations on how and why decisions are made. As a result, these defects are more ``hidden" and more challenging to eliminate. It is thus crucial to develop systematic approaches to identify and mitigate defects in a neural network in a rigorous way.

In this dissertation, we focus on …

Towards Robust, Secure, And Privacy-Aware Large Language Models Of Code, Zhou Yang

Dissertations and Theses Collection (Open Access)

The field of software engineering has witnessed a surge in large language models specifically tailored to understand and process code, which we call large language models for code (LLM4Code). The increasing popularity of LLM4Code is inseparable from three key factors: the availability of extensive datasets compiled from diverse data sources, the advancements in deep learning algorithms and computational power that facilitate the training of these powerful models, and the active engagement and collaboration within the research community fostering innovation and the rapid exchange of ideas and methodologies. As evidenced by a series of studies, LLM4Code has been experiencing rapid development …

Interactive Known-Item Search In Large Video Corpora, Zhixin Ma

Dissertations and Theses Collection (Open Access)

The surge in video volume makes it challenging to locate a specific target with a single query using automatic video retrieval systems. The interactive video retrieval offers a solution by enabling users to iteratively refine a search. Nevertheless, existing systems often present users with an overwhelming number of similar videos, which can lead to mental fatigue while inspecting results and increase difficulty in providing feedback. This dissertation studies known-item video search and addresses four key challenges. First and foremost, as the link between users and the system, the interaction must be both efficient and effective. To ensure effectiveness, the user’s …

Patterns Of Interactions In Human-Machine Teams, Kazuhiko Momose

Theses and Dissertations

Increasingly capable machines, including Artificial Intelligence (AI) agents are playing a more important role in a wide range of applications, including human daily activities and safety-critical systems. They can benefit even more when humans and such machines agents work together as a team by leveraging each other's strengths and complementing each other to enhance overall performance. To design high-performing teams, it is critical to analyze the team dynamics and understand how humans and machines interact with each other. Collaboration, Coordination, and Cooperation (3Cs) are terms typically used to describe the behavior of teams. However, these terms tend to be used …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Generative Semi-Supervised Graph Anomaly Detection, Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang

Research Collection School Of Computing and Information Systems

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the …

Learning To Handle Complex Constraints For Vehicle Routing Problems, Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility Prevention (PIP) framework to advance the capabilities of neural methods towards more complex VRPs. Our PIP integrates the Lagrangian multiplier as a basis to enhance constraint awareness and introduces preventative infeasibility masking to proactively steer the solution construction process. Moreover, we present PIP-D, which employs an auxiliary decoder and two adaptive …

Inverse Factorized Soft Q-Learning For Cooperative Multi-Agent Imitation Learning, The Viet Bui, Tien Mai, Thanh Nguyen

Research Collection School Of Computing and Information Systems

This paper concerns imitation learning (IL) in cooperative multi-agent systems.The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process. However, extending this framework to a multi-agent context introduces the need to simultaneously learn both local value functions to capture local observations and individual actions, and a joint value function for exploiting centralized learning.In this work, we introduce a new multi-agent IL algorithm designed to address these challenges. Our approach enables thecentralized learning by leveraging …

Trustworthy Web3 Domains: A Framework For Digital Identity Verification, Yi Meng Lau, Ping Fan Ke

Research Collection School Of Computing and Information Systems

As decentralized applications evolve, digital identities represented through Web3 domain names gained prominence. This study addresses the challenges of establishing trust in Web3 domain names. The decentralized nature of Web3 introduces complexities in verifying domain name authenticity, making them targets for malicious activities such as cybersquatting and phishing. We propose a comprehensive framework that enhances traditional identification, authentication, and authorization processes by incorporating technological and social trust elements. This framework enables organizations and users to systematically assess the trustworthiness of Web3 domain names, offering a structured approach to managing digital identities in decentralized environments.

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Harnessing The Power Of Ai-Instructor Collaborative Grading Approach: Topic-Based Effective Grading For Semi Open-Ended Multipart Questions, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Semi open-ended multipart questions consist of multiple sub questions within a single question, requiring students to provide certain factual information while allowing them to express their opinion within a defined context. Human grading of such questions can be tedious, constrained by the marking scheme and susceptible to the subjective judgement of instructors. The emergence of large language models (LLMs) such as ChatGPT has significantly advanced the prospect of automatic grading in educational settings. This paper introduces a topic-based grading approach that harnesses LLM capabilities alongside a refined marking scheme to ensure fair and explainable assessment processes. The proposed approach involves …

Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …

Custom Permission Misconfigurations In Android: A Large-Scale Security Analysis, Rui Li, Wenrui Diao, Debin Gao

Research Collection School Of Computing and Information Systems

Android’s popularity is due to its openness and vast app ecosystem. Global developers can use Android Studio and rich Android APIs to create their apps. Within this ecosystem, Android permissions play a crucial role in managing access to resources, with system permissions controlled by system apps and custom permissions declared by third-party apps. However, the security of custom permissions has not received enough attention from the mobile security community, resulting in a lack of thorough evaluation of security practices for app developers using custom permissions. This study systematically evaluated the misconfiguration of custom permissions by Android app developers. It is …

Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

Chain Of Preference Optimization: Improving Chain-Of-Thought Reasoning In Llms, Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved …

Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …

Generative Artificial Intelligence In Business Higher Education: A Focus Group Study, Xuenan Huo, Keng Siau

Research Collection School Of Computing and Information Systems

This research investigates the opportunities and challenges of integrating generative artificial intelligence (GenAI) into business higher education, drawing insights from an asynchronous focus group research study with doctoral students who serve dual roles as both learners and educators. Key opportunities identified through thematic analysis include knowledge acquisition, intelligent co-ideation, supportive augmentation, and personalized learning. Challenges identified include AI trustworthiness, cognitive dependency, human value, policy and instruction, assessment integrity, and identity management. This study clarifies GenAI’s specific role in business education and provides practical insights for effectively integrating GenAI to enhance learning outcomes and address emerging challenges. An analysis theory on …

User Acceptance Of Advice By Ai Agents: Expectation-System Fit Perspective, Jingyuan Cai, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Algorithms have increasing influence on our daily decisions, especially when the recommendations are presented by human-like AI agents. This study applies the Theory of Effective Use to investigate how the fit between the user’s role expectation for an AI agent and the agent’s interaction style impacts AI advice adoption. We proposed a new concept termed Perceived Expectation-System Fit (PESF) and empirically examined its impact on user perceptions and advice acceptance. We found that low PESF reduces advice acceptance by diminishing cognitive and affective trust in the AI agent. Furthermore, increased algorithm transparency increases PESF's impact on decision-making. Our findings provide …