Digital Commons Network^™

Automation Of Vulnerability And Patch Management: Information Extraction, Association, And Optimization, Kylie Mcclanahan

Graduate Theses and Dissertations

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout …

Exploring The Pedagogical Impact Of Software Development Live Streams: Informal Learning Opportunities For Software And Game Developers, Ella Kokinda

All Dissertations

Live streaming is an increasingly popular medium for throwing back the curtain on software development where streamers and viewers share their knowledge and experiences. Popular platforms like Twitch and YouTube enable developers to stream live coding sessions where people around the world can engage in real-time collaboration, feedback, knowledge sharing, and skill development. This work investigates the pedagogical implications and learning opportunities present in software and game development live streaming while focusing on the role of streaming as a learning environment and collaborative community. We begin by exploring summer camps as an informal learning opportunity for STEM education, highlighting the …

Achieving Fairness In Zoning Laws With Machine Learning, William Schimitsch

College Honors Program

Zoning is a powerful regulatory tool that determines how municipalities use and develop land. The goal of zoning is to classify land use (e.g., residential, commercial, industrial) to maximize compatibility among neighboring parcels. Local zoning decisions, however, are made by small-sized boards, often through an opaque process, which raises concerns about bias and fairness. In the United States, zoning has historically prioritized single-family housing and thus created economic barriers that limit access to certain communities. Given the task of classifying land use and the wealth of geographical, demographic, and infrastructural data describing each parcel, the problem of bias in zoning …

Guest Editorial: When Multimedia Meets Food: Multimedia Computing For Food Data Analysis And Applications, Weiqing Min, Shuqiang Jiang, Petia Radeva, Vladimir Pavlovic, Chong-Wah Ngo, Kiyoharu Aizawa, Wanqing Li

Research Collection School Of Computing and Information Systems

Food is central in our life for its fundamental role in our survival, health, mood and culture. The deployment of various networks (e.g., IoT and mobile networks), devices (e.g., hyperspectral imaging devices, electronic nose/tongue), databases (e.g., nutrition tables and food compositional databases), recipe-sharing websites (e.g., Yummly and Meishijie) and social media (e.g., Twitter and Weibo) has generated unprecedented volumes of multi-modal food data. Such multi-source multi-modal food data provides new perspectives to analyze and understand food consumption via multimedia computing. Riding on the wave of AI, food-oriented multimedia computing integrates AI, multimedia technology and food science to enable a wide …

Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FixDrive, a framework that analyses driving records from near-misses or law …

Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan

Research Collection School Of Computing and Information Systems

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration …

Acccred: Improved Accountable Anonymous Credentials With Dynamic Triple-Hiding Committees, Sijiang Xie, Rui Shi, Yang Yang, Huiqin Xie, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Accountable anonymous credentials protect user privacy while holding the accountability of ill-intentioned individuals, which is a critical feature for applications such as online payments and other financial services. Existing accountable anonymous credentials rely on a public committee of trustworthy members who are assumed not to collude and are well protected to perform privacy revocation. However, this assumption is unsound in blockchain-based cryptocurrency systems because the selected committees may involve nodes with significant stakes, and public nodes serving as committee members are vulnerable against targeted attacks from high-computing power adversaries. In this paper, we propose an improved accountable anonymous credential called …

Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-Based Benchmark, Han Zhang, Zixiang Meng, Meng Luo, Hong Han, Lizi Liao, Erik Cambria, Hao Fei

Research Collection School Of Computing and Information Systems

Empathetic Response Generation (ERG) is one of the key tasks of the affective computing area, which aims to produce emotionally nuanced and compassionate responses to user's queries. However, existing ERG research is predominantly confined to the singleton text modality, limiting its effectiveness since human emotions are inherently conveyed through multiple modalities. To combat this, we introduce an avatar-based Multimodal ERG (MERG) task, entailing rich text, speech, and facial vision information. We first present a large-scale high-quality benchmark dataset, AvaMERG, which extends traditional text ERG by incorporating authentic human speech audio and dynamic talking-face avatar videos, encompassing a diverse range of …

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 …

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 …

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 …

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

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 …

Full-Stack Web Applications: Industry Standard Frameworks, Libraries & Technologies, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging full-stack web development technologies across front-end, back-end, and DevSecOps domains. It evaluates modern tools including Django, React, and TypeScript—focusing on their key features such as compile-time error checking—through to the development of a web application. By examining documentation for the frameworks Node.js, Next.js, Tailwind CSS, and others, along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and have often replaced older technologies. Cloud solutions for tasks such as authentication and deployment will also be evaluated, along with various web-application technology stacks and …

Empirically Exploring The Physical Realizability Of Adversarial Examples, Ruoyao Wen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The development of autonomous vehicles (AVs) has been accelerated by advancements in deep neural networks (DNNs), which power the complex perception systems necessary for safe and efficient real-world navigation. However, as AVs increasingly integrate into public transportation networks, the robustness of their perception systems against potential vulnerabilities is critical. Among these threats, adversarial attacks—particularly through the use of adversarial patches—pose significant risks. These patches are carefully crafted perturbations designed to mislead DNNs, potentially compromising AV safety by causing incorrect object recognition or misclassification.

While extensive research has demonstrated high attack success rates for adversarial patches in controlled digital environments, their …

Augsso: Secure Threshold Single-Sign-On Authentication With Popular Password Collection, Changsong Jiang, Chunxiang Xu, Guomin Yang

Research Collection School Of Computing and Information Systems

Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-offailure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

Disambiguart: A Neural-Based Inference Model For Knowledge Graph Disambiguation, Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

One main challenge in constructing a knowledge graph (KG) is to deal with ambiguity. Specifically, an entity in the graph can be assigned with multiple meanings while two or more entities considered to have different meanings may actually be the same. Assigning an entity with the correct meaning may involve re-evaluation of its relevant contexts. This costly operation typically involves searching for other similar entities within the KG such that the context can be determined. In this paper, a new model called DisambiguART is proposed leveraging multi-channel matching and inference in a self-organizing neural network for sense disambiguation in knowledge …

Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu

Research Collection School Of Computing and Information Systems

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …

Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently …

Robust Threshold Ecdsa With Online-Friendly Design In Three Rounds, Guofeng Tang, Haiyang Xue

Research Collection School Of Computing and Information Systems

Threshold signatures, especially ECDSA, enhance key protection by addressing the single-point-of-failure issue. Threshold signing can be divided into offline and online phases, based on whether the message is required. Schemes with low-cost online phases are referred to as “online-friendly”. Another critical aspect of threshold ECDSA for real-world applications is robustness, which guarantees the successful completion of each signing execution whenever a threshold number t of semi-honest participants is met, even in the presence of misbehaving signatories. The state-of-the-art online-friendly threshold ECDSA with-out robustness was developed by Doerner et al. in S&P'24, requiring only three rounds. Recent work by Wong et …

Gamba: Marry Gaussian Splatting With Mamba For Single-View 3d Reconstruction, Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

We tackle the challenge of efficiently reconstructing a 3D asset from a single image at millisecond speed. Existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations. Despite promising results, these approaches encounter practical limitations due to lengthy optimizations and significant memory consumption. In this work, we introduce Gamba, an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: (1) Efficient Backbone Design: introducing a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length, thereby accommodating a substantial number …

Hierarchical Learning-Based Graph Partition For Large-Scale Vehicle Routing Problems, Yuxin Pan, Ruohong Liu, Yize Chen, Zhiguang Cao, Fangzhen Lin

Research Collection School Of Computing and Information Systems

Neural solvers based on the divide-and-conquer approach for Vehicle Routing Problems (VRPs) in general, and capacitated VRP (CVRP) in particular, integrates the global partition of an instance with local constructions for each subproblem to enhance generalization. However, during the global partition phase, misclusterings within subgraphs have a tendency to progressively compound throughout the multi-step decoding process of the learning-based partition policy. This suboptimal behavior in the global partition phase, in turn, may lead to a dramatic deterioration in the performance of the overall decomposition-based system, despite using optimal local constructions. To address these challenges, we propose a versatile Hierarchical Learning-based …

Worldcuisines: A Massive-Scale Benchmark For Multilingual And Multicultural Visual Question Answering On Global Cuisines, Genta Indra Winata, Et. Al

Research Collection School Of Computing and Information Systems

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside …

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 …

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 Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

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 …

Supporting Novice Programmers With Scaffolded And Open-Ended Generative Ai Interfaces: Insights From A Design-Based Research Study, Justin Olmanson, Azadeh Hassani, Gretchen K. Larsen

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In this study, we explore student experiences in coding and learning programming with scaffolded and unscaffolded generative AI interfaces. Specifically, we supported higher education students in using ChatGPT, an open ended interface for interacting with generative AI; and Giuseppe, a specialized interface with an OpenAI backend service that offers personalized supports specifically for helping students overcome early-stage challenges in learning to code, and working on education technology prototyping projects. This study contributes to the field by offering design insights for scaffolding initial learning interactions between generative AI interfaces and novice programmers. Our findings suggest that those new to coding welcome …

Learning Educational Technology Prototyping With Generative Ai, Justin Olmanson, Azadeh Hassani, Gretchen K. Larsen

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In this study, we use ethnographic methods, grounded theory, and an iterative analytical approach to explore participant experiences and strategies for engaging generative AI in support of both learning how to prototype educational technologies and learning to code. We examine how ChatGPT and Giuseppe (a scaffolded co-coding interface of our own design) influence students’ approaches to prototyping and programming. This study contributes to the field by: identifying specific challenges and affordances of generative AI in prototyping and educational technology development contexts; and offering insights into how educators, students, and learning technology developers can integrate generative AI in formative educational technology …