UWSpace
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Recent Submissions
Maasai Women in Architecture: Navigating the Journey from Thorny Branches to Resilient Roots.
(University of Waterloo, 2025-05-09) Khan, Zhoya
"Maasai Architecture: Navigating the Journey from Thorny Branches to Resilient Roots" investigates the relationship between cultural preservation and the empowerment of Maasai women through architectural design. Traditional Maasai architecture, shaped by nomadic practices, communal living, and natural, local materials, is increasingly threatened by pressures from tourism, colonization, and shifting land ownership. These forces have transformed the Maasai culture and spaces surrounding them, unfortunately leaving the Maasai women with limited opportunities and spaces for growth. Historically, Maasai women have been the primary builders of their homes, with their intimate knowledge of the land and construction practices crucial for sustaining their community. However, as cultural change accelerates, this role is gradually being erased. Women’s activities are gradually being restricted to a private service, while men’s are being directed toward the wider public community. To address this, the research aims to design a space that honors Maasai heritage while creating new opportunities for women to thrive.
The project envisions a women's empowerment center that integrates Maasai culture and traditional construction techniques with modern architectural innovations to address present-day challenges and meet the holistic needs of Maasai women. By incorporating locally sourced materials and culturally significant forms, alongside modern features, the design aims to be both symbolic and functional - a space of resilience but also one that fosters empowerment. The proposed center will offer spaces for education, economic opportunities, healthcare, and community support, empowering Maasai women to adapt and succeed.
These multifunctional spaces will help the Maasai women with economical, intellectual, and emotional support. Additionally, the center will serve as a hub for cultural preservation, celebrating and passing on the wisdom and practices of Maasai women. In African culture, women’s agency and leadership is essential to passing on cultural heritage from one generation to the next. Through their creativity, knowledge, and key role in social practices and cultural expression, women are fundamental to maintaining traditions and cultural identity across the African continent.
This journey "from thorny branches to resilient roots" represents the transformation of Maasai culture and spaces—evolving to provide strength, security, and opportunity, while still remaining rooted in important cultural values. As external pressures on Maasai communities increase, this architectural project aims to preserve both the vernacular physical structures, forms and building techniques as well as the socio-cultural fabric that defines the women's identity. This vision sees Maasai women not just as passive recipients of change but as active participants in shaping their future. By embracing their cultural roots, they become the protectors of their traditions while also leading their community towards growth and innovation.
New Attack Detection Methods for Connected and Automated Vehicles
(University of Waterloo, 2025-05-08) Bian, Shuhao
Ensuring the security of Connected and Automated Vehicles (CAVs) against adversarial threats remains a critical challenge in cyber-physical systems. This thesis investigates attack detection methodologies and presents novel dual-perspective detection frameworks to enhance CAVs resilience.
We first propose a vehicle dynamics-based attack detector that integrates the Unscented Kalman Filter (UKF) with machine learning techniques. This approach monitors physical system behaviour and identifies anomalies when sensor readings deviate from predicted states. Our enhanced model captures nonlinear vehicle dynamics while maintaining real-time performance, enabling the detection of sophisticated attacks that traditional linear models would miss.
We develop a complementary trajectory-based detection framework that analyzes driving behaviour rationality to address the limitations of purely physics-based detection. This system evaluates vehicle trajectories within their environmental context, incorporating road conditions, traffic signals, and surrounding vehicle data. By leveraging neural networks for trajectory prediction and evaluation, our approach can identify malicious interventions even when attackers manipulate vehicle behaviour within physically plausible limits.
Integrating these two detection perspectives—one based on vehicle dynamics modelling and the other on trajectory rationality analysis—provides a comprehensive security framework that significantly improves detection accuracy while reducing false positives. Experimental results demonstrate our system’s effectiveness against various attack vectors, including false data injection, adversarial control perturbations, and sensor spoofing attacks.
Our research contributes to autonomous vehicle security by developing a holistic detection approach that considers both immediate physical anomalies and broader behavioural inconsistencies, enhancing system resilience against increasingly sophisticated cyber-physical threats.
Program Reduction: Versatility, Insights, and Efficacy
(University of Waterloo, 2025-05-08) Zhang, Mengxiao
Given a program P and a property ψ it preserves, the goal of program reduction is to minimize this program while ensuring that the minimized version Pmin still preserves ψ. Program reduction is a widely used technique to facilitate compiler debugging, as most compiler bugs are triggered by program inputs, which often contain a significant amount of code unrelated to the bug. Although program reduction automates eliminating such bug-irrelevant components from a program, it can take hours to complete due to its trial-and-error strategy. Furthermore, even if the reduction process produces a smaller program after a long wait, the resulting program is not guaranteed to be optimal, as program reduction is an NP-complete problem. As a result, compiler developers consistently seek program reduction approaches that offer faster reduction speeds or produce smaller outputs. It is critical to design superior program reduction approaches to further explore the potential of this field.
This thesis aims to help enhance program reduction approaches in the following three ways.
The first study aims to enhance the versatility of program reduction approaches. While existing techniques, such as C-Reduce and Perses, can effectively reduce a bug-triggering program as a whole, they fail to consider the varied degrees of relevance each remaining element has to the bug. To address this limitation, this study proposes PPR, a new program reduction technique designed to minimize a pair of programs w.r.t. certain properties. Given a seed program Ps and its variant Pv with differing properties (e.g., Pv crashes a compiler while Ps does not), PPR produces a pair of minimized programs that preserve these properties separately, with reduced differences highlighting bug-related elements.
Evaluation results demonstrate that PPR effectively reduces pairs of programs, producing outputs of comparably small size to those generated by Perses and C-Reduce. In addition, PPR significantly outperforms the Delta Debugging algorithm in isolating bug-related differences.
The second study concentrates on the understanding of a state-of-the-art program reduction approach ProbDD. ProbDD, a variant of ddmin, employs a probabilistic model to estimate the likelihood of each element being relevant to ψ, achieving superior performance compared to the Delta Debugging algorithm. However, the theoretical probabilistic model underlying ProbDD is intricate and not fully understood. To address this, the study conducts the first in-depth theoretical analysis of ProbDD, comprehending and demystifying this state-of-the-art approach from multiple perspectives. Building upon these insights, the study further proposes CDD, a simplified counter-based model that retains the core strengths of ProbDD. The evaluation demonstrates that CDD can achieve the same performance as ProbDD, despite being much simplified.
The third study integrates large language models (LLMs) to improve the efficacy of program reduction. Existing program reduction techniques typically fall into two categories: generic approaches applicable to a wide range of languages but lacking domain-specific knowledge, and language-specific approaches that exploit detailed language knowledge but fail to generalize across multiple languages. However, effectively combining both language generality and language-specific expertise in program reduction is yet to be explored. To this end, this chapter proposes LPR, the first LLM-aided technique leveraging LLMs to perform language-specific program reduction for multiple languages. The key insight is to synergize the language generality of existing program reducers, such as Perses, with the language-specific semantics learned by LLMs. The evaluation shows that LPR surpasses Vulcan by producing 24.93%, 4.47%, and 11.71% smaller programs, while reducing the reduction time by 10.77%, 34.88%, 36.96% on benchmarks in C, Rust and JavaScript, separately.
Collectively, these studies advance the field of program reduction by enhancing its versatility, insights, and efficacy. By making reduction more effective, faster, and easier to understand, this thesis facilitates efficient debugging and robust compiler development.
Disturbance Vulnerability Analysis and Reduction for Nonlinear Systems using Modes of Instability
(University of Waterloo, 2025-05-08) Wang, Jinghan
Engineered systems naturally experience large disturbances which have the potential to disrupt desired operation because the system may fail to recover to a desired exponentially stable equilibrium point (SEP). It is valuable to determine the mechanism of instability when the system is subject to a particular finite-time disturbance, because this information can be used to improve vulnerability detection, and to design controllers capable of mitigating these disturbances to enhance system resilience and ensure reliable performance, thereby reducing vulnerability.
For a large class of nonlinear systems there exists a particular unstable equilibrium point (UEP) on the region of attraction (RoA) boundary of the desired SEP such that the unstable eigenvector of the Jacobian at this UEP represents the mode of instability for the disturbance. Unfortunately, it is challenging to find this mode of instability, especially in high dimensional systems, because it is often computationally intractable to compute this particular UEP for a given disturbance. Consider a particular finite time disturbance applied to a nonlinear system which possesses a SEP representing desired behavior. The system is able to recover from the disturbance if its post disturbance initial condition (IC) lies inside the RoA of the desired SEP. In cases where the system fails to recover, the nonlinear mode of instability for the disturbance represents the subset of system dynamics most responsible for this failure to recover.
This thesis develops a novel algorithm for numerically computing the mode of instability for parameter-dependent nonlinear systems without prior knowledge of the particular UEP, resulting in a computationally efficient method. The key idea is to first consider the setting where the system recovers, and to average the Jacobian along the system trajectory from the post-disturbance state up until the Jacobian becomes stable. As the system approaches inability to recover, the averaged Jacobians converge to the Jacobian at the particular UEP, and can be used to extract the unstable eigenvector representing the mode of instability. Convergence guarantees are provided for computing the mode of instability, both for the theoretical setting in continuous time, and for the proposed algorithm which relies on numerical integration. Numerical examples illustrate the successful application of the method to identify the mechanism of instability in power systems subject to temporary short circuits.
Then a novel approach to control design for reducing disturbance vulnerability of nonlinear systems using knowledge of the mode of instability is developed. The main idea is to tune controller parameter values so as to drive the post-disturbance IC further inwards away from the RoA boundary by driving it in the direction opposite to the mode of instability. To achieve this, the problem is formulated as a nonconvex optimization problem, and an efficient algorithm is developed to solve it. Local convergence guarantees are provided for this method. Numerical examples illustrate the successful application of the method to reduce the vulnerability of power systems subject to temporary short circuits.
Towards Urban Digital Twins With Gaussian Splatting, Large-Language-Models, and Cloud Mapping Services
(University of Waterloo, 2025-05-08) Gao, Kyle
Computer Vision
Remote Sensing
Gaussian Splatting
Point Cloud
3D Modelling
Urban Digital Twin
GIS
Large Language Models