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Recent Submissions

VR Streaming as a New Frontier: Investigating Virtual Cameras as a Multifaceted Bridge Between Streamers and Viewers
(University of Waterloo, 2026-02-27) Wu, Liwei
Although live streaming and virtual reality (VR) have been widely studied, their combination, VR streaming as a mass media format, has received less attention. In VR streaming, the streamer uses a VR headset to share their experience in a virtual environment, while viewers watch on traditional 2D devices like computers or smartphones. As VR adoption increases in consumer markets, VR streaming is expected to become a key part of online content ecosystems. However, few studies have examined VR streaming in real-life settings, especially the critical role that virtual cameras can play during the streaming process. Just as physical cameras are essential for storytelling and viewer engagement in traditional media like film, virtual cameras are also crucial for creating effective communication and engaging interaction in VR streaming. Yet the common ways VR streamers currently use virtual cameras, the associated challenges they encounter, and the opportunities to enhance the virtual cameras and their overall streaming experience still remain largely unexplored. This thesis explores the essential role of the virtual camera in VR streaming for fostering connections and engagement between streamers and viewers through three projects. In the first project, I collected and analyzed VR streaming videos to observe VR streaming common practices and understand their associated challenges. In the second project, I interviewed media domain experts to investigate the connections between traditional media and VR streaming, as well as explored design considerations to enhance current virtual cameras. In the final project, I explored the structured representation of VR streaming with a design canvas and how to efficiently support streamers understand and explore current and future VR streaming formats.
The Spirit in the Machine: Romantic Resurgence in the Digital Age
(University of Waterloo, 2026-02-26) Vines, Hannah
Generation Z (Gen Z), often described as the “digital generation,” is the first cohort to experience childhood and adolescence extensively mediated by digital technologies. This has coincided with a widespread retreat from analog to digital spaces, frequently described as “digital escapism.” Existing sociological and psychological literature tends to explain this shift through sociopolitical stressors, individual pathology, or engineered addiction (Haidt, 2024; Jouhki et al., 2022; Kardefelt-Winther, 2014). This paper offers an alternative interpretation by theorizing digital escapism as a cultural phenomenon shaped by shared narratives, symbols, and meaning-making practices. Drawing on the strong program in cultural sociology (Alexander, 2003), this paper argues that contemporary narratives of techno-utopianism and posthumanism provide key symbolic frameworks through which Gen Z interprets their relationship with technology. These narratives idealize technological mediation, promote intimate human-computer interaction, and encourage transcendence of embodied and social limitations. Rather than viewing Gen Z’s digital retreat as a novel response to technological change, this paper situates their practices and discourse within broader cultural patterns that have roots in enduring historical traditions, challenging narratives of technological determinism. By foregrounding culture and meaning, this paper contributes to digital sociology by demonstrating how technological practices are embedded within symbolic systems that precede and exceed individual choice or technological design. The analysis shows that digital escapism is not simply a reaction to technological affordances, but a culturally mediated response shaped by powerful collective imaginaries. The paper concludes by suggesting that understanding digital engagement through cultural narratives transcends psychological or materialistic explanations, addressing a critical gap in the literature for analyzing the social implications of digital technologies and the evolving relationship between technology and contemporary social life.
How Does Experience Influence Developer Perceptions of Atoms of Confusion?
(Software REBELs, 2026) Shi, Guoshuai; Kazemi, Farshad; McIntosh, Shane; Godfrey, Michael W.
Atoms of Confusion (AoCs) are small, syntactically valid code patterns that can increase cognitive load during program comprehension. Earlier research suggested that AoCs are common and potentially harmful, but more recent studies have questioned whether their effects generalize beyond less experienced developers. This confirmatory study aims to reexamine whether the presence of AoCs slows comprehension or alters repair preferences. Moreover, we examine whether these effects are moderated by developers’ programming experience. We investigate task completion time and the kind of repairs developers prefer when interacting with code containing AoCs. We propose a two-phase study consisting of a pre-screening questionnaire and a controlled experiment. The questionnaire will function as a qualification instrument. In the experiment, participants will complete eight Java comprehension tasks, four with an AoC and four without. For each task, developers are asked to identify a seeded defect and to rank three functionally equivalent repairs differing in AoC inclusion. Task completion time and the top-ranked repair will be analyzed using mixed-effects linear and multinomial regression models, with AoC presence as the manipulated factor and programming experience as a covariate.
In Silico Multi-Scale Investigation of Lung Tissue Mechanics and Injury
(University of Waterloo, 2026-02-25) Singh, Dilaver
Biofidelity in lung tissue mechanical response and injury prediction is a critical aspect of human body modeling, since the lungs are one of the life-sustaining organs and thus present a high priority injury and fatality risk. Human body models (HBMs) are becoming integral to safety systems design across a range of industries including automotive, defense, and sports. The lungs in particular present numerous challenges to continuum scale HBMs due to their high mechanical compliance, complex heterogeneous structure, and the transient nature of respiration. Existing continuum-scale lung models in impact HBMs are limited in that they typically use properties from excised lung tissue samples, which do not consider tensile pre-strains in the alveolar walls from lung inflation to functional residual capacity (FRC) which is the nominal in vivo conditions of the lung. Furthermore, the effects of surface tension forces, which are an important aspect of lung response, have not been characterized in the existing literature for the deviatoric deformations relevant to HBMs. Additionally, existing methods for predicting pulmonary contusion (PC) are limited to using reconstructed simulations of impact scenarios, and determining empirical correlations to various response metrics in the lung (such as strain, strain rate, etc.). Consequently, the resulting injury criteria or metrics are limited in their applicability to other models or boundary conditions, and are not mechanistically linked to any injury pathology, nor are linked with the alveolar microstructure where actual lung injury occurs. The focus of the current research was to address these limitations using a multi-scale lung modeling approach to relate the macroscopic continuum scale lung response to the microstructural features of the alveoli, and that could be implemented in contemporary HBMs. The specific objectives of this work were: (O1) to develop an alveolar scale model of lung parenchyma, and to use that model to (O2) inform a continuum scale model of lung tissue, and (O3) inform a pulmonary contusion injury prediction method. A finite element model of a representative volume of lung parenchyma was developed using a generalized tetrakaidecahedron geometry to represent an alveolar cluster. The alveolar cluster model used periodic boundary constraints to model symmetry conditions on each face, and used pressure-driven and displacement-driven boundary conditions to simulate the mechanical response of the alveolar wall network. The material properties of the alveolar wall were determined from experimentally measured stress-stretch curves of excised lung tissue, and pressure-volume curves of saline-filled whole lungs that do not include surface tension forces. An explicit implementation of the surface tension membrane was included in the model, derived from experimental data of pulmonary surfactant surface tension forces, as well as from pressure-volume curves of air-filled whole lungs. The cluster model was used to simulate the macroscopic response of lung parenchyma, and predicted that both the surface tension membrane, and alveolar pre-strains at the functional residual capacity (FRC) lung volume, stiffened the macroscopic response of the cluster. The cluster model was also used to characterize the alveolar wall strains as a function of macroscopic deformation, to determine relations between continuum-scale deformations and alveolar strains at the microstructural scale. The macroscopic response of lung parenchyma predicted by the alveolar cluster model in uniaxial tension/compression and shear, was used to determine stress-stretch properties of a continuum scale lung model that included surface tension membrane forces and alveolar pre-strains at FRC, denoted as the FRC Lung model. An Ogden hyperelastic model was used to capture the deviatoric response, and the bulk properties were derived from an analysis using the rule of mixtures for porous materials. The stress-stretch response predicted by the model without alveolar pre-strains was in general agreement with the experimental data on excised lung tissue from the literature. The FRC Lung model was validated using available experimental data that directly loaded the lungs including an impact experiment by Yen et al. (1988) where the model demonstrated good agreement. The alveolar wall strain predictions and the surface area change predictions of the cluster model were validated against available data on lungs in the physiological range of motion (i.e. volume changes from respiration). Importantly, the model demonstrated that reported alveolar injury thresholds from overdistension, also generally corresponded to the physiological limits of alveolar strain. The alveolar wall strain predictions of the cluster model were used to develop injury thresholds based on alveolar overdistension, by determining thresholds of macroscopic (continuum scale) deviatoric deformation that resulted in alveolar strains that exceeded the physiological limits. The resulting continuum scale strain thresholds were assessed in full-scale HBM simulations of thoracic pendulum impacts, and resulted in contusion predictions that agreed well with expected outcomes, and also matched or outperformed existing calibrated methods. The developed model demonstrated that existing data on excised lung parenchyma, excised alveolar wall, pulmonary surfactant surface tension, whole-lung pressure-volume with saline, and whole-lung pressure-volume with air, were all in general agreement when interpreted with the alveolar-scale model. The multi-scale modeling approach for lung tissue undertaken herein, successfully related microstructural features and injury thresholds at the alveolar scale, to macroscopic lung response and an injury prediction method for input into a continuum scale human body model. Future work can extend the methods presented here to investigate additional features of lung tissue, or to other biological tissues.
On the Performance of Pippenger's algorithm for Multi-Scalar Multiplication
(University of Waterloo, 2026-02-25) Oleksandr, Hrabar
Multi-scalar multiplication (MSM) is core to many zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) systems and remains a primary performance bottleneck in proof generation and verification. In practice, zk-SNARK implementations typically rely on Pippenger’s bucket method as the standard algorithm for MSM. This thesis targets high-throughput MSM, computing many scalar–point multiplications and summing them for a large number of scalars, up to $2^{21}$ on a specific curve, with the primary goal of reducing the number of group operations. Our work consists of three main parts. First, we review background and classical MSM algorithms, with more focus on standard Pippenger's method, Pippenger's with Booth encoded digits and the Bos–Coster approach. Second, we apply a family of small-factor scalar recoding techniques that extract small factors to shorten the effective scalar bitlength, and we compare the number of point operations for the afore-mentioned methods. Third, we introduce a strategy to reduce the number of point operations in Pippenger's algorithm. It is based on clustering of scalar window digits across multiple rows. Firstly, we describe the simplest case of window digit clustering in individual columns and then its generalization to window digit clustering in multiple columns. We implement these ideas in Rust, study sensitivity to window size, and evaluate combinations of recoding and reuse. Across our benchmarks, multiple columns window digit clustering reduces total point operations by $2.59\%$–$18.09\%$ relative to Booth encoded Pippenger's method, while individual column clustering yields improvements up to $8 \%$ for small number of scalars. Overall, structured reuse together with modest window tuning provides consistent operation savings, indicating a practical path to faster MSMs at scale.