VictorJuiz/thesis huggingface dataset:
Privacy-Preserving Network Anomaly Detection via Homomorphic Encryption
Keywords: Homomorphic Encryption

Network Anomaly Detection refers to the detection of abnormal network activities due to security breaches or other network issues. Traditional methods of detection often rely on known signatures of anomalies. More recently, the advancement of machine learning has become a more robust mechanism to address this issue. However, the nature of network anomaly detection directly contradicts with the notion of data privacy and confidentiality. Being able to detect anomalies while preserving the privacy of the data has become a hotly researched topic. This thesis proposes a privacy-preserving Random Forest classifier tailored for anomaly detection settings. The system leverages Fully Homomorphic Encryption (FHE) encryption to achieve privacy-preserving classification on encrypted data. This work improves the computation and communications costs compared to previous works. Furthermore, the system is hardware-accelerated by GPUs, resulting in a speedup of up to 160x over a single-threaded CPU. Importantly, the optimization techniques place special emphasis on the scalability of the overall system, so that it can process higher throughput by horizontally scaling the hardware resources. This thesis also proposes a client-server-based protocol for generic random forest predictions, suitable for use cases such as Machine-Learning-as-a-Service (MLaaS). The privacy-preserving Random Forest was evaluated by common classification metrics on a network detection dataset, CIC-DS-2017. Experiments have shown that our system is able to sustain a detection throughput of 15 Kbps with high detection accuracy (F1 $>$ 0.90) on a system with an Nvidia A100 GPU.
Inclusive Accessible or Culturally Relevant? Two Perspectives on Accounting for Culture in Community-based Stroke Physical Activity Programs
Keywords: Community Services
Culturally Relevant
Physical Activity
Stroke

With the growing prevalence of stroke, recognition of health disparities experienced by ethnically marginalized communities, and growing cultural diversity across Canada, community-based organizations have a distinct opportunity to address the unique barriers and cultural needs of culturally diverse individuals with stroke. This thesis explores how eight program leaders within Canadian community-based organizations account for ethnicity and culture in the development of stroke physical activity programs. Three themes were identified based on interviews with program leaders: (1) multiple definitions of culturally relevant programs, (2) inclusive & accessible and culturally relevant program attributes, and (3) inclusive & accessible mindset versus culturally relevant mindset. The findings of this thesis highlight the variability of program leaders’ perspectives when defining culturally relevant and their approaches to designing and delivering programs for ethnically and culturally diverse people with stroke, including whether or how they integrate cultural needs into programs, which they describe is often a decision made by program leaders rather than a requirement of programs or organizations.
Environmental Modulations of Microglia in Mouse Brain
Keywords: Microglia
Neuroinflammation
Two Photon

Microglia, the resident immune cells of the brain, can adopt pro- or anti- inflammatory states in response to different stimuli. Utilizing advanced techniques such as two-photon serial tomography and deep learning, we examined the effects of Δ9-tetrahydrocannabinol (THC), lipopolysaccharide (LPS), and diet induced obesity (DIO) on microglial density and soma size across both male and female mice. Our findings reveal that THC led to lower levels in microglial density and soma size in females, aligning with its anti-inflammatory properties. Conversely, LPS increased density and soma size in both sexes, indicative of its pro-inflammatory role. DIO showed no significant impact, although a trend suggested potential male-specific pro-inflammatory effects or female-specific protective effects. Our study extends previous research on microglia reactivity and morphological changes, shedding light on potential sex-specific responses and enrich our understanding of microglia response to different environmental stimuli.
Opportunities, Challenges, and Curricular Insights Identified by Graduate Orthodontic Educators During the 2020/21/22 Academic Years at the University of Toronto
Keywords: Clinical Curriculum
COVID-19
Didactic Curriculum
Educator Insights
Online Education
Orthodontics

This study investigated the opportunities and challenges associated with pandemic-related changes to didactic and clinical teaching. Seven instructors that deliver didactic, and/or clinical components of the graduate orthodontics program at the University of Toronto were interviewed virtually on their teaching experiences during the 2020/2021/22 (Fall term) academic years. The data were analyzed using the thematic analysis technique described by Braun and Clarke (2006). The key curricular insights, and the opportunities and challenges of the modified didactic and clinical teaching were identified. Educators’ preferred mode of didactic teaching varied, and preferences were course-specific. Educators preferred to return to the pre-pandemic clinical curriculum. There was enthusiasm towards adopting a hybrid model and implementing online and digital tools in both didactic and clinical curricula. Educators indicated the need for institutional support (technological/ infrastructural/human resources, incentives, and training) to promote adoption of online didactic teaching, and better preparedness for the future.
Cardiovascular Physiology During Orthostasis in Healthy Older Women
Keywords: cardiology
orthostasis

The ability to sustain an upright posture is a common daily stressor for humans and requires integrated cardiovascular and autonomic adjustments to adequately compensate for the increased gravitational load that accompanies standing. This thesis examined the normal circulatory response to a 10-minute active stand in a group of self-reported healthy older women. Concurrent right heart catheterization and arterial line monitoring were used to describe changes in hemodynamic variables between supine and standing positions. Standing was associated with an increase in heart rate (8±5 to 14±6 bpm) which was maintained above supine levels over the duration of the 10-minute stand. Blood pressure did not change significantly with standing. Cardiac filling pressures tended to fall, while stroke volume (-31±6%) and mixed venous oxygen saturation (-10±4%) decreased persistently in all participants. In summary, the physiologic response to a 10-minute active stand test in healthy older women involves a complex cardiovascular and circulatory response.
Sex-specific Differences in Heart Rate Variability in Bipolar Disorder
Keywords: Autonomic Dysfunction
Bipolar Disorder
Cardiovascular Disease
Heart rate variability
Vascular-bipolar link
Wearable Technology

Women diagnosed with bipolar disorder (BD) were reported to have elevated cardiac risk relative to men, yet the etiology underlying this has not been determined. Heart rate variability (HRV) is a predictor of cardiac health that can evaluate the state of the autonomic nervous system. We assessed 24-hour HRV parameters (i.e., Root Mean Square of Successive Differences (RMSSD), Standard Deviation of Normal-to-Normal (NN) intervals Index (SDNNI), Standard Deviation of Average NN intervals (SDANN), and HRV Triangular Index (RTI)) from 35 participants using wearable technology to investigate differences in HRV between men and women with BD. When controlling for family history of psychiatric disorders, we observed lower SDANN in women, but no significant differences for the other parameters. Autonomic dysfunction may be a mechanism underlying the observed difference, but our conclusions are limited by the sample size. Future studies with larger samples are encouraged to minimize Type I/II errors.
Hybrid Electromagnetic Acoustic Transducer (EMAT) Systems for the Non-Destructive Detection of Corrosion-Induced Defects in Steel
Keywords: Corrosion
Defects
Electromangetic Acoustic Transducer
Finite Element Modelling
Non-Destructive Testing
Pipelines

Four combinations of Electromagnetic Acoustic Transducers operated at two frequencies are tested on an industrially representative 1018 Low Carbon steel plate and pipe section with an artificial pitting defect. The combination involving a Periodic Permanent Magnet EMAT as the transmitter and a Magnetostrictive Meander Line Coil EMAT as the receiver was found to be the most favourable due to its strong signal strength in non-defect regions and signal reduction in areas with defects. It was also generally found that operating at the lower of a pair of operating frequencies and using the Magnetostrictive EMAT as the receiver led to better overall defect sensitivity. Two of the four combinations were hybrid versions, which demonstrated heightened signal strength over their homogeneous counterparts. Finite element simulations and additional experiments were conducted to theoretically supplement and verify the above results and trends.
Detection of Vaulting Gait with Multi-Modal Non-Linear State Estimation
Keywords: Bipedal locomotion
Control theory
Non-linear systems
State estimation
Vaulting

A methodology for automatically detecting distinct gait modes is presented in this thesis. Two distinct gait models are parameterized based on collected data from the literature, representing a normal gait pattern and a vaulting gait pattern. Structural observability analysis is performed on the dynamic models, taking into account available output measurements from a simple, low cost sensor package. A state estimation system, comprised of Unscented Kalman Filter variants and an Interacting Multiple Model estimator, is applied to simulated and live data to automatically detect the gait mode.
Prescient Migration for Data-Dependent Distributed Transactions
Keywords: Data Dependencies
Databases
Distributed Systems
Migration

Deterministic databases reduce the need for expensive commit protocols, which enables scaling transaction processing in distributed environments. However, workloads with intra-transaction cross-node dependencies still impose significant overheads. Our aim is to efficiently support distributed transactions with intra-transaction dependencies, which we call data-dependent distributed transactions. We propose using a combination of 1) transaction scheduling and transaction migration policies to reduce the overhead of data dependencies, and 2) data migration policies to reduce the number of distributed transactions. We implement these policies in the Caracal deterministic database. We evaluate these policies and identify the types of workloads that benefit from these policies.
An Investigation and Exploration into the Use of Repetitive Transcranial Magnetic Stimulation as an Intervention for Promoting Positive Self-Regulatory Behavior in Children with Autism Spectrum Disorder
Keywords: Autism spectrum disorder
Emotional regulation
Problem behavior
Repetitive transcranial magnetic stimulation

Background: Children with autism spectrum disorder (ASD) often present with impairments in social, educational, and occupational settings. Impairments in these settings may be caused by deficits in self-regulation and can lead to unwanted behaviors. Thus, interventions targeted toward self-regulation are needed.Objectives: (1) To identify how repetitive transcranial magnetic stimulation (rTMS) may improve symptoms of ASD; and (2) to assess the feasibility of rTMS as an intervention technique for children with ASD. Methods: (1) Systematic review methodology was used to synthesize literature on rTMS in pediatric ASD. (2) A double-blind, randomized controlled trial was conducted to explore the feasibility and tolerability of rTMS. Results: (1) Further investigation is needed to determine the most efficacious rTMS metrics. (2) rTMS shows therapeutic potential for ASD, but barriers to feasibility must be addressed. Conclusion: This dissertation will inform future clinical trials exploring the feasibility of therapeutic interventions for pediatric ASD.
Development of 197Hg-Labelled Panitumumab for Auger Electron Radioimmunotherapy of Triple-Negative Breast Cancer
Keywords: Auger electron radioimmunotherapy
Mercury-197
Panitumumab (mAb of EGFR)
Triple-negative breast cancer

Objective: The cytotoxicity of anti-EGFR monoclonal antibody panitumumab directly radiolabelled with 197Hg or conjugated to S-2-(4-isothiocyanatobenzyl)-1,4,7,10-tetraaza-1,4,7,10-tetra(2-carbamoylmethyl)cyclododecane (p-SCN-Bn-TCMC) and radiolabelled with 197Hg was evaluated in EGFR-overexpressing human MDA-MB-468 triple-negative breast cancer (TNBC) cells. Methods: 197Hg-TCMC-panitumumab and 197Hg-panitumumab were constructed and characterized in vitro using analytical techniques that measured their homogeneity, EGFR binding affinity, radiochemical purity, and binding specificity to MDA-MB-468 cells. In addition, the cytotoxicity of the radioimmunoconjugates was studied in a clonogenic survival assay. Results: 197Hg-TCMC-panitumumab was homogeneous with approximately 2 TCMC molecules bound per panitumumab molecule and had high EGFR binding affinity (1.16 x 10-13 M). 197Hg-TCMC-panitumumab and 197Hg-panitumumab had high radiochemical purity (90% measured using instant thin layer silica gel chromatography) and demonstrated EGFR-specific binding to MDA-MB-468 cells. Clonogenic survival of MDA-MB-468 cells showed that 197Hg-TCMC-panitumumab and 197Hg-panitumumab were significantly more cytotoxic than panitumumab, which did not affect the survival of MDA-MB-468 cells. These radioimmunoconjugates are promising therapeutic agents for TNBC and warrant further in vitro and in vivo preclinical evaluations (e.g. serum stability study, γ-H2AX assay, cell fractionation assay, biodistribution study, tumour imaging study).
International Graduate Students' Experience with University Career Services: A Case Study at a Southern Ontarian University
Keywords: career development
career services
case study
international students

This study explores international graduate students’ experiences using university career services for their career development at a southern Ontario university. The research draws on findings from the previous literature regarding services provided by the university career centres and international graduate students’ experience in using them. Employing a qualitative single-case study approach, I analyzed two career centres’ websites and interviewed two career centre staff and seven international graduate students at the institution. The inductive, problem-oriented study aimed to discuss how its findings spoke to the existing literature about international graduate students’ challenges in using university career services. The findings revealed that career centres provided diverse career services, but those services were not sufficient and customized for international graduate students’ specific needs. The study enriches the existing knowledge and suggests the need for more tailored and accessible career services for international graduate students’ career development.
Influence of Stormwater Management Ponds on Chloride Transport to Urban Headwater Streams
Keywords: aquatic toxicity
contaminants
road salt
water quality
winter

Sodium chloride (NaCl) is the most common de-icing agent used on roads and parking lots in North America. During the winter and spring, chloride (Cl-) is readily transported from paved surfaces to stormwater management facilities in a matter of hours to days. According to earlier studies, densimetric stratification in end-of-pipe stormwater management facilities such as wet ponds can result in latencies in Cl- transfer. As a result, wet ponds may generate Cl- pulses in streams that exceed thresholds of acute toxicity to aquatic biota. This study identified the prevalence of this phenomenon at five headwater streams receiving discharge from wet stormwater ponds within the Greater Toronto Area over two years. Results suggest that pulses exceeding acute toxicity thresholds are not ubiquitous and are limited to late winter and spring months.
The Ubiquitous p value: An Exploration of the Survey Sampling Methods Associated with p values in Medical Education Research.
Keywords: p-values
Survey Research

This study explored trends in the use of p values in medical education research studies among articles published in Academic Medicine (and its predecessors) between 1926 and 2023. Three hundred articles meeting inclusion criteria were analyzed for respondent selection method and the use of p values. Since p values describe the probability that a study’s findings are due to sampling error, only studies that used probability sampling should have reported p values. However, 65% of articles that used an attempted census (all members of the population were invited to participate) or non-probability sampling used p values to describe their findings. Stricter peer reviews and more education with respect to what p values mean, and their inappropriate use with non-probability sampling is needed to ensure the collection of accurate, valid, and useable survey data.
Direct Assessment of the Hydrophobicity of Ionic Surfactants via the Characteristic Curvature (Cc) Concept
Keywords: HLD-NAC
Hydrophobicity
Microemulsion
SAXS
Solubilization
Surfactant

Surfactants possess both hydrophilic and lipophilic moieties that play important roles in numerous applications including personal care, cleaning products, and in the pharmaceutical and food industries. Assessing the hydrophilicity-lipophilicity (H-L) of a surfactant is necessary to predict the surfactant’s behavior in surfactant-oil-water (SOW) systems and to optimize the performance of these systems. This work assesses the H-L of ionic surfactants through the concept of characteristic curvature (Cc) using two innovative reference-free methods: a solubilization method and a method based on Small-Angle X-ray Scattering (SAXS). The Cc is determined via a Hydrophilic-Lipophilic-Difference (HLD) and Net (Hn)-Average (Ha) Curvature (NAC) framework through the definition of Cc as Cc = -Hn∙L (L being the surfactant’s tail length parameter) at the characteristic condition. The solubilization studies revealed that after the original HLD equation for ionic surfactants is multiplied by a factor “bi”, the new HLDbi = bi∙HLD reflects the chemical potential and curvature interpretation, validating the Cc concept. The SAXS studies successfully match the Cc assessed through solubilization methods, providing a direct way to assess the H-L of ionic surfactants without bias presented by cosurfactant in phase inversion studies as well as minimizing the potential computational uncertainty of the solubilization method.
Sustainable State Sharding and Token Liquidity Provision for the Blockchain Ecosystem
Keywords: Atomic
Automated Market Maker
Decentralized Exchange
Ethereum
Liquidity
State sharding

With the increasing adoption of blockchain technology, certain challenges must be tackled for a sustainable future. One such challenge is the ever-growing size of a blockchain ledger which increases the stress on and cost of mining hardware equipment. This thesis provides a solution to the above problem via atomic state sharding of account-based state blockchains. Our solution reduces the disk requirement of each participating miner linearly with respect to the number of shards while maintaining low bandwidth requirements. A second challenge investigated here is the increased cost of liquidity provisions for tokens with low trading volume. Lower liquidity makes the price of a token more vulnerable to manipulation. This thesis presents a design for a decentralized exchange that enhances liquidity for low trading volume tokens via liquidity sharing. Experiments show that our solution enhances liquidity by over 2.6 − 5.9×.
Quantile Formulation Risk-Aware Reinforcement Learning for Goal-Based Portfolio Optimisation
Keywords: Goal-Based Investing
Portfolio Optimisation
Reinforcement Learning
Risk Measures

We study the problem of active portfolio management where a risk-aware investor seeks to construct an optimal dynamic allocation with respect to a prescribed risk profile subject to the constraints imposed by their budgetand desired realised gain. Specifically, we consider investors of whom seek to construct allocations that minimise the rank dependent expected utility of their terminal wealth subject to a budget and goal-based investing constraint (or aspiration criterion) that specifies a lower bound on the probability that the investor posts some fixed positive return. We show that an appropriate regularisation of He and Zhou's HF/A model admits optimal allocations that can be characterized explicitly through the notion of isotonic projections in a more general class of market models than those which they consider. We then turn to the risk-aware reinforcement learning (RL) task this problem defines and further affirm the paradigm's potential in tackling constrained portfolio optimisation problems; we employ the deep policy gradient (DPG) framework developed in recent work of Jaimungal & Pesenti (2022) and illustrate the learned behaviours of the agent beneath a variety of risk profiles and aspiration criteria.
Estimating Indoor Airborne Concentrations of SARS-CoV-2 Using Quantitative Filter Forensics
Keywords: COVID-19
dust
filter
portable air cleaner
ventilation
virus

Room-scale, week-long environmental surveillance is needed to better understand where SARS-CoV-2 is at high airborne concentrations in built environments. This study used portable air filters (PACs) and quantitative filter forensics (QFF) to assess airborne concentrations of SARS-CoV-2 RNA in COVID-symptomatic homes, classrooms, and dining locations throughout Toronto. PACs were deployed for one week each, and dust from filters was collected via vacuuming. SARS-CoV-2 RNA from filter dust was quantified using reverse transcription-polymerase chain reaction (RT-PCR) and PAC metadata were used to estimate airborne concentrations of SARS-CoV-2. The highest concentrations of SARS-CoV-2 were found in isolation rooms. Classrooms had lower concentrations during summer. Limitations include unknown recovery efficiency of RNA from filters and the dynamics of concentrations due to the temporal averaging provided by QFF. This study concludes that isolation was effective at keeping high airborne SARS-CoV-2 concentrations in isolation rooms and seasonal variation in concentrations in classrooms was observed.
Image-Based Prediction of Building Attributes with Deep Learning
Keywords: Building
Climate change
Deep learning
Greenhouse gas
Machine learning
Material stock analysis

Construction material use causes about 11% of global GHG emissions and is an accelerating driver of global warming. In this research, we use image-based machine learning to predict the floor area and age of buildings which are strongly correlated with embodied GHG emissions. The ability to automatically estimate building attributes from street view images can enable large-scale analysis of the built environment and provide better differentiability compared to patch-wise or pixel-wiseestimation from satellite images. A ResNet-18 model is used for feature extraction, and area and age predictions are formulated as a regression problem and a classification problem, respectively. On area prediction, our model achieves a Mean Absolute Percentage Error of 22.32%. On age prediction, our model achieves a Balanced Accuracy (BA) of 78.05% and Accuracy of 79.05% when there are 3 age classes, but the BA and Accuracy drop to 61.94% and 63.53%, respectively when there are 6classes.