VictorJuiz/thesis huggingface dataset:
Evaluating Control-flow Integrity with Syscall Reachability Analysis
Keywords: Control-Flow Integrity
Memory Vulnerabilities
Program Analysis
Security
Taint Analysis

In order to defend against memory exploits, control-flow integrity (CFI) has been widely researched as a defence and seen significant industry adoption. While attacks have demonstrated its limitations in preventing control-flow hijacking, we argue that CFI can still prevent end-to-end exploits by limiting an attacker's access to the syscall interface. We design and implement a specialization of static taint analysis called syscall reachability analysis to over-approximate the set of syscall gadgets available to the attacker under different CFI policies, which we propose as a quantitative upper bound on exploitability. Evaluating over a set of representative C/C++ programs, We find that most programs do not make very many sensitive syscalls and that in most of the remaining cases examined, the harm can be mitigated through sanitization of syscall arguments.
Multi-stakeholder Perspectives on Acceptability of Pediatric Autism Therapies: A Mixed Methods Study
Keywords: autism intervention
autism spectrum disorder
autism therapies
perspectives

Exploring perspectives on acceptability of pediatric autism therapies (occupational therapy (OT, speech language pathology (SLP) services, applied behaviour analysis (ABA)-based therapies and physiotherapy (PT)) is critical to ensure services are appropriate and informed by lived experiences. This mixed-method survey study investigated multi-stakeholder (autistic people, parents of autistic children, clinicians) perspectives on acceptability, which reflects the extent to which people delivering or receiving a healthcare intervention consider it to be appropriate. The survey was created with stakeholder feedback. There were 157 completed responses, the majority of which were from clinicians. Results showed individuals across all stakeholder groups to be accepting of OT and SLP services, dependent on aspects of service delivery. Autistic and parent respondents were less accepting of ABA than clinicians, and shared ethical concerns about all therapies, with a focus on ABA. Further work is needed to understand what makes certain aspects of therapies (i.e., external reinforcement) unacceptable.
VRNNGAN:A Recurrent VAE-GAN Framework for Synthetic Time-Series
Keywords: Deep Learning
Machine Learning

Time-series data generation is a machine learning task growing in popularity, and has been a mod- elling focus of deep generative methods. In this paper, we propose Variational Recurrent Neural Network GAN (VRNNGAN), a novel GAN framework for synthetic time-series generation. VRN- NGAN uses a recurrent Variational Autoencoder (VAE) as the generator and a bidirectional RNN as the discriminator. The recurrent VAE captures temporal dynamics into a learned time-varying latent space while the adversarial training encourages VRNNGAN to generate realistic time-series data. We evaluate VRNNGAN against other relevant deep generative methods on three datasets and perform four evaluations on the generated synthetic data.. We propose a novel metric for evaluating time-series GAN models using the time-step percentiles built from the generated synthetic data. We find that VRNNGAN achieves the best predictive score across all datasets and baseline methods and is competitive with the top-performing alternative in the majority of other experiments.
Network Coded Information Raining Over IEEE 802.16j
Keywords: information raining
IEEE 802.16j
network coding

Information raining has been shown to address the problem of delivering Internet access to high-speed rail passengers. However, a wireline repeater or heterogeneous wireless relay design complicates implementation. Recent developments in extending mobile multihop relaying to the IEEE 802.16e standard have made it feasible to deploy an information raining using a common radio and physical layer. Two automatic repeat request techniques and two network coding techniques are proposed. An upper and lower bound on delay is established for a single fully network coded relay system operating in static signal to noise ratio conditions. Simulations involving a physical layer model demonstrate that network coding schemes can attain the maximum downlink capacity.
Optimizing Correction of Motion and Physiological Artifact in Clinical fMRI
Keywords: optimization
physiology
clinical
multivariate
image processing
functional MRI

BOLD fMRI (Blood-Oxygenation Level Dependent functional Magnetic Resonance Imaging) measures the haemodynamic correlates of brain function, with research and clinical applications. However, fMRI is limited by relatively weak signal, and large, complex noise sources. A variety of preprocessing algorithms have been developed to remove artifacts and improve signal detection, but there is no literature consensus on optimal preprocessing strategies. Furthermore, it is not well understood how fMRI experimental design choices interact with preprocessing steps. This thesis develops a statistical framework for selecting the set of preprocessing choices (“pipelines”), using data-driven metrics of (R) reproducibility of brain maps, and (P) prediction of experimental stimuli. These metrics were used to evaluate standard pipeline steps on data from young healthy subjects, who performed a set of brief tasks in an fMRI cognitive assessment battery. It is shown that (1) preprocessing choices have significant, consistent effects on the detection of brain networks in fMRI. However, (2) optimizing pipelines on a subject- and task-specific basis, compared to the standard fMRI approach of applying a single fixed set of preprocessing choices, improves (P, R) and independent test measures of between-subject activation overlap. This indicates that signal detection in standard fMRI may be limited by sub-optimal pipeline choices. Even after optimizing standard pipeline choices, physiological noise is a major confound in fMRI analysis; this includes BOLD signal changes due to respiration and pulsatile blood flow. As a potential solution, the PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis) algorithm is developed. This multivariate, data-driven model estimates physiological noise, without respiratory and cardiac measurements. The estimated noise has a spatial distribution consistent with non-neuronal tissues, and its dimensionality is correlated with cardiac and respiratory variability. Removing this physiological noise increases (P, R) of analysis results. The PHYCAA model provides novel information about the structure of physiological noise in fMRI, and a principled method of removing physiological artifact. The results of this thesis were obtained using data from a prototype fMRI cognitive assessment battery, designed for clinical use. The datasets involve brief scanning sessions with complex cognitive tasks. These findings are therefore relevant for clinical implementation of fMRI.
Seismic Analysis of Steel Wind Turbine Towers in the Canadian Environment
Keywords: seismic hazard
probability of damage
incremental dynamic analysis
finite element
seismic analysis
tubular steel
wind turbine tower

The seismic response of steel monopole wind turbine towers is investigated and their risk is assessed in the Canadian seismic environment. This topic is of concern as wind turbines are increasingly being installed in seismic areas and design codes do not clearly address this aspect of design. An implicit finite element model of a 1.65MW tower was developed and validated. Incremental dynamic analysis was carried out to evaluate its behaviour under seismic excitation, to define several damage states, and to develop a framework for determining its probability of damage. This framework was implemented in two Canadian locations, where the risk was found to be low for the seismic hazard level prescribed for buildings. However, the design of wind turbine towers is subject to change, as is the design spectrum. Thus, a methodology is outlined to thoroughly investigate the probability of reaching predetermined damage states under seismic loading for future considerations.
Establishment of Zebrafish Models for Studying Mesenchymal Stromal Cell Therapy for Cardiac Disease
Keywords: Zebrafish kidney stromal cells
Transgenic transposon vectors
Cell transplantation into zebrafish embryos

Bone marrow (BM)-derived mesenchymal stromal cells (MSCs) can be induced to express cardiac-specific markers by embryonic cardiomyocytes in vitro. To determine whether this phenomenon occurs in vivo, we have developed a cell transplantation system using zebrafish embryonic recipients. We were unable to isolate expandable zebrafish kidney stromal (ZKS) cells from the kidney, the human BM equivalent; hence, we analyzed the established ZKS1 cell line. We found that ZKS1 expresses stromal genes, but also expresses hematopoietic genes not normally expressed by MSCs. Furthermore, we were unable to differentiate ZKS1 cells into adipocytes, osteoblasts or cardiomyocytes in vitro. We created a transgenic ZKS1(CMV:eGFP) cell line which, after transplantation into zebrafish blastulae, was observed within the host heart, among other tissues. Finally, pT2/S2tnnt2-GM2 and pT2/S2tnnt2-DsRed transposons were generated to mark ZKS1 cardiac differentiation. The zebrafish model established here will be useful for studying the molecular mechanisms of exogenous MSC cardiac differentiation in vivo.
Large-scale Dynamic Traffic Assignment Modeling of Autonomous Vehicles A Case Study of Greater Toronto and Hamilton Area
Keywords: Autonomous Vehicle
Dynamic Traffic Assignment
Greater Toronto and Hamilton Area GTHA
Heterogenous
Large Scale
Mesoscopic

With the rapid advancement of technology and people's tendency toward a driving-free lifestyle, autonomous vehicles (AVs) are expected to dominate the roads in the future. Therefore, it is crucial to quantify their transportation implications for metropolitan areas. In this project, we built a dynamic model to assess the impact of AVs on the performance of the Greater Toronto and Hamilton Area (GTHA) transportation system in both mixed traffic flow and the whole AV conditions. To this end, first, a large-scale and multi-regional network is developed for the GTHA in the AIMSUN Next® platform. Then, a generic approach is applied to model AVs in mesoscopic resolution. The results revealed that the level of aggressiveness of the AVs plays a critical role in the magnitude of their effect, where the presence of aggressive AVs improved the system performance while cautious AVs resulted in deterioration.
Long-range Charge Transport in Linear Molecular Chains
Keywords: Charge Transport
Molecular Electronics

Charge transport (CT) is the flow of charge through a medium, in this work we focus on electron CT. Due to the longer timescale of CT in longer chains, nuclear degrees of freedom must be included. In this work, we use the Landauer-B ̈uttiker probe (LBP) method to incorporate dephasing and energy exchange to the system electrons. We first present short systems of length < 1 nm to demonstrate the transition from tunneling to hopping transport mechanism. Next, we demonstrate non-trivial behaviours in high intersite tunneling energy systems. Following, we simulate longer systems up to 50 nm, showing the advantages of different transport mechanisms. We further simulate an alternating-rigidity system based on Geobacter Sulfurreducen by alternating the dephasing conditions. Finally, we apply the LBP method to study chiral-induced spin selectivity in proteins. While the results are preliminary, simulations show asymmetry of the transmission probability for different electron spin species.
SigVM: Enabling Event-driven Execution for Truly Decentralized Smart Contracts
Keywords: blockchain
programming languages
secuirty
smart contract

This paper presents SigVM, a novel blockchain virtual machine that supports an event-driven execution model, enabling developers to build truly decentralized smart contracts. Under traditional blockchain virtual machine such as Ethereum, the events have to be monitored uninterruptedly and the corresponding function in Smart Contract should be called manually by users once acquiring events. Under SigVM, contracts can emit signal events, on which other contracts can listen. Once an event is triggered, corresponding handler functions are automatically executed as signal transactions. We build an end-to-end blockchain platform SigChain and a contract language compiler SigSolid to realize the potential of SigVM. Experimental results show that our benchmark applications can be reimplemented with SigVM in an autonomous way, eliminating the dependency on unreliable mechanisms like off-chain relay servers. The development effort of reimplementing these contracts with SigVM is small, i.e., we modified on average 2.6% of the contract code.
Multi-modal, Multi-functional Phase-change Palladium Porphylets
Keywords: Cancer
Droplet
Mass Spectrometry
Photodynamic Therapy
Porphyrin
Ultrasound

Photodynamic therapy (PDT) utilizes sensitizers, such as porphyrin, and light to locally generate cytotoxic reactive oxygen species (ROS) to treat tumors. The accumulation of sensitizers in the tumor is critical for effective ROS generation. Porphyrin liposomes (porphysomes) have been demonstrated to be an effective and passive accumulation platform for PDT but cannot be used for active delivery. Porphyrin microbubbles are capable of active delivery with ultrasound for subsequent PDT but are too large and short-lived for passive delivery. In this thesis, phase-change porphyrin droplets (AKA porphylets) are investigated as an agent that can bridge porphysomes and porphyrin microbubbles. Like porphysomes, porphylets can passively accumulate in tumors due to their stability and small diameters. Like porphyrin microbubbles, porphylets can respond to ultrasound for active delivery. Such an agent can be utilized as a multi-modal, multi-functional "one-for-all" platform that is capable of therapy and delivery quantification without the need for companion diagnostic agents.
Collaborative Professional Development: The Journey of Five Core French Teachers Navigating the COVID-19 Pandemic in Ontario Public Schools
Keywords: Collaborative Professional Development
Core French
COVID-19 Pandemic
Ontario
Public Schools
Teacher agency

In this study, I bring forward five elementary Core French teachers’ experiences of teaching during the COVID-19 pandemic in a large school board in the Greater Toronto Area in Ontario, Canada. The study evolved from a collaborative professional development group I initiated within the school board in September of 2020. The aim was to support each other, so that we in turn could help our students. The objective of my research is to explore the extent to which the collaboration group enabled us to develop teacher agency as Core French teachers despite the challenges associated with unstable school planning. This study will look at how Core French teachers 1) planned their programs, 2) supported students, particularly those who are learning the main school language, also referred to as English Language Learners, and 3) utilized technology to support learning throughout the different stages of teaching through the pandemic.
Toward Patient-centred Care in Precision Oncology: What Matters to Patients
Keywords: Cancer screening
ctDNA
Hereditary cancer syndromes
Patient-centred care
Precision oncology
Qualitative research

Aim: To explore patients’ experiences and perspectives on using precision oncology to identify domains of patient-centred care that are important in this context. Methods: Qualitative interpretive descriptive study consisting of semi-structured interviews with hereditary cancer syndromes patients who would be eligible for circulating tumour DNA (ctDNA) testing. Results: 22 patients were interviewed. Connectedness to the healthcare system was the central element to participants’ experiences of patient-centred care. It was also central to their perspectives on patient-centred delivery of ctDNA. Four domains influenced the degree participants felt connected to the system: patient-provider communication, navigation, coordination of care, and technology. ctDNA was seen as a tool to increase participants’ connectedness to the system by increasing touchpoints with providers, informing decision-making, and triggering further investigations. Conclusion: Patient-centred precision oncology should strive toward connectedness to the system (i.e., ensuring patients are in regular contact with and supported by the healthcare system (e.g., their providers)).
Meaningful Datatypes: Ontologically-Sound Dependent Type Systems for Data Science
Keywords: data science
datatypes
dependent types
meaningful datatypes
ontology

Data science incorporates many processes, techniques and domains to transform data into meaningful insights that inform decision-making. In practice, data science relies heavily on simplistic datatypes like strings or integers to represent common and significant complex real-world phenomena like time, mereology, and provenance. Current solutions to this problem are opaque, lack standardization, and require manual intervention to validate. To address these issues, we introduce the meaningful type safety framework (MeTS) to enforce the integrity of real-world interpretations via type-checking with dependent types. MeTS is also grounded in real-world census datasets from Statistics Canada, which also serve as the basis for a novel census data ontology and a theorem to prove the formal ontological soundness of a MeTS type environment. The MeTS framework, including its ontological foundations, challenges the status quo of data science, and introduces new avenues of research for the functional programming, data science, and knowledge modeling communities.
Investigating the Role of 3-ketosphinganine Reductase (KDSR) in Acute Myeloid Leukemia
Keywords: Acute Myeloid Leukemia
Lipidome
Lipidomics
Metabolism
Sphingolipids

AML is characterized by accumulation of poorly differentiated, non-functional myeloblasts in the bone marrow and blood. Despite therapeutic advancements, AML remains a disease with poor long-term survival rates. To develop improved therapeutic strategies, this study aims to understand and target sphingolipid metabolism in AML. Sphingolipids are a heterogenous class of lipids that share an 18-carbon amino alcohol backbone. Bioactive sphingolipids regulate leukemic cell survival, apoptosis, chemoresistance, and differentiation. In this study, we show that early sphingolipid synthesis enzyme, 3-ketosphinganine reductase (KDSR), is essential for AML pathogenesis. KDSR is overexpressed in primary AML patient samples compared to healthy controls. Knockdown of KDSR impairs proliferation, viability, and self-renewal properties in AML cell lines. Using liquid-chromatography mass-spectrometry, we show that lack of KDSR activity induces a significant change in the cellular lipidome. Altogether, our findings suggest that KDSR promotes AML pathogenesis by regulating lipid homeostasis, thereby proposing KDSR as a novel target in AML.
Measurement and Characterization of Neighbourhood Scale Air Quality in the Vicinity of an Urban Rail Yard in Toronto, Ontario
Keywords: Air Quality
Emissions
Measurement
Pollution
Rail
Rail Yard

This study explores potential neighbourhood scale air quality impacts of an urban rail yard in Toronto, Ontario. Measurements of NO, NO2, NOx, O3, PM10, PM2.5, BC and UFP were completed at two urban background stations near the rail yard. Two mobile monitoring campaigns measuring BC and UFP were also carried out on the surrounding road network. Fixed station measurements approximately 150 m from the nearest tracks exhibited statistically significant evidence of elevated indicator pollutant concentrations during downwind conditions. On-road mobile measurements exhibited strong influences from on-road traffic compared to other neighbourhood sources such as the rail yard. Paired analysis between the two fixed stations to estimate maximum potential rail yard pollutant enhancements demonstrated measurable impacts when contributions from confounding local sources were typically minimized, such as during downwind conditions and overnight hours. Future research could benefit from collaboration with rail operators for collection of detailed rail yard activity data.
Sex Differences in Stroke Dysbiosis in Rodent Models of Focal Cortical Ischemia
Keywords: Focal cortical ischemia
Gut-microbiota-brain-axis
Rodent models
Sex differences
Stroke

Stroke is a devastating neurological disease that differentially affects males and females across the lifespan. Interestingly, clinical, and preclinical research findings indicate that stroke induced changes in gut microbiota composition, termed stroke dysbiosis, plays an important role in stroke outcomes. Given that there are also sex differences in gut microbiota composition throughout the lifespan, there has been little study of sex differences in stroke dysbiosis. In this study, we utilized rodent models of focal cortical ischemia to study sex differences in stroke dysbiosis. Using 16s rRNA sequencing approaches, we observed that the trajectory of stroke dysbiosis differs between males and females over time, however this was not associated with a concomitant increase in biomarkers of increased intestinal permeability. Furthermore, we assessed whether probiotic interventions may improve functional outcomes following stroke injury. Interestingly, we observed functional recovery following probiotic treatment in a sensorimotor model of focal cortical ischemia. This highlights the potential use of probiotics as an adjunct therapeutic for ischemic stroke.
Soil Nitrogen Mineralization and Nitrous Oxide Dynamics in an Extensive Agro-Green Roof System
Keywords: extensive green roof
nitrogen mineralization
nitrous oxide
rooftop agriculture

Understanding microbially mediated nutrient cycling processes, including soil nitrogen (N) dynamics is key to maximizing the potential for extensive green roofs to support crop production. In this thesis, I used three substrate treatments (Organic, FLL, and FLL+) and four cropping treatments (bush-bean and Sedum spp. planted in monoculture and together in polyculture) and a no plant control. Measuring various plant parameters, soil N mineralization rates (n=48), and soil nitrous-oxide (N2O) (n=36) across extensive green roof modules, I found that both crop and vegetation productivity, as well as N mineralization rates were significantly affected across substrate treatments, however no significance was detected for soil N2O production across all experimental treatments. Overall, findings from this study show that FLL substrates demonstrate ability to support crop production, and that substrate type is key to maintaining soil nutrient fluxes for crop growth on extensive green roofs.