eScholar

eScholar stores, preserves and disseminates digital copies of the research and scholarly output of eScholar faculty, researchers and students. These can include the following items:

  • Monographs
  • Pre- and post-prints of academic journal articles
  • Theses and dissertations
  • Major projects and papers
  • Reports/working papers and conference proceedings

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Now showing 1 - 5 of 9

Recent Submissions

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    Anomaly detection in kernel-level process events using machine learning-based context analysis
    (2020-08-01) Ezeme, Okwudili M.; Mahmoud, Qusay H.; Azim, Akramul
    The limitation of the use of input-validation approach in anomaly detection at the application layer is that a malicious software like Stuxnet worm can successfully return looped expected values to a monitoring application while executing the attack. To overcome these application layer-based anomaly detector limitations, we focus on anomaly detection at the kernel layer. Anomaly detection using kernel-level traces has unique advantages in detecting security threats early, but challenges associated with understanding the patterns for online and offline monitoring are enormous. However, the problem of the intricate pattern of the kernel-level events can be solved with effective machine learning approaches but requires a deep understanding of the data in the application domain. In this thesis, we design and implement machine learning frameworks based on deep learning (DL) and clustering that capture the context of a process in both inter- process and intra-process interactions via kernel-level event profile analysis. Since the context is learned from the kernel-level events, we provide cybersecurity solutions without compromising the privacy of the software applications because there is no one-to-one mapping between the kernel events and the source code of the process. During operation, we label patterns deviating from the benign context as malicious and we can kill or restart the process when we detect that it has been compromised. Software applications have a high degree of reliability. Therefore, data collected from these applications to create machine learning models is not balanced and introduces a bias in the machine learning model. We solve this challenge with a novel generative adversarial network (GAN)-based oversampling technique that inherently removes noise and outliers from the data without the use of the computationally expensive strategy of input sample inversion. We test the proposed frameworks with several publicly available benchmark anomaly datasets of Unmanned Aerial Vehicle (UAV), network logs, and images with varying profiles that impact the order, distribution, and execution time contexts of the applications. In all the test cases, the results of the frameworks in this thesis show a 3% - 13% improvement in the Precision, Accuracy, and Recall over the benchmark approaches used for comparison.
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    State of the health system: an exploration of the patients first act and recent outcomes
    (2019-08-01) Sleeman, Anne; Lemonde, Manon
    There is evidence that integrated healthcare systems are considered a solution to the challenge of maintaining access and integrity in healthcare. This is important to uphold the Canada Health Act (1982) principles of universality and accessibility. Furthermore, strong primary care services are the foundation for a high-performing healthcare system and Ontario has focused on improving primary care delivery for many years, through various means. Bill 41, the Patients First Act, was recently legislated in 2016 to promote the integration of primary care services in Ontario, as well as home care and public health, all under the mandate of the Local Health Integration Networks (LHINs). In order to transform and improve our healthcare system, we must reflect on past outcomes of healthcare reform that led to the enactment of Bill 41. It is also important to understand the consequences of any legislation and policy direction in healthcare, as well as its effect on all key stakeholders, including healthcare professionals, patients, services and institutions. Without adequate buy-in and support for new policies, change management will be tumultuous and limit the improvements that are intended to enhance service accessibility for patients. This Major Research Paper will explore the proposed benefits and actual challenges of adopting Bill 41. Furthermore, the paper presents a historical review of primary care reforms; a critical analysis of policy implementation challenges; and discussion of controversial responses to new healthcare legislation in the media, with specific consideration for the recent shift in government power.
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    Neural mechanisms in processing of emotion in real and virtual faces using functional-near infrared spectroscopy (fNIRS)
    (2025-08-01) Rapanan, Dylan; Livingstone, Steven; Stojanoski, Bobby
    As avatars permeate social media, gaming, and telecommunications, understanding how the brain reads emotions from virtual faces is increasingly important. We recorded functional near-infrared spectroscopy (fNIRS) data from adults viewing real photographs and matched computer-generated faces expressing Anger, Disgust, Fear, Joy, Sadness, Surprise, or Neutral (control). General-linear-model mapping revealed higher activation in virtual faces in the left occipital region, and higher activation in Neutral and Surprise compared to the other emotions in parietal and occipital regions. Functional-connectivity analysis revealed higher connectivity in real faces across the brain, and higher connectivity across the brain in Anger and Fear compared to the other emotions. Collectively, the results demonstrate differences in activation in occipital areas, and differential processing of face and emotion types across the whole brain. These neural signatures provide quantitative targets for refining the realism and emotional efficacy of digital characters in virtual and augmented environments.
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    An autoethnography of double consciousness and educational exclusion: Black womanhood, cultural erasure, and the search for belonging
    (2025-08-01) Scott, Nickiesha; Eamer, Allyson
    This autoethnography explores my experience as a Black woman navigating the Canadian education system from childhood to my current educator role. Grounded in critical race theory and the concept of double consciousness, the study examines how race, gender and class interest shape identity, belonging and academic possibility. Through personal narrative and scholarly engagement, I reflect on the internalized silences, shifting identities, and systemic inequalities that shaped my sense of self across educational spaces. While the educational trajectories of both my mother and daughter provide necessary context, this autoethnography centers my journey, illustrating how structural barriers, cultural erasure, and misrecognition defined my education and early academic career. My daughter’s experiences within today’s education system, including a health crisis nearly a decade ago, even more effectively illustrate how institutional practices continue to neglect the nuances of Black student life. Moreover, these experiences highlight an urgency for intersectional and humanizing approaches to student support. Through the interconnectedness of memory, critical self-awareness, and theory, this paper challenges the myth of meritocracy and calls for a more relational, humanizing, and responsible understanding of educational equity.
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    Quantification and analysis of second balls in soccer
    (2025-09-01) Sears, Jackson; Hung, Patrick; Tashiro, Jayshiro
    In soccer, second balls are crucial to control possession and create attacking chances, but have remained largely unexplored. In this thesis, a mathematical framework is created to identify, classify, and extract second balls from data. Building on this foundation, the novel Expected Second Ball Value (xSBV) model uses machine learning and Markov chains to estimate both the probability of winning a second ball and the likelihood that the following possession leads to a goal. Predictive models achieved a top-3 accuracy of 60% for second ball location and an ROC-AUC score of 0.79 for predicting the winning team. The key results highlighted specific areas to target for higher success rates and produced a ranking of players based on their second-ball winning ability. This thesis extends existing literature for second ball analysis, offering valuable applications for player evaluation and tactical decision-making.