Past Student Presentations

Date: Tuesday, January 28, 2025
Time: 2:00 pm – 3:30 pm
Room: Science Board Room (UA 4170)
Number of Presentations: 3

Presentation 1:

Title of Presentation: Novel optimizers for multi-objective machine learning

Presenter Name: Farzaneh Nikbakhtsarvestani (PhD Candidate, Computer Science, Ontario Tech University)

Supervisor: Dr. Mehran Ebrahimi (Faculty of Science, Ontario Tech University) and Dr. Shahryar Rahnamayn (Faculty of Engineering and Applied Science, Ontario Tech University)

Abstract: Multi-objective optimization is a prevalent challenge in the area of deep learning. There is a lack of robust multi-objective optimization methods applicable in deep learning capable of training networks by simultaneously optimizing conflicting multiple loss functions. Its applications include a wide range of deep neural network branches such as multiloss, multi-task, multi-modal, and crossmodal learning. In this thesis, we develop two novel optimizers: Multi-objective Adam Optimizer (MAdam) and Opposition-base Multi-objective Adam Optimizer (OMAdam). MAdam is a multi-objective extension of the well-known Adam optimization algorithm. MAdam is a classical population-based approach that uses the gradient information of multiple objectives to accelerate population convergence toward an optimal minimum. The method applied a non-dominated sorting algorithm to keep selective population and improve the diversity across the landscape. In OMAdam, opposition based scheme was introduced into MAdam framework as global search is necessary for escaping local optima in gradient-based multi-objective optimization approaches. The Opposition-based MAdam, explores multiple directions over the landscape, that leads to independence from specific initialization. These two optimizers are scalable in every deep learning tasks. In a series of experiments for MAdam and OMAdam applications, we investigate multi objective binary and multi-class classification tasks using novel Madam and OMAdam optimizers. The aim of the study is to provide a range of solutions on the Pareto front to balance trade-offs between conflicting loss functions and to select corresponding model parameters that reflect the clinical priorities of practitioners (classes and sub-classes). In this regard, we employed these two novel optimizers as AI diagnostic techniques based on the 10-class MNIST and 2-class, 8-class, and 11-class MedMNIST datasets. In each experiment, the Pareto front of model parameters allows for variations in performance metrics and enable the prioritization of specific classes and sub-classes targeted by the end-users. MAdam and OMAdam optimizers achieve F1-scores comparable to those of the single objective Adam optimizer among their many solutions on the Pareto front. Also, we investigate the functionality of Madam and OMAdam optimizers in another deep learning task: Multi-objective regression task. In this study, we modify the optimization process to include conflicts losses for fitting error, model complexity, and Huber loss, which represent the model’s performance on data consistency, regularization, and resistance to outliers, respectively. we incorporate these conflicting loss terms to MAdam and OMAdam framework. These schemes, address the challenges of noise sensitivity and overfitting in scenarios dominated by collinearity or high-dimensional feature spaces. We demonstrate how these two novel optimizers can efficiently train artificial neural networks by balancing these conflicting objectives without the need for multiple retraining cycles. Our approach constructs a Pareto front that provides a spectrum of solutions, each offering a different trade-off between model’s accuracy , complexity and robustness. These methods broadens the applicability of MAdam and OMAdam optimizers in deep learning, particularly for critical applications like financial forecasting and healthcare predictions, where decision-making relies heavily on the robustness and accuracy of predictive models.

Presentation 2:

Title of Presentation: Balancing Security and Usability: A PII-Driven Framework for Password and Honeyword Generation in Modern Authentication Systems

Presenter Name: Hannah Adjei (PhD Candidate, Computer Science, Ontario Tech University

Supervisor: Dr. Miguel Vargas Martin

Abstract: The growing demand for strong yet easy-to-use authentication systems is pushing forward the development of innovative password-generation techniques to respond to the increasing threat of cyber-attacks and the critical need for user-friendly security measures.

This research investigates a novel approach to creating strong yet memorable and usable passwords that utilize Personally Identifiable Information (PII) extracted from user data. A systematic methodology was employed to evaluate PII-based passwords alongside randomly generated PII passwords (RaPII) and purely random passwords (Ra-Pass). Password strength was assessed using zxcvbn scores, cosine similarity measured honeywords’ closeness to actual passwords, and Levenshtein distance quantified memorability. Our comprehensive analysis, including ANOVA followed by Tukey HSD tests, underscored significant distinctions among the different password categories. Building on the results, which highlight the enhanced usability and robustness of passwords from PII, a comprehensive user study utilizing the System Usability Scale (SUS) provided more profound insights into users’ interactions and perceptions of these passwords. This research contributes a solid framework for developing PII-driven password and honeyword systems, integrating statistical analysis with a focus on user experience. We address current cybersecurity challenges and aim to set the foundation for future improvements in authentication technologies. By effectively blending the familiarity of PII with rigorous security protocols, it seeks to provide secure, user-focused solutions.

Presentation 3:

Title of Presentation: Gazed-enabled Implicit Interaction for Scatterplots

Presenter Name: Tania Shimabukuro (MSc Candidate, Computer Science, Ontario Tech University)

Supervisor: Dr. Christopher Collins (Faculty of Science, Ontario Tech University)

Abstract: How we look at the world can tell much about our focus and interests. Yet, this information is not used when displaying information visualizations. This research investigates novel display and interaction designs for create gaze-responsive visualizations optimized to support analysis of the items of interest. Specifically, we investigate four opportunities for implicit gaze-driven interaction for subtle visualization optimizations using eye tracking to improve standard scatter plot tasks: rendering order changes, hover speed modifications, local reference axes, and click accuracy improvements. A user study compared and evaluated the effectiveness of these techniques. Preliminary results show that the technique "hover speed," which consists of changing speed to show a tooltip based on the gaze-driven area of interest, was the favourite among the participants. Opinions on the "reference axis" technique were divided. Overall, the interest model improved their performance in both types of tasks, indicating that gaze-driven techniques can support data analytics tasks positively.