Adila: Fairness-informed Collaborative Team Formation

Team formation aims at forming a collaborative group of experts to accomplish complex tasks, which is a recognized objective in the industry. While state-of-the-art neural team formation models can efficiently analyze massive sets of candidate experts to form effective collaborative teams, they overlook fairness. In this work, we adopt state-of-the-art probabilistic and deterministic greedy reranking algorithms to achieve fairness with respect to (1) popularity or (2) gender in neural models in view of two notions of fairness, demographic parity and equality of opportunity. Specifically, we ensure a minimum representation for experts from the disadvantaged, nonpopular or female, groups by reranking the neural model’s ranked list of recommended experts. Our experiments on two large-scale benchmark datasets demonstrate three key findings: (i) neural team formation models heavily suffer from biases toward popular and male experts; (ii) probabilistic greedy reranking algorithms can substantially mitigate such biases while maintaining teams’ efficacy; (iii) in the presence of extreme biases, e.g., 95% male vs. 5% female experts in the training datasets, post-processing reranking methods alone fall short, urging further tandem integration of pre-process and in-process debiasing techniques

An SOA-Based Approach of Adaptive E-Tutoring Systems

The educational technology landscape continually evolves, and e-tutoring systems are pivotal in modern pedagogy. Traditional e-tutoring methods often need help with adaptability and user-friendliness across various devices and platforms. To address these challenges, this research introduces a novel approach that leverages service-oriented architecture (SOA) principles, enhancing scalability and flexibility. The SOA configuration streamlines communication between system components, optimizing question delivery and response evaluation. Additionally, the research contributes adaptive interfaces that intelligently engage users based on their device configurations and preferences, offering facial, vocal, or textual interactions. These interfaces ensure a consistent and tailored learning experience across PCs, laptops, and mobile devices. The study also considers critical success factors like User-Friendly Design and Technical Competence. This research presents a comprehensive solution to enhance e-tutoring systems for modern, adaptive, and engaging learning environments

NOVEL APPROACHES TO FAST OCV CHARACTERIZATION AND IMPROVED CAPACITY ESTIMATION IN LITHIUM ION BATTERIES

This thesis considers the problem of open circuit voltage (OCV) to state of charge (SOC) characterization in li-ion batteries for battery reuse applications. The traditional approach to OCV-SOC characterization is done by collecting voltage and current data through a slow discharge and charge process; this process usually takes about 60 hours. Such OCV-SOC characterization is performed on a few sample batteries because the OCV-SOC characterization is considered to be the same for new batteries coming out of the same manufacturing process. However, the characteristics of a battery may change as it is used for years in different environmental and usage conditions. Hence, they may need to be re-characterized before secondary use. Unlike primary characterization, the secondary characterization may have to be done faster in order to save time and cost. This thesis presents a faster approach for OCV-SOC characterization. The proposed approach in this thesis consists of constant-current profiles that halves in magnitude after a specified time. Such reductions allows us to fully deplete the battery; similarly, the battery is charged back with a reducing current profile in order to make sure the battery is fully charged. The resulting current profile reduces the total characterization time by 1/5. Secondly, we explore the idea of discharge and charge capacity of batteries. A traditional low-rate-OCV test consists of constant-current charging which results in a voltage drop based on the internal resistance and charging/discharging current. This thesis presents an approach to counteract this voltage drop, by appropriately over-charging and over-discharging the battery to obtain the most accurate representation of the capacity of the battery

Non-Hermitian physics achieved via non-local Gilbert damping

In this thesis, we study a simple model for a ferromagnet starting with Heisenberg exchange interaction including the effects of dissipation. Gilbert damping is consid- ered and generalized from an on-site term to include non-local damping interactions between neighbouring spins. The strength of the damping interaction between neigh- bours can be tuned individually to provide the freedom to change the parameters of the system and explore the range of possible non-Hermitian behaviours. We consider the example of a honeycomb lattice ferromagnet featuring Dirac cones and two sub- lattices and analyse the resulting spectra and eigenstates. Under periodic boundary conditions, we find the Dirac magnons that are present in the Hermitian case are split into pairs of exceptional points connected by nodal lines. Open boundary condi- tions are then studied, including cases with a periodic boundary along one direction and an open boundary along the other, to explore the presence of the non-Hermitian skin effect in this model. We find the spectra under open boundary conditions differ substantially from that of periodic boundary conditions and the eigenstates are often localized on the boundary. Lastly, we discuss open questions and future directions for exploring non-Hermitian physics in magnetic systems

The Role of Nonprofit Organizations in the Context of Increased Living Costs in Ontario

This research highlights the crucial role of non-profit organization in supporting the well-being of local communities in Ontario, particularly amidst increased cost of living. The increased cost of living has led to a higher CPI, negatively impacting the quality of life for many Ontarians and Canadians. While the vulnerable population become increasingly uncertain about the future, the role of non-profit organizations becomes prominent as effective community leaders. This research will illustrate how non-profit organizations address the most significant challenges the vulnerable population face today in terms of their quality of life, which are: housing insecurity, food insecurity, and transportation. The research methodology section will illustrate a case study of a local non-profit organization within the Windsor-Essex region in Ontario. The results indicate a growing demand and reliance on non-profit organizations of their services

Spy 1: A Potential Driving Force of the Breast Cancer Stem Cell Population

Breast cancer is the most commonly diagnosed cancer in women. Despite recent improvements in diagnostics and treatment options, the tremendous heterogeneity of the disease often complicates treatment. Triple Negative Breast Cancer (TNBC) occurs in 10-15% of breast cancer diagnoses and typically has poorer outcomes. This is largely due to lack of targeted therapies and the existence of a population of cells known as breast cancer stem cells (BCSCs); a population known to be high in TNBC. BCSCs are more resistant to therapy and capable of driving patient relapse. Cell cycle mediators may play a key role in driving expansion of this population of dangerous cells. Spy1, a cyclin-like protein, promotes cell cycle progression through the G1/S, and the G2/M phase of the cell cycle and has been shown to be elevated in TNBC patients. Additionally, Spy1 is known to expand the brain tumour initiating cell population in brain cancers. Using an in vitromodel of TNBC (MDA-MB-231 cell line), the relative abundancy of the BCSC population can be assessed to determine if increased levels of Spy1 can expand the BCSC population resulting in more aggressive, invasive and resistant cancers. BCSCs can be identified using markers known to label this population such as the CD44 high/CD24 low, and ALDH isoforms. This work seeks to determine if Spy1 is capable of regulating the BCSC population and may allow for the development of targeted therapy to increase the survival rate of those diagnosed with TNBC

African Canadian Othermothering in the Urban Secondary School

With limited curriculum guidelines on anti-racist pedagogy and knowing that teachers are heirs to the legacy of European colonialism and imperialism in educational practices (Henry, 1998), this study seeks to explore what teachers are doing about it right now in Canada amidst the conservative pushback on Critical Race theory in America. Drawing inspiration from Henry\u27s (1998) othermothering - an anti-racist pedagogy descending from the mothers and grandmothers of African American women that offers maternal assistance to the children of blood mothers within the African American community -this case study examines how individual Black female teachers and educators in Canada disrupt and challenge power and policy in classroom practice in order to meet the pyschoeducational needs of the Canadian urban child. The research introduces a critical listening methodology to illustrate the experiences and expertise of 2 Francophone African Canadian female educators with anti-racist education in Canada

Autonomous Inertial Based Vehicle Navigation Assisted by Convolutional Neural Network for Obstacle Avoidance

Autonomous vehicles are expected to aid a safe, convenient and easier driving experience. There is a huge demand for a cost effective, accurate, stable, and precise navigation system for autonomous vehicles. Inertial navigation systems combined with visual camera-based guidance are showing great promise with stable and intelligent autonomy. GPS based navigation alone is not enough to provide pin-point accuracy and needs a direct line of sight with the satellite. In contrast, inertial navigation works anywhere anytime. The focus for this research is to deploy an autonomous vehicle navigation system designed with sensor fusion technology that is supported by an inertial measurement unit (IMU) and smart camera. The proposed navigation system has an embedded IMU and camera to generate a complete assistive map of the surrounds with relative positions in any environment. The mapping of the surroundings works by performing a sensor data fusion from the camera, position data from IMU, as well as a heat graph, indicating if obstacles are close based on colour intensity. However, inertial sensor drift can degrade the sensor fusion and obstacle map due to error in position and distance of obstacles detected. To mitigate this sensor drift error, sensor fusion between the IMU and smart camera is processed to obtain an accurate environment. This sensor fusion incorporates artificial intelligence through a deep convolutional neural network (CNN). In general, convolutional neural networks are modelled after the human brain to train the vehicle to identify obstacles of interests in any environment. The smart camera mounted on the vehicle feeds real-time video footage for inference with the trained CNN and is used for real-time obstacle avoidance. This research is work-in-progress to correct sensor drift in the embedded IMU using a trained CNN to create a fully autonomous navigation system providing more autonomy towards achieving reliable assistive navigation

Statistical consulting in academia: A review

This paper reviews the state of statistical consulting in academia by performing a literature review on this topic in chapters 1 and 2. Chapter 1 overviews general aspects of statistical consulting and types of centers that conduct such services in academia. In Chapter 2 we summarise the literature about the common logistics and processes for conducting statistical consulting in academia. In Chapters 3 and 4, we analyze data on statistical consulting centers for the largest 100 universities in the USA. We also review the literature on the future of statistical consulting in academia in the era of big data and data science. The paper aims to define what statistical consulting in academia means, how it is conducted, and what its benefits are. The data analysis tries to figure out the factors that may influence the existence of statistical consulting centers, specifically in the USA