Insider threat is one of the most damaging security threats to the safety of data, systems, and intellectual property of institutions. Typical threats caused by malicious insiders are trade secrets / intellectual property theft, disclosure of classified information, theft of personal information and system sabotage. Malicious actions of insider threats are performed by authorized personnel […]

BOLD-100 is a promising new drug that initial studies have shown has potent activity against the SARS-CoV-2 (the cause of COVID-19) in cell culture experiments. Before being able to start clinical studies with BOLD-100, additional research into the mechanism of action is required, plus testing the safety and efficacy of BOLD-100 in animal models of […]

SmartBody, SmartMind (SBSM) is a 12-week online intervention combining elements of movement, mindfulness, education, and psychologically-informed coping strategies. SBSM’s philosophy is that there are physical, emotional, and psychological dimensions to all disorders. Accordingly, SBSM offers a variety of therapeutic techniques through a variety of modalities, so that each individual may tailor their own healing. While […]

There is a growing recognition in the publishing industry that standard supply chain marketing strategies have not been effective in promoting Indigenous materials and reaching Indigenous audiences. This problem has prompted us to explore how marketing processes can be amended or augmented to ensure that Indigenous organizations and educators are introduced to the resources that […]

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. […]

At a high level, the goal of this project is to create a system for producing synthetic datasets based on real data. As a large financial crime detection firm, Verafin deals with large volumes of sensitive data which must be kept private, however they are also interested in collaborating with academics to gain new insights […]

The proposed research will explore opportunities in measuring spatial variability in soil, water and topography within potato fields. Addressing this variability by doing site specific application of inputs such as fertilizer, crop protectant and seed can help to increase environmental sustainability and economic viability in potato production. This research will help develop new applications for […]

Terrestrial contaminated sites – such as abandoned oilfields, chemical spill sites, or former industrial zones – are a major environmental problem in Canada and around the world. Environmental Material Science has created new environmental monitoring equipment that generates high-resolution spatially and temporally explicit data on environmental quality. The data must be visualized and then used […]

Deep learning in medical imaging analysis has revolutionized the field in areas such as computer-aided detection and segmentation of clinical abnormalities. Several studies have been published on lung cancer screening using deep learning methodologies. Specific to lung cancer screening, algorithms have been trained to automatically detect and diagnose lesions in the lungs in low dose […]

Scrawlr is a platform for unconstrained, global interaction with all internet content and users. Scrawlr allows for user evaluation and unconstrained classification of any Scrawlr-hosted or non-Scrawlr content. For non-Scrawlr content, this evaluation and classification allowance will be first at the URL level but will subsequently be provided at the individual content component level. Scrawlr […]

In the last decade optimization is expanded in many applications from food production to sophisticated applications such as engine fuel efficiency. In the proposed package, it is tried to apply optimization techniques along with physics based analytical and semi-analytical methodologies to create a compelling framework which can help thermal-process based oil industry to reduce their […]

The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more […]