Innovative Projects Realized

Innovations in Municipal Bylaw Adjudication

This research will look at existing and possible innovations that would make municipal bylaw enforcement and adjudication in Saskatchewan more accessible, effective and efficient and would reduce the involvement of the formal court system. This may build on the example of regional co-operation offered by the Municipal Bylaw Court in Kindersley, and it may include ways to adjudicate bylaw prosecutions outside of the court system. Any proposed solutions will be suitable for the Saskatchewan context, and any necessary changes to Saskatchewan legislation will be specifically identified.

Exploring the effectiveness of a pilot parasport coach mentorship program.

Informal learning involves acquiring knowledge outside of a structured setting in which
learning is self-directed and developed from experience, exposure, and interactions with their
environments (Nelson et al., 2006). Examples of informal coach learning includes experience
as an athlete, coach observation, self-reflection, reading books, exploring the internet, and
learning from experts or mentors in the field (Fairhurst et al., 2017; Taylor et al., 2014).
Mentorship has been considered and utilized as an informal learning opportunity where coaches
seek out more experienced professionals in their field to shadow and learn from (Bloom, 2013;
Kram, 1985; Ragins & Kram, 2007). One of the first studies on coach mentorship in parasport
was conducted by Fairhurst and colleagues (2017) who interviewed six Canadian Paralympic
coaches on their experiences with formal and informal learning opportunities. The results
revealed that four out of six coaches had a mentor, three of which were informal relationships
and one from a formal mentorship program, and all coaches acted as mentors throughout their
careers. Coaches described learning highly-specialized parasport-specific skills from their
mentors, such as information pertaining to the physiology of their athletes’ disability and
developing a parasport training program, and considered this relationship to be their most
significant learning experience.

Cloud Native Big Data Engineering and Automation

XLScout is a startup engaged in democratizing innovation and connecting research and development with intellectual property (IP) departments across the world. The company is developing proprietary algorithms, using Artificial Intelligence and Machine learning, to mimic the behaviour of an expert searcher.
XLScout hosts a data vault of about 130+ million patent documents which occupies approximately 8TB of storage. Searching such documents is a cumbersome process requiring extensive effort, time and strategies that a novice searcher might not be aware of. The objective of this project is to automate the search process by allowing machines to understand users’ queries. A sustainable and adaptive text mining framework will be developed to provide NLP-based research outputs for IP search in different domains. This will provide scalable solutions for XLScout’s data vault on which the company will run proprietary AI/ML models and generate high-value analytic solutions to help the customers make informed decisions

Secure blockchain technologies

In the recent years, blockchain technologies have shown promise as infrastructure for decentralized trustless anonymous digital asset exchange. The technology promises to transform how the data is shared in many areas including financial sector, insurance and gaming industries. Yet several obstacles prevent mainstream adoption of this technology – one of these challenges is security. To facilitate trustworthy data collection, and management in blockchain, ensuring secure communication is essential.
The blockchain’s underlying cryptographic theory makes it difficult for an adversary to modify the data provenance. Yet, the technology is not immune to unauthorized access, modifications, and repudiation of origin. This research aims to address these security problems and develop methodologies to predict, track and analyze suspicious users, their behaviour, and corresponding threats in blockchain.

Multi-institute domain adaptation by adversarial constrained medical time series representation learning

Hospitals strive to perform cutting edge medical treatment, treat all patients fairly, and reduce operating costs, while also enabling caregivers to spend more time interacting with patients. Artificial intelligence and machine learning promise these things. However, medical data provides unique challenges for machine learning. Currently, if a hospital wants to include an algorithm for automated decision making, they must either secure approval to collect additional patient data or change their care practices to replicate those at other institutions. This work proposes a novel application of artificial intelligence in medicine that creates a numeric representation of patients’ electronic medical records which is constrained to be similar across all hospitals despite each hospital having different underlying operating procedures. As a result, we can directly transfer algorithms which have proven to improve care at one hospital to another, without the need for additional data collection. This research has the potential to save lives of patients who otherwise might have been overlooked, improve patient quality of life, and set a precedent for quality healthcare globally within the next three years.

The Acquisition of Land for Community Benefit: Dynamics of Organizational Structure and Management

Union: Sustainable Development Co-operative (Union Co-operative) seeks to democratize city-building by empowering its members to collectively buy, upgrade, and manage commercial and residential properties to improve the environmental, social, and economic health of Waterloo Region. This project will support the evolution of the Co-operative’s model, the development of affordable housing for refugees, and create templates that can be implemented by other communities seeking to establish affordable rents and community control of property. COVID-19 highlights the critical importance of adequate and affordable housing for sheltering in place, and the need for additional capacity to welcome refugees as the United Nation predicts increased global instability.
The research will utilize document analysis, interviews, focus groups, and facilitated design sessions.

Computational Pipeline Monitoring leak detection on multiphase fluid pipelines

Multiphase flow represents a significant portion of the products transported in Canadian pipelines. Each of the many phases in a multiphase flow has its own unique characteristics, all of which will contribute to an added layer of complexity in the detection and subsequent localization of a leak. Current technology that compares pressure variations to identify a leak is unreliable for leaks that are less than 1% of the flow volume [1]. Considering a multiphase pipeline that has a capacity of 300,000 barrels per day, a 1% leak of 3000 barrels per day can still have a disastrous impact, especially if not immediately detected. Such failures can cause harm to the safety of people, become an economic burden for companies, and create damages to public perception. The outcome of this project will fill in the gap in leak detection software and techniques to address the difficulties associated with multiphase leak detection.

Exploration of Hydrogen Silsesquioxane (HSQ) Formulations and the use of HSQ as a Precursor for Silicon Quantum Dots for Use in Polymer Coatings

The project will entail the production and characterization of hydrogen silsesquioxane (HSQ), a useful material for both lithography and production of silicon nanomaterials. This material is the workhorse for Applied Quantum Materials Inc. (AQM), as it is one of their central products that they supply to the e-beam lithography industry as well as the precursor for their silicon nanomaterials. Once studied experimental HSQ samples will be transformed into silicon nanomaterials and incorporated into polymers to produce new materials that convert solar UV into more useful red and near-infrared (NIR) light.

Development and Application of Marine Mammal Density Estimation Methods for Directional and Omnidirectional Hydrophones

Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, these survey methods are generally only performed in good weather conditions and require many trained observers. These factors make visual surveys expensive and reduce the temporal and spatial coverage of population estimates. Passive acoustic monitoring (PAM) data, which can be collected night-and-day, in all weather conditions and year-round, are a cost-effective alternative to visual data.
JASCO has data sets and ongoing data collection programs whose results that contain the vocalizations of numerous marine mammal species that are of high concern to regulators and the public, including right, blue and sei whales on the east coast and southern resident killer whales on the west coast of Canada.
This MITACS project aims to improve the methods available for detecting and identifying which species of marine mammals are present in PAM data sets by completing three sub-projects.

Infrastructure Services Requirement for Sensor based Well-Being Monitor on a Telecommunications Network

Canada population is getting older as the baby boomers enter their retirement years and the current models for communal care will not be able to scale to meet the demand and continuing to age in place and live independently is preferred leading to the best quality of life and outcomes. The recent COVID pandemic experience has made some of the challenges in communal care and provision of remote care clear. Pilots of alternative models that use technology to enable remote monitoring and supportive capabilities have occurred with some success but they have not seen widespread use or deployment after the trial. A key challenge that these all face is their dependence on communication infrastructure and services.

In this project, a major Canadian Telecommunication services provider (TELUS) will be working to understand how these home monitoring systems and services map into their network leading to the creation of the enabling network services and architectures for their widespread use. Communication networks have had to evolve many times to match the needs and requirements on new services models such as the evolution of web browsing to video streaming. This project focuses on sensors and well-being assessment systems and their implication

Explorations into the mechanism and potential of the antiviral activity of BOLD-100 as a treatment for COVID-19

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 COVID-19. The purpose of this project is to utilize a range of cell culture and animal models to test BOLD-100 against COVID-19 to better understand the drug. Additionally, investigations will look at other viral diseases that BOLD-100 might be utilized in. The results of this project will help define the potential of BOLD-100 as an antiviral drug for use in COVID-19 and beyond, and accelerate it’s development.

Unsupervised Learning Based Approach for Insider Threat Analysis

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 of organizations, which may be familiar with the organizational structure, valued properties, and security layers. Given that a malicious insider is authorized to access the organization’s systems and networks, other challenges appear in this detection problem as well. One of them is that data describing insider threat activities is typically rare and poorly documented. Thus, detecting and mitigating insider threats represent a major cybersecurity challenge to any organization. In summary, the challenges in insider threat detection include unbalanced data, limited ground truth, and possible user behaviour changes. This project aims to design an unsupervised learning-based approach for insider threat detection. Our goal is to employ unsupervised learning algorithms with different working principles, such as Autoencoder and Isolation Forest. Furthermore, we will explore various representations of data with temporal information and compare our approach to other work in the literature to analyze its effectiveness and generalizability.