Personalization with Integration of Sensor Signals in Cloud Architecture

$50B of unrealized GDP will occur by 2030 in Canada if the skill gap challenge is not addressed. With the acceleration and uncertainty of a borderless economy, Employers, Educators and Jobseekers will continue to be in constant flux. The greatest cost to Small & Medium Sized Businesses (SMBs) is the hiring, skilling and training of new hires, and the cost when they leave after a day or after 30 days.
Recent seismic talent layoffs at the conglomerates of Amazon, Twitter, Facebook have now started this ripple effect and won’t take long to impact SMBs, Jobseeker and Educators.

Automated Full-Game Ice Hockey Analytics

Computer vision involves creating algorithms capable of interpreting scenes. A key challenge is automatic generation of analytics to mimic human ability. Generating analytics from ice hockey video is one such application where human-captured analytics typically focus only on puck-centric events and it is not feasible for humans to interpret all game events.

Advance Generalizability of Graph-based Machine Learning Models for Applications Automotive Metal Forming and Impact

In modern automotive engineering, vehicles are primarily designed in the virtual space to enable a rapid vehicle design process. However, this process is heavily constrained by the time and computational requirements necessary to generate the vast number of simulations needed for vehicle design. Fortunately, modern machine learning (ML) techniques may be used to dramatically accelerate the generation of new simulation results.

An analysis of the challenges of building machine learning-based intrusion detection systems

Network attacks are becoming more complex every day. It is crucial that we use tools that can detect these sophisticated attacks on networks so that we can identify malicious behavior and prevent attacks and intrusions. The use of machine learning to create intrusion detection engines is great, and we need enough data to train these engines. The purpose of this project is to analyze the problems of existing public datasets and the challenges involved in finding the right machine learning techniques and settings for them.

Using knowledge graphs and AI to recognize similarities between politicians

IOTO International Inc’s work is recognized as innovative by some of the largest media organizations in Canada. Their Goverlytics software applies Machine Learning to political data. They intend to extend their software to provide statistical measurements of political discourse similar to performance statistics available in other domains like sports. IOTO is in the process of obtaining political data from using Natural Language Processing on political audio.

Source-level change data capture of persisted smart-contract state

Smart contracts are computer programs on the Ethereum network. For a variety of reasons, such as security concerns, we would like to be able to analyze these smart contracts. However, the source code is only available for a limited subset of these contracts. But, the result of the compilation of these contracts, the bytecode, is available for all smart contracts. The main goal of this work is the decoding of smart contracts’ bytecodes. The path towards this goal is automated decompilation of the bytecode. But, current decompilation tools are not compatible with the ethereum bytecode.

5G-LEAP - Flexible Open RAN Platform for 5G and beyond networks

5G and beyond mobile networks are expected to support a variety of emerging use-cases, such as holographic telepresence, immersive extended-reality, cloud gaming, and autonomous driving. Such a diverse set of services imposes strict requirements on the mobile network along several dimensions, such as throughput, latency, and reliability. Network slicing has been envisaged as a key enabler to satisfy these diverse requirements, by creating multiple isolated end-to-end virtual networks dedicated to different services, on top of a common physical infrastructure.

Generalization of SR.ai NLP Algorithms to New Data Sources and Stability Improvements

The field of responsible investing is rapidly expanding, with even greater attention on the importance of responsible investment with each passing year, as seen most recently in the aftermath of the impactful 2021 COP26 summit, where responsible investment was key focal point. Directing our financial resources in a sustainable direction has the potential to have a massive impact on helping us meet the Sustainable Development Goals set forth by the UN.

Statistical framework and methodology for risk and privacy in complex and high-dimensional data

Modern data collection and storage results in complex and high-dimensional databases: they include a large number of variables, with a lot of interactions. At this same time, access and release of information that is, or is derived from, personal information involves complex challenges in terms of the potential for inappropriate disclosure (e.g., identification).
In this project we propose to develop a statistical methodology that can inform the evaluation of privacy assurances while preserving the statistical utility of complex, high-dimensional health data.

Simulator for Distributed Quantum Computation

Distributed computing is a model in which components of a software system are shared among multiple computers to improve efficiency and performance. The growing interest in cloud computing scenarios that incorporate both distributed computing capabilities and heterogeneous hardware presents a significant opportunity for network operators. The aim of this Research is to develop a purpose-built discrete-event simulator for distributed quantum computing and identify further challenges and open problems arising from the design of a Distributed Quantum Computing ecosystem.

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