Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be […]

The central role of the Internet in modern society creates challenges of efficiency, flexibility, and security, especially as usage intensifies due to proliferation of mobile devices and the Internet of Things (IoT). Wireless network-based technologies such as Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC), introduce new challenges. In particular, shifting locations and hardware […]

Safety critical software systems such as those that control nir navigntion nre subject to very high standards of quality. They need to explicitly provide system requirements nnd make sure there nre enough test cases that nssure an acceptable level of quality, per requirement. However, with the current fnst pace ofsollworo development, sometimes the program and […]

The advent of 5G (fifth generation) telecommunication networks also brings new security challenges, in addition to many benefits to the community. Such is exemplified by its special nature of technology (as well as the new business model) and its deep involvement in people’s everyday life, hence more critical. We need proper security metrics to tell […]

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used […]

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go […]

In this project, we propose a continual learning approach to face the problem of catastrophic forgetting in online image classification problems. Concretely, we propose a model that learns how to mask a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital […]

MITACS interns at 1QBit will aid in the research and development of experimental usage of quantum and classical hardware devices for industry use including, healthcare, finance, advanced materials, and optimization. Interns will have the opportunity to work alongside academics and research teams. MITACS interns will gain the practical experience of applying their knowledge for industry […]

The project investigates how collaborative tasks can be enhanced in AR environments. The intern will develop three approaches to present shared information in a co-located AR setting and conduct usability studies comparing these approaches. The first approach will present information in the same location and same orientation to the collaborators, the second will present the […]

Despite advanced supply chain planning and execution systems, manufacturers and distributors tend to observe service levels below their targets. This can be explained by unexpected deviations from the plan or systems that are not properly configured. Quite often it is too expensive to have planners continually track all situations in supply chain systems at a […]

Digitizing paper records facilitates efficient storage and lookup of information. It is o,lten necessary to support advanced keyword-based querying features that allow extraction of specific information and filtering of digitized documents. However, this often requires manual intervention since records might have vastly different formats even though they essentially contain the same information. This makes digitization […]

The main objective of the project is to upgrade the existing system at Cheetah Networks to make use of Canadian cellular CAT M1 monitored network data to develop innovative QoE analytics that can be used to provide actionable insights. The system will explore applying new techniques to capture in real-time QoE visibility into experiences locally, […]