Automating Configuration Management and Deployment in Large-scale Data Centers Augmented with Edge Data Centers

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment.

Cultivating Ethos in the Tech Sector

The rapid development of technology and its ubiquitous integration with modern society has clarified for business and technology leaders the need for ethical conversations about the role technology can and should play in our lives. By interviewing primary stakeholders in the technology sector and putting their ideas in conversation with literature about the role of ethics in tech, this project will foster a critical dialogue between businesses, governments and users to overcome the ethical challenges posed by technological innovation.

Better predictions of employee events II

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.

Detection of suspicious and/or abnormal real-time events from textual live data feeds

Social media and other real-time messaging applications represent valuable sources of real-time information that remain untapped by many service operators. The project is aimed at developing methodology for detecting suspicious and/or abnormal real-time events from textual live data feeds, based on predictive and/or anomaly detection algorithms applied to time series and text features.
TRT Canada is therefore interested in developing algorithms that will be able to recognize such events based on similarities with past events in order to address the mentioned scenario.

IRIS FaceMatch: A secure face-based identity detector without racial bias using deep learning

This project aims to address the race bias of face recognition technology by developing new face feature sets and building the suggested models based on an equal number of images from varied-race photos using appropriate deep learning algorithms. This research seeks to deliver industry partner IRIS with guidelines for a developed prototype that facilitates the adoption of IRIS’s FaceMatch technology for enhancing police capabilities to find unidentified individuals based on their photos while saving labor force and other police resources.

Next Generation User Product Documentation

Product documentation is an important information tool connecting any business to its end-users and customers. Comprehensive product documentation will likely result in positive evaluation of the products by the customers and may influence their future purchasing decisions. Many studies show product documentation remains an essential element of any new product even for modern electronic devices used for information and communication technologies.

NLP sentiment analysis for contact and support centers

In today’s competitive market, customer service has become essential to any company willing to expand and increase its business. Companies cannot afford to fall short of consumer expectations. With the recent progress in Artificial Intelligence (AI) and the impressive results in different industrial areas, companies are adopting AI techniques for customer service. Most of the applications of AI in contact centers are based on the use of chatbots. These conversational agents are trained to interact with the customer and answer questions.

Supporting Community Engagement in the Maker Movement

The Ville Cooperative ( is a holistic community centre, working to empower the local community to learn, share and grow in the spirit of health, wellness, and sustainability. One of the areas in which it hopes to realise it’s vision is through the newly rebranded fabrication lab - Learning Lab @ The Ville.
The Ville’s Learning Lab, like most makerspaces and fabrication labs, is currently undersubscribed; the space is neither heavily used nor being used near its potential.

Plant level implementation of a model for real time tracking of composition changes to steel, slag and inclusions during ladle processing

The Ladle Metallurgy Furnace is used for adjustment of chemical composition and temperature, and control of tiny particles called “inclusions”. Controlling inclusions is carried out by adding calcium to modify the solid alumina or magnesium aluminate inclusions to less harmful liquid inclusions.
During ladle process, reaction of top slag, steel and inclusions occur simultaneously. Therefore, establishing a model to describe ladle process is indeed a challenge.

Spatial mapping of turbulent characteristics of tidal flow and wakes in the Minas Passage Bay of Fundy

The strong tidal currents that make in-stream tidal energy possible, are also challenging to work in since they are also very turbulent. As the flow passes over the rough bottom and shoreline variations, eddies are generated over a wide range of scales. These eddies (i.e. turbulence) create fluctuating forces on tidal turbine blades and their support structures, degrading turbine performance and operating life. Understanding and predicting the levels of turbulent flows is an important component of the marine services that Luna Ocean provides to its clients.