Innovative Projects Realized

Automated Model Tuning for Retail

Artificial intelligence, especially Machine learning algorithms, plays important roles in building recommendation systems and promotional forecasting systems for retailers. However, training a machine learning model requires the choice of a number of significant features and requires tuning a large set of configurations. Therefore, it takes a long time for humans to find the optimal configuration for one or more predictors. However, the predictive performance of existing automated tuning models is not as good as manually tuning. Besides, the approach cannot be applied to more than one model. This project, will propose a system that can automatically come up with a set of models with corresponding features and configurations for a specific problem (e.g., promotional forecasting) that provides good or acceptable performance for the prediction.

Hand Pose Reconstruction Based on Fast Multi-Touch Sensors

Serving as the most widely-used body part for communication, hand is a very important tool for human to interact with the world. Especially with the continuing development of virtual reality and augmented reality, hand pose information has gradually become an indispensable component for improving users’ experience in interacting with computing devices. Therefore, this project aims at achieving hand pose reconstruction based on capacitive sensing technology using machine learning algorithm. The capacitive sensor that will be utilized in this project is supported by the project partner, Tactual Labs who, by the end of this project, will benefit by having its current innovative capacitive controller more intelligent.

An Artificial Agent for Light Switch

Smart home devices with artificial intelligence (machine learning and deep learning) will change our lifestyles in the near future. The objective of this project is to develop an artificial agent, which will power the smart light switches produced by ecobee. The artificial agent, a machine learning program, will use the data collected by the sensors in the smart light switches and help the users operate the light switches without the users’ manual control. The goal of this project is to develop an underlying smart program to learn the behaviors and of users with the light switches. The progress of this project will help ecobee provide better smart light switches for its clients and potentially incorporate this smart program to other similar ecobee smart home devices. Therefore, the success of this project can eventually contribute the smart home industry.

Identifying vehicle accidents and high risk drivers using Machine Learning

The primary objective of the project is to approach the problem of understanding true causality of vehicle accidents and scientifically determining which vehicles and drivers are at highest risk of an accident from a machine learning perspective. Geotab has a number of identified collisions in X, Y and Z planes, and much more. The research would be aimed at using both Geotab’s data in addition to external data such as weather and topography to develop a predictive model that can identify those drivers at highest risk of an accident. This may be based solely on current driving behavior and/or the driving history.
The results of this project are important in helping our over 20,000 commercial fleet customers understand the true safety risks that exist in their fleet leveraging a novel machine learning approach that goes beyond a generic score. TO BE CONT’D

Discovery of Endocannabinoid modulating compounds for Alzheimer’s disease therapeutics development

Alzheimer’s is the most common form of dementia which worsens over time. Current therapeutic against Alzheimer’s disease provides only symptomatic treatment. This limited effectiveness provides us with an opportunity to direct our research efforts towards developing new agents to prevent or retard the disease. Studies have shown that very small amount of tetrahydrocannabinol (THC), a chemical found in marijuana, can slow the production of Amyloid beta (A?) protein. This protein is found to be the hallmarks of Alzheimer’s disease and a key contributor in its progression. Our study aims at removing the psychoactive component from marijuana, retaining it therapeutic part and screening those compounds to find a potent drug against Alzheimer’s disease.

Outils d’aide à la décision pour la planification, l’ordonnancement et la gestion de la production de solutions textiles

Pour rivaliser avec l’offre de pays à faible coût de main d’oeuvre, l’industrie canadienne du textile a dû se doter d’une technologie à l’avant-garde et offrir à sa clientèle des produits parfaitement adaptés à leurs besoins. Planifier efficacement la production d’une gamme exhaustive de produits demeure toutefois un réel défi, c’est pourquoi cette recherche vise à proposer un ensemble d’outils permettant de soutenir les activités du fabricant nord-américain de tissus Duvaltex. En mettant sur pied différents scénarios d’aménagement de la production, un tableau de bord de gestion stratégique et un modèle avancé pour la planification et l’ordonnancement des activités, la recherche fournira ainsi à Duvaltex les outils nécessaires au maintien de sa position stratégique sur l’échiquier mondial.

Integrated modelling of tropical dry forest using climate, land cover change, and ecosystem services models

This project will combine specialized land cover, climate, and ecosystem services models into a single platform via the Dinamica EGO platform, linking them together using feedback systems. Using a deforestation model in Costa Rica as a baseline, with a climate change model, CORDEX, and a water services model, SWAT, we can produce a more complete picture of the future change in Costa Rica. This will provide me the opportunity to quantify how water resources will change under different land uses and climate alterations. Costa Rica is home to sensitive ecosystems, which have a high biodiversity and produce many services for nearby populations. Additionally, the economic activities in Costa Rica have historically centered on agriculture and ranching, and these contributions continue to comprise a large portion of the GDP. This focus on land based resources makes it imperative to understand how the future of the land will change, which areas will experience increasing demands on the water supply, and which natural areas will be resilient under climate and land use changes.

Les stratégies de pensée critique?des étudiant·e·s sur le Web social

Des fausses nouvelles aux the?ories du complot, la question de l’e?valuation de l’information est au cœur de l’actualite? sociale, e?conomique ou environnementale. En effet, pour certains, Internet et les re?seaux sociaux contribueraient a? la diffusion de rumeurs vis-a?-vis desquelles les jeunes et, par conse?quent, les e?tudiant·e·s ne seraient pas suffisamment outille?s. Pourtant, plusieurs de ces re?seaux, tels que Facebook, sont de plus en plus mobilise?s dans le cadre des e?tudes, y compris a? l’universite?. Concre?tement, ce projet a donc pour ambition de brosser le portrait des strate?gies qu’utilisent les jeunes lorsqu’ils e?valuent de l’information sur les re?seaux sociaux. De fac?on plus spe?cifique, nous nous concentrerons sur les indicateurs de « pense?e critique » que l’on peut identifier chez les e?tudiant·e·s qui entrent dans l’enseignement supe?rieur. Cette recherche s’inscrit dans un objectif de comparaison internationale (Canada, Belgique et France). Cela permettra d’entrevoir, e?ventuellement, des diffe?rences entre les populations cibles. Surtout, ce projet contribuera a? avoir un meilleur portrait de la re?alite? de l’e?valuation de l’information sur le Web et, finalement, a? mieux outiller les e?tudiant·e·s en conse?quence.

Hydroponic configurations and biological nutrient solutions for shipping container plant factories

Plant factories are closed growth environment, insulated, fully automated and artificially illuminated that can increase food security in regions such as Northern Canada. However. the technologies used in large scale plant factories are not suited for smaller scale plant factories, housed in shipping container. La Boîte Maraîchère design growth chambers in shipping container, but want to develop better hydroponic practices to get closer to the yields obtained in large plant factories. Hence, research is needed to increase the knowledge on small scale plant factories hydroponic configurations. This research concerns the comparisons of innovative multistage hydroponic configurations based on nutrient film technique, floating beds and aeroponics. The main innovation is to include the aspect of crop movement in these hydroponic system, to potentially automate the crop production.

Adaptive Radiation Therapy for Cervix Cancer with Stochastic Model for Tumour Evolution

The goal of this project is to develop new models to optimize the treatment of cervix cancer

using radiation therapy. We will develop two models to achieve this goal. The first model will

model the uncertain nature of the tumour evolution as a function of time, which will help us

predict the position, shape, and size of the tumour. This model will be validated using data

from our collaborators at Princess Margaret Hospital. The tumour evolution model will then

be used to support the development of a new adaptive optimization model to determine when

and how to re-optimize multi-week treatments. Treatments are delivered over multiple weeks

and the tumour’s geometry may change significantly over this time period. By incorporating a

model that describes the tumour changes into the optimization process, we will improve

treatments for patients, while reducing the use of expensive imaging resources at the

hospital.

Demonstration-Based Initialization of Reinforcement Learning Algorithms for Efficient Robotic Control

Kindred’s Sort product is a robotic system that operates in e-commerce distribution centers to sort and handle apparel and general merchandise. The deployed system is controlled through a combination of artificial intelligence and human-in-the-loop teleoperation. The proposed project involves applying techniques from artificial intelligence (specifically machine learning and reinforcement learning) to improve the ratio of automatic control to human control. The core hypothesis of the project is that historical data collected from human teleoperation of the robots performing object-grasping tasks can be used to train the robots to pick up items automatically. This task is a challenging research problem at the cutting edge of robotic control and AI, and it will be tackled with a combination of state-of-the-art academic research and internally-developed algorithms.

Exploration of Methods and Models to Achieve Multi-Document Comprehension in the Legal Domain

The project attempts to tackle an important challenge in Artificial Intelligence (AI), to give a machine an ability to comprehend multiple documents like humans do. These can do the redundant or preliminary reading-based research performed in many domains. The project aims to create a system which can read, understand, and answer queries and/or summarize multiple legal documents in a single shot. The project aligns with ROSS’s roadmap and vision – to supplement and enhance the quality and capacity of research tools, available at an average lawyer’s disposal and boost the time spent with their clients. Such a powerful system requires the capacity to understand queries made in natural language such as English. Hence the system will be developed using applicable novel state-of-the-art AI based Natural Language Processing (NLP) techniques. TO BE CONT’D