Intelligent Lawn Mow Routing using Automatic Vehicle Locator Technology for Smart City

In Winnipeg, MB, grass and weed growth can be seen during the summer, up until the middle of October. Weeds such as dandelions are hazardous to human health as they can trigger allergic and asthmatic reactions to the public. Their roots can grow up to two feet long if not removed on time. During the grass growth period, the City of Winnipeg uses many mowers of different sizes and horse-powers to mow and remove the weeds in boulevards, athletic fields, parks and other city owned green spaces. Currently, mowing routes are decided manually by the foremen. This is inefficient and time consuming.

Solving the integration problem for loyalty programs

Paying with a mobile phone within brick and mortar retailers is becoming increasingly popular, as it adds convenience as well as security to the payment process. Many retailers that use interac terminals with tap technology allow mobile phone payments in this way but are unable to integrate loyalty points into the mobile payment process.

Generative models for controlled generation of synthetic sequence-based datasets

At a high level, the goal of this project is to create a system for producing synthetic datasets based on real data. As a large financial crime detection firm, Verafin deals with large volumes of sensitive data which must be kept private, however they are also interested in collaborating with academics to gain new insights into their data.

Improving Monitoring and Decision-making with Uncertain Sensor Data

Terrestrial contaminated sites – such as abandoned oilfields, chemical spill sites, or former industrial zones – are a major environmental problem in Canada and around the world. Environmental Material Science has created new environmental monitoring equipment that generates high-resolution spatially and temporally explicit data on environmental quality. The data must be visualized and then used to make decisions regarding if site remediation needs to occur, or if occurring, if site remediation should stop.

High-throughput linguistic content comparison and sentiment analysis

Scrawlr is a platform for unconstrained, global interaction with all internet content and users. Scrawlr allows for user evaluation and unconstrained classification of any Scrawlr-hosted or non-Scrawlr content. For non-Scrawlr content, this evaluation and classification allowance will be first at the URL level but will subsequently be provided at the individual content component level. Scrawlr will require the capacity to, in multiple languages, identify equivalent and similar content.

Speeding up Federated Learning Convergence using Transfer Learning

The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more AI based applications are being deployed to learn from personal data, privacy concerns are rising, and more specifically on sensible domains like medicine, finance or mobile related data.

Earth Data Store – A Networked Ecosystem for Earth Observation Data Analytics

The Earth Data Store project provides a cloud-based repository for information about our earth. This encompasses satellite information, environmental models, sensors, and many other types of data that allow users to ask questions of this data to help inform their understanding of the world. The outputs of the project will allow economic development to be balanced with environmental protection.

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.

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.

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.