Predict and Recommend Conditioning Programs for Coaches and Athletes

The development of a unique and innovative fitness training software application. The application will be designed for coaches and athletes and will be able to make personalized training programs for athletes, based on their sport, position and individual strengths and weaknesses. Whilst participating in the project OlyUp Technologies will make new business relationships with stakeholders in a variety of industries and expertise, which they can leverage to make their products and operations better.

Arctic Research Foundation UX/UI searchable database

The Arctic Research Foundation (ARF) is a private, non-profit organization creating a scientific infrastructure for the Canadian Arctic. Over the years, it has generated and collected a huge volume of big data. These valuable big data need to be managed. In response, we design an effective database to (a) integrate these big data (which may be of a wide variety of data types, formats, etc.) from different sources, (b) manage them, (c) catalogue the data, (d) extract useful information from the data, and (e) make the data accessible to researchers and public.

Deep Language models for Visual analytics of diversity and inclusion

The project will build an Artificial Intelligence-powered Bias Detection tool to identify bias and discrimination faced by employees of a company. The tool will take in free-text content from employee surveys, internal reviews and social media, and produce a score indicating whether the author of the content is likely facing or exhibiting bias. Algorithms will be trained to recognize bias against women, ethnic & cultural minorities, LGBTQ2+ folks, individuals with disabilities, and other special groups.

Virtual Agent Emotion Modeling for Interactive Human Training Bots

Computer Generated Solutions (CGS) is developing a chatbot to train health-related customer support human
agents. In this research, an intelligent sentiment-based chat bot is to be modeled and designed to act as a client
to train the client-facing representatives who would be able to help clients more effectively.

A hybrid two-way recommender system for candidate and job search

The recruitment industry faces information overload when matching qualified individuals with competitive jobs. A recommender system can effectively solve this problem by filtering relevant content in order to make recommendations, in this case matching candidates with jobs. The objective of this project is to develop such a system for two-way search and ranking of candidates given job description and vice versa; put simply this system will recommend candidates to employer and will recommend job opportunities to candidates. Existing recommender systems have their own unique benefits.

Mobile Applications to Facilitate Physician-to-Physicain Consultation

In the Nova Scotia healthcare system (and indeed across Canada), waitlists remain a major problem, particularly with regards to access to specialty care services. Virtual Hallway is a novel telehealth solution to this problem, providing an online platform which allows for rapid doctor-to-doctor communication with the goal of fast-tracking and improving patient care and reducing waitlist times.
The goal of this project is to design and apply the first prototype of a mobile application for the Virtual Hallway platform.

A Toolkit for Analyzing Online Conversations for Solutions Based Policy Development

The Intergovernmental Panel on Climate Change (IPCC) 2014 Synthesis Report states that “substantial [greenhouse gas] emissions reductions over the next few decades can reduce climate risks in the 21st century and beyond, increase prospects for effective adaptation, reduce the costs and challenges of mitigation in the longer term, and contribute to climate-resilient pathways for sustainable development.” Yet, despite this imperative, energy conversations in Canada have become fragmented and polarized (Kevins & Soroka, 2018; Lefsrud et al., 2015) for renewable (Hoberg, 2019) and non-renewable en

Automatic Question-Answer Generation from Educational Texts

As online educational platforms and blogs such as Coursera, Edublog, Korbit Technologies Inc. etc. are becoming increasingly popular, a great way for students to learn is to solve QA problems on educational texts. Students also learn by getting answers to their questions. Since manual QA generation of huge content of educational texts seems impracticable, an important research goal is to create natural question-answer generation systems from reading comprehension materials. Neural sequential models (e.g.

Automatic detection and classification of abnormal human blood cells using computer vision and deep learning

This research proposal aims to enhance the performance and add more features to the automated microscope system that is being developed by Smart Labs ltd. This research goal is to increase the overall accuracy of the system while running in real-time. The current prototype has an accuracy of 91% and can process 17 frames per second. Moreover, it can only classify 2 types of cell abnormalities using traditional image processing techniques.

Using a mobile application to support at-risk student re-entry into post-secondary education in the era of COVID-19

The aim of this 2-year project is to do research to inform the development of, and fully test and develop a mobile application designed to improve the experience of (particularly at-risk) post-secondary (PSE) students in addressing COVID-19-related issues. Our key concern is that COVID-19 has not only disrupted important and significant developmental experiences that improve student experience and success, but it has also caused challenges in students’ lives away from university that will spill over into their experience of being post-secondary students.