The Internet of Things (IoT) is a new emerging paradigm and is rapidly gaining ground in different applications of significant engineering importance including but not limited to smart buildings, and smart public environments. The main enabling factor of this promising paradigm is integration of identification, localization, and navigation technologies with smart hand-held devices equipped with sensing, processing, and communication capabilities.
The goal of this project that will be conducted in collaboration with Heyday is to create a technology that uses a given messaging platform (e.g. Facebook Messenger, web chat widget) that allows users to communicate easily and smoothly with their preferred brands or retailers. This technology should allow the automation of answers and interaction between users and retailers. The technology that we would like to develop will be based on advanced Natural Language Processing (NLP) and machine learning techniques.
Deep neural network (DNN) is a class of machine learning algorithms which is inspired by biological neural networks. DNNs are themselves general function approximations, which is the reason they can be applied to almost any machine learning problem. Their applications can be found in visual object recognition in computer vision, translating texts in unsupervised learning, etc. DNNs are prone to overfitting because DNNs usually have many more parameters than the available training data. However, they usually have a low error on the test data.
In the last few decades, Socially Responsible Investments (SRI) have growingly become a relevant issue. This trend is expected to continue and strengthen going forward. Moreover, the steady trend of rationalization and marketization of the social sector over the past four decades has set expectations and norms of rationalized practices such as Social Performance Measurement (SPM) encompassing practices such as impact evaluation, outcome measurement, and program monitoring adopted by an organization to measure its progress toward its social goals, reflecting internal influences.
Automatic analysis of sport videos is an attractive research area in computer vision that is driving the sport analytics towards a more technological edge. By automatically analyzing sport videos, lots of information could be drawn that benefits the teams, coaches, referees, players and even the fans, such as: extracting strategy of the game, technique and performance of each individual player, performance of the referee in a competition, and etc. This area of research, although attracted many researchers in computer vision community, is still in its infancy.
TAS (Techno Aero Services Inc.) is a Canadian recruitment agency specialized in aeronautics, engineering, and technology. At TAS, candidates currently submit resumes to a database through a web interface. These resumes are then manually processed by recruiters before a suitable candidate is matched to a position. Through this laborious manual process, great prospective candidates often get lost in the piles.
Information on the proportions of clay, silt and sand-sized mineral particles in soils and in soil products used for construction is critical for understanding their physical and chemical properties and for their proper use and management. However, these soil mineral particles are usually glued together with inorganic cements (calcium carbonate, iron and aluminum oxides/hydroxides) and organic matter which can make measurements of the proportions of the individual mineral size separates using current methods problematic.
In this project, our goal is to set up a framework of data collection to support user profiling which could be used to identify influential users in decision-making. The profile will be built based on the information of individual users obtained by collecting user activities in rewarding challenges that encourage employees, customers and partners to participate. In order to derive the profile, natural language processing tools are applied to extract useful information.
Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, these survey methods are generally only performed in good weather conditions and require many trained observers.
Hockey has long been shown to be among the least predictable of all professional sports. Recent developments in data collection methods have created the demand for more detailed and advanced predictive modelling techniques to extract value from and apply the data to real world problems. This project focuses on predicting important outcomes in hockey at both team and player levels. Game winners and scores will be predicted using Bayesian approaches tailored to accommodate evaluative statistics and relevant pre-game factors.