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 […]

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Determining position, speed and stride length using machine learning with sensor fusion based on ultrawideband local positioning system technology

Sensors that track human movement are becoming more and more popular in all kinds of applications including healthcare, sport and general human movement. However, traditional sensors generally have problems tracking individuals indoors and they are not very accurate when measuring subtle movements. Using innovative technology, new wearable sensors have been developed to track human movement […]

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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 […]

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Toronto’s City Diplomacy: Arts, Culture, and Heritage

Our project, Toronto’s City Diplomacy: Arts, Culture, and Heritage, brings together scholars and practitioners in the cultural disciplines to consider how the Greater Toronto and Hamilton Area (GTHA) engages in cultural diplomacy and cultural networks locally and globally. With Hot Docs Canadian International Documentary Festival (Hot Docs) as our non-profit partner, this project examines how […]

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Teaching artificial agents to play complex video games from demonstrations

The goal of this research project is to develop novel technics to teach artificial agents how to play complex video games using reinforcement learning and demonstrations. Namely, we wish to propose a novel approach for learning from demonstrations, in which an agent simultaneously learns a behavior and the corresponding reward signal. This training procedure will […]

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Jordan Shapes for Deep Learning

The proposed project aims to develop a systematic approach for improving deep-learning-based computer vision systems by augmenting the local pixel data with the global shape data (more specifically, Jordan curves) and by adjusting system architectures to accommodate the augmented input. Three canonical computer vision problems will be investigated in this project. They are respectively image […]

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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 […]

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

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 […]

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Motion fields with deep reinforcement learning for real-time character animation

Character motion in games and animations often have high requirements of realism, aesthetics, and interactivity. For instance, in soccer simulation games, users control the players to move in different directions and perform actions such as passing and shooting. Modern data-driven approaches like motion fields provide convenient ways to synthesizing natural motions from a given database […]

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