Blockchain-based secure transaction logging system

Smart-city platform developed by B-CITI company stores sensitive information of platform users (citizens) in a database to provide them services. The proposed research project’s aim is to build a system that records each user interaction with the database. The records are immutable and, easily linkable and traceable. The records can be used as forensics evidence for proving and validating the integrity of the data in the database. For this, we use permissioned blockchain that is managed by multiple untrusted entities to generate trust in the system.

Semantic versioning of model changes in decision support systems

Nowadays, almost any company in Canada in operation heavily relies on software solutions to improve their productivity. However, they are often facing the problem of having too many options to choose from for the software best fit for their needs. Decision support systems (DSS) help enterprises to take significant business decisions, such as finding the best software solution. Our industrial partner has developed a DSP that incrementally builds a decision model with customers preferences, choices, and ratings.

Learning priors for data-efficient causal discovery

The inference of causal relationships is a problem of fundamental interest in science. Compared to models that rely on mere correlations, causal models allow us to anticipate the effect of a change in a system. Such causal models have applications ranging from government policy making to personalized medicine. However, learning causal models from data is a challenging task, since it requires large data sets and, in some cases, the conduction of costly or invasive experiments. In this project, we propose a new method to learn causal models using less data.

Recommender System for Game-Like Machine Learning Systems

Machine learning (ML) offers a tremendous opportunity for developing new algorithms in various field of activities. Taking image classification for instance, usually the best practices are found from trial and errors and usually taking several approaches and compare one versus another. However, it is not obvious for a non-expert, and even for an expert, what ML pipeline should work best on a given dataset.

Minimiser les efforts d’annotation lors du développement d’un modèle prédictif en traitement des langues – Phase 1

Développer un modèle prédictif en traitement automatique des langues requière la création d’un corpus annoté : un texte et des annotations que l’on tentera de reproduire automatiquement. Il s’agit d’une activité à la fois complexe (les annotations sont souvent du ressort d’un expert) et coûteuse (annotations méticuleuses à produire en grande quantité). Le projet vise à développer une expertise pour minimiser les interventions (annotations) permettant d’obtenir un modèle prédictif d’une qualité donnée.

Implementation and Characterization of a Prototype of Optical Free-space Interconnect for Space Applications

As we advanced into the information age, the need for high capacity communication channels is becoming ubiquitous. As the next generation of satellites is being deployed there is a need for efficient interconnections between them, especially for those forming low earth orbiting constellations for the coming Internet-of-Space applications. The limited radio frequency spectrum available is not sufficient to implement these communication links, and thus, free-space optical interconnects (FSOIs) are expected to become the technology of choice to interconnect satellites.

Energ-AI: Artificial Intelligence in Electrical Power Engineering

Classical engineering, referring to the three fields of civil, mechanical and electrical engineering, is currently based on traditional working methods. For example, the validation of plans is often done in paper version and the engineer must interpret photos and drawings manually, which introduces a risk of error due to the human factor. In addition, the shortage of labor in this area means that the economic potential of this industry in Canada cannot be exploited to its full value.

Multivariate sequential data generator with long term, non-linear dependency

Financial data are known to be generated from complex distributions, often assumed to be changing over time. The aim of this project is to build a simulator of multivariate time series, parameterized as simply as possible by a user, with the freedom to build different models, to assess their general behavior and key properties. This will facilitate predictions of multiple sequences and their interactions, through a simple interface of possible configurations to the user, who could have a basic knowledge of statistics and limited knowledge of the mathematical details of the underlying models.

Association automatique entre symboles et textes dans des documents d’ingénierie

La plupart des documents d’ingénierie comportent des symboles pour caractériser les systèmes qu’ils représentent. Ils comportent également des annotations (identifiants, notes, spécifications) sous forme textuelle pour préciser certaines propriétés importantes ou identifier les composants. Si l’association entre les symboles et les composants est intuitive pour un expert humain, il en va autrement pour un ordinateur. Ce projet vise à permettre la découverte automatique de tels liens d’association par un ordinateur, en utilisant des techniques d’apprentissage automatique.

Optimiser la résilience de la forêt urbaine

Le projet consiste à développer un système et une application web qui permettra aux aménagistes forestiers urbains de choisir la meilleure espèce d’arbre à planter à différents endroits de la ville afin de maximiser les retombées économiques et sociales et la résilience du couvert arboré face aux changements globaux.