Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

29 670 projets achevés

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801
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Projets par catégorie

Optimizing Security Orchestration, Automation, and Response for Incident response

This research project aims to develop cost-effective solutions to aid organizations in defending against cyber-attacks. With limited resources, security operations centers are struggling to defend against the vast volume of cyber-attacks. The project proposes reducing the work effort and amount of labor needed to perform tasks such as manual inspection and incident responses. By enhancing the functionality of current plugins and developing new ones for the Security Orchestration, Automation, and Response tool, cybersecurity experts will spend less time monitoring and more time implementing better security metrics, resulting in a more secure system and organization. The project’s expected benefit to partner organizations is a reduction in the cost of services and improved cybersecurity measures, resulting in fewer cyber-attacks and better protection for both the organization and its clients.

Voir la description complète du projet
Superviseur du corps professoral :

Rozita Dara

Étudiant :

Partenaire :

ISA Cybersecurity

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Guelph

Programme :

Accelerate

Monitoring and optimizing environmental conditions for improved CEA productivity

Consistent plant production is critical for plant product quality and marketability. Plant quality (leafy greens, cannabis or other plants) is influenced by the plant species/cultivar and environmental conditions (temperature, relative humidity, light level, light quality, CO2 levels, water quality and quantity, nutrient levels and air movement). Understanding existing variability of the environmental conditions is critical for any plant production operation with the knowledge quantified in one location transferrable to other locations. The objective of this study is to evaluate and monitor the environmental conditions in a CEA operation and modify existing sensor systems to understand temporal and spatial variability.

Voir la description complète du projet
Superviseur du corps professoral :

Mark Lefsrud

Étudiant :

Partenaire :

Fermes Urbaines Ôplant

Discipline :

Engineering

Secteur :

Agriculture

Université :

McGill University

Programme :

Accelerate

Adversarial Threats on a Penetration Testing Solution

Malicious adversaries are increasingly aiming to bypass security controls. There is a race to “owning” vulnerable machines and it is advantageous to malicious adversaries if the existing vulnerabilities are not patched. The research will be performed on a vulnerability assessment and management platform, specifically designed to assist organizations in identifying and mitigating cyber risks. It is unclear how effective the solution is against malicious insiders. For instance, in an enterprise environment, a malicious insider may circumvent the alerts that the platform may generate if it has taken over that machine. The research will focus on testing exploit techniques that can allow a threat actor to bypass the platform’s detection mechanisms and establish malware and persistence mechanisms within the target. By taking this approach, the project aims to identify the susceptibility of the platform to insider attacks and develop strategies to address them before malicious actors can exploit them. Proactively identifying and mitigating potential cyber threats is critical in today’s digital landscape, and this research can help organizations better understand and address their vulnerabilities.

Voir la description complète du projet
Superviseur du corps professoral :

Hassan Khan

Étudiant :

Partenaire :

Fiera Capital

Discipline :

Computer science

Secteur :

Finance and Insurance

Université :

University of Guelph

Programme :

Accelerate

Threat actor group profiling

Understanding the current panorama of threat actor groups worldwide is critical to building efficient cybersecurity programs. Information about threat actor groups’ motivations, tools, tactics and techniques they use to attack, and the type of targets they have in their sights provide valuable information to cybersecurity teams. To achieve this goal is essential to generate intelligence processing such information. Unfortunately, humans cannot process the amount of data generated daily, so implementing machine learning models is required for data processing and categorizing threat actor groups. With the proper profiling of threat actor groups, cybersecurity teams will strengthen their policies, security controls and processes, efficiently targeting their resources to threats that could impact them instead of wasting resources on pointless activities.

Voir la description complète du projet
Superviseur du corps professoral :

Charlie Obimbo

Étudiant :

Partenaire :

eSentire

Discipline :

Computer science

Secteur :

Cyber Security; Information and Communications Technology; Technology

Université :

University of Guelph

Programme :

Accelerate

Towards Fully Automated Tumor and Organ-at-Risk Detection and Segmentation from PSMA PET and SPECT Scans of Prostate Cancer Patients

Prostate cancer is the third deadliest cancer in men and early detection is crucial. PSMA is a protein that is highly present in prostate cells, making it a promising target for imaging and treatment. Total metabolic tumor volume (TMTV) is a measure of tumors’ characteristics, but it is currently not measured in clinical settings due to the labor-intensive and time-consuming process of manually delineating the borders of all tumors in PET images. AI can automate this process, but it struggles with low-quality images and small tumors. Our proposal is to develop AI-based object detection methods to locate lesions before segmenting them to improve accuracy. PSMA can also be used for personalized radioligand therapy, where radioactive drugs attached to PSMA molecules are injected into the patient to kill cancer cells. AI can aid in automating the process of manually delineating the borders of tumors and organs at risk in PET images, simplifying existing protocols, and predicting patient response and outcome. Personalized radioligand therapy could maximize cancer irradiation while minimizing toxicity to healthy organs, leading to better efficacy.

Voir la description complète du projet
Superviseur du corps professoral :

Arman Rahmim

Étudiant :

Partenaire :

Microsoft Canada Development Centre

Discipline :

Life Sciences

Secteur :

Technology; Health and Related Sciences & Technology; Artificial Intelligence

Université :

The University of British Columbia

Programme :

Elevate

Pondération dynamique de modèles prédictifs à court terme de la charge sur le réseau électrique du Québec

Le projet vise a developper des strategies permettant de combiner plusieurs modeles d’intelligence artificielle (IA) etudies au
sein de l’ecosysteme d’intelligence artificielle de l’equipe de prevision de la demande de !’unite prevision des apports et de la demande d’Hydro-Quebec. Ces modeles combines permettront d’obtenir de meilleures predictions a court terme de la charge
sur le reseau electrique du Quebec. Un premier outil sera developpe et valide au sein de plusieurs experiences IA temps reel, en vue de permettre de qualifier en condition reelle d’exploitation les modeles IA actuels et futurs qui iront en production, ainsi que les strategies d’entrainement et de ponderation dynamique, en plus d’orienter en parallele les axes de modernisation de la production.

Voir la description complète du projet
Superviseur du corps professoral :

Fabian Bastin

Étudiant :

Partenaire :

Hydro-Quebec

Discipline :

Engineering

Secteur :

Energy and Utilities; Technology; Green/Alternative Energy

Université :

Université de Montréal

Programme :

Accelerate

Increasing Grid Resilience using Game-theoretic Demand Side Management

Demand Side Management is a scheme that manages production, consumption and storage of energy of an aggregation of households in a neighborhood. The automated algorithms communicate between households to ensure that grid constraints are respected and households use energy optimally to maximize the use of green energy and save money. A promising tool for these control algorithms is game theory which gives mathematical guarantees for fairness and equity between households such that all participants in this scheme are treated equally while respecting their individual preferences. Game-theoretic control algorithms in the area of energy management are novel and have not been applied to real-world settings. One major hindrance of the implementation in the real world is that currently there are no safety and stability guarantees for these types of algorithm. In this project we want to develop such mathematical guarantees for a specific game-theoretic controller which is ideally suited for the Demand Side Management application.

Voir la description complète du projet
Superviseur du corps professoral :

Dominic Liao-McPherson

Étudiant :

Partenaire :

ETH Zurich

Discipline :

Engineering

Secteur :

Education

Université :

The University of British Columbia

Programme :

Globalink Research Award

Controllable and editable character performance using Implicit Neural Representation approaches

Nowadays, many of the movie characters whose performances move us on screen are at least in part digital. From superhero stunts to de-aged beloved actors and actresses, visual effects artists have to create digital characters and painstakingly reproduce performances to convince audiences. New Deep Learning (DL) technologies are emerging to help alleviate the processes. For instance, Deep Fakes have been quite successful at swapping facial performances. Other promising approaches are emerging under the large umbrella of Implicit Neural Representation (INRs) such as Neural Radiance Fields (NeRFs). We wish to explore novel ways to automate parts of the workflows involved in creating so-called Digital Doubles using NeRF-like algorithms.

Voir la description complète du projet
Superviseur du corps professoral :

David Lindell

Étudiant :

Partenaire :

DNEG

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

University of Toronto

Programme :

Accelerate

Exploration of RL-based agents in the context of space robotic systems

This research will explore machine learning methods in order to devise a control scheme for robotic manipulators(Candarm3) in the context of space exploration. The objective is to develop an early prototype for an autonomous learning agent which can carry out standard control tasks without any operator supervision.
The primary machine learning methods that will be studied will revolve around deep-reinforcement learning methods, in which an agent iteratively improves its performance in a given task. This is done through simulating training exercises, where the agent is rewarded for performing well. The agent modifies its behaviour in order to maximize its expected reward in future training exercises.

Voir la description complète du projet
Superviseur du corps professoral :

Chi-Guhn Lee

Étudiant :

Partenaire :

MacDonald, Dettwiler and Associates Inc (Brampton, ON)

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

Développement d’outils d’évaluation, de suivi et de mesures de la maturité et de la transformation numérique au sein des PME

Videns accompagne actuellement plusieurs PME dans le secteur de l’assurance dans leur initiative de transformation numérique. Nos services d’accompagnement visent à soutenir les PME dans leurs démarches vers une transformation numérique répondant à leurs besoins et alignée à leurs objectifs stratégiques.
L’accompagnement de Videns est divisé en 4 volets : l’analyse de la situation actuelle, l’évaluation du potentiel de transformation numérique, la planification et la création d’une feuille de route détaillée, et l’accompagnement pour la mise en oeuvre des solutions identifiées. Les 4 volets ont lieu sur une durée de 4 mois et ont pour but d’uniformiser les pratiques en matière d’accompagnement des PME. L’objectif final est de contribuer à la création et à la mise en place d’outils d’évaluation et de mesure standards pour aider les PME dans leur transformation numérique.

Voir la description complète du projet
Superviseur du corps professoral :

Ryad Titah

Étudiant :

Partenaire :

Videns Analytics

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

HEC Montréal

Programme :

Accelerate

Mary England – Sparking the Interest of Canada’s Youth in Sustainability within Advanced Manufacturing and Entrepreneurship.

The intern will take part in a multifaceted project surrounding sustainability in industry and clean advancements in advanced manufacturing techniques within a multitude of different sectors. The intern will perform an in-depth study on Canada’s status regarding advanced manufacturing and utilize social media platforms to relay novel information to promote interest, engagement and an entrepreneurial spirit in these topics among youth. In turn, the partner organization’s (Cansbridge Fellowship) network will gain increased interest and expand, allowing for furthering of the mission to bridge Canada’s innovation knowledge, manufacturing, capital and capability to the global stage with business on an international level. The academic supervisor (Dr. Joseph McDermid) will provide guidance and expertise on advanced manufacturing and future advancements in industry. The partner organization will provide ongoing support and mentorship through the capable network of successful entrepreneurs and innovators. The intern will emphasize outreach and recruitment within content delivery for the partner organization (Cansbridge Fellowship) to promote the program and foster innovation within Canada’s youth while incorporating excellence in knowledge, leadership, mentorship and entrepreneurship for the Cansbridge Fellowship program.

Voir la description complète du projet
Superviseur du corps professoral :

Joseph McDermid

Étudiant :

Partenaire :

Cansbridge Fellowship

Discipline :

Engineering

Secteur :

Education; Other services (except public administration)

Université :

McMaster University

Programme :

Business Strategy Internship

Blunose AR Reloaded;Upgraded App with Advanced AR capabilities and more

Speed Eco has developed an interactive educational selfguided local history app for tourism and self guided tours. As the bluenose is of significance to the local history and of interest to tourists and locals, speedEco is developing the appropriate software and tools to allow for virtual reality, a 3D rendering of the boat and a more interactive tour experience.

Voir la description complète du projet
Superviseur du corps professoral :

Trishla Shah

Étudiant :

Partenaire :

PiRat Ghost History Hunt

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Nova Scotia Community College

Programme :

Business Strategy Internship

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