Innovative Projects Realized

Rapid assessment of decision biases using reach-decision tasks in web-based applications

Imagine being asked two questions during a job interview: 1) Are you more collaborative or more individual? 2) Would you prefer working from home or working in the office? Now imagine that you feel strongly that you are collaborative, and slightly prefer working from home. An interviewer might look at those two responses and feel they are contradictory. However, if they knew that you were more indecisive about working at home, it would make more sense. Here, we propose to use movement dynamics recoded via mobile apps to provide this more detailed decision information. For Paradigm, our partner organization that specializes in assessment, this new assessment platform will mean they can more easily access this rich decision information, resulting in better information collected from more people in less time.

Hydrogeochemical investigation of elevated geogenic uranium in a subarctic region

Northern Canada faces environmental changes from growing resource extraction and global warming, which make an understanding of baseline conditions critical. In the Dawson Range, Yukon, naturally elevated concentrations of uranium have recently been discovered in groundwater at levels that exceed federal water-quality guidelines. This region is also the focus of advanced mineral exploration and falls within traditional territories of several First Nations. Mining activities may enhance uranium mobilization through the generation of waste rock and tailings. Thawing of permafrost might cause a similar effect by altering hydrological and geochemical conditions in groundwater. This project’s goal is to understand and communicate the baseline controls on uranium mobilization in the Dawson Range through analyses of water and rock samples and involvement with local industry, First Nations, and government.

Towards an Intelligent and Secure 5G Ecosystem for the Transformation and Digitalization of Societies Through Artificial Intelligence

Artificial intelligence (AI) has transformed our way of perceiving and interacting with technology, by providing state-of-the-art solutions for challenging problems across the tech-spectrum. The main objective of this cluster of projects is to investigate, develop, adapt, integrate and evaluate state-of-the-art machine learning (ML) techniques, which are suitable for modeling and prediction using datasets collected for complex real-world telecommunications applications. Given the applications of interest for Ericsson Inc., we will focus on ML techniques:
1. to process complex operational data (time series or high dimensional) from real time large-scale wireless and IoT networks;
2. to enable intelligent decision making and data sharing and provenance, and modeling using technologies, such as blockchain, that can scale for real-time systems;
3. for lifecycle management of operating 4G and 5G wireless networks, by addressing the need for long-term deployment, self-profiling, and anomaly detection; and
4. to augment human-computer interactions for real-time decision in support of operation and management of large-scale industrial systems.
Training ML models in such cases typically leads to complex optimization problems, using massive amounts of noisy and incomplete training data.

Intégration des technologies et thérapies associés aux soins de santé personnalisés (SSP)

Le secteur des soins de santé personnalisés (SSP) soulève de nombreux défis en matière d’adhésion, d’intégration et de réglementation. Il est alors nécessaire de faire l’état des défis et des enjeux concernant les SSP dans le système de santé.
Lorsqu’on parle d’intégration ou d’adhésion des SSP, on fait référence à leur utilisation par les professionnels de la santé et plus particulièrement par les médecins. Pour qu’une nouvelle technologie ou thérapie soit intégrée au système de santé, il faut qu’elle soit bien connue et bien comprise par les médecins. Ainsi, ils seront en mesure de l’utiliser en pratique courante. Cette intégration et cette adhésion sont d’autant plus facilitées par l’obtention du remboursement par les assureurs privés et par les assureurs publics. Le secteur public se fait via le régime d’assurance médicament du Québec (RAMQ). Pour obtenir le remboursement d’une nouvelle thérapie ou technologie, des agences d’évaluations des technologies doivent évaluer la valeur clinique et économique d’un produit. Ces agences se basent sur certains critères d’évaluation. Ils émettent ensuite une recommandation au ministre de la Santé et des services sociaux (MSSS) à savoir si oui ou non l’innovation devrait être remboursée par la RAMQ.

Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable. To this end, this project will develop innovative feature learning methods that can distill raw spatiotemporal data, integrate with establish expert knowledge and system models, and present decision-supporting information with transparency and trustworthiness. With a focus on security monitoring applications in the safety-critical CPS, new scientific tools and practice guides developed by the project will benefit the research and development of AI-based & 5G-enabled CPS products and solutions for Ericsson while enhancing the smart infrastructure security for the general public of Canada.

Data driven energy efficient base station sleep control for 5G systems

The objective of this project is to develop a software system which can optimally control the base station sleep states in 5G networks to save energy. The 5G wireless networks are required to be green and yield very low carbon dioxide emissions. Compared with that of 4G wireless networks, the power efficiency of 5G is expected to be increased to 100-fold. High energy efficiency is a critical requirement in 5G network design and operation. We propose station sleep strategies based on machine learning, stochastic programming and robust optimization models which, by leveraging demand patterns learned from historical load data, provide statistically optimal energy efficiency and delay-bounded QoS.

Self-Adaptive Penetration Tests with Deep-Reinforced Intelligent Agents

Penetration testing is a key security tactic, where defenders thinks like an attacker to predict the latter’s actions and develop effective defense. However, for large-scale cyber-physical infrastructures like the smart grid, traditional penetration tests on individual devices or networks are insufficient to exhaust all potential exploits or to reveal infrastructure-level vulnerabilities invisible to the local system. The project aims to close the gap by developing collaborative autonomous agents that can inspect a large-scale infrastructure to identify critical vulnerabilities that would be otherwise invisible to the operators and defenders. To this end, the project will develop innovative deep reinforcement learning agents that will automatically conduct penetration tests in complex dynamic environments and adaptively update their strategies to identify the most impactful exploits. The project will deliver a systematic methodology that enables proactive search for critical vulnerabilities in 5G-connected smart critical infrastructures and promote early defense actions to mitigate the potential risks.

Intelligent Cyber-Physical Situational Awareness for Smart Infrastructures

The availability of big data in smart infrastructures have become a strategical asset for operators to understand the situation of the infrastructure and monitor potential threats. However, most of the data still have not circulated beyond traditional corporation and technological boundaries, which have limited the visibility that could have been provided by the abundant data. To break the barriers and raise the awareness of situations for the smart infrastructure, the project will develop an effective framework based on networked microgrids, which employs artificial intelligence to collect, align and analyze the cyber-physical data to provide a clear understanding of the environment and events in networked regional power grids. The advanced situational awareness technique developed by the project will allow more accurate evaluation of the risks and more effective mitigations against them, so that the networked systems and infrastructures can be better protected in the incoming era of Internet-of-Things and 5G communications.

Dynamic Model for Lead Cable of Inconel Self-Powered NeutronDetectors

CANDU reactors use a large number of self-powered, in-core flux detectors (ICFDs) for their

reactor regulating system (RRS) and their two shutdown systems (SDS-1 and SDS-2).

Dynamic characteristics of ICFDs and lead cables are very important to reactor control and

safety. Although, to date, several studies have modeled dynamic characteristics of ICFDs

and lead cables, additional effort is deemed necessary to model the dynamic response of

lead cables, both at their beginning of life and as they age. The objective of the project proposal is to develop a dynamic model for the lead cables based on the physics of the neutron and gamma interactions contributing to the signal and to use the developed model to simulate previous experiments in Chalk River and observations at OPG.The ability to predict the characteristics of these lead cables at their beginning of life and as they age will benefit OPG’s ability to ensure safe operation of its reactors.

Measuring the effectiveness of a novel treatment of Chronic Lateral Epicondylitis: the ArmLock sleeve.

Lateral epicondylitis is a common source of lateral elbow pain and causes restrictions in performance during daily activities as the pain increases with wrist and hand movements. It is necessary to explore new treatments that decrease the symptoms of lateral epicondylitis. We aim to investigate the effects of a new non-surgical treatment (the ArmLock Sleeve) on pain, movement, and performance in daily activities in adults diagnosed with lateral epicondylitis. We also want to investigate the acceptance of the ArmLock Sleeve by the study participants. The partner organization will benefit by having its product (the ArmLock Sleeve) validated for use by its clients. The feedback will also help the partner organization to scale up its product by marketing it to a wider range of users and/or industries.

Translating behavioural science into effective hiring practices

While companies are looking to hire the most qualified candidate to fill their positions, they often have a difficult time identifying the right candidate for the job using traditional hiring practices. However, one major barrier is that companies may over-rely on traditional hiring methods such as interviews that may not be the best way to select the most qualified candidates. This is because employers may overly-rely on their experience and intuition, even though they are often led astray. The current project seeks to provide a systematic literature review to examine whether work done in cognitive and behavioural science can address these issues and improve the hiring process.

Diversity and Abundance of Beneficial and Pest Insects in Canadian Prairie Agroecosystems – Year two

The proposed research project will assess the insect fauna present associated with prairie wetlands, as well as those found in adjacent fields of crop plants (canola, barley, wheat) and restored grasslands. Insects will be collected using various trapping methods to sample taxa exhibiting different lifestyles. Collected specimens will be identified as specifically as possible to determine taxa found in sampled habitats. This will provide information regarding species diversity and richness of insects in prairie wetlands, which act as nutrition for the waterfowl that Ducks Unlimited are focused on protecting. This information can further be used to assess the ecological health of habitats where Canadian waterfowl exist, as well as determine if beneficial or pest insects may be present in the vegetation surrounding those wetlands which may be affecting local waterfowl or other types of animals, or which may exert an effect on the croplands that prairie potholes exists in.