Large software systems are updated incrementally to add new features or fix bugs. It is a common practice in the software industry to have each incremental change reviewed by a peer to detect software quality issues and transfer knowledge among team members. While peer review boasts technical and non-technical benefits, it is still primarily based on low-level textual differencing, which place the prior and updated versions of the software source code next to one another.
The proposed research project targets anomaly detection of event data. The project has a duration of six months and aims to achieve two objectives: (1) to evaluate the effectiveness of a novel approach for real-world data, and (2) compare it to alternative methods. The intern will use existing research resources, and will apply them to real-world data provided by the partner, Acerta Analytics Solutions, Inc. to evaluate the different methods.
Blockchain is an emerging technology that has the potential to change the way financial participants transact with each other. It enables direct transfer of value and financial assets between participants over networks without the need for a central authority (internet of value). It does this by combining the functionality of different technologies - distributed systems, smart contracts, mutual consensus verification, and cryptography. Given its potential Scotiabank is investing in technical research and business application.
We are conducting research on using techniques from Artificial Intelligence (specifically Machine Learning, Reinforcement learning, and computer vision) to automate the ability of a robotic arm equipped with a hand-like gripper to pick a wide variety of items. The robot uses the visual scene, provided through cameras, in order to choose which item to pick, and needs to then plan and execute a grasp. This is an open research problem at the cutting edge of robotics and AI and we plan to use a combination of state of the art academic research as well as internally developed algorithms.
TurnMeUp is an iOS app for always-on voice communications. Users leave the app running in the background and can talk to the recipient (also using the app) at any time. This app is especially useful for coworkers listening to their own music in the background without needing to enter and exit voice call sessions manually. To conserve bandwidth and ensure that users listen to music without being unnecessarily interrupted, TurnMeUp sends voice signals to the recipient only if the user is speaking.
Developing a model for a system can come with a lot of uncertainty, especially in the early stages of development. Recent research has be done into removing uncertainty during early stage models. Doctalk plans to use modern research to develop a viable product for market, while contributing to the process of the research being applied to the development of the product.
The company wants to develop a state of art recommendation system for the clients. A recommendation system is a piece of software that provides productsâ suggestions to customers on a website. For example the products suggestions that can be seen on Amazonâs web page are generated by its recommendation engine.
The typical recommendation engines work by utilizing the existing user-product preferences information. They recommend products to a user by comparing his preferences to other similar usersâ preferences. The typical example of this is Users who bought item-A also bought item-B.
Using web crawling technology in coordination with state of the art machine learning techniques, the project aims to mine useful, structured information about the worldâs suppliers from the web. Recent advances in artificial intelligence have increased the viability of such autonomous systems for extracting coherent information from arbitrary human-produced content. By leveraging these technologies, our goal is to build improved supplier discovery and recommendation systems.
The intern will work on applying new advances from the field of Machine Learning to models which make predictions about time-series data. The models have the desirable property modeling the distribution of outcomes in a way that we can sample from, allowing us to account for uncertainty in the modelâs predictions. By making more accurate predictions with more accurate gauges of uncertainty, Electronica will be able to construct portfolios which give more desirable risk-adjusted returns to investors.
Dynamic modeling is one of the most important tools for the power system operation and planning purposes. In order to study the behavior of the system, which is subjected to disturbances, a valid knowledge of parameters of system components is essentially required. The objective of this project is to propose an applicable algorithm to identify the parameters of the power system componentsâ models. For the identification purpose, the actual power systemsâ subsections data collected by phasor measurement units (PMUs) are employed.