We seek to replace or enhance the traditional underwriting approach (namely identification of insureds via a pre-defined fixed set of risk criteria) with one based on a set of dynamic protocols that are responsive to human behavioral factors for continual health improvement. We seek to provide a live and interactive in-market research dataset that can be used to explore the benefit of and improve data-driven approaches (namely artificial intelligence or AI) for immediate use in life & health insurance product development and actuarial risk assessment.
UppstArt is a blockchain-based system for arts e-commerce. UppstArt integrates Ethereum blockchain to handle the online sale of art. UppstArt allows buyers to track the ownership provenance of artworks and resell their purchases any time. UppstArt also allows artists receive a royalty percentage every time their artworks are resold (Pending Canadian Legislation Artist Resale Rights). UppstArt is directed to living artists that produce original paintings.
Model-free Reinforcement Learning (RL) has recently demonstrated its great potential in solving difficult intelligent tasks. However, developing a successful RL model requires an extensive model tuning and tremendous training samples. Theoretical analysis of these RL methods, more specifically policy optimization methods, only stay in a simple setting where the learning happens in the policy space. This project attempts to advance the analysis of the policy optimization methods to a more realistic setting in the parameter space.
MatchWork enables non-profit employment support organizations to support marginalized people to find meaningful employment opportunities. This includes people with physical and mental challenges, veterans, new immigrants and refugees.
In recent years, deep learning has led to unprecedented advances in a wide range of applications including natural language processing, reinforcement learning, and speech recognition. Despite the abundance of empirical evidence highlighting the success of neural networks, the theoretical properties of deep learning remain poorly understood and have been a subject of active investigation. One foundational aspect of deep learning that has garnered great intrigue in recent years is the generalization behavior of neural networks, that is, the ability of a neural network to perform on unseen data.
Compilers are large software projects consisting of many separate but common components like code generators, garbage collectors, and runtime diagnostic tools, to name but a few. Historically compiler developers have had to write each of these components from scratch. The Eclipse OMR project was created to provide generic components for use in new compilers and language runtime environments. OMR has enough flexibility to accommodate a wide range of programming languages without sacrificing performance, portability, or robustness.
In today’s candidate-driven market, talent recruitment represents a major challenge for many companies. Younger millennials have different expectations of their work environment than previous generations. They are heavy users of technology in almost all their activities, including job search. They are also used to multiple social medias and expect faster feedback. In this changing environment, traditional job web sites do not answer the challenges facing today’s talent recruitment. It becomes obvious that we need a new approach to tackle this problem.
Against a backdrop of alarming privacy breaches at social media, banking, and government websites, the European Union will see the General Data Protection Regulation (GDPR) come into force as of May 25 2018. GDPR will drastically increase fines for data loss and breach, introduces new requirements to obtain individual consent for data use, and defines new responsibilities for data processors and controllers.
In this project, we are interested in device-free methods that passively sense, monitor, and track peoples indoor presence, location, and movement using off-the-shelf Wi-Fi-enabled devices. We use information extracted from the physical layer of wireless links to detect and interpret human presence, location, and physical activities. The current design and implementation of Wi-Fi-based systems exhibit some temporal inconsistencies and limitations due to the complexity of the wireless signal propagation in indoor environment and the challenging nature of human's behavior itself.