Amélioration des performances et du processus de mise-au-point d’agents virtuels (AV) selon une approche hybride combinant du traitement automatique de la langue ou TAL (natural language processing), de l'apprentissage statistique (machine learning) et du génie cognitif à base de règles (les outils et API de Coginov). Notamment, nous expérimenterons l’utilisation de synonymes en contexte et l’amplification des données textuelles pour fins de traitements statistiques.
Systems today often handle massive amount of data with little regard to privacy or security issues that may arise. This problem has become more acute as more and more individuals and organizations are using cloud-based data storage. Encryption techniques has often provided a key role in protecting sensitive information, however many challenges remain in using these types of techniques in this new cloud-based setting.
Discovery Agents is a leader in mobile, augmented reality educational technology, with products and services intended to enhance student and teacher experiences within informal and formal educational settings. With a growing industry demand for diverse science, technology, engineering and mathematics (STEM) professionals, this study proposes to examine the impact of the Discovery Agents Mission Builder tool on STEM learning and perceptions among middle school students.
Recent advances in applications of deep learning in natural language processing has provided potential opportunities in building robust information retrieval and conversational models that require far less hand-crafted features for understanding the intent of queries and ultimately building question-answering systems. In particular, there has been several advances in factoid question-answering systems and some recent attempts to moving beyond factoid questions.
Most cameras today, take photographs by measuring the amount of light in a scene. This means that they discard any information about the directionality of light, which results in a two-dimensional representation of the three-dimensional scene. We have developed a new type of camera that can detect the direction of light as well as image intensity. The 3D scene can be estimated by leveraging this additional information. Due to our manufacturing constraints, the signal from our camera is not ideal, hence the reconstruction process is not straightforward.
Farmers of North America (FNA) and FNA Strategic Agriculture Institute (FNA STAG) are two Canadian organizations dedicated to maximizing farm profitability. They collect and analyze demographic, legal, marketing and relevant data about its producers and partnering commodity organizations to understand the farmer market need and create strategies for business operation functionality. With this project, the organizations will get two database systems, the market/consumer research and distributed database.
Dynamic Software Updating (DSU) is a necessity in the operation of a large computing infrastructure that must deliver high availability. An issue very relevant to the industry is that legacy applications running in data centers were often designed and constructed without due consideration for the need of partial upgrades in the future. The goal of this research is the development of a framework that allows for an automatic retrofitting of legacy applications to enable a selected set of individual methods to be dynamically updated.
Unstructured data refers to data that is present in reports, web pages, newspapers and other media. Such data is the most common data that we see around us and yet no modern tools exist to extract information from it. In this project we will develop techniques to extract the data and apply it to geoscientific reports in order to aid in the discovery of new mines and other geoscience applications.
In recent years monitoring and protection of food and water resources became a priority of governments worldwide. Bio-hazards are potential threat for these resources thus need to be addressed both in industry and in academia. Therefore, developing an accurate, fast and cost effective technique for detection of pathogenic strains called for increased demand on the areas targeted by the fiber-optic systems.
Machine learning is an active field of research and development to provide tools and technologies for finding significant patterns in data. Behind every face detection and face recognition software in digital cameras or social network websites a constantly under-development machine learning algorithm is working. Nowadays in any practical applications of machine learning we have to analyze huge amounts of data. Using classical approaches to train machine learning algorithms for some classes of algorithms is either very slow, requiring a lot of computing resources, or inefficient.