In recent years, face recognition algorithms based on deep neural networks have achieved human-level performance when tested on face recognition database. However, when put into real-world application, those algorithms are not robust enough, due to factors such as different lighting conditions, camera distance, and face orientations.
In the telecommunications industry, many schemas exist to cap or limit bandwidth at certain levels for clients. However, there are no real options available to allow clients to intelligently utilize spare bandwidth above their committed purchase rates. We propose to design, implement and evaluate novel bandwidth allocation mechanisms for high speed networks like the Cybera network. Cybera is a not-for-profit, technology-neutral organization responsible for driving Albertas economic growth through the use of digital technology.
ROSS Intelligence enables legal professionals to find analyze legal issues and find hidden information and cuts down on research time by using artificial intelligence specialized in legal research. Recent advances in neural networks applied to
natural language processing have brought results that are close to human performance in some tasks. However, this approach is still nascent in legal research and it has been identified as potentially fruitful.
For this project, a data mining, visualization, and modeling technique will be developed and tested specifically for emails, using publicly available datasets. The mining will consist of gathering email and other potentially related datasets and cleaning those datasets. Cleaning will consist of removing duplicate or unnecessary information, as well as labeling data with basic information in order to ease training in the later steps. Next that data will be visualized in some form (graphs, charts, etc.) so that it may be more easily understood and a training model can be development.
Irwin's Industrial Safety is a leading provider of safety consulting, safety training, and safety operations management. Over the last 4 years, Irwins Safety has compiled data on project safety and efficiency over a wide scope of projects. Moving forward, Irwins Safety seeks research into how this data can best be analyzed, visualized, and used to optimize future projects. Before data can be analyzed, data scripting will be applied to transform the data into a suitable format.
2012 marked a pivotal milestone in the field of neural networks. The intersection of general purpose computing using Graphics Processing Units (GPUs), labelled big datasets, and very large neural networks (called deep neural networks) enabled a break-through in machine learning that has led to impressive results in many fields and applications, such as self-driving vehicles and real-time language translation. Recently, the advances offered by these techniques have been applied to the areas of music and speech synthesis, which have opened up exciting new areas of applications.
With increasing security risks in critical network infrastructures and emerging cloud technologies with shared capabilities, as well as increasing regulatory requirements on privacy, and data protection, there is a growing need for new approaches to manage security and privacy compliance.
In this project the intern will develop an intelligent virtual concierge and integrate it with Curatios social network platform. The intelligent concierge will learn about each user over time and act as a personal assistant of the user in using the social network platform.
Critical systems such as transportation systems require a high level of safety that can only be achieved with formal proof. Such formal proofs are typically expressed in some logic that can be verified by theorem provers. The diversity of theorem provers and logics has a negative consequence: the same theorem is proved many times and it is difficult for these systems to co-operate, because they do not implement the same logic. Logical frameworks are a class of theorem provers that overcome this issue by providing a generic framework in which we can represent and specify various logics.
Due to rapid development of technology, such as the Internet of Things, collecting data is easier and cheaper than ever before. As a result, municipal governments and urban centres across Canada are being inundated with dataâdata that have potential to improve public service. Despite this, local governments do not have enough data expertise to extract insight from these overwhelming datasets, which are often unstructured and âdirtyâ (i.e., incomplete, inaccurate, and/or erroneous).