The implementation of data structures usually requires checking for certain mathematical properties such as equality. Those properties are usually implemented in methods that reason about the objects stored in these data structures. However, the implementation of such methods is fairly complex, and may exhibit software bugs that may not necessarily lead to program crashes. Therefore, it is often hard to reproduce such bugs.
Artificial Intelligence (AI) research has grown rapidly in recent years as the result of faster computers and better algorithms. AI models can be trained to automate the decision process and provide results. However, if the model is not properly or sufficiently trained, the outcome will likely be unpredictable and inaccurate. Besides, training data is not easily available in a lot of applications. To address these issues, our strategy is to integrate classical Computer Vision (CV) algorithms and Deep Learning (DL) techniques. CV can provide solutions without training data.
In Canada, less than 30% of the geography is covered by cellular systems. There are lots of human activities in these uncovered remote areas for either gaining nature resources or outdoor experience. In this scenario, the walkie talkie is the only and vital method helping people to build connection between each other, which can ensures their safety. Nevertheless, the signal of walkie talkie devices can be easily attenuated and/or blocked by complicated terrains such as the forest, large rock messes, and mountains.
The forthcoming 5G networks will be much more complex than their predecessors. They are on the verge of a generational transformation driven by the coverage, connectivity, availability, speed and latency demands of 5G. 5G networks will use network slicing to open up the network “as a service” to various third parties and their diversified applications, e.g., from autonomous vehicle control to massive machine-type communication for IoT devices.
We investigate new possibilities for industrial data visualization and analysis with the latest Virtual Reality and Augmented Reality (AR/VR) technologies. In this cluster research, we will apply the state-of-art visual analytics methodologies to the forestry, manufacturing and mining industries, and evaluate the potential benefits for the newly introduced technologies.
Habanero Consulting group is partnering with Postdoctoral Researcher Ryan Taylor and Professor Bryan Gick from the University of British Columbia, and Fernando Nieto Morales from the Colegio de Mexico. They will apply the most current research from the social sciences to strengthen Habanero?s cultural transformation techniques and create a tool to more quickly and accurately diagnose impediments to organizational improvement.
“Cultural memory and diversity in Canadian film festival programming” will work with the Kingston Canadian Film Festival and the Vulnerable Media Lab at Queen’s University to research best practices related to film and video preservation, media digitization, and public programming. Specifically, interns will investigate the role of historic films made by diverse Canadians – including women, Indigenous and Métis, Inuit, and LGBT2Q+ people – within national film festivals, considering the social roles that these films and their screening cultures play.
This project will explore the ways that businesses communicate internally, with their employees and other stakeholders. In order to determine what the current best practices are in terms of how to communication to employees, through which platforms or media, or using specific strategies, the intern will conduct a thorough review of academic and ‘grey’ literature (not quite academic and not quite popular, for example, business magazines). The intern will compile a report for the partner organization in order to help with their own internal communication best practices and product development.
This research is focused on the design and development of a new digital platform for capturing energy and environmental performance data from building systems. Through interviews with key UBC staff and researchers this research will heavily focus on identifying matches between the key pains, reasons and capabilities needs of building owners and municipalities and new information technology features and functions.
IBM QRadar needs to be able to categorize events generated by hundreds of different network devices in order to function as a Security Information and Event Management (SIEM). This categorization is currently a manual process and our aim is to automate this task. We have a database of over 579,000 events coming from over 300 devices that have been manually classified over the years. We also have the classification categories: 18 high level categories, broken down into 500+ subcategories; these categories broadly correspond to security threats.