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.
The objective of this project is to develop a software system which can optimally control the base station sleep states in 5G networks to save energy. The 5G wireless networks are required to be green and yield very low carbon dioxide emissions. Compared with that of 4G wireless networks, the power efficiency of 5G is expected to be increased to 100-fold. High energy efficiency is a critical requirement in 5G network design and operation.
For different software packages created using different tools to interoperate, an intermediate layer called API bindings is needed. These bindings can be created by hand, but that takes time and needs to be updated whenever one of the packages changes.
Recent research has shown that people can perceive which affective state one is in simply by looking at body movements (without facial expression). Because of this, it has been possible to train machine learning algorithms to automatically recognize the affect of users from their body movements, to be used in human-computer interaction. This project consists in trying to adapt and deploy the same type of algorithms in a VR environment, where only partial movement information is available (hands and head positions of the user).
Traditionally a Service Function Chain (SFC) consists of a set of dedicated network service boxes such as firewall, load balancers, and application delivery controllers that are concatenated together to support a specific service. With a new service, new devices must be installed and interconnected in certain order. This can be a very complex, time-consuming, and error-prone process, requiring careful planning of topology changes and network outages and incurring high OPEX.
Frailty is an especially problematic expression of population ageing. It is a condition characterised by loss of biological reserves across multiple organ systems and vulnerability to physiological decompensation after a stressor event. Older people with frailty are at increased risk of adverse outcomes including disability, hospitalisation, nursing home admission and mortality. This project addresses the issue of predicting frailty in patients by automatically processing medical notes taken by health professionals when consulting those patients.