In this project, our goal is to set up a framework of data collection to support user profiling which could be used to identify influential users in decision-making. The profile will be built based on the information of individual users obtained by collecting user activities in rewarding challenges that encourage employees, customers and partners to participate. In order to derive the profile, natural language processing tools are applied to extract useful information.
Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, these survey methods are generally only performed in good weather conditions and require many trained observers.
Hockey has long been shown to be among the least predictable of all professional sports. Recent developments in data collection methods have created the demand for more detailed and advanced predictive modelling techniques to extract value from and apply the data to real world problems. This project focuses on predicting important outcomes in hockey at both team and player levels. Game winners and scores will be predicted using Bayesian approaches tailored to accommodate evaluative statistics and relevant pre-game factors.
Networks are moving towards being adaptive. This means that automation will be used to replace processes which are today highly manual. This project proposes a development of knowledge in the area of algorithms required to enable adaptive networks. The project will train two PhD students to understand optical networks and devise optimization algorithms in the areas of interest. In particular, the algorithms will be devised to be fast and near-optimal to enable their implementation in the network in accordance with operators goals of making the network near-optimal and adaptive.
Characterization of the energy distribution of ions generated by the plasma in an Inductively Coupled Plasma Mass Spectrometer (ICP-MS) instrument is necessary for a new ion source in that it influences the ion sampling process, transmission efficiency, focusing, and mass analysis in ICP-MS. These energy distribution phenomena are also analogous to the ion beam that has been generated from an electron impact ionization (EI) source. Similarly, better understanding of the ion beam profile results in a better optimization of the EI source for superior performance.
As the world aging process quickened, the need for healthcare solutions to support seniors living on their own is recognized as a serious medical and social problem. Though an extensive amount of research has been carried out to investigate human activity based on a range of device-oriented (e.g., wearable) and device-free (e.g., vision based) sensing technologies. Monitoring activities of clinical relevance for senior well-being (e.g., eating, sleeping and falls) is still very challenging.
Typical accounting systems are built for accountants, and they are great for filing a year-end. However, they are a burden at running day-to-day consulting companies where every hour has value. The GroupThinq accounting application tackles this problem by providing a framework to control, in real-time, every aspect of a company, helping teams to run projects, connecting teams across organizational silos, and making visible the value of time for every team member.
For a software application to be released, it may need a number of standard certifications, which should be issued from different associations and for a wide range of software features (e.g., security, perfor-mance, scalability). We call these associations trusted third parties. The main problem with these trust-ed third parties is that they are single points of failures. For example, if one of these parties gets hacked many software source codes could be stolen.
Smart, connected, and autonomous vehicles enable crash prevention, enhanced safety, mobility and environmental benefits. Despite the potential benefits of smart, connected, and autonomous vehicles, significant security and privacy challenges remain to be addressed before widespread deployment for intelligent transportation systems may begin. In this project, we identify and analyze the risks and vulnerabilities associated with cyber-attacks related to smart, connected, and autonomous vehicles.