Recent technological advancements led to the emergence of technology-enabled collaborative environments in which students work together on different activities. In such settings, the first step is to determine the participants who are present and take part in the collaborative activities. We are interested in the attendance-taking process itself as a collaborative activity, and plan to learn more about its theory and practice, and to study new ways in which it can be applied using cutting edge collaborative technologies.
The blood of cancer patients contains DNA that has been shed by their tumours (circulating tumour DNA or ctDNA). As such, any genetic mutations in the tumour may be detected in the patient's blood. Theoretically, patients can be followed and monitored through analysis of ctDNA, which would reveal if the patient is responding to treatment, is in remission, or if mutations are becoming more or less prevalent as the result of time and/or treatment pressure. The challenge is development of a reliable method of detecting ctDNA, with adequate sensitivity and specificity to guide treatment.
Learning Analytics is the collection and analysis of data traces related to learning in order to inform and improve learning processes and/or their outcomes. In digital environments, this data can be easily captured. Hence, there is a particularly great opportunity to visualize these traces and put them in the hands of students to support their learning. This can help address the longknown problems of students not consistently receiving relevant, personalized and timely feedback on their learning and as a consequence not engaging as active agents of their own learning.
This research project will provide the intern with the opportunity to work with end users (students and faculty) to refine, further develop and test a prototype of NursApp, a learning management system (LMS) designed to create opportunities for students to use technology to learn in post-secondary education. The intern will work with co-owners to conduct focus groups with students and use feedback to refine, further develop and integrate social media components into the NursApp.
High-volume online stream processing, also known as fast data processing, is becoming increasingly important in a number of different commercial sectors. Unlike big data processing in which data is processed asynchronously in batches, fast data processing performs synchronous data analysis that generates actionable results within a specified deadline. One of the key challenges in building a fast data processing system is in scaling with increasing volumes of data. In our proposed research, we plan to build a system to efficiently manage the available memory across the entire deployment.
Two Hat Security is a company that develops next generation moderation tools for social networking apps. Since visual content (e.g. images, videos) is one of the most important types of data shared by social networking apps, an important problem for the company is to identify images/videos that are offensive or inappropriate. For example, certain images/videos might contain violence, nudity, or certain objects (knife, gun, bikini, etc.) that are considered offensive.
Flow, the psychological state of being totally absorbed into an activity, has been suggested to be an important topic for future research as it represents optimal experience. This research seeks to understand 1-how to facilitate flow while engaging with virtual reality products and 2-the outcomes of experiencing flow in regards to attitudes towards virtual reality products and purchase intentions. While learning best-practices in facilitating flow while using virtual reality, we can also conduct marketing research specifically for Zenfri Inc.s game The Last Taxi.
The visualization/decision support work encompassed by this application addresses key elements of the upgrade path for that strategic part of IBM Canada's smarter cities product and service portfolio as urban transport systems evolve, their escalating complexity requires more advanced visualization tools and practices.
Internet display advertising is a substantially growing industry, where advertising spots are dynamically allocated according the product characteristics and the target audience. This is commonly seen on Facebook or Google. However, accurate audience targeting is still an enormous challenge in practice, and often evokes frustration for advertisers and users. However, with the rapid growth and change in this industry, most advertisers have limited knowledge and skills to design advertising campaigns.
This research project aims at creating a robust, efficient and reliable tool for Information Extraction (IE) from vast amounts of textual data related to the financial domain. Named entities recognition, a subtask of information extraction, seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.