Software Defined Security Orchestrator for Content Delivery Networks

Denial of service attacks deny a service, such as visiting a website or access to a network, by deliberately congesting the server or the network resources. In addition to delivering digital contents to end-users, content delivery networks (CDNs) are supposed to protect the content origins, such as Netflix or Amazon Video, against denial of service threats. However, denial of service attacks not only evade a CDN’s protection but also exploit its resources to damage content providers and the CDN itself. As such, traditional security mechanisms are no longer sufficient.

Design and Implementation of a database driven website for IIBC Canada-based online Application

This project seeks to design and implement an online web-based application system for IIBC that will enable people to easily apply for Work permit, Study permit, Permanent Residence, etc. This will provide real-time access to data, making visa application easy for people from all countries to migrate to Canada. With this web application system in place, IIBC will reduce the time taken in handling the manual process, making their services effective and efficient, while generating extra revenue due to ease of processes for customers.

Automated Generation of Software Tools to Support Data Ingestion for Environmental Modelling and Monitoring

Environmental modelling and monitoring software systems, which are very important in assessing the effects of climate change, require open data from a large number of sources including all levels of government, NGOs such as watershed management authorities, consultants and business. This data needs to be brought together into internal databases and to be kept up-to-date to perform the required underlying computations. Collecting this data manually and keeping it current requires an incredible amount of error-prone manual labour.

Developing Prediction Models on S&P 500 Index using Social Sentiment and News Events

Project is to import ten year’s of historical data on news events, public sentiment metrics and the price movement of S&P 500 related equities for study and analysis through the latest Data Mining and Machine Learning techniques. The goal is to uncover correlation and causality between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly). Specifically, the research would answer the question which features (metrics) generated from initial news and sentiment data have predictive power and which don't.

An investigation on software quality measurement

Software failure may result in substantial damage, especially to human life and financial loss. High-quality software is recognized as a product that has been specified correctly, and that meets its expected specifications. It is important that the quality characteristics be specified, measured and evaluated. In this internship, the primary objective is to create the software quality deviation artifact through comparing the user expected quality against the final observed quality of a software product. For this purpose, the quality measurement process is focused.

Adaptive User Interfaces for Product Recommender Systems

We are in the process of creating and growing a team of researchers, expert in the field of machine learning and data-mining. Ultimately, our aim is to create solutions to eliminate the need to manually define personalization strategies. We are in the process of signing partnership agreements with retailers capable of collecting large-scale datasets of customer behaviour. Through a data-sharing/consulting partnership we plan to perform research on the design of recommender systems customized for the data-sets available to brick and mortar retailers.

Models, algorithms and technologies for the treatment of atrial fibrillation caused by heart blockages

Heart failure is among the causes of death in developed countries. Scientific and medical research has made improvements in treating this condition. Mathematical modelling and computer simulation would help in developing the necessary technologies to detect and treat heart blockages and also give a better understanding and protocols for ablation, the clinical procedure used to treat this condition.

Advancing Visualization for Mobile E-Commerce Clickstream Data

We propose to design and build an advanced visual analytics tool to support the analysis of large-scale e-commerce datasets. This data is generated by software platforms that collect information about the performance of e-commerce systems, consumer behaviour, and messages sent by retailers to consumers. Current e-commerce tools provide only simple overview statistics because of the scale and complexity of this data, but more sophisticated analysis could lead to much more effective strategies for e-commerce engagement.

A Sequential Model to Recognize Depression Acuity Using Social Media and Physical Activity

Over 350 million people worldwide suffer from depression. A key part in diagnosing depression is screening questionnaires, which rely on patient self-reports of the recent past. With the advent of social media and wearable devices, there is an opportunity for a novel approach to detecting when a patient diagnosed with chronic depression becomes acute. In this project, we use social media data and physical activity data to detect depression acuity. Social media is indicative of an individual’s mental state. Physical activity is an indicator of physical wellness.

Leveraging data analytics in modern tax function

Investigating geographical footprints of income shifting by multinational enterprises. PwC owns a large data set across all industries in Canada from its tax consulting engagements and annual standard tax filings from clients. This growing data source is an opportunity for accurate tax benchmarking, trend analysis and gaining deeper insights by transforming them into market differentiating knowledge that can be dynamically shared and accessed by multiple teams.

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