Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Solar community heating and cooling system with borehole thermal energy storage – Review of systems

Abstract: There is a substantial need to accelerate the advancement and implementation of advanced clean energy technologies to solve challenges of the energy crisis, climate change, and sustainable processes. Solar heating and cooling technologies are feasible solutions among clean energy technologies. This paper presents a detailed literature review on studies performed around the solar district energy systems with integrated thermal storage. They are mainly either for heating or cooling. The combined district heating and cooling system with both systems integrated with borehole thermal energy storage (BTES) has not been fully explored. A low-temperature distribution fluid, suitable for use in distributed heat pumps around the community with BTES, has also not been practically installed yet. Such system, could reduce the transmission/distribution heat loss within the community, and lower the required amount of energy production and storage, compared to the other systems. This could make the entire system techno-economically more attractive while not compromising energy efficiency of the system.

Gaussian Mixture Modeling of Keystroke Patterns for Biometric Applications

Abstract: The keystroke patterns produced during typing have been shown to be unique biometric signatures. Therefore, these patterns can be used as digital signatures to verify the identity of computer users remotely over the Internet or locally at a specific workstation. In particular, keystroke recognition can enhance the username and password security model by monitoring the way that these strings are typed. To this end, this paper proposes a novel up--up keystroke latency (UUKL) feature and compares its performance with existing features using a Gaussian mixture model (GMM)-based verification system that utilizes an adaptive and user-specific threshold based on the leave-one-out method (LOOM). The results show that the UUKL feature significantly outperforms the commonly used key hold-down time (KD) and down--down keystroke latency (DDKL) features. Overall, the inclusion of the UUKL feature led to an equal error rate (EER) of 4.4% based on a database of 41 users, which is a 2.1% improvement as compared to the existing features. Comprehensive results are also presented for a two-stage authentication system that has shown significant benefits. Lastly, due to many inconsistencies in previous works, a formal keystroke protocol is recommended that consolidates a number of parameters concerning how to improve performance, reliability, and accuracy of keystroke-recognition systems.

An Interactive Node-Link Visualization of Convolutional Neural Networks

Abstract: Convolutional neural networks are at the core of state-of-the-art approaches to a variety of computer vision tasks. Visualizations of neural networks typically take the form of static diagrams, or interactive toy-sized networks, which fail to illustrate the networks’ scale and complexity, and furthermore do not enable meaningful experimentation. Motivated by this observation, this paper presents a new interactive visualization of neural networks trained on handwritten digit recognition, with the intent of showing the actual behavior of the network given user-provided input. The user can interact with the network through a drawing pad, and watch the activation patterns of the network respond in real-time. The visualization is available at http://scs.ryerson.ca/~aharley/vis/.