Convolutional neural networks (CNNs) have become a paradigm for designing vision based intelligent systems. These models are controlled by a vast amount of parameters, which are learned thanks to the availability of annotated ...

The growing interest in the impact of gender on technology brings attention to some concerns with regard to gender bias in design and its implications for the user experience. A first exploration with users of dating ...

This United States-based study uses a grounded theory approach to develop a theoretical understanding of change in teacher practice. This change in practice is mediated through a specific type of community of practice ...

This thesis addresses the topic of example-based colour transfer from the domain of image processing. Colour transfer is often recast as a distribution transfer problem in which the actual probability density function of ...

In human conversations, the words being said may not always contain the whole of the information intended to be conveyed; instead, such information may be conveyed through the way those words are being said. Thus understanding ...

The Internet of Things (IoT) envisions a large number of physical devices connecting over the Internet at an unparalleled rate, to enable ubiquitous and pervasive computing scenarios. By adopting the service-oriented ...

The quality of realistic virtual human faces has increased in recent years with the availability of new technologies such as photogrammetry systems to scan actors and reproduce their shape and texture digitally with minute ...

Neural networks are sets of algorithms that together can approximate general functions. To approximate a function, the network must first be trained by a framework that can give informed feedback to reinforce correct ...

Several parallel trends, including the growing number of Internet reliant devices/services, increasing Internet penetration rates, and the continuing popularity of bandwidth-hungry multimedia content contribute to the ...

Principal Component Analysis (PCA) is one of the most well-known unsupervised learning techniques used for dimensionality reduction and feature extraction. The main task of PCA is to compute a low-dimensional space that ...