Chrisina Draganova

TL;DR: The aim of this work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image, which indicates that machines can estimate theAge of a person almost as reliably as humans.

TL;DR: This work develops a method for incorporation into an on-line system to provide carefully targeted guidance and feedback to students to address the level of knowledge of the individual and guides them towards a greater understanding of particular concepts.

TL;DR: This paper investigates the application of the snap-drift neural network (SDNN) to the provision of guided student learning in formative assessments and indicates that significant learning support is provided for the students.

TL;DR: Two modal learning ideas are presented: The Snap-Drift Neural Network (SDNN), which toggles its learning between two modes, is incorporated into an on-line system to provide carefully targeted guidance and feedback to students; and an adaptive function neural network (ADFUNN), in which adaptation applies simultaneously to both the weights and the individual neuron activation functions.

TL;DR: This poster illustrates a real world implementation of the progressive enhancement technique by following the steps in building a restaurant food menu that elegantly scrolls within a fixed page design.