Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

NodeTrix: Hybrid Representation for Analyzing Social Networks

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily answer the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this prob...

Pixels to Voxels: Modeling Visual Representation in the Human Brain

The human brain is adept at solving difficult high-level visual processing problems such as image interpretation and object recognition in natural scenes. Over the past few years neuroscientists have made remarkable progress in understanding how the human brain represents categories of objects and actions in natural scenes [1, 2]. However, all current models of high-level human vision operate o...

Convolutional Network Representation for Visual Recognition

Image representation is a key component in visual recognition systems. In visual recognition problem, the solution or the model should be able to learn and infer the quality of certain visual semantics in the image. Therefore, it is important for the model to represent the input image in a way that the semantics of interest can be inferred easily and reliably. This thesis is written in the form...

Adaptive density estimation using Stein’s blockwise method

The Effect of Visual Representation, Textual Representation, and Glossing on Second Language Vocabulary Learning

In this study, the researcher chose three different vocabulary techniques (Visual Representation, Textual Enhancement, and Glossing) and compared them with traditional method of teaching vocabulary. 80 advanced EFL Learners were assigned as four intact groups (three experimental and one control group) through using a proficiency test and a vocabulary test as a pre-test. In the visual group, stu...