Although filling the gap in reading comprehension gained momentum with the rise of the top-down approach, Vygotsky’ concept of scaffolding and the dual code  theory provided a strong support for the use of paratext to enhance comprehension. Scaffolding is dependent on other-regulation, one type of which is object-regulation. From this vantage-point, various types of paratext can function as sou...

This paper proposes an improved dark channel prior for removing haze from images. Dark channel prior is an effective method for removing haze. Dark channel is an image in the same size as the hazy image which is obtained by dividing the RGB images into windows and for each window, the minimum of each R, G and B channels are calculated. Then again the minimum of these three values is calculated ...

Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail sky region of image, resulting inaccurately estimated parameters, and given failure many methods to address large band haze, we propose simple yet effective for single-image based on an improved bright prior dark channel prior. First, use Otsu by particle swarm optimi...

Khalaf Y Guy’s Hospital Campus, Kings College London, UK Email: [email protected]

This article summarizes research on the roles of prior knowledge in learning. Educators often focus on the ideas that they want their audience to have. But research has shown that a learnerÕs prior knowledge often confounds an educatorÕs best efforts to deliver ideas accurately. A large body of findings shows that learning proceeds primarily from prior knowledge, and only secondarily from the p...

Estimating the parameters of sparse multinomial distributions is an important component of many statistical learning tasks. Recent approaches have used uncertainty over the vocabulary of symbols in a multinomial distribution as a means of accounting for sparsity. We present a Bayesian approach that allows weak prior knowledge, in the form of a small set of approximate candidate vocabularies, to...

Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where an expressive dependency structure among these can influence, or even dictate, what assignments are possible. Commonly used models typically ignore expressive dependencies since the traditional way of incorporating non-local dependencies is inefficient and hence lead to expe...