Postava chlapca a mládenca v konceptoch slovenskej literatúry (nielen) pre deti a mládež

In the text, it is possible to observe the character of a young man which in a literary-historical perspective corresponds to several literary types in Slovak literature. The interpretation focuses on modelling and transforming a plebeian and obedient hero into a modern, educated and confident bachelor. At the same time, it marginally touches on the image of a man / father, who changes into an uncertain, unreliable and often absent parent on the axis of the dominant and patriarchal. The text also presents poetological aspects of the protagonists' creation: a realistic image in Kukučín's short stories, a slightly subjectivised and expressive image of a boy of interwar prose, a naive and stylised type from the 1950s fiction, an immediate, an authentic and plastic hero modelled by modern prose in novels for young adults. Transformations of construction can also be observed at the linguo-stylistic level of texts, the way of narration, and the choice of narrator.

Photo-Realistic Image Dehazing and Verifying Networks via Complementary Adversarial Learning

Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.

The specifics of the NEETs population in Bulgaria and Romania

NEETs are a social category specific to today's society characterized by increasing inequalities between people, precariousness, and insecurity. At the level of the majority of the population but also among the authorities and specialists (economists, sociologists, psychologists, etc.) the dominant perception is negative on this category of populations often associated with the underclass, dangerous class (Avis, 2014), people getting nowhere (Bynner, Ferri & Shepherd, 1997) or people at risk (Conrad, 2005). The interest for this category of the population is even higher for Bulgaria and Romania because the share of NEETs is the highest in the EU: 23.8% in Romania and 20.7% in Bulgaria (Eurostat database). This paper aims to analyze this category of population to obtain a more realistic image of NEETs, especially in the two Eastern European countries. One of the few positive effects resulting from the accentuation of interest for this category of the population consists in increasing the number of documents (articles, reports, books, etc.) that analyze NEETs. By using alternative research methodologies (literature review) these documents can provide relevant information on NEETs and can provide several clarifications on their situation at the national and European levels.

A survey on deep learning-based Monte Carlo denoising

Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its flexibility and generality. However, the integration has to balance estimator bias and variance, which causes visually distracting noise with low sample counts. Existing solutions fall into two categories, in-process sampling schemes and post-processing reconstruction schemes. This report summarizes recent trends in the post-processing reconstruction scheme. Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples. Many of these techniques show promising results in real-world applications, and this report aims to provide an assessment of these approaches for practitioners and researchers.

A New Image Completion Method Inserting an Image Generated by Sketch Image

Recently, many studies on the image completion methods make us erase obstacles and fill the hole realistically but putting a new object in its place cannot be solved with the existing Image Completion. To solve this problem, this paper proposes Image Completion which filled a new object that is created through sketch image. The proposed network use pix2pix image translation model for generating object image from sketch image. The image completion network used gated convolution to reduce the weight of meaningless pixels in the convolution process. And WGAN-GP loss is used to reduce the mode dropping. In addition, by adding a contextual attention layer in the middle of the network, image completion is performed by referring to the feature value at a distant pixel. To train the models, Places2 dataset was used as background training data for image completion and Standard Dog dataset was used as training data for pix2pix. As a result of the experiment, an image of dog is generated well by sketch image and use this image as an input of the image completion network, it can generate the realistic image as a result.

Generation of high dynamic range for enhancing the panorama environment

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.