Impact of Adversarial Examples on the Efficiency of Interpretation and Use of Information from High-Tech Medical Images

In this paper we discuss the possibility of adversarial examples appearance in high-tech medical images (Computer tomography and Magnetic resonance imaging), due to the noise inherent in the technology of their formation, and therefore we suggest ways to counteract this effect. As the idea of the paper we put two questions: 1.Can individual instances of real high-tech medical images work as AE when being analyzed with the use of neural networks? 2. Is it possible to defend oneself against such "natural" adversarial attacks with the simplest possible means? In our research, we tried the following defence methods: adversarial training, Gaussian data augmentation and bounded RELU (see section 3 for a detailed description). We conducted the experiment with the use of the neural network - a variant of convolutional network structure combining U-Net with the region proposal networks. As the source data two datasets were chosen - the Lung Image Database Consortium image collection containing 1018 lung cancer screening thoracic CT scans and Brain MRI DataSet containing clinical imaging data of glioma patients (a total of 274 cases). The experiments showed that the degree of manifestation of AE varies depending on the type of training model. When training a model not using techniques of defences on adversarial examples, the number of incorrectly recognized images is quite large (200 per 10,000 for CT and 285 per 10,000 for MRI). By proper selecting of the activation function of CNN, it can be reduced to 60 and 68, respectively. With augmentation of training dataset by Gaussian noised images, this number drops to 21 and 26. An even greater reduction in the number of incorrectly recognized images is achieved using the Adversarial Training method - 12 and 15. Thus, it is shown that the adversarial effect is possible after the application of adversarial training techniques, but the degree of noise in such an image will be much higher than before using these techniques, and it will be easy enough for the doctor to recognize them visually and exclude them from further consideration

Using topological data analysis for building Bayesan neural networks

For the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational efficiency with the ability to analyze the learning process. The proposed approach is based on Bayesianization of a deterministic neural network by randomizing parameters only at the interface level, i.e., the formation of a Bayesian neural network based on a given network by replacing its parameters with probability distributions that have the parameters of the original model as the average value. Evaluations of the efficiency metrics of the neural network were obtained within the framework of the approach under consideration, and the Bayesian neural network constructed through variation inference were performed using topological data analysis methods. The Bayesianization procedure is implemented through graded variation of the randomization intensity. As an alternative, two neural networks with identical structure were used — deterministic and classical Bayesian networks. The input of the neural network was supplied with the original data of two datasets in versions without noise and with added Gaussian noise. The zero and first persistent homologies for the embeddings of the formed neural networks on each layer were calculated. To assess the quality of classification, the accuracy metric was used. It is shown that the barcodes for embeddings on each layer of the Bayesianized neural network in all four scenarios are between the corresponding barcodes of the deterministic and Bayesian neural networks for both zero and first persistent homologies. In this case, the deterministic neural network is the lower bound, and the Bayesian neural network is the upper bound. It is shown that the structure of data associations within a Bayesianized neural network is inherited from a deterministic model, but acquires the properties of a Bayesian one. It has been experimentally established that there is a relationship between the normalized persistent entropy calculated on neural network embeddings and the accuracy of the neural network. For predicting accuracy, the topology of embeddings on the middle layer of the neural network model turned out to be the most revealing. The proposed approach can be used to simplify the construction of a Bayesian neural network from an already trained deterministic neural network, which opens up the possibility of increasing the accuracy of an existing neural network without ensemble with additional classifiers. It becomes possible to proactively evaluate the effectiveness of the generated neural network on simplified data without running it on a real dataset, which reduces the resource intensity of its development

Socialist Realism in Central and Eastern European Literatures: Institutions, Dynamics, Discourses

This is the first published work to offer a variety of alternative perspectives on the literary and cultural Sovietization of Central and Eastern Europe after World War II and emphasize the dialogic relationship between the 'centre' and the 'satellites' instead of the traditional top-down approach. The introduction of the Soviet cultural model was not quite the smooth endeavour that it was made to look in retrospect; rather, it was always a work in progress, often born out of a give-and-take with the local authorities, intellectuals and interest groups. Relying on archival resources, the authors examine one of the most controversial attempts at a cultural unification in Europe by providing an overview with a focus on specific case-studies, an analysis of distinct particularities with attention to the patterns of negotiation and adaptation that were being developed in the process

State Laughter: Stalinism, Populism, and Origins of Soviet Culture

The Stalinist reign of terror was not all gloom and darkness. Much of it was, or aimed to be, entertaining, full of laughter and joy. This book explores how, and why, humor was a necessary component of one of the most oppressive regimes of the twentieth century. It covers a variety of genres, from film comedy to satirical theatre, from war caricature to court speeches at show trials, from Stalin’s political writings to traditionally bawdy folk verses and fables. The authors combine close textual analysis with reflections on genres of the comic in general. The book offers the first comprehensive analysis of state-sponsored humoristic genres of popular culture in Stalin’s Soviet Union. Tracing the development of genres associated with official humor, satire, and comedy of the Stalin era from the late 1920s to the early 1950s, the authors argue against the conventional view that humor was a feature mostly of subversive texts of the time. According to the authors, satire and popular humor were a foundational element instilling state ideology and legitimizing Stalinist culture. The book is grounded in Soviet intellectual and cultural history and, more generally, in literary theories of laughter and the comic. The authors introduce, and demonstrate possible applications for, a number of innovative concepts

Correlation of Educational Material Ontology with the Individual Knowledge Structure of Students

In this paper we experimentally study the influence of the storage structure of the educational material, in particular ontologies, on the effectiveness of its use for students that have various levels of skills. The foreign language is used as a subject domain. The results of the research show that ontological structures are effective for storing the teaching material, but they must be transformable, in order not to cause the conflict with the already̵̵existing̵̵individual̵̵knowledge̵̵structure̵̵of̵̵the̵̵students