The aim of this study was to evaluate whether smooth surfaces varying in surface chemistry could be perceptually distinguished with the sense of touch. A set of ten glass surfaces was prepared which varied systematically in terms of the molecular composition of a thin coating of low topography. The contact angle, contact angle hysteresis, and surface energy were evaluated as objective physical parameters characterizing each coating. Additionally, the interaction forces between a human finger and the different coatings were quantified and compared in terms of tactile friction coefficients. The surfaces were evaluated psychophysically in terms of perceived similarities and were then ranked according to pleasantness. The participants could perceptually distinguish between surfaces varying in surface chemistry and a primary and secondary perceptual dimension were identified as sufficient to distinguish them. The primary dimension correlates with surface free energy, but both tactile friction and surface energy contribute to this dimension depending on whether the coatings are organic or inorganic. The secondary dimension could not be identified explicitly in terms of a physical quantity but is discussed in terms of recent developments in the literature. Coated glass is characterized by high friction coefficient upon interaction with a human finger as well as significant hysteresis in the stroking directions (lower applied load and higher friction in the backward stroke). Despite the complexity of the tribology, pleasantness can be clearly linked to it, where low friction (high contact angle) materials receive a higher ranking.

1 Московский государственный технический университет им. Н.Э. Баумана, 105005, Москва, 2-я Бауманская ул., д. 5, стр. 1 2 Институт радиотехники и электроники им. В.А. Котельникова РАН, 125009, Москва, ул. Моховая, 11-7 Статья поступила в редакцию 19 ноября 2019 г. Аннотация. Данная работа посвящена использованию сверточной нейронной сети для распознавания речи. Исследован способ обучения нейросети, произведенный на архиве из 7100 звуковых дорожек с проиндексированными метками, речевые сигналы в которых были преобразованы в log-mel спектрограммы. Обучение нейронной сети происходило на входящем сигнале, имеющем плавное распределение и нормализацию. В статье описана способность созданной сети распознавать разные произнесенные слова и определять, является ли входящий сигнал тишиной или фоновым шумом, что было достигнуто путем проработки 4000 образцов клипов шума. Рассматривается способность сети одновременно классифицировать несколько преобразованных входящих сигналов, независимо от точного положения речи во времени. Описан процесс создания виртуального устройства, способного считывать сигнал с микрофона с определенной частотой дискретизацией звука. В настоящей работе была получена нейросеть, которая может быть усовершенствована для понимания большего числа голосовых команд и использована в нескольких сферах жизнедеятельности человека. Ключевые слова: нейронные сети, глубокое обучение, распознавание речи.Abstract. This work is devoted to the use and development of speech recognition of neural networks. The process of neural network learning has been explored with the archive containing 7100 tracks with indexed tags. Speech signals in those tracks were converted into log-mel spectrograms. Neural network training has occurred onto an entering signal which possessed smooth distribution and normalization. The article describes the ability of the created network to recognize different spoken words and

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I n this paper, Ukrainian word embeddings and their properties are examined. Provided are a theoretical description, a brief account of the most common technologies used to produce an embedding, and lists of implemented algorithms. Word2wec, the first technology for calculating word embeddings, is used to demonstrate modern approaches of calculating using neural networks. Word2wec and FastText, which evolved from word2vec, are compared, and FastText’s benefits are described. Word embeddings have been applied to solving majority of the practical tasks of natural language processing. One of the latest such applications have been in the automatic construction of translation dictionaries. A previous analysis indicates that most of the words found in English-Ukrainian dictionaries are absent in the Great Electronic Dictionary of the Ukrainian Language (VESUM) project. For embeddings in Ukrainian based on word2vec, Glove, lex2vec, and FastText, the Gensim open-source library was used to demonstrate the potential of calculated models, and the results of repeating known calculation experiments are provided. They indicate that the hypothesis about the existence of biases and stereotypes in such models does not pertain to the Ukrainian language. The quality of the word embeddings is assessed on the basis of testing analogies, and adapting lexical data from a Ukrainian associative dictionary in order to construct a selection of data for assessing the quality of word embeddings is proposed. Listed are necessary tasks of future research in the field of creating and utilizing Ukrainian word embeddings.