On Use of the Correction Coefficients at Assessment of Anthropogenic Trans-formation of the Water Bodies

Оцінюючи рівень трансформації водних об’єктів, приймаємо стан природних підсистем – лісу, луків, боліт як ідеальний непорушений. Водночас під впливом господарської діяльності вони частково деградовані. З врахуванням ступеня цієї деградації коефіцієнт трансформації басейнів зростає, неврахування яких може дати негативні наслідки. At assessment of the water bodies’ transformation state of the natural subsystems (forests, meadows, wetlands etc.) is usually accepted as ideal, undisturbed. At the same time they are to a certain extent degraded because of the human economic activity. Fail of account of this degradation rate coefficient can result in incorrect assessment of the basins’ transformation, this can have unfavorable consequences.Роботу виконано в Інституті гідробіології НАН Україн

Neural networks in Machine learning

В статті розглянуті основи роботи з нейронними мережами, особливу увагу приділено моделі мережі під назвою «перцептрон», запровадженої Френком Розенблаттом. До того ж було розкрито тему найпоширеніших мов програмування, що дозволяють втілити нейронні мережі у життя, шляхом створення програмного забезпечення, пов`язаного з ними.The paper covers the basic principles of Neural Networks’ work. Special attention is paid to Frank Rosenblatt’s model of the network called “perceptron”. In addition, the article touches upon the main programming languages used to write software for Neural Networks

OAHEGA : EMOTION RECOGNITION DATASET

The dataset consists of 6 distinct emotions : Happy, Angry, Sad, Neutral, Surprise and Ahegao. Images are RGB and presented as cropped faces with corresponding emotions. The images were collected by scrapping social nets as Facebook and Instagram, scrapping YouTube videos and already available datasets as IMDB and AffectNet. 1) dataset.zip contains folders with corresponding classes. 2) data.csv contains pathes to images and corresponding labels

OAHEGA : EMOTION RECOGNITION DATASET

The dataset consists of 6 distinct emotions : Happy, Angry, Sad, Neutral, Surprise and Ahegao. Images are RGB and presented as cropped faces with corresponding emotions. The images were collected by scrapping social nets as Facebook and Instagram, scrapping YouTube videos and already available datasets as IMDB and AffectNet. 1) dataset.zip contains folders with corresponding classes. 2) data.csv contains pathes to images and corresponding labels

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OAHEGA : EMOTION RECOGNITION DATASET

The dataset consists of 6 distinct emotions : Happy, Angry, Sad, Neutral, Surprise and Ahegao. Images are RGB and presented as cropped faces with corresponding emotions. The images were collected by scrapping social nets as Facebook and Instagram, scrapping YouTube videos and already available datasets as IMDB and AffectNet. 1) dataset.zip contains folders with corresponding classes. 2) data.csv contains pathes to images and corresponding labels

EmoAtCap : Emotional attitude captioning dataset

The dataset is related to emotional attitude captioning of images. Images were taken from IMDB dataset and annotated for a specific purpose. The dataset consists of 3835 annotated images along with sentiment for each image-caption pair