Description
The "Fawkes" procedure is discussed as a method of protection against unauthorized use and recognition of facial images from social networks. As an example, the results of an experiment are given, confirming the fact of a low result of face image recognition within CNN, when the "Fawkes" procedure is applied with the parameter mode = "high". Based on a comparative analysis with the original...
In this paper, computer studies of the effectiveness of the use of transfer learning methods for solving the problem of recognizing human brain tumors based on its MRI images are carried out. The deep convolutional networks VGG-16, ResNet-50, Inception_v3, and MobileNet_v2 were used as the basic models. Based on them, various strategies for training and fine-tuning models for recognizing brain...
Recently, deep learning has obtained a central position toward our daily life automation and delivered considerable improvements as compared to traditional algorithms of machine learning. Enhancing of image quality is a fundamental image processing task and. A high-quality image is always expected in several tasks of vision, and degradations like noise, blur, and low-resolution, are required...
Very-high-energy gamma ray photons interact with the atmosphere to give rise to cascades of secondary particles - Extensive Air Showers (EASs), which in turn generate very short flashes of Cherenkov radiation. This flashes are detected on the ground with Imaging Air Cherenkov Telescopes (IACTs). In the TAIGA project, in addition to images directly detected and recorded by the experimental...
Machine learning methods including convolutional neural networks
(CNNs) have been successfully applied to the analysis of extensive air
shower images from imaging atmospheric Cherenkov telescopes (IACTs).
In the case of the TAIGA experiment, we previously demonstrated that
both quality of selection of gamma ray events and accuracy of
estimates of the gamma ray energy by CNNs are good...
We give an overview of the CMS experiment activities to apply Machine Learning (ML) techniques to Data Quality Monitoring (DQM).
In the talk special attention will be paid to ML for Muon System and muon physics object DQM. ML application for data certification (anomaly detection) and release validation will be discussed.
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is mainly designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters by detecting reactor anti-neutrinos. The total event rate from DAQ is about 1 kHz and the estimated volume of raw data is about 2 PB/year. But the event rate of reactor anti-neutrino is only about 60/day. So one of challenges...
Introduction
In world practice, the number of published articles in leading scientific publications is indicators of the results of scientific activities of researchers, research organizations and higher educational institutions. International publication activity reflects the level of development of national science against the background of other countries, especially in the field of basic...
The modern Big Data ecosystem provides tools to build a flexible platform for processing data streams and batch datasets. Supporting both the functioning of modern giant particle physics experiments and the services necessary for the work of many individual physics researchers generate and transfer large quantities of semi-structured data. Thus, it is promising to apply cutting-edge...
The Large Hadron Collider experiments at CERN produce a large amount of data which are analyzed by the High Energy Physics (HEP) community in hundreds of institutes around the world.
Both efficient transport and distribution of data across HEP centres are, therefore, crucial.
The HEP community has thus established high-performance interconnects for data transport---notably the Large Hadron...
Accurate simulations of elementary particles in High Energy Physics (HEP) detectors are fundamental to accurately reproduce and interpret the experimental results and to correctly reconstruct particle flows. Today, detector simulations typically rely on Monte Carlo-based methods which are extremely demanding in terms of computing resources. The need for simulated data at future experiments -...
Identifying news that affects financial markets is an important task on the way to predicting financial markets. A large number of articles are devoted to this topic. But the main problem for analyzing news is neural networks what used. These neural networks are created to analyze user reports about a particular object, be it a restaurant, a movie or a purchased item. In such reports, the...
Pneumonia is a life-threatening lung disease caused by either a bacterial or viral infection. It can be life-threatening if not acted on at the right time, and so early diagnosis of pneumonia is vital. The aim of this work is the automatic detection of bacterial and viral pneumonia on the basis of X-ray images. Four different pre-trained deep convolutional neural networks (CNN): VGG16,...
This paper proposes a method for predicting and assessing land conditions based on satellite image processing using neural networks. In some regions, mainly based on agriculture and cattle breeding, the threat of irreversible soil changes has appeared, in particular desertification, which can lead to serious environmental and economic problems. Therefore, it is necessary to identify both the...
Machine learning methods and, in particular, deep neural networks are often used to solve the problem of image classification. There is a tendency to increase the amount of training data and the size of neural networks. The process of training a deep neural network with millions parameters can take hundreds of hours on modern computing nodes. Parallel and distributed computing can be used to...
High-resolution images processing for land-surface monitoring is fundamental to analyse the impact of different geomorphological processes on earth surface for different climate change scenarios. In this context, photogrammetry is one of the most reliable techniques to generate high-resolution topographic data, being key to territorial mapping and change detection analysis of landforms in...
The Spark – Hadoop ecosystem includes a wide variety of different components and can be integrated with any tool required for Big Data nowadays. From release-to-release developers of these frameworks optimize the inner work of components and make their usage more flexible and elaborate.
Anyway, since inventing MapReduce as a programming model and the first Hadoop releases data skew was and...
The report will present the results on the development of the algorithmic block of the Information System (IS) for radiobiological studies, created within a joint project of MLIT and LRB JINR, in terms of solving the segmentation problem for morphological research to study the effect of ionizing radiation on biological objects. The problem of automating the morphological analysis of...
Existence of exact closed-form formula for the price of derivative is a rather rare event in derivative pricing, therefore, to determine the price of derivative, one has to apply various numerical methods, including finite difference methods, binomial trees and Monte Carlo simulations. Alternatively, derivative prices can be approximated with deep neural networks.
We study pricing of...
Started in natural sciences, the high demand for analyzing a vast amount of complex data reached such research areas as economics and social sciences. Big Data methods and technologies provide new efficient tools for researches. In this paper, we discuss the main principles and architecture of the digital analytical platform aimed to support socio-economic applications. Integrating specific...
Предлагаемая работа посвящена разработке программной системы для проведения взаимной классификации семейств популяционных алгоритмов оптимизации и задач многомерной непрерывной оптимизации. Одной из целей настоящего исследования является разработка методов предсказания эффективности работы включенных в систему алгоритмов и выбора из них наиболее эффективных алгоритмов для решения заданной...
To accurately detect texts containing elements of hatred or enmity, it is necessary to take into account various features: syntax, semantics and discourse relations between text fragments. Unfortunately, at present, methods for identifying discourse relations in the texts of social networks are poorly developed. The paper considers the issue of classification of discourse relations between two...
