N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other). For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

Model of predicting customer behavior based on big data analysis technologies

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

Methodology for eliminating imbalance of image data sets

The methodology of solving the problem of eliminating class imbalance in image data sets is presented. The proposed methodology includes the stages of image fragment extraction, fragment augmentation, feature extraction, duplication of minority objects, and is based on reinforcement learning technology. The degree of imbalance indicator was used as a measure to determine the imbalance of the data set. An experiment was performed using a set of images of the faces of patients with skin rashes, annotated according to the severity of acne. The main steps of the methodology implementation are considered. The results of the classification showed the feasibility of applying the proposed methodology. The accuracy of classification on test data was 85%, which is 5% higher than the result obtained without the use of the proposed methodology. Key words: class imbalance, unbalanced data set, image fragment extraction, augmentation.

Middleware architecture for data consistency in multi-cloud systems

Multi-cloud middleware must perform many different resource management, control, and monitoring functions that must interoperate but may differ in implementation in each cloud service provider. A mechanism for monotonic recording model implementation for multi-cloud systems with a geo-distributed middleware architecture is proposed in the article. It is shown, the middleware modules location defines the algorithm of synchronization of start moments of adjusting intervals required to generating the global sequence numbers for customer's data recording into the databases of multi-cloud systems. Figs.: 2. Tabl.: 1. Refs.: 10 titles. Keywords: middleware architecture, geo-distributed middleware architecture, multicloud systems.

Modeling the volatity of cryptocurrency markets

The application of the model of geometric Brownian motion (GBM) for the problem of modeling and forecasting prices for cryptocurrencies is analyzed. For prediction the solution of the stochastic differential equation of the GBM model is used, which has a linear drift and diffusion coefficients. Different scenarios of price movement are considered. Keywords: geometric Brownian motion (GBM), modeling, forecasting, cryptocurrency.

Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

Building a classifier in the personality recognition system based on eye tracking data

Identification systems that use biometric characteristics to solve the problem of access to information systems are becoming more common. The article proposes a new method of biometric identification of computer systems users, based on the determination of the integral Volterra model of the human oculo-motor system (OMS) according to experimental research "input-output" using innovative eye tracking technology. With the help of the Tobii Pro TX300 eye tracker, the data of OMC responses to test visual stimuli were obtained, displayed as bright dots on the computer screen at different distances from the start position in the "horizontal" direction. Based on the data obtained, the transition functions of the first, second and third orders of the OMS for two people were determined. To construct a personality classifier, the informativeness of the proposed heuristic features, determined on the basis of the transition functions in terms of the probability of correct recognition (PCR), is investigated. Pairs of features are established that are resistant to computational errors and have a high PCR value - in the range 0.92 - 0.97. Fig.: 8. Table: 5. Bibliography: 30 items. Key words: biometric identification, personality recognition, Volterra model, oculo-motor system, eye tracking technology, informativeness of features, classification.

Computational approach to solving problems of spatial processing of point sets on two-dimensional regular grids

The paper presents an approach to solving problems of spatial processing on sets of points on a plane. The presented method consists in plotting regions of an arbitrary geometric shape near given points of the set on a regular grid and determining the intersection points of the regions using spatial hash tables to improve the efficiency of operations. The proposed approach is implemented in the form of software for determining the spatial relationships between points as a sequence of operations with discretized sets and allows visualization of research results. Figs.: 2. Refs.: 13. Keywords: spatial processing task; point set; plane; regular grid; spatial hash table.

Method of analysis of the current state of inertial characteristics of an autonomous mobile robot of variable configuration

The features of the dynamics of an autonomous mobile robot (AMR) of variable configuration with the relative movement of attachments are considered. Analysis of the current state of inertial characteristics during AMR operation will ensure stable movement along a given trajectory, successful implementation of technological operations by attachments, and preservation of the robot's functionality when operating under undetermined or extreme conditions. At the design stage, the analysis of the inertial characteristics of the AMR will make it possible to refine the parameters of the mathematical model, improve the algorithms for adaptive control and correct the commands for the actuators. Figs.: 2. Tabl.: 2. Bibliogr.: 10 titles. Keywords: autonomous mobile robot, inertial characteristics.