bluuebunny/crossref_metadata_2025 huggingface dataset:
ADAPTIVE HUMAN MACHINE INTERACTION APPROACH FOR FEATURE SELECTION-EXTRACTION TASK IN MEDICAL DATA MINING
Authors: Perova Iryna , Bodyanskiy Yevgeniy
Published: February 2021

Feature Selection task is one of the most complicated and actual in the areas of Data Mining and Human Machine Interaction. Many approaches to its solving are based on non-mathematical and presentative hypothesis. New approach to evaluation of medical features information quantity, based on optimized combination of feature selection and feature extraction methods is proposed. This approach allows us to produce optimal reduced number of features with linguistic interpreting of each of them. Hybrid system of feature selection/extraction based on Neural Network-Physician interaction is investigated. This system is numerically simple, can produce feature selection/extraction with any number of factors in online mode using neural network-physician interaction based on Oja’s neurons for online principal component analysis and calculating distance between first principal component and all input features. A series of experiments confirms efficiency of proposed approaches in Medical Data Mining area and allows physicians to have the most informative features without losing their linguistic interpreting.
STOCHASTIC MODELLING OF CASH FLOW FOR PERSONAL INSURANCE FUND USING THE CLOUD DATA STORAGE
Authors: Bazilevych Kseniia , Mazorchuk Mary , Parfeniuk Yurii , Dobriak Viktoriia , Meniailov Ievgen , Chumachenko Dmytro
Published: February 2021

In this paper the model and applied information system are proposed for modelling cash flow of insurance funds with use cloud data storage. Cash flow valuation is performed taking into account contextual characteristics of an insured under conditions of stochastic uncertainty. The proposed insurance cash flow model of the financial fund of insurance company allow to account the financial solvency probability based on the analysis of contextual properties of insureds, that increase the estimation accuracy of the modeling cash flows. Also, the model takes into account insurance bonus non-payment during the insurance company operation. In this paper, we propose a structure of an informational system, which is called “Insurance Life”. This system supports downloading and uploading data with the cloud service pCloud according to safety requirements of data. The main results of the research are introduced into practice of the operation of insurance companies in forms of software applications and results of calculations.
INTERACTIVE KNOWLEDGE VISUALIZATION TOOLS FOR EXHIBITION CURATION
Authors: He Jing , Quantz Joachim
Published: February 2021

This paper presents interactive knowledge visualization tools supporting knowledge workers in the process of curating digital content for exhibitions, showrooms, visitor centers or museums. The tools developed in the research project DKT (Digital Curation Technologies), funded by the Federal Ministry of Education and Research (BMBF), use language and knowledge technologies (such as information extraction, image recognition, classification and clustering) to automatically process digital multimedia content and then provide interactive visualizations of the results. The tools are thus not meant to replace knowledge workers but rather to support them and allow them to handle more content in a shorter span of time while maintaining or even increasing the quality of the curation process. Given this particular application scenario, the performance and accuracy of current state-of-the-art algorithms from Artificial Intelligence, though far from being perfect, is already good enough. The focus of the project work presented in this paper is on information extraction and text content.
PRIMITIVE VISUAL RELATION FEATURE DESCRIPTOR APPLIED TO STEREO VISION
Authors: Rosas Dario , Ponomaryov Volodymyr , Reyes-Reyes Rogelio
Published: February 2021

In this study, we present a novel local image descriptor, which is very efficient to compute densely, with semantic information based on visual primitives and relations between them, namely, coplanarity, cocolority, distance and angle. The designed feature descriptor covers both geometric and appearance information. The proposed descriptor has demonstrated its ability to compute dense depth maps from image pairs with a good performance evaluated by the Bad Matched Pixel criterion. Since novel descriptor is very high dimensional, we show that a compact descriptor can be sustitable. An analysis of size reduction was performed in order to reduce the computational complexity with no lose of quality by using different algorithms like max-min or PCA. This novel descriptor has a better results than state-of-the-art methods in stereo vision task. Also, an implementation in GPU hardware is presented performing time reduction using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.
COMPUTATION OF HEAT AND MASS DISTRIBUTION IN SINTER LAYER BASED ON PDEs
Authors: Krasnikov Kyrylo S.
Published: February 2021

The article presents mathematical model of interconnected physical processes on sintering machine during agglomeration of iron ore pellets. The mathematical model uses a system of partial differential equations. Velocity of the horizontal movement of the layer and the vertical velocity of the air movement through the layer as well as phase transition and simple chemical reactions of pellet and air components are taken into account in this model. The purpose of simulation is to determine the time dependency of sinter temperature distributed along the length and height of the layer and then define rational parameters for optimization of metallurgical process. In addition, concentration of sinter and gas components distributed along the height of the layer is computed. The numerical experiment shows that temperature front, which is lower in the layer cross-section, is sharper in comparison with the upper front, where the obtained agglomerate is cooled, as water requires a considerable amount of energy to evaporate. The obtained results are qualitatively consistent with the data in scientific literature.
MULTIDIMENSIONAL SEQUENCE CLUSTERING WITH ADAPTIVE ITERATIVE DYNAMIC TIME WARPING
Authors: Mashtalir Sergii , Mikhnova Olena , Stolbovyi Mykhailo
Published: February 2021

Multimedia sequence matching is an urgent problem nowadays for the field of artificial intelligence. Despite great progress in this field of computer science, huge data arrays require near to real time processing, which significantly limits applicable methods. In this paper, the authors make an attempt of time series approach modification and enhancement with orientation for non-stationary data of different length. The processing procedure is enriched by sequences alignment with iterative dynamic time warping which implies matching two temporal data segments. Computational complexity is reduced due to Kohonen self-organizing maps applied for the purpose of clustering. Mathematical presentation is given in scalar, vector and matrix forms in order to cover all the possible use cases. An example of video sequence processing with the novel approach is provided to show its efficiency. The proposed technique can also be successfully implemented for natural language and signal processing, bioinformatics and financial data analysis.
LINGUISTIC ANALYSIS OF USER MOTIVATIONS OF INFORMATION CONTENT FOR UNIVERSITY ENTRANT’S WEB-FORUM
Authors: Zhezhnych Pavlo , Shilinh Anna , Melnyk Viktor
Published: February 2021

Educational web-forums are an effective source of meeting the information and communication needs of the participants in educational activities. Information content of the educational web-forum for entrants is a reflection of the motivational intentions of communication participants who publish posts in the web-community. The subjects of the posts or the choice of the thematic section characterize the motivational intentions of both sides of the communicative activity. It should be borne in mind that motivational intentions are based not only on the technical, but also on the psychological aspects of the author's activities. So, an important task is to carry out a computer-linguistic analysis of the peculiarities of the formation of the information content of an educational web-forum for entrants created by motivated users. This article discusses the linguistic analysis of individual postings that form the content of a web-forum with template key phrases. Participant communications are divided into parts that present information about certain motivational intentions of the participants in the entry campaign. Linguistic analysis of motivational intentions of higher educational institutions and potential entrants is also contained in the article.
A WORK DISTRIBUTION STRATEGY FOR GLOBAL SEQUENCE ALIGNMENT
Authors: Kalare Kailash , Tembhurne Jitendra
Published: February 2021

The sequence alignment comprises to identify similarities and dissimilarities between two given sequences. In this paper, we propose a work distribution strategy for the implementation of DNA global sequence alignment. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach dealt the issues with the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory of the system. The proposed strategy is implemented in shared memory architecture using OpenMP programming for large biological sequences. Parallelization using OpenMP directive is relatively easy and execute the code fast. We experimented on the Dell Precision Tower 7910 with Intel Xeon processor with 32GB RAM and 28 CPU cores. The efficient use of global memory and cache memory optimization dominate the results of execution time. The results demonstrate the significantly high speed up using OpenMP as compared with other implementations.
BRAIN TISSUES SEGMENTATION ON MR PERFUSION IMAGES USING CUSUM FILTER FOR BOUNDARY PIXELS
Authors: Alkhimova Svitlana , Krenevych Andrii
Published: February 2021

The fully automated and relatively accurate method of brain tissues segmentation on Т2-weighted magnetic resonance perfusion images is proposed. Segmentation with this method provides a possibility to obtain perfusion region of interest in images with abnormal brain anatomy that is very important for perfusion analysis. In the proposed method the result is presented as a binary mask, which marks two regions: brain tissues pixels with unity values and skull, extracranial soft tissue and background pixels with zero values. The binary mask is produced based on the location of boundary between two studied regions. Each boundary point is detected with CUSUM filter as a change point for iteratively accumulated points at time of moving on a sinusoidal-like path along the boundary from one region to another. The evaluation results for 20 clinical cases showed that proposed segmentation method could significantly reduce the time and efforts required to obtain desirable results for perfusion region of interest detection on Т2-weighted magnetic resonance perfusion images with abnormal brain anatomy.
SPECIALIZED AUTOMATED SYSTEM FOR CONTROL AND SUPPORT OF RAIL CARGO TRANSPORTATION
Authors: Fomenko Andrii , Vyshnia Volodymyr
Published: February 2021

Today, Ukraine has faced the significant problem of railway cargo theft during transit and in the parking lots. Large losses of railways in this case require intensification of the fight against these crimes. The objective of the research is to find out the ways for preventing cargo encroachment by creating a network of weigh checkpoints (WCPs) on the railroad, which is intended for automatic control of the actual weight of goods in train carriages without moving them with further comparison of the accompanying data of cargo documents using electronic communication. In the event of a discrepancy between the indications of WCP device and the accompanying information on the loads, there is a fixed shortage of cargo in the carriage. The optimization of the general demand for WCP on the railways in practice allows law enforcement officers to use the research carried out as a means to effectively prevent crimes on the railroad.
SHAPE DESCRIPTOR FOR OBJECT CLASSIFICATION
Authors: Koriukalov Oleksandr , Tereshchenko Vasyl
Published: February 2021

In this article we propose an efficient modification of a previously published shape descriptor, which is fast and simple to compute. Proposed descriptor was developed for object classification and should be used with classifiers: SVM, KNN, etc. Object classification is a common task of computer vision, which has many applications in different areas: computer intelligence, robotic vision, smart cameras, autonomous driving, etc. Because the properties of objects are largely determined by their geometric features, shape analysis and classification are essential to almost every applied scientific and technological area. Main steps of the proposed algorithm are as follows: to find the object bounds; to smooth the bound contour using extremes based approach (if needed); to find side contour feature for each of N object rotation; to gather common feature vector; to classify object contour, using pre-trained classifier (SVM, KNN). In this work, in addition to method modifications (saving object proportions, rotation invariance, applying KNN classifier), we provide a wide comparison of our algorithm with existing approaches. The described method provided state-of-the-art performance on 100 leaves and Mpeg7 datasets, and showed good results on our own Mushroom dataset separately or together with texture or color based features.
CODE-BASED HYBRID CRYPTOSYSTEM: COMPARATIVE STUDIES AND ANALYSIS OF EFFICIENCY
Authors: Gorbenko Yurii , Kiian Anastasiia , Pushkar’ov Andriy , Korneiko Oleksandr , Smirnov Serhii , Kuznetsova Tetiana
Published: February 2021

In this paper the basic principles of construction and operation of McEliece and Niederreiter cryptosystems based on the use of error-correcting codes were considered. A new hybrid cryptosystem that combines the rules of encryption according to the above-mentioned schemes is proposed. Also, this paper presents the analysis and comparative studies from the standpoint of stability, the volume of public and private keys, length of ciphertext and relative speed of information transmission of the new proposed scheme and McEliece and Niederreiter cryptosystems. It is considered from an analytical point of view and with the help of graphic images. Comparative studies revealed that the hybrid cryptosystem retains the positive aspects of its predecessors, as well as allows us to increase the relative transmission rate with the preservation of the stability indicator to the classical and quantum cryptanalysis. One disadvantage is the increase in decoding time by adding information extracted as in Niederreiter scheme, but the increase in this indicator is not critical. Despite the demonstrated benefits, it remains open to all cryptosystems to reduce the amount of the used key data, which, in the case of quantum computers to maintain stability, still needs to be increased once.
NOISE IMMUNITY OF THE ALGEBRAIC GEOMETRIC CODES
Authors: Kuznetsov Alexandr , Kolovanova Ievgeniia , Smirnov Oleksii , Kuznetsova Tetiana
Published: February 2021

Linear block noise-immune codes constructed according to algebraic curves (algebraic geometric codes) are considered, their design properties are evaluated, algorithms of construction and decoding are studied. The energy efficiency of the transmission of discrete messages by M-ary orthogonal signals in the application of algebraic geometric codes is studied; the achievable energy gain from the use of noise-immune coding is estimated. The article shows that in discrete channels without memory it is possible to obtain a significant energy gain, which increases with the transition to long algebraic geometric codes constructed from curves with a large number of points relative to the genus of the curve. It is found that the computational complexity of implementing algebraic geometric codes is comparable to other known noise-immune codes, for example, Reed-Solomon codes and others. Thus, high energy efficiency in combination with the acceptable computational complexity of implementation confirms the prospects of algebraic geometric codes use in modern telecommunication systems and networks to improve the noise immunity of data transmission channels.
OPPORTUNITIES TO MINIMIZE HARDWARE AND SOFTWARE COSTS FOR IMPLEMENTING BOOLEAN FUNCTIONS IN STREAM CIPHERS
Authors: Kuznetsov Alexandr , Potii Oleksandr , Poluyanenko Nikolay , Ihnatenko Serhii , Stelnyk Igor , Mialkovsky Danylo
Published: February 2021

Currently, nonlinear Boolean functions are actively investigated worldwide. However, many questions remain unanswered. The theory of nonlinear Boolean functions that are suitable for use in cryptographically strong algorithms is significantly incomplete. Despite the existence of numerous publications on these themes, many issues related to the interconnection of design characteristics affecting the generator’s performance and its cryptographic characteristics still remain unsolved. The possibility of generating a special type of sequence, called de Bruijn sequence, at minimal hardware and software costs to implement nonlinear Boolean functions in stream encryption systems, is the main subject of this work. The paper presents the possible structure boundaries (algebraic degree of a Boolean function, the number of monomials in a function) of iterative de Bruijn sequence bitrate generators for various generated sequence characteristics, such as linear complexity and autocorrelation function. The profile of the linear complexity of the studied sequences is close to the expected value of the linear complexity, as well as for a truly random sequence.
SYMBOLIC MODELS OF THE PULSE ENERGY CONVERSION SYSTEMS DYNAMICS
Authors: Koschinsky S. L. , Sholonik A. P. , Ustinov P. S.
Published: February 2021

The derivation of developed symbolic models of dynamics of voltage mode controlled buck converter operated both in discontinuous and continuous conducting modes is given.
AVAILABILITY VERSUS PERFORMANCE
Authors: Fiorini Pierre M. , Lipsky Lester
Published: February 2021

We discuss analytic procedures for evaluating the availability of parallel computer systems comprised of P processors with N tasks subject to failures and repairs. In addition, we argue, via analytic and numeric examples, that not incorporating the task-stream into the model is an inadequate approach for evaluating system performance.
SELF-CALIBRATION OF ULTRASONIC TRANSDUCERS IN AN INTELLIGENT DATA ACQUISITION SYSTEM
Authors: Papageorgiou Chris , Laopoulos Theodore
Published: February 2021

The rapid growth of powerful single-chip microcomputers for monitoring and data acquisition applications permits nowadays the design of advanced measuring systems. The work reported here is presenting an advanced on-line monitoring configuration in order to improve the performance and extend the lifetime of ultrasonic transducers by applying an automated testing and calibration technique. The operation of this instrumentation system is based on the fast measurement of frequency and amplitude, performed by the proposed configuration. The combination of this information with the time of flight of each pulse-train is then used to derive practically all characteristics of ultrasonic transducers. Due to its low cost and small size, the system can be used either for characterization and classification of transducers, or as a self-testing and automated calibration section within any high performance ultrasonic system.
INCOMPLETE CHOLESKY FACTORIZATION IN FIXED MEMORY WITH FLEXIBLE DROP-TOLERANCE STRATEGY
Authors: Saukh Sergey
Published: February 2021

We propose an incomplete Cholesky factorization for the solution of large positive definite systems of equations and for the solution of large-scale trust region sub-problems. The factorization is based on the two- parameter (m, p) drop-tolerance strategy for insignificant elements in the incomplete factor matrix. The factorization proposed essentially reduces the negative processes of irregular distribution and accumulation of errors in factor matrix and provides the optimal rate of memory filling with essential nonzero elements. On the contrary to the known p - retain and t - drop-tolerance strategies, the (m, p) strategy allows to form the factor matrix in fixed memory.
CLUSTER COMPUTING OF HIGH FREQUENCY ELECTROMAGNETIC FIELD PROPAGATION
Authors: Walendziuk Wojciech , Forenc Jaroslaw , Jordan Andrzej
Published: February 2021

The paper presents parallel implementations of the FDTD (Finite Differences Time Domain) method in cluster systems of PC’s. Those two- and three- dimensional algorithms are based on the stripes decomposition of the analyzed area. What is more, the communication among particular computation nodes building the cluster system is discussed. On the basis of the obtained results, the analysis of the efficiency of the presented algorithms was done.
DESIGNING A MULTI-PRECISION NUMBER THEORY LIBRARY
Authors: Hritcu Catalin , Goriac Iulian , Gordan Raluca Mihaela , Erbiceanu Elena
Published: February 2021

The aim of this paper is twofold. First, we present the basic principles and point out the main difficulties in writing a library supporting operations with arbitrarily large numbers. Aspects such as library structure, number representation, algorithm selection, memory management, etc., are discussed and exemplified on the most efficient libraries developed. Secondly, we present work in progress regarding the design of a new multi-precision library, MpNT. Comparisons between our library and the existing ones show that it achieves high performance.
THE SYSTEM OF VIDEO-DATA PROCESSING FOR THE AUTONOMOUS CONTROL OF MOBILE ROBOT
Authors: Vershok Denis , Sadykhov Rauf , Selikhanovich Andrei , Schilling Klaus , Roth Hubert
Published: February 2021

This paper describes the system of video-data processing based on monocular vision for autonomous control of mobile robot. The system allows detecting obstacles in a robot environment modeled as a set of straight-line segment. The given system consists of three basic stages and uses original algorithms, ensuring the required precision and realization of the system in the real time. The first stage uses a fast edge detection algorithm on the basis of two- dimensional Walsh transform. The algorithm of modified Hough transform is used for detection of straight-line segments. The third stage «segment tracking» uses Kalman filtration for tracking segments in a monocular sequence of images.
METHODOLOGY FOR DISTRIBUTED DESIGNING OF DISTRIBUTED VIRTUAL INSTRUMENTS
Authors: Winiecki Wieslaw
Published: February 2021

A generic architecture of a networked Distributed Virtual Measurement Instrument (DVI) is presented. General assumptions for distributed design of networked DVI’s are described. The generic architecture of a distributed, networked and platform-independent environment for DVI designing, together with the generic methodology for distributed designing of networked DVI is proposed. Currently employed DVI design methodologies are presented and assessed.
MODERN APPROACH TO PROTECTION OF COMPUTER SYSTEMS AND NETWORKS
Authors: Dudykevych Valerij , Piskozub Andrian , Lomnytskyj Igor
Published: February 2021

This article is dedicated to the use of firewalling method and intrusion detection method, which can be applied both separately and together to computer systems and networks, to expedience of the use of that or other method, tools, that perform these methods. We will try to carry out the analysis of the modern state of security questions in computer networks, to give recommendations how to attain the greatest level of protection of computer systems and networks by most effective way - within the shortest time interval, with minimum financial expenditures.
MEASURING CLASSIFICATION ACCURACIES USING IMPROVED THERMAL INFRARED DATA
Authors: Cheruku Venkateswarulu , Yenduri Sumanth , Iyengar S. S.
Published: February 2021

Image classification is one of the major aspects in digital image analysis of remotely sensed data. In this paper, we present the effects on classification accuracy if improved thermal data are used instead of raw thermal data. We use two methods, Artificial Neural Networks (ANN) and Maximum Likelihood Approach (MLH) to demonstrate our purpose. Using each method different combinations of raw and improved data are tested to classify in order to compare the accuracies. As a final note, the findings are discussed.
PIECEWISE UNIFORM PRODUCT POLAR QUANTIZATION
Authors: Peric Zoran H. , Milovic Daniela M.
Published: February 2021

In this paper, simple and complete asymptotical analysis is given for a piecewise uniform product polar quantizer (PUPPQ) with respect to mean-square error (MSE) i.e. distortion (D). PUPPQ is based on uniform product polar quantizers. PUPPQ consists of L different uniform product polar quantizers. Uniform product polar quantizer conditions for optimality and all main equations for number of phase divisions and optimal number of levels for each partition are presented. These systems, although not optimal, may have asymptotic performance close to the optimum. Furthermore, the analysis and implementation can be simpler than those of optimal systems. PUPPQ has implementation complexity between optimal nonuniform polar quantization (NPQ) and uniform product polar quantization (UPPQ). The gain of PUPPQ over optimum uniform product polar quantization is also obtained (2 dB for the rate of 8 bits/sample).
A NEURAL NET FOR 2D-SLOPE AND SINUSOIDAL SHAPE DETECTION
Authors: Bruckmann A. , Klefenz F. , Wunsche A.
Published: February 2021

2D-slope and sinusoidal shape detection are application specific tasks which are widely discussed in the literature. A neural network is presented which is able to learn a set of different slopes or a set of sinusoids of different frequencies and to detect test patterns after the training stage. The neural net is composed of input neurons, delay neurons and output neurons. The delay neurons form a set of tapped delay lines. Each delay line adapts to its specific signal propagation velocity. The signal propagation velocity vector field of the delay lines is learned by collectively tuning the signal propagation velocities. The neural net is fed with a set of spatiotemporal training patterns, such as bars of different slopes or sinusoids of different frequencies. After training, the net is tested with a random set of 2D- patterns. Unsupervised learning with a Boltzmann temperature term is assumed.
AN ARTIFICIAL NEURAL PROCESS TO CREATE CONTINUOUS OBJECT BOUNDARIES IN MEDICAL IMAGE ANALYSIS
Authors: Pathegama Mahinda , Gol Ozdemir
Published: February 2021

Computer-aided analysis for cell images acquired by an electron microscope involves a range of image processing steps including edge detection and thresholding. The major problem encountered in automatic cell analysis is the possible presence of incomplete boundaries of cell features, which prevent the generation of cell feature details including all measurements as the boundaries include very tiny gaps. This paper presents a novel edge-linking technique based on an artificial neural process, which uses directional sensitivity derivatives from an edged image. The input signals applied to the neural layer are integrated with direction-sensitive information produced by an auxiliary algorithm, which interrogates all the pixels in the 2-D image in order to designate the specified direction in which each edge-end pixel should propagate. The proposed edge-linking technique, implemented as an image-processing algorithm for direction-sensitive selectiveness, provides an effective solution to the problem of porous boundaries encountered in biological cell image analysis.
SOME MODELS OF RASTER CORRELATORS OF BINARY IMAGES
Authors: Zholtikov Ruslan R. , Tatur Mikhail M.
Published: February 2021

In paper the outcomes of mathematical modeling of statistical recognition of binary images are proposed. The offered hypothesis that the pixels constituting boundary of recognition object and an image background are secondary attributes at recognition is experimentally confirmed. As consequence, recognition reliability can be raised due to exception of these pixels at recognition. Basing on the offered example of training of models on the fixed training sample and taking into account that the prototype model is a special case of the rejecting model we have noted that recognition reliability of offered model cannot be lower than reliability of the prototype. The obtained results will be used in hardware realization of binary comparators.
LEARNING AND UNDERSTANDING BASED ON NEURAL NETWORK TREES
Authors: Zhao Qiangfu
Published: February 2021

Models for machine learning can be categorized roughly into two groups: symbolic and non-symbolic Generally speaking, symbolic model based learning can provide understandable results, but cannot adapt to changing environments efficiently. On the other hand, non-symbolic model based learning can adapt to changing environments, but the results are usually "black-boxes”. In our study, we introduced a hybrid model called neural network tree (NNTree). An NNTree is a decision tree (DT) with each non-terminal node containing an expert neural network (ENN). Results obtained so far show that an NNTree can be re-trained incrementally using new data. In addition, an NNTree can be interpreted easily if we restrict the number of inputs for each ENN. Thus, it is possible to perform recognition, learning and understand using the NNTree model alone.
COMPUTING OF LYAPUNOV EXPONENTS TECHNIQUES USING NEURAL NETWORKS
Authors: Golovko Vladimir , Savitsky Yury
Published: February 2021

The authors examine neural network techniques for computing of Lyapunov spectrum using observations from unknown dynamical system. Such an approach is based on applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using scalar time series. The results of experiments are discussed.
SOME METHODS OF ADAPTIVE MULTILAYER NEURAL NETWORKS TRAINING
Authors: Makhnist Leonid , Maniakov Nikolaj , Maniakov Nikolaj
Published: February 2021

Is proposed two new techniques for multilayer neural networks training. Its basic concept is based on the gradient descent method. For every methodic are showed formulas for calculation of the adaptive training steps. Presented matrix algorithmizations for all of these techniques are very helpful in its program realization.
CODE COMPRESSION FOR THE EMBEDDED ARM/THUMB PROCESSOR
Authors: Xu Xianhong , Jones Simon
Published: February 2021

Previous code compression research on embedded systems was based on typical RISC instruction code. THUMB from ARM Ltd is a compacted 16-bits instruction set showing a great code density than its original 32-bits ARM instruction. Our research shows that THUMB code is compressible and a further 10-15% code size reduction on THUMB code can be expected using our proposed new architecture – Code Compressed THUMB Processor. In our proposal, Level 2 cache or additional RAM space is introduced to serve as the temporary storage for decompressed program blocks. A software implementation of the architecture is proposed and we have implemented a software prototype based on ARM922T processor, which runs on the ARMulator.
HART PROTOCOL ANALYSER BASED IN LABVIEW
Authors: Dias Pereira J. M. , Postolache Octavian , Silva Girao P.
Published: February 2021

Analysis of Highway Addressable Remote Transducer (HART) protocol is important in reliability evaluation of HART based systems. The present work proposes a virtual instrument based solution for HART signal analysis that includes a digital oscilloscope with GPIB interface, a HART modem, a PC with a GPIB interface board and a RS232 port, and a software component developed in LabVIEW. The HART analyser permits the visualisation of signals that correspond to the HART protocol, gives access to data coming from field instruments and has the ability to analyse and modify field instrument performance.
SOLVING THE RECTILINEAR STEINER MINIMAL TREE PROBLEM WITH A BRANCH AND CUT ALGORITHM
Authors: Emanet Nahit , Ozturan Can
Published: February 2021

This paper presents a new branch-and-cut algorithm that allows us to reduce the solution time of the concatenation phase of the rectilinear Steiner minimal tree problem in the plane. Our branch-and-cut algorithm is used on an integer programming formulation using what we call cutsec, and sec constraints. We present implementation details of our branch-and-cut program called NEOSteiner and provide computational results on test instances from the SteinLib library.
MULTI-LEVEL PARALLEL FRAMEWORK
Authors: Craus Mitica , Rudeanu Laurentiu
Published: February 2021

This article deals with the pyramidal framework designed to be used in the parallelization of the ant-like algorithms. Such algorithms have several things in common: they run in cycles and the process can be divided among different "processing units". The parallel implementation of the Ant Colony Optimization algorithm for the Traveling Salesman Problem is an application of this system. The topology of the framework architecture is similar to a B-tree and contains three types of processing nodes: a single master (the root), several sub-masters corresponding to the internal nodes of the tree and several slaves as leaves. First the master reads the problem instance, wraps it up in a message that is sent to all the other processing nodes and initializes the central data structures. Then, the slaves take over the control by starting the algorithm while the master and the sub-masters are waiting for requests to update the data. The framework has an object-oriented design and was implemented in C++, using the MPI library.
IDENTIFICATION AND FORECASTING OF SHARP CHANGES IN ECONOMICAL SYSTEMS BY TRACKING OF LOCAL LYAPUNOV EXPONENTS
Authors: Malyuk Dmitry , Boyarintsev Georgy
Published: February 2021

An approach to detect sharp changes in economical systems was developed in this paper. We will give brief introduction in current methods of prediction of nonlinear and chaotic time-series and give definition of local Lyapunov exponents (LLE). Then author’s approach will be described. Also some numerical results and discussions will be given.
BASES OF ELEMENTARY ARITHMETICS IN GALOIS’S FIELDS
Authors: Petryshyn Lubomyr
Published: February 2021

It is resulted the theoretical bases and structure of facilities for transformation and digital processing of messages in the Galois code systems and proved efficiency of their application in comparison with the known methods of binary coding. The lacks of implementation dynamics of arithmetic operations in binary codes and methods of their elimination are analyzed at the Galois code and increasing of digital processing speed.
INVESTIGATION OF THE NONLINEAR FLUTTER OF VISCOELASTIC PLATES IN A SUPERSONIC FLOW
Authors: Khudayarov Bakhtiyar
Published: February 2021

In this paper the flutter of nonlinear viscoelastic plates in a supersonic flow is investigated. The basic direction of work is consisted in taking into account of viscoelastic material’s properties at supersonic speeds. Quasi-steady aerodynamic panel loadings are determined using piston theory. The vibration equations relatively of deflection are described by Integrо-differential equations in partial derivatives. The plate nonlinear partial integro-differential equation is transformed info a set of nonlinear ordinary IDE through a Bubnov-Galerkin’s approach. The resulting system of IDE is solved through the Badalov-Eshmatov integration method. Critical speeds for plate flutter are defined.
FAULT TOLERANT MEMORY SYSTEM WITH ACTIVE REDUNDANCY FOR CRITICAL APPLICATIONS
Authors: Cascaval Petru , Cascaval Doina
Published: February 2021

This paper proposes a fault tolerant RAM memory architecture with active redundancy dedicated to critical applications. To assure high reliability, additional modules are provided in the system. To reach a high safety, memory system is composed by built-in self-testing RAM devices (BIST?RAM). Memory modules are tested online one by one, in a sequential order, during the normal operation. A primary module under testing is temporary replaced by another one. If one module is diagnosed as being failed it is eliminated from the system automatically and replaced with a spare module. The memory control unit responsible for performing the reconfiguration of the memory system has a cellular structure and introduces an acceptable extra delay for the memory access.
OPEN SOURCE SOFTWARE FOR THE DEVELOPMENT OF INFORMATIONAL SYSTEMS: COMPARATIVE ANALYSUS AND DEVELOPMENT PROSPECTS IN UKRAINE
Authors: Boiko Ivan , Lupenko Sergiy , Luckiv Andriy
Published: February 2021

In the article possibilities of opened software for information systems development and its perspectives in Ukraine are analyzed; the aspects of software development, using and cooperation are considered; the main problems which arise at software development and ways of their decision are resulted; the question of legal software using in work of researcher is reflected.
REAL-WORLD ACCESS CONTROL SYSTEMATIC FAILURES; REALITY OR VIRTUAL REALITY?
Authors: Attfield Philip , Huang Ming-Yuh
Published: February 2021

This paper examines the true causes of systematic failures of real-world access control within the context of modern business transactions. Today’s business transactions depend heavily on systems that were developed and protected by off-the-shelf, checklist-mentality security technologies/products such as firewalls, intrusion detection systems and anti-virus software. This dependency, as well as the oversight of system level security requirements, frequently leads to incorrect and incomplete security implementation at the business process and transaction levels. To fully illustrate the critical issues faced by today’s system, this paper utilizes a real-life cyber crime case for analytical purposes. This case was successfully prosecuted by a jury trial at the US Federal Court in Seattle during the period of 1999-2000. It revealed many fatal system security failures and business process trust collapses in an environment involving multiple online web-based systems. The paper then shows how such failures are directly attributed from the inappropriate application of technologies/products based on false assumptions of trust, as well as the lack of appropriate security engineering process during the systems development phase. Observations and recommendations are also made regarding what can be done to enhance security and trust requirements at the levels of business transactions and processes.
ON PRIVACY CLASSIFICATION IN UBIQUITOUS COMPUTING SYSTEMS
Authors: Cvrcek Dan , Matyas Vaclav , Kumpost Marek
Published: February 2021

Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This led us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability – the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling – and linking – users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets.
SECURING DEVICES COMMUNITIES IN SPONTANEOUS NETWORKS
Authors: Prigent Nicolas , Bidan Christophe
Published: February 2021

We define a community as a set of devices able to communicate permanently or erratically and that share a long term trust relation. Small corporate networks or home networks are typical examples of such communities. Historically, the devices of the same community communicated over physically isolated wired networks. They are currently used over spontaneous networks, the characteristics of which have implications, in terms of their security and the mechanisms that can be used to protect such. In this article, we present a fully decentralized service of automated configuration of the security mechanisms dedicated to communities of devices that communicate over spontaneous networks. This service is located on each device of the community and manages information related to the environment of the device and to the security policy. Based on this information, it configures dynamically and automatically the security services available on the device to ensure its security and that of the community to which it belongs.
THE PROBLEMS OF INFORMATION DEFENCE IN DIAGNOSIS INTELLIGENT SYSTEMS OF MICROPROCESSOR DEVICES
Authors: Lokazyuk Viktor , Pomorova Oksana
Published: February 2021

The method for protection of diagnosis intelligent system of microprocessor devices is represented in the paper. This method based on background authentication of the user in the process of keyboarding. The user’s keystroke dynamics characteristics are the means of authentication. For realization of the user authentication method uses the artificial neural networks of ART2 architecture.
AUTONOMOUS DECENTRALIZED DATA CONSISTENCY FOR HIGH-ASSURANCE EMBEDDED SYSTEM
Authors: Shiibashi Akio , Mori Kinji
Published: February 2021

Advancement in computer and communication technologies have resulted in an explosive growth in embedded systems. The market and users requirements have been rapidly changing and diversified. Under these evolving situations, the assurance to keep the continuous system operation of embedded systems is becoming more and more important. The Autonomous Decentralized System (ADS) has been proposed for resolving the on-line property to achieve the step-by-step expansion, maintenance and fault-propagation prevention for high assurance. This architecture is effective to improve the reliability and reduce the development cost and product cycle time to market by data-driven mechanism. The technologies have been applied in the IC card system for train fare collection and its effectiveness has been proven.
EFFICIENCY ESTIMATION OF PARALLEL ALGORITHM OF ENHANCED HISTORICAL DATA INTEGRATION ON COMPUTATIONAL GRID
Authors: Turchenko V. , Triki C. , Grandinetti Lucio , Sachenko Anatoly
Published: February 2021

The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.
INTRUSION RECOGNITION USING NEURAL NETWORKS
Authors: Golovko Vladimir , Kochurko Pavel
Published: February 2021

Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.
FACE IDENTIFICATION ALGORITHM BASED ON MESH-DERIVED SYNTHETIC LINEAR DESCRIPTORS
Authors: Sadykhov R. Kh. , Samokhval V. A.
Published: February 2021

This paper presents appearance-based face identification algorithm by means of synthetic linear filters. The objective of our research is to construct facial descriptor in the form of linear filter, which should produce high and low outputs for intra- and inter-class recognition problem correspondingly. This filter can be synthesized from 2,5D sparse mesh derived from a given set of images of a person. As ever the filter is created it is then used as facial descriptor, i.e. serves as personal ID for face identification.
MECHANISMS FOR COORDINATION OF MASTER PLANNING AND LOT SIZING WITHIN A HIERARCHICAL PRODUCTION PLANNING MODEL
Authors: Leisten Rainer , Reusch Pascal
Published: February 2021

This paper presents a simulation based analysis of different coordination mechanisms in Hierarchical Production Planning. According to the results of the simulations the effects of Aggregation and Capacity Assignments on the overall costs are discussed.
FAST NEURAL NETWORK FOR GENERALIZED TRIGONOMETRIC TRANSFORMATIONS SYNTHESIS
Authors: Liskevych Oleh , Yatsymirskyy Mykhaylo
Published: February 2021

The structure of the fast hardware neural network, based on generalized trigonometric transformations algorithm is developed. The network is appointed for optimal by some given criteria transformation selection and synthesis in adaptive digital signal processing system.
SOFTWARE IMPLEMENTED HARDWARE-TRANSIENT FAULTS DETECTION
Authors: Saha Goutam Kumar
Published: February 2021

This paper examines a software implemented self-checking technique that is capable of detecting processorregisters' hardware-transient faults. The proposed approach is intended to detect run-time transient bit-errors in memory and processor status register. Error correction is not considered here. However, this low-cost approach is intended to be adopted in commodity systems that use ordinary off-the-shelf microprocessors, for the purpose of operational faults detection towards gaining fail-safe kind of fault tolerant system.
CONSISTENCY OF DISTRIBUTED SYSTEM WITH ACTIVE INITIATOR PROCESS WITHOUT USELESS CHECKPOINTS
Authors: Gopalan N. P. , Nagarajan K.
Published: February 2021

Checkpointing mechanism is the one of the best attractive approach for providing software fault tolerance in distributed message passing systems. This paper aims to implement a distributed checkpointing technique, which eliminates the drawbacks of the centralized approach like “domino effect”, “useless checkpoint” (checkpoints that do not contribute to global consistency), and “hidden and zigzag” dependencies. The proposed checkpointing protocol has a checkpoint initiator, but, coordination among the local checkpoints is done in a distributed fashion. This guaranty that no message would be lost in case of failure occurs, has been maintained in this work by exchange of information among the processes. However, there is no central checkpoint initiator, but each of the processes takes turn to act as an initiator. Processes take local checkpoints only after being notified by the initiator. The processes synchronize their activities of the current checkpointing interval before finally committing their checkpoints. Thus, the checkpointing pattern described in this paper takes only those checkpoints that will contribute to the consistent global snapshot thereby eliminating the number of useless checkpoints.