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The publishing of the present issue (Volumen 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

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Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

Volumen 20 (2020): Edición 2 (June 2020)

Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

12 Artículos
Acceso abierto

Emotional Valence Coded in the Phonemic Content – Statistical Evidence Based on Corpus Analysis

Publicado en línea: 12 Jun 2020
Páginas: 3 - 21

Resumen

Abstract

This study investigates the relationship between the phonemic content of texts in English and the emotional valence they inspire. The sublexical content is presented in terms of biphones composed by one vowel and one consonant. The statistical analysis of a vast corpus of emotionally evaluated sentences reveals a strong correlation between this sublexical presentation and the evaluations of valence provided by the readers. An initial test performed with other valence-rated prose texts makes believing that the feature observed within the corpus can be useful for the emotion classification of texts.

Palabras clave

  • Natural language processing
  • Sound-symbolism
  • Corpus linguistics
  • Text emotion recognition
Acceso abierto

Developing the Expert Decision-Making Algorithm Using the Methods of Multi-Criteria Analysis

Publicado en línea: 12 Jun 2020
Páginas: 22 - 29

Resumen

Abstract

The paper deals with the development of an expert decision-making algorithm using the AHP and TOPSIS methods of multi-criteria analysis. In the proposed algorithm, the AHP and TOPSIS methods are used in combination; in particular the AHP method is used to determine the vector of the alternatives’ weights, while the TOPSIS method is used assessing and ranking the alternatives. The obtained algorithm ensures optimal decision making based on the experts’ assessments. A simple web application has been developed to demonstrate the algorithm’s performance.

Palabras clave

  • Decision-making
  • multi-criteria analysis
  • expert
  • decision matrix
  • AHP
  • TOPSIS
Acceso abierto

Analytical Overview and Applications of Modified Black-Litterman Model for Portfolio Optimization

Publicado en línea: 12 Jun 2020
Páginas: 30 - 49

Resumen

Abstract

The paper makes analytical overviews of the Markowitz portfolio and the Capital Asset Pricing models and motivates the advances of the Black-Litterman (BL) one. This overview implies that for a small set of assets the BL model needs the characteristics of a specific market point, which cannot be taken from a global market index. The paper derives analytic relations for the new specific market point with analytical approximation of the efficient frontier. The BL model insists also expert views, which influence the portfolio solution. The paper derives formalization of the expert views from the difference between the evaluated implied returns and historical mean assets returns. Such form of expert views makes modifications of the BL model. This allows comparisons between Markowitz (MV) and BL portfolio performance. Benefits of this research are demonstrated with market data and comparison of the MV and BL portfolio results.

Palabras clave

  • Size Portfolio optimization
  • mean-variance portfolio model
  • Black-Litterman portfolio model
  • active portfolio management
  • decision making
Acceso abierto

Fuzzy Group Full Consistency Method for Weight Determination

Publicado en línea: 12 Jun 2020
Páginas: 50 - 58

Resumen

Abstract

In this paper, the FUll COnsistency Method (FUCOM) is extended to work in a collective manner, to solve a fuzzy optimization problem and to obtain the fuzzy weights of criteria. The employment of a predefined order of criteria decreases the number of fuzzy comparisons needed in the evaluation phase. The defuzzified values of the optimal weight coefficients are calculated by Graded Mean Integration Representation formula. This feature also reduces time complexity without affecting the quality of the solution. Two practical examples are presented to verify the reliability and feasibility of the proposed fuzzy group FUCOM. The obtained results demonstrate that the new fuzzy group weight determination method can obtain appropriate criteria importance.

Palabras clave

  • Multi-criteria decision methods
  • determination of fuzzy weight coefficients
  • fuzzy optimization
  • fuzzy group full consistency method
Acceso abierto

Image Copy Detection Based on Local Binary Pattern and SVM Classifier

Publicado en línea: 12 Jun 2020
Páginas: 59 - 69

Resumen

Abstract

Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.

Palabras clave

  • Content-based copy Detection
  • Digital watermarking
  • Local Binary Pattern
  • Support Vector Machine
  • Image forensics
  • Image hashing
Acceso abierto

Implementation of Classic Image Transformation Algorithm to Quantum State, Boundary Extraction and Transformation of Half-Tone Image to Binary

Publicado en línea: 12 Jun 2020
Páginas: 70 - 78

Resumen

Abstract

The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.

Palabras clave

  • Qubit
  • entanglement
  • quantum circuit
  • quantum gate
  • wave function
  • quantum algorithm
Acceso abierto

DevOps Project Management Tools for Sprint Planning, Estimation and Execution Maturity

Publicado en línea: 12 Jun 2020
Páginas: 79 - 92

Resumen

Abstract

The goal of DevOps is to cut down the project timelines, increase the productivity, and manage rapid development-deployment cycles without impacting business and quality. It requires efficient sprint management. The objective of this paper is to develop different sprint level project management tools for quick project level Go/No-Go decision making (using real-time projects data and machine learning), sprint estimation technique (gamified-consensus based), statistical understanding of overall project management maturity, project sentiment & perception. An attempt is made to device a model to calibrate the perception or the tone of a project culture using sentiment analysis.

Palabras clave

  • DevOps
  • Machine Learning (ML)
  • effort estimation
  • planning poker
  • sentimental analysis
Acceso abierto

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Publicado en línea: 12 Jun 2020
Páginas: 93 - 104

Resumen

Abstract

Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

Palabras clave

  • Ontology design patterns
  • OWL
  • credit risk scorecard
  • decision support
  • BCBS 239
Acceso abierto

Advanced Embedded Control of Electrohydraulic Power Steering System

Publicado en línea: 12 Jun 2020
Páginas: 105 - 121

Resumen

Abstract

The article presents a developed embedded system for control of electrohydraulic power steering based on multivariable uncertain plant model and advanced control techniques. The plant model is obtained by identification procedure via “black box” system identification and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the model. Three types of controller are designed: Linear-Quadratic Gaussian (LQG) controller, H controller and μ-controller. The main result is a performed comparative analysis of time and frequency domain properties of control systems. The results show the better performance of systems based on µ-controllers. Also the robust stability and robust performance are investigated. All three systems achieved robust stability which guarantees their workability, but only the system with µ-controller has robust performance against prescribed uncertainties. The control algorithms are implemented in specialized 32-bit microcontroller. A number of real world experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.

Palabras clave

  • Linear-Quadratic Gaussian (LQG) controller
  • Kalman filter
  • H controller
  • μ-controller
  • electrohydraulic steering system
  • embedded control
Acceso abierto

QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization

Publicado en línea: 12 Jun 2020
Páginas: 122 - 140

Resumen

Abstract

Air pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area of air quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service (QoS) levels against accuracy, sensing throughput and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms of reliable delivery of the messages under employed protocol.

Palabras clave

  • Air quality monitoring
  • Internet of Things
  • Message Queue Telemetry Transport protocol
  • smart city
  • ESP8266
Acceso abierto

Bayesian Regularized Neural Network for Prediction of the Dose in Gamma Irradiated Milk Products

Publicado en línea: 12 Jun 2020
Páginas: 141 - 151

Resumen

Abstract

Gamma irradiation is a well-known method for sterilizing different foodstuffs, including fresh cow milk. Many studies witness that the low dose irradiation of milk and milk products affects the fractions of the milk protein, thus reducing its allergenic effect and make it potentially appropriate for people with milk allergy. The purpose of this study is to evaluate the relationship between the gamma radiation dose and size of the protein fractions, as potential approach to decrease the allergenic effect of the milk. In this paper, an approach for prediction of the dose in gamma irradiated products by using a Bayesian regularized neural network as a mean to save recourses for expensive electrophoretic experiments, is developed. The efficiency of the proposed neural network model is proved on data for two dairy products – lyophilized cow milk and curd.

Palabras clave

  • Bayesian neural network
  • milk products
  • milk allergy
  • protein fraction
Acceso abierto

Retraction

Publicado en línea: 12 Jun 2020
Páginas: 152 - 152

Resumen

12 Artículos
Acceso abierto

Emotional Valence Coded in the Phonemic Content – Statistical Evidence Based on Corpus Analysis

Publicado en línea: 12 Jun 2020
Páginas: 3 - 21

Resumen

Abstract

This study investigates the relationship between the phonemic content of texts in English and the emotional valence they inspire. The sublexical content is presented in terms of biphones composed by one vowel and one consonant. The statistical analysis of a vast corpus of emotionally evaluated sentences reveals a strong correlation between this sublexical presentation and the evaluations of valence provided by the readers. An initial test performed with other valence-rated prose texts makes believing that the feature observed within the corpus can be useful for the emotion classification of texts.

Palabras clave

  • Natural language processing
  • Sound-symbolism
  • Corpus linguistics
  • Text emotion recognition
Acceso abierto

Developing the Expert Decision-Making Algorithm Using the Methods of Multi-Criteria Analysis

Publicado en línea: 12 Jun 2020
Páginas: 22 - 29

Resumen

Abstract

The paper deals with the development of an expert decision-making algorithm using the AHP and TOPSIS methods of multi-criteria analysis. In the proposed algorithm, the AHP and TOPSIS methods are used in combination; in particular the AHP method is used to determine the vector of the alternatives’ weights, while the TOPSIS method is used assessing and ranking the alternatives. The obtained algorithm ensures optimal decision making based on the experts’ assessments. A simple web application has been developed to demonstrate the algorithm’s performance.

Palabras clave

  • Decision-making
  • multi-criteria analysis
  • expert
  • decision matrix
  • AHP
  • TOPSIS
Acceso abierto

Analytical Overview and Applications of Modified Black-Litterman Model for Portfolio Optimization

Publicado en línea: 12 Jun 2020
Páginas: 30 - 49

Resumen

Abstract

The paper makes analytical overviews of the Markowitz portfolio and the Capital Asset Pricing models and motivates the advances of the Black-Litterman (BL) one. This overview implies that for a small set of assets the BL model needs the characteristics of a specific market point, which cannot be taken from a global market index. The paper derives analytic relations for the new specific market point with analytical approximation of the efficient frontier. The BL model insists also expert views, which influence the portfolio solution. The paper derives formalization of the expert views from the difference between the evaluated implied returns and historical mean assets returns. Such form of expert views makes modifications of the BL model. This allows comparisons between Markowitz (MV) and BL portfolio performance. Benefits of this research are demonstrated with market data and comparison of the MV and BL portfolio results.

Palabras clave

  • Size Portfolio optimization
  • mean-variance portfolio model
  • Black-Litterman portfolio model
  • active portfolio management
  • decision making
Acceso abierto

Fuzzy Group Full Consistency Method for Weight Determination

Publicado en línea: 12 Jun 2020
Páginas: 50 - 58

Resumen

Abstract

In this paper, the FUll COnsistency Method (FUCOM) is extended to work in a collective manner, to solve a fuzzy optimization problem and to obtain the fuzzy weights of criteria. The employment of a predefined order of criteria decreases the number of fuzzy comparisons needed in the evaluation phase. The defuzzified values of the optimal weight coefficients are calculated by Graded Mean Integration Representation formula. This feature also reduces time complexity without affecting the quality of the solution. Two practical examples are presented to verify the reliability and feasibility of the proposed fuzzy group FUCOM. The obtained results demonstrate that the new fuzzy group weight determination method can obtain appropriate criteria importance.

Palabras clave

  • Multi-criteria decision methods
  • determination of fuzzy weight coefficients
  • fuzzy optimization
  • fuzzy group full consistency method
Acceso abierto

Image Copy Detection Based on Local Binary Pattern and SVM Classifier

Publicado en línea: 12 Jun 2020
Páginas: 59 - 69

Resumen

Abstract

Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.

Palabras clave

  • Content-based copy Detection
  • Digital watermarking
  • Local Binary Pattern
  • Support Vector Machine
  • Image forensics
  • Image hashing
Acceso abierto

Implementation of Classic Image Transformation Algorithm to Quantum State, Boundary Extraction and Transformation of Half-Tone Image to Binary

Publicado en línea: 12 Jun 2020
Páginas: 70 - 78

Resumen

Abstract

The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.

Palabras clave

  • Qubit
  • entanglement
  • quantum circuit
  • quantum gate
  • wave function
  • quantum algorithm
Acceso abierto

DevOps Project Management Tools for Sprint Planning, Estimation and Execution Maturity

Publicado en línea: 12 Jun 2020
Páginas: 79 - 92

Resumen

Abstract

The goal of DevOps is to cut down the project timelines, increase the productivity, and manage rapid development-deployment cycles without impacting business and quality. It requires efficient sprint management. The objective of this paper is to develop different sprint level project management tools for quick project level Go/No-Go decision making (using real-time projects data and machine learning), sprint estimation technique (gamified-consensus based), statistical understanding of overall project management maturity, project sentiment & perception. An attempt is made to device a model to calibrate the perception or the tone of a project culture using sentiment analysis.

Palabras clave

  • DevOps
  • Machine Learning (ML)
  • effort estimation
  • planning poker
  • sentimental analysis
Acceso abierto

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Publicado en línea: 12 Jun 2020
Páginas: 93 - 104

Resumen

Abstract

Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

Palabras clave

  • Ontology design patterns
  • OWL
  • credit risk scorecard
  • decision support
  • BCBS 239
Acceso abierto

Advanced Embedded Control of Electrohydraulic Power Steering System

Publicado en línea: 12 Jun 2020
Páginas: 105 - 121

Resumen

Abstract

The article presents a developed embedded system for control of electrohydraulic power steering based on multivariable uncertain plant model and advanced control techniques. The plant model is obtained by identification procedure via “black box” system identification and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the model. Three types of controller are designed: Linear-Quadratic Gaussian (LQG) controller, H controller and μ-controller. The main result is a performed comparative analysis of time and frequency domain properties of control systems. The results show the better performance of systems based on µ-controllers. Also the robust stability and robust performance are investigated. All three systems achieved robust stability which guarantees their workability, but only the system with µ-controller has robust performance against prescribed uncertainties. The control algorithms are implemented in specialized 32-bit microcontroller. A number of real world experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.

Palabras clave

  • Linear-Quadratic Gaussian (LQG) controller
  • Kalman filter
  • H controller
  • μ-controller
  • electrohydraulic steering system
  • embedded control
Acceso abierto

QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization

Publicado en línea: 12 Jun 2020
Páginas: 122 - 140

Resumen

Abstract

Air pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area of air quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service (QoS) levels against accuracy, sensing throughput and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms of reliable delivery of the messages under employed protocol.

Palabras clave

  • Air quality monitoring
  • Internet of Things
  • Message Queue Telemetry Transport protocol
  • smart city
  • ESP8266
Acceso abierto

Bayesian Regularized Neural Network for Prediction of the Dose in Gamma Irradiated Milk Products

Publicado en línea: 12 Jun 2020
Páginas: 141 - 151

Resumen

Abstract

Gamma irradiation is a well-known method for sterilizing different foodstuffs, including fresh cow milk. Many studies witness that the low dose irradiation of milk and milk products affects the fractions of the milk protein, thus reducing its allergenic effect and make it potentially appropriate for people with milk allergy. The purpose of this study is to evaluate the relationship between the gamma radiation dose and size of the protein fractions, as potential approach to decrease the allergenic effect of the milk. In this paper, an approach for prediction of the dose in gamma irradiated products by using a Bayesian regularized neural network as a mean to save recourses for expensive electrophoretic experiments, is developed. The efficiency of the proposed neural network model is proved on data for two dairy products – lyophilized cow milk and curd.

Palabras clave

  • Bayesian neural network
  • milk products
  • milk allergy
  • protein fraction
Acceso abierto

Retraction

Publicado en línea: 12 Jun 2020
Páginas: 152 - 152

Resumen

Planifique su conferencia remota con Sciendo