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Special Zeszyt on New Developments in Scalable Computing

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Special issue on Innovations in Intelligent Systems and Applications

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Special Thematic Zeszyt on Optimal Codes and Related Topics

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Tom 17 (2017): Zeszyt 5 (December 2017)
Special Zeszyt With Selected Papers From The Workshop “Two Years Avitohol: Advanced High Performance Computing Applications 2017

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Tom 16 (2016): Zeszyt 6 (December 2016)
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016

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Zeszyt Title: Special Zeszyt on Application of Advanced Computing and Simulation in Information Systems

Tom 16 (2016): Zeszyt 4 (December 2016)

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Tom 15 (2015): Zeszyt 7 (December 2015)
Special Zeszyt on Information Fusion

Tom 15 (2015): Zeszyt 6 (December 2015)
Special Zeszyt on Logistics, Informatics and Service Science

Tom 15 (2015): Zeszyt 5 (April 2015)
Special Zeszyt on Control in Transportation Systems

Tom 15 (2015): Zeszyt 4 (November 2015)

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Special Zeszyt

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Tom 13 (2013): Zeszyt Special-Zeszyt (December 2013)

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The publishing of the present issue (Tom 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.

Tom 13 (2013): Zeszyt 3 (September 2013)

Tom 13 (2013): Zeszyt 2 (June 2013)

Tom 13 (2013): Zeszyt 1 (March 2013)

Tom 12 (2012): Zeszyt 4 (December 2012)

Tom 12 (2012): Zeszyt 3 (September 2012)

Tom 12 (2012): Zeszyt 2 (June 2012)

Tom 12 (2012): Zeszyt 1 (March 2012)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

Tom 17 (2017): Zeszyt 3 (September 2017)

Informacje o czasopiśmie
Format
Czasopismo
eISSN
1314-4081
Pierwsze wydanie
13 Mar 2012
Częstotliwość wydawania
4 razy w roku
Języki
Angielski

Wyszukiwanie

12 Artykułów
Otwarty dostęp

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Data publikacji: 04 Oct 2017
Zakres stron: 3 - 28

Abstrakt

Abstract

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.

Słowa kluczowe

  • Data clustering
  • swarm intelligence
  • artificial bee colony
  • meta-heuristics
  • optimization
Otwarty dostęp

Multicriteria Fuzzy Sets Application in Economic Clustering Problems

Data publikacji: 04 Oct 2017
Zakres stron: 29 - 46

Abstrakt

Abstract

This paper presents an approach for small and medium-sized enterprises selection in economic clusters, where the problem of integration is defined as “ill structured under condition of uncertainty”. The proposed solution demonstrates applying several fuzzy multi-criteria decision making algorithms along with discussion over specific input data requirements. The results are compared with classical multi-criteria decision-making algorithm PROMETHEE II.

Słowa kluczowe

  • Multi-criteria decision
  • fuzzy sets algorithms
  • intercriteria analysis
  • small and medium-sized enterprises
  • economic clustering
Otwarty dostęp

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Data publikacji: 04 Oct 2017
Zakres stron: 47 - 58

Abstrakt

Abstract

This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Słowa kluczowe

  • Resource allocation
  • simulated annealing
  • virtual service
  • cloud computing
  • energy consumption
Otwarty dostęp

Particle Swarm Optimization Based on Smoothing Approach for Solving a Class of Bi-Level Multiobjective Programming Problem

Data publikacji: 04 Oct 2017
Zakres stron: 59 - 74

Abstrakt

Abstract

As a metaheuristic, Particle Swarm Optimization (PSO) has been used to solve the Bi-level Multiobjective Programming Problem (BMPP). However, in the existing solving approach based on PSO for the BMPP, the upper level and the lower level problem are solved interactively by PSO. In this paper, we present a different solving approach based on PSO for the BMPP. Firstly, we replace the lower level problem of the BMPP with Kuhn-Tucker optimality conditions and adopt the perturbed Fischer-Burmeister function to smooth the complementary conditions. After that, we adopt PSO approach to solve the smoothed multiobjective programming problem. Numerical results show that our solving approach can obtain the Pareto optimal front of the BMPP efficiently.

Słowa kluczowe

  • Bi-level multiobjective programming problem
  • scalarization method
  • optimality conditions
  • smoothing method
  • particle swarm optimization
Otwarty dostęp

Bi-Level Model for Public Rail Transportation under Incomplete Data

Data publikacji: 04 Oct 2017
Zakres stron: 75 - 91

Abstrakt

Abstract

The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

Słowa kluczowe

  • Bi-level optimization
  • rail transportation
  • max-flow problem
  • network design
Otwarty dostęp

Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database

Data publikacji: 04 Oct 2017
Zakres stron: 92 - 108

Abstrakt

Abstract

Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

Słowa kluczowe

  • Association Rule Hiding
  • Data Mining
  • Gene Pattern
  • Transactional database
  • Multiplicative perturbation
  • Additive perturbation
Otwarty dostęp

Modules for Rapid Application Development of Web-Based Information Systems (RADWIS)

Data publikacji: 04 Oct 2017
Zakres stron: 109 - 127

Abstrakt

Abstract

This paper describes a model of modular system for Rapid Application Development of Web-based Information Systems (RADWIS). The existing modular systems on technology, framework and platform level does not fully solve the problems of functionality reuse, rapid application development and balance between the complexity, size and functionality. The proposed modular system addresses these problems in a new way. The current work fills the gap between the modular systems on the framework and platform level. The model uses flexible, reusable modules, which can be built with different technologies. They are installable and shareable with the standard dependency manager of the technology and can communicate using web services. The modules use NoSQL approaches in SQL databases. A workflow engine module, based on the Petri Nets theory, allows a graphical and formal mathematical solution for a wide variety of problems.

Słowa kluczowe

  • Modules
  • Web-based information systems
  • Petri Nets
  • NoSQL in SQL
Otwarty dostęp

Secret Image Enhanced Sharing Using Visual Cryptography

Data publikacji: 04 Oct 2017
Zakres stron: 128 - 139

Abstrakt

Abstract

In the conventional visual cryptographic scheme, an image is divided into several image shares, which are distributed among the members of a group, and the original image is retrieved by combining the shares of all the members. This secret image becomes accessible to every individual member and there is an inherent risk of any one of the members in the group using the valuable information for illegal purposes as an intruder. To overcome this problem, the proposed algorithm Secret Image Enhanced Sharing using Visual Cryptography (SIESVC) diligently facilitates any member in the group to retrieve either only a part or the complete secret image based purely on his access privilege rights only.

Słowa kluczowe

  • Visual Cryptography
  • Secret Image Enhanced Sharing using Visual Cryptography (SIESVC)
Otwarty dostęp

Approach for Analysis and Improved Usage of Digital Cultural Assets for Learning Purposes

Data publikacji: 04 Oct 2017
Zakres stron: 140 - 151

Abstrakt

Abstract

This paper presents an approach for analysis and improved usage of digital cultural assets for non-formal learning purposes in digital culture ecosystems. The digital culture ecosystems, their features, and an exemplar are discussed.

Słowa kluczowe

  • Digital culture ecosystem
  • analysis
  • improved usage
  • digital cultural assets
  • non-formal learning
Otwarty dostęp

Learned Features are Better for Ethnicity Classification

Data publikacji: 04 Oct 2017
Zakres stron: 152 - 164

Abstrakt

Abstract

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

Słowa kluczowe

  • Ethnicity recognition
  • race classification
  • Convolutional Neural Network (CNN)
  • VGG Face
  • Support Vector Machine (SVM)
Otwarty dostęp

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Data publikacji: 04 Oct 2017
Zakres stron: 165 - 185

Abstrakt

Abstract

Cloud computing is emerging as a significant new paradigm in the fields of Service-oriented computing, software engineering, etc. The paper aims to characterize the cloud environment and to study the cloud optimization problems. About 50 papers are collected from the standard journals, and it is first reviewed chronologically to find out the contributions in cloud security. After reviewing, the various challenges addressed in the cloud environment and its performance analysis is discussed. In the next section, the exploration of the meta-heuristic study of cloud optimization is done. The algorithms used in the cloud security challenges are discussed and reviewed. The algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) are exploited for the finding the cloud security problems. As the research outcome, case studies are taken and reviewed. Cloud computing is a vast field, and a lot of problems in it had to be addressed and solved.

Słowa kluczowe

  • Cloud computing
  • optimization
  • metaheuristic
  • Cloud environment
Otwarty dostęp

Erratum to the paper “Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction”

Data publikacji: 04 Oct 2017
Zakres stron: 186 - 186

Abstrakt

12 Artykułów
Otwarty dostęp

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Data publikacji: 04 Oct 2017
Zakres stron: 3 - 28

Abstrakt

Abstract

Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.

Słowa kluczowe

  • Data clustering
  • swarm intelligence
  • artificial bee colony
  • meta-heuristics
  • optimization
Otwarty dostęp

Multicriteria Fuzzy Sets Application in Economic Clustering Problems

Data publikacji: 04 Oct 2017
Zakres stron: 29 - 46

Abstrakt

Abstract

This paper presents an approach for small and medium-sized enterprises selection in economic clusters, where the problem of integration is defined as “ill structured under condition of uncertainty”. The proposed solution demonstrates applying several fuzzy multi-criteria decision making algorithms along with discussion over specific input data requirements. The results are compared with classical multi-criteria decision-making algorithm PROMETHEE II.

Słowa kluczowe

  • Multi-criteria decision
  • fuzzy sets algorithms
  • intercriteria analysis
  • small and medium-sized enterprises
  • economic clustering
Otwarty dostęp

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Data publikacji: 04 Oct 2017
Zakres stron: 47 - 58

Abstrakt

Abstract

This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Słowa kluczowe

  • Resource allocation
  • simulated annealing
  • virtual service
  • cloud computing
  • energy consumption
Otwarty dostęp

Particle Swarm Optimization Based on Smoothing Approach for Solving a Class of Bi-Level Multiobjective Programming Problem

Data publikacji: 04 Oct 2017
Zakres stron: 59 - 74

Abstrakt

Abstract

As a metaheuristic, Particle Swarm Optimization (PSO) has been used to solve the Bi-level Multiobjective Programming Problem (BMPP). However, in the existing solving approach based on PSO for the BMPP, the upper level and the lower level problem are solved interactively by PSO. In this paper, we present a different solving approach based on PSO for the BMPP. Firstly, we replace the lower level problem of the BMPP with Kuhn-Tucker optimality conditions and adopt the perturbed Fischer-Burmeister function to smooth the complementary conditions. After that, we adopt PSO approach to solve the smoothed multiobjective programming problem. Numerical results show that our solving approach can obtain the Pareto optimal front of the BMPP efficiently.

Słowa kluczowe

  • Bi-level multiobjective programming problem
  • scalarization method
  • optimality conditions
  • smoothing method
  • particle swarm optimization
Otwarty dostęp

Bi-Level Model for Public Rail Transportation under Incomplete Data

Data publikacji: 04 Oct 2017
Zakres stron: 75 - 91

Abstrakt

Abstract

The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

Słowa kluczowe

  • Bi-level optimization
  • rail transportation
  • max-flow problem
  • network design
Otwarty dostęp

Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database

Data publikacji: 04 Oct 2017
Zakres stron: 92 - 108

Abstrakt

Abstract

Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

Słowa kluczowe

  • Association Rule Hiding
  • Data Mining
  • Gene Pattern
  • Transactional database
  • Multiplicative perturbation
  • Additive perturbation
Otwarty dostęp

Modules for Rapid Application Development of Web-Based Information Systems (RADWIS)

Data publikacji: 04 Oct 2017
Zakres stron: 109 - 127

Abstrakt

Abstract

This paper describes a model of modular system for Rapid Application Development of Web-based Information Systems (RADWIS). The existing modular systems on technology, framework and platform level does not fully solve the problems of functionality reuse, rapid application development and balance between the complexity, size and functionality. The proposed modular system addresses these problems in a new way. The current work fills the gap between the modular systems on the framework and platform level. The model uses flexible, reusable modules, which can be built with different technologies. They are installable and shareable with the standard dependency manager of the technology and can communicate using web services. The modules use NoSQL approaches in SQL databases. A workflow engine module, based on the Petri Nets theory, allows a graphical and formal mathematical solution for a wide variety of problems.

Słowa kluczowe

  • Modules
  • Web-based information systems
  • Petri Nets
  • NoSQL in SQL
Otwarty dostęp

Secret Image Enhanced Sharing Using Visual Cryptography

Data publikacji: 04 Oct 2017
Zakres stron: 128 - 139

Abstrakt

Abstract

In the conventional visual cryptographic scheme, an image is divided into several image shares, which are distributed among the members of a group, and the original image is retrieved by combining the shares of all the members. This secret image becomes accessible to every individual member and there is an inherent risk of any one of the members in the group using the valuable information for illegal purposes as an intruder. To overcome this problem, the proposed algorithm Secret Image Enhanced Sharing using Visual Cryptography (SIESVC) diligently facilitates any member in the group to retrieve either only a part or the complete secret image based purely on his access privilege rights only.

Słowa kluczowe

  • Visual Cryptography
  • Secret Image Enhanced Sharing using Visual Cryptography (SIESVC)
Otwarty dostęp

Approach for Analysis and Improved Usage of Digital Cultural Assets for Learning Purposes

Data publikacji: 04 Oct 2017
Zakres stron: 140 - 151

Abstrakt

Abstract

This paper presents an approach for analysis and improved usage of digital cultural assets for non-formal learning purposes in digital culture ecosystems. The digital culture ecosystems, their features, and an exemplar are discussed.

Słowa kluczowe

  • Digital culture ecosystem
  • analysis
  • improved usage
  • digital cultural assets
  • non-formal learning
Otwarty dostęp

Learned Features are Better for Ethnicity Classification

Data publikacji: 04 Oct 2017
Zakres stron: 152 - 164

Abstrakt

Abstract

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

Słowa kluczowe

  • Ethnicity recognition
  • race classification
  • Convolutional Neural Network (CNN)
  • VGG Face
  • Support Vector Machine (SVM)
Otwarty dostęp

Emphasis on Cloud Optimization and Security Gaps: A Literature Review

Data publikacji: 04 Oct 2017
Zakres stron: 165 - 185

Abstrakt

Abstract

Cloud computing is emerging as a significant new paradigm in the fields of Service-oriented computing, software engineering, etc. The paper aims to characterize the cloud environment and to study the cloud optimization problems. About 50 papers are collected from the standard journals, and it is first reviewed chronologically to find out the contributions in cloud security. After reviewing, the various challenges addressed in the cloud environment and its performance analysis is discussed. In the next section, the exploration of the meta-heuristic study of cloud optimization is done. The algorithms used in the cloud security challenges are discussed and reviewed. The algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) are exploited for the finding the cloud security problems. As the research outcome, case studies are taken and reviewed. Cloud computing is a vast field, and a lot of problems in it had to be addressed and solved.

Słowa kluczowe

  • Cloud computing
  • optimization
  • metaheuristic
  • Cloud environment
Otwarty dostęp

Erratum to the paper “Two-Dimensional l1-Norm Minimization in SAR Image Reconstriction”

Data publikacji: 04 Oct 2017
Zakres stron: 186 - 186

Abstrakt

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