Open Access Published since April 16, 2011

Open Computer Science

ISSN: 2299-1093

Editor-in-chief: Egon L. van den Broek

About this journal

CALL FOR PAPERS

Special Issue on Programming Models and Algorithms for Big Data

COORDINATING EDITORS:
Prof. Dr. Fadi Al-Turjman, Faculty of Engineering, Near East University, Turkey
Dr. Shahid Mumtaz, Instituto de Telecomunicações Aveiro, Aveiro, Portugal

The deadline for submissions is January 17, 2023, but individual papers will be reviewed and published online on an ongoing basis.

For more details, please see the CFP Programming Models and Algorithms for Big Data file in Supplementary Materials below.

Open Computer Science is a fully peer-reviewed, open access, electronic-only journal publishing works of wide significance, originality and relevance. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-term preservation, no space constraints and quick publication.

Open Computer Science is listed inter alia by Web of Science (ESCI - Emerging Sources Citation Index) and SCOPUS. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.

Open Computer Science publishes original and significant research results in the following fields:

algorithms and complexity theory
artificial intelligence
bioinformatics
networking and security systems
programming languages
system and software engineering
theoretical foundations of computer science

The journal considers submissions of different types of articles, including "Research Articles" as well as “Review Articles”, which present the existing literature and achievements on the specific topic from new perspectives. We accept both theoretical and empirical contributions.

Why read

Open Computer Science is a premier source of a high quality research. Thanks to Open Access all articles are freely available to the academic community worldwide.

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Authors benefit from:

fair and constructive peer-review
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quick publication of accepted papers*
Open Access for all articles**
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long-term preservation of the content***

* The journal uses continuous publication model − accepted manuscripts are published quickly after the acceptance.
** Thanks to Open Access, all articles are freely available to the academic community worldwide without any restrictions, there are more liberal policies on copyrights (authors retain copyright) and on self-archiving (no embargo periods).
*** The journal is archived in Portico.

Detailed information on Editorial Policy, Publication Ethics, Instructions for Authors etc. can be found below and in the Supplementary Information section.

Publication fees

From January 1, 2021, articles accepted for publication in Open Computer Science are subject to Article Processing Charges (APCs). The publication fee amounts to 1000 EUR. Authors who have no funding for the submitted research can request a waiver or a discount during the submission process.

Read more about our pricing policy, available discounts and waiver policy.

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Volume 12 Issue 1

January 2022

Regular Articles

Open Access February 21, 2022

An ROI-based robust video steganography technique using SVD in wavelet domain

Urmila Pilania, Rohit Tanwar, Prinima Gupta

Page range: 1-16

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Abstract

Steganography is a technique that embeds secret information in a suitable cover file such as text, image, audio, and video in such a manner that secret information remains invisible to the outside world. The study of the literature relevant to video steganography reveals that a tradeoff exists in attaining the acceptable values of various evaluation parameters such as a higher capacity usually results in lesser robustness or imperceptibility. In this article, we propose a technique that achieves high capacity along with required robustness. The embedding capacity is increased using singular value decomposition compression. To achieve the desired robustness, we constrain the embedding of the secret message in the region of interest in the cover video file. In this manner, we also succeed in maintaining the required imperceptibility. We prefer Haar-based lifting scheme in the wavelet domain for embedding the information because of its intrinsic benefits. We have implemented our suggested technique using MATLAB. The analysis of results on the prespecified parameters of the steganography justifies the effectiveness of the proposed technique.

Open Access February 23, 2022

SIKM – a smart cryptographic key management framework

Saman Shojae Chaeikar, Ali Ahmadi, Sasan Karamizadeh, Nakisa Shoja Chaeikar

Page range: 17-26

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Abstract

For a secure data transmission in symmetric cryptography, data are encrypted and decrypted using an identical key. The process of creating, distributing, storing, deploying, and finally revoking the symmetric keys is called key management. Many key management schemes are devised that each one is suitable for a specific range of applications. However, these schemes have some common drawbacks like the hardness of key generation and distribution, key storage, attacks, and traffic load. In this article, a key management framework is proposed, which is attack resistant and transforms the current customary key management workflow to enhance security and reduce weaknesses. The main features of the proposed framework are eliminating key storage, smart attack resistant feature, reducing multiple-times key distribution to just one-time interpreter distribution, and having short key intervals – minutely, hourly, and daily. Moreover, the key revocation process happens automatically and with no revocation call.

Open Access February 23, 2022

Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model

Koyel Datta Gupta, Rinky Dwivedi, Deepak Kumar Sharma

Page range: 27-36

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Abstract

In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.

Open Access February 24, 2022

3D chaotic map-cosine transformation based approach to video encryption and decryption

Mohit Dua, Drishti Makhija, Pilla Yamini Lakshmi Manasa, Prashant Mishra

Page range: 37-56

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Abstract

Data security is vital for multimedia communication. A number of cryptographic algorithms have been developed for the secure transmission of text and image data. Very few contributions have been made in the area of video encryption because of the large input data size and time constraints. However, due to the massive increase in digital media transfer within networks, the security of video data has become one of the most important features of network reliability. Block encryption techniques and 1D-chaotic maps have been previously used for the process of video encryption. Although the results obtained by using 1D-chaotic maps were quite satisfactory, the approach had many limitations as these maps have less dynamic behavior. To overcome these drawbacks, this article proposes an Intertwining Logistic Map (ILM)-Cosine transformation-based video encryption technique. The first step involved segmenting the input video into multiple frames based on the frames per second (FPS) value and the length of the video. Next, each frame was selected, and the correlation among the pixels was reduced by a process called permutation/scrambling. In addition, each frame was rotated by 90° in the anticlockwise direction to induce more randomness into the encryption process. Furthermore, by using an approach called the random order substitution technique, changes were made in each of the images, row-wise and column-wise. Finally, all the encrypted frames were jumbled according to a frame selection key and were joined to generate an encrypted video, which was the output delivered to the user. The efficiency of this method was tested based on the state of various parameters like Entropy, Unified Average Change in Intensity (UACI), and correlation coefficient (CC). The presented approach also decrypts the encrypted video, and the decryption quality was checked using parameters such as mean square error (MSE) and peak signal-to-noise ratio (PSNR).

Open Access February 24, 2022

Security and privacy issues in federated healthcare – An overview

Jansi Rani Amalraj, Robert Lourdusamy

Page range: 57-65

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Abstract

Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.

Open Access March 3, 2022

Designing of fault-tolerant computer system structures using residue number systems

Victor Krasnobayev, Alexandr Kuznetsov, Anastasiia Kiian

Page range: 66-74

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Abstract

This article discusses computing systems that operate in residue number systems (RNSs). The main direction of improving computer systems (CSs) is increasing the speed of implementation of arithmetic operations and the reliability of their functioning. Encoding data in RNS solves the problem of optimal redundancy, i.e., the creation of such computing systems provides maximum reliability with restrictions on weight and size characteristics. This article proposes new structures of fault-tolerant CSs operating in RNS in the case of the application with an active fault-tolerant method. The use of the active fault-tolerant method (dynamic redundancy) in the RNSs provides higher reliability. In addition, with an increase in the digits of CSs, the efficiency of using the proposed structures increases.

Open Access March 8, 2022

A method for detecting objects in dense scenes

Chuanyun Xu, Yu Zheng, Yang Zhang, Gang Li, Ying Wang

Page range: 75-82

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Abstract

Recent object detectors have achieved excellent performance in accuracy and speed. Even with such impressive results, the most advanced detectors are challenging in dense scenes. In this article, we analyze and find the reasons for the decrease in detection accuracy in dense scenes. We started our work in terms of region proposal and location loss. We found that low-quality proposal regions during the training process are the main factors affecting detection accuracy. To prove our research, we established and trained a dense detection model based on Cascade R-CNN. The model achieves an accuracy of mAP 0.413 on the SKU-110K sub-dataset. Our results show that improving the quality of recommended regions can effectively improve the detection accuracy in dense scenes.

Open Access March 10, 2022

An effective integrated machine learning approach for detecting diabetic retinopathy

Penikalapati Pragathi, Agastyaraju Nagaraja Rao

Page range: 83-91

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Abstract

Millions of people across the world are suffering from diabetic retinopathy. This disease majorly affects the retina of the eye, and if not identified priorly causes permanent blindness. Hence, detecting diabetic retinopathy at an early stage is very important to safeguard people from blindness. Several machine learning (ML) algorithms are implemented on the dataset of diabetic retinopathy available in the UCI ML repository to detect the symptoms of diabetic retinopathy. But, most of those algorithms are implemented individually. Hence, this article proposes an effective integrated ML approach that uses the support vector machine (SVM), principal component analysis (PCA), and moth-flame optimization techniques. Initially, the ML algorithms decision tree (DT), SVM, random forest (RF), and Naïve Bayes (NB) are applied to the diabetic retinopathy dataset. Among these, the SVM algorithm is outperformed with an average of 76.96% performance. Later, all the aforementioned ML algorithms are implemented by integrating the PCA technique to reduce the dimensions of the dataset. After integrating PCA, it is noticed that the performance of the algorithms NB, RF, and SVM is reduced dramatically; on the contrary, the performance of DT is increased. To improve the performance of ML algorithms, the moth-flame optimization technique is integrated with SVM and PCA. This proposed approach is outperformed with an average of 85.61% performance among all the other considered ML algorithms, and the classification of class labels is achieved correctly.

Open Access March 15, 2022

Greatest-common-divisor dependency of juggling sequence rotation efficient performance

Joseph A. Erho, Juliana I. Consul, Bunakiye R. Japheth

Page range: 92-102

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Abstract

In previous experimental study with three-way-reversal and juggling sequence rotation algorithms, using 20,000,000 elements for type LONG in Java, the average execution times have been shown to be 49.66761ms and 246.4394ms, respectively. These results have revealed appreciable low performance in the juggling algorithm despite its proven optimality. However, the juggling algorithm has also exhibited efficiency with some offset ranges. Due to this pattern of the juggling algorithm, the current study is focused on investigating source of the inefficiency on the average performance. Samples were extracted from the previous experimental data, presented differently and analyzed both graphically and in tabular form. Greatest common divisor values from the data that equal offsets were used. As emanating from the previous study, the Java language used for the rotation was to simulate ordering of tasks for safety and efficiency in the context of real-time task scheduling. Outcome of the investigation shows that juggling rotation performance competes favorably with three-way-reversal rotation (and even better in few cases) for certain offsets, but poorly with the rests. This study identifies the poorest performances around offsets in the neighborhood of square root of the sequence size. From the outcome, the study therefore strongly advises application developers (especially for real-time systems) to be mindful of where and how to in using juggling rotation.

Open Access March 16, 2022

Construction of a gas condensate field development model

Alexander K. Skiba

Page range: 103-111

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Abstract

This article has developed and verified a mathematical aggregated approximate model of developing a gas condensate field using a cyclic process. The essence of the cyclic process is to pump the drained gas into the productive formation to reduce the pressure drop into the deposit. This process allows for increased condensate recovery in the future. The model discussed in this article is a continuous dynamic system with control parameters. It is a modification of the dynamic aggregated model of a purely gas field, designed for planning for a sufficiently long period with limited information about the state of the reservoir (the initial flow rate of wells, the initial recoverable gas reserves, the initial reservoir pressure, the dependence of potential condensate content per unit volume of fatty gas on the reservoir pressure). A non-standard approach underlies the model construction. Logical simplifications and a priori assumptions about the processes occurring in the field during its development are at its core. The instruments in the model are the increase in production and injection wells and the proportion of injection wells involved in the production. The purpose of the article is to calculate various variants of the dynamics of the fundamental indicators of the development of a gas condensate field for a sufficiently long-term period at the stage of preliminary design.

Open Access March 19, 2022

A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure

Fariba Sarhangnia, Shima Mahjoobi, Samaneh Jamshidi

Page range: 112-122

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Abstract

Link prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in the f -measure criterion shows the best performance.

Open Access March 21, 2022

Rough set-based entropy measure with weighted density outlier detection method

Tamilarasu Sangeetha, Amalanathan Geetha Mary

Page range: 123-133

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Abstract

The rough set theory is a powerful numerical model used to handle the impreciseness and ambiguity of data. Many existing multigranulation rough set models were derived from the multigranulation decision-theoretic rough set framework. The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pattern extraction. The proposed approach mainly focuses on anomaly detection in qualitative data with multiple granules. The approximations of the dataset will be derived through multiequivalence relation, and then, the rough set-based entropy measure with weighted density method is applied on every object and attribute. For detecting outliers, threshold value fixation is performed based on the estimated weight. The performance of the algorithm is evaluated and compared with existing outlier detection algorithms. Datasets such as breast cancer, chess, and car evaluation have been taken from the UCI repository to prove its efficiency and performance.

Open Access March 24, 2022

Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks

Tosin Adewumi, Foteini Liwicki, Marcus Liwicki

Page range: 134-141

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Abstract

Word2Vec is a prominent model for natural language processing tasks. Similar inspiration is found in distributed embeddings (word-vectors) in recent state-of-the-art deep neural networks. However, wrong combination of hyperparameters can produce embeddings with poor quality. The objective of this work is to empirically show that Word2Vec optimal combination of hyper-parameters exists and evaluate various combinations. We compare them with the publicly released, original Word2Vec embedding. Both intrinsic and extrinsic (downstream) evaluations are carried out, including named entity recognition and sentiment analysis. Our main contributions include showing that the best model is usually task-specific, high analogy scores do not necessarily correlate positively with F 1 scores, and performance is not dependent on data size alone. If ethical considerations to save time, energy, and the environment are made, then relatively smaller corpora may do just as well or even better in some cases. Increasing the dimension size of embeddings after a point leads to poor quality or performance. In addition, using a relatively small corpus, we obtain better WordSim scores, corresponding Spearman correlation, and better downstream performances (with significance tests) compared to the original model, which is trained on a 100 billion-word corpus.

Open Access March 30, 2022

Post-quantum cryptography-driven security framework for cloud computing

Henry Chima Ukwuoma, Gabriel Arome, Aderonke Thompson, Boniface Kayode Alese

Page range: 142-153

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Abstract

Data security in the cloud has been a major issue since the inception and adoption of cloud computing. Various frameworks have been proposed, and yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies an urgent need to proffer a model that will provide adequate data security for both classical and quantum computing. Thus, most cryptosystems will be rendered susceptible and obsolete, though some cryptosystems will stand the test of quantum computing. The article proposes a model that comprises the application of a variant of McEliece cryptosystem, which has been tipped to replace Rivest–Shamir–Adleman (RSA) in the quantum computing era to secure access control data and the application of a variant of N-th degree truncated polynomial ring units (NTRU) cryptosystem to secure cloud user data. The simulation of the proposed McEliece algorithm showed that the algorithm has a better time complexity than the existing McEliece cryptosystem. Furthermore, the novel tweaking of parameters S and P further improves the security of the proposed algorithms. More so, the simulation of the proposed NTRU algorithm revealed that the existing NTRU cryptosystem had a superior time complexity when juxtaposed with the proposed NTRU cryptosystem.

Open Access May 16, 2022

BiSHM: Evidence detection and preservation model for cloud forensics

Prasad Purnaye, Vrushali Kulkarni

Page range: 154-170

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Abstract

The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP).

Open Access May 23, 2022

Two hide-search games with rapid strategies for multiple parallel searches

Peter E. Creasey

Page range: 171-180

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Abstract

Making a rapid unpredictable decision from N N choices of unequal value is a common control task. When the cost of predictability can be modelled as a penalty hidden under a single option by an intelligent adversary, then an optimal strategy can be found efficiently in O ( N log N ) O\left(N\log N) steps using an approach described by Sakaguchi for a zero-sum hide-search game. In this work, we extend this to two games with multiple parallel predictions, either coordinated or drawn independently from the optimal distribution, both of which can be solved with the same scaling. An open-source code is provided online at https://github.com/pec27/rams.

Open Access May 23, 2022

A student-based central exam scheduling model using A* algorithm

Mehmet Sinan Başar, Sinan Kul

Page range: 181-190

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Abstract

In this study, a student-based placement model using the A* algorithm is proposed and applied to solve the problem of placing the courses in exam sessions. The application area of the model is midterm and final exams, conducted by the Open Education Faculty. The reason for choosing open education exams for the practice is that the exams are applied across the country and more than 100,000 students participate. The main problem is to obtain a suitable distribution that can satisfy many constraints simultaneously. In the current system, the lessons in the sessions were placed once using the curriculum knowledge. This placement plan is applied in all exams. When the placement is done according to the curriculum information, the courses in the sessions cannot be placed effectively and efficiently due to a large number of common courses and the large number of students taking the exam. This makes the booklets more expensive and the organization more prone to errors. Both the opening of new programs and the increase in the number of students regularly lead to the necessity of placing the classes in sessions dynamically each semester. In addition, to prevent conflicts with the calendars of other central exams, it is necessary to conduct all exams in three sessions. A better solution was obtained by using a different model than the currently used model in the study. With this solution, distribution of the courses of successful students with few courses to all sessions is provided, and difficult courses of unsuccessful students who have a large number of courses were gathered in the same session. This study can support future studies on two issues: the first issue is the approach of using the course that will be taken by most students instead of the courses taught in most departments in the selection of the course to be placed in the booklet. The second issue is to try to find the most suitable solution by performing performance tests on many algorithms whose performance has been determined by many academic studies.

Volume 4 (2014)

Volume 3 (2013)

Volume 2 (2012)

Volume 1 (2011)

Cite Score	1.1
Index Copernicus Value	97.86
Journal Citation Indicator	0.13
SCImago Journal Rank	0.147
Source Normalized Impact per Paper	0.691

Submit

MANUSCRIPTS
Open Computer Science encourages the submission of both substantial full-length bodies of work and shorter manuscripts that report novel findings that might be based on a more limited range of experiments. There are no specific length restrictions for the overall manuscript or individual sections; however, we urge the authors to present and discuss their findings in a concise and accessible manner. All submissions should be written in clear and concise English.

Important documents necessary for publication, i.a. Instruction for Authors, LaTex Template, Open Access License and more, can be found in the Supplementary Information section available above.

Manuscripts should be submitted to the journal via online submission system Editorial Manager available for this journal at http://www.editorialmanager.com/opencs/. In case of problems, please contact the Editorial Office of this journal via email opencomputerscience@degruyter.com.

Manuscripts can be written in TeX, LaTeX (strongly recommended). Please note that we do not accept papers in Plain TEX format. Text files can be also submitted as standard DOCUMENT (.DOC) which is acceptable if the submission in LATEX is not possible. For an initial submission, the authors are strongly advised to upload their entire manuscript, including tables and figures, as a single PDF file. Authors are strongly advised to submit the final version of the paper using the journal’s LATEX template.
For detailed information, please see Instruction for Authors.

EDITORIAL POLICY
Unpublished material: Submission of a manuscript implies that the work described is not copyrighted, published or submitted elsewhere, except in abstract form. The corresponding author should ensure that all authors approve the manuscript before its submission.
Ethical conduct of research: The authors must describe and confirm safeguards to meet ethical standards when applicable. See Editorial Policy for details.
Conflict of interest: When authors submit a manuscript, they are responsible for recognizing and disclosing financial and/or other conflicts of interest that might bias their work and/or could inappropriately influence his/her judgment. If no specified acknowledgement is given, the Editors assume that no conflict of interest exists. Authors are encouraged to fill in the ICMJE Conflicts of Interest Form (available here) and send it in the electronic format to the Managing Editor.
Copyright: All authors retain copyright, unless – due to their local circumstances – their work is not copyrighted.
The copyrights are governed by the Creative-Commons Attribution Only license (CC-BY) which is compliant with Plan-S. We may also publish a paper under CC-BY-NC-ND license on author's request. The corresponding author grants the journal the license to use of the article, by signing the License to Publish. Scanned copy of license should be sent to the journal, as soon as possible.
Data distribution: We encourage authors to make the data available for the public. We offer a possibility to publish additional files together with the article as Supplementary Materials. Data and other materials can be also stored using one of the repositories such as Open Science Framework (OSF, https://osf.io), preferably selected from those indexed on FAIRsharing.org or re3data.org.
Authorship: Authorship should be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study. All those who have made significant contributions should be listed as co-authors. Where there are others who have participated in certain substantive aspects of the research project, they should be named in an Acknowledgement section. The corresponding author should ensure that all appropriate co-authors (according to the above definition) and no inappropriate co-authors are included in the author list of the manuscript, and that all co-authors have seen and approved the final version of the paper and have agreed to its submission for publication.
Peer Review process: The Editors reserve the right to decline the submitted manuscript without review, if the studies reported are not sufficiently novel or important to merit publication in the journal. Manuscripts deemed unsuitable (insufficient originality or of limited interest to the target audience) are returned to the author(s) without review. The Editor seeks advice from experts in the appropriate field. Research articles and communications are refereed by a minimum of two reviewers, review papers by at least three. Authors are requested to suggest persons competent to review their manuscript. However, please note that this will be treated only as a suggestion, and the final selection of reviewers is exclusively the Editor's decision. The final decision of acceptance is made by Managing Editor or, in case of conflict, by the Editor-in-Chief.
Scientific Misconduct: This journal publishes only original manuscripts that are not also published or going to be published elsewhere. Multiple submissions/publications, or redundant publications (re-packaging in different words of data already published by the same authors) will be rejected. If they are detected only after publication, the journal reserves the right to publish a Retraction Note. In each particular case Editors will follow COPE’s Core Practices and implement the advices.

Editorial

Editor-in-Chief
Egon L. van den Broek, Department of Information and Computing Sciences, Utrecht University, The Netherlands

Senior Editors
Christos Anagnostopoulos, University of Glasgow, UK
Miguel Correia, Instituto Superior Técnico, Universidade de Lisboa, Portugal
George A. Papadopoulos, University of Cyprus, Cyprus

Editorial Contact
Deepan Selvaraj
Assistant Managing Editor, Compuscript
AssistantManagingEditor@degruyter.com

Editorial Advisory Board
Norman C. Beaulieu, University of Alberta, Canada
Erdal Cayirci, University of Stavanger, Norway
Liqun Chen, HP Labs, UK
John M. Cioffi, Stanford University, USA
Sudhir Dixit, HP Labs, India
Marco Dorigo, Université Libre de Bruxelles, Belgium
Alexander Egyed, Johannes Kepler University Linz, Austria
Georgios B. Giannakis, University of Minnesota, USA
Raj Jain, Washington University in St. Louis, USA
P. Vijay Kumar, Indian Institute of Science, India
Der-Tsai Lee, Academia Sinica, Taiwan
Jason Yi-Bing Lin, National Chiao Tung University, Taiwan
Shu Lin, University of California, Davis, USA
Charles E. Perkins, WiChorus Corporation, USA
Mauro Pezze, University of Lugano, Switzerland and University of Milano Bicocca, Italy
Arnold L. Rosenberg, Northeastern University and Colorado State University, USA
Uwe Schöning, University of Ulm, Germany
Jie Wu, Temple University, USA
Ronald R. Yager, Iona College, USA
Philip S. Yu, University of Illinois at Chicago, USA
Jun Zhang, University of Kentucky, USA

Editorial Board
Davide Ancona, University of Genova, Italy
Mehmet E. Aydin, University of the West of England (UWE Bristol), UK
Farshad Badie, Berlin School of Business & Innovation, Germany
Ladislav Beránek, University of South Bohemia, Czech Republic
Stephan Chalup, The University of Newcastle, Australia
Adrian Chifu, Aix-Marseille University, France
Felix J. Garcia Clemente, Universidad de Murcia, Spain
Riccardo Dondi, Universita degli Studi di Bergamo, Italy
Frank Drewes, Umeå University, Sweden
Adérito Fernandes-Marcos, Universidade Aberta, Portugal
João M. Fernandes, University of Minho, Portugal
Hugo Gamboa, NOVA University of Lisbon, Portugal
Angel Garrido, UNED, Spain
Juraj Giertl, Technical University of Kosice, Slovakia
Tomasz Górski, Polish Naval Academy, Poland
Goffredo Maria Haus, Università degli Studi di Milano, Italy
Florentina Hristea, University of Bucharest, Romania
Liangxiao Jiang, China University of Geosciences, China
Jiří Kléma, Czech Technical University, Republic of Czech
Josip Knezović, University of Zagreb, Croatia
Renato De Leone, Universita degli Studi di Camerino, Italy
Simon Lui, Singapore University of Technology and Design, Singapore
José Machado, University of Minho, Portugal
Francisco Martins, Universidade de Lisboa, Portugal
Imran Memon, Zhejiang University, China
Pedro Merino, Universidad de Málaga, Spain
Massimo Merro, University of Verona, Italy
Hugo Miranda, Universidade de Lisboa, Portugal
Hrvoje Mlinarić, University of Zagreb, Croatia
Gianpiero Monaco, University of L'Aquila, Italy
Ekaterina Muravleva, Skolkovo Institute of Science and Technology, Russia
Roberto Muscia, University of Trieste, Italy
Valerie Novitzká, Technical University of Kosice, Slovakia
Vitalii B. Novoseltsev, Russian Academy of Sciences, Russia
Mitsunori Ogihara, University of Miami, USA
Milos Oravec, Slovak University of Technology, Slovakia
Radek Ošlejšek, Masaryk University, Czech Republic
Francisco J. García-Peñalvo, University of Salamanca, Spain
Mikhail Posypkin, Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Russia
Łukasz Radliński, West Pomeranian University of Technology, Poland
Jorge A. Ruiz-Vanoye, Autonomous Popular University of Puebla State, Mexico
Michael Ruzhansky, Imperial College London, UK
Marek Rychly, Brno University of Technology, Czech Republic
Shaaban Sahmoud, Fatih Sultan Mehmet Vakif University, Istanbul, Turkey
Sukumar Senthilkumar, Universiti Sains Malaysia, Malaysia
Victor Sheng, University of Central Arkansas, USA
Theodore Simos, T.E., King Saud University, Saudi Arabia, Ural Federal University, Russian Federation, TEI of Sterea Hellas, Greece, Democritus University of Thrace, Greece
Jorge Sá Silva, University of Coimbra, Portugal
Frans van der Sluis, University of Copenhagen, Denmark
William Steingartner, Technical University of Kosice, Slovakia
Simon Tjoa, St. Pölten University of Applied Sciences, Austria
Dominika Tkaczyk, Crossref, Ireland
Angelo Trotta, University of Bologna, Italy
José Valente de Oliveira, Universidade do Algarve, Portugal
Jose Luis Vazquez-Poletti, Universidad Complutense de Madrid, Spain
Sebastian Ventura, University of Córdoba, Spain
Deepali Virmani, BPIT,GGSIPU, India
Thomas Canhao XU, Beijing Normal University-Hong Kong Baptist University United International College, China
Claudio Zandron, Università degli Studi di Milano-Bicocca, Italy
Erliang Zeng, University of Iowa, USA
Karl-Heinz Zimmermann, Hamburg University of Technology, Germany
Julius Žilinskas, Vilnius University, Lithuania
Hiba Zuhair, Al-Nahrain University, Iraq
Benyamin Ahmadnia, Tulane University, USA
Tai-Yu Lai, National Taipei University of Technology, Taiwan
Anna Formica, Consiglio　Nazionale delle Ricerche, Italy
Rashmi Agrawal, Manav　Rachna International Institute of Research and Studies, India
Jurriaan Hage, Utrecht University, Netherlands

Publisher
DE GRUYTER Poland
Bogumiła Zuga 32A Str.
01-811 Warsaw, Poland
T: +48 22 701 50 15

Managing Editor
Monika Maleszka
Monika.Maleszka@degruyter.com

(Deutsch)

Type: Journal
Language: English
Publisher: De Gruyter Open Access
First published: April 16, 2011
Publication Frequency: 1 Issue per Year
Audience: researchers in all subfields of computer science