Special Issue "Networks, Communication, and Computing"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 March 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Andras Farago

Department of Computer Science, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, P.O. Box 830688, MS-EC31 Richardson, TX 75083-0688, USA
Website | E-Mail
Interests: communication networks and their protocols; network design/analysis methods; algorithms; complexity

Special Issue Information

Dear Colleagues,

Networks, communication, and computing have become ubiquitous and inseparable parts of everyday life. This Special Issue is devoted to the exploration of the many-faceted relationship of these areas. We aim at exploring the current state-of-the-art of research in networks, communication, and computing, with particular interest to the interactions among these fields.
The topics of interest in this Special Issue covers the scope of the ICNCC 2016 Conference (http://www.icncc.org/index.html)
Extended versions of papers presented at ICNCC 2016 are sought, but this Special Issue is also open to all who want to contribute by submitting a research paper relevant to the area.
Topics of interest for submission include, but are not limited to:
•Coding Techniques
•Modeling and Simulation of Communication Systems
•Network Architecture and Protocol, Optical Fiber/Microwave Communication
•Satellite Communication
•Wired and Wireless Communication
•Wireless Sensor Networks and related topics
•Artificial Intelligence
•Computer Graphics and Virtual Reality
•Speech/Image Processing
•Data Mining Algorithms
•Distributed Computing
•Grid and Cloud Computing
•Software Architecture
•Bioinformatics
•Evolutionary Algorithms
•Software Engineering
•Ubiquitous Computing
•Semantic Web and related topics
•Case studies about the interactions of networks, communication and computing

Prof. Dr. Andras Farago
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Networks
  • Communication
  • Computing and algorithms
  • Computing applications
  • Modeling and simulation
  • Network architecture

Published Papers (6 papers)

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Research

Open AccessArticle Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization
Algorithms 2017, 10(2), 69; doi:10.3390/a10020069
Received: 31 March 2017 / Revised: 16 May 2017 / Accepted: 10 June 2017 / Published: 13 June 2017
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Abstract
Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to
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Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
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Open AccessArticle Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks
Algorithms 2017, 10(2), 63; doi:10.3390/a10020063
Received: 31 March 2017 / Revised: 12 May 2017 / Accepted: 25 May 2017 / Published: 30 May 2017
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Abstract
The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression,
[...] Read more.
The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
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Open AccessArticle A Flexible Pattern-Matching Algorithm for Network Intrusion Detection Systems Using Multi-Core Processors
Algorithms 2017, 10(2), 58; doi:10.3390/a10020058
Received: 15 March 2017 / Revised: 17 May 2017 / Accepted: 20 May 2017 / Published: 24 May 2017
Cited by 1 | PDF Full-text (1812 KB) | HTML Full-text | XML Full-text
Abstract
As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based
[...] Read more.
As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based NIDSs. In this paper, we describe our proposal for an efficient and flexible pattern-matching algorithm for inspecting packet payloads using a head-body finite automaton (HBFA). The proposed algorithm takes advantage of multi-core GPP parallelism and single-instruction multiple-data operations to achieve higher throughput compared to that resulting from traditional deterministic finite automata (DFA) using the Aho-Corasick algorithm. Whereas the head-body matching (HBM) algorithm is based on pre-defined DFA depth value, our HBFA algorithm is based on head size. Experimental results using Snort and ClamAV pattern sets indicate that the proposed algorithm achieves up to 58% higher throughput compared to its HBM counterpart. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
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Open AccessArticle An Asynchronous Message-Passing Distributed Algorithm for the Generalized Local Critical Section Problem
Algorithms 2017, 10(2), 38; doi:10.3390/a10020038
Received: 27 January 2017 / Revised: 14 March 2017 / Accepted: 22 March 2017 / Published: 24 March 2017
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Abstract
This paper discusses the generalized local version of critical section problems including mutual exclusion, mutual inclusion, k-mutual exclusion and l-mutual inclusion. When a pair of numbers (li, ki) is given for each process Pi,
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This paper discusses the generalized local version of critical section problems including mutual exclusion, mutual inclusion, k-mutual exclusion and l-mutual inclusion. When a pair of numbers (li, ki) is given for each process Pi, it is the problem of controlling the system in such a way that the number of processes that can execute their critical sections at a time is at least li and at most ki among its neighboring processes and Pi itself. We propose the first solution for the generalized local (li, |Ni| + 1)-critical section problem (i.e., the generalized local li-mutual inclusion problem). Additionally, we show the relationship between the generalized local (li, ki)-critical section problem and the generalized local (|Ni| + 1 − ki, |Ni| + 1 − li)-critical section problem. Finally, we propose the first solution for the generalized local (li, ki)-critical section problem for arbitrary (li, ki), where 0 ≤ li < ki + |Ni| + 1 for each process Pi. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
Open AccessArticle DNA Paired Fragment Assembly Using Graph Theory
Algorithms 2017, 10(2), 36; doi:10.3390/a10020036
Received: 26 January 2017 / Revised: 27 February 2017 / Accepted: 17 March 2017 / Published: 24 March 2017
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Abstract
DNA fragment assembly requirements have generated an important computational problem created by their structure and the volume of data. Therefore, it is important to develop algorithms able to produce high-quality information that use computer resources efficiently. Such an algorithm, using graph theory, is
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DNA fragment assembly requirements have generated an important computational problem created by their structure and the volume of data. Therefore, it is important to develop algorithms able to produce high-quality information that use computer resources efficiently. Such an algorithm, using graph theory, is introduced in the present article. We first determine the overlaps between DNA fragments, obtaining the edges of a directed graph; with this information, the next step is to construct an adjacency list with some particularities. Using the adjacency list, it is possible to obtain the DNA contigs (group of assembled fragments building a contiguous element) using graph theory. We performed a set of experiments on real DNA data and compared our results to those obtained with common assemblers (Edena and Velvet). Finally, we searched the contigs in the original genome, in our results and in those of Edena and Velvet. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
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Open AccessArticle Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
Algorithms 2017, 10(1), 16; doi:10.3390/a10010016
Received: 29 November 2016 / Revised: 5 January 2017 / Accepted: 11 January 2017 / Published: 18 January 2017
Cited by 3 | PDF Full-text (2302 KB) | HTML Full-text | XML Full-text
Abstract
Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing
[...] Read more.
Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method. Full article
(This article belongs to the Special Issue Networks, Communication, and Computing)
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