Special Issue "Computational Aspects Related to Unconventional, Bio-Inspired and Quantum Methods"

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 March 2017).

Special Issue Editors

Dr. Theodore Andronikos
E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu 49100, Greece
Interests: quantum computation and quantum automata; quantum programming languages; probabilistic automata and automata on infinite objects; dynamic scheduling algorithms for parallel and distributed heterogeneous systems; temporal logics for automated synthesis and verification of reactive systems; Internet Programming, Query languages for the web (XML, XPath)
Dr. Dimitrios Tsoumakos
E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu 49100, Greece
Interests: systemic and data-specific aspects of distributed; large-scale computing; optimization of multi-engine analytics; RDF graphs and graph workloads; distributed data compression; elasticity, profiling and resource scheduling; distributed data management; data management in cloud computing
Dr. Spyros Sioutas
E-Mail Website
Guest Editor
Department of Informatics, Ionian University, Greece
Interests: algorithmic data management; spatio-temporal database systems; distributed data structures and P2P overlays; cloud infrastructures; indexing; query processing and query optimization
Special Issues and Collections in MDPI journals
Dr. Ioannis Panagis
E-Mail Website
Guest Editor
Faculty of Law, University of Copenhagen, Studiestræde 6, 1455 København K, Denmark
Interests: web information systems; web-services; information retrieval; data structures; software engineering; social networks; natural language processing; text mining; big data
Dr. Konstantinos Giannakis
E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu 49100, Greece
Interests: automata theory; algorithms; quantum and unconventional computation; membrane computing; probabilistic models of computation and logic; SPARQL queries and finite automata; semantic web

Special Issue Information

Dear Colleagues,

During the last 20 years, bio-inspired computation models and mechanisms have been widely used, both in theory and practice, offering novel and interesting results. Natural Computing is a term with a twofold notion: It describes both nature-inspired computation methods and computational processes observed in natural environment. Besides its close connection with the branch of the Theory of Computation, Natural Computing deals with the development of algorithms and techniques inspired by actual natural processes.

The interdisciplinary concept of “Unconventional Computing” means the implementation of non-standard methods and models of the computational process. The research on alternative or unconventional computation methods is initiated mainly because of the apparent limits induced by the nature of the materials and the methods used in current computing technologies (see Moore's law). Therefore, various bio-inspired computing methods have already been proposed and studied, both in practice and theory. Hence, there is an ongoing effort to investigate the possibility of using such methods/materials for actual computation purposes, with promising and interesting results so far.

This Special Issue will try to shed light on problems related to the above concepts. It aims to gather works that will contribute to this emerging field. Hence, original and unpublished research articles on these topics are welcome. The main topics of the proposed Special Issue are the theoretical and practical aspect of unconventional and non-traditional computation methods and algorithms.

Dr. Theodore Andronikos
Dr. Dimitrios Tsoumakos
Dr. Spyros Sioutas
Dr. Ioannis Panagis
Dr. Konstantinos Giannakis
Guest Editors

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. Computation 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 1000 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

  • natural computing
  • quantum computing
  • quantum and quantum-inspired algorithms
  • molecular and DNA computing
  • chemical computing
  • molecular machines incorporating information processing
  • membrane computing
  • neural networks
  • evolutionary computing
  • computation graphs
  • computation on networks and graphs
  • computing based on dynamical systems
  • non-standard approaches
  • fuzzy computing
  • computation approaches going beyond the Turing model
  • application of non-standard computational methods
  • physics of computation
  • non-traditional approaches to classical problems
  • parallel computing
  • algorithms based on ant colonies, bees foraging etc.
  • logics of unconventional computing
  • physical limits to mechanical computation
  • unconventional methods in education and learning approaches

Published Papers (5 papers)

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Research

Open AccessArticle
Tensor-Based Semantically-Aware Topic Clustering of Biomedical Documents
Computation 2017, 5(3), 34; https://doi.org/10.3390/computation5030034 - 18 Jul 2017
Cited by 9
Abstract
Biomedicine is a pillar of the collective, scientific effort of human self-discovery, as well as a major source of humanistic data codified primarily in biomedical documents. Despite their rigid structure, maintaining and updating a considerably-sized collection of such documents is a task of [...] Read more.
Biomedicine is a pillar of the collective, scientific effort of human self-discovery, as well as a major source of humanistic data codified primarily in biomedical documents. Despite their rigid structure, maintaining and updating a considerably-sized collection of such documents is a task of overwhelming complexity mandating efficient information retrieval for the purpose of the integration of clustering schemes. The latter should work natively with inherently multidimensional data and higher order interdependencies. Additionally, past experience indicates that clustering should be semantically enhanced. Tensor algebra is the key to extending the current term-document model to more dimensions. In this article, an alternative keyword-term-document strategy, based on scientometric observations that keywords typically possess more expressive power than ordinary text terms, whose algorithmic cornerstones are third order tensors and MeSH ontological functions, is proposed. This strategy has been compared against a baseline using two different biomedical datasets, the TREC (Text REtrieval Conference) genomics benchmark and a large custom set of cognitive science articles from PubMed. Full article
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Open AccessArticle
Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience
Computation 2017, 5(3), 32; https://doi.org/10.3390/computation5030032 - 04 Jul 2017
Cited by 1
Abstract
Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections [...] Read more.
Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the “Connectosome”, and propose new venues of computational data structures based on a conceptual framework called “Grand Ensemble” which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming. Full article
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Open AccessArticle
Artificial Immune Classifier Based on ELLipsoidal Regions (AICELL)
Computation 2017, 5(2), 31; https://doi.org/10.3390/computation5020031 - 17 Jun 2017
Abstract
Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on [...] Read more.
Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes. Full article
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Open AccessArticle
Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition
Computation 2017, 5(2), 26; https://doi.org/10.3390/computation5020026 - 01 Jun 2017
Cited by 11
Abstract
Emotion recognition from speech may play a crucial role in many applications related to human–computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human’s emotions may [...] Read more.
Emotion recognition from speech may play a crucial role in many applications related to human–computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human’s emotions may be recognized using several modalities such as analyzing facial expressions, speech, physiological parameters (e.g., electroencephalograms, electrocardiograms) etc. However, measuring of these modalities may be difficult, obtrusive or require expensive hardware. In that context, speech may be the best alternative modality in many practical applications. In this work we present an approach that uses a Convolutional Neural Network (CNN) functioning as a visual feature extractor and trained using raw speech information. In contrast to traditional machine learning approaches, CNNs are responsible for identifying the important features of the input thus, making the need of hand-crafted feature engineering optional in many tasks. In this paper no extra features are required other than the spectrogram representations and hand-crafted features were only extracted for validation purposes of our method. Moreover, it does not require any linguistic model and is not specific to any particular language. We compare the proposed approach using cross-language datasets and demonstrate that it is able to provide superior results vs. traditional ones that use hand-crafted features. Full article
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Open AccessArticle
Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection
Computation 2017, 5(2), 20; https://doi.org/10.3390/computation5020020 - 03 Apr 2017
Cited by 1
Abstract
It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this [...] Read more.
It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this orientation, early pathway-based approaches coupled expression data with whole pathway interaction topologies but it was the recent approaches that zoomed into subpathways (local areas of the entire biological pathway) that provided more targeted and context-specific candidate disease biomarkers. Here, we explore the application potential of PerSubs, a graph-based algorithm which identifies differentially activated disease-specific subpathways. PerSubs is applicable both for microarray and RNA-Seq data and utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as reference for biological pathways. PerSubs operates in two stages: first, identifies differentially expressed genes (or uses any list of disease-related genes) and in second stage, treating each gene of the list as start point, it scans the pathway topology around to build meaningful subpathway topologies. Here, we apply PerSubs to investigate which pathways are perturbed towards mouse lung regeneration following H1N1 influenza infection. Full article
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