Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time.

The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry.

The journal publishes high-quality, refereed articles. It emphasizes a balanced coverage of both theory and practice. It fully acknowledges and vividly promotes a breadth of the discipline of Informations Sciences.

Topics include:

Foundations of Information Science:
Information Theory, Mathematical Linguistics, Automata Theory, Cognitive Science, Theories of Qualitative Behaviour, Artificial Intelligence, Computational Intelligence, Soft Computing, Semiotics, Computational Biology and Bio-informatics.

Implementations and Information Technology:
Intelligent Systems, Genetic Algorithms and Modelling, Fuzzy Logic and Approximate Reasoning, Artificial Neural Networks, Expert and Decision Support Systems, Learning and Evolutionary Computing, Expert and Decision Support Systems, Learning and Evolutionary Computing, Biometrics, Moleculoid Nanocomputing, Self-adaptation and Self-organisational Systems, Data Engineering, Data Fusion, Information and Knowledge, Adaptive ad Supervisory Control, Discrete Event Systems, Symbolic / Numeric and Statistical Techniques, Perceptions and Pattern Recognition, Design of Algorithms, Software Design, Computer Systems and Architecture Evaluations and Tools, Human-Computer Interface, Computer Communication Networks and Modelling and Computing with Words

Applications:
Manufacturing, Automation and Mobile Robots, Virtual Reality, Image Processing and Computer Vision Systems, Photonics Networks, Genomics and Bioinformatics, Brain Mapping, Language and Search Engine Design, User-friendly Man Machine Interface, Data Compression and Text Abstraction and Summarization, Virtual Reality, Finance and Economics Modelling and Optimisation

Special Issue on Membrane Computing
Submission Date: 2021-05-31

Aim and Scope

Membrane Computing is a computing paradigm, a branch of Natural Computing, inspired from the structure and functioning of living cells, and the organization of cells in tissues and other structures, including the brain. This computing paradigm, initially introduced by Gheorghe Păun in 1998, provides distributed parallel computing devices (called, generically, P systems) processing multisets of objects (symbols, strings, numbers) by rewriting or biologically inspired evolution rules. Membrane Computing contains elements of theoretical computer science as well as neural computation and nature-inspired optimisation and, thus, can be viewed as a contact point between automata, formal languages and bio-inspired computation.

As parallel and unconventional computing devices, P systems have proved to overcome the well-known limitations imposed by the conventional techniques based on sequential machines and von Neumann architecture. Besides, due to their generality, they are extremely versatile and can be used both as “classic” computing models, as well as a framework for modelling biological and non-biological processes, also in control and optimisation.

The bibliography of the domain is already very comprehensive, including more than 120 PhD theses, tens of collective volumes, monographs and contributions in renowned journals, with topics ranging from significant theoretical results to relevant real-life applications.

Themes

This special issue collects original research works about recent advances in Membrane Computing. With this special issue, we wish to promote Membrane Computing to the general Computer Science community. Hence, we expect the articles to be accessible to/understandable by non-specialists in Membrane Computing or in Theoretical Computer Science. Theoretical results, applications, and implementation aspects are welcome. The list of topics includes but is not limited to:

    New P system architectures
    Studies on computational power (that is the capability of producing the same string), computing efficiency and computational complexity of P systems
    P systems as number generators to solve optimisation problems
    P systems as neural systems to aid optimisation algorithms
    Applications of P systems to engineering problems
    Computational modelling based on Membrane Computing of biomollecular processes (micro-level) and real ecosystems (macro-level)

Submission Guidelines

The manuscripts should be prepared according to the "Guide for Authors" section of the journal found at: https://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-authors

and submission should be done through the journal's submission website:
https://www.editorialmanager.com/ins/default.aspx
by selecting "SI: MC" and also clearly indicating the full title of this special issue "Membrane Computing" in comments to the Editor-in-Chief.

Each submitted paper will be reviewed by expert reviewers. Submission of a paper will imply that it contains original unpublished work and is not being submitted for publication elsewhere.

Important dates

    Submission deadline: May 31st, 2021
    Notification of first round: August 31st, 2021
    Re-submission deadline: October 1st, 2021
    Final decision: November 30th, 2021

Guest Editors

Ferrante Neri
University of Nottingham, UK
ferrante.neri@nottingham.ac.uk

Special Issue on Recent Progress in Autonomous Machine Learning
Submission Date: 2021-07-01

Autonomous Machine Learning (AML) refers to a learning system having flexible characteristic to evolve both its network structure and parameters on the fly. It is capable of initiating its learning process from scratch with/without a predefined network structure while its knowledge base is automatically constructed in real-time. AML is built upon two fundamental principles: one-pass learning strategy and self-evolving network structure. The former one reflects a situation where a data point is directly discarded once learned to assure bounded memory and computational burdens while the latter lies in the self-reconfiguration aptitude of AML where its network size can increase or reduce in respect to varying data distributions. AMLs have been proven to be useful in handling real-time data streams where a learning system confronts never-ending information flow which does not follow static or predictable data distributions rather drifting overtime with different types, magnitudes and types. Variants of AMLs are capable of quickly reacting to those drifting distributions regardless of how slow, fast, sudden, gradual, cyclic changing distributions might be while retaining computationally light characteristics. In addition, the AMLs have grown into various application domains not only limited to regression, classification, clustering but also control and reinforcement learning. In a nutshell, it is enabled by the fact that AMLs aim to balance between stability and plasticity of a learning system.

Recent challenges in machine learning renders innovation of AMLs urgently needed. The advent of deep learning technologies is a concrete example. Existing DNNs mostly rely on a static and offline learning principle limiting its feasibility in the streaming environments. On the other hand, DNNs are well-known for its feature learning power being able to handle unstructured problems with large input dimension and target classes. The network structure of DNNs are difficult to evolve because of the absence of local and spatial contexts. The multi-layer nature of DNNs complicate the self-evolving strategy. Insertion of a new layer definitely leads to the catastrophic forgetting problem. Another research opportunity of AMLs is identified in the context of lifelong/continual learning where the goal is not only to adapt to changing environments but also to actualize a lifelong learning agent with knowledge retention property. That is, a learning agent must not suffer from the catastrophic forgetting problem when adapting to a new context. The fact that AMLs are normally designed in the local learning environment should be useful for this purpose. Only relevant knowledge is stimulated by new tasks while others remain silent. The application of AMLs in the transfer learning domain deserves in-depth study. Unlike traditional AML involving only a single stream, the case of multi-streams remains an open issue. The main goal of this problem is to create a domain-invariant network handling both source stream and target stream equally well. The challenge of this topic is evident in the covariate shift problem between source stream and target stream. As with the single stream case, the concept drift occurs here in each stream in different time periods.

This special issue aims to bring together recent research works of AMLs. Particular interest lies in the integration of AMLs in handling advanced issues of machine learning as abovementioned. We solicit original works that have not been published nor under consideration in other publication venues.

Topic of Interest

The topic of interest includes the following but not limited to

    Novel network architecture of AMLs
    AMLs to handle unstructured problems such as texts, videos, speech, etc.
    AMLs to handle weakly supervised learning problem.
    AMLs to handle semi-supervised learning problem.
    Active learning for AMLs.
    AMLs to handle continual learning problem.
    AMLs to handle multi-stream problem.

Important Dates

Manuscript Submission Deadline: July 1st, 2021
First Round of Reviews: September 30th, 2021
Revised Paper Submission: December 10th, 2022
Second Round of Reviews: February 1st, 2022
Expected Publication Date: May 1st, 2022

Submission Instruction

To be considered in this special issue, author should select VSI: Recent Progress in AML in the Elsevier editorial system.

Guest Editors

Mahardhika Pratama
email: mpratama@ntu.edu.sg
School of Computer Science and Engineering
Nanyang Technological University
Singapore
 
Edwin Lughofer
email: edwin.lughofer@jku.at
Department of Knowledge-Based Mathematical Systems
Johannes Keppler University
Austria
 
Plamen P. Angelov
email: p.angelov@lancaster.ac.uk
School of Computing and Communications
Lancaster University
UK