Special issue: Building secure parallel and distributed networks and systems

Security and privacy have been the major concern when people build parallel and distributed networks and systems. While the attack systems have become more easy-to-use, sophisticated, and powerful, interest has greatly increased in the field of building more effective, intelligent, adaptive, active and high performance defense systems which are distributed and networked. This special issue focuses on the issues of building secure parallel and distributed networks and systems.

Special issue : securing distributed networks and systems

With decades of progress toward ubiquitous networks and systems, distributed computing systems have played an increasingly important role in the industry and society. However, not many distributed networks and systems are secure and reliable in the sense of defending against different attacks and tolerating failures automatically, thus guaranteeing properties such as performance, and offering security against intentional threats. This special issue focuses on securing distributed networks and systems.

Arakawa & Gins, a special issue of Inflexions

This issue of Inflexions was edited by Jondi Keane and Trish Glazebrook, with web design by Leslie Plumb.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Special issue : recent advances in soft computing : theories and applications

Soft Computing is an interdisciplinary area that encompasses a variety of computing paradigms. Examples of some popular soft computing paradigms include fuzzy computing, neural computing, evolutionary computing, and probabilistic computing. Soft computing paradigms, in general, aim to produce computing systems/machines that exhibit some useful properties, e.g. making inference with vague and/or ambiguous information, learning from noisy and/or incomplete data, adapting to changing environments, and reasoning with uncertainties. These properties are important for the systems/machines to be useful in assisting humans in our daily activities. Indeed, soft computing paradigms have been demonstrated to be capable of tackling a wide range of problems, e.g. optimization, decision making, information processing, pattern recognition, and intelligent data analysis. A number of papers pertaining to some recent advances in theoretical development and practical application of different soft computing paradigms are highlighted in this special issue.