In recent years, the interest of the scientific community in perforated plates for ballistic protection has increased. Perforated plates do not represent protection by themselves, rather, they are used in the armour systems of armoured vehicles, in conjunction with base armour, since they are intended to induce bend stresses, where a penetrating core fracture occurs. The fragments are subsequently stopped by base armoured vehicle armour. Although for the first time used several decades ago, perforated plates are found to be attractive even today. The main reason is the combination of very convenient properties. Besides high mass effectiveness, they possess a high multi-impact resistance, since their perforations arrest cracks. Therefore, a relatively wide array of materials is suitable for perforated plate fabrication, ranging from alloy steel to some types of cast iron. Being made of metallic materials, raw material costs are relatively low compared to ceramics or composite materials, making them very attractive for present and future armoured vehicles. Finally, armour system consisting of a perforated plate and base plate at some distance, reduce the effectiveness of both shaped charge jets and act as blast mitigators.

A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.

Data Mining extrapolates patterns drawing conclusions from data. Outliers detection identifies those objects that fall some standard deviations away from the mean and is an important tool of commercial data mining. Characterizing the manner of outliers can lead to new knowledge, such as the manner of fraudulent transactions. However, outliers may represent meaningless aberrations and hence there is no rigid mathematical or statistical definition of what constitutes an outlier, and, in many scenarios, determination of the outlier is ultimately a subjective exercise. Standard deviation is a central actor in outlier detection and yet exhibits sensitivity to values and can be distorted, inflated, by a single or even a few observations of borderline and extreme values. It can mask the situation where less extreme outliers or anomalies go undetected because of the existence of the most extreme outliers. This study proposes a novel outlier identification model using an enhanced normal distribution method. The model can explore different types of outliers giving an end-user the ability to fully or partially eliminate outliers found in a retail point of sale (POS) dataset. Experiments revealed that the enhanced normal distribution method appeared more accurate than the standard normal distribution method, and results were also evaluated subjectively by the client, who found most of the outliers to be truly outliers and some representing potentially fraudulent transactions.

An image hiding scheme in a 2-dimensional coupled map lattice of matrices is presented in this paper. Scalar variables at each node of the lattice are replaced by nilpotent matrices. The spatiotemporal divergence process is employed to hide the secret digital image in the state map of the nodal variables. The presented image hiding scheme does not require the computation of the difference image between two patterns produced by the perturbed and the unperturbed initial conditions. Computational experiments are used to demonstrate the efficacy of the presented technique. (C) 2019 Elsevier Ltd. All rights reserved.

For the fabrication of reciprocating engine cylinder heads we touch upon relevant mechanical properties and comparisons between the use of two aluminium alloys - A: 9 % Si, 1 % Cu and B: 7 % Si, 3 % Cu. Manufacturing costs are reduced by means of a lowered Si content, particularly with respect to machining, but also this makes the material less dependent on those critical raw materials of importance to the European Union. Our study tests these mechanical properties: ultimate tensile strength, proof strength elongation and modulus of elasticity. It also carries out fatigue tests by the stair-case method. Microstructure is found to correlate to the mechanical properties. Even when tensile properties of alloy A are slightly higher, the fatigue performance at elevated temperatures makes alloy B more attractive for this application owing to: longer life expectancy of the part; lower machining costs; and lower quantities of the required critical raw material.

A deceptive problem with known analytical solution is introduced. Arguably its solution search landscape is such that heuristic methods will find it difficult to search for the solution. The problem is tunable offering a test bed by which to examine the performance of different methods of heuristic and evolutionary search.

Nature likely implements modularization in multicellular developmental biology using the chemical context of the cell, cell division generational distance, and genetic triggers. Inspired in this, Evomorph is a proposed heuristic method of Artificial Intelligence that pairs these concepts with Evolutionary Computation. It is described here as a flexible template matching for object detection in Machine Vision.

Deep Learning and other Artificial Neural Network based solutions are rarely transparent, and white-box solutions are often called for. This paper explains how Multi-run Subtree Encapsulation can provide equivalent white box solutions to facilitate Explainable Artificial Intelligence.

Short time series prediction technique based on Bernstein polynomials is presented in this paper. Firstly, the straightforward Bernstein polynomial extrapolation scheme is improved by extending the interval of approximation. Secondly, the forecasting scheme is designed in the evolutionary computational setup which is based on the conciliation between the coarseness of the algebraic prediction and the smoothness of the time average prediction. Computational experiments with the test time series suggest that this time series prediction technique could be applicable for various forecasting applications.

This novel algorithm uses standard Genetic Programming (GP) to evolve a grammar. It is applied to the automatic parallelization of sequential software. Alternative parallel schedules are generated for a computational resource constrained illustrative example demonstrating the power of the methodology.

A novel algorithm uses standard Genetic Programming (GP) to evolve an Attribute Grammar (AG) and this is tested on a problem with known solution in automatic code parallelization. Standard GP first generates a vector of real numbers and its elements are in turn applied to the grammar. As the parse tree is being produced the choices in the grammar depend on the attributes being input to the current node of the parse tree. Experiments reveal different levels of success at finding solutions to different versions of the test problem. It is speculated that the novel method may find a role in computational medicine in stem cell research and in the modelling of epigenetic disease.

This book constitutes the refereed proceedings of the 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011, held in Daejeon, Korea, in September 2011. The 94 revis

The reliance of Evolutionary Algorithms on haploid genotypes has proved a difficult area for non-stationary function optimization. While it is generally accepted that various approaches involving diploidy can better cope with these kinds of problems, none of these paradigms have gained wide acceptance in the GA community. We describe Shades, a new haploid system which uses Polygenic Inheritance. Polygenic inheritance differs from most implementations of GAs in that several genes contribute to each phenotypic trait. A Knapsack non-stationary function optimization problems from the literature is described, and it is shown how Shades outperforms diploidy for this task.

A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.

The paper is an engineering exposition of the Stochastic Interpolation Framework, a novel mathematical approach to data regularization, which recovers a function from input data that is a representation of this data. The framework is an area-based method that comprises a two-step procedure: de-convolution and convolution, involving row-stochastic matrices. Varying the extent of convolution with respect to de-convolution in the framework obtains a gamut of functional recovery ranging from interpolation to approximation, to peak sharpening. Construction of the row stochastic matrices is achieved by means of a mollifier, a positive function which serves as the generator of the row space of these matrices. The properties of the recovered function will depend on the choice of this mollifier. For example, only if the mollifier is differentiable so is the recovered function, and the framework can obtain derivatives anywhere in the domain. The mollifier can be a probability distribution function. Thus, the framework connects interpolation to statistical analysis. Two novel applications in image analysis illustrate the potential of the framework for security applications: as an alternative method of lossy image compression, and as an alternative method to zoom-up an image.

The mesh refinement decisions of an experienced user of high-velocity impact numerical approximation finite differences computations are discovered as a set of comprehensible rules by means of Genetic Programming. These rules that could automatically trigger adaptive mesh refinement to mimic the expert user, detect mesh cells that require refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations are investigated in order to identify the optimal ones for indicating mesh refinement. A high-velocity impact phenomena example of a tungsten ball that strikes a steel plate illustrates this methodology.

This Genetic Programming based tool simulates activities and resource allocations in the Program (Project) Evaluation and Review Technique method of project control. Users constrain the optimization problem by means of a visual interface and Genetic Programming discovers a umber of acceptable solutions that satisfy the user constraints. It evolves computer programs that, when executed, produce a variable length vector of real numbers. This vector is then interpreted according to the grammar that abides by the user constraints. The tool has a wide application in the management of large and complex projects as it handles the a priori simulation of events that may delay or compromise the project, and enables the project owners and project managers to come up with robust and innovative contingency measures to decrease the likelihood of project failure before project start-up.

This special issue is based on the 2007 ECSIS Symposium on Bio-inspired Learning and Intelligent Systems for Security (BLISS-07) that was held in Edinburgh, Scotland, UK. That successful symposium emphasized reliable, versatile, and intelligent systems employed by a broad range of security applications. The goal was to integrate developers of intelligent systems with those who use them in security applications, including project managers, system integrators, and end users. As here used intelligent systems denote those artificial computational systems that operate in part or fully autonomously and that display behavior that if it were to be observed in animals, would normally become associated with intelligence of one sort or another. Systems with different degrees of autonomy of operation benefit greatly from incorporating aspects and mechanisms that are found in a broad range of biological systems, from survivability and adaptation of the simple living structures to learning, creativity, cognition and various forms of intelligence that are normally associated with humans. These features are often incorporated into algorithms by mimicking the biological processes that provide the inspiration. Such intelligent systems have been applied to a wealth of practical problems, including those in security. Examples of such applications discussed at the symposium include the detection and prevention of cybercrimes and identity theft, internet security, security of financial systems, security of public transportation systems, emergency response systems, combining space-based systems with geographical information systems, etc. A subset of selected papers presented at BLISS-07 have been chosen for this special issue of Journal of Soft Computing. These papers deal with a broad range of applications relevant to security including Voice over Internet Protocol (VOIP), identifying faces, image analysis, determining the source of an unknown airborne contaminant release, encryption for communication, among others. Two additional papers were contributed from a similar special issue being edited by Dr. Daniel Howard. All papers contained herein have relevance for security in either the civilian or defense arenas. We thank the authors who have all taken considerable effort to expound on their work for permanent archival in this journal. We similarly thank the reviewers who have facilitated this process, enhancing the quality of this special issue. We especially thank the editors of Journal of Soft Computing, particularly Brunella Gerla, for patience as we worked to make this special issue a reality. We hope the reader will capture a piece of the excitement and collegiality present in Edinburgh for the BLISS-07 conference as preserved in these papers. S. E. Haupt (&) Applied Research Laboratory, The Pennysylvania State University, State College, PA, USA e-mail: seh19@psu.edu