International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

A Multi-scale View of the Emergent Complexity of Life: A Free-energy Proposal

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales

A general graphical user interface for automatic reliability modeling

Reported here is a general Graphical User Interface (GUI) for automatic reliability modeling of Processor Memory Switch (PMS) structures using a Markov model. This GUI is based on a hierarchy of windows. One window has graphical editing capabilities for specifying the system's communication structure, hierarchy, reconfiguration capabilities, and requirements. Other windows have field texts, popup menus, and buttons for specifying parameters and selecting actions. An example application of the GUI is given

Design of an electrochemical micromachining machine

Electrochemical micromachining (μECM) is a non-conventional machining process based on the phenomenon of electrolysis. μECM became an attractive area of research due to the fact that this process does not create any defective layer after machining and that there is a growing demand for better surface integrity on different micro applications including microfluidics systems, stress-free drilled holes in automotive and aerospace manufacturing with complex shapes, etc. This work presents the design of a next generation μECM machine for the automotive, aerospace, medical and metrology sectors. It has three axes of motion (X, Y, Z) and a spindle allowing the tool-electrode to rotate during machining. The linear slides for each axis use air bearings with linear DC brushless motors and 2-nm resolution encoders for ultra precise motion. The control system is based on the Power PMAC motion controller from Delta Tau. The electrolyte tank is located at the rear of the machine and allows the electrolyte to be changed quickly. This machine features two process control algorithms: fuzzy logic control and adaptive feed rate. A self-developed pulse generator has been mounted and interfaced with the machine and a wire ECM grinding device has been added. The pulse generator has the possibility to reverse the pulse polarity for on-line tool fabrication.The research reported in this paper is supported by the European Commission within the project “Minimizing Defects in Micro-Manufacturing Applications (MIDEMMA)” (FP7-2011-NMPICT- FoF-285614)