Abstract
Support Vector Machines (SVMs) have achieved a considerable attention due to their theoretical foundations and good empirical performance when compared to other learning algorithms in different applications. However, the SVM performance strongly depends on the adequate calibration of its parameters. In this work we proposed a hybrid multi-objective architecture which combines meta-learning (ML) with multi-objective particle swarm optimization algorithms for the SVM parameter selection problem. Given an input problem, the proposed architecture uses a ML technique to suggest an initial Pareto front of SVM configurations based on previous similar learning problems; the suggested Pareto front is then refined by a multi-objective optimization algorithm. In this combination, solutions provided by ML are possibly located in good regions in the search space. Hence, using a reduced number of successful candidates, the search process would converge faster and be less expensive. In the performed experiments, the proposed solution was compared to traditional multi-objective algorithms with random initialization, obtaining Pareto fronts with higher quality on a set of 100 classification problems.

Abstract
In this work we present a tabu search metaheuristic method for solving the permutation flow shop scheduling problem with sequence dependent setup times and the objective of minimising total weighted tardiness. The problem is well known for its practical applications and for the difficulty in obtaining good solutions. The tabu search method proposed is based on the insertion neighbourhood, and is characterised by the selection and evaluation of a small subset of this neighbourhood at each iteration; this has consequences both on diversification and intensification of the search. We also propose a speed-up technique based on book keeping information of the current solution, used for the evaluation of its neighbours. © 2014 Inderscience Enterprises Ltd.

Abstract

Abstract
Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.

Abstract
This paper describes a Hybrid User Centered Development Methodology (HUCDM). This is a simple, iterative and incremental development process that has as building blocks the principles of User Centered Design (UCD), specified in the International Organization for Standardization 9241-210. In its base lies the disciplined structure of development processes as well as practices and values from agile software development methods. The process consists of four main phases: planning, design, implementation and maintenance/operation. The prototyping and evaluation are carried out across the entire process. The HUCDM was implemented in an Educational Software Small and Medium Enterprise (SME) developer. The first feature based on this methodology was Courseware Sere. The quality of this educational resource has been internationally recognized. This Courseware was finalist in the national contest of multimedia products and thus got the interest of multinational companies such as BP - British Petroleum, which financed a new phase for the product development.

Abstract
Due to its availability and low cost, the use of wireless communication technologies increases in domains beyond the originally intended usage areas, e.g. M2M communication in industrial applications. Such industrial applications often have specific security requirements. Hence, it is important to understand the characteristics of such applications and evaluate the vulnerabilities bearing the highest risk in this context. We present a comprehensive overview of security issues and features in existing WLAN, NFC and ZigBee standards, investigating the usage characteristics of these standards in industrial environments. We apply standard risk assessment methods to identify vulnerabilities with the highest risk across multiple technologies. We present a threat catalogue, conclude in which direction new mitigation methods should progress and how security analysis methods should be extended to meet requirements in the M2M domain. © 2014 IEEE.