A number of guidelines for Psychosocial Risk Management in organizations have been proposed in recent decades; however, some reviews on the subject also highlights that the terms Stress and Psychosocial Risks (PRs) are not mentioned explicitly in most pieces of legislation, leading to lack of clarity on the terminology used. To improve the way of dealing with this type of vulnerability and to allow organizations to successfully manage PRs, this work proposes and characterizes a workable problem-solving method in which the PRs can be evaluated for the entropy they generate within the organization. The analysis and development of such a system is based on a series of logical formalisms for Knowledge Representation and Reasoning that are grounded on Logic Programming, complemented with an Artificial Neural Network approach to computing.

The identification, classification and recording of events leading to deterioration of wine quality is essential for developing appropriate strategies avoid them. This work introduces an adverse event reporting and learning system that can help preventing hazards and ensure the quality wines. The Eindhoven Classification Method (ECM) has been extended and adapted to the incidents of the wine industry. Logic Programming was used for Knowledge Representation and Reasoning (KRR) in order to model the universe of discourse, even in the presence of incomplete data, information or knowledge. On the other hand, the evolutionary process of the body of knowledge is to be understood as a process of devaluation, enabling the automatic extraction of knowledge and the generation of reports to identify the most relevant causes of errors lead to a poor wine quality. In addition, the answers to the problem are object of formal evidence through theorem proving.

Vulnerable Road Users (VRUs) are all those with an increased vulnerability on the road, in particular non-motorised ones. Until now, the emphasis has been in politics more focused on drivers, vehicles and infrastructures. However, recent developments show a shift in other directions, with researchers now devoting efforts to improve VRUs' safety. Hence, this work focuses on pedestrian walking and crossing behaviour, attitudes, motivations and habits, being grounded on an approach to Knowledge Representation and Reasoning centred on logic programming, which establishes a formal logical inference engine that is complemented with an Artificial Neural Network line to computation.

From simple entertainment gadgets to real world based applications, a little bit of everything is in a mobile platform. Thus, it's not a surprise that enterprise managers, decision makers or business analytics use it also to support their most regular daily business activities. Following the same fast adoption path of other applications, their analytical needs quickly appeared in their horizon as one of the most useful applications that they could bring in their “pockets”. The data structures of such applications are very large to be received by mobile platforms. Data cubes still are quite out from currently mobile platforms' data manipulation boundaries. However, a lot of efforts were developed during the last few years to provide smaller hyper cubes with the same exploitation potentialities, requiring less computational resources as disk storage and processing time, appealing to better data compression techniques or multidimensional views selection. In this paper we present the most usual techniques for compressing and processing data cubes. In order to support such approach we designed and developed the Pocket Cubes system, an application especially oriented to prepare data cubes to be used on mobile platforms.

Parallel perceptrons (PPs) are very simple and efficient committee machines (a single layer of perceptrons with threshold activation functions and binary outputs, and a majority voting decision scheme), which nevertheless behave as universal approximators. The parallel delta (P-Delta) rule is an effective training algorithm, which, following the ideas of statistical learning theory used by the support vector machine (SVM), raises its generalization ability by maximizing the difference between the perceptron activations for the training patterns and the activation threshold (which corresponds to the separating hyperplane). In this paper, we propose an analytical closed-form expression to calculate the PPs' weights for classification tasks. Our method, called Direct Parallel Perceptrons (DPPs), directly calculates (without iterations) the weights using the training patterns and their desired outputs, without any search or numeric function optimization. The calculated weights globally minimize an error function which simultaneously takes into account the training error and the classification margin. Given its analytical and noniterative nature, DPPs are computationally much more efficient than other related approaches (P-Delta and SVM), and its computational complexity is linear in the input dimensionality. Therefore, DPPs are very appealing, in terms of time complexity and memory consumption, and are very easy to use for high-dimensional classification tasks. On real benchmark datasets with two and multiple classes, DPPs are competitive with SVM and other approaches but they also allow online learning and, as opposed to most of them, have no tunable parameters.

In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or programs in an extension to the Logic Programming language, which makes possible to handle incomplete or even contradictory information in an evolutionary environment. Indeed, we present a new insight for the construction of evolutive systems that combines the potential of the knowledge representation and reasoning mechanisms, present in the logic programming languages. Here, in an evolutionary setting, the candidate solutions to model the universe of discourse are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried by those logical theories or programs. From a point of view of the process, the quality-of-information of the universe of discourse is assessed on the fly, being therefore possible to select the best logical theory or program that models it, in terms of the same time line.

We propose a method, called Direct Kernel Perceptron (DKP), to directly calculate the weights of a single perceptron using a closed-form expression which does not require any training stage. The weigths minimize a performance measure which simultaneously takes into account the training error and the classification margin of the perceptron. The ability to learn non-linearly separable problems is provided by a kernel mapping between the input and the hidden space. Using Gaussian kernels, DKP achieves better results than the standard Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) for a wide variety of benchmark two-class data sets. The computational cost of DKP linearly increases with the dimension of the input space and it is much lower than the corresponding to SVM.

The Parallel Perceptron (PP) is a simple neural network which has been shown to be a universal approximator, and it can be trained using the Parallel Delta (P-Delta) rule. This rule tries to maximize the distance between the perceptron activations and their decision hyperplanes in order to increase its generalization ability, following the principles of the Statistical Learning Theory. In this paper we propose a closed-form analytical expression to calculate, without iterations, the PP weights for classification tasks. The calculated weights globally optimize a cost function which takes simultaneously into account the training error and the perceptron margin, similarly to the P-Delta rule. Our approach, called Direct Parallel Perceptron (DPP) has a linear computational complexity in the number of inputs, being very interesting for high-dimensional problems. DPP is competitive with SVM and other approaches (included P-Delta) for two-class classification problems but, as opposed to most of them, the tunable parameters of DPP do not influence the results very much. Besides, the absence of an iterative training stage gives to DPP the ability of on-line learning.