A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map

Innovative self management system for guided cardiac rehabilitation

This paper describes the design and development of a system for cardio rehabilitation of patients that suffered a myocardial infarction. The proposed solution focuses on exercise prescriptions and the encouragement of healthy behaviors. The innovative strategy of the design takes into account health promotion models to provide safe, assistive exercise training sessions, personalized feedbacks, and educational contents.

Detecting false testimonies in reputation systems using self-organizing maps

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

The influence of transparency on self-evaluation as part of the university accreditation process in Peru.

In order to show the choice of transparency as the guiding principle of the accreditation process, the article evaluates its influence on the fundamental subprocess of self-evaluation, thereby confirming that transparency is an essential tool for continuous improvement of academic processes and those of educational quality management. It fosters educational innovation and permits the sustainability of the continuous accreditation process over time, resulting in greater probabilities of university self-regulation through systemization of the process, with the objective of continuous improvement of university degree programs. The article analyzes the influence of transparency on each activity of the self-evaluation process according to the Peruvian accreditation model prepared under the total quality approach, as a reference for other accreditation models, proposing concrete transparency actions and evaluating its influence on the stakeholder groups in the self-evaluation process, as well as on the efficiency and effectiveness of the process. It is concluded that transparency has a positive influence on the training of human capital and the formation of the university?s organizational culture, facilitating dissemination, understanding and involvement of the stakeholder groups in the continuous improvement of accreditation activities and increasing their acceptance of change and commitment to the process. It is confirmed that transparency contributes toward increasing the efficiency index of the self-evaluation process by reducing operating costs through adequate, accessible, timely contribution of information by the stakeholders and through the optimization of the time spent gathering relevant information. In addition, it is concluded that transparency contributes toward increasing the effectiveness index of self-evaluation by facilitating the achievement of its objectives through synthetic, useful, reliable interpretation of the education situation and the formulation of feasible improvement plans based on the adequacy, relevance, visibility, pertinence and truthfulness of the information analyzed.

Self perception of decision making competence in spanish football players

Background: It is known that competence to make decisions is a fundamental aspect of sport competition. Objective: This study has analyzed the decision profile of a sample of Spanish football players of different levels of expertise. Methods: 690 Spanish football players of national and international level completed the decision making questionnaire, which cover three dimensions ? perceived decision competence, decision anxiety and commitment with decision learning. MANCOVA and ANOVA analysis were carried out to analyse the differences in each dimension based on the level of expertise. Results: Results showed that perception of decision making competence increased and the anxiety decreased with the level of expertise. Conclusions: This study confirmed the usefulness of this questionnaire in the process of training for coaches and sport psychologists.

Self-perceptions of decision making competence in Spanish football players

Background: It is known that competence to make decisions is a fundamental aspect of sport competition. Objective: This study has analyzed the decision profile of a sample of Spanish football players of different levels of expertise. Methods: 690 Spanish football players of national and international level completed the decision mak- ing questionnaire, which cover three dimensions ? perceived decision competence, decision anxiety and commit- ment with decision learning. MANCOVA and ANOVA analysis were carried out to analyse the differences in each dimension based on the level of expertise. Results: Results showed that perception of decision making competence increased and the anxiety decreased with the level of expertise. Conclusions: This study confirmed the usefulness of this questionnaire in the process of training for coaches and sport psychologists.