Mixture-based probabilistic graphical models for the partial label ranking problem

The Label Ranking problem consists in learning preference models from training datasets labeled with a ranking of class labels, and the goal is to predict a ranking for a given unlabeled instance. In this work, we focus on the particular case where both, the training dataset and the prediction given as output allow tied labels (i.e., there is no particular preference among them), known as the Partial Label Ranking problem. In particular, we propose probabilistic graphical models to solve this problem. As far as we know, there is no probability distribution to model rankings with ties, so we transform the rankings into discrete variables to represent the precedence relations (precedes, ties and succeeds) among pair of class labels (multinomial distribution). In this proposal, we use a Bayesian network with Naive Bayes structure and a hidden variable as root to collect the interactions among the different variables (predictive and target). The inference works as follows. First, we obtain the posterior-probability for each pair of class labels, and then we input these probabilities to the pair order matrix used to solve the corresponding rank aggregation problem. The experimental evaluation shows that our proposals are competitive (in accuracy) with the state-of-the-art Instance Based Partial Label Ranking (nearest neighbors paradigm) and Partial Label Ranking Trees (decision tree induction) algorithms

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements

The effect of countries’ health and environmental conditions on restaurant reputation

Corporate reputation enhancement in the restaurant industry has recently been increasingly driven by the central importance of consumer review websites and customers’ greater awareness of sustainable practices regarding health and the environment. In this context, the research question of the present study was if there is a relationship between health and environmental conditions, and restaurants’ corporate reputation on a country level. Trying to answer this question, the present study sought to analyze the e ects of countries’ health and environmental conditions on their restaurants’ corporate reputation, thereby contributing to the existing knowledge about how sustainable environments influence the industry’s competitiveness. The research design included di erent methodological approaches, and was divided into three main phases: restaurant corporation identification, reputation database design, and results. To this end, reputation data from a consumer review website were gathered for a sample of restaurant corporations and establishments connected to the European countries on the Healthiest Country Index.The methods were based on regression analysis. The results indicate that restaurant reputation improves in healthy, sustainable environments, specifically in countries ranked as the healthiest. These findings provide a better understanding of how aspects related to health and environmental sustainability influence corporate reputation.FCT: UIDB/04020/2020;info:eu-repo/semantics/publishedVersio

Diseño y comercialización de uniformes de seguridad industrial para los corteros de caña de azúcar y accesorios de protección

Este artículo quiere señalar una de las caras del Diseño de Modas en un campo poco explotado, y en un ambiente donde por razones culturales no siempre se tienen en cuenta las tendencias de la moda. En la búsqueda de temas de investigación se encontró que con los trabajadores de caña de azúcar, especialmente con los corteros, se podría aportar en el diseño de su ropa de trabajo, con el fin de mejorar sus condiciones físicas y ambientales. Se visitaron trapiches e ingenios para conocer las condiciones laborales, los vestuarios y accesorios; las diseñadoras luego de desplazarse a campo y vestirse como los corteros, tomaron sus herramientas, y una vez terminada la investigación diseñaron 6 trajes especiales con accesoriosde seguridad para estos trabajadores, que incrementan la protección contra agentes naturales y posibles accidentes. El resultado fue un diseño seguro, cómodo y estético, que incorpora la imagen de hombres de campo. Queda demostrado que el diseño de modas no solamente se aplica a cuerpos armoniosos o de pasarela, sino que es para todos, no sólo por la necesidad primaria de vestir, sino por un sentido estético y de protección

Desarrollo de un sistema de toma de decisiones autónomo y ejemplo de aplicación a servicios de seguridad bajo demanda

Existen multitud de sistemas de adquisición de datos autónomos basados en diferentes metodologías, muchos de ellos se imparten en la educación universitaria y otros por el contrario surgen de un exhaustivo trabajo de investigación científica. Estos sistemas se aplican hoy en día en un gran número de áreas, siendo todas ellas muy diversas aunque con importantes nexos comunes. En el presente artículo se propone, a partir de un proyecto fin de carrera, un sistema autónomo de adquisición de datos y reconocimiento del entorno, con un alto índice de escalabilidad e integración a nuevos espacios. Para probar su eficacia se propone también una aplicación robotizada que utiliza estas características en el área de la seguridad, un campo en el que resultan especialmente claras las propiedades diferenciadoras del método propuesto

Improvement of cardiometabolic markers after fish oil intervention in young Mexican adults and the role of PPARα L162V and PPARγ2 P12A

Polyunsaturated fatty acids (PUFA) contained in fish oil (FO) are ligands for peroxisome proliferator-activated receptors (PPAR) that may induce changes in cardiometabolic markers. Variation in PPAR genes may influence the beneficial responses linked to FO supplementation in young adults. The study aimed to analyze the effect of FO supplementation on glucose metabolism, circulating lipids and inflammation according to PPARα L162V and PPARγ2 P12A genotypes in young Mexican adults. 191 young, non-smoking subjects between 18 and 40 years were included in a one-arm study. Participants were supplemented with 2.7 g/day of EPA+DHA, during six weeks. Dietary analysis, body composition measurements and indicators for glucose metabolism, circulating lipids, and markers for inflammation were analyzed before and after intervention. An overall decrease in triglycerides (TG) and an increase in HS-ω3 index were observed in all subjects [-4.1 mg/dL, (SD:±51.7), P=.02 and 2.6%, (SD:±1.2), P\u3c.001 respectively]. Mean fasting insulin and glycated hemoglobin (HbA1c%) were significantly decreased in all subjects [-0.547mlU/L, (SD:±10.29), P=.034 and-0.07%, (SD:±0.3), P\u3c.001 respectively], whereas there was no change in body composition, fasting glucose, adiponectin and inflammatory markers. Subjects carrying the minor alleles of PPARα L162V and PPARγ2 P12A had higher responses in reduction of TG and fasting insulin respectively. Interestingly, doses below 2.7 g/day (1.8 g/day) were sufficient to induce a significant reduction in fasting insulin and HbA1c% from baseline (P=.019 and P\u3c.001). The observed responses in triglycerides and fasting insulin in the Mexican population give further evidence of the importance of FO supplementation in young people as an early step towards the prevention of cardiometabolic disease. Trial registration: ClinicalTrials.gov NCT02296385

Simuladores de Planificadores de Sistemas en Tiempo Real

En este artículo se presenta un simulador desarrollado que permite ejecutar diferentes planificadores de Tiempo Real, como el algoritmo de planificación cíclica, Algoritmo de la Razón Monótona (RMA) y EDF (Earliest Deadline First) para un conjunto de procesos con unos datos dados y muestra los resultados obtenidos. Mediante este simulador se facilita a los alumnos el aprendizaje de los algoritmos de planificación.This paper presents a simulator that has been developed to allow the execution of scheduling algorithms such as the Cyclic Non­preemptive Executive, Rate­monotonic scheduling (RMS) and Earliest Deadline First (EDF) for a given set of processes with different values and the simulator displays the results. With this simulator, students are able to learn about scheduling algorithms.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadores; Vicerrectorado para la Garantía de la Calidad

MALDI Profiling of Human Lung Cancer Subtypes

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer