AYNEC: All you need for evaluating completion techniques in knowledge graphs

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.Ministerio de Economía y Competitividad TIN2016-75394-

Effects of 4-Week training intervention with unknown loads on power output performance and throwing velocity in junior team handball players

PURPOSE:To compare the effect of 4-week unknown vs known loads strength training intervention on power output performance and throwing velocity in junior team handball players. METHODS:Twenty-eight junior team-handball players (17.2 ± 0.6 years, 1.79 ± 0.07 m, 75.6 ± 9.4 kg)were divided into two groups (unknown loads: UL; known loads: KL). Both groups performed two sessions weekly consisting of four sets of six repetitions of the bench press throw exercise, using the 30%, 50% and 70% of subjects' individual 1 repetition maximum (1RM). In each set, two repetitions with each load were performed, but the order of the loads was randomised. In the KL group, researchers told the subjects the load to mobilise prior each repetition, while in the UL group, researchers did not provide any information. Maximal dynamic strength (1RM bench press), power output (with 30, 50 and 70% of 1RM) and throwing velocity (7 m standing throw and 9 m jumping throw) were assessed pre- and post-training intervention. RESULTS:Both UL and KL group improved similarly their 1RM bench press as well as mean and peak power with all loads. There were significant improvements in power developed in all the early time intervals measured (150 ms) with the three loads (30, 50, 70% 1RM) in the UL group, while KL only improved with 30% 1RM (all the time intervals) and with 70% 1RM (at certain time intervals). Only the UL group improved throwing velocity in both standing (4.7%) and jumping (5.3%) throw (p > 0.05). CONCLUSIONS:The use of unknown loads has led to greater gains in power output in the early time intervals as well as to increases in throwing velocity compared with known loads. Therefore unknown loads are of significant practical use to increase both strength and in-field performance in a short period of training

Human Identities and Nation Building: Comparative Analysis, Markets, and the Modern University

The purpose of this article is to discuss the dilemma of the multi-university in sustainable education, research, and outreach by addressing some of the ways in which universities, must generate actions that seek to address these challenges, develop strategic relationships, and maximize their potential in the areas of teaching, research and service to society. Significantly, we examine how sustainability is experienced by nations—in our case Mexico—by analyzing higher education and its mission in developing citizens and economic sovereignty. The author’s goal is to establish a new paradigm by which practitioners and researchers can collaborate to produce the ideas that stimulate sustainable development

Observational Δν\Delta\nu-ρˉ\bar\rho relation for δ\delta Sct stars using eclipsing binaries and space photometry

Delta Scuti ($\delta$ Sct) stars are intermediate-mass pulsators, whose intrinsic oscillations have been studied for decades. However, modelling their pulsations remains a real theoretical challenge, thereby even hampering the precise determination of global stellar parameters. In this work, we used space photometry observations of eclipsing binaries with a $\delta$ Sct component to obtain reliable physical parameters and oscillation frequencies. Using that information, we derived an observational scaling relation between the stellar mean density and a frequency pattern in the oscillation spectrum. This pattern is analogous to the solar-like large separation but in the low order regime. We also show that this relation is independent of the rotation rate. These findings open the possibility of accurately characterizing this type of pulsator and validate the frequency pattern as a new observable for $\delta$ Sct stars.Comment: 11 pages, including 2 pages of appendix, 2 figures, 2 tables, accepted for publication in ApJ

CALA: Classifying Links Automatically based on their URL

Web page classification refers to the problem of automatically assigning a web page to one or moreclasses after analysing its features. Automated web page classifiers have many applications, and many re- searchers have proposed techniques and tools to perform web page classification. Unfortunately, the ex- isting tools have a number of drawbacks that makes them unappealing for real-world scenarios, namely:they require a previous extensive crawling, they are supervised, they need to download a page beforeclassifying it, or they are site-, language-, or domain-dependent. In this article, we propose CALA, a toolfor URL-based web page classification. The strongest features of our tool are that it does not require aprevious extensive crawling to achieve good classification results, it is unsupervised, it is based exclu- sively on URL features, which means that pages can be classified without downloading them, and it issite-, language-, and domain-independent, which makes it generally applicable. We have validated ourtool with 22 real-world web sites from multiple domains and languages, and our conclusion is that CALAis very effective and efficient in practice.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-