Los alumnos prefieren diferentes estrategias didácticas de la enseñanza de las ciencias en función de sus características motivacionales

We carried out empirical research into the relationship between students' motivational pattems and their preferences for different teaching procedures in science education. According to the results obtained, students tend to prefer those teaching procedures and strategies that allow their motivational needs to be met and reject those conflicting with them

Up to fifth-order Raman scattering of InP under nonresonant conditions

We present Raman spectra of InP measured under nonresonant conditions revealing multiphonon processes up to fifth order. Using an incident photon energy in the absorption region of the compound but far from any of its interband transitions, nonresonant multiphonon processes of order higher than two, which have not been reported so far in a zinc-blende-type semiconductor, have been observed in indium phosphide. In this way it has been possible to detect contributions not only from the longitudinal optical phonons but also from the transverse optical phonons in the higher-order peaks. We find a very good agreement between multiples of the TO- and LO-phonon frequencies at the zone center and the higher-order phonons measured in the experiments. The trend of strong intensity reductions observed when passing from first to second as well as from second to third order is not maintained when going from third to fourth, and from fourth to fifth order

SVMs for Automatic Speech Recognition: a Survey

Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact. During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed. These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research

An open source framework based on Kafka-ML for Distributed DNN inference over the Cloud-to-Things continuum

The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies higher transmission latency and bandwidth consumption. Moreover, it challenges the real-time monitoring of physical objects, e.g., the Internet of Things (IoT). Edge systems bring computing closer to end devices and support time-sensitive applications. However, Edge systems struggle with state-of-the-art Deep Neural Networks (DNN) due to computational resource limitations. This paper proposes a technology framework that combines the Edge-Cloud architecture concept with BranchyNet advantages to support fault-tolerant and low-latency AI predictions. The implementation and evaluation of this framework allow assessing the benefits of running Distributed DNN (DDNN) in the Cloud-to-Things continuum. Compared to a Cloud-only deployment, the results obtained show an improvement of 45.34% in the response time. Furthermore, this proposal presents an extension for Kafka-ML that reduces rigidness over the Cloud-to-Things continuum managing and deploying DDNN

Evolution of dwarf galaxies: characterizing star formation scenarios

This is an electronic version of the lecture presented at the IX Scientific Meeting of the Spanish Astronomical Society (SEA), held on September 13-17, 2010, in Madrid

On the generalised Chaplygin gas: worse than a big rip or quieter than a sudden singularity?

Although it has been believed that the models with generalised Chaplygin gas do not contain singularities, in a previous work we have studied how a big freeze could take place in some kinds of phantom generalised Chaplygin gas. In the present work, we study some types of generalised Chaplygin gas in order to show how different sorts of singularities could appears in such models, in the future or in the past. We point out that: (i) singularities may not be originated from the phantom nature of the fluid, and (ii) if initially the tension of the brane in a brane-world Chaplygin model is large enough then an infrared cut off appears in the past.Comment: 19 pages, 6 figures. Discussion expanded and references added. Version to appear in the International Journal of Modern Physics