Evolution in the Design and Functionality of Rubrics: from “Square” Rubrics to “Federated” Rubrics

The assessment of learning remains one of the most controversial and challenging aspects for teachers. Among some recent technical solutions, methods and techniques like eRubrics emerge in an attempt to solve the situation. Understanding that all teaching contexts are different and there can be no single solution for all cases, specific measures are adapted to contexts where teachers receive support from institutions and communities of practice. This paper presents the evolution of the eRubric service [1] which started from a first experience with paper rubrics, and, with time and after several I+D+R [2] educational projects, has evolved thanks to the support of a community of practice [3] and the exchange of experiences between teachers and researchers. This paper shows the results and functionality of the eRubrics service up to the date of publicationa.) Project I+D+i EDU2010-15432: eRubric federated service for assessing university learning http://erubrica.uma.es/?page_id=434. b.) Centre for the Design of eRubrics. National Distance Education System -Sined- Mexico. [http://erubrica.uma.es/?page_id=389

La formación del directivo : evolución del entorno económico y la comunicación empresarial

El artículo destaca la importancia de la comunicación empresarial en los tiempos actuales como una herramienta indispensable para el fondo de comercio de las empresas y la buena imagen pública en un entorno altamente competitivo.Peer ReviewedPostprint (published version

Study of Video Annotations In External Practices Of University Learning

The digital video as code and learning technology has extensive scientific literature (Bartolome, 1997; Aguaded and Sánchez, 2008). However, the increase of digital video services on the Internet has facilitated and increased the use of video for education. With a recent important increase of videos as contained in the MOOC (Massive Open Online Course). This context has also created the expansion of educational practices based on models for collaborative learning and mediated by technology (Computer Supported Learning collaborative -CSCL-). The study of these practices is proving to be effective for teachers in service and initial training practices if it is analyzed collectively (Hosack, Br tools, 2010;. Picci, Calvani, & Bonaiuti, 2012; Etscheidt & Curran, 2012; Ingram , 2014). There is interest in literature reviews on the reflective capabilities with the use of video for initial teacher training (Orland-Barak & Rachamim, 2009; Rich and Hannafin 2009; Rich & Trip, 2011) to which we expand in (Wallet, Cebrian & Desenne, 2015). This work is part of a research project in progress [1] which aims to implement a federated portfolio model of multimedia evidences. This model uses a digital portfolio (from now on we will call ePortfolios) with three different federated tools (1. Digital rubric or eRubric, 2. Webquest and 3. Open Video Annotations -Ova-) created by our research and development group Gtea [2 ]. The OVA tool was created within the MOOC of edX in collaboration with Harvard University in 2013 [3]. So it, we need to create another standalone tool to design their own interface to use this tool in this project. This design was evaluated through user usability and satisfaction (Monedero, Cebrian & Desenne, 2015). This study focuses on the ease and functionality of the OVA tool so that students to collect evidence on their digital multimedia portfolios. Especially, analyzes the competences that students show when annotate video in order to explain their learning experiences and respond to the skills that are required in the eRubrics in different teaching contexts (external and laboratory practices).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. R+D+i project: Study of the Impact of federated eRubrics on the evaluation of external practices competences Plan Nacional de I + D + i de Excelencia (2014-2017) Ministerio de Economía y competitividad, nº EDU2013-41974-P web: http://goo.gl/CN6ID

Método híbrido para o cálculo dos custos de interrupções em processos eletro-eletrônicos causados por faltas em sistemas de distribuição de energia elétrica

Neste trabalho é apresentado um método híbrido para análise de riscos de interrupções em processos sensíveis provocadas por faltas nos sistemas de distribuição de energia elétrica. Com a utilização deste método, são avaliados índices relativos às interrupções de longa duração e às variações de tensão de curta duração (afundamentos e elevações) em cada consumidor da rede de distribuição de energia elétrica. O método permite, de forma inovadora, a obtenção, para cada ocorrência na rede, dos valores de magnitude da tensão e da duração do evento. Em cada barra do sistema, as freqüências de ocorrências de cada índice são obtidas e classificadas por faixas de magnitude e duração. O método parte de um conjunto de informações de configuração, de parâmetros da rede e dos dispositivos de proteção e, através de um conjunto de simulações aleatórias de curto-circuito, é possível mapear as áreas de risco relativas aos fenômenos de interrupção e variações de tensão de curta duração (VTCDs). Dispondo ainda do conjunto de curvas de sensibilidade dos processos industriais, avaliam-se as freqüências de disrupções de processos, esperadas por ano no ponto analisado, isto é, o número de vezes que um processo é interrompido pelos eventos de curta duração considerados, para então serem avaliados os custos anuais associados.This paper shows a new hybrid method for risk assessment regarding interruptions in sensitive processes due to faults in electric power distribution systems. This method determines indices related to long duration interruptions and short duration voltage variations, such as voltage sags and swells in each customer supplied by the distribution network. Frequency of such occurrences and their impact on customer processes are determined for each bus and classified according to their corresponding magnitude and duration. The method bases on information regarding network configuration, system parameters and protective devices and randomly generates a number of fault scenarios to assess risk areas regarding long duration interruptions and voltage sags and swells. Based on process sensitivity curves, the method determines frequency indices regarding disruption in customer processes that represent possible process interruptions due to the considered short duration events to eventually determine the associated annual costs

Evaluation of Clustering Algorithms on HPC Platforms

[EN] Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson-Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and by the Spanish "Agencia Estatal de Investigacion" under grant PID2020-112827GB-I00 /AEI/ 10.13039/501100011033, and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Cebrian, JM.; Imbernón, B.; Soto, J.; Cecilia-Canales, JM. (2021). Evaluation of Clustering Algorithms on HPC Platforms. Mathematics. 9(17):1-20. https://doi.org/10.3390/math917215612091

Por el Ilustrissimo y Excelentísimo Señor Don Fray Juan Cebrian Arzobispo de Zaragoza, del Consejo de Estado de su Magestad : tratase fundar que como a ordinario eclesiastico en su Diocesi [sic], le pertenece privativamente mandar a los Capitulares que reciban el orden annexo a sus beneficios ...

Precede al texto en p. 1 viñeta con la inscripción: "Jesus, Maria, Joseph"Errores en la paginación sin afectar al computo finalCopia digital : Diputación Provincial de Zaragoza. Servicio de Archivos y Bibliotecas, 2010Datos de tít. tomados de p. 1Ejemplar sin pie de imprenta, al final del texto aparece: "Zaragoça ... 1655"Sign.: A-M\p2\sInic. grab

High-throughput fuzzy clustering on heterogeneous architectures

[EN] The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data'' because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40x compared to the sequential counterpart version.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE), RTI2018-096384-B-I00, RTI2018-098156-B-C53 and RTC-2017-6389-5.Cebrian, JM.; Imbernón, B.; Soto, J.; García, JM.; Cecilia-Canales, JM. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems. 106:401-411. https://doi.org/10.1016/j.future.2020.01.022S401411106Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530(7589), 144-147. doi:10.1038/530144aCecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Stephens, N., Biles, S., Boettcher, M., Eapen, J., Eyole, M., Gabrielli, G., … Walker, P. (2017). The ARM Scalable Vector Extension. IEEE Micro, 37(2), 26-39. doi:10.1109/mm.2017.35Wright, S. A. (2019). Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. Future Generation Computer Systems, 92, 900-902. doi:10.1016/j.future.2018.11.020Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504Lee, J., Hong, B., Jung, S., & Chang, V. (2018). Clustering learning model of CCTV image pattern for producing road hazard meteorological information. Future Generation Computer Systems, 86, 1338-1350. doi:10.1016/j.future.2018.03.022Pérez-Garrido, A., Girón-Rodríguez, F., Bueno-Crespo, A., Soto, J., Pérez-Sánchez, H., & Helguera, A. M. (2017). Fuzzy clustering as rational partition method for QSAR. Chemometrics and Intelligent Laboratory Systems, 166, 1-6. doi:10.1016/j.chemolab.2017.04.006H.S. Nagesh, S. Goil, A. Choudhary, A scalable parallel subspace clustering algorithm for massive data sets, in: Proceedings 2000 International Conference on Parallel Processing, 2000, pp. 477–484.Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Havens, T. C., Bezdek, J. C., Leckie, C., Hall, L. O., & Palaniswami, M. (2012). Fuzzy c-Means Algorithms for Very Large Data. IEEE Transactions on Fuzzy Systems, 20(6), 1130-1146. doi:10.1109/tfuzz.2012.2201485Flores-Sintas, A., Cadenas, J., & Martin, F. (1998). A local geometrical properties application to fuzzy clustering. Fuzzy Sets and Systems, 100(1-3), 245-256. doi:10.1016/s0165-0114(97)00038-9Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Flores-Sintas, A., M. Cadenas, J., & Martin, F. (2001). Detecting homogeneous groups in clustering using the Euclidean distance. Fuzzy Sets and Systems, 120(2), 213-225. doi:10.1016/s0165-0114(99)00110-4Wang, H., Potluri, S., Luo, M., Singh, A. K., Sur, S., & Panda, D. K. (2011). MVAPICH2-GPU: optimized GPU to GPU communication for InfiniBand clusters. Computer Science - Research and Development, 26(3-4), 257-266. doi:10.1007/s00450-011-0171-3Kaltofen, E., & Villard, G. (2005). On the complexity of computing determinants. computational complexity, 13(3-4), 91-130. doi:10.1007/s00037-004-0185-3Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. doi:10.1007/bf02289588Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681. doi:10.1016/j.neucom.2017.06.053Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. 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A robust protocol for in vivo THz skin measurements

This work presents an experimental setup to control the way in which pressure interferes with the repeatability of in vivo THz skin measurements. By integrating a pressure sensor circuit into our THz system, it is possible to identify which measurements were taken within a previously specified pressure range. The live response of the pressure sensor helps to acquire data within the desired pressure leading to greater consistency of data between measurements. Additionally, a protocol is proposed to help achieve repeatable results and to remove the effects of the natural variation of the skin through the course of the day. This technique has been shown to be able to quantify the changes induced in the skin following the application of a moisturising skin product and shows the measured result to be significantly different from natural skin variation. This research therefore prepares the way for further studies on the effectiveness of different skin products using in vivo THz measurements

Slanted Stixels: A way to represent steep streets

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a Fully Convolutional Network (FCN), which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.Comment: Journal preprint (published in IJCV 2019: https://link.springer.com/article/10.1007/s11263-019-01226-9). arXiv admin note: text overlap with arXiv:1707.0539

On the relationship between subjective and objective measures of virtual reality experiences : a case study of a serious game

In this paper we present a Virtual Reality game related to Cultural Heritage. We contribute with an analysis of subjective measures taken from questionnaires filled by users after the VR experience, and objective measures taken from logs during the VR game. Specifically, we were interested on study data globally and in groups of user behaviour. Analysing data globally we see a high value of users’ subjective perceptions. Nevertheless, we found differences of subjective measures when splitting the Novice group. Specifically, the subjective perception of Strugglers is considerably lower than the rest of groups, and this difference is significant. Then, we propose strategies to provide a better experience to Strugglers. We also found correlations between objective and subjective data when they were analysed globally (i.e. without using groups), but these measures did not correlate when they were analysed using behaviour groups