Aplicando la metodología flipped-teaching en el Grado de Ingeniería Informática: una experiencia práctica

En el Grado de Ingeniería Informática de la Universitat Politècnica de València se llevó a cabo durante el curso 2014-15 una experiencia piloto de aplicación de la metodología Flipped-Teaching en todas las asigna-turas obligatorias de segundo curso del grado. La metodología Flipped-Teaching (o clase inversa) consiste en invertir el modelo tradicional de docencia, de modo que la lección magistral habitual de aula se sustituye por un conjunto de materiales en línea (vídeos, lecturas, etc.) que el alumno debe revisar previa a su asistencia a clase. Por su parte, las sesiones de aula se transforman en sesiones fundamental-mente prácticas, con actividades individuales o en grupo, pensadas principalmente para la resolución de ejercicios y problemas, la aclaración de dudas y la discusión sobre aspectos relevantes. En este trabajo se presenta la organización de la docencia, los métodos utilizados, así como la evaluación de la experiencia y los resultados obtenidos para una de las asignaturas del Grado de Ingeniería Informática en las que se aplicó esta metodología, en concreto "Concurrencia y Sistemas Distribuidos". La metodología Flipped-Teaching nos ha permitido aumentar la motivación y participación de los estudiantes así como mejorar su proceso de autoaprendizaje. La motivación de los alumnos ha sido enorme, reflejándose claramente tanto en su participación activa en la clase como por los buenos resultados de evaluación obtenidos

Empowering users regarding the sensitivity of their data in social networks through nudge mechanisms

The use of online social networks (OSNs) is a continuous trade-off between relinquishing some privacy in exchange for getting some social benefits like maintaining (or creating new) relationships, getting support, influencing others’ opinions, etc. OSN users are faced with this decision each time they share information. The amount of information or its sensitivity is directly related to the amount of users’ loss of privacy. Currently, there are several approaches for assessing the sensitivity of the information based on the willingness of users to provide them, the monetary benefits derived from extracting knowledge of them, the amount of information they provide, etc. In this work, we focus on quantifying data sensitivity as the combination of all of the approaches and adapting them to the OSN domain. Furthermore, we propose a way of scoring publication sensitivity as the accumulative value of the sensitivity of the information types included in it. Finally, an experiment with 196 teenagers was carried out to assess the effectiveness of empowering users regarding the sensitivity of the publication. The results show a significant effect on users’ privacy behavior by the nudge message and the sensitivity included in it

Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets

The sports betting market has emerged as one of the most lucrative markets in recent years. Trading in sports betting markets entails predicting odd movements in order to bet on an outcome, whilst also betting on the opposite outcome, at different odds in order to make a profit, regardless of the final result. These markets are mainly composed by humans, which take decisions according to their past experience in these markets. However, human rational reasoning is limited when taking quick decisions, being influenced by emotional factors and offering limited calibration capabilities for estimating probabilities. In this paper, we show how artificial techniques could be applied to this field and demonstrate that they can outperform even the bevahior of high-experienced humans. To achieve this goal, we propose a case-based reasoning model for trading in sports betting markets, which is integrated in an agent to provide it with the capabilities to take trading decisions based on future odd predictions. In order to test the performance of the system, we compare trading decisions taken by the agent with trading decisions taken by human traders when they compete in real sports betting markets.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and project TIN2011-27652-C03-01. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; García Fornes, AM. (2013). Using a Case-Based Reasoning Approach for Trading in Sports Betting Markets. Applied Intelligence. 38(3):465-477. https://doi.org/10.1007/s10489-012-0381-9S465477383Aamodt A (1990) Knowledge-intensive case-based reasoning and sustained learning. In: Topics in case-based reasoning. Springer, Berlin, pp 274–288Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Ahn JJ, Byun HW, Oh KJ, Kim TY (2012) Bayesian forecaster using class-based optimization. Appl Intell 36(3):553–563Alberola JM, Garcia-Fornes A, Espinosa A (2010) Price prediction in sports betting markets. In: Proceedings of the 8th German conference on multiagent system technologies, pp 197–208Arias-Aranda D, Castro JL, Navarro M, Zurita JM (2009) A cbr system for knowing the relationship between flexibility and operations strategy. In: Proceedings of the 18th international symposium on foundations of intelligent systems, ISMIS’09, pp 463–472Ates C (2004) Prediction markets are only human: subadditivity in probability judgments. In: MSC in finance and international businessBerlemann M, Schmidt C (2001) Predictive accuracy of political stock markets—empirical evidence from a European perspective. Technical report 2001-57Betfair (2009) http://www.betfaircorporate.comChen Y, Goel S, Pennock D (2008) Pricing combinatorial markets for tournaments. In: STOC’08: proceedings of the 40th annual ACM symposium on theory of computing. ACM Press, New York, pp 305–314Debnath S, Pennock DM, Giles CL, Lawrence S (2003) Information incorporation in online in-game sports betting markets. In: Proceedings of the 4th ACM conference on electronic commerce, EC ’03. ACM Press, New York, pp 258–259. doi: 10.1145/779928.779987Fischoff B, Slovic P, Lichtenstein S (1977) Knowing with certainty: the appropriateness of extreme confidence. J Exp Psychol Human Percept Perform 3:552–564Forsythe R, Rietz T, Ross T (1999) Wishes, expectations and actions: a survey on price formation in election stock markets. J Econ Behav Organ 39(1):83–110Fortnow L, Kilian J, Pennock DM, Wellman MP (2005) Betting Boolean-style: a framework for trading in securities based on logical formulas. Decis Support Syst 39(1):87–104. doi: 10.1016/j.dss.2004.08.010Gayer G (2010) Perception of probabilities in situations of risk: a case based approach. Games Econ Behav 68(1):130–143Guo M, Pennock D (2009) Combinatorial prediction markets for event hierarchies. In: Proc of the 8th AAMAS’09. Int foundation for autonomous agents and multiagent systems, pp 201–208Huang W, Lai K, Nakamori Y, Wang S (2004) Forecasting foreign exchange rates with artificial neural networks: a review. Int J Inf Technol Decis Mak 3(1):145–165Hüllermeier E (2007) Case-based approximate reasoning. Theory and decision library, vol 44. Springer, BerlinKim K-J, Ahn H (2012) Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36(4):887–898LeBaron B (1998) Agent based computational finance: suggested readings and early research. J Econ Dyn ControlLiu Y, Yang C, Yang Y, Lin F, Du X, Ito T (2012) Case learning for cbr-based collision avoidance systems. Appl Intell 36(2):308–319Love BC (2008) Behavioural finance and sports betting markets. In: MSC in finance and international businessLuque C, Valls JM, Isasi P (2011) Time series prediction evolving Voronoi regions. Appl Intell 34(1):116–126Mantaras RLD, McSherry D, Bridge D, Leake D, Smyth B, Craw S, Faltings B, Maher M, Lou C, Forbus MCK, Keane M, Aamodt A, Watson I (2005) Retrieval, reuse, revision and retention in case-based reasoning. Knowl Eng Rev 20(3):215–240Moody J (1995) Economic forecasting: challenges and neural network solutions. In: Proceedings of the international symposium on artificial neural networksOntañón S, Plaza E (2009) Argumentation-based information exchange in prediction markets. Argument Multi-Agent Syst 5384:181–196Ontañón S, Plaza E (2011) An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems. Multiagent Grid Syst 7:95–108Palmer R, Arthur W, Holland J, Lebaron B, Tayler P (1994) Artificial economic life: a simple model of a stock market. Physica D 75:264–274Pennock D, Debnath S, Glover E, Giles C (2002) Modelling information incorporation in markets, with application to detecting and explaining events. In: Proceedings of the 18th annual conference on uncertainty in artificial intelligence (UAI-02), San Francisco, CA. Morgan Kaufmann, San Mateo, pp 404–405Pennock DM, Lawrence S, Nielsen FÅ, Giles CL (2001) Extracting collective probabilistic forecasts from web games. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’01. ACM Press, New York, pp 174–183. doi: 10.1145/502512.502537Plott CR (2000) Markets as information gathering tools. South Econ J 67(1):2–15Qian B, Rasheed K (2007) Stock market prediction with multiple classifiers. 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VMFS: herramienta visual para la enseñanza del funcionamiento de un sistema de ficheros

Se ha desarrollado una herramienta que permite a los alumnos conocer de forma sencilla las distintas partes de que consta un sistema de ficheros en el sistema operativo MINIX. En concreto, mediante VMFS1 es posible estudiar cómo se gestiona en MINIX la asignación del espacio en disco a ficheros, así como las distintas estructuras de datos que se emplean en dicha gestión para implementar diferentes tipos de ficheros. Además, al tratarse de una intuitiva aplicación gráfica, resulta una herramienta muy adecuada para realizar prácticas en el marco de una asignatura orientada a la enseñanza de conceptos básicos y técnicas fundamentales de los sistemas operativos, cuyos alumnos normalmente carecen de grandes conocimientos de programación

Desarrollo de prototipos hardware para una maqueta de tren con fines docentes

El uso de elementos reales con fines docentes es cada vez más frecuente. El presente trabajo presenta la experiencia de la puesta en marcha de una maqueta de trenes describiendo los problemas surgidos a la hora de realizar el control por computador de la misma, así como las soluciones propuestas. El trabajo se centra en la descripción de la infraestructura hardware desarrollada sobre una maqueta comercial, para permitir el control individual de los elementos móviles (trenes, cambios de vía…) mediante un ordenador

Feedback Efectivo en Prácticas de Programación

Las asignaturas de carácter práctico como la programación, presentan históricamente un alto índice de abandonos y unas tastas de aprobados bajas. Una característica de estas asignaturas es que el material que se aprende, necesita ser afianzado para aprender nuevos conceptos, por lo tanto, un feedback progresivo y continuo es esencial para la motivación de los alumnos. En este artículo, presentamos una experiencia docente que obtiene dicho feedback mediante el uso de la plataforma educativa. El impacto a diferentes niveles de esta experiencia es analizado en un grupo de alumnos.Alberola Oltra, JM.; García Fornes, AM. (2013). Feedback Efectivo en Prácticas de Programación. VAEP-RITA. Versión Abierta Español-Portugués. 1(2):88-96. http://hdl.handle.net/10251/60536S88961

Analyzing the effect of gain time on soft task scheduling policies in real-time systems

In hard real-time systems, gain time is defined as the difference between the Worst Case Execution Time (WCET) of a hard task and its actual processor consumption at runtime. This paper presents the results of an empirical study about how the presence of a significant amount of gain time in a hard real-time system questions the advantages of using the most representative scheduling algorithms or policies for aperiodic or soft tasks in fixed-priority preemptive systems. The work presented here refines and complements many other studies in this research area in which such policies have been introduced and compared. This work has been performed by using the authors' testing framework for soft scheduling policies, which produces actual, synthetic, randomly generated applications, executes them in an instrumented Real-Time Operating System (RTOS), and finally processes this information to obtain several statistical outcomes. The results show that, in general, the presence of a significant amount of gain time reduces the performance benefit of the scheduling policies under study when compared to serving the soft tasks in background, which is considered the theoretical worst case. In some cases, this performance benefit is so small that the use of a specific scheduling policy for soft tasks is questionable. © 2012 IEEE.This work is partially funded by research projects PROMETEO/2008/051, CSD2007-022, and TIN2008-04446.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Espinosa Minguet, AR.; García Fornes, AM. (2012). Analyzing the effect of gain time on soft task scheduling policies in real-time systems. IEEE Transactions on Software Engineering. 38(6):1305-1318. https://doi.org/10.1109/TSE.2011.95S1305131838

VivesDebate: A new annotated multilingual corpus of argumentation in a debate tournament'.

The application of the latest Natural Language Processing breakthroughs in computational argumentation has shown promising results, which have raised the interest in this area of research. However, the available corpora with argumentative annotations are often limited to a very specific purpose or are not of adequate size to take advantage of state-of-the-art deep learning techniques (e.g., deep neural networks). In this paper, we present VivesDebate, a large, richly annotated and versatile professional debate corpus for computational argumentation research. The corpus has been created from 29 transcripts of a debate tournament in Catalan and has been machine-translated into Spanish and English. The annotation contains argumentative propositions, argumentative relations, debate interactions and professional evaluations of the arguments and argumentation. The presented corpus can be useful for research on a heterogeneous set of computational argumentation underlying tasks such as Argument Mining, Argument Analysis, Argument Evaluation or Argument Generation, among others. All this makes VivesDebate a valuable resource for computational argumentation research within the context of massive corpora aimed at Natural Language Processing tasks

An intelligent self-configurable mechanism for distributed energy storage systems

Next generation of smart grid technologies demand intel- ligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multiagent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this paper we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-con gurable mechanism which allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.This work has been partially supported by projects TIN2012-36586-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). An intelligent self-configurable mechanism for distributed energy storage systems. Cybernetics and Systems. 45(3):292-305. https://doi.org/10.1080/01969722.2014.894859S292305453Abbey , C. and G. Joos . “Coordination of Distributed Storage with Wind Energy in a Rural Distribution System.” Paper presented at Industry Applications Conference, 42nd IAS Annual Meeting, September 23–27, 2007, New Orleans, USA .Alberola , J. M. , V. Julian , and A. Garcia-Fornes . “Multi-Dimensional Transition Deliberation for Organization Adaptation in Multiagent Systems.” Paper presented at the 11th International Conference on Aut. Agents and MAS (AAMAS12), June 4–8, 2012, Valencia, Spain .Chouhan , N. S. and M. Ferdowsi . “Review of Energy Storage Systems.” Paper presented at North American Power Symposium (NAPS), October 4–6, 2009, Mississippi, USA.Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. IEEE Transactions on Power Systems, 20(2), 1035-1042. doi:10.1109/tpwrs.2005.846054Costa , L. , F. Bourry , J. Juban , and G. Kariniotakis . “Management of Energy Storage Coordinated with Wind Power under Electricity Market Conditions.” Paper presented at 10th International Conference on Probabilistic Methods Applied to Power Systems, May 25–29, 2008, Rincón, Puerto Rico .Eyer , J. and G. Corey . “Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide.” Sandia National Laboratories, 2010. Technical Report .Jiang , Z. “Agent-Based Control Framework for Distributed Energy Resources Microgrids.” Paper presented at International Conference on Intelligent Agent Technology, December 18–22, 2006, Hong Kong .Karnouskos , S. and T. N. De Holanda . “Simulation of a Smart Grid City with Software Agents.” Paper presented at Third UKSim European Symposium on Computer Modeling and Simulation, November 25–27, 2009, Athens, Greece .Ketter, W., Collins, J., & Reddy, P. (2013). Power TAC: A competitive economic simulation of the smart grid. Energy Economics, 39, 262-270. doi:10.1016/j.eneco.2013.04.015Lakshman, A., & Malik, P. (2010). Cassandra. ACM SIGOPS Operating Systems Review, 44(2), 35. doi:10.1145/1773912.1773922Logenthiran, T., Srinivasan, D., Khambadkone, A. M., & Aung, H. N. (2012). Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator. IEEE Transactions on Smart Grid, 3(2), 925-933. doi:10.1109/tsg.2012.2189028Maly, D. K., & Kwan, K. S. (1995). Optimal battery energy storage system (BESS) charge scheduling with dynamic programming. IEE Proceedings - Science, Measurement and Technology, 142(6), 453-458. doi:10.1049/ip-smt:19951929Mihailescu , R. C. , M. Vasirani , and S. Ossowski . “Dynamic Coalition Formation and Adaptation for Virtual Power Stations in Smart Grids.” Paper presented at 2nd International Workshop on Agent Technologies for Energy Systems, May 2, 2011, Taipei, Taiwan .Mohd , A. , E. Ortjohann , A. Schmelter , N. Hamsic , and D. Morton . “Challenges in Integrating Distributed Energy Storage Systems into Future Smart Grid.” Paper presented at IEEE International Symposium on Industrial Electronics, June 30–July 2, 2008, Cambridge, UK .Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2010). Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments. IEEE Transactions on Smart Grid, 1(2), 120-133. doi:10.1109/tsg.2010.2055903Momoh , J. A. “Smart Grid Design for Efficient and Flexible Power Networks Operation and Control.” Paper presented at IEEE PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Nguyen, C. P., & Flueck, A. J. (2012). Agent Based Restoration With Distributed Energy Storage Support in Smart Grids. IEEE Transactions on Smart Grid, 3(2), 1029-1038. doi:10.1109/tsg.2012.2186833Nourai , A. “Installation of the First Distributed Energy Storage System (DESS) At American Electric Power.” Sandia National Laboratories, 2007. Technical Report.Oyarzabal , J. , J. Jimeno , J. Ruela , A. Engler , and C. Hardt . “Agent Based Micro Grid Management System.” Paper presented at International Conference on Future Power Systems, November 16–18, 2005, Amsterdam, Netherlands .Pinson, P., Chevallier, C., & Kariniotakis, G. N. (2007). Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power. IEEE Transactions on Power Systems, 22(3), 1148-1156. doi:10.1109/tpwrs.2007.901117Pipattanasomporn , M. , H. Feroze , and S. Rahman . “Multi-agent Systems in a Distributed Smart Grid: Design and Implementation.” Paper presented at IEEE/PES Power Systems Conference and Exposition, March 15–18, 2009, Seattle, USA .Reddy , P. P. and M. M. Veloso . “Factored Models for Multiscale Decision Making in Smart Grid Customers.” Paper presented at the Twenty-sixth AAAI Conference on Artificial Intelligence, July 22–26, 2012, Toronto, Canada .Ribeiro, P. F., Johnson, B. K., Crow, M. L., Arsoy, A., & Liu, Y. (2001). Energy storage systems for advanced power applications. Proceedings of the IEEE, 89(12), 1744-1756. doi:10.1109/5.975900Schutte , S. and M. Sonnenschein . “Mosaik-Scalable Smart Grid Scenario Specification.” Paper presented at Proceedings of the 2012 Winter Simulation Conference (WSC), December 9–12, 2012, Berlin, Germany .Sioshansi, R., Denholm, P., Jenkin, T., & Weiss, J. (2009). Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy Economics, 31(2), 269-277. doi:10.1016/j.eneco.2008.10.005Szkuta, B. R., Sanabria, L. A., & Dillon, T. S. (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Transactions on Power Systems, 14(3), 851-857. doi:10.1109/59.780895Van Dam, K. H., Houwing, M., Lukszo, Z., & Bouwmans, I. (2008). Agent-based control of distributed electricity generation with micro combined heat and power—Cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering, 32(1-2), 205-217. doi:10.1016/j.compchemeng.2007.07.012Vosen, S. (1999). Hybrid energy storage systems for stand-alone electric power systems: optimization of system performance and cost through control strategies. International Journal of Hydrogen Energy, 24(12), 1139-1156. doi:10.1016/s0360-3199(98)00175-xVytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Agent-Based Micro-Storage Management for the Smart Grid.” Paper presented at Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010a, Toronto, Canada .Vytelingum , P. , T. D. Voice , S. Ramchurn , A. Rogers , and N. R. Jennings . “Intelligent Agents for the Smart Grid.” Paper presented at the 9th International Conference on Autonomous Agents and Multiagent Systems, May 10–14, 2010b, Toronto, Canada

Deadline Prediction Scheduling based on Benefits

This paper describes a scheduling algorithm that composes a scheduling plan which is able to predict the completion time of the arriving tasks. This is done by performing CPU booking. This prediction is used to establish a temporal commitment with the client that invokes the execution of the task. This kind of scheduler is very useful in scenarios where Service-Oriented Computing is deployed and the execution time is used as a parameter for QoS. This scheduler is part of an architecture that is based on the Distributed Goal-Oriented Computing paradigm, which allows agents to express their own goals and to reach them by means of service compositions. Moreover, the scheduler is also able to prioritize those tasks which provide greater benefits to the OS. In this work, the scheduler has been designed in several iterations and tested by means of a set of experiments that compare the scheduler algorithm with a representative set of scheduling algorithms. © 2012 Elsevier B.V. All rights reserved.This work is supported by the TIN2009-13839-C03-01 project of the Spanish government, PROMETEO/2008/051 project, FEDER funds and CONSOLIDER-INGENIO 2010 under grant CSD2007-00022.Palanca Cámara, J.; Navarro Llácer, M.; García-Fornes, A.; Julian Inglada, VJ. (2013). Deadline Prediction Scheduling based on Benefits. Future Generation Computer Systems. 29(1):61-73. https://doi.org/10.1016/j.future.2012.05.007S617329