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

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.</p

Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic evaluation of complete professional argumentative debates. In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.</p

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. 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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

Toward the Prevention of Privacy Threats:How Can We Persuade Our Social Network Platform Users?

Complex decision-making problems, such as the privacy policy selection, when sharing content in online social network (OSN) platforms can significantly benefit from artificial intelligence systems. With the use of computational argumentation, it is possible to persuade human users to modify their initial decisions to avoid potential privacy threats and violations. In this paper, we present a study performed with the participation of 186 teenage users aimed at analyzing their behaviors when we try to persuade them to modify the post/publication of sensitive content on OSN platforms with different arguments. The results of the study revealed that the personality traits and the social interaction data (e.g., number of comment posts, friends, and likes) of our participants were significantly correlated with the persuasive power of the arguments. Therefore, these sets of features can be used to model OSN users and estimate the persuasive power of different arguments when used in human-computer interactions. The findings presented in this paper are helpful for personalizing decision support systems aimed at educating and preventing privacy violations on OSN platforms using arguments.</p

Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks

One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided

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

Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks

One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided

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