The role of oblivion, memory size and spatial separation in dynamic language games

In this paper we present some multiagent simulations in which the individuals try to reach a uniform vocabulary to name spatial movements. Each agent has initially a random vocabulary that can be modified by means of interactions with the other agents. As the objective is to name movements, the topic of conversation is chosen by moving. Each agent can remember a finite number of words per movement, with certain strength. We show the importance of the forgetting process and memory size in these simulations, discuss the effect of the number of agents on the time to agree and present a few experiments where the evolution of vocabularies takes place in a divided range.This paper has been sponsored by the Spanish Interdepartmental Commission of Science and Technology (CICYT), project numbers TEL1999-0181, and TIC 2001-0685-C02-01

Some strategies for the simulation of vocabulary agreement in multi-agent communities

In this paper, we present several experiments of belief propagation in multi-agent communities. Each agent in the simulation has an initial random vocabulary (4 words) corresponding to each possible movement (north, south, east and west). Agents move and communicate the associated word to the surrounding agents, which can be convinced by the 'speaking agent', and change their corresponding word by 'imitation'. Vocabulary uniformity is achieved, but strong interactions and competition can occur between dominant words. Several moving and trusting strategies as well as agent roles are analyzed.This paper has been sponsored by the Spanish Interdepartmental Commission of Science and Technology (CICYT), project number TEL1999-0181

Some applications of semi-discrete variational integrators to classical field theories

We develop a semi-discrete version of discrete variational mechanics with applications to numerical integration of classical field theories. The geometric preservation properties are studied.Comment: 14 page

The DDG^G-classifier in the functional setting

The Maximum Depth was the first attempt to use data depths instead of multivariate raw data to construct a classification rule. Recently, the DD-classifier has solved several serious limitations of the Maximum Depth classifier but some issues still remain. This paper is devoted to extending the DD-classifier in the following ways: first, to surpass the limitation of the DD-classifier when more than two groups are involved. Second to apply regular classification methods (like $k$NN, linear or quadratic classifiers, recursive partitioning,...) to DD-plots to obtain useful insights through the diagnostics of these methods. And third, to integrate different sources of information (data depths or multivariate functional data) in a unified way in the classification procedure. Besides, as the DD-classifier trick is especially useful in the functional framework, an enhanced revision of several functional data depths is done in the paper. A simulation study and applications to some classical real datasets are also provided showing the power of the new proposal.Comment: 29 pages, 6 figures, 6 tables, Supplemental R Code and Dat

Human and Object Recognition with a High-resolution tactile sensor

This paper 1 describes the use of two artificial intelligence methods for object recognition via pressure images from a high-resolution tactile sensor. Both meth- ods follow the same procedure of feature extraction and posterior classification based on a supervised Supported Vector Machine (SVM). The two approaches differ on how features are extracted: while the first one uses the Speeded-Up Robust Features (SURF) descriptor, the other one employs a pre-trained Deep Convolutional Neural Network (DCNN). Besides, this work shows its applica- tion to object recognition for rescue robotics, by distinguishing between differ- ent body parts and inert objects. The performance analysis of the proposed methods is carried out with an experiment with 5-class non-human and 3-class human classification, providing a comparison in terms of accuracy and compu-tational load. Finally, it is discussed how feature-extraction based on SURF can be obtained up to five times faster compared to DCNN. On the other hand, the accuracy achieved using DCNN-based feature extraction can be 11.67% superior to SURF.Proyecto DPI2015-65186-R European Commission under grant agreement BES-2016-078237. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

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