40 research outputs found
Ranking and significance of variable-length similarity-based time series motifs
The detection of very similar patterns in a time series, commonly called
motifs, has received continuous and increasing attention from diverse
scientific communities. In particular, recent approaches for discovering
similar motifs of different lengths have been proposed. In this work, we show
that such variable-length similarity-based motifs cannot be directly compared,
and hence ranked, by their normalized dissimilarities. Specifically, we find
that length-normalized motif dissimilarities still have intrinsic dependencies
on the motif length, and that lowest dissimilarities are particularly affected
by this dependency. Moreover, we find that such dependencies are generally
non-linear and change with the considered data set and dissimilarity measure.
Based on these findings, we propose a solution to rank those motifs and measure
their significance. This solution relies on a compact but accurate model of the
dissimilarity space, using a beta distribution with three parameters that
depend on the motif length in a non-linear way. We believe the incomparability
of variable-length dissimilarities could go beyond the field of time series,
and that similar modeling strategies as the one used here could be of help in a
more broad context.Comment: 20 pages, 10 figure
Assessing confidence in cased based reuse step
Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving process, since the solution for a new problem is based in the available solutions of the retrieved cases. In classification tasks a trivial reuse method is commonly used, which takes into account the most frequently solution proposed by the set of retrieved cases. We propose an alternative reuse process; we call confidence-reuse method, which make a qualitative assessment of the information retrieved. This approach is focused on measuring the solution accuracy, applying some confidence predictors based in a k-NN classifier with the aim of analyzing and evaluating the information offered by the retrieved cases.Peer Reviewe
Towards next generation coordination infrastructures
Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges
Spatio-Temporal Reasoning for Reliable Facial Expression Interpretation
Understanding human behaviours and emotions has received contributions from image analysis and pattern recognition techniques in order to tackle this challenge. The most popular facial expression classifiers deal with eyebrows and lips while avoiding eyelid motion. According to psychologists, eye motion is relevant for trust and deceit analysis as well for dichotomizing near facial expressions. Unlike previous approaches, we include the eyelid motion by constructing an appearance-based tracker (ABT). Subsequently, a Case-Based Reasoning (CBR) approach is applied by training a case-base with seven facial actions. We classify new facial expressions with respect to previous solutions, previously assessing confidence for the proposed solutions. Therefore, the proposed system yields efficient classification rates comparable to the best previous facial expression classifiers. The ABT and CBR combination provides trusty solutions by evaluating the confidence of the solution quality for eyebrows, mouth and eyes. Consequently, this method is robust and accurate for facial motion coding, and for confident classifications. The training is progressive, the quality of the solution increases with respect to previous solutions and do not need re-training processes
Lessons learned from supplementing archaeological museum exhibitions with virtual reality
Archaeological excavations provide us with important clues about the past. Excavated artefacts represent an important connection to civilisations that no longer exist and help us understand some of their customs, traditions and common practices. With the help of academics and practitioners from various disciplines the results of archaeological excavations can be analysed and a body of knowledge about the corresponding society can be created and shared with members of the general public. Museums have traditionally served the purpose of communicating this knowledge and backing it up with the help of the excavated artefacts. Many museum visitors, however, find it difficult to develop a coherent understanding of the corresponding society only based on the artefacts and annotations showed in museums. Effective modern techniques that have high potential in helping museum visitors with better understanding of the past are 3D reconstruction and Virtual Reality. 3D reconstruction offers a cost effective way of recreating historical settlements in a computer-generated virtual environment, while Virtual Reality helps with immersing people into such environments and reaching a high degree of realism. With the help of these technologies it becomes possible to relive history, imagine yourself being a part of the reconstructed society and learn about its culture firsthand. The combination of 3D reconstruction and Virtual Reality \anton{represents} a very powerful learning tool, however this tool has been rarely used in a museum setting and its correct use has not been properly investigated. In this paper we present a study into using Virtual Reality in itinerant archaeological exhibitions. We discuss the lessons we have learned from developing an interactive Virtual Reality simulation of the Neolithic settlement of La Draga. These lessons feature our analysis of qualitative and quantitative feedback of museum visitors, as well as what we have learned from analysing their navigation and interaction patterns
FamĂlies botĂ niques de plantes medicinals
Facultat de Farmà cia, Universitat de Barcelona. Ensenyament: Grau de Farmà cia, Assignatura: Botà nica FarmacÚutica, Curs: 2013-2014, Coordinadors: Joan Simon, CÚsar Blanché i
Maria Bosch.Els materials que aquĂ es presenten sĂłn els recull de 175 treballs dâuna famĂlia botĂ nica dâinterĂšs medicinal realitzats de manera individual. Els treballs han estat realitzat
per la totalitat dels estudiants dels grups M-2 i M-3 de lâassignatura BotĂ nica FarmacĂšutica
durant els mesos dâabril i maig del curs 2013-14. Tots els treballs sâhan dut a terme a travĂ©s de la plataforma de GoogleDocs i han estat tutoritzats pel professor de lâassignatura i revisats i finalment co-avaluats entre els propis estudiants. Lâobjectiu principal de lâactivitat ha estat fomentar lâaprenentatge autĂČnom i col·laboratiu en BotĂ nica farmacĂšutica
The Noos Representation Language
The aim of this thesis is the design and implementation of a representation language for developing knowledge systems that integrate problem solving and learning. Our proposal is that this goal can be achieved with a representation language with representation constructs close to knowledge modeling frameworks and with episodic memory and reflective capabilities. We have developed Noos, a reflective object-centered representation language close to knowledge modeling frameworks. Noos is based on the task/method decomposition principle and the analysis of models required and constructed by problem solving methods. Noos is formalizated using feature terms, a formal approach to object-centered representations, that provides a formalism for integrating different learning techniques. The integration of machine learning tasks has as implication that the knowledge modeling of the implemented knowledge system has to include modeling of learning goals. Moreover, machine learning techniques have t..