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

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

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

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

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..

The Noos Approach

D). Backtracking is engaged when a method fails in solving a task. In that case, another remaining non-failed method in fM i g F (D) will be selected and reflected down. Moreover, since a method M can have subtasks, and each subtask may have several alternative methods to solve it, metalevel inference ensures that backtracking is engaged in M. Then, the possible combinations of methods for each subtask are tried, following the local preference orderings for each subtask, until a solution is found. Next Chapter will discuss the role of experience and memory in Noos problem solving and our proposal for integrating learning techniques in Noos. In the next Chapter we will present the Noos elements concerning to the integration of learning. The reader can find in Appendix A the rest of elements provided in the Noos development environment

Reflection in Noos: An object-centered representation language for knowledge modelling

this paper is to present the reflective capabilities of the NOOS language, so we present first some intuitions about the language and later a formalization of it. An example of using NOOS is also presented, but the reader may be interested in other more detailed applications of NOOS for case-based reasoning (CBR) systems [Arcos and Plaza 93], integrating induction and CBR [Armengol and Plaza 94], and the support NOOS gives for knowledge modelling [Arcos and Plaza 94]. The next section introduces the basic capabilities of NOOS language. In section 3 we will present the formal description of NOOS using Reflective Dynamic Logic (RDL), a logical framework to describe reflective logical architectures [Sierra 95]. Section 4 takes a knowledge modelling analysis of diagnosis tasks performed by R. Benjamin [Benjamins 94] and shows how it can be implemented in NOOS. Finally, section 5 discusses related work and our future work