Monte Carlo schemata searching for physical activity recognition

Proceeding of: 7th International Conference on Intelligent Networking and Collaborative Systems (INCoS 2015), 2-4 September 2015, Taipei, TaiwanMedical literature have recognized physical activity as a key factor for a healthy life due to its remarkable benefits. However, there is a great variety of physical activities and not all of them have the same effects on health nor require the same effort. As a result, and due to the ubiquity of commodity devices able to track users' motion, there is an increasing interest on performing activity recognition in order to detect the type of activity carried out by the subjects and being able to credit them for their effort, which has been detected as a key requirement to promote physical activity. This paper proposes a novel approach for performing activity recognition using Monte Carlo Schemata Search (MCSS) for feature selection and random forests for classification. To validate this approach we have carried out an evaluation over PAMAP2, a public dataset on physical activity available in UCI Machine Learning repository, enabling replication and assessment. The experiments are conducted using leave-one-subject-out cross validation and attain classification accuracies of over 93% by using roughly one third of the total set of features. Results are promising, as they outperform those obtained in other works on the same dataset and significantly reduce the set of features used, which could translate in a decrease of the number of sensors required to perform activity recognition and, as a result, a reduction of costs.This work was partially funded by European Union’s CIP Programme (ICT-PSP-2012) under grant agreement no. 325146 (SEACW project)

Evolvable hardware: An outlook

In this paper, we explore the potential of Evolvable Hardware (EHW) for online adaptation in real-time applications. We follow a top-down approach here. We first review existing adaptation and learning techniques and take a look at their suitability for driving hardware evolution. Then we discuss some research problems whose solution will improve the performance of EHW.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

DOI: 10.1017/S000000000000000 Printed in the United Kingdom Honesty and Deception in Populations of Selfish, Adaptive Individuals

Biologists have mostly studied under what circumstances honest signaling is stable. Stability, however, is not sufficient to explain the emergence of honest signaling. We study the evolution of honest signaling between selfish, adaptive individuals and observe that honest signaling can emerge through learning. More importantly, honest signaling may emerge in cases where it is not evolutionarystable. In such cases, honesty and dishonesty co-exist. Furthermore, honest signaling does not necessarily emerge in cases where it is evolutionary stable. We show that the latter is due to the existence of other, more important equilibria and that the importance of equilibria is related to Pareto-optimality.

The limits and robustness of reinforcement learning in Lewis signalling games

Lewis signalling games are a standard model to study the emergence of language. We introduce win-stay/lose-inaction, a random process that only updates behaviour on success and never deviates from what was once successful, prove that it always ends up in a state of optimal communication in all Lewis signalling games, and predict the number of interactions it needs to do so: N3 interactions for Lewis signalling games with N equiprobable types. We show three reinforcement learning algorithms (Roth-Erev learning, Q-learning, and Learning Automata) that can imitate win-stay/lose-inaction and can even cope with errors in Lewis signalling games. © 2014 Taylor & Francis.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Building a genetic programming framework: The added-value of design patterns

info:eu-repo/semantics/publishe

Honesty and deception in populations of selfish, adaptive individuals

Biologists have mostly studied under what circumstances honest signaling is stable. Stability, however, is not sufficient to explain the emergence of honest signaling. We study the evolution of honest signaling between selfish, adaptive individuals and observe that honest signaling can emerge through learning. More importantly, honest signaling may emerge in cases where it is not evolutionary stable. In such cases, honesty and dishonesty co-exist. Furthermore, honest signaling does not necessarily emerge in cases where it is evolutionary stable. We show that the latter is due to the existence of other, more important equilibria and that the importance of equilibria is related to Pareto-optimality.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Substitution Matrix Based Kernel Functions for Protein Secondary Structure Prediction

Different approaches to using substitution matrices in kernel functions for protein secondary structure prediction (PSSP) with support vector machines are investigated. This work introduces a number of kernel functions that calculate inner products between amino acid sequences based on the entries of a substitution matrix (SM), i.e. a matrix that contains evolutionary information about the substitutability of the different amino acids that make up proteins. The starting point is always the same, i.e. a pseudo inner product (PI) between amino acid sequences making use of a SM. It is shown what conditions a SM should satisfy in order for the PI to make sense and subsequently it is shown how a substitution distance (SD) based on the PI can be defined. Next, different ways of using both the PI and the SD in kernel functions for support vector machine (SVM) learning are discussed. In a series of experiments the different kernel functions are compared with each other and with other kernel functions that do not make use of a SM. The results show that the information contained in a SM can have a positive influence on the PSSP results, provided that it is employed in the correct way