Landmark-based music recognition system optimisation using genetic algorithms

Audio fingerprinting allows us to label an unidentified music fragment within a previously generated database. The use of spectral landmarks aims to obtain a robustness that lets a certain level of noise be present in the audio query. This group of audio identification algorithms holds several configuration parameters whose values are usually chosen based upon the researcher’s knowledge, previous published experimentation or just trial and error methods. In this paper we describe the whole optimisation process of a Landmark-based Music Recognition System using genetic algorithms. We define the actual structure of the algorithm as a chromosome by transforming its high relevant parameters into various genes and building up an appropriate fitness evaluation method. The optimised output parameters are used to set up a complete system that is compared with a non-optimised one by designing an unbiased evaluation model.     

Socio-Cultural Connections and Ruptures: Social Roles of Older Adults in Chile

This research focused on determining the sociocultural factors that build the social perception of the social roles of senior citizens in Chile in the 2002–2012 period. For this purpose, public opinion was studied in newspapers El Mercurio and La Cuarta. Specialized opinion included the Senior Citizen Program of Pontificia Universidad Católica de Chile, the National Service for Senior Citizens, the Social Observatory for Aging and Old Age of Universidad de Chile, and the Society of Geriatrics and Gerontology of Chile. A comprehensive methodology was used, along with a bibliographical four-stage design. The following ten sociocultural factors were identified: Health and Quality of Life, Studies and Data, Social Images of Aging, Gerontological Concepts and Sociopolitical Participation, Gerontological Policies, Plans and Principles, Aging, Production and Gender, Institutionalization, Demographic Growth, Gerontological Training, and Exclusion and Old Age. This work suggests modifying the conceptual premise that emphasizes the inexistence of a social role for elderly people with a vision centered on the heterogeneity of social roles under permanent sociocultural construction.     

Looking Back to Move Forward: Measuring the Impact of Existing Digital Resources Relevant to Irish Archaeology

The proliferation of digital resources is an important manifestation of data sharing and knowledge sharing practice. There is an increasing recognition of the importance of understanding the actual impact of these resources on research and research practice. Impact assessment methodologies are emerging from this nascent scholarship and offer a means of demonstrating performance and utility. This study took a multi-strand approach, inspired by the Toolkit for the Impact of Digitised Scholarly Resources (TIDSR), to evaluate seven digital resources relating to Irish archaeology. The results indicated significant factors that can influence the impact of a digital resource, positively or negatively.     

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead mother?s abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.     

Cartogram visualization for nonlinear manifold learning models

Real-world applications of multivariate data analysis often stumble upon the barrier of interpretability. Simple data analysis methods are usually easy to interpret, but they risk providing poor data models. More involved methods may instead yield faithful data models, but limited interpretability. This is the case of linear and nonlinear methods for multivariate data visualization through dimensionality reduction. Even though the latter have provided some of the most exciting visualization developments, their practicality is hindered by the difficulty of explaining them in an intuitive manner. The interpretability, and therefore the practical applicability, of data visualization through nonlinear dimensionality reduction (NLDR) methods would improve if, first, we could accurately calculate the distortion introduced by these methods in the visual representation and, second, if we could faithfully reintroduce this distortion into such representation. In this paper, we describe a technique for the reintroduction of the distortion into the visualization space of NLDR models. It is based on the concept of density-equalizing maps, or cartograms, recently developed for the representation of geographic information. We illustrate it using Generative Topographic Mapping (GTM), a nonlinear manifold learning method that can provide both multivariate data visualization and a measure of the local distortion that the model generates. Although illustrated here with GTM, it could easily be extended to other NLDR visualization methods, provided a local distortion measure could be calculated. It could also serve as a guiding tool for interactive data visualization.     

Service oriented evolutionary algorithms

This work presents a service oriented architecture for evolutionary algorithms, and an implementation of this architecture using a specific technology (called OSGiLiath). Service oriented architecture is a computational paradigm where users interact using services to increase the integration between systems. The presented abstract architecture is formed by loosely coupled, highly configurable and language-independent services. As an example of an implementation of this architecture, a complete process development using a specific service oriented technology is explained. With this implementation, less effort than classical development in integration, distribution mechanisms and execution time management has been attained. In addition, steps, ideas, advantages and disadvantages, and guidelines to create service oriented evolutionary algorithms are presented. Using existing software, or from scratch, researchers can create services to increase the interoperability in this area.     

Virtual life: self and identity redefined in the new media age

In this article, the author discusses her virtual reality installation The Parallel Dimension and goes on to explore the conflicts of identity experienced when a person is confronted with VR as a total immersion. She suggests that the various questions arising through this experience may lead us to a more ambivalent concept of the self.     

Fuzzy clustering based ET image fusion

Electrical tomography (ET) is a technique to visually reconstruct inhomogeneous medium distributions by injecting currents or voltages at the boundary of the medium and measuring the resulted changes in the investigated fields. The ET techniques have been widely used in industrial practices owing to the low cost, rapid response time, non-existent radiation exposure, and non-intrusive characteristics comparing to other tomographic modalities. However, the spatial resolution of ET images using single modality or single-driven patterns (adjacent pattern vs. opposite pattern for imaging reconstruction) is low, which may limit its applications. In this research, the application of fuzzy clustering based fusion techniques for ET imaging is studied. Both multi-modality imaging and multi-driven patterns are of interest. Specifically, two modality images are fused: Electrical Capacitance Tomography (ECT), which performs well for imaging material of large permittivity difference, and Electrical Resistance Tomography (ERT), which is suited for imaging materials having large conductivity differences. The research also explores the fusion of adjacent and opposite patterns for either ECT or ERT modalities. Experiments show that the proposed method can construct high quality ET images by discovering the strong complementary natures of the modalities and/or driven patterns.     

Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework

When evaluating transportation infrastructure projects and determining which of them will be carried out from a set of projects and given a budget constraint, several criteria need to be considered in the decision. Standard evaluation practices imply the aggregation of impacts into one utility function which is later optimized. Nevertheless these techniques used for translation of different measuring units into monetary terms are highly controversial. Multicriteria techniques can explicitly deal with different measuring units, however, they are not suitable to model interdependence relationships of projects that share a common characteristic (same route, location or target population, for instance). In this research we model this transportation planning problem, the multi-objective transportation infrastructure project selection problem (MTIPSP), as a constrained multi-objective optimization problem with quadratic objective functions, using a variation of the multi-objective 0–1 knapsack problem plus some additional constraints. Given the combinatorial nature of the problem, an evolutionary-based framework is used for the identification of Pareto solutions, and later, those with non-attractive properties are filtered using a Knee Identification Procedure. The final selection of the projects portfolio is made using a well known multicriteria decision aid method and including the decision makers’ preferences based on the existing context.