New framework for score segmentation and analysis in OpenMusic

We present new tools for the segmentation and analysis of musical scores in the OpenMusic computer-aided composition environment. A modular object-oriented framework enables the creation of segmentations on score objects and the implementation of automatic or semi-automatic analysis processes. The analyses can be performed and displayed thanks to customizable classes and callbacks. Concrete examples are given, in particular with the implementation of a semi-automatic harmonic analysis system and a framework for rhythmic transcription.

Machine learning techniques for automatic opinion detection in non-traditional textual genres

This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.

Extractive text summarization: can we use the same techniques for any text?

In this paper we address two issues. The first one analyzes whether the performance of a text summarization method depends on the topic of a document. The second one is concerned with how certain linguistic properties of a text may affect the performance of a number of automatic text summarization methods. For this we consider semantic analysis methods, such as textual entailment and anaphora resolution, and we study how they are related to proper noun, pronoun and noun ratios calculated over original documents that are grouped into related topics. Given the obtained results, we can conclude that although our first hypothesis is not supported, since it has been found no evident relationship between the topic of a document and the performance of the methods employed, adapting summarization systems to the linguistic properties of input documents benefits the process of summarization.

Gender analysis of moxifloxacin clinical trials

Purpose: To determine the inclusion of women and the sex-stratification of results in moxifloxacin Clinical Trials (CTs), and to establish whether these CTs considered issues that specifically affect women, such as pregnancy and use of hormonal therapies. Previous publications about women’s inclusion in CTs have not specifically studied therapeutic drugs. Although this type of drug is taken by men and women at a similar rate, adverse effects occur more frequently in the latter. Methods: We reviewed 158 published moxifloxacin trials on humans, retrieved from MedLine and the Cochrane Library (1998–2010), to determine whether they complied with the gender recommendations published by U.S. Food and Drug Administration Guideline. Results: Of a total of 80,417 subjects included in the moxifloxacin CTs, only 33.7% were women in phase I, in contrast to phase II, where women accounted for 45%, phase III, where they represented 38.3% and phase IV, where 51.3% were women. About 40.9% (n = 52) of trials were stratified by sex and 15.3% (n = 13) and 9% (n = 7) provided data by sex on efficacy and adverse effects, respectively. We found little information about the influence of issues that specifically affect women. Only 3 of the 59 journals that published the moxifloxacin CTs stated that authors should stratify their results by sex. Conclusions: Women are under-represented in the published moxifloxacin trials, and this trend is more marked in phase I, as they comprise a higher proportion in the other phases. Data by sex on efficacy and adverse effects are scarce in moxifloxacin trials. These facts, together with the lack of data on women-specific issues, suggest that the therapeutic drug moxifloxacin is only a partially evidence-based medicine.

A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Detecting implicit expressions of affect in text using EmotiNet and its extensions

In the past years, an important volume of research in Natural Language Processing has concentrated on the development of automatic systems to deal with affect in text. The different approaches considered dealt mostly with explicit expressions of emotion, at word level. Nevertheless, expressions of emotion are often implicit, inferrable from situations that have an affective meaning. Dealing with this phenomenon requires automatic systems to have “knowledge” on the situation, and the concepts it describes and their interaction, to be able to “judge” it, in the same manner as a person would. This necessity motivated us to develop the EmotiNet knowledge base — a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. In this article, we briefly present the process undergone to build EmotiNet and subsequently propose methods to extend the knowledge it contains. We further on analyse the performance of implicit affect detection using this resource. We compare the results obtained with EmotiNet to the use of alternative methods for affect detection. Following the evaluations, we conclude that the structure and content of EmotiNet are appropriate to address the automatic treatment of implicitly expressed affect, that the knowledge it contains can be easily extended and that overall, methods employing EmotiNet obtain better results than traditional emotion detection approaches.

Main ecological gradients and landscape matrix affect wild rabbit Oryctolagus cuniculus (Linnaeus, 1758) (Mammalia: Leporidae) abundance in a coastal region (South-East Spain)

Landscape analysis with transects, in the Marina Baja area (province of Alicante, Spain), has contributed to establish the influence of different landscape matrices and some environmental gradients on wild rabbit Oryctolagus cuniculus (Linnaeus, 1758) (Mammalia: Leporidae) abundance (kilometric abundance index, KAI). Transects (n = 396) were developed to estimate the abundance of this species in the study area from 2006 to 2008.Our analysis shows that rabbits have preferences for a specific land use matrix (irrigated: KAI = 3.47 ± 1.14 rabbits/km). They prefer the coastal area (KAI = 3.82 ± 1.71 rabbits/km), which coincides with thermo-Mediterranean (a bioclimatic belt with a tempered winter and a hot and dry summer with high human density), as opposed to areas in the interior (continental climate with lower human occupation). Their preference for the southern area of the region was also noted (KAI = 8.22 ± 3.90 rabbits/km), which coincides with the upper semi-arid area, as opposed to the northern and intermediate areas (the north of the region coinciding with the upper dry and the intermediate area with the lower dry). On the other hand, we found that the number of rabbits increased during the 3-year study period, with the highest abundance (KAI = 2.71 ± 1.30 rabbits/km) inMay. Thus, this study will enable more precise knowledge of the ecological factors (habitat variables) that intervene in the distribution of wild rabbit populations in a poorly studied area.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

Biological significance of dead biomass retention trait in Mediterranean Basin species: an analysis between different successional niches and regeneration strategies as functional groups

Standing dead biomass retention is considered one of the most relevant fuel structural traits to affect plant flammability. However, very little is known about the biological significance of this trait and its distribution between different functional groups. Our aim was to analyse how the proportion of dead biomass produced in Mediterranean species is related to the successional niche of species (early-, mid- and late-successional stages) and the regeneration strategy of species (seeders and resprouters). We evaluated biomass distribution by size classes and standing dead biomass retention in nine dominant species from the Mediterranean Basin in different development stages (5, 9, 14 and 26 years since the last fire). The results revealed significant differences in the standing dead biomass retention of species that presented a distinct successional niche or regeneration strategy. These differences were restricted to the oldest ages studied (>9 years). Tree and small tree resprouters, typical in late-successional stages, presented slight variations with age and a less marked trend to retain dead biomass, while seeder shrubs and dwarf shrubs, characteristic of early-successional stages, showed high dead biomass loads. Our results suggest that the species that tend to retain more dead branches are colonising species that may promote fire in early-successional stages.