No abstract

Modern agriculture is facing unique challenges in building a sustainable future for food production, in which the reliable detection of plantation threats is of critical importance. The breadth of existing information sources, and their equivalent sensors, can provide a wealth of data which, to be useful, must be transformed into actionable knowledge. Approaches based on Information Communication Technologies (ICT) have been shown to be able to help farmers and related stakeholders make decisions on problems by examining large volumes of data while assessing multiple criteria. In this paper, we address the automated identification (and count the instances) of the major threat of olive trees and their fruit, the Bactrocera Oleae (a.k.a. Dacus) based on images of the commonly used McPhail trap’s contents. Accordingly, we introduce the “Dacus Image Recognition Toolkit” (DIRT), a collection of publicly available data, programming code samples and web-services focused at supporting research aiming at the management the Dacus as well as extensive experimentation on the capability of the proposed dataset in identifying Dacuses using Deep Learning methods. Experimental results indicated performance accuracy (mAP) of 91.52% in identifying Dacuses in trap images featuring various pests. Moreover, the results also indicated a trade-off between image attributes affecting detail, file size and complexity of approaches and mAP performance that can be selectively used to better tackle the needs of each usage scenario.

No abstract

The aim of our study was to investigate the prevalence of smoking in a Greek working population. A questionnaire regarding smoking habit was collected from 1,005 out of 1,200 blue and white-collar employees (response rate: 84%). The overall smoking prevalence was 48.4% and did not differ by sex, age, education, and occupation. The mean cigarette consumption per day was 25.54, with no difference observed by occupation. The above-mentioned findings, if confirmed by further research, are alarming and inconsistent with the prevalent pattern of smoking habits in the West.

A collection of 64 fig (Ficus carica L.) accessions was characterized through the use of RAPD markers, and results were evaluated in conjunction with morphological and agronomical characters, in order to determine the genetic relatedness of genotypes with diverse geographic origin. The results indicate that fig cultivars have a rather narrow genetic base. Nevertheless, RAPD markers could detect enough polymorphism to differentiate even closely related genotypes (i.e., clones of the same cultivar) and a unique fingerprint for each of the genotypes studied was obtained. No wasteful duplications were found in the collection. Cluster analysis allowed the identification of groups in accordance with geographic origin, phenotypic data and pedigree. Taking into account the limited information concerning fig cultivar development, the results of this study, which provide information on the genetic relationships of genetically distinct material, dramatically increase the fundamental and practical value of the collection and represent an invaluable tool for fig germplasm management.

Considering music as a sequence of events with multiple complex dependencies, the Long Short-Term Memory (LSTM) architecture has proven very efficient in learning and reproducing musical styles. However, the generation of rhythms requires additional information regarding musical structure and accompanying instruments. In this paper we present DeepDrum, an adaptive Neural Network capable of generating drum rhythms under constraints imposed by Feed-Forward (Conditional) Layers which contain musical parameters along with given instrumentation information (e.g. bass and guitar notes). Results on generated drum sequences are presented indicating that DeepDrum is effective in producing rhythms that resemble the learned style, while at the same time conforming to given constraints that were unknown during the training process.

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an "aggressive" accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

This paper presents an algorithm that efficiently retrieves audio data similar to an audio query. The proposed method utilises a feature extraction method for acoustical music sequences. The extracted features are grouped by Minimum Bounding Rectangles (MBRs) and indexed by means of a spatial access method. We also present a novel false alarm resolution method that utilises a reverse order schema while calculating the distance of the query and results, in order to avoid costly operations. Performance evaluation results show that the proposed technique achieves considerable performance improvement in comparison to an existing method.