Abstract
This article presents MixMash, an interactive tool which streamlines the process of music mashup creation by assisting users in the process of finding compatible music from a large collection of audio tracks. It extends the harmonic mixing method by Bernardes, Davies and Guedes with novel degrees of harmonic, rhythmic, spectral, and timbral similarity metrics. Furthermore, it revises and improves some interface design limitations identified in the former model software implementation. A new user interface design based on cross-modal associations between musical content analysis and information visualisation is presented. In this graphic model, all tracks are represented as nodes where distances and edge connections display their harmonic compatibility as a result of a force-directed graph. Besides, a visual language is defined to enhance the tool's usability and foster creative endeavour in the search of meaningful music mashups.

Abstract
This paper approaches the permanent struggle that less favoured regions must deal with regarding economic opportunities, job creation, income and regional production increase. Since an increased demand for nature and protected areas is taking place in a more and more urban society, some innovation potential is emerging. The study we have developed is focused on sustainable tourism practices in a specific natural area (Malcata Mountain Reserve), using electric mobility, which is known for its zero emission, no polluting and noise-free travelling. The broader study is carried out under the Interreg Funding Program in the Moveletur Project. Our aims are to promote a model of sustainable and clean tourism for visitors of natural areas, to create a network of green tourism itineraries connecting sites of natural and/or cultural value using electric vehicles and to empower tourism sector entrepreneurs with a new added-value service for their activity. Joint work with other natural areas is required to increase results. After the project is finished (by the end of 2018) there will be an improved knowledge about natural and cultural values that natural areas hold and that can be used for visitors’ enjoyment. There will be a more respectful way of ‘doing tourism’ in natural areas and hopefully it will address employment creation and improved territorial competitiveness. Finally, tourism experiences will have more quality and the project will promote smart villages’ further development by using technological components. © 2019, Springer International Publishing AG, part of Springer Nature.

Abstract
The rise of online social networks has reshaped the way information is published and spread. Users can now post in an effortless way and in any location, making this medium ideal for searching breaking news and journalistic relevant content. However, due to the overwhelming number of posts published every second, such content is hard to trace. Thus, it is important to develop methods able to detect and analyze whether a certain text contains journalistic relevant information. Furthermore, it is also important that this detection system can provide additional information towards a better comprehension of the prediction made. In this work, we overview our system, based on an ensemble classifier that is able to predict if a certain post is relevant from a journalistic perspective which outperforms the previous relevant systems in their original datasets. In addition, we describe REMINDS: a web platform built on top of our relevance system that is able to provide users with the visualization of the system's features as well as additional information on the text, ultimately leading to a better comprehension of the system's prediction capabilities. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.

Abstract
We explore the task of computational beat tracking for musical audio signals from the perspective of putting an end-user directly in the processing loop. Unlike existing “semi-automatic” approaches for beat tracking, where users may select from among several possible outputs to determine the one that best suits their aims, in our approach we examine how high-level user input could guide the manner in which the analysis is performed. More specifically, we focus on the perceptual difficulty of tapping the beat, which has previously been associated with the musical properties of expressive timing and slow tempo. Since musical examples with these properties have been shown to be poorly addressed even by state of the art approaches to beat tracking, we re-parameterise an existing deep learning based approach to enable it to more reliably track highly expressive music. In a small-scale listening experiment we highlight two principal trends: i) that users are able to consistently disambiguate musical examples which are easy to tap to and those which are not; and in turn ii) that users preferred the beat tracking output of an expressive-parameterised system to the default parameterisation for highly expressive musical excerpts. © 2021, Springer Nature Switzerland AG.

Abstract
The present study focuses on the estimation of the human capital stock for the Cape Verdean economy in the period 1950-2012. Adapting the methodology proposed by Barro and Lee, based on past schooling values, we found that between 1950 and 2012 the Cape Verdean working-age population showed a gradual improvement in the levels of schooling, rising from 0.7 years of schooling in the 1950s to 5.4 in late 2012. Thus, in each year, the average years of schooling increased only 0.08 years, meaning that, in net terms and on average, only 7.6 per cent of the working-age population was attending some level of formal education. The availability of a time series of number of average schooling years in Cape Verde opens up possibilities for assessing the impact of human capital on the country's economic development.

Abstract
A cost-effective optical sensor for continuous in-situ monitoring of turbidity and suspended particulate matter concentration (SPM), with a production cost in raw materials less than 20 (sic), is presented for marine or fluvial applications. The sensor uses an infrared LED and three photodetectors with three different positions related to the light source-135 degrees, 90 degrees and 0 degrees-resulting in three different types of light detection: backscattering, nephelometry and transmitted light, respectively. This design allows monitoring in any type of environment, offering a wide dynamic range and accuracy for low and high turbidity or SPM values. An ultraviolet emitter-receiver pair is also used to differentiate organic and inorganic matter through the differences in absorption at different wavelengths. The optical transducers are built in a watertight structure with a radial configuration where a printed circuit board with the electronic signal coupling is assembled. An in-lab calibration of the sensor was made to establish a relation between suspended particulate matter (SPM) or the turbidity (NTU) to the photodetectors' electrical output value in Volts. Two di fferent sizes of seashore sand were used (180 mu m and 350 mu m) to evaluate the particle size susceptibility. The sensor was tested in a fluvial environment to evaluate SPM change during sediment transport caused by rain, and a real test of 22 days continuous in-situ monitoring was realized to evaluate its performance in a tidal area. The monitoring results were analysed, showing the SPM change during tidal cycles as well as the influence of the external light and biofouling problems.