Abstract

Abstract
Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD) matrix factorisation algorithm for rating prediction. Additionally, we explore different (i) profiling approaches (hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together with location and StV post-recommendation filtering. While the most promising results occur with the hotel-based version, the best theme-based version shows a remarkable memory conciseness when compared with its hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams. The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done.

Abstract
This paper addresses one of the key components in today's industrialization approach: virtualization. The work describes the virtualization of a typical production process, the digital twin in the scope of Industry 4.0, involving different devices, such as robotic arms, conveyors, automatic warehouses and vision systems. It includes both legacy and recent equipment, with different characteristics and communication capabilities, ranging from RS232 serial communication to TCP/IP-based communication, or even I/O-based interaction for devices with no communication capabilities. The developed approach aims at industrial implementations, while allowing for educational purposes. For a standardized approach, the OPC UA protocol is used for high-level communication between the various systems. Several results are described showing the success of the methodology and application.

Abstract
Authenticity, traceability and certification are key to assure both quality and confidence to wine consumers and an added commercial value to farmers and winemakers. Grapevine variety stands out as one of the most relevant factors to be considered in wine identification within the whole wine sector value chain. Ampelography is the science responsible for grapevine varieties identification based on (i) in-situ visual inspection of grapevine mature leaves and (ii) on the ampelographer experience. Laboratorial analysis is a costly and time-consuming alternative. Both the lack of experienced professionals and context-induced error can severely hinder official regulatory authorities’ role and therefore bring about a significant impact in the value chain. The purpose of this paper is to assess deep learning potential to classify grapevine varieties through the ampelometric analysis of leaves. Three convolutional neural networks architectures performance are evaluated using a dataset composed of six different grapevine varieties leaves. This preliminary approach identified Xception architecture as very promising to classify grapevine varieties and therefore support a future autonomous tool that assists the wine sector stakeholders, particularly the official regulatory authorities.

Abstract
Counting subgraph occurrences is a hard but very important task in complex network analysis, with applications in concepts such as network motifs or graphlet degree distributions. In this paper we present a novel approach for this task that takes advantage of knowing that a large fraction of subgraph types does not appear at all on real-world networks. We describe a pattern-growth methodology that is able to iteratively build subgraph patterns that do not contain smaller non-occurring subgraphs, significantly pruning the search space. By using the g-trie data structure, we are able to efficiently only count those subgraphs that we are interested in, reducing the total computation time. The obtained experimental results are very promising allowing us to avoid the computation of up to 99.78% of all possible subgraph patterns. This showcases the potential of this approach and paves the way for reaching previously unattainable subgraph sizes. © Springer Nature Switzerland AG 2019.

Abstract
In the world of mobile robot navigation, the ultrasonic sensors stand out for presenting attractive features at an affordable cost. The main problem in the use of these devices lies in the difficulty of correctly interpreting the obtained data, which means that their efficiency is limited. This paper focuses on the improvement and implementation of a low cost location system based on ultrasonic sensors. Through the combination of mathematical techniques and signal processing it is possible to make the system more accurate and reliable. The developed system includes the data acquisition, the signal filtering, and the trigonometric methods to estimate the coordinates of a target and can be assembled in a mobile robot.