Abstract
This paper explores the usage of unmanned aerial vehicles (UAVs) to acquire remotely sensed very high-resolution imagery for classification of an agrosilvopastoral system in a rural region of Portugal. Aerial data was obtained using a low-cost UAV, equipped with an RGB sensor. Acquired imagery undergone a photogrammetric processing pipeline to obtain different data products: an orthophoto mosaic, a canopy height model (CHM) and vegetation indices (VIs). A superpixel algorithm was then applied to the orthophoto mosaic, dividing the images into different objects. From each object, different features were obtained based in its maximum, mean, minimum and standard deviation. These features were extracted from the different data products: CHM, VIs, and color bands. Classification process – using random forest algorithm – classified objects into five different classes: trees, low vegetation, shrubland, bare soil and infrastructures. Feature importance obtained from the training model showed that CHM-driven features have more importance when comparing to those obtained from VIs or color bands. An overall classification accuracy of 86.4% was obtained. © Springer Nature Switzerland AG 2019.

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. © Springer Nature Switzerland AG 2019.

Abstract
Viticulture has a major impact in the European economy and over the years the intensive grapevine production led to the proliferation of many varieties. Traditionally these varieties are manually catalogued in the field, which is a costly and slow process and being, in many cases, very challenging to classify even for an experienced ampelographer. This article presents a cost-effective and automatic method for grapevine varieties classification based on the analysis of the leaf’s images, taken with an RGB sensor. The proposed method is divided into three steps: (1) color and shape features extraction; (2) training and; (3) classification using Linear Discriminant Analysis. This approach was applied in 240 leaf images of three different grapevine varieties acquired from the Douro Valley region in Portugal and it was able to correctly classify 87% of the grapevine leaves. The proposed method showed very promising classification capabilities considering the challenges presented by the leaves which had many shape irregularities and, in many cases, high color similarities for the different varieties. The obtained results compared with manual procedure suggest that it can be used as an effective alternative to the manual procedure for grapevine classification based on leaf features. Since the proposed method requires a simple and low-cost setup it can be easily integrated on a portable system with real-time processing to assist technicians in the field or other staff without any special skills and used offline for batch classification. © Springer Nature Switzerland AG 2019.

Abstract
Large-scale password data breaches are becoming increasingly commonplace, which has enabled researchers to produce a substantial body of password security research utilising real-world password datasets, which often contain numbers of records in the tens or even hundreds of millions. While much study has been conducted on how password composition policies-sets of rules that a user must abide by when creating a password-influence the distribution of user-chosen passwords on a system, much less research has been done on inferring the password composition policy that a given set of user-chosen passwords was created under. In this paper, we state the problem with the naive approach to this challenge, and suggest a simple approach that produces more reliable results. We also present pol-infer, a tool that implements this approach, and demonstrates its use in inferring password composition policies. © 2019 IEEE.

Abstract
Live Programming is an idea pioneered by programming environments from the earliest days of computing, such as those for Lisp and Smalltalk. One thing they had in common is liveness: an always accessible evaluation and nearly instantaneous feedback, usually focused on coding activities. In this paper, we argue for Live Software Development (LiveSD), bringing liveness to software development activities beyond coding, to make software easier to visualize, simpler to understand, and faster to evolve. Multiple challenges may vary with the activity and application domain. Research on this topic needs to consider the more important liveness gaps in software development, which representations and abstractions better support developers, and which tools are needed to support it. © 2019 Association for Computing Machinery.

Abstract
In the context of the CloudDBAppliance (CDBA) project, fault tolerance and high-availability are provided in layers: within each appliance, within a data centre and between data centres. This paper presents the proposed replication architecture for providing fault tolerance and high availability within a data centre. This layer configuration, along with specific deployment constraints require a custom replication architecture. In particular, replication must be implemented at the middleware-level, to avoid constraining the backing operational database. This paper is focused on the design of the CDBA Replication Manager along with an evaluation, using micro-benchmarking, of components for the replication middleware. Results show the impact, on both throughput and latency, of the replication mechanisms in place.