Abstract
In the last few years, sustainability has become one of the priority lines for many companies and organizations, especially public administrations. This trend has been even more evident in some regions where the preservation of natural resources is of utmost importance, not only from an environmental perspective, but also from an economic one. In this context, technology has become one of the key factors to achieve sustainability goals. An example of these technologies are Unmanned Aerial Vehicles (UAVs) which are being used more and more with sustainability purposes. However, although some efforts have been made to propose software approaches to model sustainability, some examples that model the impact of technology on sustainability are still needed. This paper presents an instance of a sustainability metamodel for the UAVs domain. This model allows to specify the impact of UAV-based processes on sustainability, and also to identify potential limitations that may hinder its applicability. Finally, the paper provides some suggestions to complete the metamodel based on the instantiation process.

Abstract
The continuous process industry is currently facing a serious need for systems that allow to monitor and manage their factories. Despite the existing industrial visualization screens provide for a partial answer to the identified issue, there is a set of new technologies who might help on developing the existing industrial visualization systems and provide and improved aid to the human controllers who monitor and manage the production processes. The presented paper aims at not only delivering a serious and focused characterization on the existing industry visualization screens, but above all, it intends to present several opportunities, challenges and new perspectives for the use of innovative visualization technologies (such as 2.5D/3D screens, augmented and virtual reality) in those industries whose production process assumes itself as continuous. This contribute, from our perspective will serve as a basis for further research on the field.

Abstract
Purpose - This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot's size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach - Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings - Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications - Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications - This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications - The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value - This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.

Abstract
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when related social feedback is unavailable. The second strategy, a posteriori, is focused on predicting the popularity of news using related social feedback. However, both strategies present shortcomings related to data availability and time of prediction. To overcome such shortcomings, we propose a hybrid strategy of time-based ensembles using models from both strategies. Using news data from Google News and popularity data from Twitter, we show that the proposed ensembles significantly improve the early and accurate prediction of rare cases of highly popular news.

Abstract
The growing dependency of our society on increasingly complex software systems, combining mobile and cloud-based applications and services, makes the test activities even more important and challenging. However, sometimes software tests are not properly performed due to tight deadlines, due to the time and skills required to develop and execute the tests or because the developers are too optimistic about possible faults in their own code. Although there are several frameworks for mobile test automation, they usually require programming skills or complex configuration steps. Hence, in this paper, we propose a framework that allows creating and executing tests for Android applications without requiring programming skills. It is possible to create automated tests based on a set of pre-defined actions and it is also possible to inject data into device sensors. An experiment with programmers and non-programmers showed that both can develop and execute tests with a similar time. A real world example using a fall detection application is presented to illustrate the approach. © 2016 ACM.

Abstract
Modern distributed real-time embedded applications have high processing requirements associated with strict deadlines. For some applications, such constraints cannot be fulfilled by existing single-core embedded platforms. A solution is to parallelize the execution of the applications, by allowing networked nodes to distribute their workload to remote nodes with spare capacity. In that context, this paper presents a holistic timing analysis for fixedpriority fork-join parallel/distributed tasks. Furthermore, we extend the holistic approach to consider the interaction between parallel threads and messages interchanged through a flexible time triggered switched Ethernet network, and we show how the pessimism on the worst case response time computation of such tasks can be reduced by considering the pipeline effect that occurs in such distributed systems. To evaluate the performance and correctness of the holistic model, this paper includes a numerical evaluation based on a real automotive application. The obtained results show that the proposed method is effective in distributing the load by different nodes, allowing a significant reduction of the worst case response time of the tasks. Moreover, the paper also reports an implementation of the model on a Linux library, called parallel/distributed real-time, as well as the corresponding results obtained on a real testbed. The obtained results are in accordance with the predictions of the holistic timing analysis.