Abstract
Cable-driven robots have received some attention by the scientific community and, recently, by the industry because they can transport hazardous materials with a high level of safeness which is often required by construction sites. In this context, this research presents an extension of a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. The proposed robot is formed by a rotating claw and a set of four cables, enabling four degrees of freedom. In addition, this paper proposes a new Vision-Guided Path-Planning System (V-GPP) that provides a visual interpretation of the scene: the position of the robot, the target and obstacles location; and optimizes the trajectory of the robot. Moreover, it determines a collision-free trajectory in 3D that takes into account the obstacles and the interaction of the cables with the scene. A set of experiments make possible to validate the contribution of V-GPP to the SPIDERobot while operating in realistic working conditions, as well as, to evaluate the interaction between the V-GPP and the motion controlling system. The results demonstrated that the proposed robot is able to construct architectural structures and to avoid collisions with obstacles in their working environment. The V-GPP system localizes the robot with a precision of 0.006 m, detects the targets and successfully generates a path that takes into account the displacement of cables. Therefore, the results demonstrate that the SPIDERobot can be scaled up to real working conditions.

Abstract
The expansion of social networks has contributed to the propagation of information relevant to general audiences. However, this is small percentage compared to all the data shared in such online platforms, which also includes private/personal information, simple chat messages and the recent called ‘fake news’. In this paper, we make an exploratory analysis on two social networks to extract features that are indicators of relevant information in social network messages. Our goal is to build accurate machine learning models that are capable of detecting what is journalistically relevant. We conducted two experiments on CrowdFlower to build a solid ground truth for the models, by comparing the number of evaluations per post against the number of posts classified. The results show evidence that increasing the number of samples will result in a better performance on the relevancy classification task, even when relaxing in the number of evaluations per post. In addition, results show that there are significant correlations between the relevance of a post and its interest and whether is meaningfully for the majority of people. Finally, we achieve approximately 80% accuracy in the task of relevance detection using a small set of learning algorithms. © 2017 Copyright is held by the owner/author(s).

Abstract
In the last decade, technological advances and improved manufacturing processes have significantly dropped the price tag of mobile devices such as smartphones and tablets whilst augmenting their storage and computational capabilities. Their ubiquity fostered research on mobile edge-clouds, formed by sets of such devices in close proximity, with the goal of mastering their global computational and storage resources. The development of crowdsourcing applications that take advantage of such edge-clouds is, however, hampered by the complexity of network formation and maintenance, the intrinsic instability of wireless links and the heterogeneity of the hardware and operating systems in the devices. In this paper we present a middleware to deal with this complexity, providing a building block upon which crowd-sourcing applications may be built.We motivate the development of the middleware through a discussion of real-world applications, and present the middleware's architecture along with the associated components and current development status. The middleware takes form as a Java API for Android devices that allows for the establishment of links using heterogeneous communication technologies (e.g., Wifi-Direct, Bluetooth), and the combination of these links to form a logical edge-cloud network. On top of this functionality, services for edge computation, storage, and streaming are also being developed. © 2017 Association for Computing Machinery.

Abstract
The green computing has an important role in today's software technology. Either speaking about small IoT devices or large cloud servers, there is a generic requirement of minimizing energy consumption. For this purpose, we usually first have to identify which parts of the system is responsible for the critical energy peaks. In this paper we suggest a new method to measure the energy consumption based on Low Level Virtual Machine (LLVM)/Clang tooling. The method has been tested on 2 open source systems and the output is visualized via the well-known Kcachegrind tool. © Copyright 2017 by the paper's authors.

Abstract

Abstract
Software developers usually work as a team and in order to improve the team organization and performance several companies use agile methodologies. The efficiency of this methodologies can be improved by the use of project management tools. Several software developers are difficult to motivate on the usage of these tools, as they often claim being disruptive to their workflow. This paper presents the use of gamification to incite software developers to use this kind of tools with the necessary regularity. The main focus of this work is on the tasks completion and the regular use of the project management tool, while insuring the respect of the values proposed by the agile manifesto to software development. The gamification process is described in detail, followed by an application the project management software SCRAIM.