Abstract
There is an increasing number of mobile robot applications. The demanding of the Industry 4.0 pushes the robotic areas in the direction of the decision. The autonomous robots should actually decide the path according to the dynamic environment. In some cases, time requirements must also be attended and require fast path planning methods. This paper addresses a comparison between well-known path planning methods using a realistic simulator that handles the dynamic properties of robot models including sensors. The methodology is implemented in SimTwo that allows to compare the A* and RRT* algorithms in different scenarios with dynamic and real time constraint scenarios. Copyright

Abstract
The emergence and popularization of online social networks suddenly made available a large amount of data from social organization, interaction and human behavior. All this information opens new perspectives and challenges to the study of social systems, being of interest to many fields. Although most online social networks are recent, a vast amount of scientific papers was already published on this topic, dealing with a broad range of analytical methods and applications. Therefore, the development of a tool capable of gather tailored information from social networks is something that can help a lot of researchers on their work, especially in the area of Natural Language Processing (NLP). Nowadays, the daily base medium where people use more often text language lays precisely on social networks. Therefore, the ubiquitous crawling of social networks is of the utmost importance for researchers. Such a tool will allow the researcher to get the relevant needed information, allowing a faster research in what really matters, without loosing time on the development of his own crawler. In this paper, we present an extensive analysis of the existing social networks and their APIs, and also describe the conception and design of a social network crawler which will help NLP researchers. © 2019 Association for Computing Machinery.

Abstract
Jet impingement is widely implemented in a variety of engineering applications and industrial processes where high average heat transfer coefficients and the uniformity of the heat transfer over the impinging surface are required to enhance the process and to avoid local hot (or cold) spots. Multiple jet impingement involves several parameters that interfere with the performance of the process, and there are no universal optimal solutions. To ensure the optimization of the process, it is important to understand the influence of these parameters in the heat transfer over the target surface. To perform this study an experimental research will be performed on a purpose-built test facility which has been commissioned, using a Particle Image Velocimetry system. However, to reduce time and costs associated to the experimental tests, it is important to perform a Design of Experiments, that allows to reduce the number of trials, focusing on the parameters that have a greater influence on the process performance. Taguchi’s method allows the optimization of the process through the selection of the most suitable parameters values. This work presents the method that must be followed before the development of experiments related to the multiple jet impingement over a complex surface, from the design of the experimental setup to the design of the matrix of experiments. © 2019, Springer Nature Switzerland AG.

Abstract
Open Design (OD) is characterized as fast-growing community-based generative process, that is supported in a digital repository or platform, in order to develop a specific topic area or field. Many examples of OD are available nowadays, from software (probably the most common) to hardware, and from science and technology to business. One of the most known platforms of OD is an open editable content encyclopedia, the Wikipedia. Trough the involvement of volunteers (community that do not receive any payment for the work) in writing articles in a collaborative way was possible to develop, most probably, the extensive multi-language encyclopedia in the World started from scratch within 18 years. Based on analysis made from available Wikipedia statistical data, it is possible to conclude that from a stochastic behaviour the variables reached a transition phase in which a presence of a hysteresis between all variables were reached in between 60 to 80 months, and the editors (agents of the community) are an important asset for the system "transformation". (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
When learning from images, it is desirable to augment the dataset with plausible transformations of its images. Unfortunately, it is not always intuitive for the user how much shear or translation to apply. For this reason, training multiple models through hyperparameter search is required to find the best augmentation policies. But these methods are computationally expensive. Furthermore, since they generate static policies, they do not take advantage of smoothly introducing more aggressive augmentation transformations. In this work, we propose repeating each epoch twice with a small difference in data augmentation intensity, walking towards the best policy. This process doubles the number of epochs, but avoids having to train multiple models. The method is compared against random and Bayesian search for classification and segmentation tasks. The proposal improved twice over random search and was on par with Bayesian search for 4% of the training epochs.

Abstract
Evaluating scientists based on their scientific production is a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics rank individual entities (e.g., researchers, journals, faculties) without looking at the whole data (i.e., the whole network). Network algorithms, such as PageRank, have been used to measure node importance in a network, and have been applied to author ranking. However, traditional PageRank only uses network topology and ignores relevant features of scientific collaborations. Multiple extensions of PageRank have been proposed, more suited for author ranking. These methods enrich the network with information about the author’s productivity or the venue and year of the publication/citation. Most state-of-the-art (STOA) feature-enriched methods either ignore or do not combine effectively this information. Furthermore, STOA algorithms typically disregard that the full network is not known for most real-world cases.Here we describe OTARIOS, an author ranking method recently developed by us, which combines multiple publication/citation criteria (i.e., features) to evaluate authors. OTARIOS divides the original network into two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science, and compare it against STOA methods. When matching OTARIOS’ produced ranking with a ground-truth ranking (comprised of best paper award nominations), we observe that OTARIOS is >30% more accurate than traditional PageRank (i.e., topology based method) and >20% more accurate than STOA (i.e., competing feature enriched methods). We obtain the best results when OTARIOS considers (i) the author’s publication volume and publication recency, (ii) how recently the author’s work is being cited by outsiders, and (iii) how recently the author’s work is being cited by insiders and how individual he is. Our results showcase (a) the importance of efficiently combining relevant features and (b) how to adequately perform author ranking in incomplete networks.