Abstract
This book chapter proposes a description of smart gateways and cyber-physical systems (CPS) for the industrial internet of things (I-IOT). It also presents a case study where a smart gateway is developed to be used in different types of industrial equipment for the shop floor. The case study is developed under the specifications of different industries in the region of Castelo Branco. It is a proof that the 4th industrial revolution will be the engine for SME innovation, independence of the regions and their financial strength. It is also proof that the cooperation between universities, industries and startups can evolve to break barriers and add value in the improvement of regional industries competitiveness. Topics that will be addressed on the chapter can be used for developers, students, researchers and enthusiasts to learn topics related to I-IOT, such as data acquisitions systems, wired and wireless communication devices and protocols, OPC servers and LabVIEW programming.

Abstract
Smart Governence as its roots in e-government, in the principles of good governance, and in the assumptions of citizens' participation and involvement in public decision-making and is considered one of the six main characteristics of smart cities. The present investigation was intended to answer the question: 'What smart governance practices are being implemented in European smart cities' through an extensive literature review and content analysis of the websites of six European smart cities: Amsterdam; Barcelona; Copenhagen; Lisbon; Manchester and Stockholm. The objective was to identify the presence of factors related with eparticipation; e-services; and the functioning of local public administration on the city's websites. It was concluded that all the smart cities analyzed presented some factors related with smart governance, but with different levels of development and application. © 2019 AISTI.

Abstract
We present the status and plans for the Keck All sky Precision Adaptive optics (KAPA) program. The program includes four key science projects, an upgrade to the Keck I laser guide star (LGS) adaptive optics (AO) facility to improve image quality and sky coverage, AO telemetry based point spread function (PSF) estimates for all science exposures, and an educational component focused on broadening the participation of women and underrepresented groups in instrumentation. All of these elements have pathfinder relevance for the ELTs. For the purpose of this conference we will focus on the AO facility upgrade which includes implementation of a new laser, wavefront sensor and real-time controller to support laser tomography, the laser tomography system itself, and modifications to an existing near-infrared tip-tilt sensor to support multiple natural guide star (NGS) and focus measurements.

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.

Abstract