Abstract
Using Fourier methods to reconstruct the phase measured by a wavefront sensor (WFS) can significantly re- duce the number of computations required, as well as easily enable predictive reconstruction methods based on knowledge of the adaptive optics system, atmospheric turbulence and wind profile. Previous work on Fourier re- construction has focused on the Shack-Hartmann WFS. With increasing interest in the highly sensitive Pyramid WFS we present the development of Fourier reconstruction tools tailored to the Pyramid sensor. We include the development of the Fourier model, it's use for formulating error budgets and a laboratory demonstration of Fourier reconstruction with a Pyramid WFS.

Abstract
The new paradigms of Industry 4.0 demand the collaboration between robot and humans. They could help and collaborate each other without any additional safety unlike other manipulators. The robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This paper proposes a system that acquires the environment space, based on a kinect sensor, performs the path planning of a UR5 manipulator for pick and place tasks while avoiding the objects, based on the point cloud from kinect. Results allow to validate the proposed system. © Springer International Publishing AG 2018.

Abstract

Abstract
Ensemble models have been proven successful for batch recommendation algorithms, however they have not been well studied in streaming applications. Such applications typically use incremental learning, to which standard ensemble techniques are not trivially applicable. In this paper, we study the application of three variants of online gradient boosting to top-N recommendation tasks with implicit data, in a streaming data environment. Weak models are built using a simple incremental matrix factorization algorithm for implicit feedback. Our results show a significant improvement of up to 40% over the baseline standalone model. We also show that the overhead of running multiple weak models is easily manageable in stream-based applications.

Abstract
The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.

Abstract
Following the reflection around the emergence of “research university” in the context of the slow but progressive increase in value of science and technology and research and development in Portugal, a study applied to the knowledge transfer and the process of innovation in the university, in the context of a master dissertation in information science (IS), study area of information management, is presented. The university is one of the most important institutions in the context of the national innovation system (SNI), being part of its mission the creation and transfer of knowledge. At the University of Porto (U.Porto), projects, such as the University of Porto Innovation unit (U.Porto Inovação) and the Science and Technology Park of the University of Porto (UPTEC) seek to support the university's innovation value chain, promoting the reinforcement and solidification of knowledge transfer and of the relations between the university and companies, as well as the promotion and support to the creation of companies with a technological, scientific, and creative base, and the attraction of numerous innovation centers of national and international companies. This chapter points out an informational perspective on I&D+i (research and development and innovation) and entrepreneurship, based on the systemic theory and the quadripolar method, as theoretical and methodological guidance tools, and an information management/knowledge management approach of innovation models for the knowledge economy, the national and international referents, and corresponding set of indicators. An exploratory study, which allowed the identification of internal and external agents, the resources, the relations between actors and institutions, the processes and flows, and the main inputs and outputs, is presented. The most relevant result is embodied in a model of innovation indicators in an academic context and applied to the University of Porto.