Abstract
Internationalization-at-home activities present relevant opportunities for innovation in the teaching-learning process. These activities provide a very broad set of advantages, including the development of soft skills and increased motivation of students. This article aims to contribute to the debate on internationalization of higher education institutions by exploring students’ perceptions and experiences after participating in an international collaboration project involving 153 students in 5 universities in Germany, Mexico, Portugal, Scotland and the United States of America during the Fall/Winter semester 2017. The focus of this study is students’ satisfaction and perceptions. Results demonstrate that although students found the idea of collaborating with peers from other universities very appealing, high levels of satisfaction depended on commitment, both their own and that of their peers. The feelings during the project were predominantly positive, although students recognized that they should have communicated more with their partners and put more effort in the collaboration. This article provides useful evidence for instructors that are considering an international collaboration activity for their students. It demonstrates the deliverables of such initiatives as well as the clear advantage gained by receiving students’ feedback. Hopefully it will inspire other instructors and contribute to the diffusion of international collaboration as a teaching-learning practice.

Abstract
This paper describes a Decision Support System (DSS) that aims to plan and maintain the weekly self-promotion space for an over the air TV station. The self-promotion plan requires the assignment of several self-promotion advertisements to a given set of available time slots over a pre-specified planning period. The DSS consists of a data base, a statistic module, an optimization module, and a user interface. The input data is provided by the TV station and by an external audiometry company, which collects daily audience information. The statistical module provides estimates based on the data received from the audiometry company. The optimization module uses a genetic algorithm that can find good solutions quickly. The interface reports the solution and corresponding metrics and can also be used by the decision makers to manually change solutions and input data. Here, we report mainly on the optimization module, which uses a genetic algorithm (GA) to obtain solutions of good quality for realistic sized problem instances in a reasonable amount of time. The GA solution quality is assessed using the optimal solutions obtained by using a branch-and-bound based algorithm to solve instances of small size, for which optimality gaps below 1% are obtained.

Abstract
Over the years robotics has made great progress. Nowadays, robots begin to be part of the life of any person, designated social robotic. Humanoid robots are fascinating and have several advantages, such as they can work in places where there is a risk of contamination, risk of health, danger of life, places that are difficult to access. They also are able to access different types of terrain and to climb stairs. NAO robot is currently the humanoid platform with high sensory capacity that it has lower costs in the market. This robot is similar to human in order to have a more real and natural with society. Using the capabilities of the robot and adding other sensors, we can have a more powerful machine in our society. NAO robot is presented in this paper as a domestic robot. © CLAWAR Association.

Abstract
FASTEN is an H2020 project under a bilateral call UE-Brazil. Embraer is a global aerospace company, with manufacturing and assembly lines in Europe, Brazil and USA. FASTEN aims to advance IoT and IoT enabled applications to support Industry 4.0 concepts, namely in the area of automation and additive manufacturing. The project results will be demonstrated through two pilots: one in Brazil, lead by a ThyssenKrupp use case, and the other in Europe, at Embraer facilities in Portugal. The project results for the Embraer use case will be presented, with emphasis on bilateral collaboration gains provided by exploiting common frameworks for development and open architecture, and future opportunities for exploitation discussed.

Abstract
This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Abstract
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.