Abstract
This paper describes the development of an application that allows an ABB robot arm to automatically perform the portrait of people. The Portraiture Robot performs the picture of a human face on paper. The developed system consists of 4 steps: (i) image acquisition through a webcam, (ii) image processing to retrieve the contours and features of the person's face, (iii) vectorization of the coordinates in the image plane, and (iv) conversion of the coordinates to the RAPID programming language. To get only the person's face, is performed a background subtraction and to obtain only the necessary information from the image are used filtering techniques to remove the features and contours of the person's face. To convert these points into x, y coordinates, the contours are vectorised and sent to a file, saved according to a defined protocol, and allowing to create a program for the robot. The developed application allows processing of all blocks listed above in real-time and in a robust manner, having the ability to adapt to any environment and allowing continued use. The work was validated through the participation in the 2014 Portuguese Robotics Open, and in an ISEP exhibition that occurred in Maia, always with good results.

Abstract
The concept of Temporary User-Centred Networks (TUNs) for transport systems is introduced. Affinity in these networks is defined as the time-specific degree of equivalence between travel patterns of users in the system. TUNs reveal latent social structures typically invisible to their users, enabling circumstantial collaboration opportunities amongst them. To make TUNs explicit we quantify affinity as a combination of two measures: journey similarity and journey substitutability. In the urban public transport domain, TUNs enable the diffusion of knowledge across the system in real-time. This can assist passengers adjusting travel decisions to their preferences and objectives according to service status. An enriched Automated Fare Collection (AFC) system dataset is used to demonstrate the market potential of TUNs in the urban public transport domain.

Abstract
This paper introduces a mathematical model (together with a relaxed version) and solution approaches for the multi-facility glass container production planning (MF-GCPP) problem. The glass container industry covers the production of glass packaging (bottle and jars), where a glass paste is continuously distributed to a set of parallel molding machines that shape the finished products. Each facility has a set of furnaces where the glass paste is produced in order to meet the demand. Furthermore, final product transfers between facilities are allowed to face demand. The objectives include meeting demand, minimizing inventory investment and transportation costs, as well as maximizing the utilization of the production facilities. A novel mixed integer programming formulation is introduced for MF-GCPP and solution approaches applying heuristics and meta-heuristics based on mathematical programming are developed. A multi-population genetic algorithm defines for each individual the partitions of the search space to be optimized by the MIP solver. A variant of the fix-and-optimize improvement heuristic is also introduced. The computational tests are carried on instances generated from real-world data provided by a glass container company. The results show that the proposed methods return competitive results for smaller instances, comparing to an exact solver method. In larger instances, the proposed methods are able to return high quality solutions.

Abstract
Participation of consumers in Demand Response (DR) programs improves system stability and reliability as well as market efficiency. Retailers and distributors purchase DR to advance business and system reliability, respectively. Meanwhile, large consumers, Distribution System Operators (DSOs), Load Service Entities (LSEs), and DR aggregators sell DR to increase their own profits. In this context, DR aggregators are key elements of power systems that enhance the participation of consumers in electricity markets. These market participants can negotiate their aggregated DR with other market players in Demand Response eXchange (DRX) markets, and participate in the energy and ancillary service markets. Hence, this paper proposes a stochastic model to optimize the performance of a DR aggregator to take part in the day-ahead energy, ancillary services and intraday DRX markets. In order to mitigate the negative impacts of uncertainties, Conditional Value at Risk (CVaR) is also incorporated to the proposed model. Numerical studies indicate that the proposed model for DR aggregator can arrange its offering/bidding strategies to participate in the mentioned markets simultaneously.

Abstract
This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross-spectrum iris and periocular recognition. Six submissions were evaluated for crossspectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.

Abstract
The Robot@Factory competition was recently included in Robotica, the main Portuguese Robotics Competition. This robot competition takes place in an emulated factory plant, where automatic guided vehicles (AGVs) must cooperate to perform tasks. To accomplish their goals, the AGVs must deal with localization, navigation, scheduling, and cooperation problems that must be solved autonomously. This robot competition can play an important role in education due to its inherent multidisciplinary approach, which can motivate students to bridge different technological areas. It can also play an important role in research and development, because it is expected that its outcomes will later be transferred to real-world problems in manufacturing or service robots. By presenting a scaled-down factory shop floor, this competition creates a benchmark that can be used to compare different approaches to the challenges that arise in this kind of environment. The ability to alter the environment, in some restricted areas, can usually promote the test and evaluation of different localization mechanisms, which is not possible in other competitions. This paper presents one of the possible approaches to build a robot capable of entering this competition. It can be used as a reference to current and new teams.