Abstract
"The Fifth Empire" is a single-player action adventure game in 2D. Centering around an adaptation of the myth the Fifth Empire (Nebuchadnezzar's dream and its interpretation by prophet Daniel) [1] is an epic allegory of the human condition, with a reinterpretation of European and Portuguese history as backdrop. This is a project that is still in process, which main goal is the development of an attractive videogame, with a strong and enriched educational purpose, since the game takes place in a real and historical setting. Moreover, through entertainment, the game should promote curiosity about the Portuguese history and motivate a more detailed study by the target audience.

Abstract
The combustion of coal wastes resulting from mining is of particular environmental concern and therefore the importance of the proper management involving real-time assessment of their status and identification of probable evolution scenarios is recognized. Continuous monitoring of combustion temperature and emission levels of certain gases opens the possibility to plan corrective actions to minimize their negative impact in the surroundings. Optical fiber technology is well-suited to this purpose and in this work it is described the main attributes of a fiber optic sensing system projected to gather data on distributed temperature and gas emission in these harsh environments.

Abstract
Three-dimensional object detection and recognition is increasingly in manipulation and navigation applications in autonomous service robots. It involves clustering points of the point cloud from an unstructured scene into objects candidates and estimating features to recognize the objects under different circumstances such as occlusions and clutter. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D unstructured scenes in an open-ended manner? (2) How to acquire and utilize high-level knowledge obtained from the user (e. g. category label) to improve the system performance? This approach starts with a pre-processing phase to remove unnecessary information and prepare a suitable point cloud. Clustering is then applied to detect object candidates. Subsequently, all object candidates are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to assign category labels to the detected objects. To examine the performance of the proposed approach, a leave-one-out cross validation algorithm is utilized to compute precision and recall. The experimental results show the fulfilling performance of this approach on different types of objects.

Abstract
We tested in a robotics experiment a dynamic neural field model for learning a precisely timed musical sequence. Based on neuro-plausible processing mechanisms, the model implements the idea that order and relative timing of events are stored in an integrated representation whereas the onset of sequence production is controlled by a separate process. Dynamic neural fields provide a rigorous theoretical framework to analyze and implement the necessary neural computations that bridge gaps between sensation and action in order to mediate working memory, action planing, and decision making. The robot first memorizes a short musical sequence performed by a human teacher by watching color coded keys on a screen, and then tries to execute the piece of music on a keyboard from memory without any external cues. The experimental results show that the robot is able to correct in very few demonstration-execution cycles initial sequencing and timing errors.

Abstract
The paper firstly reviews the relevant concepts on virtual enterprise operations as well as industrial maintenance processes. Then a virtual enterprise enabling platform is presented. The architecture of the platform and its main modules are briefly introduced. Within this platform, a smart object extension is highlighted. This smart object is used to collect data from remote equipment and pass it to the Virtual Enterprise Management Platform (VEMP) through a gateway. The data collected by the smart object will be aggregated and monitored, using the business intelligence tools of the platform, enabling the implementation of maintenance strategies, rising fault conditions that will trigger a repair business process. In the final part of the paper, it is discussed a business case for a SME with worldwide operations.

Abstract
This paper presents an algorithm, in the context of speech analysis and pathologic/dysphonic voices evaluation, which splits the signal of the glottal excitation into harmonic and noise components. The algorithm uses a harmonic and noise splitter and a glottal inverse filtering. The combination of these two functionalities leads to an improved estimation of the glottal excitation and its components. The results demonstrate this improvement of estimates of the glottal excitation in comparison to a known inverse filtering method (IAIF). These results comprise performance tests with synthetic voices and application to natural voices that show the waveforms of harmonic and noise components of the glottal excitation. This enhances the glottal information retrieval such as waveform patterns with physiological meaning.