Abstract
Runtime adaptability is expected to adjust the application and the mapping of computations according to usage contexts, operating environments, resources availability, etc. However, extending applications with adaptive features can be a complex task, especially due to the current lack of programming models and compiler support. One of the runtime adaptability possibilities is the use of specialized code according to data workloads and environments. Traditional approaches use multiple code versions generated offline and, during runtime, a strategy is responsible to select a code version. Moving code generation to runtime can achieve important improvements but may impose unacceptable overhead. This paper presents an aspect-oriented programming approach for runtime adaptability. We focus on a separation of concerns (strategies vs. application) promoted by a domain-specific language for programming runtime strategies. Our strategies allow runtime specialization based on contextual information. We use a template-based runtime code generation approach to achieve program specialization. We demonstrate our approach with examples from image processing, which depict the benefits of runtime specialization and illustrate how several factors need to be considered to eficiently adapt the application. © 2015 ACM.

Abstract
Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system - ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.

Abstract
The Setplay Framework (available from SourceForge as free software) is composed of a C++ library (Project name: fcportugalsetplays), a fully functional RoboCup Simulation 2D demonstration team (fcportugalsetplaysagent2d), and a complete graphical tool (SPlanner), that can be used to design and plan the collaborative behavior between the soccer player agents. In order to demonstrate the usage of the Setplay library, a complete 2D simulation team, based on Agent2D, was developed. This example team uses the framework to execute previously planned collaborative behavior. This framework can be used both within simulated environments, such as the Robocup Soccer Simulation leagues, and with real soccer playing robots. This paper presents the free software Setplay Framework, and provides the necessary information for any team to use the framework with the goal of providing collaborative behavior to a team of soccer playing robots.

Abstract
3D object detection and recognition is increasingly used for manipulation and navigation tasks in service robots. It involves segmenting the objects present in a scene, estimating a feature descriptor for the object view and, finally, recognizing the object view by comparing it to the known object categories. 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 on-line experiences of a robot, and thus become gradually available over time, rather than 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 scenes in an open-ended manner? (2) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? This approach starts with a pre-processing step to remove irrelevant data and prepare a suitable point cloud for the subsequent processing. Clustering is then applied to detect object candidates, and object views are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to predict the categories of the detected objects. A leave-one-out cross validation algorithm is used to compute precision and recall, in a classical off-line evaluation setting, for different system parameters. Also, an on-line evaluation protocol is used to assess the performance of the system in an open-ended setting. Results show that the proposed system is able to interact with human users, learning new object categories continuously over time.

Abstract

Abstract
Spectrum allocation for current wireless communication systems is performed by the regulatory and licensing bodies, who allocate spectrum bands for given applications. This strict allocation severely limits the effectiveness and flexibility of the spectrum use. Cognitive radio (CR) has been demonstrated as a key emerging technology to provide flexible and efficient use of the available spectrum by allocating frequency bands dynamically, and to improve the performance of radio systems in congested or jammed environments. Frequencies that are reserved or usually occupied can be exploited if the cognitive radio system identifies them as being free. Such a system is also able to monitor and deal with degrading communication performance or regulatory constraints. It automatically adjusts radio settings to use the best wireless channels in its environment, ensuring appropriate quality of service, efficiency and versatility. The SCREEN project proposes to extend the concept of cognitive radio to space and particularly to SatCom applications. This is an on-going project funded by the Horizon 2020 European Union programme. CR has never been used or tested in space, since previous research has been focused in terrestrial technologies. By addressing this topic and demonstrating its capabilities and benefits for space applications, SCREEN will contribute to a better management of this scarce resource that is bandwidth. While it has already been demonstrated that CR technology radically improves the performance for terrestrial applications at many different levels, the same benefits also apply in Space and especially in the SatCom segment, where the services provided need to ensure quality to the clients, for market competitiveness. CR has the potential to enable different approaches for managing the growing satellite communication demands and provides flexibility to explore new types of hybrid networks. SatCom operators will benefit from having the flexibility to allocate frequency slots dynamically, according to the instantaneous traffic patterns, instead of reserving fixed bands within regulatory constraints. Additionally, by optimising the spectrum management, SatCom operators can accommodate more users at the same time, without sacrificing the network performance. In this paper we will describe the overall concept behind the SCREEN project and present the results of a complete framework analysis, consisting of technical conclusions, market and impact analyses, regulatory considerations/constraints and requirements. Based on this analysis we further present functional, performance and test requirements for the project, which will show the project direction and outcome, together with the expected benefits that this technology will bring to Space applications. Copyright