Abstract
Gesture-controlled applications typically are tied to specific gestures, and also tied to specific recognition methods and specific gesture-detection devices. We propose a concern-separation architecture, which mediates the following concerns: gesture acquisition; gesture recognition; and gestural control. It enables application developers to respond to gesture-independent commands, recognized using plug-in gesture-recognition modules that process gesture data via both device-dependent and device-independent data formats and callbacks. Its feasibility is demonstrated with a sample implementation.

Abstract
This paper extends the authors earlier proposal for providing Ada with support for fine-grained parallelism with an execution model based on the concept of abstract executors, detailing the progress guarantees that these executors must provide and how these can be assured even in the presence of potentially blocking operations. The paper also describes how this execution model can be applied to real-time systems. © Springer International Publishing Switzerland 2015.

Abstract
This paper discusses the concepts of game design and gamification of content, based on the development of a serious game aimed at making the process of learning sign language enjoyable and interactive. In this game the player controls a character that interacts with various objects and non player characters, with the aim of collecting several gestures from the Portuguese Sign Language corpus. The learning model used pushes forward the concept of gamification as a learning process valued by students and teachers alike, and illustrates how it may be used as a personalized device for amplifying learning. Our goal is to provide a new methodology to involve students and general public in learning specific subjects using a ludic, participatory and interactive approach supported by ICT-based tools. Thus, in this paper we argue that perhaps some education processes could be improved by adding the gaming factor through technologies that are able to involve students in a way that is more physical (e.g. using Kinect and sensor gloves), so learning becomes more intense and memorable.

Abstract
Due to the limited predictability and associated uncertainty of renewable energy resources, renewable-based electricity systems are confronted with instability problems. In such power systems, implementation of Demand Response (DR) programs not only can improve the system stability but also enhances market efficiency and system reliability. By implementing cloud-based engineering systems the utilization of DR will be increased and consequently DR will play a more crucial role in the future. Therefore, DR aggregators can efficiently take part in energy, balancing and ancillary services markets. In this paper, a model has been developed to optimize the behavior of a DR aggregator to simultaneously participate in the mentioned markets. To this end, the DR aggregator optimizes its offering/bidding strategies based on the contracts with its customers. In the proposed model, uncertainties of renewable energy resources and the prices of electricity markets are considered. Numerical studies show the effectiveness of the proposed model.

Abstract
Many real life prediction problems involve predicting a structured output. Multi-target regression is an instance of structured output prediction whose task is to predict for multiple target variables. Structured output algorithms are usually computationally and memory demanding, hence are not suited for dealing with massive amounts of data. Most of these algorithms can be categorized as local or global methods. Local methods produce individual models for each output component and combine them to produce the structured prediction. Global methods adapt traditional learning algorithms to predict the output structure as a whole. We propose the first rule-based algorithm for solving multi-target regression problems from data streams. The algorithm builds on the adaptive model rules framework. In contrast to the majority of the structured output predictors, this particular algorithm does not fall into the local and global categories. Instead, each rule specializes on related subsets of the output attributes. To evaluate the performance of the proposed algorithm, two other rule-based algorithms were developed, one using the local strategy and the other using the global strategy. These methods were compared considering their prediction error, memory usage, computational time, and model complexity. Experimental results on synthetic and real data show that the local-strategy algorithm usually obtains the lowest error. However, the proposed and the global-strategy algorithms use much less memory and run significantly much faster at the cost of a slightly increase in the error, which make them very attractive when computation resources are an important factor. Also, the models produced by the latter approaches are much easier to understand since considerably less rules are produced.

Abstract
Verification and testing are two of the most costly and time consuming steps during the development of safety critical systems. The advent of complex and sometimes partially unpredictable computing architectures such as multicore commercial-of-the-shelf platforms, together with the composable development approach adopted in multiple industrial domains such as avionics and automotive, rendered the exhaustive testing of all situations that could potentially be encountered by the system once deployed on the field nearly impossible. Run-time verification (RV) is a promising solution to help accelerate the development of safety critical applications whilst maintaining the high degree of reliability required by such systems. RV adds monitors in the application, which check at run-time if the system is behaving according to predefined specifications. In case of deviations from the specifications during the runtime, safeguarding measures can be triggered in order to keep the system and its environment in a safe state, as well as potentially attempting to recover from the fault that caused the misbehaviour. Most of the state-of-the-art on RV essentially focused on the monitor generation, concentrating on the expressiveness of the specification language and its translation in correct-by-construction monitors. Few of them addressed the problem of designing an efficient and safe run-time monitoring (RM) architecture. Yet, RM is a key component for RV. The RM layer gathers information from the monitored application and transmits it to the monitors. Therefore, without an efficient and safe RM architecture, the whole RV system becomes useless, as its inputs and hence by extension its outputs cannot be trusted. In this paper, we discuss the design of a novel RM architecture suited to safety critical applications. © Springer International Publishing Switzerland 2015.