Abstract
Many military training centers still use traditional means to train and practice air traffic control. To represent reality, they use physical models of an airfield and aircrafts, or advanced computer simulations. LMS systems are also frequently used for management of online contents of courses. We are connecting simulations with LMS systems, in order to support trainers' tracking of trainees' progress. This paper presents a technological architecture that allows a trainee to use a virtual air traffic control (ATC) simulator to practice exercises available at the LMS and updates the LMS with the trainee's progress and actions within the simulator. It also presents details of a first approach to the implementation of this architecture. © 2011 IEEE.

Abstract
Laparoscopy is a surgical procedure on which operations in the abdomen are performed through small incisions using several specialized instruments. The laparoscopic surgery success greatly depends on surgeon skills and training. To achieve these technical high-standards, different apprenticeship methods have been developed, many based on in vivo training, an approach that involves high costs and complex setup procedures. This paper explores Virtual Reality (VR) simulation as an alternative for novice surgeons training. Even though several simulators are available on the market claiming successful training experiences, their use is extremely limited due to the economic costs involved. In this work, we present a low-cost laparoscopy simulator able to monitor and assist the trainee's surgical movements. The developed prototype consists of a set of inexpensive sensors, namely an accelerometer, a gyroscope, a magnetometer and a flex sensor, attached to specific laparoscopic instruments. Our approach allows repeated assisted training of an exercise, without time constraints or additional costs, since no human artificial model is needed. A case study of our simulator applied to instrument manipulation practice (hand-eye coordination) is also presented. © 2011 IEEE.

Abstract
Fraud detection is a key activity with serious socioeconomical impact. Inspection activities associated with this task are usually constrained by limited available resources. Data analysis methods can provide help in the task of deciding where to allocate these limited resources in order to optimise the outcome of the inspection activities. This paper presents a multi-strategy learning method to address the question of which cases to inspect first. The proposed methodology is based on the utility theory and provides a ranking ordered by decreasing expected outcome of inspecting the candidate cases. This outcome is a function not only of the probability of the case being fraudulent but also of the inspection costs and expected payoff if the case is confirmed as a fraud. The proposed methodology is general and can be useful on fraud detection activities with limited inspection resources. We experimentally evaluate our proposal on both an artificial domain and on a real world task.

Abstract
We analyze the fundamental limits of secret-key agreement over MIMO quasi-static fading channels. In the low-power and high-power regimes, we establish closed-form expressions for secret-key capacity. In the low-power regime, we show that the optimal signaling strategy is independent of the eavesdropper's fading realization. The low-power secret-key capacity is achieved by transmitting along the direction corresponding to the maximal eigenvalue of the legitimate channel. By combining this signaling strategy with reconciliation and privacy amplification, one obtains a semi-blind key-distillation strategy in which the knowledge of the eavesdropper's fading is required for privacy amplification alone. © 2011 IEEE.

Abstract
For beginners in computer programming, the learning curve can be in many cases quite steep, especially if it is their first contact with this area. Plus, the traditional learning methodologies are usually based on doing countless exercises that aim to cover many areas, but are often disconnected from each other and can become tiresome, as they offer little immediate rewards to the student. Nowadays serious games technology offers tools that may have potential to help computer programming students to become more engaged on their learning through a 'learn while having fun approach. This paper aims to generally describe our approach on the creation of a platform for deploying serious computer games for the teaching of any computer programming language. We will begin by describing the game mechanics, followed by the general system architecture and its data model, finalizing with a small conclusion.

Abstract
This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed intelligent system is thoroughly evaluated, reporting the numerical results from a real-world case study. © 2011 IEEE.