Abstract
Functional encryption (FE) is a powerful cryptographic primitive that generalizes many asymmetric encryption systems proposed in recent years. Syntax and security definitions for FE were proposed by Boneh, Sahai, and Waters (BSW) (TCC 2011) and independently by O'Neill (ePrint 2010/556). In this paper we revisit these definitions, identify several shortcomings in them, and propose a new definitional approach that overcomes these limitations. Our definitions display good compositionality properties and allow us to obtain new feasibility and impossibility results for adaptive token-extraction attack scenarios that shed further light on the potential reach of general FE for practical applications.

Abstract
The development of smart orthopaedic implants is being considered as an effective solution to ensure their everlasting life span. The availability of electric power to supply active mechanisms of smart prostheses has remained a critical problem. This paper reports the first implementation of a new concept of energy harvesting systems applied to hip prostheses: the multi-source generation of electric energy. The reliability of the power supply mechanisms is strongly increased with the application of this new concept. Three vibration-based harvesters, operating in true parallel to harvest energy during human gait, were implemented on a Metabloc TM hip prosthesis to validate the concept. They were designed to use the angular movements on the flexion-extension, abduction-adduction and inward-outward rotation axes, over the femoral component, to generate electric power. The performance of each generator was tested for different amplitudes and frequencies of operation. Electric power up to 55 µJ/s was harvested. The overall function of smart hip prostheses can remain performing even if two of the generators get damaged. Furthermore, they are safe and autonomous throughout the life span of the implant. © Springer-Verlag Berlin Heidelberg 2013.

Abstract

Abstract
The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players' actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets' negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets' players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets' data, using MASCEM - a multi-agent electricity market simulator that simulates market players' operation in the market.

Abstract
In University Management, one of the recurring problems that each department has to solve, each year, is the Teaching Service Distribution (TSD) or Teaching Assignment Problem (TAP). The problem of TSD consist to assign teachers to courses classes - lectures, tutorials, practical or laboratory - taking into account these preferences and qualifications for teaching. This is a crucial stage, since it is almost imperative that the TSD is fully defined before the process of schedules generating. However, most institutions of higher education, don't have a specific software tool to support the process of TSD. In this paper we propose a new approach for solving the TSD consisting on the formulation of the problem as a distributed scheduling problem with the formation with coalitions formation. The problem is solved in the context of a multi-agent system where the real agents are modeled by computational agents, with their interests, but may cooperate in alliance groups.

Abstract
This paper proposes a new real-time communication scheme for 802.11e wireless networks. This scheme is called Group Sequential Communication (GSC). The GSC improves the efficiency of the Hybrid Coordination Function Controlled Channel Access (HCCA) mechanism by reducing the protocol overheads of the 802.11e amendment. The GSC approach eliminates the polling scheme used in traditional scheduling algorithm, by means of a virtual token passing procedure among members of the real-time group to whom is granted a high-priority and sequential access to communication medium. In order to improve the reliability of the proposed scheme, it is also proposed an error recovery mechanism based on block acknowledgment. The GSC was implemented in network simulator software and the performance results were compared to HCCA scheme, showing the efficient of the proposed approach when dealing with traditional industrial communication scenarios.