Abstract
The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Abstract
An intensity curvature sensor using a Photonic Crystal Fiber (PCF) with three coupled cores is proposed. The three cores were aligned and there was an air hole between each two consecutive cores. The fiber had a low air filling fraction, which means that the cores remain coupled in the wavelength region studied. Due to this coupling, interference is obtained in the fiber output even if just a single core is illuminated. A configuration using reflection interrogation, which used a section fiber with 0.13 m as the sensing head, was characterized for curvature sensing. When the fiber is bended along the plane of the cores, one of the lateral cores will be stretched and the other compressed. This changes the coupling coefficient between the three cores, changing the output optical power intensity. The sensitivity of the sensing head was strongly dependent on the direction of bending, having its maximum when the bending direction was along the plane of the cores. A maximum curvature sensitivity of 2.0 dB/m(-1) was demonstrated between 0 m and 2.8 m.

Abstract
In this paper, a new fast and computationally light weight methodology is proposed to pinpoint a robot in a structured scenario. The localisation algorithm performs a tracking routine to pinpoint the robot's position as it moves in a known map. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper briefly describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way. Experimental results on the performance of the proposed methodology are presented in this paper in two different scenarios: 1) in the Middle Size Soccer Robotic League (MSL), with artificial vision data from an omni directional robot, and 2) in an indoor environment with a Laser Range Finder data from a differential traction robot (called Robot Vigil). © 2012 IEEE.

Abstract
Text alignment is one of the main processes for obtaining parallel corpora. When aligning two versions of a book, results are often affected by unpaired sections - sections which only exist in one of the versions of the book. We developed Text : : Perfide : : BookSync, a Perl module which performs books synchronization (structural alignment based on section delimitation), provided they have been previously annotated by Text : : Perfide : : BookCleaner. We discuss the need for such a tool and several implementation decisions. The main functions are described, and examples of input and output are presented. Text : : Perfide : : PartialAlign is an extension of the partialAlign.py tool bundled with hunalign which proposes an alternative methods for splitting bitexts.

Abstract
In this paper we propose a method to develop an omnidirectional kick (behavior) for a humanoid robot. This behavior uses a path planning module to create a trajectory that the foot must follow to propel the ball in the intended direction. Two additional modules were required when performing the movement: Inverse kinematics module which computes the value of the joints to position the foot at a given position and the stability module which is responsible for the robot's stability. Simulation tests are performed under different ball positions, relative to the robot's orientation, and for various ball directions. The results obtained showed the usefulness of the approach since the behavior performs accurately the intended motion and is able to kick the ball in all the directions desired.

Abstract
This paper presents a multi-population biased random-key genetic algorithm (BRKGA) for the single container loading problem (3D-CLP) where several rectangular boxes of different sizes are loaded into a single rectangular container. The approach uses a maximal-space representation to manage the free spaces in the container. The proposed algorithm hybridizes a novel placement procedure with a multi-population genetic algorithm based on random keys. The BRKGA is used to evolve the order in which the box types are loaded into the container and the corresponding type of layer used in the placement procedure. A heuristic is used to determine the maximal space where each box is placed. A novel procedure is developed for joining free spaces in the case where full support from below is required. The approach is extensively tested on the complete set of test problem instances of Bischoff and Ratcliff [1] and Davies and Bischoff [2] and is compared with 13 other approaches. The test set consists of 1500 instances from weakly to strongly heterogeneous cargo. The computational experiments demonstrate that not only the approach performs very well in all types of instance classes but also it obtains the best overall results when compared with other approaches published in the literature.