Abstract
Background: Assessment of disagreement among multiple measurements for the same subject by different observers remains an important problem in medicine. Several measures have been applied to assess observer agreement. However, problems arise when comparing the degree of observer agreement among different methods, populations or circumstances. Methods: The recently introduced information-based measure of disagreement (IBMD) is a useful tool for comparing the degree of observer disagreement. Since the proposed IBMD assesses disagreement between two observers only, we generalized this measure to include more than two observers. Results: Two examples (one with real data and the other with hypothetical data) were employed to illustrate the utility of the proposed measure in comparing the degree of disagreement. Conclusion: The IBMD allows comparison of the disagreement in non-negative ratio scales across different populations and the generalization presents a solution to evaluate data with different number of observers for different cases, an important issue in real situations. A website for online calculation of IBMD and respective 95% confidence interval was additionally developed. The website is widely available to mathematicians, epidemiologists and physicians to facilitate easy application of this statistical strategy to their own data.

Abstract
This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the cross-entropy (CE) concepts with the standard analytical GCR assessment. A mathematical analysis of the CE equations is carried out to demonstrate that the optimal change of measure or distortion can be obtained by simply dividing the annualized GCR indices for two different configurations of the generating system. Under these hypotheses, a straightforward algorithm based on fast Fourier transform is proposed to systematically obtain the optimal distorted unavailabilities for all generating units in the system. The accuracy and computational performance of the proposed approach are compared with the standard CE optimization process using different generating systems. The IEEE-RTS 79, IEEE-RTS 96, and two configurations of the Brazilian South-Southeastern system are all used for this purpose.

Abstract
This paper presents a set of novel modifications that can be applied to any grid-based path planning algorithm from the A* family used in mobile robotics. Five modifications are presented regarding the way the robot sees an obstacle and its target to plan the robot's path. The modifications make it possible for the robot to get to the target faster than traditional algorithms, as well as to avoid obstacles that move as fast as (or even faster than) the robot. Some simulations were made using a crowded and highly dynamic environment with twelve randomly moving obstacles. In these first simulations, a middle sized 5DPO robot was used. Also, real experiments were made with a small-sized version of a 5DPO robot to validate the algorithm's effectiveness. In all simulations and real robot experiments the objects are considered to be moving at a constant speed. Finally, we present an overall discussion and conclusion of this paper. © 2012 The Brazilian Computer Society.

Abstract
Raven is a Multi-Object Adaptive Optics (MOAO) scientific demonstrator which will be used on-sky at the Subaru observatory from 2014. Raven is currently being built and tested at the University of Victoria AO Lab. This paper presents an overview of the optomechanical design and the software architecture of Raven, and gives the current status of this project. Raven includes three open loop wavefront sensors (WFSs), a laser guide star WFS and two figure/truth WFSs. Two science channels containing deformable mirrors (DMs) feed light to the Subaru IRCS spectrograph. Central to the Raven is a Calibration Unit which contains multiple sources, a telescope simulator including two phase screens and a ground layer DM that can be used to calibrate and test Raven in the lab. Preliminary results on calibration and open-loop AO correction using a tomographic reconstructor are presented.

Abstract
The widespread availability of large data-sets poses both an opportunity and a challenge to logic programming. A first approach is to couple a relational database with logic programming, say, a Prolog system with MySQL. While this approach does pay off in cases where the data cannot reside in main memory, it is known to introduce substantial overheads. Ideally, we would like the Prolog system to deal with large data-sets in an efficient way both in terms of memory and of processing time. Just In Time Indexing (JITI) was mainly motivated by this challenge, and can work quite well in many application. Exo-compilation, designed to deal with large tables, is a next step that achieves very interesting results, reducing the memory footprint over two thirds. We show that combining exo-compilation with Just In Time Indexing can have significant advantages both in terms of memory usage and in terms of execution time. An alternative path that is relevant for many applications is User-Defined Indexing (UDI). This allows the use of specialized indexing for specific applications, say the spatial indexing crucial to any spatial system. The UDI sees indexing as pluggable modules, and can naturally be combined with Exo-compilation. We do so by using UDI with exo-data, and incorporating ideas from the UDI into high-performance indexers for specific tasks.

Abstract
This demonstration proposal aims to present a 3D multi-user training simulator, developed in the 3D Open Simulator virtual world platform, in order to enable the practice and training of mechanical maintenance for F-16 engines, specifically the installation of a Pratt & Whitney F100 engine in a F-16 aircraft - a process that requires three skilled engine technicians to do a set of cooperative execution tasks. The main purpose of the simulator is to provide trainees and trainers with more opportunities to conduct training, enabling technician training to be enhanced with cooperation and context prior to the training phase with actual physical engines. In this proposal, we describe the architecture of the system and the sample simulation tasks.