Abstract
Performance estimation denotes a task of estimating the loss that a predictive model will incur on unseen data. These procedures are part of the pipeline in every machine learning task and are used for assessing the overall generalisation ability of models. In this paper we address the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in these datasets and currently there is no settled way to do so. We compare different variants of cross-validation and different variants of out-of-sample approaches using two case studies: One with 53 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that cross-validation approaches can be applied to stationary synthetic time series. However, in real-world scenarios the most accurate estimates are produced by the out-of-sample methods, which preserve the temporal order of observations.

Abstract
CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm has many beneficial properties, including automatic step-size adaptation, efficient covariance updates that incorporates the current samples as well as the evolution path and its invariance properties. Its update rules are composed of well established heuristics where the theoretical foundations of some of these rules are also well understood. In this paper we will fully derive all CMA-ES update rules within the framework of expectation-maximisation-based stochastic search algorithms using information-geometric trust regions. We show that the use of the trust region results in similar updates to CMA-ES for the mean and the covariance matrix while it allows for the derivation of an improved update rule for the step-size. Our new algorithm, Trust-Region Co-variance Matrix Adaptation Evolution Strategy (TR-CMA-ES) is fully derived from first order optimization principles and performs favourably in compare to standard CMA-ES algorithm. © 2017 ACM.

Abstract

Abstract

Abstract
In this manuscript a novel method is presented for left ventricle (LV) tracking in three-dimensional ultrasound data using a hybrid approach combining segmentation and tracking-based clues. This is accomplished by coupling an affine motion model to an existing LV segmentation framework and introducing an energy term that penalizes the deviation to the affine motion estimated using a global Lucas–Kanade algorithm. The hybrid nature of the proposed solution can be seen as using the estimated affine motion to enhance the temporal coherence of the segmented surfaces, by enforcing the tracking of consistent patterns, while the underlying segmentation algorithm allows to locally refine the estimated global motion. The proposed method was tested on a dataset composed of 24 4D ultrasound sequences from both healthy volunteers and diseased patients. The proposed hybrid tracking platform offers a competitive solution for fast assessment of relevant LV volumetric indices, by combining the robustness of affine motion tracking with the low computational burden of the underlying segmentation algorithm. © 2017 Elsevier Ltd

Abstract
In the current world, performance is one of the most important issues concerning work and competition. Performance is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms as the level of the learner's attention affects learning results. When students are doing learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage. The goal of this research is to propose a system that measures the level of attentiveness in real scenarios, and detects patterns of behavior associated to different attention levels among different students. This system measures attention and uses this information for training a decision support system that shows the level of attention of a group of students in real time.