The Cognitive Benefit of Dynamic Representations on Procedural Skill Acquisition: A Computational Modeling Approach

Cognitive computational modeling is a viable methodology for further investigation of the hitherto inconclusive findings on the cognitive benefits of dynamic versus static visualization components of instructions. This is more so as contemporary cognitive architectures such as the Adaptive Control of Thought–Rational (ACT–R) 6.0 are increasingly applied to traditional cognitive psychology research problems. The application of this methodology is, however, restricted by the limited capability of existing architectures for implementing detailed atomic motor actions such as those involved in complex skill acquisition and performance. This article presents a 2-component computational modeling methodology for investigating the cognitive processes involved in the acquisition and performance of skilled motor tasks. The approach specifies a novel combination of a sequence-of-point technique with a movement control mechanism to implement variously acquired cognitive mental task representations and their intertwined role in postlearning performance as evident in the atomic control of motor actions. This paradigm is validated for 2 experiments using incrementally developed cognitive models developed in ACT–R 6.0. The model's quantitative outputs correlate significantly with equivalent empirical human data. This has implications for multimedia instructional design, especially where rapid, transferrable skill acquisition is desired on initial exposure.     

A fine-grained Random Forests using class decomposition: an application to medical diagnosis

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable data set without the need for feature engineering required by techniques like support vector machines and deep learning. For ensembles, the decomposition is a natural way to increase diversity, a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of data sets that are mainly related to the medical domain. Results reported in this paper show clearly that our method has significantly improved the accuracy of Random Forests.     

Deep imitation learning for 3D navigation tasks

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

     

New trends on digitisation of complex engineering drawings

Neural Computing and Applications - Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming...     

Coherent electron radiation in a synchrotron

The coherent radiation of electrons in a synchrotron was investigated experimentally for a wavelength of 10 cm, corresponding to the 50th harmonic of the electron frequency of revolution for various electron distributions inside the separatrix. In most cases, the theory of coherent radiation of electrons is confirmed. A disparity with the theory is observed for almost uniform electron distributions. The radiation was used for measurements of the electron phase oscillations and adiabatic damping of the phase oscillation amplitudes. The experiment was carried out on the synchrotron at the Physics Institute, Academy of Sciences, USSR at 280 Mev with an initial betratron acceleration.In conclusion, the authors thank Prof. P. A. Cherenkov, Prof. M. S. Rabinovich, Prof. A. M. Prkhorov, and L. V. Iogansen for valuable discussions.