Upper Facial Action Unit Recognition

This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (AUs) by using Facial Action Coding System (FACS). Haar wavelet, Haar-Like and Gabor wavelet coe cients are compared, using Adaboost for feature selection. The binary classi cation results by using Support Vector Machines (SVM) for the upper face AUs have been observed to be better than the current results in the literature, for example 96.5% for AU2 and 97.6% for AU5. In multi-class classi cation case, the Error Correcting Output Coding (ECOC) has been applied. Although for a large number of classes, the results are not as accurate as the binary case, ECOC has the advantage of solving all problems simultaneously; and for large numbers of training samples and small number of classes, error rates are improved

Delta Divergence: A Novel Decision Cognizant Measure of Classifier Incongruence

In pattern recognition, disagreement between two classifiers regarding the predicted class membership of an observation can be indicative of an anomaly and its nuance. Since, in general, classifiers base their decisions on class a posteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergence. However, existing divergences are not particularly suitable to gauge classifier incongruence. In this paper, we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure, referred to as delta divergence. In contrast to existing measures, it focuses on the dominant (most probable) hypotheses and, thus, reduces the effect of the probability mass distributed over the non dominant hypotheses (clutter). The proposed measure satisfies other important properties, such as symmetry, and independence of classifier confidence. The relationship of the proposed divergence to some baseline measures, and its superiority, is shown experimentally

Deep Convolutional Neural Network Ensembles Using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance

Approximation of Ensemble Boundary Using Spectral Coefficients

IEEE A spectral analysis of a Boolean function is proposed for approximating the decision boundary of an ensemble of classifiers, and an intuitive explanation of computing Walsh coefficients for the functional approximation is provided. It is shown that the difference between the first- and third-order coefficient approximations is a good indicator of optimal base classifier complexity. When combining neural networks, the experimental results on a variety of artificial and real two-class problems demonstrate under what circumstances ensemble performance can be improved. For tuned base classifiers, the first-order coefficients provide performance similar to the majority vote. However, for weak/fast base classifiers, higher order coefficient approximation may give better performance. It is also shown that higher order coefficient approximation is superior to the Adaboost logarithmic weighting rule when boosting weak decision tree base classifiers

Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection

The state of classifier incongruence in decision making systems incorporating multiple classifiers is often an indicator of anomaly caused by an unexpected observation or an unusual situation. Its assessment is important as one of the key mechanisms for domain anomaly detection. In this paper, we investigate the sensitivity of Delta divergence, a novel measure of classifier incongruence, to estimation errors. Statistical properties of Delta divergence are analysed both theoretically and experimentally. The results of the analysis provide guidelines on the selection of threshold for classifier incongruence detection based on this measure

A decision cognizant Kullback-Leibler divergence

In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback–Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback–Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems

ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology

We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence

Passivation behaviour of Alloy 31 (UNS N08031) in polluted phosphoric acid at different temperatures

The influence of temperature (20–80 °C) and chloride concentration (0.06–0.42 wt.% KCl) on the electrochemical behaviour of the UNS N08031 was studied in 40 wt.% polluted phosphoric acid solution. Passivation behaviour was investigated by using potentiostatic tests at different potentials. From the linear regions of the log i vs. log t transients, the parameter n was obtained. The results showed that the applied potential hardly affects on the passivation rate n. However, n values decreased when temperature increased. The values of n demonstrated that the passive film formed on Alloy 31 was compact and highly protective.The authors express their gratitude to the MAEC of Spain (PCI Mediterraneo C/8196/07, C/018046/08, D/023608/09 and D/030177/10), to Programa de Apoyo a la Investigacion y Desarrollo de la UPV (PAID-06-09) and to the Generalitat Valenciana (GV/2011/093) for the financial support and to Dr. Asuncion Jaime for her translation assistance.Escrivá Cerdán, C.; Blasco Tamarit, ME.; García García, DM.; García Antón, J.; Guenbour, A. (2012). Passivation behaviour of Alloy 31 (UNS N08031) in polluted phosphoric acid at different temperatures. Corrosion Science. 56:114-122. https://doi.org/10.1016/j.corsci.2011.11.014S1141225

Dynamical Principles of Emotion-Cognition Interaction: Mathematical Images of Mental Disorders

The key contribution of this work is to introduce a mathematical framework to understand self-organized dynamics in the brain that can explain certain aspects of itinerant behavior. Specifically, we introduce a model based upon the coupling of generalized Lotka-Volterra systems. This coupling is based upon competition for common resources. The system can be regarded as a normal or canonical form for any distributed system that shows self-organized dynamics that entail winnerless competition. Crucially, we will show that some of the fundamental instabilities that arise in these coupled systems are remarkably similar to endogenous activity seen in the brain (using EEG and fMRI). Furthermore, by changing a small subset of the system's parameters we can produce bifurcations and metastable sequential dynamics changing, which bear a remarkable similarity to pathological brain states seen in psychiatry. In what follows, we will consider the coupling of two macroscopic modes of brain activity, which, in a purely descriptive fashion, we will label as cognitive and emotional modes. Our aim is to examine the dynamical structures that emerge when coupling these two modes and relate them tentatively to brain activity in normal and non-normal states

A BPMN extension to support discrete-event simulation for healthcare applications:an explicit representation of queues, attributes and data-driven decision points

Stakeholder engagement in simulation projects is important, especially in healthcare where there is a plurality of stakeholder opinions, objectives and power. One promising approach for increasing engagement is facilitated modelling. Currently, the complexity of producing a simulation model means that the ‘model coding’ stage is performed without the involvement of stakeholders, interrupting the possibility of a fully-facilitated project. Early work demonstrated that with currently-available software tools we can represent a simple healthcare process using Business Process Model and Notation (BPMN) and generate a simulation model automatically. However, for more complex processes, BPMN currently has a number of limitations, namely the ability to represent queues and data-driven decision points. To address these limitations, we propose a conceptual design for an extension to BPMN (BPMN4SIM) using Model Driven Architecture. Application to an elderly emergency care pathway in a UK hospital shows that BPMN4SIM is able to represent a more-complex business process