Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the benefit of various applications such as identification of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challenges arising for both science(e.g., methodology underpinned by psychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our findings and first attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets

Feature Extraction Techniques for Human Emotion Identification from Face Images

Emotion recognition has been one of the stimulating issues over the years due to the irregularities in the complexity of models and unpredictability between expression categories. So many Emotion detection algorithms have developed in the last two decades and still facing problems in accuracy, complexity and real-world implementation. In this paper, we propose two feature extraction techniques: Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method. In Mouth based feature extraction method mouth area is calculated and based on that value the emotions are classified. In the MSER method, the features are extracted by using connecting components and then the extracted features are given to a simple ANN for classification. Experimental results shows that the Mouth area based feature extraction method gives 86% accuracy and MSER based feature extraction method outperforms it by achieving 89% accuracy on DEAP. Thus, it can be concluded that the proposed methods can be effectively used for emotion detection

Generalized symmetric nonextensive thermostatistics and q-modified structures

We formulate a convenient generalization of the q-expectation value, based on the analogy of the symmetric quantum groups and q-calculus, and show that the q->q^{-1} symmetric nonextensive entropy preserves all of the mathematical structure of thermodynamics just as in the case of non-symmetric Tsallis statistics. Basic properties and analogies with quantum groups are discussed.Comment: 9 pages, 1 figure. To appear in Mod. Phys. Lett.

Anyonic behavior of quantum group gases

We first introduce and discuss the formalism of $SU_q(N)$-bosons and fermions and consider the simplest Hamiltonian involving these operators. We then calculate the grand partition function for these models and study the high temperature (low density) case of the corresponding gases for $N=2$. We show that quantum group gases exhibit anyonic behavior in $D=2$ and $D=3$ spatial dimensions. In particular, for a $SU_q(2)$ boson gas at $D=2$ the parameter $q$ interpolates within a wider range of attractive and repulsive systems than the anyon statistical parameter.Comment: LaTeX file, 19 pages, two figures ,uses epsf.st

Quantitative RT-PCR luminometric hybridization assay with an RNA-internal standard for cytokeratin-19 mRNA in peripheral blood of patients with breast cancer. Clin Biochem

6 normal PBMC and is highly specific as none of the 26 healthy controls tested had detectable CK-19 mRNA levels, while 10 out of 14 (71.4%) and 9 out of 37 (24.3%) patients with stage IV and stage I/II breast cancer, respectively, were tested positive. Conclusion: The developed quantitative RT-PCR hybridization assay for CK-19 is reproducible, highly sensitive and specific, and can be used for a large-scale prospective evaluation of clinical samples

A Hybrid Spam Detection Method Based on Unstructured Datasets

This document is the accepted manuscript version of the following article: Shao, Y., Trovati, M., Shi, Q. et al. Soft Comput (2017) 21: 233. The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-015-1959-z. © Springer-Verlag Berlin Heidelberg 2015.The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation-based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.Peer reviewedFinal Accepted Versio

Diverging results of areal and volumetric bone mineral density in Down syndrome

Population with Down syndrome (DS) has lower areal BMD, in association with their smaller skeletal size. However, volumetric BMD and other indices of bone microarchitecture, such as trabecular bone score (TBS) and calcaneal ultrasound (QUS), were normal. INTRODUCTION: Patients with DS have a number of risk factors that could predispose them to osteoporosis. Several studies reported that people with DS also have lower areal bone mineral density, but differences in the skeletal size could bias the analysis. METHODS: Seventy-five patients with DS and 76 controls without intellectual disability were recruited. Controls were matched for age and sex. Bone mineral density (BMD) was measure by Dual-energy X-ray Absorptiometry (DXA), and volumetric bone mineral density (vBMD) was calculated by published formulas. Body composition was also measured by DXA. Microarchitecture was measured by TBS and QUS. Serum 25-hidroxyvitamin D (25OHD), parathyroid hormone (PTH), aminoterminal propeptide of type collagen (P1NP), and C-terminal telopeptide of type I collagen (CTX) were also determined. Physical activity was assessed by the International Physical Activity Questionnaires (IPAQ-short form). To evaluate nutritional intake, we recorded three consecutive days of food. RESULTS: DS individuals had lower height (151 ± 11 vs. 169 ± 9 cm). BMD was higher in the controls (lumbar spine (LS) 0.903 ± 0.124 g/cm2 in patients and 0.997 ± 0.115 g/cm2 in the controls; femoral neck (FN) 0.761 ± .126 g/cm2 and 0.838 ± 0.115 g/cm2, respectively). vBMD was similar in the DS group (LS 0.244 ± 0.124 g/cm3; FN 0.325 ± .0.073 g/cm3) and the controls (LS 0.255 ± 0.033 g/cm3; FN 0.309 ± 0.043 g/cm3). Microarchitecture measured by QUS was slightly better in DS, and TBS measures were similar in both groups. 25OHD, PTH, and CTX were similar in both groups. P1NP was higher in the DS group. Time spent on exercise was similar in both groups, but intensity was higher in the control group. Population with DS has correct nutrition. CONCLUSIONS: Areal BMD is reduced in DS, but it seems to be related to the smaller body and skeletal size. In fact, the estimated volumetric BMD is similar in patients with DS and in control individuals. Furthermore, people with DS have normal bone microarchitecture

The transmission of unconventional monetary policy to bank credit supply : evidence from the TLTRO

We assess the transmission of the Targeted Longer-Term Refinancing Operations (TLTRO) to the bank credit supply for the Euro area (2014:05-2018:01) and for Portugal (2011:01-2018:01), using a panel data setup. For the Euro area, we find a positive relationship between the TLTRO and the amount of credit granted to the real economy. For the vulnerable countries, the effects of the TLTRO on the stock of credit increased from 2016 to 2017. Among the group of small banks, the effects are stronger in less vulnerable countries. We also find that competition has no statistically significant impact on the transmission of the TLTRO to the bank credit supply for the Euro area. For Portugal, using a difference-in-differences model, we find no statistically significant impact of the TLTRO on credit granted by banks. Finally, bidding banks set lower interest rates than non-bidding banks and the difference seems to be larger in 2017. In Portugal, the effects of the TLTRO on loan interest rates also increased from 2016 to 2017 and are stronger for small banks.info:eu-repo/semantics/publishedVersio