A Large Visual, Qualitative, and Quantitative Dataset for Web Intelligence Applications

The Web is the communication platform and source of information par excellence. The volume and complexity of its content have grown enormously, with organizing, retrieving, and cleaning Web information becoming a challenge for traditional techniques. Web intelligence is a novel research area to improve Web-based services and applications using artificial intelligence and automatic learning algorithms, for which a large amount of Web-related data are essential. Current datasets are, however, limited and do not combine visual representation and attributes of Web pages. Our work provides a large dataset of 49,438 Web pages, composed of webshots, along with qualitative and quantitative attributes. This dataset covers all the countries in the world and a wide range of topics, such as art, entertainment, economics, business, education, government, news, media, science, and the environment, addressing different cultural characteristics and varied design preferences. We use this dataset to develop three Web Intelligence applications: knowledge extraction on Web design using statistical analysis, recognition of error Web pages using a customized convolutional neural network (CNN) to eliminate invalid pages, and Web categorization based solely on screenshots using a CNN with transfer learning to assist search engines, indexers, and Web directories.This work has been funded by the grant awarded by the Central University of Ecuador through budget certification No. 34 of March 25, 2022 for the development of the research project with code: DOCT-DI-2020-37

Towards a Better Performance in Facial Expression Recognition: A Data-Centric Approach

Facial expression is the best evidence of our emotions. Its automatic detection and recognition are key for robotics, medicine, healthcare, education, psychology, sociology, marketing, security, entertainment, and many other areas. Experiments in the lab environments achieve high performance. However, in real-world scenarios, it is challenging. Deep learning techniques based on convolutional neural networks (CNNs) have shown great potential. Most of the research is exclusively model-centric, searching for better algorithms to improve recognition. However, progress is insufficient. Despite being the main resource for automatic learning, few works focus on improving the quality of datasets. We propose a novel data-centric method to tackle misclassification, a problem commonly encountered in facial image datasets. The strategy is to progressively refine the dataset by successive training of a CNN model that is fixed. Each training uses the facial images corresponding to the correct predictions of the previous training, allowing the model to capture more distinctive features of each class of facial expression. After the last training, the model performs automatic reclassification of the whole dataset. Unlike other similar work, our method avoids modifying, deleting, or augmenting facial images. Experimental results on three representative datasets proved the effectiveness of the proposed method, improving the validation accuracy by 20.45%, 14.47%, and 39.66%, for FER2013, NHFI, and AffectNet, respectively. The recognition rates on the reclassified versions of these datasets are 86.71%, 70.44%, and 89.17% and become state-of-the-art performance.This work was funded by grant CIPROM/2021/17 awarded by the Prometeo program from Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital of Generalitat Valenciana (Spain), and partially funded by the grant awarded by the Central University of Ecuador through budget certification no. 34 of March 25, 2022, for the development of the research project with code: DOCT-DI-2020-37

Volcanic ash delimitation using Artificial Intelligence based on Pix2Pix

Volcanic eruptions emit ash that can be harmful to human health and cause damage to infrastructure, economic activities and the environment. The delimitation of ash clouds allows to know their behavior and dispersion, which helps in the prevention and mitigation of this phenomenon. Traditional methods take advantage of specialized software programs to process the bands or channels that compose the satellite images. However, their use is limited to experts and demands a lot of time and significant computational resources. In recent years, Artificial Intelligence has been a milestone in the computational treatment of complex problems in different areas. In particular, Deep Learning techniques allow automatic, fast and accurate processing of digital images. The present work proposes the use of the Pix2Pix model, a type of generative adversarial network that, once trained, learns the mapping of input images to output images. The architecture of such a network consisting of a generator and a discriminator provides the versatility needed to produce black and white ash cloud images from multispectral satellite images. The evaluation of the model, based on loss and accuracy plots, a confusion matrix, and visual inspection, indicates a satisfactory solution for accurate ash cloud delineation, applicable in any area of the world and becomes a useful tool in risk management.Comment: 18 pages, in Spanish language, 15 figure

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Improving Facial Expression Recognition Through Data Preparation and Merging

Human emotions present a major challenge for artificial intelligence. Automated emotion recognition based on facial expressions is important to robotics, medicine, psychology, education, security, arts, entertainment and more. Deep learning is promising for capturing complex emotional features. However, there is no training dataset that is large and representative of the full diversity of emotional expressions in all populations and contexts. Current facial datasets are incomplete, biased, unbalanced, error-prone and have different properties. Models learn these limitations and become dependent on specific datasets, hindering their ability to generalize to new data or real-world scenarios. Our work addresses these difficulties and provides the following contributions to improve emotion recognition: 1) a methodology for merging disparate in-the-wild datasets that increases the number of images and enriches the diversity of people, gestures, and attributes of resolution, color, background, lighting and image format; 2) a balanced, unbiased, and well-labeled evaluator dataset, built with a gender, age, and ethnicity predictor and the successful Stable Diffusion model. Single- and cross-dataset experimentation show that our method increases the generalization of the FER2013, NHFI and AffectNet datasets by 13.93%, 24.17% and 7.45%, respectively; and 3) we propose the first and largest artificial emotion dataset, which can complement real datasets in tasks related to facial expression.This work has been funded by grant CIPROM/2021/017 awarded by the MEEBAI Project (Prometheus Programme for Research Groups on R&D Excellence) from Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital of Generalitat Valenciana (Spain), and partially by the grant awarded by the Central University of Ecuador through budget certification No. 34 of March 25, 2022 for the development of the research project with code DOCT-DI-2020-37

[Conocimiento básico asociado al fatalismo generado por el COVID-19 en estudiantes de medicina de Bolivia]

El objetivo fue determinar si el conocimiento básico está asociado al fatalismo que fue generado por el COVID-19 en estudiantes de medicina de Bolivia. Estudio transversal analítico, generado a través de una encuesta virtual, con escalas validadas para medir el conocimiento y el fatalismo ante la posibilidad de enfermarse por COVID-19, esto en 4 facultades de medicina de Bolivia. Se obtuvo resultados descriptivos y analíticos de dicha asociación, ajustado por otras variables. En el análisis multivariado se encontró que hubo un mayor nivel de conocimiento conforme aumentaba el año académico (3er año p=0,012, 4to año p=0,031, 5to año p=0,001 y el internado p=0,013; todos a comparación del 1er año), en cambio, hubo más conocimiento entre los estudiantes que fueron menos fatalistas (RPa: 0,76; IC95%: 0,68-0,85%; valor p<0,001) y entre los que estudiaban en algunas universidades (UNIFRANZ p<0,001 y UNITEPC p<0,001, ambas a comparación de la UMSS); ajustados por el sexo y la edad de los encuestados. En conclusión, el que los estudiantes hayan tenido percepciones fatalistas se asoció de forma inversa al conocimiento que tuvieron respecto a la enfermedad; además, hubo asociación según el año de estudios y la universidad donde estudiaba

Latin American perceptions of fear and exaggeration transmitted by the media with regard to COVID-19: frequency and association with severe mental pathologies

IntroductionThe COVID-19 pandemic contributed to the spread of abundant misinformation by the media, which caused fear and concern.ObjectiveTo determine the association between the pathologies of the mental sphere and the perceptions of fear and exaggeration transmitted by the media with respect to COVID-19 in Latin America.MethodologyThe present study has an analytical cross-sectional design that is based on a validated survey to measure fear and exaggeration transmitted by the media and other sources (Cronbach's α: 0.90). We surveyed more than 6,000 people, originally from 12 Latin American countries, who associated this perceived exaggeration with stress, depression, and anxiety (measured through DASS-21, Cronbach's α: 0.96).ResultsSocial networks (40%) or television (34%) were perceived as the sources that exaggerate the magnitude of the events. In addition, television (35%) and social networks (28%) were perceived as the sources that generate much fear. On the contrary, physicians and health personnel are the sources that exaggerated less (10%) or provoked less fear (14%). Through a multivariate model, we found a higher level of global perception that was associated with whether the participant was older (p = 0.002), had severe or more serious anxiety (p = 0.033), or had stress (p = 0,037). However, in comparison with Peru (the most affected country), there was a lower level of perception in Chile (p &lt; 0.001), Paraguay (p = 0.001), Mexico (p &lt; 0.001), Ecuador (p = 0.001), and Costa Rica (p = 0.042). All of them were adjusted for gender and for those having severe or major depression.ConclusionThere exists an association between some mental pathologies and the perception that the media does not provide moderate information

Subcutaneous anti-COVID-19 hyperimmune immunoglobulin for prevention of disease in asymptomatic individuals with SARS-CoV-2 infection: a double-blind, placebo-controlled, randomised clinical trialResearch in context

Summary: Background: Anti-COVID-19 hyperimmune immunoglobulin (hIG) can provide standardized and controlled antibody content. Data from controlled clinical trials using hIG for the prevention or treatment of COVID-19 outpatients have not been reported. We assessed the safety and efficacy of subcutaneous anti-COVID-19 hyperimmune immunoglobulin 20% (C19-IG20%) compared to placebo in preventing development of symptomatic COVID-19 in asymptomatic individuals with SARS-CoV-2 infection. Methods: We did a multicentre, randomized, double-blind, placebo-controlled trial, in asymptomatic unvaccinated adults (≥18 years of age) with confirmed SARS-CoV-2 infection within 5 days between April 28 and December 27, 2021. Participants were randomly assigned (1:1:1) to receive a blinded subcutaneous infusion of 10 mL with 1 g or 2 g of C19-IG20%, or an equivalent volume of saline as placebo. The primary endpoint was the proportion of participants who remained asymptomatic through day 14 after infusion. Secondary endpoints included the proportion of individuals who required oxygen supplementation, any medically attended visit, hospitalisation, or ICU, and viral load reduction and viral clearance in nasopharyngeal swabs. Safety was assessed as the proportion of patients with adverse events. The trial was terminated early due to a lack of potential benefit in the target population in a planned interim analysis conducted in December 2021. ClinicalTrials.gov registry: NCT04847141. Findings: 461 individuals (mean age 39.6 years [SD 12.8]) were randomized and received the intervention within a mean of 3.1 (SD 1.27) days from a positive SARS-CoV-2 test. In the prespecified modified intention-to-treat analysis that included only participants who received a subcutaneous infusion, the primary outcome occurred in 59.9% (91/152) of participants receiving 1 g C19-IG20%, 64.7% (99/153) receiving 2 g, and 63.5% (99/156) receiving placebo (difference in proportions 1 g C19-IG20% vs. placebo, −3.6%; 95% CI -14.6% to 7.3%, p = 0.53; 2 g C19-IG20% vs placebo, 1.1%; −9.6% to 11.9%, p = 0.85). None of the secondary clinical efficacy endpoints or virological endpoints were significantly different between study groups. Adverse event rate was similar between groups, and no severe or life-threatening adverse events related to investigational product infusion were reported. Interpretation: Our findings suggested that administration of subcutaneous human hyperimmune immunoglobulin C19-IG20% to asymptomatic individuals with SARS-CoV-2 infection was safe but did not prevent development of symptomatic COVID-19. Funding: Grifols