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Por meio da fala, que privilegia a natureza funcional e interativa do texto, é possível averiguar as circunstâncias espaço-temporais, as condições de produção e recepção do discurso, os propósitos explícitos como informar, explicar, convencer etc. Condições essas que permitem aproximar a interação entre humanos à interação entre humanos e robôs tomando-a natural e sensível às informações. No entanto, não basta compreender o que é falado, faz-se necessário o reconhecimento de emoções para a interação desejada. Verificou-se a validez do uso de redes neurais para seleção de características e para o reconhecimento de emoções. Para isso propõe-se o uso de Redes Neurais e comparação de modelos, como redes neurais recorrentes e redes neurais profundas, com intuito de realizar a classificação das emoções através dos sinais de fala para verificar a qualidade do reconhecimento. Espera-se possibilitar a implementação em robôs de um ambiente doméstico, como o robô HERA da equipe RoboFEI@Home, que tem como foco robôs de serviço autônomos para o ambiente doméstico. Foram realizados testes utilizando-se apenas os Coeficientes Cepstrais da Frequência-Mel, bem como testes com diversas características do Delta-MFCC, contraste espectral e o espectrograma-Mel. Para realizar o treinamento, validação e testes das redes neurais, usufruiu-se a base de dados eNTERFACE'05, que possui 42 locutores de 14 nacionalidades diferentes falando o idioma inglês. Os dados da base escolhida são vídeos que, para o uso nas redes neurais, foram convertidos em áudios. Constatou-se como resultado uma classificação de 52% de acertos quando empregada a rede neural profunda, quando verificado o uso da rede neural recorrente, sendo a classificação com acurácia igual 44%. Os resultados apresentam maior acurácia quando apenas os Coeficientes Cepstrais da Frequência-Mel são usados para a classificação, utilizando o classificador com a Rede Neural Profunda e em apenas um caso é possível observar um maior acerto por parte da Rede Neural Recorrente, que se dá no uso de diversas características e na configuração de 73 para o tamanho do Batch e 100 épocas de treinamento Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatio-temporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing etc. These conditions allow bringing the interaction between humans closer to the Human-Robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said, it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of Neural Networks and comparison of models, such as recurrent neural networks and deep neural networks, in arder to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation in robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with severa! characteristics of Delta-MFCC, spectral contrast and the melspectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE'05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are vídeos that, for use in neural networks, were converted into audios. It was found as a resulta classification of 52% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the Deep Neural Network and in only one case it is possible to observe a greater accuracy by the Recurrent Neural Network, which occurs in the use of various features and setting 73 for Batch size and 100 training epochs

Colon capsule endoscopy (CCE) was introduced in 2006 as a novel way to visualise the colonic mucosa. Initially, CCE validity was limited by low completion rates (CR) and poor image quality. Through technical progress and improved bowel preparations, CCE now offers an adjunct to diagnostic colonoscopy. As referred in this review, several studies have shown promising results regarding polyp detection rates by the use of CCE. Improvements in CR and quality of bowel preparation are needed for CCE to be on a par with conventional colonoscopy. Research in artificial intelligence is evolving to aid in diagnostics and staging using CCE.

We extend the expressivity of classical conditional reasoning by introducing situation as a new parameter. The enriched conditional logic generalises the defeasible conditional setting in the style of Kraus, Lehmann, and Magidor, and allows for a refined semantics that is able to distinguish, for example, between expectations and counterfactuals. We introduce the language for the enriched logic and define an appropriate semantic framework for it. We analyse which properties generally associated with conditional reasoning are still satisfied by the new semantic framework, provide a suitable representation result, and define an entailment relation based on Lehmann and Magidor's generally-accepted notion of RationalClosure.

Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension of the propositionally closed DL. We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical.

Objectives: The SARS-CoV-2 virus pandemics led to a global shut down in indoor, as well as outdoor team sports. Although airborne transmission and distance of infectious particle is already well described in literature, information about safety of outdoor team sports and in particular in football is [for full text, please go to the a.m. URL]

Hintergrund/Fragestellung/Problem: Die medizinische Versorgung von Menschen mit Behinderung wird trotz der 2009 in Kraft getretenen UN-Behindertenrechtskonvention weiterhin als defizitär beschrieben [ref:1]. Seit 2015 besteht die Möglichkeit Medizinische Zentren für [zum vollständigen Text gelangen Sie über die oben angegebene URL]

Hintergrund und Stand (inter)nationaler Forschung: Rehabilitative Versorgungsforschung und Knowledge Translation im Feld der Rehabilitation bedarf einer geteilten, gemeinsamen Definition des Gegenstandsbereichs der Rehabilitation. Im Rahmen von Cochrane Rehabilitation wird gegenwärtig eine solche [zum vollständigen Text gelangen Sie über die oben angegebene URL]

A fundamental concept in eukaryotic DNA replication is the temporal separation of G1 origin licensing from S phase origin firing. Re-replication and genome instability ensue if licensing occurs after DNA synthesis has started. In humans and other vertebrates, the E3 ubiquitin ligase CRL4Cdt2 starts to degrade the licensing factor Cdt1 after origins fire, raising the question of how cells prevent re-replication in early S phase. Here, using quantitative microscopy, we show that Cdt1 inhibits DNA synthesis during an overlap period when cells fire origins while Cdt1 is still present. Cdt1 inhibits DNA synthesis by suppressing CMG helicase progression at replication forks through the MCM-binding domain of Cdt1, and DNA synthesis commences once Cdt1 is degraded. Thus, instead of separating licensing from firing to prevent re-replication in early S phase, cells separate licensing from DNA synthesis through Cdt1-mediated inhibition of CMG helicase after firing.Highlights– Cdt1 is present together with fired origins of replication at the start of S phase– Cdt1 delays DNA synthesis by inhibiting CMG helicase progression after origins fire– Cdt1 inhibits CMG helicase progression through the MCM-binding domain of Cdt1