Reconhecimento de impressões digitais com baixo custo computacional para um sistema de controle de acesso

Orientador: Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 2005Inclui bibliografi

Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human–robot interaction, and cross-cultural communication.Peer reviewe

Eggshell microbiology and quality of hatching eggs subjected to different sanitizing procedures

O objetivo deste trabalho foi avaliar o efeito de diferentes procedimentos de desinfecção alternativos à fumigação com formaldeído sobre a redução da contagem microbiana e a qualidade de casca de ovos de matrizes Cobb de 42 semanas de idade. Um total de 10.080 ovos limpos coletados dos ninhos foi distribuído de maneira aleatória, em delineamento de blocos ao acaso, entre os seguintes tratamentos: fumigação com 13,33 g m-3 de paraformaldeído, fumigação com 5–10 ppm de ozônio, 6,36 mW cm-2 de irradiação de luz UV-C, pulverização com 1,56% de peróxido de hidrogênio, pulverização com 0,13% de ácido peracético, pulverização com água (controle úmido) e sem desinfecção (controle seco). Por tratamento, foram coletadas oito amostras de quatro ovos cada uma, momentos antes e após as desinfecções, para contagem de Enterobacteriaceae e bactérias mesófilas aeróbicas totais da casca. Somente os ovos submetidos aos tratamentos com formaldeído e UV apresentaram redução significativa nas contagens de bactérias mesófilas aeróbicas totais, quando comparados aos do grupo controle seco. Os tratamentos não influenciaram a espessura e a resistência da casca. A exposição de luz UV é eficaz em reduzir a contagem microbiana da casca de ovos de matrizes de 42 semanas de idade, sem afetar sua qualidade, e pode ser considerada alternativa ao uso de formaldeído para desinfecção.The objective of this work was to evaluate the effect of different disinfection procedures as alternatives to formaldehyde fumigation on eggshell microbial load and quality of eggs from a 42-week-old Cobb commercial breeder flock. A total of 10,080 clean eggs collected from the nests were randomly distributed in a randomized complete block design, among the following treatment groups: 13.33 g m-3 formaldehyde fumigation, 5–10 ppm ozone fumigation, 6.36 mW cm-2 UV-C light irradiation, spraying with 1.56% hydrogen peroxide, spraying with 0.13% peracetic acid, spraying with water (wet control), and no disinfection procedure (dry control). Per treatment, eight samples of four eggs each were collected before and after the disinfection procedure, in order to count the number of Enterobacteriaceae and total aerobic mesophilic bacteria on the eggshell. Only eggs subjected to the formaldehyde and UV treatments showed a significant reduction in total aerobic mesophilic bacterial count on the eggshell, when compared with those of the dry control group. Treatments did not affect eggshell thickness and resistance force. UV light exposure is effective in reducing microbial load on 42-week-old breeder flock eggshells, without affecting their quality, and can be considered an alternative to formaldehyde disinfection

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost