Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set

The convolutional neural network as a tool to classify electroencephalography data resulting from the consumption of juice sweetened with caloric or non-caloric sweeteners

Sweetener type can influence sensory properties and consumer’s acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we applied the convolutional neural network (CNN) to evaluate brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a Deep Neural Network with tree branches that applied a Convolutions and pooling for different feature filtering and selection. The CNN received raw signal as input for multiclass classification and with supervised training was able to extract underling features and patterns from the signal with better performance than handcrafted filters like FFT. Our results indicated that CNN is an useful tool for electroencephalography (EEG) analyses and classification of perceptually similar tastes

Enfoque metodológico para cuantificar los efectos cognitivos en el análisis sensorial de alimentos = A methodological approach to quantify the cognitive effects in sensorial analysis of food

Este trabajo tuvo como objetivo formular un modelo de análisis sensorial que permita cuantificar la acción del estímulo gustativo en el contexto cognitivo, a partir de la actividad eléctrica cerebral. Los experimentos fueron realizados en dos etapas: (a) Determinación del umbral de percepción del sabor y (b) investigación de la percepción de sabor bajo el umbral de la actividad consciente, utilizando electroencefalograma (EEG). El procesamiento digital de señales en los voluntarios fue realizado usando el análisis tiempo-frecuencia por el método AGR (Adaptative Gaussian Representation). Este método evalúa cómo la información de la señal evoluciona en el espacio tiempo-frecuencia usando coeficientes representativos de este espacio. Se pudo verificar que el 3er coeficiente de AGR se destacó por el ruido y por tanto representó mejor los resultados del EEG. Así fue posible verificar que el coeficiente presentó separación lineal de la concentración de sacarosa, es decir, se detectó en el comportamiento tiempo-frecuencia del EEG la separación entre las concentraciones de sacarosa, independientemente de la manifestación del sujeto experimental. Esos resultados sugieren que la metodología descrita en este artículo puede ser utilizada como una herramienta complementaria al análisis sensorial

Development of a device for acquiring the corona effect on food

O presente trabalho teve como objetivo o desenvolvimento de um equipamento capaz de gerar e captar o efeito corona em alimentos de forma automatizada e instantânea. Para isto, foi desenvolvido um gerador de alta tensão e alta frequência, um sistema de captação com eletrodo e câmera digital e um software de gerenciamento do sistema, que recebe e trata os dados coletados através da câmera digital. Para comprovar a eficiência do sistema, foram feitos vários testes com frutas (maçã, limão, ameixa, uva e pera) e os resultados obtidos foram comparados com imagens geradas por um sistema kirlian e com simulações computacionais através de algoritmo genético e elementos finitos e permitem assim, concluir que o sistema proposto foi capaz de captar imagens que contém informações das características físico-químicas do alimento.This study aimed to develop a device capable of generating and acquiring the corona effect on food automatically and instantaneously. In order to make it possible, a high voltage and high frequency generator was developed, with a system with electrodes and digital camera controlled by a software that receives and handles incoming data from a digital camera. To test de system, experiments where made with fruits (apples, lemons, plums, grapes and pears) and the results where compared to a standard system and with computational simulation made with Genetic Algorithm and Finite Elements and it was conclude that the system is able to acquire images that contain information on the physical-chemical proprieties of the material

Simulation, control and automation of a conveyor belt tunel oven with embedded technology

Baseado na grande evolução dos dispositivos eletrônicos nos últimos 35 anos e dos novos hardwares de baixo custo e alto poder computacional, esta tese tem como objetivo testar a seguinte hipótese: É possível o controle e automação de um forno em escala piloto com informações de sensores e simulação em tempo real utilizando computação embarcada de baixo custo. Para isto, modelagem matemática e simulação do perfil de temperatura do forno e do alimento foram realizadas para que o sistema de controle possa ter informação da temperatura no alimento em tempo real, contando apenas com os sensores fixos no forno. A informação desta simulação alimenta o controle PID, garantindo que o perfil de temperatura desejado para o aquecimento/cozimento do alimento seja obedecido, melhorando a qualidade do produto final. O sistema de controle possui duas unidades, a escrava que é localizada na lateral do forno é composta de um Arduino mini, da instrumentação para o controle dos sensores e atuadores do forno e a unidade mestre, que utiliza um Raspberry pi onde o software de controle com interface gráfica realiza as simulações, o controle PID e a comunicação via bluetooth com a unidade escrava. Experimentos foram realizados para testar e validar a simulação do perfil térmico do forno, do alimento e o controlador PID. Concluiu-se que é possível um controle de um forno industrial em escala piloto com simulação em tempo real utilizando computação de baixo custo.Based on the dramatic evolution of electronic devices in the last 35 years and the recent advent of low cost computational hardware, embedded sensors have become a cost-effective solution for real time machine monitoring and simulation. The objective of this thesis is to present both software and hardware intended for the real-time simulation and control of a conveyor belt tunnel oven using low cost embedded hardware, to ensure high quality food production. For this purpose, mathematical modelling and simulation of the temperature profile inside the oven was performed in order to supply the control system with the calculated temperature of the food in real time, using only the fixed sensors inside the oven. This simulation information is passed to the PID controller, ensuring that the desired temperature profile for heating the food is achieved, improving the quality of the final product. The system has two units, a slave that is located on the main body consisting of an Arduino mini and the instrumentation for controlling the sensors and actuators. The second unit is the master unit, which utilises a Raspberry pi to host the control software, the graphical user interface the PID controller and manages the Bluetooth connection with the slave unit. Experiments were performed in order to test and validate the thermal profile simulation of the oven and the food, as well as to test the PID controller. It was concluded that it is possible to control a prototype scale industrial oven using real time simulation with a low-cost computation unit

Development of a device for acquiring the corona effect on food

O presente trabalho teve como objetivo o desenvolvimento de um equipamento capaz de gerar e captar o efeito corona em alimentos de forma automatizada e instantânea. Para isto, foi desenvolvido um gerador de alta tensão e alta frequência, um sistema de captação com eletrodo e câmera digital e um software de gerenciamento do sistema, que recebe e trata os dados coletados através da câmera digital. Para comprovar a eficiência do sistema, foram feitos vários testes com frutas (maçã, limão, ameixa, uva e pera) e os resultados obtidos foram comparados com imagens geradas por um sistema kirlian e com simulações computacionais através de algoritmo genético e elementos finitos e permitem assim, concluir que o sistema proposto foi capaz de captar imagens que contém informações das características físico-químicas do alimento.This study aimed to develop a device capable of generating and acquiring the corona effect on food automatically and instantaneously. In order to make it possible, a high voltage and high frequency generator was developed, with a system with electrodes and digital camera controlled by a software that receives and handles incoming data from a digital camera. To test de system, experiments where made with fruits (apples, lemons, plums, grapes and pears) and the results where compared to a standard system and with computational simulation made with Genetic Algorithm and Finite Elements and it was conclude that the system is able to acquire images that contain information on the physical-chemical proprieties of the material

Enfoque metodológico para cuantificar los efectos cognitivos en el análisis sensorial de alimentos

This work aims to propose a sensorial analysis method which allows quantitative evaluation of taste stimuli within a cognitive context using electroencephalogram monitoring. The experiment was conducted in two steps: (a) Determination of flavor perception threshold and (b) investigation of the flavor perception below conscious threshold using electroencephalogram (EEG). The volunteer’s digital signal processing was performed using time-frequency analysis by the AGR method. This method evaluates the signal behavior in the time-frequency framework by means of representative coefficients. It could be verified that the 3rd AGR representative coefficient was noiseless and then EEG information was better characterized. Thus it was possible to verify that the coefficient provided linear separation of sucrose concentration, therefore, was able to represent the EEG behavior in the time-frequency framework by separating sucrose concentrations independent of subject response. These results suggest that the methodology described in this article can be used as a tool to complement the sensory analysis.Este trabajo tuvo como objetivo formular un modelo de análisis sensorial que permita cuantificar la acción del estímulo gustativo en el contexto cognitivo, a partir de la actividad eléctrica cerebral. Los experimentos fueron realizados en dos etapas: (a) Determinación del umbral de percepción del sabor y (b) investigación de la percepción de sabor bajo el umbral de la actividad consciente, utilizando electroencefalograma (EEG). El procesamiento digital de señales en los voluntarios fue realizado usando el análisis tiempo-frecuencia por el método AGR (Adaptative Gaussian Representation). Este método evalúa cómo la información de la señal evoluciona en el espacio tiempo-frecuencia usando coeficientes representativos de este espacio. Se pudo verificar que el 3ercoeficiente de AGR se destacó por el ruido y por tanto representó mejor los resultados del EEG. Así fue posible verificar que el coeficiente presentó separación lineal de la concentración de sacarosa, es decir, se detectó en el comportamiento tiempo-frecuencia del EEG la separación entre las concentraciones de sacarosa, independientemente de la manifestación del sujeto experimental. Esos resultados sugieren que la metodología descrita en este artículo puede ser utilizada como una herramienta complementaria al análisis sensorial