Educational software in english to the awareness in a preschool education supervised context

Information and communication technologies play an increasingly important role in society, in the sense that all areas and professions make use of digital resources. The school can not be brushed off this reality, aim to create full subjects and integrated in society today. Educational software can be used very early in the education of children, but they must be carefully and monitoring. This article aims to present the results of the use of educational software in English to the awareness of context with children of pre-school education in kindergarten, nursery center Redemptorist Fathers - The smallest fox in White Castle, a 21 group children under 5 years. Early awareness of foreign language such as English can be started with digital multimedia capabilities and various software available on the market. However, the small study described the case reveals some resistance from parents and educators, in the preparation of these to choose and monitor the use of ICT by children, in addition to also highlight the self-interest of the children involved and their learning a few words in English language in different contexts of daily worked. The study opens perspectives on close monitoring needs of such uses and training of educators in the field of use of resources multilingual awareness in pre-school education.

The use of digital educational resources : the contribution of research in the supervised teaching practice in basic education

This article aims to reflect on the impact of Information and Communication Technologies (ICT) in the educational context, focusing on the potential contributions of the use of Digital Educational Resources (RED) in the process of teaching and learning. For this purpose, the results of the use of the RED will be presented:. Digital Classroom - The World's 1st Year Carochinha The study was accomplished in a class of the 1st grade of the 1st CEB, composed of 27 students, aged 6-7 years in Castelo Branco City Schools Group within the Supervised Teaching Practice. The results obtained after the analysis and processing of the data showed that when using this RED students show they have acquired the content covered by the fact that they enhanced levels of greater interest, commitment, motivation, commitment and initiative in the course of activities proposals. But, perhaps because they are students of 1st year of the 1st CEB, do not neglect the presence and monitoring of the teacher and the use of paper-based resources. This means that there should be a complementarity that reconciles the human factor (teacher), with the use of digital media resources and paper support resources (Manual).

Redes sociais – estudo exploratório das conceções dos jovens sobre riscos e desafios

Relatório final apresentado para a obtenção do grau de mestre na Especialidade profissional do ensino do 1.º e 2.º ciclos do ensino básico

Temperamento – Haverá diferenças no temperamento da criança em contexto de creche e em contexto familiar?

Relatório de estágio apresentado para obtenção do grau de Mestre em Educação Pré-Escolar

Sperimentazione di Deep Metric Loss per Self-Supervised Information Retrieval Systems su CORD19.

Dopo lo sviluppo dei primi casi di Covid-19 in Cina nell’autunno del 2019, ad inizio 2020 l’intero pianeta è precipitato in una pandemia globale che ha stravolto le nostre vite con conseguenze che non si vivevano dall’influenza spagnola. La grandissima quantità di paper scientifici in continua pubblicazione sul coronavirus e virus ad esso affini ha portato alla creazione di un unico dataset dinamico chiamato CORD19 e distribuito gratuitamente. Poter reperire informazioni utili in questa mole di dati ha ulteriormente acceso i riflettori sugli information retrieval systems, capaci di recuperare in maniera rapida ed efficace informazioni preziose rispetto a una domanda dell'utente detta query. Di particolare rilievo è stata la TREC-COVID Challenge, competizione per lo sviluppo di un sistema di IR addestrato e testato sul dataset CORD19. Il problema principale è dato dal fatto che la grande mole di documenti è totalmente non etichettata e risulta dunque impossibile addestrare modelli di reti neurali direttamente su di essi. Per aggirare il problema abbiamo messo a punto nuove soluzioni self-supervised, a cui abbiamo applicato lo stato dell'arte del deep metric learning e dell'NLP. Il deep metric learning, che sta avendo un enorme successo soprattuto nella computer vision, addestra il modello ad "avvicinare" tra loro immagini simili e "allontanare" immagini differenti. Dato che sia le immagini che il testo vengono rappresentati attraverso vettori di numeri reali (embeddings) si possano utilizzare le stesse tecniche per "avvicinare" tra loro elementi testuali pertinenti (e.g. una query e un paragrafo) e "allontanare" elementi non pertinenti. Abbiamo dunque addestrato un modello SciBERT con varie loss, che ad oggi rappresentano lo stato dell'arte del deep metric learning, in maniera completamente self-supervised direttamente e unicamente sul dataset CORD19, valutandolo poi sul set formale TREC-COVID attraverso un sistema di IR e ottenendo risultati interessanti.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Supervised and weakly supervised counting-by-segmentation: the fluorescent microscopy use case

This thesis focuses on automating the time-consuming task of manually counting activated neurons in fluorescent microscopy images, which is used to study the mechanisms underlying torpor. The traditional method of manual annotation can introduce bias and delay the outcome of experiments, so the author investigates a deep-learning-based procedure to automatize this task. The author explores two of the main convolutional-neural-network (CNNs) state-of-the-art architectures: UNet and ResUnet family model, and uses a counting-by-segmentation strategy to provide a justification of the objects considered during the counting process. The author also explores a weakly-supervised learning strategy that exploits only dot annotations. The author quantifies the advantages in terms of data reduction and counting performance boost obtainable with a transfer-learning approach and, specifically, a fine-tuning procedure. The author released the dataset used for the supervised use case and all the pre-training models, and designed a web application to share both the counting process pipeline developed in this work and the models pre-trained on the dataset analyzed in this work.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Depth Estimation in Stereo Biomedical Images via Proxy-Supervised Deep Learning

In order to estimate depth through supervised deep learning-based stereo methods, it is necessary to have access to precise ground truth depth data. While the gathering of precise labels is commonly tackled by deploying depth sensors, this is not always a viable solution. For instance, in many applications in the biomedical domain, the choice of sensors capable of sensing depth at small distances with high precision on difficult surfaces (that present non-Lambertian properties) is very limited. It is therefore necessary to find alternative techniques to gather ground truth data without having to rely on external sensors. In this thesis, two different approaches have been tested to produce supervision data for biomedical images. The first aims to obtain input stereo image pairs and disparities through simulation in a virtual environment, while the second relies on a non-learned disparity estimation algorithm in order to produce noisy disparities, which are then filtered by means of hand-crafted confidence measures to create noisy labels for a subset of pixels. Among the two, the second approach, which is referred in literature as proxy-labeling, has shown the best results and has even outperformed the non-learned disparity estimation algorithm used for supervision.

Self-supervised Information Retrieval basato su deep metric learning

La tesi ha lo scopo di ricercare, esaminare ed implementare un sistema di Machine Learning, un Recommendation Systems per precisione, che permetta la racommandazione di documenti di natura giuridica, i quali sono già stati analizzati e categorizzati appropriatamente, in maniera ottimale, il cui scopo sarebbe quello di accompagnare un sistema già implementato di Information Retrieval, istanziato sopra una web application, che permette di ricercare i documenti giuridici appena menzionati.

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

Remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) fish-bone settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - K-means: k=0.68; k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). Results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.

A questão da licenciatura em Educação Fisica : a transição a pratica profissional

Universidade Estadual de Campinas. Faculdade de Educação Física

A dimensão prática na preparação profissional em educação física : concepção e organização acadêmica

Universidade Estadual de Campinas . Faculdade de Educação Física

Inclusão, necessidades especiais e Educação Fisica : considerações sobre a ação pedagogica no ambiente escolar

Universidade Estadual de Campinas . Faculdade de Educação Física

A prática de exercícios físicos em pacientes com transtornos alimentares

CONTEXTO: Diferentes estudos discutem a relação da prática excessiva de exercícios físicos com transtornos alimentares como estratégia para perda de peso. OBJETIVO: Revisar a literatura sobre a prática de exercícios físicos em pacientes com transtornos alimentares, discutindo definições, critérios diagnósticos e propostas terapêuticas. MÉTODOS: Levantamento bibliográfico foi realizado por meio de MedLine, LiLacs e Cochrane Library, com os termos "transtornos alimentares", "anorexia", "bulimia", "exercício físico excessivo", "atividade física", "exercício obrigatório", "exercício compulsivo" e "exercício excessivo". RESULTADOS: Dos 80 artigos encontrados, foram selecionados 12 que incluíam a investigação de um padrão de atividade física considerado excessivo em indivíduos acima dos 18 anos e uso de algum instrumento de avaliação para essa finalidade. A prática de exercícios físicos em pacientes com transtornos do comportamento alimentar é revisada. CONCLUSÃO: Não há consenso sobre critérios diagnósticos e instrumentos para considerar o exercício físico como inadequado ou excessivo e seu uso como recurso para perder peso. Por outro lado, a prática de exercícios físicos durante o tratamento de pacientes com transtornos alimentares pode ser benéfica desde que orientada e supervisionada.