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Abstract Earlier linguistic research suggested that Malacca Creole Portuguese (MCP) had existed without diglossia with Portuguese ever since the Dutch conquest of Portuguese Malacca in 1642, yet it had experienced some contact with Portuguese in the 19th and 20th centuries. The present study adds significantly to this discussion. It considers a range of information from sociohistorical studies and archival sources (including linguistic data) relating to the Dutch (1642–1795, 1818–1823) and early British (1795–1818, 1823–1884) colonial periods. For the Dutch period, it is seen that contact with other Creole Portuguese communities is likely to have persisted for some time. Most significant, however, is the finding that 19th century texts in Portuguese and creole Portuguese, recently identified in archival sources in London and Graz, show that Portuguese continued to be part of the Malacca sociolinguistic setting until the early British period, and that missionary Indo-Portuguese also had a presence at that time. It is concluded that, rather than presenting a narrow lectal range akin to that of the MCP community in the late 20th century, the creole lectal grid in the 19th century was more complex, and included dimensions of a continuum in a diglossic relationship with Portuguese.
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To what extent is students' understanding of computer science culturally situated? This, possibly philosophical question, has come to the surface at Uppsala University, Uppsala, Sweden, where many Chinese students study computer science together with the local students. We did an exploratory study using email interviews to see if our intuitions could be relied on. We collected data from Chinese students studying in master programs and analysed the data using a phenomenographic perspective. A complex intertwined relationship between the content of their learning (the WHAT), the ways in which they went about studying (the HOW), the aims of their studies (the WHY), and the competencies developed from the intercultural context they studied in (the WHERE) was observed. In this paper we offer some insights from the results of the pilot study and discuss how they have shaped our on-going study in the field.
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Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.
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The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.
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COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.
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This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.
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Although there is a substantial body of research on the second language acquisition of adults, there is little specific research on the learning experiences of senior and very senior adults. This thesis investigates and discovers the experience of being a senior from a traditional Confucian Heritage Culture aged between 55 and 75 years old, learning English as a foreign language through various interventions, including, the introduction of an adapted version of synthetic phonics to improve pronunciation, alongside the use of andragogical and geragogical principles to accommodate and encourage the development of agency and self-directed learning. This research adopted a case study methodology to investigate the lived experiences of seniors, and investigated the participants’ subjective constructions of the situation, learning experiences, challenges, circumstances, needs, and wants with regard to the situation. Therefore, an open and exploratory case study design was selected to understand the participants and report the findings. Furthermore, this thesis identifies the challenges faced by senior and very senior learners who are post-work and post-family rearing to make recommendations from the findings to complement, enhance and empower their learning
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Maria Celeste Natário, Renato Epifânio, Carlos Ascenso André, Gonçalo Cordeiro, Inocência Mata, Jorge Rangel, Maria Antónia Espadinha
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Resumo O fascínio do Ocidente pela dicção poética oriental está atestado em várias latitudes e línguas, e resultou numa profícua produção na área da poesia. Sabe-se que a reinvenção da poesia chinesa da autoria de Pound, em grande medida na origem da sua proposta de revolução do idioma poético, nas primeiras décadas do séc. XX, assentou, na verdade, numa falácia; numa concepção errada da natureza da escrita chinesa (e japonesa) como essencialmente pictográfica e ideogramática, na base de propriedades expressivas reconhecidas na poesia que resultariam numa particular eficácia na apreensão e tradução do real. Pessanha enaltece, em termos similares aos da exaltação poundiana, a escrita da poesia chinesa clássica. Interessa-nos rever alguns inventários dos traços da dicção poética chinesa e japonesa que explicam que ela seja tomada como metonímia e metáfora da poesia, ou como meta e utopia da poesia, para perceber o que terá levado autores muito díspares a tentar a mão nos haikus, processo em que sondaremos algumas formulações poéticas em língua portuguesa. Consideramos também que esse fascínio por uma (sonhada) origem da dicção poética, quando cruzada com o habitar (não metafórico, neste caso) do pequenino enclave de Macau, de autores que nele lançaram raízes, resultou em alguns exercícios poéticos particularmente felizes e singulares. Serão trazidos à colação nesta abordagem poemas de Eugénio de Andrade, Sophia de Mello Breyner Andresen, José Tolentino Mendonça, Yao Feng, Fernanda Dias e Fernando Sales Lopes.
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"Student engagement is a catch-all term, irresistible to educators and policy makers, and serving many agendas and purposes. This ground-breaking book provides a powerful theory of student engagement, rooted in critical theory and social justice. It sets out a compelling argument for student engagement to promote social justice and to repel neoliberalism in, and through, higher education, addressing three key questions: -Student engagement in what? -Student engagement for what? -Student engagement for whom? The answers draw on Habermas, Honneth, Gramsci, Foucault, and Giroux in examining ideology, power, recognition, resistance, and student engagement, with examples drawn from across the world. It sets out key features, limitations and failures of neoliberalism in higher education, and indicates how student engagement can resist it. Student engagement calls for higher education institutions to be sites for challenge, debate on values and power, action for social justice, and for students to engage in the struggle to resist neoliberalism, taking action to promote social justice, democracy, and the public good. This book is essential reading for educators, researchers, managers and students in higher education, social scientists and social theorists. It is a call to reawaken higher education for social justice, human rights, democracy and freedoms"--
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