A Prática como Componente Curricular em licenciaturas de Letras: um estudo discursivo das relações entre teoria e prática

O objetivo deste trabalho foi investigar, a partir da reforma das licenciaturas nas universidades ocorrida em 2001, a constituição do eixo disciplinar Prática como Componente Curricular (PCC) nos currículos de licenciatura de cursos de Letras, Português / Espanhol da região sudeste brasileira, focando-nos na relação entre teoria e prática. Para tal discussão, utilizamo-nos das reflexões de Deleuze (1968) a fim de problematizar as possibilidades de repetição total ou de diferenciação total; Vázquez (1977), ao trazer sua discussão de práxis que trata da indissociabilidade da teoria e da prática e Schwartz (2010), para incorporar a discussão sobre o âmbito do trabalho, em particular no que concerne a impossibilidade de antecipação completa da atividade a ser realizada por um profissional. Operamos, também, com os preceitos da Análise do Discurso de base enunciativa (MAINGUENEAU, 1998, 2003) quando tratamos os enunciados como socio-historicamente situados em nossas análises. Para atingirmos nosso fim, realizamos uma contextualização documental que contou com a análise do Parecer CNE/CP 28/2001, no qual estão as determinações sobre carga horária e definição dos eixos de disciplinas da licenciatura, sendo eles: Acadêmico Científico, Prática como Componente Curricular e Estágio Supervisionado. Voltamo-nos, também, para os Projetos Políticos Pedagógicos das universidades analisadas, a fim de investigar qual o entendimento de prática construído nesses documentos. Por fim, recorremos às ementas das disciplinas obrigatórias de PCC oferecidas pelas universidades que compuseram o córpus, buscando identificar as marcas que aproximam a temática da disciplina com o trabalho que considere a prática docente, já que o eixo em questão pressupõe essa discussão. Como critérios de seleção de córpus, consideramos: contemplar uma universidade de cada estado da Região Sudeste; duas universidades que possuem disciplinas que contenham exclusivamente horas de PCC e outras duas que contenham, em uma mesmo disciplina, horas dos eixos de PCC e Acadêmico Científico. Com isso, as universidades analisadas são: UERJ, UFSCar, UFES e UFTM.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.

Statistical parametric speech synthesis based on speaker and language factorization

An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language. © 2012 IEEE.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

N-best error simulation for training spoken dialogue systems

A recent trend in spoken dialogue research is the use of reinforcement learning to train dialogue systems in a simulated environment. Past researchers have shown that the types of errors that are simulated can have a significant effect on simulated dialogue performance. Since modern systems typically receive an N-best list of possible user utterances, it is important to be able to simulate a full N-best list of hypotheses. This paper presents a new method for simulating such errors based on logistic regression, as well as a new method for simulating the structure of N-best lists of semantics and their probabilities, based on the Dirichlet distribution. Off-line evaluations show that the new Dirichlet model results in a much closer match to the receiver operating characteristics (ROC) of the live data. Experiments also show that the logistic model gives confusions that are closer to the type of confusions observed in live situations. The hope is that these new error models will be able to improve the resulting performance of trained dialogue systems. © 2012 IEEE.

Systematic assessment of the training needs of health library staff

Urquhart, C., Spink, S., Thomas, R. & Durbin, J. (2005). Systematic assessment of the training needs of health library staff. Library and Information Research, 29(93), 35-42. Sponsorship: National Library for Health (NLH)

Training needs analysis of healthcare library staff, undertaken for South Yorkshire Workforce Development Confederation

Urquhart, C., Durbin, J. & Spink, S. (2004). Training needs analysis of healthcare library staff, undertaken for South Yorkshire Workforce Development Confederation. Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: South Yorkshire WDC (NHS)

Assessing training and professional development needs of library staff. Report for National Library of Health

Urquhart, C., Spink, S. & Thomas, R., Assessing training and professional development needs of library staff. Report for National Library of Health. (2005). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth Sponsorship: National Library for Health (NHS Information Authority)

Training for professional librarians in Slovakia by distance-learning methods: an overview of the PROLIB and EDULIB projects

Tedd, L.A., Dahl, K., Francis, S.,Tet?evov?, M.& ?ihlavn?kov?, E.(2002).Training for professional librarians in Slovakia by distance-learning methods: an overview of the PROLIB and EDULIB projects. Library Hi Tech, 20(3), 340-351. Sponsorship: European Union and the Open Society Institute

Automatic Detection of Relevant Head Gestures in American Sign Language Communication

An automated system for detection of head movements is described. The goal is to label relevant head gestures in video of American Sign Language (ASL) communication. In the system, a 3D head tracker recovers head rotation and translation parameters from monocular video. Relevant head gestures are then detected by analyzing the length and frequency of the motion signal's peaks and valleys. Each parameter is analyzed independently, due to the fact that a number of relevant head movements in ASL are associated with major changes around one rotational axis. No explicit training of the system is necessary. Currently, the system can detect "head shakes." In experimental evaluation, classification performance is compared against ground-truth labels obtained from ASL linguists. Initial results are promising, as the system matches the linguists' labels in a significant number of cases.

An alignment based similarity measure for hand detection in cluttered sign language video

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a web cam in a user’s home. Moreover, the signers’ clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The Histogram of Oriented Gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a Support Vector Machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the Vocabulary Guided Pyramid Match Kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Research Question, Study Design and Continuous Research Education and Training Exercises (CREATE) Program.

The clinical research project starts with identifying the optimal research question, one that is ethical, impactful, feasible, scientifically sound, novel, relevant, and interesting. The project continues with the design of the study to answer the research question. Such design should be consistent with ethical and methodological principles, and make optimal use of resources in order to have the best chances of identifying a meaningful answer to the research question. Physicians and other healthcare providers are optimally positioned to identify meaningful research questions the answer to which could make significant impact on healthcare delivery. The typical medical education curriculum, however, lacks solid training in clinical research. We propose CREATE (Continuous Research Education And Training Exercises) as a peer- and group-based, interactive, analytical, customized, and accrediting program with didactic, training, mentoring, administrative, and professional support to enhance clinical research knowledge and skills among healthcare professionals, promote the generation of original research projects, increase the chances of their successful completion and potential for meaningful impact. The key features of the program are successive intra- and inter-group discussions and confrontational thematic challenges among participating peers aimed at capitalizing on the groups' collective knowledge, experience and skills, and combined intellectual processing capabilities to optimize choice of research project elements and stakeholder decision-making.

Students perceptions about attention to diversity in Secondary Education Teachers Initial Training

This work aims to analyze the perceptions of students enrolled in the Master's Degree in Secondary Education Teaching, Training and Language Teaching at the University of Jaen, about the initial training received on attention to diversity. A descriptive methodology has been followed using an ad hoc questionnaire as data collection instrument. The results show favorable attitudes of future secondary teachers for diversity, having received an adequate training in curricular and organizational aspects, making it able to fully achieve inclusion of students with special educational needs in the classroom.