Variação das unidades fraseoterminológicas da culinária entre português brasileiro e português europeu

A criação de uniões, como a União Europeia e o Mercosul, o aumento do intercâmbio de mercadorias, de informações e conhecimentos, etc. estabelece novos trabalhos na área da Terminologia Científica e Técnica, tanto bilíngue como monolíngue, inclusive entre variantes de uma mesma língua, como o Português Brasileiro (PB) e o Português Europeu (PE), o que torna relevante o conhecimento das variantes fraseoterminológicas entre duas normas linguísticas para o especialista e para o tradutor. Sendo a Culinária uma área que proporciona vários tipos de intercâmbios, como linguístico, cultural, mercantil, etc. e, dessa forma, necessitando trocar conhecimentos, nosso estudo propõe, através de uma perspectiva interdisciplinar que engloba a Terminologia, numa ótica variacionista, a Fraseologia e a Linguística de Corpus, estabelecer critérios para identificar, emparelhar, contrastar e descrever as unidades fraseoterminológicas (UFT) da Culinária do PB e do PE, almejando, por conseguinte, estruturá-las numa ferramenta que seja útil aos especialistas, estudantes e tradutores dessa área. O desenvolvimento deste trabalho está organizado em sete capítulos. O primeiro, apresenta a Culinária, traçando um panorama histórico dessa área, e estabelece o mapa conceitual da Culinária que, além de servir para a organização das relações conceituais no dicionário, limita o universo da pesquisa. O segundo aborda a variação em Terminologia, bem como as principais tendências da Terminologia que aceitam a variação terminológica. O terceiro explana a Fraseologia, desde a língua corrente até à língua de especialidade, e estabelece os critérios para recolha dos candidatos a UFT da Culinária. O quarto apresenta brevemente a Linguística de Corpus e traça os caminhos seguidos para a constituição dos dois corpora textuais da Culinária, compostos de receitas culinárias e técnicas de preparo, os quais serviram para o levantamento da terminologia. O quinto trata da coleta e organização das unidades fraseoterminológicas da Culinária em PB bem como das respectivas variantes em PE e seu armazenamento em Base de Dados. O sexto, analisa a variação entre os pares de UFT selecionados para esse fim, descreve os contrastes detectados, e apresenta uma tipologia contrastiva dessas UFT variantes entre PB e PE. O sétimo apresenta o projeto do Dicionário Fraseológico Contrastivo de Culinária: Português Brasileiro - Português Europeu, descrevendo suas partes e o sistema de remissivas. Com base nas reflexões teóricas e na análise dos dados recolhidos, pudemos, além de identificar, emparelhar e descrever as diferentes formas assumidas do discurso da Culinária pelas UFT, chegar a um projeto de dicionário fraseoterminológico, cuja microestrutura possibilitará, mais que compreender o significado da UT, encontrar elementos para produzir um texto, visando, desse modo, as necessidades reais de tradutores e redatores, que carecem de recursos para o uso adequado das UFT presentes nas línguas de especialidade. Os resultados obtidos reafirmam que a variação terminológica é um fenômeno inerente aos domínios de especialidade, assim como às línguas naturais em que estão inseridas e, portanto, não deve ser ignorado na hora de elaborar dicionários terminológicos.

Basic cognitive experiences and definitions in the Longman Dictionary of Contemporary English

This study applies theories of cognitive linguistics to the compilation of English learners’ dictionaries. Specifically, it employs the concepts of basic level categories and image schemas, two basic cognitive experiences, to examine the ‘definition proper’ of English dictionaries for foreign learners. In the study, the definition proper refers to the constituent part of a reference work that provides an explanation of the meanings of a word, phrase or term. This rationalization mainly consists of defining vocabulary, sense division and arrangement, as well as the means of defining (i.e. paraphrase, true definition, functional definition, and pictorial illustration). The aim of the study is to suggest ways of aligning the consultation and learning of definitions with dictionary users’ cognitive experiences. For this purpose, an analysis of the definition proper of the fourth edition of the Longman Dictionary of Contemporary English (LDOCE4) from the perspective of basic cognitive experiences has been undertaken. The study found that, generally, the lexicographic practices of LDOCE4 are consistent with theories of cognitive linguistics. However, there exist shortcomings that result from disregarding basic cognitive experiences.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Working group report: Dictionary of Large Hadron Collider signatures

We report on a plan to establish a `Dictionary of LHC Signatures', an initiative that started at the WHEPP-X workshop in Chennai, January 2008. This study aims at the strategy of distinguishing 3 classes of dark matter motivated scenarios such as R-parity conserved supersymmetry, little Higgs models with T-parity conservation and universal extra dimensions with KK-parity for generic cases of their realization in a wide range of the model space. Discriminating signatures are tabulated and will need a further detailed analysis.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Super Resolution Using a Single Image Dictionary

To perform super resolution of low resolution images, state-of-the-art methods are based on learning a pair of lowresolution and high-resolution dictionaries from multiple images. These trained dictionaries are used to replace patches in lowresolution image with appropriate matching patches from the high-resolution dictionary. In this paper we propose using a single common image as dictionary, in conjunction with approximate nearest neighbour fields (ANNF) to perform super resolution (SR). By using a common source image, we are able to bypass the learning phase and also able to reduce the dictionary from a collection of hundreds of images to a single image. By adapting recent developments in ANNF computation, to suit super-resolution, we are able to perform much faster and accurate SR than existing techniques. To establish this claim, we compare the proposed algorithm against various state-of-the-art algorithms, and show that we are able to achieve b etter and faster reconstruction without any training.

Efficient Dictionary for Salted Password Analysis

User authentication is essential for accessing computing resources, network resources, email accounts, online portals etc. To authenticate a user, system stores user credentials (user id and password pair) in system. It has been an interested field problem to discover user password from a system and similarly protecting them against any such possible attack. In this work we show that passwords are still vulnerable to hash chain based and efficient dictionary attacks. Human generated passwords use some identifiable patterns. We have analysed a sample of 19 million passwords, of different lengths, available online and studied the distribution of the symbols in the password strings. We show that the distribution of symbols in user passwords is affected by the native language of the user. From symbol distributions we can build smart and efficient dictionaries, which are smaller in size and their coverage of plausible passwords from Key-space is large. These smart dictionaries make dictionary based attacks practical.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

Dictionary-Learning-Based Post-Filter for HMM-Based Speech Synthesis

Oversmoothing of speech parameter trajectories is one of the causes for quality degradation of HMM-based speech synthesis. Various methods have been proposed to overcome this effect, the most recent ones being global variance (GV) and modulation-spectrum-based post-filter (MSPF). However, there is still a significant quality gap between natural and synthesized speech. In this paper, we propose a two-fold post-filtering technique to alleviate to a certain extent the oversmoothing of spectral and excitation parameter trajectories of HMM-based speech synthesis. For the spectral parameters, we propose a sparse coding-based post-filter to match the trajectories of synthetic speech to that of natural speech, and for the excitation trajectory, we introduce a perceptually motivated post-filter. Experimental evaluations show quality improvement compared with existing methods.