Elaboração do Plano de Classificação de fotografia para a divisão do Arquivo Municipal e Gestão Documental da Câmara Municipal de Sesimbra

Elaboração do plano de classificação do Arquivo Fotográfico da Divisão do Arquivo Municipal e Gestão Documental. Para a elaboração da morosa tarefa arquivística procedeu-se, primeiramente, à caracterização da estrutura orgânica e funcional do Arquivo Municipal de Sesimbra, quer no sistema de gestão documental, quer no plano de classificação do arquivo corrente. Atende-se, ainda, à classificação de clientes (quantificação e qualificação). Numa segundo fase, reconsideramos a fotografia como objeto direto da arquivística – a fotografia como documento de arquivo; a fotografia nos arquivos portugueses; as normas de classificação de fotografias, nacionais e internacionais e, finalmente, tomamos contato com as espécies fotográficas de Américo Ribeiro e Idaleciano Cabecinha, fotografias que representam aspetos singulares da vila piscatória de Sesimbra e suas gentes. Apresenta-se, no terminus desta investigação, o plano de classificação da Divisão do Arquivo Municipal e Gestão Documental. São descritos, por conseguinte, (i) objetivos, (ii) sistema e estrutura de classificação, (iii) descrição analítica codificada e, (iv) os elementos constitutivos do referido plano de classificação. Apresentamos, finalmente, um estudo arquivométrico de um dos acervos fotográficos – Fundo Doação Valdemar – do fotógrafo Idaleciano Cabecinha. Determinamos, assim, quais os temas e subtemas mais representativos da estética mimética de Cabecinha.

A classificação de documentos fotográficos: um estudo em arquivos, bibliotecas e museus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Pathophysiology and classification of the vibration white finger.

Skin, arteries and nerves of the upper extremities can be affected by vibration exposure. Recent advances in skin and vascular biology as well as new investigative methods, have shown that neurovascular symptoms may be due to different vascular and neurological disorders which should be differentiated if proper management is to be evaluated. Three types of vascular disorder can be observed in the vibration white finger: digital organic microangiopathy, a digital vasospastic phenomenon and arterial thrombosis in the upper extremities. An imbalance between endothelin-1 and calcitonin-gene-related peptide is probably responsible for the vasospastic phenomenon. Moreover, paresthesiae can be due to either a diffuse vibration neuropathy or a carpal tunnel syndrome. A precise diagnosis is then necessary to adapt the treatment to individual cases. A classification describing the type and severity of the vascular lesions is presented. Asymptomatic lesions are included for adequate epidemiological studies and risk assessment of vibrating tools. Monitoring of vibration exposed workers should include not only a questionnaire about symptoms, but also a clinical evaluation including diagnostic tests for the screening of early asymptomatic neurovascular injuries.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Preoperative assessment and classification of benign laryngotracheal stenosis: a consensus paper of the European Laryngological Society.

Adult and pediatric laryngotracheal stenoses (LTS) comprise a wide array of various conditions that require precise preoperative assessment and classification to improve comparison of different therapeutic modalities in a matched series of patients. This consensus paper of the European Laryngological Society proposes a five-step endoscopic airway assessment and a standardized reporting system to better differentiate fresh, incipient from mature, cicatricial LTSs, simple one-level from complex multilevel LTSs and finally "healthy" from "severely morbid" patients. The proposed scoring system, which integrates all of these parameters, may be used to help define different groups of LTS patients, choose the best treatment modality for each individual patient and assess distinct post-treatment outcomes accordingly.

Bosniak classification of renal cystic lesions according to multidetector computed tomography findings

Renal cystic lesions are usually diagnosed in the radiologists' practice and therefore their characterization is crucial to determine the clinical approach to be adopted and prognosis. The Bosniak classification based on computed tomography findings has allowed for standardization and categorization of lesions in increasing order of malignancy (I, II, IIF, III and IV) in a simple and accurate way. The present iconographic essay developed with multidetector computed tomography images of selected cases from the archives of the authors' institution, is aimed at describing imaging findings that can help in the diagnosis of renal cysts.

Schémas de classification et repérage des documents administratifs électroniques dans un contexte de gestion décentralisée des ressources informationnelles

Les employés d’un organisme utilisent souvent un schéma de classification personnel pour organiser les documents électroniques qui sont sous leur contrôle direct, ce qui suggère la difficulté pour d’autres employés de repérer ces documents et la perte possible de documentation pour l’organisme. Aucune étude empirique n’a été menée à ce jour afin de vérifier dans quelle mesure les schémas de classification personnels permettent, ou même facilitent, le repérage des documents électroniques par des tiers, dans le cadre d’un travail collaboratif par exemple, ou lorsqu’il s’agit de reconstituer un dossier. Le premier objectif de notre recherche était de décrire les caractéristiques de schémas de classification personnels utilisés pour organiser et classer des documents administratifs électroniques. Le deuxième objectif consistait à vérifier, dans un environnement contrôlé, les différences sur le plan de l’efficacité du repérage de documents électroniques qui sont fonction du schéma de classification utilisé. Nous voulions vérifier s’il était possible de repérer un document avec la même efficacité, quel que soit le schéma de classification utilisé pour ce faire. Une collecte de données en deux étapes fut réalisée pour atteindre ces objectifs. Nous avons d’abord identifié les caractéristiques structurelles, logiques et sémantiques de 21 schémas de classification utilisés par des employés de l’Université de Montréal pour organiser et classer les documents électroniques qui sont sous leur contrôle direct. Par la suite, nous avons comparé, à partir d'une expérimentation contrôlée, la capacité d’un groupe de 70 répondants à repérer des documents électroniques à l’aide de cinq schémas de classification ayant des caractéristiques structurelles, logiques et sémantiques variées. Trois variables ont été utilisées pour mesurer l’efficacité du repérage : la proportion de documents repérés, le temps moyen requis (en secondes) pour repérer les documents et la proportion de documents repérés dès le premier essai. Les résultats révèlent plusieurs caractéristiques structurelles, logiques et sémantiques communes à une majorité de schémas de classification personnels : macro-structure étendue, structure peu profonde, complexe et déséquilibrée, regroupement par thème, ordre alphabétique des classes, etc. Les résultats des tests d’analyse de la variance révèlent des différences significatives sur le plan de l’efficacité du repérage de documents électroniques qui sont fonction des caractéristiques structurelles, logiques et sémantiques du schéma de classification utilisé. Un schéma de classification caractérisé par une macro-structure peu étendue et une logique basée partiellement sur une division par classes d’activités augmente la probabilité de repérer plus rapidement les documents. Au plan sémantique, une dénomination explicite des classes (par exemple, par utilisation de définitions ou en évitant acronymes et abréviations) augmente la probabilité de succès au repérage. Enfin, un schéma de classification caractérisé par une macro-structure peu étendue, une logique basée partiellement sur une division par classes d’activités et une sémantique qui utilise peu d’abréviations augmente la probabilité de repérer les documents dès le premier essai.

Méthodes de détection et de classification des naines brunes

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Elaboration d'une boîte à outils pour les archives audiovisuelles

Travail réalisé à l'EBSI, Université de Montréal, sous la direction de M. Yvon Lemay dans le cadre du cours SCI6111 - Politique de gestion des archives, à l'automne 2012.

From archives to archival science Elements for a discursive construction

This work tries to outline the theoretical background established by archival science manuals, based mainly on discourse analysis as a key discipline to understand which the differences are and points of conceptual commonality in archival science traditions studied that to some extent, are additional. As archival science is a product of historically constructed knowledge, the context of conceptual production ultimately reflects in the different schools of thought and in the methodological approach. The manuals discussed here were selected by their canon and their wide dissemination in the archival science field, and are: the Manual for the arrangement and description of archives of the Association of Dutch Archivists (1898), and Jenkinson's A manual of archive administration including the problems of war archives and archive making. We considered how in these manuals two key concepts are discussed as for organization methodology in archival science: description and classification, establishing, from these concepts, which are the intertwined discourses in these texts and relating them to their historical aspects.

Catalog rules: series for archives material, Springfield, Illinois, 1938.

Accompanied by "Revision no. 1- " ( v.) Published: [Springfield, 1939- ]

Phylogenetics, Ancestral State Reconstruction, And A New Infrafamilial Classification Of The Pantropical Ochnaceae (medusagynaceae, Ochnaceae S.str., Quiinaceae) Based On Five Dna Regions.

Ochnaceae s.str. (Malpighiales) are a pantropical family of about 500 species and 27 genera of almost exclusively woody plants. Infrafamilial classification and relationships have been controversial partially due to the lack of a robust phylogenetic framework. Including all genera except Indosinia and Perissocarpa and DNA sequence data for five DNA regions (ITS, matK, ndhF, rbcL, trnL-F), we provide for the first time a nearly complete molecular phylogenetic analysis of Ochnaceae s.l. resolving most of the phylogenetic backbone of the family. Based on this, we present a new classification of Ochnaceae s.l., with Medusagynoideae and Quiinoideae included as subfamilies and the former subfamilies Ochnoideae and Sauvagesioideae recognized at the rank of tribe. Our data support a monophyletic Ochneae, but Sauvagesieae in the traditional circumscription is paraphyletic because Testulea emerges as sister to the rest of Ochnoideae, and the next clade shows Luxemburgia+Philacra as sister group to the remaining Ochnoideae. To avoid paraphyly, we classify Luxemburgieae and Testuleeae as new tribes. The African genus Lophira, which has switched between subfamilies (here tribes) in past classifications, emerges as sister to all other Ochneae. Thus, endosperm-free seeds and ovules with partly to completely united integuments (resulting in an apparently single integument) are characters that unite all members of that tribe. The relationships within its largest clade, Ochnineae (former Ochneae), are poorly resolved, but former Ochninae (Brackenridgea, Ochna) are polyphyletic. Within Sauvagesieae, the genus Sauvagesia in its broad circumscription is polyphyletic as Sauvagesia serrata is sister to a clade of Adenarake, Sauvagesia spp., and three other genera. Within Quiinoideae, in contrast to former phylogenetic hypotheses, Lacunaria and Touroulia form a clade that is sister to Quiina. Bayesian ancestral state reconstructions showed that zygomorphic flowers with adaptations to buzz-pollination (poricidal anthers), a syncarpous gynoecium (a near-apocarpous gynoecium evolved independently in Quiinoideae and Ochninae), numerous ovules, septicidal capsules, and winged seeds with endosperm are the ancestral condition in Ochnoideae. Although in some lineages poricidal anthers were lost secondarily, the evolution of poricidal superstructures secured the maintenance of buzz-pollination in some of these genera, indicating a strong selective pressure on keeping that specialized pollination system.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Hippocampal Dysplasia With Balloon Cells: Case Report And Discussion On Classification.

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