Letteratura scientifica selezionata sul tema "Illustrations – classification"
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Articoli di riviste sul tema "Illustrations – classification":
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Luo, Yang, e Yuewu Lin. "Effects of Illustration Types on the English Reading Performance of Senior High School Students with Different Cognitive Styles". English Language Teaching 10, n. 9 (31 luglio 2017): 1. http://dx.doi.org/10.5539/elt.v10n9p1.
Abstract (sommario):
Illustration is always used as an example to make the written text or the utterance more clear in general. In Winarski’s opinion (1997), one picture equals thousands of words. That is to say, illustrations are capable to express the meaning of unfamiliar language or a great deal of information in the reading material by vivid pictures, tables, drawings, paintings and so on. As a result, illustrations are applied to many different fields including English language teaching. Based upon Song’s 3 types of illustration classification (2005), decorational illustrations, explainable illustrations and promotive illustrations, this paper tries to investigate the effects of illustrations on the reading performance of senior high school students with different cognitive styles (field-dependence, field-mix and field-independence) in the process of English reading. The result shows that: 1). There is a significant correlation between illustration types and reading performance in terms of field-dependent students. The coefficient of explainable illustration to reading peformance is the highest, while the lowest coefficient is decorational illustration. 2). As for field-mixed participants, their reading performance is also closely associated with illustrations. However, the coefficients are lower than that of field-dependent participants. Decorational illustration has no obviously relation to reading performance. Explainable illustration also reaches the highest coefficient, and it can better improve student’ reading score than promotive illustration. 3). Speaking of field-independent students, no correlation has been found between decorational, promotive illustration and reading performance. However, there exists a significant correlation between explainable illustration and reading performance for field-independent participants.
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Ha, Yeoncheol, e Seung-Sep Kim. "Classification of large ornithopod dinosaur footprints using Xception transfer learning". PLOS ONE 18, n. 11 (29 novembre 2023): e0293020. http://dx.doi.org/10.1371/journal.pone.0293020.
Abstract (sommario):
Large ornithopod dinosaur footprints have been confirmed on all continents except Antarctica since the 19th century. However, oversplitting problems in ichnotaxa have historically been observed in these footprints. To address these issues and distinguish between validated ichnotaxa, this study employed convolutional neural network-based Xception transfer learning to automatically classify ornithopod dinosaur tracks. The machine learning model was trained for 162 epochs (i.e., the number of full cycles of all training data through the model) using 274 data images, excluding horizontally flipped images. The trained model accuracy was 96.36%, and the validation accuracy was 92.59%. We demonstrate the performance of the machine learning model using footprint illustrations that are not included in the training dataset. These results show that the machine learning model developed in this study can properly classify footprint illustration data for large ornithopod dinosaurs. However, the quality of footprint illustration data (or images) inherently affects the performance of our machine learning model, which performs better on well-preserved footprints. In addition, because the developed machine-learning model is a typical supervised learning model, it is not possible to introduce a new label or class. Although this study used illustrations rather than photos or 3D data, it is the first application of machine-learning techniques at the academic level for verifying the ichnotaxonic assignments of large ornithopod dinosaur footprints. Furthermore, the machine learning model will likely aid researchers to classify the large ornithopod dinosaur footprint ichnotaxa, thereby safeguarding against the oversplitting problem.
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Hong, Wenting, Yongmei Liu, Weiqing Tong e Yonghao Ma. "Automatic Recognition of Garment Illustrations Based on CNN". AATCC Journal of Research 8, n. 1_suppl (settembre 2021): 128–34. http://dx.doi.org/10.14504/ajr.8.s1.16.
Abstract (sommario):
In the process of garment production, obtaining and identifying garment illustrations, transforming them into the required information, and then implementing the information into automated production can improve the production efficiency to a great extent. However, the research on recognition of garment illustration and pattern image is mostly based on category classification, but very little on the identification of parts and details. The Inception module in the GoogleNet Inception and its improvement development models enhance parameter utilization, accelerate computation, and have no special requirements for hardware. The Mask R-CNN, a convolutional neural network, is a modified model from based on Faster R-CNN for instance splitting tasks. Based on these two models, this paper proposes a method to identify garment illustrations using a self-built database. The experimental results show that this method outperforms the related algorithms.
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Shi, Rui. "GSAIC: GeoScience Articles Illustration and Caption Dataset". Highlights in Science, Engineering and Technology 9 (30 settembre 2022): 289–97. http://dx.doi.org/10.54097/hset.v9i.1858.
Abstract (sommario):
The scientific investigation of geoscience includes data collection, sample classification and semantic, consisting of a large number of images. An image-text search model that can well assist the research work of geoscience. However, the existing image-text datasets are mainly in the field of daily life and lack academic image-text datasets. In order to help geoscience researchers to investigate through the image and text, and to provide a new benchmark for researchers in the fields of data mining and information retrieval, this paper proposes a novel parallel material of geoscience academic illiustrateion and caption (GSAIC) based on GAKG, which contains over 900,000 illustrations of earth science papers and the corresponding captions. GSAIC filters out high-quality illustrations and captions through a classifier, and with the support of experts annotations. The GSAIC will support several tasks Including text search for images, retrieving corresponding images or papers based on academic image descriptions and academic illustration classification tasks, for geoscience scenarios Finally, both the GSAIC benchmark and classifier are publicly accessible.
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Lan, Ziwen, Keisuke Maeda, Takahiro Ogawa e Miki Haseyama. "Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships". Sensors 23, n. 10 (16 maggio 2023): 4798. http://dx.doi.org/10.3390/s23104798.
Abstract (sommario):
In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.
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Low, Christopher M., Jonathan M. Morris, Jane S. Matsumoto, Janalee K. Stokken, Erin K. O’Brien e Garret Choby. "Use of 3D-Printed and 2D-Illustrated International Frontal Sinus Anatomy Classification Anatomic Models for Resident Education". Otolaryngology–Head and Neck Surgery 161, n. 4 (9 luglio 2019): 705–13. http://dx.doi.org/10.1177/0194599819860832.
Abstract (sommario):
Objective To examine the impact of 2-dimensional (2D) illustrations and 3-dimensonal (3D)–printed anatomic models of the frontal sinuses according to the International Frontal Sinus Anatomy Classification in the education of otolaryngology and radiology residents. Study Design A crossover study design with half of the study participants randomized to the 2D illustration intervention first and the other half randomized to the 3D-printed model first. Setting Regularly scheduled resident didactic lectures at a tertiary care center. Subjects and Methods Forty-one otolaryngology and radiology residents were assessed with pre- and postintervention questionnaires that included subjective and objective methods of assessment. Results Overall, there was a statistically significant improvement in total number of answers correct and in confidence score between the pre- and postintervention assessments ( P < .0001). The primary outcome of order of intervention (ie, 2D → 3D vs 3D → 2D) did not result in statistically significant differences in postevaluation scores. In regard to the secondary outcome of learner preference for educational modality, radiology residents favored the 2D illustrations to understand anatomic relationships, while otolaryngology residents preferred the 3D model to be more helpful in surgical planning ( P = .0075). Conclusion There is no difference between 2D-illustrated and 3D-printed International Frontal Sinus Anatomy Classification anatomic models in overall educational outcome, despite the preference of learners. Together, these models can be used as helpful tools in frontal sinus education for otolaryngology and radiology trainees.
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Marretta, Sandra Manfra, Alexander J. Schloss e Undo S. Klippert. "Classification and Prognostic Factors of Endodontic-Periodontic Lesions in the Dog". Journal of Veterinary Dentistry 9, n. 2 (giugno 1992): 27–30. http://dx.doi.org/10.1177/089875649200900203.
Abstract (sommario):
The classification of the possible pathologic relationships between the periodontium and endodontium in man includes three separate groups. This classification system is adapted for utilization in the dog to clarify the appropriate treatment and prognosis of dog's teeth affected with endodontic-periodontic lesions. This article describes the classification and treatment planning with illustrations of each type.
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SCHLAGINTWEIT, FELIX, e MIKE SIMMONS. "THE ORIGINAL PUBLISHED DESCRIPTION OF AN EMBRYONIC APPARATUS FROM THE ORBITOLINIDAE (FORAMINIFERA) (LOWER CRETACEOUS OF BORNEO) WITH A BRIEF COMMENTARY ON THE AGE OF ORBITOLINID OCCURRENCES IN BORNEO". Acta Palaeontologica Romaniae, n. 19 (2) (15 maggio 2023): 21–24. http://dx.doi.org/10.35463/j.apr.2023.02.04.
Abstract (sommario):
The family Orbitolinidae originates with Martin (1890), who described the external and internal test features of orbitolinids from what can now understood to be the Lower Cretaceous of western Borneo. Overlooked in subsequent literature, this description includes the illustration of a transverse section passing through a megalospheric embryo of a specimen assigned to Orbitolina concava Lamarck and regarded as being Cenomanian in age. However, the illustration, description, and dimensions refer this specimen to Palorbitolina lenticularis (Blumenbach, 1805) comprising a relatively large, subdivided chamber (= periembryonic chambers) surrounding a large ‘Centralkammer’. It is worth mentioning that the original illustration by Blumenbach (1805) from the Lower Cretaceous of south-eastern France as Madreporites lenticularis did not show this taxonomically important feature in the modern classification of the Orbitolinidae and with special respect to the subfamily Orbitolininae. The re-interpretation of Martin’s illustrations and a literature review challenge Cenomanian ages ascribed to orbitolinid occurrences in Borneo.
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Yue, Qing. "The Use of Textbook Illustrations in Teaching English Reading in Junior Secondary School". Frontiers in Humanities and Social Sciences 3, n. 4 (20 aprile 2023): 17–22. http://dx.doi.org/10.54691/fhss.v3i4.4726.
Abstract (sommario):
Although the progress of modern information technology and society has made modern junior high school English textbooks gradually develop in the direction of multi-modality, in actual teaching, teachers make less use of textbook illustrations and have difficulties in using them. Based on the two dimensions of classification and function of illustrations, the article Save the Sharks! from the Unit 13 Reading section of the Grade 9 English textbook of PEP is used as an example to discuss the effective use of textbook illustrations in reading teaching in conjunction with actual teaching. Teachers can use the illustrations in the three stages of reading, namely pre-reading, while-reading and post-reading, to help them enrich the classroom content and present a better quality of reading classroom.
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THAMBUGALA, KASUN M., HIRAN A. ARIYAWANSA, ZUO-YI LIU, EKACHAI CHUKEATIROTE e KEVIN D. HYDE. "Towards a natural classification of Dothideomycetes 6: The genera Dolabra, Placostromella, Pleosphaerellula, Polysporidiella and Pseudotrichia (Dothideomycetes incertae sedis)". Phytotaxa 176, n. 1 (20 agosto 2014): 55. http://dx.doi.org/10.11646/phytotaxa.176.1.8.
Abstract (sommario):
The type specimens of Dolabra, Placostromella, Pleosphaerellula, Polysporidiella and Pseudotrichia were re-examined in order to suggest their familial and higher placement according to the morphology based on modern taxonomic concepts. An overview of the history and descriptions and illustrations of these genera are provided. Based on morphological similarities, Placostromella is placed in Parmulariaceae, while Pseudotrichia is transferred to Montagnulaceae. Pleosphaerellula is placed in Pleosporales, genera incertae sedis and Polysporidiella is retained in Dothideomycetes, genera incertae sedis as it is not typical of any existing family of Dothideomycetes. According to published phylogenetic data, Dolabra belongs in Chaetothyriomycetidae, genera incertae sedis (Eurotiomycetes). Recollection, epitypifycation and multi-gene molecular analyses are needed for all type species of these genera in order to clarify their familial status. By illustrating and redescribing the type species we expect to stimulate interest for these fungi to be recollected.
Tesi sul tema "Illustrations – classification":
1
Lefebvre, Grégoire. "Sélection et fusion de signatures visuelles parcimonieuses : application à la classification d'images naturelles". Bordeaux 2, 2007. http://www.theses.fr/2007BOR21463.
Abstract (sommario):
Les travaux effectués dans le cadre de cette thèse portent sur la classification automatique des images numériques en fonction de leur contenu visuel. L'objectif est d'assigner à une image de test une identité parmi celles d'un ensemble de catégories connues. Pour cela, on cherche à extraire un ensemble de signatures visuelles parcimonieuses, qui lui soit spécifique, puis à sélectionner et structurer l'information discriminante, avant de proposer une classification adaptée à leur nature et à l'application visée. De nombreuses méthodes ont été proposées dans la littérature pour décrire le contenu visuel des images. Parmi les plus efficaces, on compte les techniques d'extraction de points d'intérêt et de descriptions des singularités locales. Ce travail reprend ce principe et propose de nouvelles techniques de sélection et de fusion de signatures locales, basées sur l'analyse de cartes de neurones auto-organisatrices. Un nouveau support de l'information du contenu visuel est alors proposé comme étant l'activation d'un modèle neuronal multimodal. Les méthodes proposées permettent de se focaliser sur les éléments spécifiques d'une catégorie, vis-à-vis des autres catégories en compétition. Elles permettent ainsi une certaine robustesse aux changements de prises de vue, aux variations d'illumination et aux occultations partielles. Les techniques proposées sont évaluées et comparées aux approches usuelles selon des protocoles expérimentaux rigoureux. Les résultats ainsi obtenus montrent les très bonnes performances des approches introduites dans les domaines de la classification d'images, de la reconnaissance de visages et du filtrage de contenus tendancieuxThis thesis is concerned with automatic classification. The objective is to assign an identity to a test image among a set of known category. The underlying approach aim at extracting a specific set of parsimonious visual signatures, then selecting and melting discriminative information, before designing a classification scheme adapted to the context. Many methods have been proposed in order to describe visual content. One of the most effective is based on points of interest extraction and local singularity description. In the thesis, this principle is used to define next local signature and combination, based on self-organizing neural maps. A novel image information support s then proposed, being the activation of a multimodal neural model. The proposed methods focus on specific elements of one image class versus the other categories. It permits robustness to viewpoint changes, illumination variations and partial occlusions. The proposed techniques are evaluated and compared to usual methods using various international databases. These experiments show the effectiveness of the proposed approaches, in particular, in the domains of image classification, face recognition and objectionable content exclusion
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Le, Saux Bertrand Honoré Henri. "Classification non exclusive et personnalisation par apprentissage : application à la navigation dans les bases d'images". Versailles-St Quentin en Yvelines, 2003. http://www.theses.fr/2003VERS0013.
Abstract (sommario):
In the context of Content-Based Image Retrieval, we have been interested in methods to summarize and to help browsing in the image databases. We have developed an unsupervised lassification method which allows to categorize the feature space to group visually similar images. By defining a new competitive agglomeration objective function in which competition is made adaptive to the cluster densities, ARC (Adaptive Robust Competition) is able to deal with the following issues:* estimate automatically the number of clusters,* handle noisy data, * cope with various classe densities nd shapes. In a second step, we have refined the categories with a supervised classification based on the support vector machine (SVM) method. It provides the user with the ability to give feedback on the relevance of the cluster frontiers obtained by our unsupervised methodDans le cadre de la recherche d'images par le contenu, nous nous sommes intéressés aux méthodes de résumé et d'aide à la navigation pour les bases d'images. Nous avons développé une méthode de classification non-exclusive capable de catégoriser l'espace de description des images pour regrouper les images d'apparences visuelles similaires. En définissant une nouvelle fonction de Compétition Agglomérative où la compétition s'adapte à la densité des atégories, l'algorithme ARC (Adaptive Robust Competition) permet de résoudre les difficultés suivantes : * déterminer automatiquement le nombre de classes,* gérer les données bruitées diffuses,* prendre en compte les densités et les formes variables des classes. Dans un deuxième temps, nous permettons à l'utilisateur de contrôler la pertinence des classes obtenues. Un apprentissage basé sur une machine à vecteurs de support permet de personnaliser les classes d'images
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Etievent, Emmanuel. "Assistance à l'indexation vidéo par analyse du mouvement". Lyon, INSA, 2002. http://theses.insa-lyon.fr/publication/2002ISAL0015/these.pdf.
Abstract (sommario):
Cette étude s'inscrit dans un projet multidisciplinaire sur le thème de l'indexation de séquences audiovisuelles, en particulier dans le cadre des archives de télévision. L'objectif était de permettre des recherches dans les bases de documents audiovisuels, comme les archives de télévision, en se fondant sur le contenu sémantique et visuel des vidéos. Dans ce cadre, nous proposons des outils semi-automatiques d'analyse d'image en vue d'assister la tâche du documentaliste chargé de l'indexation. Ainsi, nous avons expérimenté une méthode d'analyse du mouvement dans le but de simplifier la tâche d'indexation des objets présents dans la vidéo. Notre approche se fonde sur le suivi de primitives appelées points d'intérêt, à l'aide d'une méthode de type multi-hypothèses. Nous avons tout d'abord donné un aperçu de l'importance actuelle des bases audiovisuelles. Nous avons ensuite considéré le rôle que l'analyse d'image peut jouer dans le domaine de l'indexation selon le double aspect du contenu des vidéos, visuel et sémantique. Dans ce cadre, nous avons identifié quelles étaient les diverses applications de l'analyse du mouvement. Concernant notre approche, une expérimentation préalable nous a permis de vérifier la stabilité des points d'intérêt dans des vidéos compressées. L'expérimentation principale a porté simultanément sur le suivi et la détection des points d'intérêt. Nous avons évalué notre approche dans la perspective de l'assistance à l'indexation des objets, en utilisant des séquences réelles issues d'archives de télévision. Nous nous sommes intéressés d'une part à la question du paramétrage de l'algorithme de suivi, pour déterminer s'il pouvait être utilisé par des non spécialistes, et d'autre part à la répartition des points d'intérêt dans l'image, qui est apparue très importante pour pouvoir détecter les principaux objets présents. Enfin, nous avons considéré l'intégration des outils d'analyse d'image dans un système d'indexation complet, en particulier du point de vue de l'interaction avec le documentaliste.
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Lu, Ying. "Transfer Learning for Image Classification". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.
Abstract (sommario):
Lors de l’apprentissage d’un modèle de classification pour un nouveau domaine cible avec seulement une petite quantité d’échantillons de formation, l’application des algorithmes d’apprentissage automatiques conduit généralement à des classifieurs surdimensionnés avec de mauvaises compétences de généralisation. D’autre part, recueillir un nombre suffisant d’échantillons de formation étiquetés manuellement peut s’avérer très coûteux. Les méthodes de transfert d’apprentissage visent à résoudre ce type de problèmes en transférant des connaissances provenant d’un domaine source associé qui contient beaucoup plus de données pour faciliter la classification dans le domaine cible. Selon les différentes hypothèses sur le domaine cible et le domaine source, l’apprentissage par transfert peut être classé en trois catégories: apprentissage par transfert inductif, apprentissage par transfert transducteur (adaptation du domaine) et apprentissage par transfert non surveillé. Nous nous concentrons sur le premier qui suppose que la tâche cible et la tâche source sont différentes mais liées. Plus précisément, nous supposons que la tâche cible et la tâche source sont des tâches de classification, tandis que les catégories cible et les catégories source sont différentes mais liées. Nous proposons deux méthodes différentes pour aborder ce problème. Dans le premier travail, nous proposons une nouvelle méthode d’apprentissage par transfert discriminatif, à savoir DTL(Discriminative Transfer Learning), combinant une série d’hypothèses faites à la fois par le modèle appris avec les échantillons de cible et les modèles supplémentaires appris avec des échantillons des catégories sources. Plus précisément, nous utilisons le résidu de reconstruction creuse comme discriminant de base et améliore son pouvoir discriminatif en comparant deux résidus d’un dictionnaire positif et d’un dictionnaire négatif. Sur cette base, nous utilisons des similitudes et des dissemblances en choisissant des catégories sources positivement corrélées et négativement corrélées pour former des dictionnaires supplémentaires. Une nouvelle fonction de coût basée sur la statistique de Wilcoxon-Mann-Whitney est proposée pour choisir les dictionnaires supplémentaires avec des données non équilibrées. En outre, deux processus de Boosting parallèles sont appliqués à la fois aux distributions de données positives et négatives pour améliorer encore les performances du classificateur. Sur deux bases de données de classification d’images différentes, la DTL proposée surpasse de manière constante les autres méthodes de l’état de l’art du transfert de connaissances, tout en maintenant un temps d’exécution très efficace. Dans le deuxième travail, nous combinons le pouvoir du transport optimal (OT) et des réseaux de neurones profond (DNN) pour résoudre le problème ITL. Plus précisément, nous proposons une nouvelle méthode pour affiner conjointement un réseau de neurones avec des données source et des données cibles. En ajoutant une fonction de perte du transfert optimal (OT loss) entre les prédictions du classificateur source et cible comme une contrainte sur le classificateur source, le réseau JTLN (Joint Transfer Learning Network) proposé peut effectivement apprendre des connaissances utiles pour la classification cible à partir des données source. En outre, en utilisant différents métriques comme matrice de coût pour la fonction de perte du transfert optimal, JTLN peut intégrer différentes connaissances antérieures sur la relation entre les catégories cibles et les catégories sources. Nous avons effectué des expérimentations avec JTLN basées sur Alexnet sur les jeux de données de classification d’image et les résultats vérifient l’efficacité du JTLN proposé. A notre connaissances, ce JTLN proposé est le premier travail à aborder ITL avec des réseaux de neurones profond (DNN) tout en intégrant des connaissances antérieures sur la relation entre les catégories cible et sourceWhen learning a classification model for a new target domain with only a small amount of training samples, brute force application of machine learning algorithms generally leads to over-fitted classifiers with poor generalization skills. On the other hand, collecting a sufficient number of manually labeled training samples may prove very expensive. Transfer Learning methods aim to solve this kind of problems by transferring knowledge from related source domain which has much more data to help classification in the target domain. Depending on different assumptions about target domain and source domain, transfer learning can be further categorized into three categories: Inductive Transfer Learning, Transductive Transfer Learning (Domain Adaptation) and Unsupervised Transfer Learning. We focus on the first one which assumes that the target task and source task are different but related. More specifically, we assume that both target task and source task are classification tasks, while the target categories and source categories are different but related. We propose two different methods to approach this ITL problem. In the first work we propose a new discriminative transfer learning method, namely DTL, combining a series of hypotheses made by both the model learned with target training samples, and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant, and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary. On this basis, we make use of similarities and dissimilarities by choosing both positively correlated and negatively correlated source categories to form additional dictionaries. A new Wilcoxon-Mann-Whitney statistic based cost function is proposed to choose the additional dictionaries with unbalanced training data. Also, two parallel boosting processes are applied to both the positive and negative data distributions to further improve classifier performance. On two different image classification databases, the proposed DTL consistently out performs other state-of-the-art transfer learning methods, while at the same time maintaining very efficient runtime. In the second work we combine the power of Optimal Transport and Deep Neural Networks to tackle the ITL problem. Specifically, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data. By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data. Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories. We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning. To the best of our knowledge, the proposed JTLN is the first work to tackle ITL with Deep Neural Networks while incorporating prior knowledge on relatedness between target and source categories. This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks
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Nettl, Bruno. "Gender (and Other) Identities in Singing Style and Vocal Tone Color. Ethnomusicological Perspectices and Two Brief Illustrations". Bärenreiter Verlag, 2012. https://slub.qucosa.de/id/qucosa%3A71817.
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Augereau, Olivier. "Reconnaissance et classification d’images de documents". Thesis, Bordeaux 1, 2013. http://www.theses.fr/2013BOR14764/document.
Abstract (sommario):
Ces travaux de recherche ont pour ambition de contribuer à la problématique de la classification d’images de documents. Plus précisément, ces travaux tendent à répondre aux problèmes rencontrés par des sociétés de numérisation dont l’objectif est de mettre à disposition de leurs clients une version numérique des documents papiers accompagnés d’informations qui leurs sont relatives. Face à la diversité des documents à numériser, l’extraction d’informations peut s’avérer parfois complexe. C’est pourquoi la classification et l’indexation des documents sont très souvent réalisées manuellement. Ces travaux de recherche ont permis de fournir différentes solutions en fonction des connaissances relatives aux images que possède l’utilisateur ayant en charge l’annotation des documents.Le premier apport de cette thèse est la mise en place d’une méthode permettant, de manière interactive, à un utilisateur de classer des images de documents dont la nature est inconnue. Le second apport de ces travaux est la proposition d’une technique de recherche d’images de documents par l’exemple basée sur l’extraction et la mise en correspondance de points d’intérêts. Le dernier apport de cette thèse est l’élaboration d’une méthode de classification d’images de documents utilisant les techniques de sacs de mots visuelsThe aim of this research is to contribute to the document image classification problem. More specifically, these studies address digitizing company issues which objective is to provide the digital version of paper document with information relating to them. Given the diversity of documents, information extraction can be complex. This is why the classification and the indexing of documents are often performed manually. This research provides several solutions based on knowledge of the images that the user has. The first contribution of this thesis is a method for classifying interactively document images, where the content of documents and classes are unknown. The second contribution of this work is a new technique for document image retrieval by giving one example of researched document. This technique is based on the extraction and matching of interest points. The last contribution of this thesis is a method for classifying document images by using bags of visual words techniques
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Goh, Hanlin. "Learning deep visual representations". Paris 6, 2013. http://www.theses.fr/2013PA066356.
Abstract (sommario):
Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deux angles. D'abord nous nous sommes intéressés à l'apprentissage non supervisé de représentations latentes ayant certaines propriétés à partir de données en entrée. Il s'agit ici d'intégrer une connaissance à priori, à travers un terme de régularisation, dans l'apprentissage d'une machine de Boltzmann restreinte (RBM). Nous proposons plusieurs formes de régularisation qui induisent différentes propriétés telles que la parcimonie, la sélectivité et l'organisation en structure topographique. Le second aspect consiste au passage graduel de l'apprentissage non supervisé à l'apprentissage supervisé de réseaux profonds. Ce but est réalisé par l'introduction sous forme de supervision, d'une information relative à la catégorie sémantique. Deux nouvelles méthodes sont proposées. Le premier est basé sur une régularisation top-down de réseaux de croyance profonds à base de RBMs. Le second optimise un cout intégrant un critre de reconstruction et un critre de supervision pour l'entrainement d'autoencodeurs profonds. Les méthodes proposées ont été appliquées au problme de classification d'images. Nous avons adopté le modèle sac-de-mots comme modèle de base parce qu'il offre d'importantes possibilités grâce à l'utilisation de descripteurs locaux robustes et de pooling par pyramides spatiales qui prennent en compte l'information spatiale de l'image. L'apprentissage profonds avec agrÉgation spatiale est utilisé pour apprendre un dictionnaire hiÉrarchique pour l'encodage de reprÉsentations visuelles de niveau intermÉdiaire. Cette mÉthode donne des rÉsultats trs compétitifs en classification de scènes et d'images. Les dictionnaires visuels appris contiennent diverses informations non-redondantes ayant une structure spatiale cohérente. L'inférence est aussi très rapide. Nous avons par la suite optimisé l'étape de pooling sur la base du codage produit par le dictionnaire hiérarchique précédemment appris en introduisant introduit une nouvelle paramétrisation dérivable de l'opération de pooling qui permet un apprentissage par descente de gradient utilisant l'algorithme de rétro-propagation. Ceci est la premire tentative d'unification de l'apprentissage profond et du modèle de sac de mots. Bien que cette fusion puisse sembler évidente, l'union de plusieurs aspects de l'apprentissage profond de représentations visuelles demeure une tache complexe à bien des égards et requiert encore un effort de recherche importantRecent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm. This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area
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Blot, Michaël. "Étude de l'apprentissage et de la généralisation des réseaux profonds en classification d'images". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS412.
Abstract (sommario):
L'intelligence artificielle connait une résurgence ces dernières années. En cause, la capacité croissante à rassembler et à stocker un nombre considérable de données digitalisées. Ces immenses bases de données permettent aux algorithmes de machine learning de répondre à certaines tâches par apprentissage supervisé. Parmi les données digitalisées, les images demeurent prépondérantes dans l’environnement moderne. D'immenses datasets ont été constitués. De plus, la classification d'image a permis l’essor de modèles jusqu'alors négligés, les réseaux de neurones profonds ou deep learning. Cette famille d'algorithmes démontre une grande facilité à apprendre parfaitement des datasets, même de très grande taille. Leurs capacités de généralisation demeure largement incomprise, mais les réseaux de convolutions sont aujourd'hui l'état de l'art incontesté. D'un point de vue recherche et application du deep learning, les demandes vont être de plus en plus exigeantes, nécessitant de fournir un effort pour porter les performances des réseaux de neurone au maximum de leurs capacités. C'est dans cet objectif que se place nos recherches dont les contributions sont présentées dans cette thèse. Nous nous sommes d'abord penchés sur la question de l'entrainement et avons envisagé d’accélérer celui ci grâce à des méthodes distribuées. Nous avons ensuite étudié les architectures dans le but de les améliorer sans toutefois trop augmenter leurs complexités. Enfin nous avons particulièrement étudié la régularisation de l'entrainement des réseaux. Nous avons envisagé un critère de régularisation basée sur la théorie de l'information que nous avons déployé de deux façons différentesArtificial intelligence is experiencing a resurgence in recent years. This is due to the growing ability to collect and store a considerable amount of digitized data. These huge databases allow machine learning algorithms to respond to certain tasks through supervised learning. Among the digitized data, images remain predominant in the modern environment. Huge datasets have been created. moreover, the image classification has allowed the development of previously neglected models, deep neural networks or deep learning. This family of algorithms demonstrates a great facility to learn perfectly datasets, even very large. Their ability to generalize remains largely misunderstood, but the networks of convolutions are today the undisputed state of the art. From a research and application point of view of deep learning, the demands will be more and more demanding, requiring to make an effort to bring the performances of the neuron networks to the maximum of their capacities. This is the purpose of our research, whose contributions are presented in this thesis. We first looked at the issue of training and considered accelerating it through distributed methods. We then studied the architectures in order to improve them without increasing their complexity. Finally, we particularly study the regularization of network training. We studied a regularization criterion based on information theory that we deployed in two different ways
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Vuillerot, Carole. "Métrologie et évaluation fonctionnelle motrice dans les maladies neuromusculaires de l’enfance : Illustrations à partir de la Mesure de Fonction Motrice (MFM) et d’une classification en grades de sévérité d’atteinte fonctionnelle motrice (NM-Score)". Thesis, Lyon 1, 2012. http://www.theses.fr/2012LYO10081/document.
Abstract (sommario):
Les progrès de la recherche et de la prise en charge des maladies neuromusculaires de l’enfance ont prolongé la survie des patients. L’évaluation s’impose donc pour le suivi des patients et aussi en recherche clinique car les premiers essais cliniques tant attendus commencent à paraître. Une métrologie rigoureuse et adaptée est alors indispensable parce qu'il n'est possible ni de se contenter d’une quantification approximative ni d'utiliser des outils non adaptés à des pathologies évolutives. Nous résumons l’état des connaissances sur la métrologie appliquée à l’évaluation fonctionnelle motrice des patients atteints de maladies neuromusculaires et proposons une revue de la littérature sur les outils disponibles avec des analyses précises de leurs propriétés métrologiques. La Mesure de Fonction Motrice, développée à partir de 1998, présente des qualités intéressantes en termes de validité et de fiabilité. Nous avons analysé sa sensibilité au changement dans différentes populations de patients adultes et enfants. Nous proposons ensuite, une classification en grades de sévérité d’atteinte fonctionnelle motrice, le NM-score. Les études de validation ont confirmé son intérêt, sa facilité d'utilisation, sa validité et sareproductibilité. Le NM-Score permet de décrire précisément et de façon discriminante les patients en termes de fonction motrice pour la position debout et les transferts, la motricité axiale ou proximale et la motricité distale. S’intéresser à l’évaluation et à la mesure en médecine, c’est faire preuve d’une rigueur indispensable aux décisions de soins touchant des personnes vulnérables aux besoins spécifiquesAdvances in the research and treatment of childhood neuromuscular diseases have led to longer patient survivals. Evaluation is thus required not only in clinical practice for patient follow-up but also in medical research because the results of long-awaited clinical trials are beginning to emerge. A rigorous and appropriate metrology is then necessary because rough estimates or the use of improper assessment tools are no more satisfactory. We summarize here the current knowledge on the metrology applied to motor function assessment of patients with neuromuscular diseases. We propose a review of the literature on the tools available to monitor motor function with detailed analyses of their metrological properties. Developped since 1998, the Motor Function Measure presents interesting properties in terms of validity and reliability. We analyzed its sensitivity to change in different patient populations of adults and children. We then propose, the NM-Score, a classification in levels of severity of motor function decline.Validation studies have confirmed the interest of this score as well as its ease of use, validity,and reproducibility. The NM-Score is able to describe the patients precisely and discriminantly in terms of motor function for standing position and transfers, axial / proximal motor function and distal motor function. Being interested in evaluation and measurement in medicine is a sign of rigor necessary for decision-making regarding vulnerable persons with special need
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Chebbi, Imen. "Modèles de stockage et d’analyse des données massives appliquées à l’imagerie satellitaire". Electronic Thesis or Diss., Paris 8, 2021. http://www.theses.fr/2021PA080106.
Abstract (sommario):
Notre thèse s’inscrit dans le cadre spatiotemporel des images satellitaires, l’analyse du gros volume d'images devient de plus en plus difficile avec l'apparition des capteurs à très hautes résolutions spatiales, spectrales et temporelles. Afin de pouvoir situer notre thèse en rapport avec la littérature, nous avons étudié les principales étapes du pipeline de grand volume de données et nous avons travaillé sur deux contributions principales qui sont le stockage et le traitement des données. Parmi les objectifs de notre thèse est de développer une architecture adaptée pour notre système du point de vue stockage et traitement. Pour la mise en place de cette plateforme nous avons développé un cluster local maître-esclave. Dans la première contribution, il s’agit de la proposition d’un système de stockage physique intelligent et qui tient compte des données hétérogènes, on a étudié plusieurs méthodes de stockage de données massives et des méthodes de représentation des données en se basant sur le système de fichier distribué de hadoop hdfs et les avantages de Nosql permettant de stocker, récupérer et interroger les données massives. Nous avons essayé de les adapter à notre contexte des images satellitaires en se basant sur notre architecture physique ensuite les tester avec une collection de données satellitaires. La deuxième contribution de notre thèse est le traitement des images satellitaires massives après les avoir stockés dans le but de les classifier, où il s’agit de développer une approche de classifications des images satellitaires par apprentissage des labels existants en utilisant les techniques de l’apprentissage profond et les plateformes Spark et TensorflowOur work forming part of the spatiotemporal remote sensing images, the analysis of the large volume of images is becoming more difficult with the appearance of sensors with very high spatial, spectral and temporal resolutions. In order to be able to situate our thesis in relation to the literature, we studied the main stages of the large volume data pipeline and we focused on two main contributions which are data storage and data processing. Among the objectives of our thesis is to develop a suitable architecture for our system from the perspective of storage and processing. For the implementation of this platform we developed a local master-slave cluster with several machines including one dedicated for the master node and the others for the slave nodes. The first contribution is the idea of a physical storage system that is intelligent and takes into account heterogeneous data. For this, several methods of big data storage and data representation methods based on the hadoop distributed file system (HDFS) and the benefits of Nosql allowing to store, retrieve and query massive data were investigated. We tried to adapt them to our context of satellite images based on our physical architecture and then test them with in-house satellite data collection.The second contribution of our thesis is the processing of massive satellite images after having stored them in order to classify them, where the aim is to develop an approach to classify satellite images by learning the existing truth-labels. We used deep learning techniques and more particularly the adaptation of the Unet and Vggnet algorithms based on the Apache Spark and Tensorflow platform
Libri sul tema "Illustrations – classification":
1
Saiṇī, Malakīata Siṅgha. Indian sawflies biodiversity: Keys, catalogue & illustrations. Dehra Dun: Bishen Singh Mahendra Pal Singh, 2006.
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W, Berry Michael, a cura di. Survey of text mining: Clustering, classification, and retrieval ; with 57 illustrations. New York: Springer, 2004.
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Watanabe, Masayuki. Nihon aoko daizukan: The freshwater planktonic blue-greens of Japan with photographs and illustrations / by Masayuki Watanabe. Tōkyō: Seibundō Shinkōsha, 2007.
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Lesueur, Justine. Conflits de droits, illustrations dans le champ des propriétés incorporelles. Aix-en-Provence: Presses universitaires d'Aix-Marseille, 2009.
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Gergus, Erik W. A. Labs for vertebrate zoology: An evolutionary approach / Erik W.A. Gergus, Gordon W. Schuett ; illustrations by Laura White Schuett. Carmel, IN: Cooper Publishing Group, 1997.
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Watson, Leslie. Grass genera of the world: Illustrations of characters, descriptions, classification, interactive identification, information retrieval : with microfiches and floppy disks for MS-DOS microcomputers. Canberra: Australian National University, Research School of Biological Sciences, 1988.
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Zomlefer, Wendy B. Flowering plants of Florida: A guide to common families. Gainesville, FL: Biological Illustrations, 1989.
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Zomlefer, Wendy B. Guide to flowering plant families. Chapel Hill: University of North Carolina Press, 1994.
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Li, Lulu. Tu shuo Zhongguo chuan tong hang ye =: An illustration to Chinese traditional trades. 8a ed. Xi'an: Shi jie tu shu chu ban Xi'an gong si, 2006.
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Boelcke, Osvaldo. Plantas vasculares de la Argentina, nativas y exóticas. 2a ed. Buenos Aires, Argentina: Editorial Hemisferio Sur, 1992.
Capitoli di libri sul tema "Illustrations – classification":
1
Li, Ziyang. "Research on Digital Concept Art Illustrations Style Classification based on Deep Learning". In Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023), 379–88. Dordrecht: Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-370-2_40.
2
Page, Joanna. "3. Floras, Herbaria, and Botanical Illustration". In Decolonial Ecologies, 93–136. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0339.03.
Abstract (sommario):
New World plants were exhaustively catalogued in the floras and herbaria produced by the great scientific expeditions led by European naturalists in the eighteenth and nineteenth centuries, such as the Royal Botanical Expedition to New Granada (1783–1816), directed by José Celestino Mutis. Species were primarily illustrated in a way that would allow their identification according to Linnaean taxonomies. Three contemporary artists from Colombia—Alberto Baraya, María Fernanda Cardoso and Eulalia de Valdenebro—have reworked the Enlightenment norms of botanical illustration in order to draw attention to their many erasures and to chart environmental change over the past two centuries. Baraya’s Herbario de plantas artificiales (2002–) celebrates the anomalies and aberrations that were smoothed out in the European quest for a universal system of classification, exposing the relationship between modern Western science and the dynamics of economic and cultural dispossession. De Valdenebro’s seed collections contrast the homogenization and commercialization of transgenic varieties with the greater biodiversity of native seeds, whose cultivation has unfolded within a much higher degree of reciprocity between humans and their environment. In On the Marriages of Plants (2018), Cardoso reflects on Linnaeus’s use of sexual terms borrowed from the human world in her exploration of more recent research into reciprocal relationships between plants, insects, and humans. I bring these projects into dialogue with a selection of illustrations by Abel Rodríguez (Mogaje Guihu), an artist whose work preserves the ancestral knowledge of the Nonuya and Muinane communities in the Colombian Amazon. Contrasting with Linnaean abstraction, Rodríguez’s drawings and paintings depict rainforest ecosystems in ways that cast light on Amazonian concepts of cohabitation and the co-constitution of human and nonhuman subjects. These enter into conflict with two dominant Western paradigms: extraction, on the one hand, and conservation, on the other.
3
Abdullah, A. Sheik. "Data Classification by Decision Trees – An Illustration". In Swarm Intelligence and its Applications in Biomedical Informatics, 38–56. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003330189-3.
4
Holzer, Stephan, e Oliver Labs. "Illustrating the classification of real cubic surfaces". In Algebraic Geometry and Geometric Modeling, 119–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33275-6_8.
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Ghaly, Mohammed. "Constructing a Comprehensive Discourse". In Islamic Ethics and Incidental Findings, 13–23. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59405-2_2.
Abstract (sommario):
AbstractChapter two constructs a comprehensive ethical framework to facilitate the analysis of intricate bioethical issues like incidental findings (IFs) in genomics. Drawing from both secular and Islamic traditions, it synthesizes Robert Veatch's multi-layered approach to bioethics and the recommendation by Muslim ethicists to engage diverse scholarly disciplines. The “Theoretical Level” section explores Islamic metaethics rooted in theology and legal theory, centering on aligning human actions with God’s will to achieve benefit and avert harm. It examines the process of religio-ethical reasoning (ijtihād) employed by Muslim scholars to discern divine guidance on novel issues. The “Practical Level” section outlines the fivefold classification scheme for categorizing human acts based on their moral value within the Islamic tradition: prohibited, obligatory, reprehensible, recommended, and permissible. Distinct from secular schemes, this classification’s theological foundations, definitions, and moral dimensions are elucidated. Bridging theory and practice, the chapter proposes utilizing this fivefold scheme as a nuanced tool to evaluate the ethical management of IFs. It advocates a dynamic approach, acknowledging how evolving scientific understanding may shift the categorization of specific IFs over time. The chapter lays the groundwork for the subsequent analysis, where representative cases illustrating each ethical category are examined through the synthesized Islamic ethical lens, fostering constructive dialogue between religious and secular bioethical discourses on this complex issue.
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Id-youss, Lahousseine, e Abied Alsulaiman. "On the interaction between legal and religious concepts". In Handbook of Terminology, 224–41. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/hot.3.int2.
Abstract (sommario):
Legal concepts differ in each individual society reflecting the differences in that society (Cao 2007, 2010). They form part of nationally contained legal systems with their own terminological apparatus and underlying conceptual structure, their own rules of classification (Šarčević 1997). These differences have both semasiological and onomasiological perspectives. Some areas of the Moroccan law offer a good illustration for these idiosyncratic features as they are marked by peculiar characteristics, reflecting the prevalent political, social and religious realities. In this chapter, we will shed light on the interaction between law and religion, showing how this interaction manifests itself in the Moroccan Family Code (Moudawana). The study will be carried out from semasiological and onomasiological angles.
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García, Francisco Sanz, Maite Pelacho, Tim Woods, Dilek Fraisl, Linda See, Mordechai Haklay e Rosa Arias. "Finding What You Need: A Guide to Citizen Science Guidelines". In The Science of Citizen Science, 419–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4_21.
Abstract (sommario):
AbstractIn line with the growth in citizen science projects and participants, there are an increasing number of guidelines on different aspects of citizen science (e.g. specific concepts and methodologies; data management; and project implementation) pitched at different levels of experience and expertise. However, it is not always easy for practitioners to know which is the most suitable guideline for their needs. This chapter presents a general classification of guidelines, illustrating and analysing examples of each type. Drawing on the EU-Citizen.Science project, we outline criteria for categorising guidelines to enable users to find the right one and to ensure that guidelines reach their intended audience. We discuss challenges and weaknesses around the use and creation of guidelines and, as a practical conclusion, provide a set of recommendations to consider when creating guidelines.
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de Castro Moreira, Gabriel, João Felipe Coimbra Leite Costa e Diego Machado Marques. "Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling". In Springer Proceedings in Earth and Environmental Sciences, 199–219. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_16.
Abstract (sommario):
AbstractMachine learning is a broad field of study that can be applied in many areas of science. In mining, it has already been used in many cases, for example, in the mineral sorting process, in resource modeling, and for the prediction of metallurgical variables. In this paper, we use for defining estimation domains, which is one of the first and most important steps to be taken in the entire modeling process. In unsupervised learning, cluster analysis can provide some interesting solutions for dealing with the stationarity in defining domains. However, choosing the most appropriate technique and validating the results can be challenging when performing cluster analysis because there are no predefined labels for reference. Several methods must be used simultaneously to make the conclusions more reliable. When applying cluster analysis to the modeling of mineral resources, geological information is crucial and must also be used to validate the results. Mining is a dynamic activity, and new information is constantly added to the database. Repeating the whole clustering process each time new samples are collected would be impractical, so we propose using supervised learning algorithms for the automatic classification of new samples. As an illustration, a dataset from a phosphate and titanium deposit is used to demonstrate the proposed workflow. Automating methods and procedures can significantly increase the reproducibility of the modeling process, an essential condition in evaluating mineral resources, especially for auditing purposes. However, although very effective in the decision-making process, the methods herein presented are not yet fully automated, requiring prior knowledge and good judgment.
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Sadurski, Wojciech. "Standards of Scrutiny, Equal Protection, and Illicit Motives for Discrimination". In Constitutional Public Reason, 242—C8.N90. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869678.003.0008.
Abstract (sommario):
Abstract Connection between anti-discrimination law and the ideal of public reason (PR) is found in a doctrine that the invidious quality of a law claimed to be discriminatory must ultimately be traced to discriminatory legislative motives/purposes. This chapter begins by taking seriously a concept of ‘suspect classifications’ developed in the US Supreme Court’s case law under the Fourteenth Amendment, and inquires into the most likely features that may render a classification ‘suspect’, which inevitably—as the chapter argues—leads to the question of legislative motivations behind a legal classification. The chapter offers a theory which may help us understand the idea of ‘prejudice’ by looking at external ‘indicia’ of such prejudice, hence, of suspect classifications. Illustrations for this proposition are taken from the US and South Africa. Finally, using examples from India and Canada, the chapter offers some examples of how judges figure out the presence of impermissible motives behind a legal classification.
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Cohen, Robert B., Richard W. Ferguson e Michael F. Oppenheimer. "The Classification of NTBs: Some Illustrations of Impacts". In Nontariff Barriers to High-Technology Trade, 23–51. Routledge, 2019. http://dx.doi.org/10.4324/9780429038259-4.
Atti di convegni sul tema "Illustrations – classification":
1
Horvat, Saša A., Tamara N. Rončević, Ivana Z. Bogdanović e Dušica D. Rodić. "DIFFERENCES IN GRAPHIC ILLUSTRATIONS IN THE CONTENTS OF NATURAL SCIENCES IN REGULAR TEXTBOOKS AND TEXTBOOKS FOR STUDENTS WITH SPECIAL EDUCATIONAL NEEDS IN THE REPUBLIC OF SERBIA". In 5th International Baltic Symposium on Science and Technology Education. Scientia Socialis Press, 2023. http://dx.doi.org/10.33225/balticste/2023.88.
Abstract (sommario):
The most important source of knowledge in primary school teaching is the textbook. This research aimed to determine the differences in graphic illustrations in the contents of natural sciences in a regular textbook and a textbook for children with special educational needs in the Republic of Serbia. As the number of subjects that deal with the contents of natural sciences for children with special educational needs is small, as well as the number of schools that implement this type of teaching, physics is taken as a subject, because the number of common topics is quite similar. The research aim was to analyze illustrations in selected physics textbooks for the 6th grade of primary education, by the criteria for dividing illustrations by types, for determining abstractness and relative representation of illustrations. In addition, a supplementary classification of illustrations was applied. The obtained results indicate that the number of illustrations concerning the number of words is higher in textbooks for children with special educational needs, as well as that the most represented are illustrations from everyday life and greater abstraction compared to regular textbooks. Since the physics textbook for children with disabilities is quite old, these results can be examined in practice among teachers and help future textbook authors to write the best quality textbook taking into account the needs of teachers and children with special educational needs. Keywords: image analysis, teaching physics, type of illustrations, special educational needs
2
Radulescu, Victorita. "Numerical Modeling and Prediction of the Significant Parameters for Wind Monitoring". In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7518.
Abstract (sommario):
Abstract Romania has a high wind potential, representing more than 14,000 MW. After significant investments of over 5 billion euros made starting 2010, many wind farms were developed in regions with efficient potential, from the South-East part of the country. Nowadays, in January 2018, in Romania were registered 3025 MW produced by wind energy, representing around 30% of the total generated energy. To establish the efficient areas for future wind power plants a massive campaign of wind’s monitoring was developed, in the entire country. The paper presents a solution of the numerical modeling for the registered environmental data, significant atmospheric parameters. The complex realized database will allow future implementations of wind power plants. The data measured and stored refer at wind intensity and direction, pressure, temperature, humidity, solar radiation, and drew points, performed during four years with masts of height 70 m, situated at distance of 20 km each other. By numerical modeling is created a correlation and prediction of the measured data, plotted in correspondence to each elevation of the measuring stations. It was also analyzed the perturbations induced by the masts presence. Firstly, are mentioned some aspects referring to the masts installation, the solution adopted for a proper distribution through the analyzed area. The database elaboration was a challenge, due to the large amount of data recorded at intervals of 10 minutes (some parameters at 10 seconds) for a period of four years, for more than 12 parameters instantly. Besides these, there were stored and some other data referring at daily produced energy with some existent wind turbines. They will be considered as data input for future developments, with new generations of turbines, more efficient. It is created an original method to compact the database in order to use small amounts of computer memory. With the daily collected data was made and stored separately the average, maximum, and minimum wind velocity, for each day and month, from the measurements at time interval of 10 minutes. The relations between the values registered are within classification areas CL-4 and CL-8, allowing performing illustrations of over-prediction and under-prediction. The wind velocities under 4 m/s are stored in a separate folder because they are not useful in wind turbine functioning. These values are used only for estimation the future wind farms efficiency. The uncertainties are analyzed and are assessed the limits of errors, for the land classification CL-4. There are presented numerical results, some conclusions, and references.
3
Antoine, Axel, Sylvain Malacria, Nicolai Marquardt e Géry Casiez. "Interaction Illustration Taxonomy: Classification of Styles and Techniques for Visually Representing Interaction Scenarios". In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3445586.
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Lan, Ziwen, Keisuke Maeda, Takahiro Ogawa e Miki Haseyama. "GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features". In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9898071.
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Zhang, Daokun, e Wenyong Tang. "The application of Structural Reliability Analysis on hull girder ultimate strength of bulk carriers-a trial on Safety Level Approach". In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-052.
Abstract (sommario):
The International Maritime Organization is developing the Goal Based Standard, in which the Safety Level Approach(SLA) is one of the two parallel ways forward focusing on deriving explicit and reasonable safety level. During the development of Safety Level Approach, the Structural Reliability Analysis(SRA) is recognized as one of the useful tools. The application of SRA on the calibration of partial safety factors for hull girder ultimate strength is so far a typical illustration, which could be very helpful for the application of Safety Level Approach on the structural Rules in the future. China Classification Society (CCS) carries out a trial analysis with co-operation of Shanghai Jiao Tong University.
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Кутепов, Илья, Ilya Kutepov, Вадим Крысько, Vadim Krysko, Антон Крысько, Anton Krysko, Сергей Павлов et al. "Complexity of EEG Signals in Schizophrenia Syndromes". In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-140-143.
Abstract (sommario):
In the present study, 45 patients with schizophrenia syndromes and 39 healthy subjects are studied with electroencephalogram (EEG) signals. The study groups were of different genders. For each of the two groups, the signals were analyzed using 16 EEG channels. Multiscale entropy, Lempel-Ziv complexity and Lyapunov exponent were used to study the chaotic signals. The data were compared for two groups of subjects. Entropy was compared for each of the 16 channels for all subjects. As a result, topographic images of brain areas were obtained, illustrating the entropy and complexity of Lempel-Ziv. Lempel-Ziv complexity was found to be more representative of the classification problem. The results will be useful for further development of EEG signal classification algorithms for machine learning. This study shows that EEG signals can be an effective tool for classifying participants with symptoms of schizophrenia and control group. It is suggested that this analysis may be an additional tool to help psychiatrists diagnose patients with schizophrenia.
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Kolat, Tom. "Thermocouple Testing Methods, Data Analysis and Reporting Calibration Results with Emphasis on Noble Metal Types". In NCSL International Workshop & Symposium. NCSL International, 2018. http://dx.doi.org/10.51843/wsproceedings.2018.26.
Abstract (sommario):
Thermocouples are rugged, fast responding temperature sensors that very often cover large ranges of temperature. In addition, their economical affordability and ease of adaptation to a variety of configurations serve to increase their popularity and consequently the customer demand for traceable calibrations. Like all other measurement and test equipment, thermocouples, due primarily to their classification categories, exhibit a range of performances and accuracy expectations. This establishes the need for several methods of testing and analysis for effective, accurate result reporting with respect to the written standard and the temperature scale. This paper and its presentation discuss approaches employed to calibrate thermocouples and report their calibration results. It continues by illustrating some of the mathematics that can be used to furnish results on the calibration certificate. Examples of those applications with realistic thermocouple data are shown.
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Shieh, Win-Bin, Dar-Zen Chen e Yen-Chun Chen. "Kinematic Synthesis of One-DOF Geared Mechanisms According to Specified Gain Types". In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49510.
Abstract (sommario):
Although the atlas of the geared kinematic chains (GKCs) had been enumerated decades ago, few studies had focused on how these synthesized GKCs can be applied according to the kinematic requirements of geared mechanisms in practice. In this paper, the kinematic behaviors of one-DOF, single-output geared mechanisms of up to six links are analyzed based on the concept of kinematic fractionation and the formulas of global gains are established. Classification of the geared mechanisms is obtained according to the gain types, ordinary, subtractive, fractional and composite. A set of rules for the assignment of ground, input and output links of single-KU and multiple-KU geared mechanisms without redundant links are proposed according to gain types. As a result, the kinematic synthesis of the one-DOF, single-output geared mechanisms of up to six links according to their associated gain types can be easily accomplished. An exemplary design of a geared mechanism with subtractive gain type is provided for the illustration of the proposed methodology.
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Sharma, Gehendra, Janet K. Allen e Farrokh Mistree. "Classification and Execution of Coupled Decision Problems in Engineering Design for Exploration of Robust Design Solutions". In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97372.
Abstract (sommario):
Abstract Decision Support Problems (DSPs) are used to model design decisions involving multiple trade-offs. In practice, such design decisions are also coupled, that is, these decisions must be modelled by identifying and addressing the influence they exert on one another. Hence, we need to classify coupled decision problems and to introduce methods for managing uncertainty for such problems. Classification of coupled decision problems allows for the development and execution of decision templates to effect design and to archive design-related knowledge on a computer. Incorporating robustness metrics allows for the exploration of robust design solutions for coupled decision problems by managing uncertainty. In this paper, we present a classification scheme for coupled decisions using DSPs, called the Decision Scenario Matrix and we illustrate its utility by solving a coupled problem using DSPs. The design of a beam to be used as a fender is used to illustrate the efficacy of the formulation of coupled problems. In the first example, we determine a robust design, that is, determine the dimensions of the fender and simultaneously design the material recognizing that the computational models are incomplete and inaccurate. In the second example, we determine robust design solutions when design decisions are coupled, that is, determine the dimensions of the fender and select the material concurrently. Our focus, in this paper, is on illustrating the efficacy of the method rather than on the results.
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Zhou, Bin, Shenghua Liu, Bryan Hooi, Xueqi Cheng e Jing Ye. "BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/616.
Abstract (sommario):
Given a large-scale rhythmic time series containing mostly normal data segments (or `beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0.95 AUC, and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality.