Conciencia lingüística y cultural a través del léxico y la imagen: diccionario audiovisual bilingüe y bicultural FR-ES

La educación bilingüe desde etapas tempranas (Candelier & Castelotti, 2013) ha tenido un amplio desarrollo en Europa en los últimos 30 años, lo que ha desembocado en la definición y estudio de la competencia plurilingüe y pluricultural, en el desarrollo de la didáctica del plurilingüismo y de un complejo polo de conceptos en relación con la competencia cultural (intercultural, metacultural, transcultural...). En este marco, nos proponemos diseñar un diccionario visual bilingüe y bicultural francés-español que permita visualizar las diferencias conceptuales y culturales entre las lenguas y culturas francesa y española. En el uso de imágenes, preferiremos el uso de fotos, específicas y diferenciadas para las palabras de cada lengua de modo que se pueda desarrollar la conciencia de la relatividad lingüística (éveil aux langues) y de la relatividad cultural (éveil aux cultures). Gracias a un formato digital, se añadirá una versión sonora, de modo que se creará un diccionario audiovisual bicultural francés-español (BICAV bicultural FRES). Bilingual education from early stages (Candelier & Castelotti, 2013) has been developed in Europe over the last 30 years, which has led to the definition and study of multilingual and multicultural competence, the development of multilingualism didactics and complex concepts related to cultural competence (intercultural, metacultural, transcultural...). Within this framework, we propose to design a bilingual and bicultural French-Spanish visual dictionary that allows us to visualise the conceptual and cultural differences between the French and Spanish languages and cultures. Images, and specially pictures, for each word in each language, will develop awareness of linguistic relativity (éveil aux langues) and cultural relativity (éveil aux cultures). A sound version will be added thanks to a digital format in order to create a French-Spanish BILingual and BICultural AudioVisual dictionary (BILBICAV FRES). L'éducation bilingue dès les premières étapes (Candelier & Castelotti, 2013) a connu un développement important en Europe au cours des 30 dernières années, qui a conduit à la définition et à l'étude de la compétence plurilingue et pluriculturelle, au développement de la didactique du plurilinguisme et de concepts complexes en relation avec la compétence culturelle (interculturelle, métaculturelle, transculturelle...). Dans ce cadre, nous proposons de concevoir un dictionnaire visuel bilingue et biculturel français-espagnol qui permette de visualiser les différences conceptuelles et culturelles entre les langues et cultures française et espagnole. Dans l'utilisation des images, nous préférerons l'utilisation de photos, spécifiques et différenciées pour les mots de chaque langue afin de développer la conscience de la relativité linguistique (éveil aux langues) et de la relativité culturelle (éveil aux cultures). Grâce à un format numérique, une version sonore sera ajoutée, de sorte qu’un dictionnaire audiovisuel biculturel français-espagnol (BICAV bicultural FRES) sera créé.

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance Data

Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization (i.e., graphical representation of quantitative information) in stewardship research.Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed (excluding geographical maps and flowcharts). The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations.Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n = 54; 36.0%) and line charts (n = 42; 28.1%) were preferred visualization types. Visualization problems comprised color scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve color use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards.Conclusion: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship toward more effective visualizations that improve data insights, knowledge, and clinical decision-making.

Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using k -means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed. The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n=54; 36.0%) and line charts (n=42; 28.1%) were preferred visualization types. Visualization problems comprised colour scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve colour use for grouping/stratifying, improve the display of magnitude, and bring visualizations to match scientific standards. Conclusions: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship towards more effective visualizations that improve data insights, knowledge, and clinical decision-making.

Color Correction and Repair of Haze Images Under Hue-saturation-intensity Color Space and Machine Learning

To improve the visual effect and quality of haze images after fog removal, a model for color correction and repair of haze images under hue-saturation-intensity (HSI) color space combined with machine learning is proposed. First, the haze image imaging model is constructed according to the atmospheric scattering theory. Second, based on HSI color space, the color enhancement and fog removal of the haze image model is proposed, and a haze image-transmittancegallery is constructed. Third, the visual dictionary of the transmittance graph is obtained by training the k-means clustering algorithm based on density parameter optimization and support vector machine algorithm based on genetic algorithm optimization. Fourth, based on the visual dictionary and the atmospheric scattering model, the haze image is repaired and defogged, and the subjective visual effects and objective evaluation indexes of color enhancement and fog removal of haze images are compared. It is concluded that the algorithm can effectively guarantee the detail and clarity of the image after defogging.

Fuzzy encoding with hybrid pooling for visual dictionary in food recognition

<span>Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by using MSER detector with SURF descriptor was encoded by using fuzzy encoding approach. Support vector machine (SVM) with linear kernel was employed to evaluate the effect of fuzzy encoding. The results of the experiments have demonstrated a noteworthy classification performance of fuzzy encoding approach compared to the traditional approach based on hard assignment and Fisher vector technique. The effects of uncertainty and plausibility were minimized along with more discriminative and compact visual dictionary representation.</span>