900 resultados para International Classification


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Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

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Teacher assessment literacy is a phrase that is often used but rarely defined. Yet understanding teacher assessment literacy is important in an international curriculum and assessment reform context that continues to challenge teachers’ assessment practices. In this article situated examples of classroom assessment literacies are analysed using Bernstein’s (Pedagogy, symbolic control and identity: Theory, research and critique, Taylor and Francis, London, 1996; Br J Sociol Educ 20(2):157–173, 1999) theoretical tools of vertical and horizontal discourses, classification and framing. Drawing on a sociocultural view of learning, the authors define teacher assessment literacies as dynamic social practices which are context dependent and which involve teachers in articulating and negotiating classroom and cultural knowledges with one another and with learners, in the initiation, development and practice of assessment to achieve the learning goals of students. This conceptualisation of assessment literacy aims to make explicit some underpinning theoretical constructs of assessment literacy to inform dialogue and decision making for policy and practice to benefit student learning and achievement.

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Since 1959, international cooperation has been a key feature of Cuba’s commitment to egalitarian social well-being. Aspects of this experience have been well documented , in general and with reference to specific initiatives across human development and occupational sectors. Others have been little examined, of which education is one. This book describes the internationalism of Cuban education policy as practised in Cuba and in other parts of the Global “South.”

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A little-known facet of Cuban internationalism is the Cuba shares in the education of young people who want to help build a stronger media culture that represents voices from the global South. Cuba was instrumental in the establishment and operation of the International Film and Television School at San Antonio de los Baños. The Cuban government provided the location and buildings for the school, and among the range of international media professionals who teach the students are selected Cuban professors from the Institute of the Arts, based n Havana. The International Film and Television School is supported by funding from Spain and other countries, and by the willingness of international media professionals to teach short courses for little more than an honorarium. Cuba used to provide full scholarships for student from the South to study a two-year course in film or television, but now charges fees for its three-year diploma course.

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An engaging narrative is maintained throughout this edited collection of articles that address the issue of militarism in international relations. The book seamlessly integrates historical and contemporary perspectives on militarism with theory and relevant international case studies, resulting in a very informative read. The work is comprised of three parts. Part 1 deals with the theorisation of militarism and includes chapters by Anna Stavrianakis and Jan Selby, Martin Shaw, Simon Dalby, and Nicola Short. It covers a range of topics relating to historical and contemporary theories of militarism, geopolitical threat construction, political economy, and the US military’s ‘cultural turn’.

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Purpose – The purpose of this paper is to provide a new type of entry mode decision-making model for construction enterprises involved in international business. Design/methodology/approach – A hybrid method combining analytic hierarchy process (AHP) with preference ranking organization method for enrichment evaluations (PROMETHEE) is used to aid entry mode decisions. The AHP is used to decompose the entry mode problem into several dimensions and determine the weight of each criterion. In addition, PROMETHEE method is used to rank candidate entry modes and carry out sensitivity analyses. Findings – The proposed decision-making method is demonstrated to be a suitable approach to resolve the entry mode selection decision problem. Practical implications – The research provides practitioners with a more systematic decision framework and a more precise decision method. Originality/value – The paper sheds light on the further development of entry strategies for international construction markets. It not only introduces a new decision-making model for entry mode decision making, but also provides a conceptual framework with five determinants for a construction company entry mode selection based on the unique properties of the construction industry.

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Introduction to the topic or context and/or mapping of the literature Increasing degree-seeking, self-funded, international students from affluent Asian countries, who use English as an additional language (EAL), have contributed to cultural and linguistic diversities in Australian universities. Such diversities further posed challenges in pedagogy and assessment. In particular, these students' English proficiency and cultural attributes were highlighted as factors in productive group discussions, and equitable group assessment. The focus in the research literature thus far is on how EAL international students can better English proficiency and adaptability to group participation. However, little is known from sociological perspectives about the power relations involved in EAL students' choice of group members in group discussions.

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This research examines the effects of expectation (perceived attractiveness) on satisfaction, place identity, and place dependence. Place identity and place dependence are viewed as relational components of choice and relate to deeper needs. This study proposes that these two relational components depend on transactional expectations, which are emergent and determined by past experiences and visitor goals. In a theoretically elaborated and tested Structural Equation Model (SEM) this study assumes that these relationships vary according to intentions to return. The study addresses the conditions under which loyalty intentions influence the deeper place attachments (place identity and place dependence) that visitors associate with attractive cultural and natural destinations. The model is tested on a sample of 504 international tourists visiting Tanzania during fall 2010, and explains 59% of variance in the predicted dependent variables. The results are linked to a discussion on loyalty programs.

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Bridges are currently rated individually for maintenance and repair action according to the structural conditions of their elements. Dealing with thousands of bridges and the many factors that cause deterioration, makes this rating process extremely complicated. The current simplified but practical methods are not accurate enough. On the other hand, the sophisticated, more accurate methods are only used for a single or particular bridge type. It is therefore necessary to develop a practical and accurate rating system for a network of bridges. The first most important step in achieving this aim is to classify bridges based on the differences in nature and the unique characteristics of the critical factors and the relationship between them, for a network of bridges. Critical factors and vulnerable elements will be identified and placed in different categories. This classification method will be used to develop a new practical rating method for a network of railway bridges based on criticality and vulnerability analysis. This rating system will be more accurate and economical as well as improve the safety and serviceability of railway bridges.

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Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them.

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Greater than 750 individual particles have now been selected from collection flags housed in the JSC Cosmic Dust Curatorial Facility and most have been documented in the Cosmic Dust Catalogs [1]. As increasing numbers of particles are placed in Cosmic Dust Collections, and a greater diversity of particles are introduced to the stratosphere through natural and man-made processes (e.g. decaying orbits of space debris [2]), there is an even greater need for a classification scheme to encompass all stratospheric particles rather than only extraterrestrial particles. The fundamental requirements for a suitable classification scheme have been outlined in earlier communications [3,4]. A quantitative survey of particles on collection flag W7017 indicates that there is some bias in the number of samples selected within a given category for the Cosmic Dust Catalog [5]. However, the sample diversity within this selection is still appropriate for the development of a reliable classification scheme. In this paper, we extend the earlier works on stratospheric particle classification to include particles collected during the period May 1981 to November 1983.

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Visuals are a central feature of STEM in all levels of education and many areas of employment. The wide variety of visuals that students are expected to master in STEM prevents an approach that aims to teach students about every type of visual that they may encounter. This paper proposes a pedagogy that can be applied across year levels and learning areas, allowing a school-wide, cross-curricular, approach to teaching about visual, that enhances learning in STEM and all other learning areas. Visuals are classified into six categories based on their properties, unlike traditional methods that classify visuals according to purpose. As visuals in the same category share common properties, students are able to transfer their knowledge from the familiar to unfamiliar in each category. The paper details the classification and proposes some strategies that can be can be incorporated into existing methods of teaching students about visuals in all learning areas. The approach may also assist students to see the connections between the different learning areas within and outside STEM.

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Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.