1,720,980 research outputs found
ESP teacher education at the interface of theory and practice: introducing a model of mediated corpus-based genre analysis
No full textOne of the effects of the growing importance of global English in professional contexts has been the rise of ESP teaching at all levels. Despite the concurrently increasing demand for ESP teachers, pre-service teacher education programmes in Europe have so far largely neglected this important area. In order to address the professional needs of future ESP teachers, a novel and coherent framework for mediating the findings of corpus linguistics and genre analysis has been developed. The advantages of such a model of mediated corpus-based genre analysis lie in its flexibility of application to diverse ESP settings and target groups, so empowering both student teachers and their future pupils to develop autonomous language capabilities. Following this model, student teachers are familiarized with the potential of specialized corpora as a source of information regarding specific genres, such as contracts of sale, sustainability reports or company profiles, and as a tool in materials development.This model of mediated corpus-based genre analysis, which will be presented and discussed in this paper, has been implemented in an innovative teacher education project at the English Department of the University of Vienna.Feedback from both student teachers and future employers underlines the positive effects of such a linguistics-informed approach to teacher education
Fit für die globalisierte Welt? – Ein neues Ausbildungsmodul Fachsprache für EnglischlehrerInnen
No full textAutomatic model selection in cost-sensitive boosting
No full textThis paper introduces SSTBoost, a predictive classification methodology designed to target the accuracy of a modified boosting algorithm towards required sensitivity and specificity constraints. The SSTBoost method is demonstrated in practice for the automated medical diagnosis of cancer on a set of skin lesions (42 melanomas and 110 naevi) described by geometric and colorimetric features. A cost-sensitive variant of the AdaBoost algorithm is combined with a procedure for the automatic selection of optimal cost parameters. Within each boosting step, different weights are considered for errors on false negatives and false positives, and differently updated for negatives and positives. Given only a target region in the ROC space, the method also completely automates the selection of the cost parameters ratio, tipically of uncertain definition. On the cancer diagnosis problem, SSTBoost outperformed in accuracy and stability a battery of specialized automatic systems based on different types of multiple classifier combinations and a panel of expert dermatologists. The method thus can be applied for the early diagnosis of melanoma cancer or in other problems in which an automated cost-sensitive classification is require
Experience in designing and evaluating a teleconsultation system supporting shared care of oncological patients
No full textThis poster presents our experience in designing, developing and deploying a Web-based Teleconsultation System based on a Patient Centred Oncological Electronic Medical Record (PEMR) specifically designed to provide clinicians a cooperative work tool supporting the oncological patient management. An evaluation phase in a clinical setting was performed when the system was deployed in the hospitals. A second evaluation phase after two years of utilization is on goin
SSTBoost: Automatic Model Selection in Cost-sensitive Boosting
No full textThis paper introduces SSTBoost, a predictive classification methodology designed to target the accuracy of a modified boosting algorithm towards required sensitivity and specificity constraints. The SSTBoost method is demonstrated in practice for the automated medical diagnosis of cancer on a set of skin lesions (42 melanomas and 110 naevi) described by geometric and colorimetric features. A cost-sensitive variant of the AdaBoost algorithm is combined with a procedure for the automatic selection of optimal cost parameters. Within each boosting step, different weights are considered for errors on false negatives and false positives, and differently updated for negatives and positives. Given only a target region in the ROC space, the method also completely automates the selection of the cost parameters ratio, tipically of uncertain definition. On the cancer diagnosis problem, SSTBoost outperformed in accuracy and stability a battery of specialized automatic systems based on different types of multiple classifier combinations and a panel of expert dermatologists. The method thus can be applied for the early diagnosis of melanoma cancer or in other problems in which an automated cost-resistive classification is require
Bereit für professionelle Herausforderungen: universitäre Englischlehrerinnenbildung in Wien
No full textTuning Cost-sensitive Boosting and its Application to Melanoma Diagnosis
No full textThis paper investigates a methodology for effective model selection of cost-sensitive boosting algorithms. in many real situations, e.g. for automated medical diagnosis, it is crucial to tune the classification performance towards the sensitivity and specificity required by the user. To this purpose, for binary classification problems, we have designed a cost-sensitive variant of AdaBoost where (1) the model error function is weighted with separate costs for errors (false negative and false positives) in the two classes. and (2) the weights are updated differently for negatives and positives t each boosting step. Finally, (3) a practical search procedure allows to get into or as close as possible to the sensitivity and specificity constraints without an extensive tabulation of the ROC curve. This off-the-shelf methodology was applied for the automatic diagnosis of melanoma on a set of 152 skin lesions described by geometric and colorimetric features, out-performing, on the same data set, skilled dermatologists and a specialized automatic system based on a multiple classifier combinatio
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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