Lextale-Esp: a test to rapidly and efficiently assess the Spanish vocabulary size

The methods to measure vocabulary size vary across disciplines. This heterogeneity hinders direct comparisons between studies and slows down the understanding of research findings. A quick, free and efficient test of English language proficiency, LexTALE, was recently developed to remedy this problem. LexTALE has been validated and shown to be an effective tool for distinguishing between different levels of proficiency in English. The test has also been made available in Dutch, German, and French. The present study discusses the development of a Spanish version of the test: Lextale-Esp. The test discriminated well at the high and the low end of Spanish proficiency and returned a big difference between the vocabulary size of Spanish native and non-native speakers

An efficient and effective immune based classifier

Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier

The effect of noise on RWTSAIRS classifier.

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (apparently better) individuals. However, with this type of selection, there is a high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In a previous study, the Real World Tournament Selection (RWTS) method was incorporated into the resource competition phase of AIRS to prevent this problem. The new classifier, named RWTSAIRS, obtained higher accuracy than AIRS in standard datasets from UCI machine learning repository. Real-world data is not perfect and contains noise that may impact the models created from data and decision made based on data. In this study, the performance of RWTSAIRS is evaluated in noisy environments. For this purpose, class and attribute noise are injected into some datasets

Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (seemly good) individuals. This type of selection has high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In this study, the Real World Tournament Selection (RWTS) method is incorporated in resource competition phase of AIRS to prevent this issue and experiments are conducted to evaluate the accuracy of new algorithm (RWTSAIRS). The combination of cross validation and t test is used as evaluation method. Algorithms tested on benchmark datasets of the UCI machine learning repository show that RWTSAIRS obtained higher accuracy than AIRS in all cases and that the difference between accuracies of two algorithms was significant in majority of cases

Artificial immune recognition system with nonlinear resource allocation method and application to traditional Malay music genre classification

Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems. In this study, the resource allocation method of AIRS was changed with a nonlinear method. This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification. Music genre classification has a great important role in music information retrieval systems nowadays. The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification

A hybrid approach to traditional Malay music genre classification: combining feature selection and artificial immune recognition system

Music genre classification has a great important role in music information retrieval systems. In this study we propose hybrid approach for Traditional Malay Music (TMM) genre classification. The proposed approach consists of three stages: feature extraction, feature selection and classification with Artificial Immune Recognition System (AIRS). The new version of AIRS is used in this study. In Proposed algorithm, the resource allocation method of AIRS has been changed with a nonlinear method. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation. This accuracy is maximum accuracy among the classifiers used in this study

Perception of nonnative tonal contrasts by Mandarin-English and English-Mandarin sequential bilinguals

This study examined the role of acquisition order and crosslinguistic similarity in influencing transfer at the initial stage of perceptually acquiring a tonal third language (L3). Perception of tones in Yoruba and Thai was tested in adult sequential bilinguals representing three different first (L1) and second language (L2) backgrounds: L1 Mandarin-L2 English (MEBs), L1 English-L2 Mandarin (EMBs), and L1 English-L2 intonational/non-tonal (EIBs). MEBs outperformed EMBs and EIBs in discriminating L3 tonal contrasts in both languages, while EMBs showed a small advantage over EIBs on Yoruba. All groups showed better overall discrimination in Thai than Yoruba, but group differences were more robust in Yoruba. MEBs’ and EMBs’ poor discrimination of certain L3 contrasts was further reflected in the L3 tones being perceived as similar to the same Mandarin tone; however, EIBs, with no knowledge of Mandarin, showed many of the same similarity judgments. These findings thus suggest that L1 tonal experience has a particularly facilitative effect in L3 tone perception, but there is also a facilitative effect of L2 tonal experience. Further, crosslinguistic perceptual similarity between L1/L2 and L3 tones, as well as acoustic similarity between different L3 tones, play a significant role at this early stage of L3 tone acquisition.Published versio

An evidence-based recommendation on bed head elevation for mechanically ventilated patients

A semi-upright position in ventilated patients is recommended to prevent ventilator-associated pneumonia (VAP) and is one of the components in the Ventilator Bundle of the Institute for Health Care Improvement. This recommendation, however, is not an evidence-based one.status: publishe

Spelling-to-sound correspondences affect acronym recognition processes

A large body of research has examined the factors which affect the speed with which words are recognised in lexical decision tasks. Nothing has yet been reported concerning the important factors in differentiating acronyms (e.g. BBC, HIV, NASA) from non-words. It appears that this task poses little problem for skilled readers, in spite of the fact that acronyms have uncommon, even illegal, spellings in English. We used regression techniques to examine the role of a number of lexical and non-lexical variables known to be important in word processing in relation to lexical decision for acronym targets. Findings indicated that acronym recognition is affected by age of acquisition and imageability. In a departure from findings in word recognition,acronym recognition was not affected by frequency. Lexical decision responses for acronyms were also affected by the relationship between spelling and sound - a pattern not usually observed in word recognition. We argue that the complexity of acronym recognition means that the process draws phonological information in addition to semantics