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Who Can Be the Monkey? The Use of a Novel Story-based Program Incorporating Music and Dance to Enhance Peer Acceptance and Inclusion for Young Autistic Pupils in Mainstream Schools
No full textBuilding Professional Relationships and Student Confidence through Early Childhood Graduate Practitioner Competencies
No full textAbstractThis paper reports on how Early Childhood Graduate Practitioner Competencies (ECGPCs) impact on professional relationships and develop bidirectional confidence in the practical abilities of Early Childhood Studies (ECS) students in England. The study adopted an interpretive approach, seeking views through questionnaires (n=38) which were administered, through purposeful sampling, to students, mentors and academics from three universities in England offering Early Childhood Studies (ECS) degrees with ECGPCs. Findings suggest that the ECGPCs enabled focused placement students, with stakeholders recognising the potential for confidence and increased professionalism through the direction that the ECGPCs provide. In contexts of rapid change in Early Childhood policy this article argues the importance of the ECGPCs and of placement to support the graduate professional identity of the early childhood workforce. Interlinking and evidencing knowledge from research and practice enable graduates to articulate and have competencies in; ‘what they do’, ‘how they do ‘it’’ and essentially ‘why they do ‘it’’’. This is essential in promoting graduate relational/collegial professionals and advocating for stronger societal recognition and valuing of young children and the professionals working with them. With the ECGPCs being a new initiative within the United Kingdom, thisstudy is unique in that it begins the research conversation around the success and challenges that this new initiative brings to the suite of Early Childhood (EC) qualifications
The Reverberations of Andante
No full textInvited essay in response to Igor & Moreno choreographic work Andant
Deep Learning Approach for Accurate Prediction of diabetes
No full textOver the decades, diabetes has proven to be a chronic disease,causing significant impact on individuals and healthcare systemsglobally. This disease increases the threat of diseases like cardiovascularillness, blindness, and may even cause early death. Diabetesrequires a lifelong disease control, taking a significant financialtoll on, not just the patient, but the entire family. Of the globaldiabetes cases, 90% are Type 2 diabetes mellitus (T2DM). By takingpreventive measures, like early diagnosis and with proper care, theeffect of diabetes can be decreased, possibly delaying its complications.This paper studies literature on implementation of machinelearning (ML) and deep learning (DL) approaches for predictingdiabetes and evaluate their performance. The accuracy of diabetesprediction using Support Vector Machine (SVM) algorithm and theArtificial Neural Network (ANN) algorithm, on the Pima IndiansDiabetes (PID) dataset, was compared. ANN showed better accuracyas compared to SVM. Also, the Adam optimizer proved to be abetter predictor than RMSprop optimizer. The results suggest thatmachine learning and deep learning can be an effective tool forpredicting diabetes, and that some algorithms perform better thanothers. We conclude that these techniques have shown accurateresults in prediction of diabetes. Future studies should extend thismodel using more neurons in the hidden layers of ANN with afocus on developing a robust model that can be easily integratedwith clinical practice
Automated classification of remote sensing satellite images using deep learning based vision transformer
Get PDFAutomatic classification of remote sensing images using machine learning techniques is challenging due to the complex features of the images. The images are characterized by features such as multi-resolution, heterogeneous appearance and multi-spectral channels. Deep learning methods have achieved promising results in the analysis of remote sensing satellite images in the recent past. However, deep learning methods based on convolutional neural networks (CNN) experience difficulties in the analysis of intrinsic objects from satellite images. These techniques have not achieved optimum performance in the analysis of remote sensing satellite images due to their complex features, such as coarse resolution, cloud masking, varied sizes of embedded objects and appearance. The receptive fields in convolutional operations are not able to establish long-range dependencies and lack global contextual connectivity for effective feature extraction. To address this problem, we propose an improved deep learning-based vision transformer model for the efficient analysis of remote sensing images. The proposed model incorporates a multi-head local self-attention mechanism with patch shifting procedure to provide both local and global context for effective extraction of multi-scale and multi-resolution spatial features of remote sensing images. The proposed model is also enhanced by fine-tuning the hyper-parameters by introducing dropout modules and a decay linear learning rate scheduler. This approach leverages local self-attention for learning and extraction of the complex features in satellite images. Four distinct remote sensing image datasets, namely RSSCN, EuroSat, UC Merced (UCM) and SIRI-WHU, were subjected to experiments and analysis. The results show some improvement in the proposed vision transformer on the CNN-based methods
Optimizing Fake News Detection: A Hybrid Transformer-Based Model for Enhanced Performance
No full text'Nothing-to-be-glossed-here': Race in Shakespeare's Sonnets
No full textThis is the first major study of race in Shakespeare's sonnets, looking beyond the argument about how Dark is the Dark lady, to examine the representation of the Fair Youth's Whiteness and beyond to other examples of racecraft performed by the language of the Sonnets
School staff perceptions of the impact of school counselling on young people, the school and integration into the school system
No full textIntroduction: This research explored the views of staff in secondary schools on school counselling for young people. Data were drawn from the Effectiveness and Cost Effectiveness Trial of Humanistic Counselling in Schools (ETHOS) study, an RCT of school counselling across 18 state‐funded secondary schools in London. Methods: Qualitative semi‐structured interviews were held with school staff (n = 16) from a sub‐sample of 10 participating schools from the RCT. The interviews explored the perceived impact of school counselling on the school and students. Thematic analysis was conducted using the NVivo qualitative data analysis software. Results: Three key themes were identified: (1) school context: rising mental health need and varying provision for mental health; (2) school staff perspectives on the impact of counselling: increased openness and improvements in mood, dedicated space to open up, putting skills into practice, one size does not fit all, and role of personal connection; and (3) long‐term impact of counselling in schools: integration as central to success and counselling as a stepping stone for further support. Conclusion: This research provides insights into school staff views of secondary school counselling in the context of delivery through a research trial. Effective ways of integrating counselling services into schools are identified
