Detta arbete fokuserar på ämnet ensemblespel, och handlar huvudsakligen om lärarens didaktiska val vid detta. I teorikapitlet har jag övergripande beskrivit didaktik, specialpedagogik respektive gruppsykologi. Arbetet har en sociokulturell synvinkel som grund vilken också analysen utgår från. Metoden som har använts för att genomföra undersökningen är aktionsforskning med observation och loggbok vid två separata tillfällen, i arbetet med två grupper. Den ena har varit en ensemblegrupp inom högstadiet, och den andra har varit en ensemblegrupp där medlemmarna har funktionsvariationer inom autismspektrum. Syftet är att genom aktionsforskning synliggöra mina didaktiska val och anpassningar i ensemble under arbetet med elever med och utan funktionsnedsättningar inom autismspektrum under några veckor. För att nå syftet har jag ställt mig följande forskningsfråga: • Vilka musikaliska och praktiska anpassningar gör jag som lärare vid ensemblespel med ensemblegrupp inom autismspektrum? Resultat visar att de anpassningar som skapades främst med Vuxengruppen var att jag behövde vara ett aktivt stöd i form av scaffolding med särskilda deltagande under hela arbetsprocessen, samtidigt som jag fick leda aktiviteten med de övriga deltagarna. Ett viktigt resultat är att jag framförde två saker parallellt som stöd för ensemblen, mitt gitarrspel som stöd för kompet, samtidigt som jag sjöng, som stöd för sångaren. Detta skilde sig från Ungdomsgruppen som självmant kunde framföra låtarna i form av uppspel efter instruerande och där det parallella framförandet inte var lika frekvent. Det fanns gemensamma nämnare för båda grupperna som innebar bland annat flexibilitet i användning av kulturella verktyg. Dessa bestod av mängden av ackord, låtmaterialets svårighetsgrader, noter och texter samt interaktion med eleverna.

Denna studie behandlar musiklärares arbete med bedömning i ensemble på gymnasiet och musicerande på grundskolan där musiklärarnas erfarenheter, tillvägagångssätt och förhållningssätt till bedömning i deras undervisning har undersökts. Studiens syfte är att utveckla kunskap om musiklärares tillämpningar av formativ respektive summativ bedömning i ensemblespel och musicerande samt vilka möjligheter och svårigheter som det innefattar. Studien utgår från en musikdidaktisk och sociokulturell teoretisk utgångspunkt. Data har samlats in med kvalitativ metod där musiklärare från grundskolan och ensemblelärare från gymnasiet har intervjuats. Resultatet visar att den formativa bedömningen genomsyrar musiklärarnas undervisning både på grundskolan och gymnasiet där de värdesätter den formativa bedömningen högt i sin undervisning. Resultatet visar även att den summativa bedömningen kan vara svår att utföra på grund av att musikämnet är praktiskt, att den summativa bedömningen inte tar lika mycket plats som den formativa bedömningen och att bedömningsstödet som finns tillhands inte har varit hjälpsamt. Musiklärarna värdesätter lyhördhet, anpassning, flexibilitet, initiativtagande och gruppledarskap som goda kvalitéer för vad som krävs för att vara en god medmusiker i sin bedömning.

4D Flow Magnet Resonance Imaging (4D Flow MRI) is a novel non-invasive technique for imaging of cardiovascular blood flow. However, when utilized as a stand-alone analysis method, 4D Flow MRI has certain limitations including limited spatial resolution and noise artefacts, motivating the application of dedicated post-processing tools. Learning based super-resolution (SR) has here emerged as a promising utility for such work, however, more often than not, these efforts have been constricted to a narrowly defined cardiovascular domain. Rather, there has been limited exploration of how learned super-resolution models perform across \emph{multiple} cardiovascular compartments, with the wide range of hemodynamic compartments covered by the cardiovasculature as an apparent challenge. To address this, we investigate the generalizability of 4D Flow MRI super-resolution using ensemble learning. Our investigation employs ensemble learning techniques, specifically bagging and stacking, with a convolutional neural network (4DFlowNet) serving as the framework for all base learners. To assist in training, synthetic training data was extracted from patient-specific, physics-based velocity fields derived from computational fluid dynamic (CFD) simulations conducted in three key compartments: the aorta, brain and the heart. Varying base and ensemble networks were then trained on pairs of high-resolution and low-resolution synthetic data, with performance quantitatively assessed as a function of cardiovascular domain, and specific architecture. To ensure clinical relevance, we also evaluated model performance on clinically acquired MRI data from the very same three compartments.  We find that ensemble models improve performance, as compared to isolated equivalents. Our ensemble model \textit{Stacking Block-3}, improves in-silico error rate by $16.22$ points across the average domain. Additionally, performance on the aorta, brain and heart improves by $2.66$, $5.81$ and $2.00$ points respectively. Employing both qualitative and quantitative evaluation methods on the in-vivo data, we find that ensemble models produce super-resolved velocity fields that are quantitatively coherent with ground truth reference data and visually pleasing. To conclude, ensemble learning has shown potential in generalizing 4D Flow MRI across multiple cardiovascular compartments.

Nowadays, web application attacks have increased tremendously due to the large number of users and applications. Thus, industries are paying more attention to using Web application Firewalls and improving their security which acts as a shield between the app and the internet by filtering and monitoring the HTTP traffic. Most works focus on either traditional feature extraction or deep methods that require no feature extraction method. We noticed that a combination of an unsupervised language model and a classic dimension reduction method is less explored for this problem. Inspired by this gap, we propose a new unsupervised anomaly detection model with better results than the existing state-of-the-art model for anomaly detection in WAF security. This paper focuses on this structure to explore WAF security: 1) feature extraction from HTTP traffic packets by using NLP (natural language processing) methods such as word2vec and Bert, and 2) Dimension reduction by PCA and Autoencoder, 3) Using different types of anomaly detection techniques including OCSVM, isolation forest, LOF and combination of these algorithms to explore how these methods affect results.  We used the datasets CSIC 2010 and ECML/PKDD 2007 in this paper, and the model has better results.

Background. Due to an increasing number of credit card defaulters, companies arenow taking greater precautions when approving credit applications. When a customermeets certain requirements, credit card firms typically use their experience todecide whether to grant them a credit card. Additionally, a few machine learningmethods have been applied to support the final decision. Objectives. The aim of this thesis is to compare the accuracy of logistic regressionclassifier, random forest classifier, and support vector classifier with the ensemblebagging classifier for predicting credit card approval. Methods. This thesis follows a method called general experimentation to determinethe most accurate classification technique for predicting credit card approval. Thedataset is taken from Kaggle, which contains information about credit card applications.The selected algorithms are trained with training data and validate themusing validation data then evaluate their performance on the testing data by usingmetrics such as accuracy, precision, recall, F1 score, and ROC curve. Now ensemblelearning bagging technique is applied to combine the predictions of these multiplemodels using majority voting to create an ensemble model. Finally, the performanceof the ensemble model was evaluated on the testing data and compared its accuracyto that of the individual models to identify the most accurate classification techniquefor predicting credit card approval. Results. Among the four selected machine learning algorithms, the random forestclassifier performed better with an accuracy of 88.41% on the testing dataset.The second-best algorithm is the ensemble bagging classifier, with an accuracy of84.78%. Hence, the random forest classifier is the most accurate algorithm for predictingcredit card approval. Conclusions. After evaluating various classifiers, including logistic regression classifier,random forest classifier, support vector classifier, and ensemble bagging, it wasobserved that the random forest classifier outperformed the other models in termsof predicting accuracy. This indicates that the random forest classifier was better atpredicting credit card approval.

Grimaldi is a synesthetic work composed of aural and visual media including noise, tone, electronic devices, lighting and theatrical events. It is designed for solo saxophone with an accompanying ensemble of eight wind instruments and four percussion. The most important aspect of the piece is its texture, which consists of constantly changing and interacting aural and visual events. The compositional process of Grimaldi began with the selection of a form constructed of two equal arches of unequal subdivision, and the designation of texture for each section. After these selections were made, the theatrical elements were selected, based on a traditional story about the clown Joseph Grimaldi (1787-1836). By gradually refining these general selections into specific notation, and by considering them as a whole rather than individually, consistent relationships were maintained.

At its heart (or lack thereof) The Machine is Angry is a theater work. The visual components are as equally important as their acoustic counterparts. Sounds are only as valuable as the images they evoke and the intention of The Machine is to outline a picture that simultaneously conveys community and isolation; the idea that one can feel most alone when in a crowded room.

This composition for Wind Ensemble (like Concert Band but usually only one player on a part) was Scott Master's Thesis for completing a Master of Music degree in Composition at Butler University. Written in 2010, the piece is a combination of styles, philosophies, and techniques, all in balance with each other. Avant-garde and traditional techniques are used; tonality and atonality vie with each other, resulting in a combination of the two; specific musical directions are balanced by a degree of choice available to each participant. “The Memory of Persistence” is about a journey. No specific personal story is presented, nor is the piece programmatic, but the progress and development of the piece could mirror many life situations and be accessible to anyone from that point of view. There is a progression from simple to complex, from innocence to maturity, that is demonstrated in the instrumentation as well as the melodic and harmonic elements. The title of the composition is an allusion to Salvador Dali’s painting called “The Persistence of Memory.” Even the font of the score’s title page is based on Dali’s own handwriting. Dali was a major player in the Surrealism movement of the twentieth century, combining classical elements of art with unusual, surprising, or even outrageous twists. “The Memory of Persistence” does the same thing in a musical setting. Yet, even without knowledge of Dali, the listener can understand the title because the piece demonstrates persistence through difficulty while retaining the memory of the past and incorporating it into current life.

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The purpose of this pilot study was to examine the benefit of offering a percussion ensemble class in secondary public schools. I looked at two elements of music education: playing time and relevant instruction. The research questions focused on the difference in playing time and relevant instruction between percussionists in the concert band and percussionists in the percussion ensemble, as well as differences between the concert band subgroups (brass, woodwind, percussion). 6 separate instrumental groups were observed: 4 concert bands and 2 percussion ensembles (N=6). Students were randomly selected from each instrument subgroup (brass, woodwind, percussion, percussion ensemble) for observation. A mixed model ANOVA was used to compare the playing time per hour of each instrument subgroup. A second mixed model ANOVA was used to compare the relevant instruction received per hour of each instrument subgroup. As anticipated, the concert band percussionists experienced significantly less playing time and relevant instruction than both the brass and woodwind subgroups. The percussion ensemble subgroup did not experience a significant difference in either playing time or relevant instruction from the concert band percussionstudents. However, informal observations of the rehearsals indicated a difference in the scope and depth of the playing time and instruction experienced by these two subgroups. Implications from these observations are also discussed.