Background Daily, companies generate enormous amounts of customer support tickets which are grouped and placed in specialised queues, based on some characteristics, from where they are resolved by the customer support personnel (CSP) on a first-in-first-out basis. Given that these tickets require different levels of urgency, a logical next step to improving the effectiveness of the CSPs is to prioritise the tickets based on business policies. Among the several heuristics that can be used in prioritising tickets is sentiment polarity. Objectives This study investigates how machine learning methods and natural language techniques can be leveraged to automatically predict the sentiment polarity of customer support tickets using. Methods Using a formal experiment, the study examines how well Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) based sentiment polarity prediction models built for the product and movie reviews, can be used to make sentiment predictions on email support tickets. Due to the limited size of annotated email support tickets, Valence Aware Dictionary and sEntiment Reasoner (VADER) and cluster ensemble - using k-means, affinity propagation and spectral clustering, is investigated for making sentiment polarity prediction. Results Compared to NB and LR, SVM performs better, scoring an average f1-score of .71 whereas NB scores least with a .62 f1-score. SVM, combined with the presence vector, outperformed the frequency and TF-IDF vectors with an f1-score of .73 while NB records an f1-score of .63. Given an average f1-score of .23, the models transferred from the movie and product reviews performed inadequately even when compared with a dummy classifier with an f1-score average of .55. Finally, the cluster ensemble method outperformed VADER with an f1-score of .61 and .53 respectively. Conclusions Given the results, SVM, combined with a presence vector of bigrams and trigrams is a candidate solution for extracting sentiments from email support tickets. Additionally, transferring sentiment models from the movie and product reviews domain to the email support tickets is not possible. Finally, given that there exists a limited dataset for conducting sentiment analysis studies in the Swedish and the customer support context, a cluster ensemble is recommended as a sample selection method for generating annotated data.

In 2011, the National Association of Schools of Music (NASM) took an official position to recognize the importance of hearing health and injury prevention as a standard for all member-accredited institutions. This is the largest national acknowledgement promoting hearing health and safety within the music discipline and among students seeking a music degree in the United States. The purpose of the study is to describe what conductors (i.e., instructors) of college-based ensembles know about hearing health and the generation of sound intensity levels. The study aimed to describe the 1) current state of conductors’ awareness and knowledge of sound intensity levels, 2) current attitudes of conductors toward learning and sharing knowledge of sound intensity levels, and 3) current teaching practices of conductors in regard to equipment usage (e.g. sound level meter, noise dosimeter, hearing protection devices) relating to sound measurement and exposure. Findings indicate 80.2% of conductors (N = 162, 66% employed by NASM-accredited institutions) agree that sounds generated during ensemble-based instructional activities (EBIAs) in college-level schools of music are capable of harming human hearing, but 24.1% “do not know” if EBIAs they conduct ever exceed sound intensity levels capable of harming human hearing, 54.9% do not know “what services or resources” their home institutions offer/refer to students, 93% are never using a noise dosimeter, 40% have never had an audiology exam, and 70% have never used hearing protection during an EBIA. Conductors have a strong openness to change current teaching practices and inform themselves about hearing health, but few are personally informing and educating their students during the EBIA. The study serves to assist conductors and foster a new dialogue among their students, colleagues, staff, and administrators to revise current curriculum, explore sound measurement technologies, and evaluate current hearing health and safety issues inherent in the practice, performance, and teaching of sound intensity levels generated during EBIAs in college-level schools of music.

Duo Chopinesque by Michael Hennagin and Chameleon Music by Dan Welcher represent two of the most significant percussion ensemble compositions written in the last twenty years. Both works are written for the mostly mallet type of percussion ensemble wherein the keyboard instruments predominate. However, the most unique aspect of these two pieces is their use of musical quotation. Duo Chopinesque borrows Chopin's Prelude in E minor in its entirety, while Chameleon Music borrows portions from four Mozart Sonatas. This paper places each work within the history of the percussion ensemble, and in the larger history of musical quotation in the twentieth century. In addition, the compositional characteristics of both works are examined with particular emphasis on each composer's use of borrowed material from the music of Mozart and Chopin. Particular attention is paid to the relationship between quoted material and newly composed rhythmic motives.

Sona of Pi-Pa is a composition set to a poem to be performed by soprano and mixed instrumental ensemble. The formal plan is through-composed and the organization of each individual piece is largely determined by the structure of the poetic text. The text, drawn from Song of Pi-Pa by Po Chu-i, depicts the story of how the poet became overwhelmed by the chance hearing of a virtuosic performance of a woman playing the pi-pa. The general characteristics of the work reflect the assimilation of certain non-western musical and philosophical influences. Traditional western compositional techniques are also employed in the treatment of thematic materials, musical form, instrumentation, and the developmental process. The total performance time for this composition is approximately twenty-six minutes.

A Wedding Ceremony is a composition of approximately 17 minutes in duration and is scored for horn in F, two trumpets in B-flat, trombone, two percussionists (timpani, roto toms, chimes, snare, triangle, suspended cymbal), 2-part boys choir, female soprano, and organ. The work consists of five parts of a mass, the Processional, Kyrie, Alleluia!, Hosanna!, and Recessional, with texted sections being taken from the Latin mass. The work is intended for a sacred wedding service of any denomination. The work was composed with the traditional aspects of the Latin mass in combination with a contemporary setting.

The specific problems investigated involved identifying and describing the characteristics of fundamental frequency, overall intensity, and spectral content in unaccompanied and ensemble performance settings. Additionally, comparisons and descriptions of the relationships among these acoustical parameters were made. Fifteen trombonists were used as research subjects. Each subject recorded a selected musical excerpt in the following performance modes: high register unaccompanied, harmonic, and unison ensemble; and low register unaccompanied and unison ensemble. Tape recordings of these subjects were used in conjunction with certain electronic apparatus to obtain data on frequency, intensity, and spectral content. Based on these data, descriptions of these acoustical parameters and comparisons of unaccompanied and ensemble performance settings were made.

In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble classifiers in the field of heart disease diagnosis, and to find ways to explain this model. This study uses experiment and quantitative analysis to evaluate the prediction performance of eight models in terms of prediction ability, algorithmic stability, false negative rate and run-time. It is proved that the Stacking fusion model with Naive Bayes classifier, XGBoost and Random forest as the first-level learners is superior to other classifiers in prediction ability. The false negative rate of this model is also outstanding. Furthermore, the Stacking fusion model is explained from the working principle of the model and the SHAP framework. The SHAP framework explains this model’s judgement of the important factors that influence heart disease and the relationship between the value of these factors and the probability of disease. Overall, two research problems in this study help reveal the prediction performance and reliability of the cardiac disease prediction model based on the Stacking principle. This study provides practical and theoretical support for hospitals to use the Stacking principle in the diagnosis of heart disease.

Making predictions nowadays is of high importance for any company, whether small or large, as thanks to the possibility to analyze the data available, new market opportunities can be found, risks and costs can be reduced, among others. Machine learning algorithms for time series can be used for predicting future values of interest. However, choosing the appropriate algorithm and tuning its metaparameters require a great level of expertise. This creates an adoption barrier for small and medium enterprises which could not afford hiring a machine learning expert to their IT team. For these reasons, this project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account different algorithms at the same time, and then it combines their results considering also the previous performance of each algorithm to obtain a final prediction of the value. Moreover, the solution proposed and implemented in this project can also predict according to a concrete objective (e.g., optimize the prediction, or do not exceed the real value) because not every prediction problem is subject to the same constraints. We have experimented and validated the implementation with three different cases. In all of them, a better performance has been obtained in comparison with each of the algorithms involved, reaching improvements of 45 to 95%.

This thesis examines the composition of three different musical works through the use of my constraint-based patterns. I have explored the patterns through spreadsheets and also SuperCollider: a software for algorithmic composition and audio synthesis. The aim is to find how the patterns can be used to reach clear contrasts while maintaining coherence in the music, as well as finding challenges and possibilities within the patterns, while exploring how evaluation of the artistic results can contribute to improved methods. While I see the main method as autoethnographical, with the core focus on composing, I have also used feedback from other composers, and through focus groups, as a way to collect data. Throughout this thesis I describe my process of constructing patterns and composing music, accompanied by my reasoning and relevant feedback. My results from analyzing feedback, score and patterns are that while some ways of using the patterns are well suited for achieving contrast and coherence, problems arose related to (among other things) musical form and predictability. Evaluation through feedback and interviews resulted in a better understanding of the patterns, and different workflows allowed for different viability in the evaluation. The most valuable insight is that the greater the amount of composition parameters which are controlled through constraint-based patterns, the simpler each individual composition parameter has to be in order to reach contrasting results that I find satisfying. My conclusion is that I can therefore design each individual composition parameter with high coherence to reach contrasting results when the composition parameters are applied on the same musical structure.

Recent debiasing techniques are incorporated into the Ensemble Kalman-Bucy Filter (EnKBF). Specifically, a novel double randomization is applied. The EnKBF is a Monte Carlo (MC) method that approximates the Kalman-Bucy Filter (KBF), which in turn can be seen as the continuous-time version of the celebrated discrete-time Kalman Filter (KF). The KF is a method that combines sequential observations with an underlying dynamics model to predict the state of the quantity of interest. Our interest in the EnKBF comes from its relevance in high dimensions, where it overcomes the curse of dimensionality and outperforms other standard methods like the Particle Filter. We will consider debiasing techniques (also termed unbiased estimators) in order to improve the error-to-cost rate. Unbiased estimators are variance reduction techniques that produce unbiased and finite variance estimators. Applications of the EnKBF are numerous, from atmospheric sciences, numerical weather prediction, finance, machine learning, among others. Thus, improving the EnKBF is of interest. Numerical tests are done in order to evaluate the cost and the error-to-cost rate of the algorithm, where we consider Ornstein-Uhlenbeck processes. Specifically, a numerical comparison with the Multilevel Ensemble Kalman-Bucy Filter (MLEnKBF) is made using two different unbiased estimators, the coupled sum and the single term estimators. Additionally, we test two variants of the EnKBF, the Vanilla EnKBF, and the Deterministic EnKBF. We find that the error-to-cost rate is virtually the same, although the cost of the unbiased EnKBF is much higher.