Accurate knowledge of water content in seasonal snow can be helpful for water resource management. In this study, a distributed temperature index snow model based on temperature and precipitation as forcing data, is used to estimate snow storage in the Jollie catchment approximately 20km east of the main divide of the central Southern Alps, New Zealand. The main objective is to apply a frequently used assimilation method, the ensemble Kalman square root filter, to assimilate remotely sensed snow covered area into the model and evaluate the impacts of this approach on simulations of snow water equivalent. A 250m resolution remotely sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS), specifically tuned to the study location was used. Temperature and precipitation were given on a 0.055 latitude/longitude grid. Precipitation was perturbed as input into the model, generating 100 ensemble members, which represented model error. Only observations of snow covered area that had less that 25% cloud cover classification were used in the assimilation precess. The error in the snow covered area observations was assumed to be 0.1 and grow linearly with cloud cover fraction up to 1 for a totally cloud covered pixel. As the model was not calibrated, two withholding experiments were conducted, in which observations withheld from the assimilation process were compared to the results. Two model states were updated in the assimilation, the total snow accumulation state variable and the total snow melt state variable. The results of this study indicate that the model underestimates snow storage at the end of winter and/or does not detect snow fall events during the ablation period. The assimilation method only affected simulated snow covered area and snow storage during the ablation period. That corresponded to higher correlation between modelled snow cover area and the updated state variables. Withholding experiments show good agreement between observations and simulated snow covered area. This study successfully applied the ensemble Kalman square root filter and showed its applicability for New Zealand conditions.

I denna uppsats undersöks hur några musiklärare går tillväga för att bygga en fungerande blåsorkesterverksamhet i de musik- och kulturskolor de är verksamma. Vad lärarna anser om planering, vilka mål de har med verksamheten samt hur de arbetar för att uppnå sina mål är frågor som besvaras i uppsatsen. Resultaten diskuteras med utgångspunkt i teorier om samspel och ledarskap. Datamaterialet består av intervjuer med fyra musiklärare på tre olika musik-/kulturskolor. Två av skolorna finns i Sverige och en i Norge. Två av lärarna arbetar på samma skola. I resultatet visas att arbetsmetoderna för planeringen verkar vara snarlika på de fyra skolorna. Lärarna anser att det krävs bra material och kunskap om det material som används, samt ordning och reda på repetitionerna. Skapa spelglädje och en mysig miljö är något som prioriteras högt hos samtliga lärare. Målsättningarna hos de respektive lärarna beror till stor del på vilka resurser som finns och vilka kunskaper eleverna har nått från år till år. Målsättningar på de respektive musik- och kulturskolorna finns inte, utan det är upp till varje ledare/dirigent att själva bestämma målen. Gemensamt för skolorna är planering av jul- och sommarkonserter samt att konkurrensen med idrotten alltid är något de får arbeta mot. När det gäller de intervjuades förhållningssätt i arbetet, är det ett situationsanpassat förhållningssätt det till sist handlar om för att ta sig mot målet. / This thesis purports to examine how different music teachers work in establishing a functioning wind ensemble at music and culture schools they teach at. How they view the planning process, their work goals, and how they strive to achieve their goals, are questions that are answered in the paper. The results are then discussed on the basis of theories of interaction and leadership. The data consists of interviews with four music teachers at three different schools of music / arts. Two of the schools are located in Sweden and one in Norway. Two of the teachers work at the same school. The results demonstrate that the planning is similar at the four schools. The teachers believe that good material and knowledge of the material being used, is required, in addition to discipline at the rehearsals to achieve good results. To create an enjoyful and relaxing environment is prioritized by all the teachers. The goal of the teachers depends largely on the material resources they have, and what knowledge the students have achieved from the previous year. Particular objectives at each respective music and culture school do not exist: this is up to each leader / conductor to decide each case. Common to all schools is the planning of Christmas and summer concerts at the end of every semester, as well as the conflict with sports, which is always something they must deal with. As for the interviewees and their approach to their work, the strategy in achieving the goal is based on each particular situation.

Syftet med föreliggande studie är att undersöka relationen mellan styrdokumen­tens föreskrifter gällande jämställdhet och lärares hantering av detta i ensemble­undervisning inom jazz och populärmusik på gymnasiet. Jag har valt att analysera ensemblepedagogernas förhållningssätt och handlingar vid ensembleundervisning utifrån ett genusperspektiv. Denna studie tar avstamp i synen på genus som en social konstruktion, där genusmönster genereras i möten mellan människor, genom ständig upprepning av handlingar och beteenden. För att få svar på mina frågor har jag genomfört halvstrukturerade kvalitativa in­tervjuer med fyra verksamma ensemblelärare vid fyra olika gymnasieskolor. Efter att ha analyserat intervjumaterialet visar resultatet att alla informanter anser att synen på jämställdhet och människors lika värde är grundläggande för hela under­visningen. Tillvägagångssätten för hur pedagogerna hanterar jämställdhet i en­sembleundervisningen skiljer sig något. De lyfter fram olika aspekter rörande äm­net och knyter an till styrdokumentens föreskrifter på varierande vis, men samlas kring åsikten att synen på jämställdhet påverkar det pedagogiska arbetet såväl i mötet med eleverna som på ett kollegialt plan. / The purpose of this study is to investigate the relationship between the national policy documents regarding gender perception and how teachers are dealing with this in ensemble education in jazz and popular music in the Swedish upper secondary school. I have chosen to analyze the attitudes and actions of ensemble teachers from a gender perspective. This study is based on the perception of gender as a social construction,, where gender patterns are generated in the interaction between people, through constant repetition of action and behavior. In an attempt to answer these questions, I conducted semi-structured qualitative interviews with four current teachers at four upper secondary schools. After analyzing the interview material, the result shows that all the participants believe that the perception of gender and human equality is fundamental in the field of education. The procedures for how educators handle equality in ensemble teaching differ slightly. They highlight various aspects of the subject linked to policy documents governing the various ways. The teachers agree that perception of gender affects the educational work, both through interaction with the students, as well as on a collegiate level.

Syftet med studien var att få en djupare inblick i hur ensemblepedagoger ser på Time samt hur de didaktiskt arbetade med detta. Vi avsåg att få djupare inblick i olika förhållningssätt som ensemblepedagoger kan ha till Time i en undervisningssituation, samt vad begreppet Time innebär. Studien har sin teoretiska utgångspunkt i ett sociokulturellt perspektiv med fokus på hur människor använder olika redskap för att kommunicera med sin omgivning. Vi anser att vi har fått en bredare ingång i ämnet genom att alla pedagoger spelar olika instrument. Den gemensamma faktorn är att de undervisar i ensemble på folkhögskola. I undersökningen använder vi oss av den kvalitativa forskningsintervjun för att söka svar på våra frågor. Resultaten visar att alla pedagoger anser att god Timing är otroligt viktigt i samband med musikutövande. Redskapen och de tekniker som pedagogerna använde för att undervisa i Time, var varierande och viktiga. Det visade sig att en syn pedagogerna hade på Time var att det är kopplat till personligheten hos musiker. De ansåg det vara känsligt och svårt att gå in som pedagog och försöka ändra en elevs Time då det är så starkt kopplat till personligheten. / The purpose of this study is to acquire a better understanding of ensemble educators’ view of the concept of Time and how it was didactically applied. Our intentions were to gain a deeper insight in the different approaches in becoming a better ensemble educator regarding teaching Time, and what the concept of Time is. The study had its theoretical standpoint in a socio-cultural perspective, which focuses on how people use different tools to communicate with each other’s surroundings. In the study we used the qualitative research interview to attain the answers to our questions. The result indicates that the educators consider a good sense and feel of Time is important for musicians. The fact that the educators who participated in this study played different instruments provided a broad perspective regarding the subject. Each employed different tools and techniques which varied, yet were all important. The study indicated that, accordingly to the educators, one aspect of Time was connected to the personality of the musicians. This latter aspect helped show how sensitive and difficult it can be for an educator to change a student’s perception of Time, especially when considering the close ties between the concept of Time and the musician’s personality.

Concept drift refers to a problem that is caused by a change in the data distribution in data mining. This leads to reduction in the accuracy of the current model that is used to examine the underlying data distribution of the concept to be discovered. A number of techniques have been introduced to address this issue, in a supervised learning (or classification) setting. In a classification setting, the target concept (or class) to be learned is known. One of these techniques is called “Ensemble learning”, which refers to using multiple trained classifiers in order to get better predictions by using some voting scheme. In a traditional ensemble, the underlying base classifiers are all of the same type. Recent research extends the idea of ensemble learning to the idea of using committees, where a committee consists of diverse classifiers. This is the main difference between the regular ensemble classifiers and the committee learning algorithms. Committees are able to use diverse learning methods simultaneously and dynamically take advantage of the most accurate classifiers as the data change. In addition, some committees are able to replace their members when they perform poorly. This thesis presents two new algorithms that address concept drifts. The first algorithm has been designed to systematically introduce gradual and sudden concept drift scenarios into datasets. In order to save time and avoid memory consumption, the Concept Drift Introducer (CDI) algorithm divides the number of drift scenarios into phases. The main advantage of using phases is that it allows us to produce a highly scalable concept drift detector that evaluates each phase, instead of evaluating each individual drift scenario. We further designed a novel algorithm to handle concept drift. Our Dynamic Committee for Concept Drift (DCCD) algorithm uses a voted committee of hypotheses that vote on the best base classifier, based on its predictive accuracy. The novelty of DCCD lies in the fact that we employ diverse heterogeneous classifiers in one committee in an attempt to maximize diversity. DCCD detects concept drifts by using the accuracy and by weighing the committee members by adding one point to the most accurate member. The total loss in accuracy for each member is calculated at the end of each point of measurement, or phase. The performance of the committee members are evaluated to decide whether a member needs to be replaced or not. Moreover, DCCD detects the worst member in the committee and then eliminates this member by using a weighting mechanism. Our experimental evaluation centers on evaluating the performance of DCCD on various datasets of different sizes, with different levels of gradual and sudden concept drift. We further compare our algorithm to another state-of-the-art algorithm, namely the MultiScheme approach. The experiments indicate the effectiveness of our DCCD method under a number of diverse circumstances. The DCCD algorithm generally generates high performance results, especially when the number of concept drifts is large in a dataset. For the size of the datasets used, our results showed that DCCD produced a steady improvement in performance when applied to small datasets. Further, in large and medium datasets, our DCCD method has a comparable, and often slightly higher, performance than the MultiScheme technique. The experimental results also show that the DCCD algorithm limits the loss in accuracy over time, regardless of the size of the dataset.

A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion.

Dans ce mémoire nous avons, en premier lieu, identifié et présenté deux méthodes de calcul de l'ensemble de Pareto.Ces méthodes ne demandent qu'un logiciel élémentaire de programmation linéaire. En second lieu nous avons analysé une problématique issue du domaine de la fonderie. Nous avons présenté un problème de base de mélange classique. Ensuite nous avons considéré un problème bicritère de correction d'un prémélange et calculé son ensemble de Pareto. En troisième lieu nous avons constaté qu'il était possible de présenter une formulation unifiée des deux problèmes précédents sous forme d'un second problème bicritère. Cette unification fait intervenir une transformation géométrique. Finalement nous avons terminé ce mémoire par une étude sommaire de cette transformation géométrique mettant en évidence les cônes de préférence associés aux ensembles de Pareto de ces différents problèmes.

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This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work.

John Harbison's Olympic Dances was composed in 1996 and premiered in February 1997. The work was written as a piano score before it was orchestrated for a wind ensemble of 25 winds and two percussionists.The first section of the paper focuses on the various influences that have affected Harbison's compositional style. The composer's educational background includes several prominent teachers whose instruction had great impressions. Special emphasis is placed on those characteristics of Harbison's style that are most prominent in the work with which this paper is concerned, Olympic Dances. Olympic Dances was commissioned by the College Band Directors National Association and premiered at the CBDNA Twenty-ninth National Conference in Athens, Georgia, in a collaborative performance of the University of North Texas Wind Symphony and Pilobolus Dance Theatre. The second part of the paper presents an historical overview of CBDNA commissioning projects along with a summary of the genesis of the commissioning of Olympic Dances. The primary focus of the study appears in the third section of the paper. An analysis of the four movements of Olympic Dances is presented with attention to the objective elements of harmonic and melodic structures along with a focus on orchestration and scoring. This section considers the composer's thoughts on aesthetic concerns, suggested through his written program notes, and elucidated by way of an interview with the author. Special performance concerns related to rehearsal and conducting conclude this chapter. The paper also includes a transcription of the author's interview with John Harbison, a bibliography and a select discography of recent recordings of his works that are currently available.