Indiana University-Purdue University Indianapolis (IUPUI) / As the amount of information grows, the desire to efficiently filter out unnecessary information and retain relevant or interested information for people is increasing. To extract the information that will be of interest to people efficiently, we can utilize recommender systems. Recommender systems are information filtering systems that predict the preference of a user to an item. Based on historical data of users, recommender systems are able to make relevant recommendations to users. Due to its usefulness, Recommender systems have been widely used in many applications, including e-commerce and healthcare information systems. However, existing recommender systems suffer from several issues, including data sparsity and user/item heterogeneity. In this thesis, a hybrid dynamic and multi-collaborative filtering based recommendation technique has been developed to recommend search terms for physicians when physicians review a large number of patients’ information. Besides, a local sparse linear method ensemble has been developed to tackle the issues of data sparsity and user/item heterogeneity. In health information technology systems, most physicians suffer from information overload when they review patient information. A novel hybrid dynamic and multi-collaborative filtering method has been developed to improve information retrieval from electronic health records. We tackle the problem of recommending the next search term to a physician while the physician is searching for information about a patient. In this method, I have combined first-order Markov Chain and multi-collaborative filtering methods. For multi-collaborative filtering methods, I have developed the physician-patient collaborative filtering and transition-involved collaborative filtering methods. The developed method is tested using electronic health record data from the Indiana Network for Patient Care. The experimental results demonstrate that for 46.7% of test cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in. The local sparse linear model ensemble has been developed to tackle both the data sparsity and the user/item heterogeneity issues for the top-n recommendation. Multiple local sparse linear models are learned for all the users and items in the system. I have developed similarity-based and popularity-based methods to determine the local training data for each local model. Each local model is trained on Sparse Linear Method (SLIM) which is a powerful recommendation technique for top-n recommendation. These learned models are then combined in various ways to produce top-N recommendations. I have developed model results combination and model combination methods to combine all learned local models. The developed methods are tested on a benchmark dataset and its sparsified datasets. The experiments demonstrate 18.4% improvement from such ensemble models, particularly on sparse datasets.

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第18713号 / 文博第671号 / 新制||文||613(附属図書館) / 31664 / 京都大学大学院文学研究科現代文化学専攻 / (主査)教授 伊藤 和行, 教授 林 晋, 准教授 伊勢田 哲治 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DGAM

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Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.

Short-term wind power forecasting is crucial for the efficient operation of power systems with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural network-based approaches (ANNs) have been one of the most effective and popular approaches for short-term wind power forecasting because of the availability of large amounts of historical data and strong computational power. Although ANNs usually perform well for short-term wind power forecasting, further improvement can be obtained by selecting suitable input features, model parameters, and using forecasting techniques like spatial correlation and ensemble for ANNs. In this research, the effect of input features, model parameters, spatial correlation and ensemble techniques on short-term wind power forecasting performance of the ANNs models was evaluated. Pearson correlation coefficients between wind speed and other meteorological variables, together with a basic ANN model, were used to determine the impact of different input features on the forecasting performance of the ANNs. The effect of training sample resolution and training sample size on the forecasting performance was also investigated. To separately investigate the impact of the number of hidden layers and the number of hidden neurons on short-term wind power forecasting and to keep a single variable for each experiment, the same number of hidden neurons was used in each hidden layer. The ANNs with a total of 20 hidden neurons are shown to be sufficient for the nonlinear multivariate wind power forecasting problems faced in this dissertation. The ANNs with two hidden layers performed better than the one with a single hidden layer because additional hidden layer adds nonlinearity to the model. However, the ANNs with more than two hidden layers have the same or worse forecasting performance than the one with two hidden layers. ANNs with too many hidden layers and hidden neurons can overfit the training data. Spatial correlation technique was used to include meteorological variables from highly correlated neighbouring stations as input features to provide more surrounding information to the ANNs. The advantages of input features, model parameters, and spatial correlation and ensemble techniques were combined to form an ANN-based ensemble model to further enhance the forecasting performance from an individual ANN model. The simulation results show that all the available meteorological variables have different levels of impact on forecasting performance. Wind speed has the most significant impact on both short-term wind speed and wind power forecasting, whereas air temperature, barometric pressure, and air density have the smallest effects. The ANNs perform better with a higher data resolution and a significantly larger training sample size. However, one requires more computational power and a longer training time to train the model with a higher data resolution and a larger training sample size. Using the meteorological variables from highly related neighbouring stations do significantly improve the forecasting accuracy of target stations. It is shown that an ANNs-based ensemble model can further enhance the forecasting performance of an individual ANN by obtaining a large amount of surrounding meteorological information in parallel without encountering the overfitting issue faced by a single ANN model.

Syftet med studien är att undersöka hur ett ensembleledarskap kan anpassasefter ensembledeltagarnas grupputveckling, med fokus på deltagarnas upplevelserav trygghet och samarbete. Studiens forskningsfrågor är: (1) Hur kan en ensembleledareanpassa sig utifrån ensembledeltagarnas grupputveckling? och (2) Vilka utmaningar ochmöjligheter följer med ett ensembleledarskap med fokus på grupputveckling och samarbete?Studiens tre repetitioner planerades utifrån aktionsforskningscykeln, och genomfördestillsammans med tre deltagare i en pop/rock-ensemble. Efter varje repetition reviderasden kommande planeringen i enlighet med aktionsforskningens självreflekterandecykler, och innehållet baserades på tidigare forskning om grupputveckling och ledarskap.Under repetitionerna kombineras ensemblespel med lära-känna- och samarbetsövningar.Deltagarna var med och valde repertoaren som spelades och blev även inbjudna till attplanera den sista repetitionen, i enlighet med aktionsforskningens deltagande ideal. Vidden tredje och avslutande repetitionen menar studiens deltagare att de upplevt studiensensembleledarskap som mer processfokuserat, demokratiskt och som att gruppen fått mermandat i besluten som tagits jämfört med deras tidigare erfarenheter av ensembleledning.Resultatet visar att medvetenhet om grupprocesser, flexibilitet inför gruppens behov,självkännedom och förmåga att ge och ta feedback är viktiga aspekter av ett ensembleledarskapsom fokuserar på grupputveckling. Fokuset på grupputveckling för dock ocksåmed sig utmaningar och möjligheter i form av traditionella normer kring ensembleledarskap,balans mellan process- och målorientering och att grupprocessen kan vara svårfångad.

Den här undersökningen tar sin utgångspunkt i tidigare forskning som pekar på att musikun-dervisningen i Sverige rör sig inom en tämligen begränsad trygghetszon gällande bland annat genre, repertoar och innehåll. Mitt syfte med studien har varit att undersöka gymnasieelevers upplevelser av att musicera utanför det välbekanta samt undersöka tankebanor och resonemang kring repertoar i ensembleundervisningen på gymnasiet. För min undersökning har en kvalitativ metod använts. Empirin har sedan samlats in, dokumenterats och analyserats med hjälp av tran-skribering av en videoinspelning som skett under en ensemblelektion med en grupp gymnasie-elever, samt en efterföljande gruppintervju. Ensemblemomentet har gått ut på att spela ett mu-sikstycke från Grekland i en udda taktart. Gruppintervjun behandlade frågor om upplevelser, associationer, svårigheter, möjligheter, repertoarval och att röra sig utanför trygghetszonen. Min undersökning visade resultat som antyder att eleverna tycktes röra sig inom en tämligen begränsad trygghetszon i ensemblen och aldrig tidigare hade musicerat i musik utanför väster-ländsk populärmusik, trots att det står tydligt i det centrala innehållet i kursen ensemble att eleverna ska få möjlighet att musicera i musik från andra kulturer. Undersökningens respon-denter ansåg att musicera inom den här typen av musik var en positiv upplevelse och uppgav flera anledningar till att det kan var nyttigt att röra sig utanför det bekanta. Anledningarna var bland annat att det kunde vara ett smart sätt att lära sig nya taktarter, skalor och tonaliteter på. De sa även att de kunde främja kreativiteten i låtskriveri, improvisation, laborering, och gemen-skap. Således indikerar studiens resultat att röra sig utanför det bekanta kan genera kunskap som har en hel del fördelar, vilket i förlängningen kan leda till fördjupade musikaliska färdigheter.

SammanfattningNikolai Zavgorodni (2008)Titel: Rock- och popmusik i undervisningen.Syfte med denna studie är att undersöka vilken betydelse har musicerandet i elevernas musikaliska utveckling. Studien omfattar musikundervisningen i gymnasieskolan. Jag har använt mig av både kvantitativ och kvalitativ metoder i min studie. Den kvantitativa metoden var i form av enkätundersökningen och kvalitativa metoden i form av observationer men framförallt i form av definitioner, konceptioner och teoretiska konstruktioner. Resultatet visar att musicerandet har en stor betydelse för musikstuderande och rock- och popmusik har en hög status i undervisningen. Majoriteten av alla studerande anser att det är viktigt att man ska satsa mer på musicerandet än på teori på lektionerna. Det framgick även av enkätstudien att det är mycket viktig med genrebredd i musikundervisningen. Många musikstuderande i studien anser att rock och popmusikgenrer har en stor betydelse i musikundervisningen. En viktig aspekt är att läraren ska engagera sig i elevernas musikaliska utveckling. Sökord: genre, ensemble, musikundervisning, musicerandet. / Abstract Nikolai Zavgorodni (2008)Title: Rock and popmusic in education. A study on the importance of rock and popmusic playing in music education. The aim of this study is to find out what music students think about playing in music lesson and their reflections on the importance of genre in music education. The study covers music education in high school. I have used both of quanititative and qualititative methods in my study. The quantitative method vas in the form of queststionnaires and qualitative method in the form of my observations above all in the form of definitions, conceptions and theoretical constructions. The result shows that play the instuments is very important for students and rock and popmusic have a high status in music education. The majority of students prefer more to play the instruments than their have theory in the lesson. The most of music students in high school believ that rock and popmusic is very important things in music education. One importans aspect is that teacher’s commitment to the students musical development. Keywords: genre, ensemble, music education, play the instruments.

The aim of this dissertation is to investigate self-assembled structures and the phase diagram of amphiphilic molecules of diverse geometric shapes using a number of different computer simulation methods. The semi-realistic coarse-grained model, used extensively for simulation of polymers and surfactant molecules, is adopted in an off-lattice approach to study how the geometric structure of amphiphiles affects the aggregation properties. The results of simulations show that the model system behavior is consistent with theoretical predictions, experiments and lattice simulation models. We demonstrate that by modifying the geometry of the molecules, self-assembled aggregates are altered in a way close to theoretical predictions. In several two and three dimensional off-lattice Brownian Dynamics simulations, the influence of the shape of the amphiphilic molecules on the size and form of the aggregates is studied systematically. Model phospholipid molecules, with two hydrophobic chains connected to one hydrophilic head group, are simulated and the formation of stable bilayers is observed. In addition, (practically very important) mixtures of amphiphiles with diverse structures are studied under different mixing ratios and molecular structures. We find that in several systems, with Poisson distributed chain lengths, the effect on the aggregation distribution is negligible compared to that of the pure amphiphilic system with the mean length of the Poisson distribution. The phase diagrams of different amphiphilic molecular structures are investigated in separate simulations by employing the Gibbs Ensemble Monte Carlo method with an implemented configurational-bias technique. The computer simulations of the above mentioned amphiphilic systems are done in an area where physics, biology and chemistry are closely connected and advances in applications require the use of new theoretical, experimental and simulation methods for a better understanding of their self-assembling properties. Obtained simulation results demonstrate the connection between the structure of amphiphilic molecules and the properties of their thermodynamically stable aggregates and thus build a foundation for many applications of the remarkable phenomena of amphiphilic self-assembly in the area of nanotechnology.