Ensembles of jittered association rule classifiers

The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that can be used as an ensemble. We use as base classifiers sets of classification association rules. The two methods of jittering ensembling we propose are Iterative Reordering Ensembling (IRE) and Post Bagging (PB). Both methods start by learning one rule set over a single run, and then produce multiple rule sets without relearning. Empirical results on 36 data sets are positive and show that both strategies tend to reduce error with respect to the single model association rule classifier. A bias–variance analysis reveals that while both IRE and PB are able to reduce the variance component of the error, IRE is particularly effective in reducing the bias component. We show that Model Jittering Ensembling can represent a very good speed-up w.r.t. multiple model learning ensembling. We also compare Model Jittering with various state of the art classifiers in terms of predictive accuracy and computational efficiency.This work was partially supported by FCT project Rank! (PTDC/EIA/81178/2006) and by AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER), and also supported by Fundacao Ciencia e Tecnologia, FEDER e Programa de Financiamento Plurianual de Unidades de I & D. Thanks are due to William Cohen for kindly providing the executable code for the SLIPPER implementation. Our gratitude goes also to our anonymous reviewers who have helped to significantly improve this paper by sharing their knowledge and their informed criticism with the authors

Designing an orrery of the universe: the creation of new chamber music through algorithmic composition

The main focus of my research has been to investigate a number of ways to extend my practice as a composer. In response to a detailed analysis of the technical means and aesthetic intentions of my music during the decade preceding this research (a music fundamentally derived from Hebrew language and grammatical structures), together with consideration of broader cultural trends in 20th century musical modernism, I have designed a detailed process for creating ambitious musical works. This process has been explored in the composition of two chamber music projects - a flute solo and a piano trio - both of which are documented as musical scores and audio recordings. Computer technology is utilized at a number of key points: the melodic life of the projects is characterized by the development of themes through the agency of six discrete transformational algorithms, all of which can be applied simultaneously and independently controlled. This aspect of the process was achieved using IRCAM’s software Openmusic and a rhythmic search engine of my own design. The success of these projects is considered against the principles that informed their creation and through expert peer responses and critical reception. The exegesis concludes with a detailed list of possible future directions for musical composition, some of which extend and refine the role of algorithms, and some that propose diametrically opposed strategies that respond to some probable limits of algorithmic composition I have identified through this research

Automatic Selection of MapReduce Machine Learning Algorithms: A Model Building Approach

As the amount of information available for data mining grows larger, the amount of time needed to train models on those huge volumes of data also grows longer. Techniques such as sub-sampling and parallel algorithms have been employed to deal with this growth. Some studies have shown that sub-sampling can have adverse effects on the quality of models produced, and the degree to which it affects different types of learning algorithms varies. Parallel algorithms perform well when enough computing resources (e.g. cores, memory) are available, however for a limited sized cluster the growth in data will still cause an unacceptable growth in model training time. In addition to the data size mitigation problem, picking which algorithms are well suited to a particular dataset, can be a challenge. While some studies have looked at selection criteria for picking a learning algorithm based on the properties of the dataset, the additional complexity of parallel learners or possible run time limitations has not been considered. This study explores run time and model quality results of various techniques for dealing with large datasets, including using different numbers of compute cores, sub-sampling the datasets, and exploiting the iterative anytime nature of the training algorithms. The algorithms were studied using MapReduce implementations of four supervised learning algorithms, logistic regression, tree induction, bagged trees, and boosted stumps for binary classification using probabilistic models. Evaluation of these techniques was done using a modified form of learning curves which has a temporal component. Finally, the data collected was used to train a set of models to predict which type of parallel learner best suits a particular dataset, given run time limitations and the number of compute cores to be used. The predictions of those models were then compared to the actual results of running the algorithms on the datasets they were attempting to predict

Effects of improvisation techniques in leadership development

Studies show that improvisation in leadership decision making is on the rise, and it transpires in organizations 75-90% of the time, yet very little research has explored this skillset. No other leadership skillset that is applied two thirds of the time has ever been so underdeveloped. The purpose of this study was to assess the effects of a pilot workshop applying a Holistic Improvisational Leadership Model as developed by the researcher and based on the latest improvisation research. The study employed a mixed methods design to gather qualitative and quantitative data for a descriptive evaluation of the pilot training workshop. Nonproportional quota sampling and triangulation were used to maximize cross verification and validity of the data. This study explored the skills leaders acquired and applied during, immediately after, 1 month after the workshop, and in 3 months. The study was pilot-tested on 6 different groups and a total of 67 leaders from various regions, industries and organizations. Primary findings revealed that participants gained the highest benefits in working with others and their ability to lead. Executive and educational leaders gained the awareness that 79% of their decisions at work were made spontaneously as opposed to 71% for all leaders. 100% of executives and senior leaders indicated acquiring more effective listening skills. Moreover, the concept of competent risks and celebrating failure appeared to have the most transformational impact on the participants\u27 sense of self, willingness to take risks, and acquire new skills. The workshop seemed to bring participants\u27 stress level down to an optimal level and enhance mindfulness. Ultimately, it was concluded the study\u27s workshop was most effective as a continuous 3.5 hours. Learning to improvise experientially includes a process of unlearning old routines of decision making and re-learning more effective skills. Hence, the researcher recommends follow-up learning sessions to complete the cycle of learning. Utilizing grounded theory, the findings from the study led to the revision of Tabaee\u27s Holistic Improvisational Leadership Model. The researcher recommends following the model by teaching the competencies not only to leaders but to all employees for achieving OPTIMAL strategy and performance for the organization

Singing bodies: Cultural geographies of song and health in Glasgow

This thesis is a scaled enquiry into the embodied practice of collective singing in Glasgow, encompassing breathing bodies, singing collectives, sites of song, and a musical city. Using practice-led research by a singing practitioner and geographer, the thesis explores the establishment of a singing-for-breathing group attended by people living with chronic respiratory illness in Glasgow’s East End. The research charts and interprets the journey of the group through interviews with its members, reflexive creative workshops, and an autoethnographic narration of sessions. These materials show how singing and deep breathing exercises help group members relearn their breath and find their voice, gaining greater autonomy over their bodies in this non-clinical setting. Singing practice shapes and guides the breath, but also creates affective atmospheres of breath work and emotional soundscapes. The embodied and emotional geographies of singing are shown to impact the group members' day-to-day lives. Members report that breathing practice learned through singing helps the management of breathlessness caused by their chronic respiratory illness. Gaining control of breath reshapes the lifeworld experiences of members, placing the singing group as an important non-clinical intervention in their journey through illness. While the thesis is organised around a central narrative about the singing-for-breath group, the reader is also introduced to Glasgow’s wider singing cultures through three short interludes. Here, the voices of a small political song group, the psalm-singing of a Presbyterian church, and the reflective song of a deathbed choir offer insights into the varied and diverse uses and practices of song in city communities. These snapshots of collective singing practices also contextualise the singing-for-breathing group within the broader framework of Glasgow’s singing culture and open up new understandings of spaces and songs as relatable social phenomena

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes