title: Book Review: A Sufi-Jewish Dialogue: Philosophy and Mysticism in Baha Ibn Paqūda’s Duties of the Heart

authors: Simms, Norman

subjects: book review

abstract: The article reviews the book “A Sufi-Jewish Dialogue: Philosophy and Mysticism in Baha Ibn Paqūda’s Duties of the Heart”, by Diana Lobel.

text: Book Review: A Sufi-Jewish Dialogue: Philosophy and Mysticism in Baha Ibn Paqūda’s Duties of the Heart The article reviews the book “A Sufi-Jewish Dialogue: Philosophy and Mysticism in Baha Ibn Paqūda’s Duties of the Heart”, by Diana Lobel.

year: 2009

title: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

authors: Lynam, Adam David

subjects: machine learning

abstract: The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

text: Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of heat. Many areas of the dairy industry can be automated, however the detection of oestrus is an area that still requires human experts.
This thesis investigates the application of Machine Learning classification techniques, on dairy cow milking data provided by the Livestock Improvement Corporation, to predict oestrus. The usefulness of various ensemble learning algorithms such as Bagging and Boosting are explored as well as specific skewed data techniques.
An empirical study into the effectiveness of classifiers designed to target skewed data is included as a significant part of the investigation. Roughly Balanced Bagging and the novel Under Bagging classifiers are explored in considerable detail and found to perform quite favourably over the SMOTE technique for the datasets selected. This study uses non-dairy, commonplace, Machine Learning datasets; many of which are found in the UCI Machine Learning Repository.

year: 2009

title: Māori Waiata: Discourse structuring, sub-genres and aesthetics

authors: Roa, Raukura

subjects: Te reo Māori

abstract: With particular reference to a discourse-based analysis of five waiata tangi from Ngāti Hauā, it is argued here that different types of Māori waiata (songs and chants) may be characterised by different types of overall discourse structuring. It is also suggested that discourse structuring may be a significant aspect of the aesthetics of Māori waiata.

text: Māori Waiata: Discourse structuring, sub-genres and aesthetics With particular reference to a discourse-based analysis of five waiata tangi from Ngāti Hauā, it is argued here that different types of Māori waiata (songs and chants) may be characterised by different types of overall discourse structuring. It is also suggested that discourse structuring may be a significant aspect of the aesthetics of Māori waiata.

year: 2004-02