title: Intra-propositional relations and their signalling: An investigation of authentic Māori texts

authors: Whaanga, Hēmi

subjects: Language & education

abstract: I report here on one part of a research project that involved the investigation of a range of authentic Māori texts in terms of two types of meaning relation – those that operate within propositions (‘intra-propositional relations’) and those that link propositions or groups of propositions (‘inter-propositional relations’). The focus here is on intra-propositional relations (relationships within propositions). On the basis of the comparison of a number of intra-propositional relational models, a new model which draws inspiration from the work of Crombie is proposed and applied to a corpus of texts written in Māori, the emphasis being on the ways in which these relations are signalled in Māori.

text: Intra-propositional relations and their signalling: An investigation of authentic Māori texts I report here on one part of a research project that involved the investigation of a range of authentic Māori texts in terms of two types of meaning relation – those that operate within propositions (‘intra-propositional relations’) and those that link propositions or groups of propositions (‘inter-propositional relations’). The focus here is on intra-propositional relations (relationships within propositions). On the basis of the comparison of a number of intra-propositional relational models, a new model which draws inspiration from the work of Crombie is proposed and applied to a corpus of texts written in Māori, the emphasis being on the ways in which these relations are signalled in Māori.

year: 2010

title: He Taonga te Ta Moko ki Tauranga Moana: A survey of attitudes, opinions, whakāro noa iho, towards ta moko during the Tauranga Moana, Tauranga Tangata Festival (Labour Weekend 2002)

authors: Cairns, Puawai

subjects: Maori

abstract: During Labour Weekend (26 – 28 October 2002) in Tauranga, a celebration called
the Tauranga Moana, Tauranga Tangata Festival was held. The festival
represented the artists, kapa haka, lectures, sports, and personalities of Tauranga
Moana. It was during this festival that a ta moko survey was conducted, with the
support of the lead ta moko artist there, with the aim to canvass opinions and
attitudes towards ta moko. The basis of the survey was to broadly examine the
extent to which ta moko has become a form of inscribed identity for Māori. This is
a first stage in a wider research project to examine Māori attitudes towards ta
moko, but it happens that this initial step was taken in the author’s own rohe.

text: He Taonga te Ta Moko ki Tauranga Moana: A survey of attitudes, opinions, whakāro noa iho, towards ta moko during the Tauranga Moana, Tauranga Tangata Festival (Labour Weekend 2002) During Labour Weekend (26 – 28 October 2002) in Tauranga, a celebration called
the Tauranga Moana, Tauranga Tangata Festival was held. The festival
represented the artists, kapa haka, lectures, sports, and personalities of Tauranga
Moana. It was during this festival that a ta moko survey was conducted, with the
support of the lead ta moko artist there, with the aim to canvass opinions and
attitudes towards ta moko. The basis of the survey was to broadly examine the
extent to which ta moko has become a form of inscribed identity for Māori. This is
a first stage in a wider research project to examine Māori attitudes towards ta
moko, but it happens that this initial step was taken in the author’s own rohe.

year: 2003

title: Using swarm optimization to enhance autoencoder’s images

authors: Doaud, Maisa

subjects: computer science

abstract: Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

text: Using swarm optimization to enhance autoencoder’s images Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

year: 2017