title: Translated application interfaces - issues of engagement

authors: Mato, Paora James

abstract: In New Zealand, English is the language that dominates contemporary technologies. Usability testing was completed on a range of applications, available with a Māori-language interface, to gauge levels of awareness, engagement and perception. Nearly all of the respondents were unaware of the availability of these interfaces but most indicated they would prefer to use the Māori-language versions. In terms of engagement and usability, users initially engaged using Māori but switched to English when they wanted to quickly complete the task at hand. Few remained fully engaged with the Māori-language interfaces. High levels of language switching were reported and some frustration as the participants encountered new and unfamiliar uses of words. At face value the feedback suggests the translated interfaces contained unnecessary complications and that better design and content might have enhanced the user experience. However, there is evidence that extended use would enable users to become more familiar with the interfaces alluding to initial barriers created by a previous competence in another language -- in this case English. With this previous competence in mind it might be more useful to employ design concepts that would alleviate initial difficulties and serve to keep the user engaged in the target language for longer periods of time.

text: Translated application interfaces - issues of engagement In New Zealand, English is the language that dominates contemporary technologies. Usability testing was completed on a range of applications, available with a Māori-language interface, to gauge levels of awareness, engagement and perception. Nearly all of the respondents were unaware of the availability of these interfaces but most indicated they would prefer to use the Māori-language versions. In terms of engagement and usability, users initially engaged using Māori but switched to English when they wanted to quickly complete the task at hand. Few remained fully engaged with the Māori-language interfaces. High levels of language switching were reported and some frustration as the participants encountered new and unfamiliar uses of words. At face value the feedback suggests the translated interfaces contained unnecessary complications and that better design and content might have enhanced the user experience. However, there is evidence that extended use would enable users to become more familiar with the interfaces alluding to initial barriers created by a previous competence in another language -- in this case English. With this previous competence in mind it might be more useful to employ design concepts that would alleviate initial difficulties and serve to keep the user engaged in the target language for longer periods of time.

year: 2015

title: Stop playing up! A critical ethnography of health, physical education and (sub)urban schooling

authors: Fitzpatrick, Katie

subjects: Health

abstract: This thesis explores the place of health and physical education in the lives of Otara youth in New Zealand. Situated in the southern suburbs of New Zealand’s largest city, Otara, South Auckland is known for its cultural diversity, as well as for poverty and crime. It is home to large numbers of indigenous Maori and migrant Pasifika (Pacific Island) youth. Based on a year-long critical ethnography of a multiethnic high school, this thesis explores how these young people engage with and respond to the school subjects of health and physical education. It also discusses broader issues in their lives, including the social geographies within which they reside, and how they understand their bodies, sexuality, health, gender and physicalities. The subjects of health and physical education are compulsory in most schools internationally – in New Zealand they are directly linked in curriculum policy documents and in school practice – but share a somewhat uneasy relationship and differing historical positions. Considered low status in schools, these subjects are also conflated with narrow body and health norms, possibly problematic for young women, and/or are wedded to the social and cultural world of sport. Curriculum policy documents established in the last ten years offer the possibility of critical and social approaches to these subjects, but examples of critical practice remain rare. Health and physical education are thus compulsory, contentious, contradictory and complex subjects within contemporary schooling. Critical ethnographies of schooling are relatively scarce compared with conventional ethnographic accounts, but critical ethnographies of health and physical education (PE) are almost unheard of. The use of such a methodology in this study enabled an indepth account of Otara youth in the subjects of health and PE at school. It also provided a platform for storied accounts of how one teacher, Dan, enacted a critical and culturally connected pedagogy of health and PE with the young people in his classes. This thesis explores the complex potential for health and PE as key sites of learning for Pasifika and Maori youth. It examines health and PE as subjects that are both politically fraught and spaces of hope.

text: Stop playing up! A critical ethnography of health, physical education and (sub)urban schooling This thesis explores the place of health and physical education in the lives of Otara youth in New Zealand. Situated in the southern suburbs of New Zealand’s largest city, Otara, South Auckland is known for its cultural diversity, as well as for poverty and crime. It is home to large numbers of indigenous Maori and migrant Pasifika (Pacific Island) youth. Based on a year-long critical ethnography of a multiethnic high school, this thesis explores how these young people engage with and respond to the school subjects of health and physical education. It also discusses broader issues in their lives, including the social geographies within which they reside, and how they understand their bodies, sexuality, health, gender and physicalities. The subjects of health and physical education are compulsory in most schools internationally – in New Zealand they are directly linked in curriculum policy documents and in school practice – but share a somewhat uneasy relationship and differing historical positions. Considered low status in schools, these subjects are also conflated with narrow body and health norms, possibly problematic for young women, and/or are wedded to the social and cultural world of sport. Curriculum policy documents established in the last ten years offer the possibility of critical and social approaches to these subjects, but examples of critical practice remain rare. Health and physical education are thus compulsory, contentious, contradictory and complex subjects within contemporary schooling. Critical ethnographies of schooling are relatively scarce compared with conventional ethnographic accounts, but critical ethnographies of health and physical education (PE) are almost unheard of. The use of such a methodology in this study enabled an indepth account of Otara youth in the subjects of health and PE at school. It also provided a platform for storied accounts of how one teacher, Dan, enacted a critical and culturally connected pedagogy of health and PE with the young people in his classes. This thesis explores the complex potential for health and PE as key sites of learning for Pasifika and Maori youth. It examines health and PE as subjects that are both politically fraught and spaces of hope.

year: 2010

title: Solutions to the unseen domain problem for machine learning-based glaucoma detection from retinal fundus images

authors: Gunasinghe, Hansi Nawam

abstract: Ocular diseases are a significant problem faced by 70 million people worldwide yearly. Identifying an eye disease requires a medical professional with years of specialist training. Glaucoma screening is often done manually, which is time-consuming. Automatic eye disease identification could be introduced to clinics to help speed up the detection and treatment of eye diseases. Retinal fundus images are helpful for glaucoma screening. However, the existing glaucoma identification systems trained using images from a specific camera fail to perform well with those captured from a new camera. This research aims to find solutions to address the above-unseen domain problem in machine learning-based glaucoma detection systems from retinal fundus images. The study was conducted in three phases, including an initial study that identifies the problem domain, followed by the manipulation of image preprocessing and image augmentation techniques to produce a more generalised glaucoma detection system.

In the first stage, twenty-eight pre-trained deep learning models for object recognition tasks were compared as potential feature extractors for glaucoma classification from retinal fundus images of the REFUGE dataset. First, the images were automatically cropped around the optic nerve head using a template-matching algorithm. Features were extracted using the pre-trained networks from both whole and cropped images. An extended feature set was created by concatenating those two feature sets. Finally, a ten-fold cross-validation experiment was conducted to compare the performance of random forest and logistic regression classifiers against each feature set. The best setup was when features were extracted from images using the ResNet101V2 ImageNet-pre-trained neural network and classified using a random forest classifier. However, the accuracy dropped when testing it with images from a new camera. Hence, the study was directed towards the unseen domain problem in the second stage.

In the study's second phase, transfer learning-based domain generalisation was applied together with multiple image preprocessing methods: input standardisation, median filtering and multi-image histogram matching for glaucoma detection using retinal fundus images from multiple cameras. The analysis included images from the RIMONEr2 and REFUGE datasets, which were captured using three camera models. A set of experiments were conducted using all possible combinations of training and testing camera devices, using the best system found in the first stage. Images were preprocessed in six different ways using either a single or a combination of three different preprocessing methods to see their effect on generalisation. The results indicated that the stylisation of test data might lead to better generalisation while reducing the retraining of an existing system. 

As a result, we compared multi-image histogram matching with neural style transferring to identify the classification accuracy during the testing phase of a model. We trained a random forest classifier and an XGBoost classifier with AlexNet and ResNet101V2 as feature extractors and tested the system following the same strategy as in phase two. Comparative results indicated that the neural style transferring better predicts the labels for unseen images. We continued experiments with neural style transferring to test publicly available models trained on the ACRIMA dataset. The method results better when reference images are selected from the same class. Given that the class information of real clinical data is unavailable, we suggest possible strategies for choosing better reference images. 

Overall, this study provides solutions to develop robust machine learning systems that require no retraining with new fundus cameras. The experimental results indicate that the proposed combination of preprocessing methods can be successfully utilised for better domain generalisation in the context of different retinal fundus camera devices. Furthermore, test-time data augmentation with neural style transferring leads to better predictions for images taken from unseen retinal fundus cameras. This reduces model retraining and increases the reusability of a pre-trained machine learning-based glaucoma detection system.

text: Solutions to the unseen domain problem for machine learning-based glaucoma detection from retinal fundus images Ocular diseases are a significant problem faced by 70 million people worldwide yearly. Identifying an eye disease requires a medical professional with years of specialist training. Glaucoma screening is often done manually, which is time-consuming. Automatic eye disease identification could be introduced to clinics to help speed up the detection and treatment of eye diseases. Retinal fundus images are helpful for glaucoma screening. However, the existing glaucoma identification systems trained using images from a specific camera fail to perform well with those captured from a new camera. This research aims to find solutions to address the above-unseen domain problem in machine learning-based glaucoma detection systems from retinal fundus images. The study was conducted in three phases, including an initial study that identifies the problem domain, followed by the manipulation of image preprocessing and image augmentation techniques to produce a more generalised glaucoma detection system.

In the first stage, twenty-eight pre-trained deep learning models for object recognition tasks were compared as potential feature extractors for glaucoma classification from retinal fundus images of the REFUGE dataset. First, the images were automatically cropped around the optic nerve head using a template-matching algorithm. Features were extracted using the pre-trained networks from both whole and cropped images. An extended feature set was created by concatenating those two feature sets. Finally, a ten-fold cross-validation experiment was conducted to compare the performance of random forest and logistic regression classifiers against each feature set. The best setup was when features were extracted from images using the ResNet101V2 ImageNet-pre-trained neural network and classified using a random forest classifier. However, the accuracy dropped when testing it with images from a new camera. Hence, the study was directed towards the unseen domain problem in the second stage.

In the study's second phase, transfer learning-based domain generalisation was applied together with multiple image preprocessing methods: input standardisation, median filtering and multi-image histogram matching for glaucoma detection using retinal fundus images from multiple cameras. The analysis included images from the RIMONEr2 and REFUGE datasets, which were captured using three camera models. A set of experiments were conducted using all possible combinations of training and testing camera devices, using the best system found in the first stage. Images were preprocessed in six different ways using either a single or a combination of three different preprocessing methods to see their effect on generalisation. The results indicated that the stylisation of test data might lead to better generalisation while reducing the retraining of an existing system. 

As a result, we compared multi-image histogram matching with neural style transferring to identify the classification accuracy during the testing phase of a model. We trained a random forest classifier and an XGBoost classifier with AlexNet and ResNet101V2 as feature extractors and tested the system following the same strategy as in phase two. Comparative results indicated that the neural style transferring better predicts the labels for unseen images. We continued experiments with neural style transferring to test publicly available models trained on the ACRIMA dataset. The method results better when reference images are selected from the same class. Given that the class information of real clinical data is unavailable, we suggest possible strategies for choosing better reference images. 

Overall, this study provides solutions to develop robust machine learning systems that require no retraining with new fundus cameras. The experimental results indicate that the proposed combination of preprocessing methods can be successfully utilised for better domain generalisation in the context of different retinal fundus camera devices. Furthermore, test-time data augmentation with neural style transferring leads to better predictions for images taken from unseen retinal fundus cameras. This reduces model retraining and increases the reusability of a pre-trained machine learning-based glaucoma detection system.

year: 2024