title: E pā tō reo e Te Matatini, Herewini Parata

authors: Maxwell, Te Kahautu

abstract: Waita ā-ringa (action song) Lyrics composed by Te Kahautu Maxwell, Musical score composed by Te Whānau Mitai, Eric Kurei. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: E pā tō reo e Te Matatini, Herewini Parata Waita ā-ringa (action song) Lyrics composed by Te Kahautu Maxwell, Musical score composed by Te Whānau Mitai, Eric Kurei. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22

title: What are Bicultural Psychological Services?

authors: Thomas, David R.

subjects: Maori students

abstract: The present paper is intended to stimulate discussion on the development of bicultural psychological services in Aotearoa/New Zealand, focusing primarily on services provided by psychologists trained in tertiary educational institutions. Patterns of biculturalism emerging among human services in New Zealand are briefly described, then topics relevant to the provision of bicultural psychological services, that are appropriate for both Maori and Pakeha, in Aotearoa are discussed.

text: What are Bicultural Psychological Services? The present paper is intended to stimulate discussion on the development of bicultural psychological services in Aotearoa/New Zealand, focusing primarily on services provided by psychologists trained in tertiary educational institutions. Patterns of biculturalism emerging among human services in New Zealand are briefly described, then topics relevant to the provision of bicultural psychological services, that are appropriate for both Maori and Pakeha, in Aotearoa are discussed.

year: 1993

title: Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

authors: Yap, Moi Hoon

subjects: computer science

abstract: There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

text: Deep learning in diabetic foot ulcers detection: A comprehensive evaluation There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

year: 2021