title: Taumafaina o le ‘ava Samoa, a’afiaga o le mafaufau, ma le ave taavale (Traditional kava-drinking, cognition and driver fitness [Samoan])

authors: Aporosa, S. 'Apo'

abstract: ‘Ava, po'o se 'La’au mai le Atua o le Lagi', ua fa’aigoaina ai e nisi. Ua fa’atupulaia lona ola lauusiusi i so’o se vaega o le Pasefika (Moananuiakea). Ua fa’aaogaina foi mo vaila’au ma togafitiga fa’afoma’i fa’ale-aganu’u, ma o ona a'a foi ua fa’aaoga e tu'itu'i fa’amalu, ona palu fa’atasi lea i se suavai ma tatau fa’amama, ona inu lea e malolo i ai pe fa’amalolo ai foi le tino. O le ava e maoa'e tele lona taua ma lona fa’aaogaina i le aganu’u, ae talu ai ona ua inuina e tagata se suavai ava tele mo se taimi umi foi, ua fa’aleoina ai ni lagona popole ma atugaluga o le toatele ona o ni a'afiaga e ono alia'e ma tutupu mai ai, e a'afia ai le saogalemu o le aveina o se ta’avale. O le ali’i su'es'ue mai le Iunivesite o Waikato i Niu Sila o Dr Apo Aporosa, ua ia su'esu'eina ni a'afiaga po'o ni fa’afitauli e ono alia'e mai, pe afai o le a e aveina se ta’avale ae sa e taumafa/inu ava. E aofia ai aafiaga i le mafaufau lelei ma le ave taavale saogalemū. O lo’o su'esu'eina e le ali’i su'esu'e ia Dr Apo Aporosa ni a'afiaga ma se malamalama’aga fa'ale-mafaufau, po'o ni fa’alavelave e ono tutupu foi, pe afai e te aveina se ta’avale, ae sa e taumafa pe inuina foi se ava. 

(Kava, or ‘the plant of the gods’, grows widely across tropical Moananuiākea (the Pacific). Used in traditional medicine, its roots are also pounded and strained through water to make a drink with relaxant effects. Kava has deep cultural significance, but because it is drunk in large quantities over several hours, concerns have been raised about its effects on driver safety. Dr ‘Apo’ Aporosa, a Research Fellow at the University of Waikato in New Zealand, has studied the impact of traditional kava use on cognition and its implications for driver fitness. The findings of that research are presented here in the Samoan language.)

text: Taumafaina o le ‘ava Samoa, a’afiaga o le mafaufau, ma le ave taavale (Traditional kava-drinking, cognition and driver fitness [Samoan]) ‘Ava, po'o se 'La’au mai le Atua o le Lagi', ua fa’aigoaina ai e nisi. Ua fa’atupulaia lona ola lauusiusi i so’o se vaega o le Pasefika (Moananuiakea). Ua fa’aaogaina foi mo vaila’au ma togafitiga fa’afoma’i fa’ale-aganu’u, ma o ona a'a foi ua fa’aaoga e tu'itu'i fa’amalu, ona palu fa’atasi lea i se suavai ma tatau fa’amama, ona inu lea e malolo i ai pe fa’amalolo ai foi le tino. O le ava e maoa'e tele lona taua ma lona fa’aaogaina i le aganu’u, ae talu ai ona ua inuina e tagata se suavai ava tele mo se taimi umi foi, ua fa’aleoina ai ni lagona popole ma atugaluga o le toatele ona o ni a'afiaga e ono alia'e ma tutupu mai ai, e a'afia ai le saogalemu o le aveina o se ta’avale. O le ali’i su'es'ue mai le Iunivesite o Waikato i Niu Sila o Dr Apo Aporosa, ua ia su'esu'eina ni a'afiaga po'o ni fa’afitauli e ono alia'e mai, pe afai o le a e aveina se ta’avale ae sa e taumafa/inu ava. E aofia ai aafiaga i le mafaufau lelei ma le ave taavale saogalemū. O lo’o su'esu'eina e le ali’i su'esu'e ia Dr Apo Aporosa ni a'afiaga ma se malamalama’aga fa'ale-mafaufau, po'o ni fa’alavelave e ono tutupu foi, pe afai e te aveina se ta’avale, ae sa e taumafa pe inuina foi se ava. 

(Kava, or ‘the plant of the gods’, grows widely across tropical Moananuiākea (the Pacific). Used in traditional medicine, its roots are also pounded and strained through water to make a drink with relaxant effects. Kava has deep cultural significance, but because it is drunk in large quantities over several hours, concerns have been raised about its effects on driver safety. Dr ‘Apo’ Aporosa, a Research Fellow at the University of Waikato in New Zealand, has studied the impact of traditional kava use on cognition and its implications for driver fitness. The findings of that research are presented here in the Samoan language.)

year: 2023

title: Meta-Learning and the Full Model Selection Problem

authors: Sun, Quan

subjects: meta-learning

abstract: When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several projects needed to be done in a relatively short time. 

Naturally, as a computer science graduate, I started to ask myself, "What can be automated here?"; because, intuitively, part of my work is more or less a loop that can be programmed. Literally, the loop is "choose, run, test and choose again... until some criterion/goals are met". 

In other words, I use my experience or knowledge about machine learning and data mining to guide and speed up the process of selecting and applying techniques in order to build a relatively good predictive model for a given dataset for some purpose. So the following questions arise:

"Is it possible to design and implement a system that helps a data analyst to choose from a set of data mining tools? Or at least that provides a useful recommendation about tools that potentially save some time for a human analyst."

To answer these questions, I decided to undertake a long-term study on this topic, to think, define, research, and simulate this problem before coding my dream system. This thesis presents research results, including new methods, algorithms, and theoretical and empirical analysis from two directions, both of which try to propose systematic and efficient solutions to the questions above, using different resource requirements, namely, the meta-learning-based algorithm/parameter ranking approach and the meta-heuristic search-based full-model selection approach. 

Some of the results have been published in research papers; thus, this thesis also serves as a coherent collection of results in a single volume.

text: Meta-Learning and the Full Model Selection Problem When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several projects needed to be done in a relatively short time. 

Naturally, as a computer science graduate, I started to ask myself, "What can be automated here?"; because, intuitively, part of my work is more or less a loop that can be programmed. Literally, the loop is "choose, run, test and choose again... until some criterion/goals are met". 

In other words, I use my experience or knowledge about machine learning and data mining to guide and speed up the process of selecting and applying techniques in order to build a relatively good predictive model for a given dataset for some purpose. So the following questions arise:

"Is it possible to design and implement a system that helps a data analyst to choose from a set of data mining tools? Or at least that provides a useful recommendation about tools that potentially save some time for a human analyst."

To answer these questions, I decided to undertake a long-term study on this topic, to think, define, research, and simulate this problem before coding my dream system. This thesis presents research results, including new methods, algorithms, and theoretical and empirical analysis from two directions, both of which try to propose systematic and efficient solutions to the questions above, using different resource requirements, namely, the meta-learning-based algorithm/parameter ranking approach and the meta-heuristic search-based full-model selection approach. 

Some of the results have been published in research papers; thus, this thesis also serves as a coherent collection of results in a single volume.

year: 2014

title: Whanau Maori explain how the Harti Hauora Tool assists with better access to health services (vol 25, pg 471, 2019)

authors: Masters-Awatere, Bridgette

subjects: Science & Technology

abstract: In this paper, whānau Māori highlight how a Kaupapa Māori-centred intervention (the Harti Hauora Tamariki tool, hereafter Harti tool) has improved interactions with health services. The Harti tool is undergoing a randomised control trial (RCT) at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0–5 years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. The interviews conducted highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, how the Harti tool has enhanced feelings of being in control of health, with the potential to reduce the likelihood of a hospital readmission, is highlighted.

text: Whanau Maori explain how the Harti Hauora Tool assists with better access to health services (vol 25, pg 471, 2019) In this paper, whānau Māori highlight how a Kaupapa Māori-centred intervention (the Harti Hauora Tamariki tool, hereafter Harti tool) has improved interactions with health services. The Harti tool is undergoing a randomised control trial (RCT) at Waikato Hospital in New Zealand. As part of the RCT, the authors engaged in a series of qualitative interviews with whānau members of tamariki Māori (children aged 0–5 years) admitted to Waikato Hospital’s paediatric ward. Whānau who met at least one criteria for New Zealand’s domains of deprivation were included. Using a Kaupapa Māori approach to the study, participants shared their views on barriers and facilitators to accessing health resources and primary care services. The interviews conducted highlight how the Harti tool, when administered in a culturally appropriate and respectful manner that prioritised relationship-building, enabled better connection to healthcare services. Prevalent in our analysis were connections to wider determinants of health and ways to reduce existing health inequities. To conclude the paper, how the Harti tool has enhanced feelings of being in control of health, with the potential to reduce the likelihood of a hospital readmission, is highlighted.

year: 2019