title: Super-tailoring: Using self-persuasion to reduce drivers’ car use

authors: Sivasubramaniyam, Rathee Dewi

abstract: Car use is a common travel mode in many societies but it has negative impacts on the environment and public health. There have been various interventions to reduce car use but self-persuasion has not been tested as a potential intervention. Self-persuasion involves asking people to generate arguments in favour of a specific issue. Our goal was to investigate the effectiveness of self-persuasion in changing drivers’ car use attitudes and behaviours. A sample of New Zealand drivers (n = 183) completed two online questionnaires; one immediately after and one at least 2 weeks after the intervention. We randomly assigned the drivers to one of three conditions: self-persuasion (generating arguments on the benefits of reducing car use), direct-persuasion (reading arguments on the benefits of reducing car use), and control (completing a different travel-related task). There were no significant differences between the three groups of drivers on car use intentions for commuting trips, weekly car use for commuting and non-commuting trips, or attitudes towards reducing car use. We attributed the ineffectiveness of self-persuasion to the average quality of arguments generated, the effortful nature of reducing car use, and the COVID-19 situation in New Zealand. Although self-persuasion may not be an appropriate intervention in the travel behaviour domain, future research needs to continue identifying new ways to reduce car use to reduce its detrimental effects.

text: Super-tailoring: Using self-persuasion to reduce drivers’ car use Car use is a common travel mode in many societies but it has negative impacts on the environment and public health. There have been various interventions to reduce car use but self-persuasion has not been tested as a potential intervention. Self-persuasion involves asking people to generate arguments in favour of a specific issue. Our goal was to investigate the effectiveness of self-persuasion in changing drivers’ car use attitudes and behaviours. A sample of New Zealand drivers (n = 183) completed two online questionnaires; one immediately after and one at least 2 weeks after the intervention. We randomly assigned the drivers to one of three conditions: self-persuasion (generating arguments on the benefits of reducing car use), direct-persuasion (reading arguments on the benefits of reducing car use), and control (completing a different travel-related task). There were no significant differences between the three groups of drivers on car use intentions for commuting trips, weekly car use for commuting and non-commuting trips, or attitudes towards reducing car use. We attributed the ineffectiveness of self-persuasion to the average quality of arguments generated, the effortful nature of reducing car use, and the COVID-19 situation in New Zealand. Although self-persuasion may not be an appropriate intervention in the travel behaviour domain, future research needs to continue identifying new ways to reduce car use to reduce its detrimental effects.

year: 2021

title: Better Self-training for Image Classification Through Self-supervision

authors: Sahito, Attaullah

subjects: Science & Technology

abstract: Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by solving an automatically-generated pretext task—has gained prominence in deep learning. This paper investigates three different ways of incorporating self-supervision into self-training to improve accuracy in image classification: self-supervision as pretraining only, self-supervision performed exclusively in the first iteration of self-training, and self-supervision added to every iteration of self-training. Empirical results on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from scratch, and Imagenet-pretrained weights, show that applying self-supervision only in the first iteration of self-training can greatly improve accuracy, for a modest increase in computation time.

text: Better Self-training for Image Classification Through Self-supervision Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by solving an automatically-generated pretext task—has gained prominence in deep learning. This paper investigates three different ways of incorporating self-supervision into self-training to improve accuracy in image classification: self-supervision as pretraining only, self-supervision performed exclusively in the first iteration of self-training, and self-supervision added to every iteration of self-training. Empirical results on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from scratch, and Imagenet-pretrained weights, show that applying self-supervision only in the first iteration of self-training can greatly improve accuracy, for a modest increase in computation time.

year: 2022-01-01

title: Waiwharangi

authors: Maxwell, Te Kahautu

abstract: Haka. Lyrics/Musical score composed by Te Kahautu Maxwell. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (4mins)

Waiata: Tira Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

text: Waiwharangi Haka. Lyrics/Musical score composed by Te Kahautu Maxwell. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (4mins)

Waiata: Tira Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

year: 2020-02-06