title: Self-trained centroid classifiers for semi-supervised cross-domain few-shot learning

authors: Wang, Hongyu

abstract: State-of-the-art cross-domain few-shot learning methods for image classification apply knowledge transfer by fine-tuning deep feature extractors obtained from source domains on the small labelled dataset available for the target domain, generally in conjunction with a simple centroid-based classification head. Semi-supervised learning during the meta-test phase is an obvious approach to incorporating unlabelled data into cross-domain few-shot learning, but semi-supervised methods designed for larger sets of labelled data than those available in few-shot learning appear to easily go astray when applied in this setting. We propose an efficient semi-supervised learning method that applies self-training to the classification head only and show that it can yield very consistent improvements in average performance in the Meta-Dataset benchmark for cross-domain few-shot learning when applied with contemporary methods utilising centroid-based classification.

text: Self-trained centroid classifiers for semi-supervised cross-domain few-shot learning State-of-the-art cross-domain few-shot learning methods for image classification apply knowledge transfer by fine-tuning deep feature extractors obtained from source domains on the small labelled dataset available for the target domain, generally in conjunction with a simple centroid-based classification head. Semi-supervised learning during the meta-test phase is an obvious approach to incorporating unlabelled data into cross-domain few-shot learning, but semi-supervised methods designed for larger sets of labelled data than those available in few-shot learning appear to easily go astray when applied in this setting. We propose an efficient semi-supervised learning method that applies self-training to the classification head only and show that it can yield very consistent improvements in average performance in the Meta-Dataset benchmark for cross-domain few-shot learning when applied with contemporary methods utilising centroid-based classification.

year: 2023

title: Making use of action learning in business schools: The UK and New Zealand experience

authors: Hand, Leonard

subjects: business school

abstract: INTRODUCTION Most business schools originated in the latter half of the twentieth century. Often they were staffed by individuals from the business environment who were not skilled in teaching or research methods. The new academics were required to facilitate the learning of students in an academic environment. Often they adopted a narrowly focused approach to knowledge creation, scientific positivism, because the approach was well accepted in society generally and they believed such an approach was expected of them.
METHOD We provide details from our personal histories, and from the literature, to demonstrate that scientific positivism has often provided an unsuitable base for the development of teaching, learning, and critical thinking skills in business graduates. We are critical of our broad working environment during the last decades of the twentieth century, because it has been dominated by the scientific positivism approach to understanding.
ANALYSIS We argue that any search for universal solutions to problems of learning, as encouraged by scientific positivism, is futile because such universals do not exist. However, much energy is being expended business academics seeking to improve the learning environments in their classrooms. A more accommodating approach to educational research is required.
CONCLUSION Business academics may benefit, from the employment of an action learning methodology.

text: Making use of action learning in business schools: The UK and New Zealand experience INTRODUCTION Most business schools originated in the latter half of the twentieth century. Often they were staffed by individuals from the business environment who were not skilled in teaching or research methods. The new academics were required to facilitate the learning of students in an academic environment. Often they adopted a narrowly focused approach to knowledge creation, scientific positivism, because the approach was well accepted in society generally and they believed such an approach was expected of them.
METHOD We provide details from our personal histories, and from the literature, to demonstrate that scientific positivism has often provided an unsuitable base for the development of teaching, learning, and critical thinking skills in business graduates. We are critical of our broad working environment during the last decades of the twentieth century, because it has been dominated by the scientific positivism approach to understanding.
ANALYSIS We argue that any search for universal solutions to problems of learning, as encouraged by scientific positivism, is futile because such universals do not exist. However, much energy is being expended business academics seeking to improve the learning environments in their classrooms. A more accommodating approach to educational research is required.
CONCLUSION Business academics may benefit, from the employment of an action learning methodology.

year: 2006

title: Participatory data design: managing data sovereignty in IoT solutions

authors: Bowen, Judy

subjects: computer science

abstract: Within the software engineering community, deciding how to collect, store and use personal data has become about more than just understanding our users. This paper considers ethical data use which includes cultural considerations and data ownership rights. We discuss indigenous data sovereignty as a concept and how it potentially impacts technological solutions that gather personal data from users. We propose an extension to typical user-centred design processes which we call participatory data design. This incorporates the use of frameworks and tools that specifically focus on managing data within the cultural context it is gathered from. We also present a specific example of how we have used this approach in the context of a data collection project from M¯aori workers in New Zealand forestry. We conclude with a discussion of the wider implications of this approach.

text: Participatory data design: managing data sovereignty in IoT solutions Within the software engineering community, deciding how to collect, store and use personal data has become about more than just understanding our users. This paper considers ethical data use which includes cultural considerations and data ownership rights. We discuss indigenous data sovereignty as a concept and how it potentially impacts technological solutions that gather personal data from users. We propose an extension to typical user-centred design processes which we call participatory data design. This incorporates the use of frameworks and tools that specifically focus on managing data within the cultural context it is gathered from. We also present a specific example of how we have used this approach in the context of a data collection project from M¯aori workers in New Zealand forestry. We conclude with a discussion of the wider implications of this approach.

year: 2022