title: Benefit sharing: Why inclusive provenance metadata matter

authors: Golan, J

abstract: Fair and equitable benefit sharing of genetic resources is an expectation of the Nagoya Protocol. Although the Nagoya Protocol does not yet formally apply to Digital Sequence Information (“DSI”), discussions are currently underway regarding to include such data through ongoing Convention on Biological Diversity (“CBD”) negotiations. While Indigenous Peoples and Local Communities (“IPLC”) expect the value generated from genomic data to be subject to benefit sharing arrangements, a range of views are currently being expressed by Nation States, IPLC and other stakeholders. The use of DSI gives rise to unique considerations, creating a gray area as to how it should be considered under the Nagoya Protocol’s Access and Benefit Sharing (“ABS”) principles. One way for benefit sharing to be enhanced is through the connection of data to proper provenance information. A significant development is the use of digital labeling systems to ensure that the origin of samples is appropriately disclosed. The Traditional Knowledge and Biocultural Labels initiative offers a practical option for data provided to genomic databases. In particular, the BioCultural Labels (“BC Labels”) are a mechanism for Indigenous communities to identify and maintain provenance, origin and authority over biocultural material and data generated from Indigenous land and waters held in research, cultural institutions and data repositories. This form of cultural metadata adds value to the research endeavor and the creation of Indigenous fields within databases adds transparency and accountability to the research environment.

text: Benefit sharing: Why inclusive provenance metadata matter Fair and equitable benefit sharing of genetic resources is an expectation of the Nagoya Protocol. Although the Nagoya Protocol does not yet formally apply to Digital Sequence Information (“DSI”), discussions are currently underway regarding to include such data through ongoing Convention on Biological Diversity (“CBD”) negotiations. While Indigenous Peoples and Local Communities (“IPLC”) expect the value generated from genomic data to be subject to benefit sharing arrangements, a range of views are currently being expressed by Nation States, IPLC and other stakeholders. The use of DSI gives rise to unique considerations, creating a gray area as to how it should be considered under the Nagoya Protocol’s Access and Benefit Sharing (“ABS”) principles. One way for benefit sharing to be enhanced is through the connection of data to proper provenance information. A significant development is the use of digital labeling systems to ensure that the origin of samples is appropriately disclosed. The Traditional Knowledge and Biocultural Labels initiative offers a practical option for data provided to genomic databases. In particular, the BioCultural Labels (“BC Labels”) are a mechanism for Indigenous communities to identify and maintain provenance, origin and authority over biocultural material and data generated from Indigenous land and waters held in research, cultural institutions and data repositories. This form of cultural metadata adds value to the research endeavor and the creation of Indigenous fields within databases adds transparency and accountability to the research environment.

year: 2022

title: Using swarm optimization to enhance autoencoder’s images

authors: Doaud, Maisa

subjects: computer science

abstract: Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

text: Using swarm optimization to enhance autoencoder’s images Autoencoders learn data representations through reconstruction. Robust training is the key factor affecting the quality of the learned representations and, consequently, the accuracy of the application that use them. Previous works suggested methods for deciding the optimal autoencoder configuration which allows for robust training. Nevertheless, improving the accuracy of a trained autoencoder has got limited, if no, attention. We propose a new approach that improves the accuracy of a trained autoencoder’s results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?. Our proposed approach combines both the decoder part of a trained Resitricted Boltman Machine-based autoencoder with the Competitive Swarm Optimization algorithm. Experiments show that it is possible to reconstruct images using trained decoder from randomly initialized representations. Results also show that our approach reconstructed better quality images than the autoencoder in most of the test cases. Indicating that, we can use the approach for improving the performance of a pre-trained autoencoder if it does not give satisfactory results.

year: 2017

title: What do New Zealand picturebooks tell us about New Zealand national identity?

authors: Daly, Nicola

abstract: Jeffries (2004, p. 4) defines national identity as ' ... a shared sense of belonging of a group of people that depends on a common area of named place, a common set of beliefs and values, and positive feelings for a specific named geographical area'. In her examination of how New Zealand culture is portrayed in picturebooks published between 2000 and 2005, McNeur (2006) suggests that the visual and textual messages in children's picturebooks are an important contributor to the development of children's national identity. This paper describes the process of choosing a collection of 22 New Zealand children's picturebooks (known as the New Zealand PictureBook Collection) to reflect and represent diversity in New Zealand national identity, and analyses what these books tell us about the New Zealand national identity.

text: What do New Zealand picturebooks tell us about New Zealand national identity? Jeffries (2004, p. 4) defines national identity as ' ... a shared sense of belonging of a group of people that depends on a common area of named place, a common set of beliefs and values, and positive feelings for a specific named geographical area'. In her examination of how New Zealand culture is portrayed in picturebooks published between 2000 and 2005, McNeur (2006) suggests that the visual and textual messages in children's picturebooks are an important contributor to the development of children's national identity. This paper describes the process of choosing a collection of 22 New Zealand children's picturebooks (known as the New Zealand PictureBook Collection) to reflect and represent diversity in New Zealand national identity, and analyses what these books tell us about the New Zealand national identity.

year: 2016