title: Theft in the digital: Can you steal virtual property?

authors: Rumbles, Wayne

abstract: This article explores issues arising around the theft of virtual property. It first explains a number of perhaps counter intuitive concepts vital to the understanding of the importance of extending ‘real world’ criminal law and criminal liability to conduct inside virtual worlds such as World of Warcraft or Second Life. This article focuses on theft of virtual property and explores how other jurisdictions are developing responses to this issue. It then develops an analysis of the operation of ‘real world’ New Zealand criminal law rules around theft, in the context of the evolution of virtual property.

text: Theft in the digital: Can you steal virtual property? This article explores issues arising around the theft of virtual property. It first explains a number of perhaps counter intuitive concepts vital to the understanding of the importance of extending ‘real world’ criminal law and criminal liability to conduct inside virtual worlds such as World of Warcraft or Second Life. This article focuses on theft of virtual property and explores how other jurisdictions are developing responses to this issue. It then develops an analysis of the operation of ‘real world’ New Zealand criminal law rules around theft, in the context of the evolution of virtual property.

year: 2011

title: Cue word guided question generation with BERT model fine-tuned on natural question dataset

authors: Zhang, Zijing

subjects: Deep Learning

abstract: This thesis aims to develop an efficient question generator for an automated tutoring system. Given a context passage with an answer, the question generator asks questions to help the reader learn new material. By utilizing the BERT model, this thesis experiments on generating type-specific questions with a cue word. This thesis also uses an RNN encoder-decoder architecture for question generation on SQuAD as a comparative baseline and fine-tune the BERT question generation model on Google's Natural Question dataset. Ultimately, I deliver a RESTful API by the end of this year-long master program.

text: Cue word guided question generation with BERT model fine-tuned on natural question dataset This thesis aims to develop an efficient question generator for an automated tutoring system. Given a context passage with an answer, the question generator asks questions to help the reader learn new material. By utilizing the BERT model, this thesis experiments on generating type-specific questions with a cue word. This thesis also uses an RNN encoder-decoder architecture for question generation on SQuAD as a comparative baseline and fine-tune the BERT question generation model on Google's Natural Question dataset. Ultimately, I deliver a RESTful API by the end of this year-long master program.

year: 2020

title: Maori & Psychology Research Unit annual report 2008

authors: Rua, Mohi

subjects: Maori and Psychology Research Unit

abstract: Annual report of the Maori and Psychology Research Unit (MPRU) 2008. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

text: Maori & Psychology Research Unit annual report 2008 Annual report of the Maori and Psychology Research Unit (MPRU) 2008. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community.

year: 2009-03