title: Serendipity: integrated environment support for process modelling, enactment and improvement

authors: Grundy, John C.

subjects: computer science

abstract: Large cooperative work systems require work coordination, context awareness and process modelling and enactment mechanisms to be effective. The Serendipity environment provides visual languages for specifying process models and event processing. Enacted models can be modified during or after use and can act as plans of work to be done, describe work in progress, and record work done on a project. Serendipity has been integrated with an Information Systems engineering environment and office automation applications, without modification to these pre-existing tools. Animation of process models allows collaborating users to remain aware of the work contexts of their collaborators. Information about the current enacted process stage is attached to descriptions of changes made to work artefacts, recording the context of work. Such changes are also stored by the process stage, allowing collaborators to review the stage work history. Serendipity's visual event processing language allows users to specify rules and actions triggered by enactment, process or work artefact modification, or tool events. This paper describes Serendipity, our experiences using it, and its architecture and implementation.

text: Serendipity: integrated environment support for process modelling, enactment and improvement Large cooperative work systems require work coordination, context awareness and process modelling and enactment mechanisms to be effective. The Serendipity environment provides visual languages for specifying process models and event processing. Enacted models can be modified during or after use and can act as plans of work to be done, describe work in progress, and record work done on a project. Serendipity has been integrated with an Information Systems engineering environment and office automation applications, without modification to these pre-existing tools. Animation of process models allows collaborating users to remain aware of the work contexts of their collaborators. Information about the current enacted process stage is attached to descriptions of changes made to work artefacts, recording the context of work. Such changes are also stored by the process stage, allowing collaborators to review the stage work history. Serendipity's visual event processing language allows users to specify rules and actions triggered by enactment, process or work artefact modification, or tool events. This paper describes Serendipity, our experiences using it, and its architecture and implementation.

year: 1996-08

title: Scaling up semi-supervised learning: An efficient and effective LLGC variant

authors: Pfahringer, Bernhard

subjects: computer science

abstract: Domains like text classification can easily supply large amounts of unlabeled data, but labeling itself is expensive. Semi- supervised learning tries to exploit this abundance of unlabeled training data to improve classification. Unfortunately most of the theoretically well-founded algorithms that have been described in recent years are cubic or worse in the total number of both labeled and unlabeled training examples. In this paper we apply modifications to the standard LLGC algorithm to improve efficiency to a point where we can handle datasets with hundreds of thousands of training data. The modifications are priming of the unlabeled data, and most importantly, sparsification of the similarity matrix. We report promising results on large text classification problems.

text: Scaling up semi-supervised learning: An efficient and effective LLGC variant Domains like text classification can easily supply large amounts of unlabeled data, but labeling itself is expensive. Semi- supervised learning tries to exploit this abundance of unlabeled training data to improve classification. Unfortunately most of the theoretically well-founded algorithms that have been described in recent years are cubic or worse in the total number of both labeled and unlabeled training examples. In this paper we apply modifications to the standard LLGC algorithm to improve efficiency to a point where we can handle datasets with hundreds of thousands of training data. The modifications are priming of the unlabeled data, and most importantly, sparsification of the similarity matrix. We report promising results on large text classification problems.

year: 2007

title: The Indigenous Exchange Forum: transitions in mine closure

authors: Holcombe, Sarah

abstract: “The Indigenous Exchange Forum: transitions in mine closure” was held on 2–3 November 2021 over Zoom, hosted by the Centre for Social Responsibility in Mining (CSRM) in the Sustainable Minerals Institute (SMI), at The University of Queensland.
The Forum aimed to affirm, connect, and elevate the voices of First Nations leaders and interested community members impacted by mining and mine closure. In doing so, we hoped to begin to build an international network across these Indigenous communities, to enable the sharing of experiences, struggles, and sentiments in relation to mine site transition and mine-related impacts on their lands.
The intent was to recognise each other’s unique cultural position and the impact of mining and mine closure through lessons learned: first with each other and then with industry.
The Forum had a strong foundation of reciprocity. The objective was:
•To provide an independent and culturally safe space to facilitate the connectionbetween First Nations peoples, whose customary lands have been impacted bymining.
•To position First Nations voices individually and collectively in a shared learningjourney full of experiences and lessons learned through online network exchange.
•To facilitate knowledge exchange and elevate the connectivity between Canadian,Australian, and Aotearoa (New Zealand) First Nations regarding impacts, issues, andinnovative ideas and practices in response to mine site transitions and mine closure.

text: The Indigenous Exchange Forum: transitions in mine closure “The Indigenous Exchange Forum: transitions in mine closure” was held on 2–3 November 2021 over Zoom, hosted by the Centre for Social Responsibility in Mining (CSRM) in the Sustainable Minerals Institute (SMI), at The University of Queensland.
The Forum aimed to affirm, connect, and elevate the voices of First Nations leaders and interested community members impacted by mining and mine closure. In doing so, we hoped to begin to build an international network across these Indigenous communities, to enable the sharing of experiences, struggles, and sentiments in relation to mine site transition and mine-related impacts on their lands.
The intent was to recognise each other’s unique cultural position and the impact of mining and mine closure through lessons learned: first with each other and then with industry.
The Forum had a strong foundation of reciprocity. The objective was:
•To provide an independent and culturally safe space to facilitate the connectionbetween First Nations peoples, whose customary lands have been impacted bymining.
•To position First Nations voices individually and collectively in a shared learningjourney full of experiences and lessons learned through online network exchange.
•To facilitate knowledge exchange and elevate the connectivity between Canadian,Australian, and Aotearoa (New Zealand) First Nations regarding impacts, issues, andinnovative ideas and practices in response to mine site transitions and mine closure.

year: 2022