title: SOPHYA: a system for digital management of ordered physical document collections

authors: Jervis, Matthew Geoffrey

subjects: computer science

abstract: In recent years several systems have been developed to integrate the management of physical and digital documents and artefacts. These systems, which often rely on technologies such as RFID, generally detect the location of a digitally tagged item within a collection, with varying degrees of location sensitivity, ranging from a room to a smaller container such as a filing cabinet or briefcase. Despite their obvious value, such systems are not capable of detecting the precise location and ordering of individual items within the managed collection of items. In this paper we present the second generation of our earlier prototype system, called SOPHYA, which utilises a wired technology to allow management and retrieval of documents and artefacts within ordered collections.

text: SOPHYA: a system for digital management of ordered physical document collections In recent years several systems have been developed to integrate the management of physical and digital documents and artefacts. These systems, which often rely on technologies such as RFID, generally detect the location of a digitally tagged item within a collection, with varying degrees of location sensitivity, ranging from a room to a smaller container such as a filing cabinet or briefcase. Despite their obvious value, such systems are not capable of detecting the precise location and ordering of individual items within the managed collection of items. In this paper we present the second generation of our earlier prototype system, called SOPHYA, which utilises a wired technology to allow management and retrieval of documents and artefacts within ordered collections.

year: 2010

title: Using finite mixtures to robustify statistical models

authors: Rohan, Maheswaran

subjects: Robust Statistics

abstract: This thesis is concerned with robust estimation of the parameters of statistical models. Although robust estimation is a very good idea, it has some shortcomings when seen from the statistical modelling point of view. For example, there are no easily applicable principles for creating robust estimates in new situations. In this thesis, we are trying to introduce a unified method to obtain the robustified statistics for various situations such as the linear model and the generalized linear model. We wish to modify the maximum likelihood estimation procedure, which is very sensitive to the outliers. In order to reduce the effect on these estimates by outliers, we add an additional component, which would be of no interest but would contain all outliers, to the regular component forming a finite mixture. In fact, we use the finite mixture model to obtain a ``robustified'' estimate for a model parameter $\theta$, where the finite mixture form is being used as a mathematical tool to have a tractable form of analysis rather than being used as a serious model for the data. We employ the EM algorithm to obtain the our proposed robustified estimates for the parameters. Our estimates are compared with some other estimates defined in the robust statistics literature. This thesis examines the robustness of the proposed estimates using the concept of influence function. The estimates are defined iteratively, so that the implicit differentiation method of Jorgensen is used to obtain the influence functions of the estimates. We give example plots of these influence functions which are bounded. In this thesis, we give mathematical results for all cases and we use well known real data sets to investigate our method. The statistical software R is used for all investigation. Finally, we hope that this method may give a unified approach for making parameter estimation in statistical models more robust.

text: Using finite mixtures to robustify statistical models This thesis is concerned with robust estimation of the parameters of statistical models. Although robust estimation is a very good idea, it has some shortcomings when seen from the statistical modelling point of view. For example, there are no easily applicable principles for creating robust estimates in new situations. In this thesis, we are trying to introduce a unified method to obtain the robustified statistics for various situations such as the linear model and the generalized linear model. We wish to modify the maximum likelihood estimation procedure, which is very sensitive to the outliers. In order to reduce the effect on these estimates by outliers, we add an additional component, which would be of no interest but would contain all outliers, to the regular component forming a finite mixture. In fact, we use the finite mixture model to obtain a ``robustified'' estimate for a model parameter $\theta$, where the finite mixture form is being used as a mathematical tool to have a tractable form of analysis rather than being used as a serious model for the data. We employ the EM algorithm to obtain the our proposed robustified estimates for the parameters. Our estimates are compared with some other estimates defined in the robust statistics literature. This thesis examines the robustness of the proposed estimates using the concept of influence function. The estimates are defined iteratively, so that the implicit differentiation method of Jorgensen is used to obtain the influence functions of the estimates. We give example plots of these influence functions which are bounded. In this thesis, we give mathematical results for all cases and we use well known real data sets to investigate our method. The statistical software R is used for all investigation. Finally, we hope that this method may give a unified approach for making parameter estimation in statistical models more robust.

year: 2011

title: Student belonging: critical relationships and responsibilities

authors: Berryman, Mere

subjects: Belonging

abstract: In this paper, we consider New Zealand’s education system to understand what can happen when we focus only on excellence and students reaching their potential, without simultaneously investing in their sense of belonging and wellbeing. National statistics suggest we are alienating and shortchanging an increasing number of students and, for disproportionate numbers of Indigenous students, these statistics are part of a world trend. The literature, and the students themselves, highlight the need to overturn the underlying racism that persistently disadvantages clearly identifiable groups of students over others. Until we do, using equity and excellence as the most powerful drivers for reform, will continue to promote conditions where our students’ sense of belonging and wellbeing are undermined throughout their education and we will risk, failing to address the ensuing negative statistics. We conclude with a response that we have learned from working with these same students.

text: Student belonging: critical relationships and responsibilities In this paper, we consider New Zealand’s education system to understand what can happen when we focus only on excellence and students reaching their potential, without simultaneously investing in their sense of belonging and wellbeing. National statistics suggest we are alienating and shortchanging an increasing number of students and, for disproportionate numbers of Indigenous students, these statistics are part of a world trend. The literature, and the students themselves, highlight the need to overturn the underlying racism that persistently disadvantages clearly identifiable groups of students over others. Until we do, using equity and excellence as the most powerful drivers for reform, will continue to promote conditions where our students’ sense of belonging and wellbeing are undermined throughout their education and we will risk, failing to address the ensuing negative statistics. We conclude with a response that we have learned from working with these same students.

year: 2019