A historical GIS for England and Wales: a framework for reconstructing past geographies and analysing long-term change

PhDThis thesis describes the creation and possible uses of a Geographical Information System that contains the changing boundaries of the major administrative units of England and Wales from 1840 to 1974. For over 150 years the census, the General Register Office, and others have used these units to publish a wealth of data concerning the population of the country. The key issue addressed by the thesis is that changes in the administrative geography have hampered much research on long-term change in society that could have been done using these sources. The goal of the thesis is the creation of framework to allow the analysis of long-term socio-economic change that makes maximum use of the available data. This involves not only making use of the data's attribute (statistical) component, but also their spatial and temporal components. In order to do this, the thesis provides solutions to two key problems: the first is how to build a GIS containing administrative units that incorporates an accurate record of their changing boundaries and can be linked to statistical data in a flexible manner. The second is how to remove the impact of boundary changes when comparing datasets published at different dates. This is done by devising a methodology for interpolating data from the administrative units they were published using, onto a single target geography. An evaluation of the accuracy of this interpolation is performed and examples are given of how this type of research could be conducted. Taken together, these will release information locked up within historical socio-economic statistics by allowing space to be explicitly incorporated into any explorations of the data. This, in turn, allows research to explore the past with increased levels of both spatial and attribute data for longer time periods

The role of error factors in teaching

This thesis describes a wide-ranging enquiry into the nature, identification and treatment of pupil error. It takes, as its main point of departure, the work of Harlow on the role of 'error factors' in learning and it was undertaken in the belief that educators could considerably enhance the effectiveness of their teaching by making more insightful appraisals of the learning obstacles, (or error factors) of their pupils. Harlow first introduced the concept of error factors following his research into 'learning set' formation. Lewis and Pask subsequently incorporated Harlow's ideas in their own work, 'and laid particular stress on the significance of error factors which give rise to whole classes of errors. The concept of error factors and the role of error factors in teaching, is examined in this thesis in both psychological and philosophical terms, because the two approaches are mutually illuminating. Despite scant literature in this sphere the entire topic is opened up to systematic examination. Hypotheses are presented concerning the role and nature of error factors, and novel strategies are proposed for treating them. A special diagnostic framework for error factors has been formulated, an. 1 its effectiveness investigated in a series of Case Studies and pilot experiments, culminating in a major experimental investigation concerning the overall role of error factors in teaching. Strategies for error factor prediction and prevention are presented and examined, including the use of algorithms. Major consideration is given to the exploitation of error factors as powerful tools to enhance learning. There is a detailed theoretical discussion at each stage, so that the full significance of the findings can be assessed. Inferences concerning the role of error factors in teaching are examined in conjunction with methodological and other implications arising from the experimental findings. A number of weaknesses of current teaching strategies are identified, and the thesis concludes with various speculations (e. g. to do with the use of computer assisted instruction in this context) which have been prompted by this investigation

A Graph-Based Approach for the Summarization of Scientific Articles

Automatic text summarization is one of the eminent applications in the field of Natural Language Processing. Text summarization is the process of generating a gist from text documents. The task is to produce a summary which contains important, diverse and coherent information, i.e., a summary should be self-contained. The approaches for text summarization are conventionally extractive. The extractive approaches select a subset of sentences from an input document for a summary. In this thesis, we introduce a novel graph-based extractive summarization approach. With the progressive advancement of research in the various fields of science, the summarization of scientific articles has become an essential requirement for researchers. This is our prime motivation in selecting scientific articles as our dataset. This newly formed dataset contains scientific articles from the PLOS Medicine journal, which is a high impact journal in the field of biomedicine. The summarization of scientific articles is a single-document summarization task. It is a complex task due to various reasons, one of it being, the important information in the scientific article is scattered all over it and another reason being, scientific articles contain numerous redundant information. In our approach, we deal with the three important factors of summarization: importance, non-redundancy and coherence. To deal with these factors, we use graphs as they solve data sparsity problems and are computationally less complex. We employ bipartite graphical representation for the summarization task, exclusively. We represent input documents through a bipartite graph that consists of sentence nodes and entity nodes. This bipartite graph representation contains entity transition information which is beneficial for selecting the relevant sentences for a summary. We use a graph-based ranking algorithm to rank the sentences in a document. The ranks are considered as relevance scores of the sentences which are further used in our approach. Scientific articles contain reasonable amount of redundant information, for example, Introduction and Methodology sections contain similar information regarding the motivation and approach. In our approach, we ensure that the summary contains sentences which are non-redundant. Though the summary should contain important and non-redundant information of the input document, its sentences should be connected to one another such that it becomes coherent, understandable and simple to read. If we do not ensure that a summary is coherent, its sentences may not be properly connected. This leads to an obscure summary. Until now, only few summarization approaches take care of coherence. In our approach, we take care of coherence in two different ways: by using the graph measure and by using the structural information. We employ outdegree as the graph measure and coherence patterns for the structural information, in our approach. We use integer programming as an optimization technique, to select the best subset of sentences for a summary. The sentences are selected on the basis of relevance, diversity and coherence measure. The computation of these measures is tightly integrated and taken care of simultaneously. We use human judgements to evaluate coherence of summaries. We compare ROUGE scores and human judgements of different systems on the PLOS Medicine dataset. Our approach performs considerably better than other systems on this dataset. Also, we apply our approach on the standard DUC 2002 dataset to compare the results with the recent state-of-the-art systems. The results show that our graph-based approach outperforms other systems on DUC 2002. In conclusion, our approach is robust, i.e., it works on both scientific and news articles. Our approach has the further advantage of being semi-supervised

Approaches for enriching and improving textual knowledge bases

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The development of a novel agent based long term domestic energy stock model

This research has developed a novel long term domestic energy stock model of owneroccupied dwellings in England. Its primary purpose is to aid policy makers in determining appropriate policy measures to achieve CO2 emissions reductions in the housing sector. Current modelling techniques can provide a highly disaggregated technology rich environment, but they do not consider the behaviour required for technological changes to the dwelling stock. Energy efficiency improvements will only occur in the owner-occupied sector of the housing market when owners decide to carry out such improvements. Therefore, a stock model that can simulate this decision making process will be of more use for policy makers in predicting the impact of different measures designed to encourage uptake of suitable technologies. Agent based modelling has been proposed as a solution to allow the inclusion of individual household decision making into a long term domestic stock model. The agents in the model represent households and have a simple additive weighting decision making algorithm based on discrete choice survey data from the Energy Saving Trust and Element Energy. The model has then been calibrated against historic technology diffusion data. Sixteen scenarios have been developed and tested in the model. The initial Business as Usual scenarios indicate that current policies are likely to fall well short of the 2050 80% emissions reduction target, although subsequent scenarios indicate that the target is achievable. The results also indicate that care is required when setting subsidy levels when competing technologies are available, as there is the potential to suppress the diffusion of technologies that offer greater potential savings. The developed model can now be used by policy makers in testing further scenarios, and this novel approach can be applied both regionally and in other countries, subject to the collection of suitable input data

Second CLIPS Conference Proceedings, volume 2

Papers presented at the 2nd C Language Integrated Production System (CLIPS) Conference held at the Lyndon B. Johnson Space Center (JSC) on 23-25 September 1991 are documented in these proceedings. CLIPS is an expert system tool developed by the Software Technology Branch at NASA JSC and is used at over 4000 sites by government, industry, and business. During the three days of the conference, over 40 papers were presented by experts from NASA, Department of Defense, other government agencies, universities, and industry