Computing & IT

Abstract. In the shape from shading problem of computer vision one attempts to recover the threedimensional shape of an object or landscape from the shading on a single image. Under the assumptions that the surface is dusty, distant, and illuminated only from above, the problem reduces to that of solving the eikonal equation| Du|= on a domain in R2. Despite various existence and uniqueness theorems for smooth solutions, we show that this problem is unstable, which is catastrophic for general numerical algorithms.

Abstract The «anomalous» fluid isotropic phase in the DDAB/tetradecane/water system differs in important ways from microemulsion phases in related systems, and is thus a useful test for models. On the basis of absolute scaled neutron and X-ray scattering data we show here that the microstructure is best characterised as a randomly folded reverse bilayer. All previously reported examples of this structure are restricted to a narrow range of composition and temperature.

"This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain
model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends."

The categorization of documents is traditionally
topic-based. This paper presents a complementary
analysis of research and experiments on genre to show
that encouraging results can be obtained by using
genre structure (form) features. We conducted an
experiment to assess the effectiveness of using
extensible mark-up language (XML) tag information,
and part-of-speech (P-O-S) features, for the
classification of genres, testing the hypothesis that if a
focus on genre can lead to high precision on normal
textual documents, then good results can be achieved
using XML tag information in addition to P-O-S
information. An experiment was carried out on a
subsection of the initiative for the evaluation of XML
(INEX) 1.4 collection. The features were extracted and 2. Background: text classification and documents were classified using machine learning
algorithms, which yielded encouraging results for logistic regression and neural networks. We propose that utilizing these features and training a classifier may benefit retrieval for most world wide web (WWW) technologies such as XML and extensible hypertext markup language) XHTML.

The difficulties inherent with Information Retrieval are many fold. The main problem is how to find and display the users’ needs. This volume briefly explores IR background, typical components and how it has traditionally been performed in the past on a multitude of works. Retrieval is being utilised on collections by using genre, which identifies the type of document by conceptual features instead of using traditional topical information; this volume investigates research in this field. Some authors have claimed that a document can be identified by form alone.
Subsequently, a proposal is made with regard to investigating the genre of a document by using ten structural features. This would be carried out by conducting retrieval on the INEX XML collection which is to be indexed and searched using the Lucene library and classified by using two data mining algorithms.

The categorization of documents is traditionally topic-based. This paper presents a complementary analysis of research and experiments on genre to show that encouraging results can be obtained by using genre structure (form) features. We conducted an experiment to assess the effectiveness of using eXtensible Mark-Up Language (XML) tag information, and part-of-speech (P-O-S) features, for the classification of genres, testing the hypothesis that if a focus on genre can lead to high precision on normal textual documents, then good results can be achieved using XML tag information in addition to P-O-S information. An experiment was carried out on a subsection of the initiative for the evaluation of XML (INEX) 1.4 collection. The features were extracted and documents were classified using machine learning algorithms, which yielded encouraging results for logistic regression and neural networks. We propose that utilizing these features and training a classifier may benefit retrieval for most World Wide Web (WWW) technologies such as XML and eXtensible Hypertext Markup Language) XHTML.

This paper presents an overview of the ways in which genres, or structural
forms, develop in a community of practice, in this case, Wikipedia. Firstly,
we collected data by performing a small search task in the Wikipedia
search engine (powered by Lucene) to locate articles related to global car
manufacturers, for example, British Leyland, Ferrari and General Motors.
We also searched for typical biographical articles about notable people, such
as Spike Milligan, Alex Ferguson, Nelson Mandela and Karl Marx. An
examination of the data thus obtained revealed that these articles have
particular forms and that some genres connect to each other and evolve,
merge and overlap. We then looked at the ways in which the purpose
and form of a biographical article have evolved over six years within this
community. We concluded the work with a discussion on the usefulness of
Wikipedia as a vehicle for such genre investigations. This small analysis
has allowed us to start generating a number of detailed research questions
as to how forms may act as descriptors of genre and to discuss plans for
experimental work aimed at answering these questions.

This volume describes the Implementation and Evaluation stage of the project. It begins with an explanation of the project workspace in which Maven, Eclipse, and a Subversion repository were utilised.
As most research has focused solely on retrieval by topic and categorization of text this project has been carried out to try to prove that document categorization can be achieved by conducting retrieval techniques such as indexing and querying.
Finally, by applying Machine Learning classification techniques to the data extracted, it is proved that classification can be achieved with LogitBoost(74%); other algorithms, such as Naïve Bayes, iBk and MultiLayerPerceptron (Neural Network) scored only just under 70%.
In conclusion, possible enhancements and future areas of research are examined which could lead to improvements in accuracy and performance.

This paper reports on our task-based observational, logged, questionnaire study and analysis of ocular behavior pertaining to the interaction of structural features of text in Wikipedia using eye tracking. We set natural and realistic tasks searching Wikipedia online focusing on examining which features and strategies (skimming or scanning) were the most important for the participants to complete their tasks. Our research, carried out on a group of 30 participants, highlighted their interactions with the structural areas within Wikipedia articles, the visual cues and features perceived during the searching of the Wiki text. We collected questionnaire and ocular behavior (fixation metrics) data to highlight the ways in which people view the features in the articles. We found that our participants’ extensively interacted with layout features, such as tables, titles, bullet lists, contents lists, information boxes, and references. The eye tracking results showed that participants used the format and layout features and they also highlighted them as important. They were able to navigate to useful information consistently, and they were an effective means of locating relevant information for the completion of their tasks with some success. This work presents results which contribute to the long-term goals of studying the features for genre and theoretical perception research.

"This paper reports on our approach to the analysis of
genre recognition using eyetracking. We focused on a
collection of different types of email which could
represent different datasets, such as, mailing lists for calls
for papers, newsletters, etc. We found that genre analysis
based on purpose, form and layout features is potentially
effective for identifying the characteristics of these
datasets and we have highlighted some of the new
important features of genres. The results from a pilot
study showed a clear effect, with an interaction between
the email texts and the visual cues or features perceived
and also the strategies employed for the processing of the
texts. We found, in our small sample, that readers can
determine the purpose and form of genres and that during
this process some readers do skim the shape of the e-mails
(form). "

This paper reports on an approach to the analysis of genre recognition using eye-tracking. The researchers focused on eight different types of e-mail, such as calls for papers, newsletters and spam, which were chosen to represent different genres. The study in- volved the collection of oculographic behaviour data metrics, such as fixations and saccades to highlight the ways in which people view the features of genres. We found that genre analysis based on purpose and form (layout features, etc) was an effective means of identifying the characteristics of these e-mails. The research, carried out on a group of 24 participants, highlighted their interaction with the e-mail texts and the visual cues or features perceived as well as the strategies they employed for the processing of the texts. The results showed that readers can determine the purpose and form of genres, that form and content can occasionally be separable, that some features cause fixations and that some readers are prompted to respond by using saccadic behaviour (e.g. regres- sive saccades) over the shape of the e-mails (form).

This paper describes the participation of the Information Retrieval and Interaction group of Robert Gordon University in the INEX 2006 ad hoc track. We focused on two questions: What potential evidence do human assessors use to identify relevant XML elements? and How can this evidence be used by computers for the same task? . Our main strategy was to investigate evidence taken not only from the content, but also from the shallow features of how texts were displayed. We employed the vector space model and the language model combining estimates based on element full-text and the compact representation of the element. We analyzed a range of non-content priors to boost retrieval effectiveness.

This paper describes the participation of the Information Re-
trieval and Interaction group of Robert Gordon University in the INEX 2006 ad hoc track. We focused on two questions: \What potential evidence do human assessors use to identify relevant XML elements?" and How can this evidence be used by computers for the same task?". Our main strategy was to investigate evidence taken not only from the content, but also from the shallow features of how texts were displayed.
We employed vector space model and language model combining estimates based on element full-text and the compact representation of the element. We analyzed a range of non-content priors to boost retrieval effectiveness.

"This paper offers a proposal for some preliminary research on the retrieval of structured text, such as extensible mark-up language (XML). We believe that capturing the way in which a reader perceives the meaning of documents, especially genres of text, may have implications for information retrieval (IR)
and in particular, for cognitive IR and relevance. Previous research on ‘shallow’ features of structured text has shown that categorization by form is possible. Gibson’s theory of ‘affordances’ and genre offer the reader the meaning and purpose - through structure - of a text, before the reader has even begun to read it, and should therefore provide a good basis for the ‘deep’ skimming and categorization of
texts. We believe that Gibson’s ‘affordances’ will aid the user to locate, examine and utilize shallow or deep features of genres and retrieve relevant output. Our proposal puts forward two hypotheses, with a list of research questions to test them, and culminates in experiments involving the studies of human
categorization behaviour when viewing the structures of emails and web documents. Finally, we will examine the effectiveness of adding structural layout cues to a Yahoo discussion forum (currently only a bag-of-words), which is rich in structure, but only searchable through a Boolean search engine."

This thesis presents an investigation into the high value of structured text (or form) in the context of genre within Information Retrieval. In particular, how are these structured texts perceived and why are they not more heavily used within Information Retrieval & Search communities?
The main motivation is to show the features in which people can exploit genre within Information Search & Retrieval, in particular, categorisation and search tasks. To do this, it was vital to record and analyse how and why this was done during typical tasks. The literature review highlighted two previous studies (Toms & Campbell 1999a; Watt 2009) which have reported pilot studies consisting of genre categorisation and information searching. Both studies and other findings within the literature review inspired the work contained within this thesis.
Genre is notoriously hard to define, but a very useful framework of ‘Purpose and Form’, developed by Yates & Orlikowski (1992), was utilised to design two user studies for the research reported within the thesis. The two studies consisted of, first, a categorisation task (e-mails), and second, a set of six ‘simulated situations’ in Wikipedia, both of which collected quantitative data from eye tracking experiments as well as qualitative user data.
The results of both studies showed the extent to which the participants utilised the form features of the stimuli presented, in particular, how these were used, which ocular behaviours (skimming or scanning) and actual features were used, and which were the most important. The main contributions to research made by this thesis were, first of all, that the task-based user evaluations employing simulated search scenarios revealed ‘how’ and ‘why’ users make decisions while interacting with the textual features of structure and layout within a discourse community, and, secondly, an extensive evaluation of the quantitative data revealed the features that were used by the participants in the user studies and the effects of the interpretation of genre in the search and categorisation process as well as the perceptual processes used in the various communities. This will be of benefit for the re-development of information systems. As far as is known, this is the first detailed and systematic investigation into the types of features, value of form, perception of features, and layout of genre using eye tracking in online communities, such as Wikipedia.