Youth, personhood and 'practices of the self'

In contrast to most previous research in the field, this paper argues that the concept of 'youth' is best understood as an example of the governmental formation of a specific type of person. It is constructed at the intersection of a variety of diverse problematisations, being produced by the processes of individuation/ normalisation and the regulation of relations of time. Within programs such as those pertinent to the management of sex, an array of technologies structure the practices by which individuals pattern their own conduct - thereby fashioning a kind of habitus. This forms part of a general strategy of enrolling the objects of these programs in their own self-reformation. Consequently, `youth' can be understood as `the doing of specific types of work on the self'. By utilising this framework, the paper not only seeks to identify and better understand some of the sexual subjectivities associated with the construction of youth, it also seeks to offer some new directions for research in the area.

Human cortical processing of colour and pattern

Investigated human visual processing of simple two-colour patterns using a delayed match to sample paradigm with positron emission tomography (PET). This study is unique in that the authors specifically designed the visual stimuli to be the same for both pattern and colour recognition with all patterns being abstract shapes not easily verbally coded composed of two-colour combinations. The authors did this to explore those brain regions required for both colour and pattern processing and to separate those areas of activation required for one or the other. 10 right-handed male volunteers aged 18–35 yrs were recruited. The authors found that both tasks activated similar occipital regions, the major difference being more extensive activation in pattern recognition. A right-sided network that involved the inferior parietal lobule, the head of the caudate nucleus, and the pulvinar nucleus of the thalamus was common to both paradigms. Pattern recognition also activated the left temporal pole and right lateral orbital gyrus, whereas colour recognition activated the left fusiform gyrus and several right frontal regions.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Augmenting web service discovery by cognitive semantics and abduction

We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

The effect of language models on phonetic decoding for spoken term detection

Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task.

The Autistic Behavioural Indicators Instrument (ABII) : development and instrument utility in discriminating autistic disorder from speech and language impairment and typical development

The Autistic Behavioural Indicators Instrument (ABII) is an 18-item instrument developed to identify children with Autistic Disorder (AD) based on the presence of unique autistic behavioural indicators. The ABII was administered to 20 children with AD, 20 children with speech and language impairment (SLI) and 20 typically developing (TD) children aged 2-6 years. Results indicated that the ABII discriminated children diagnosed with AD from those diagnosed with SLI and those who were TD, based on the presence of specific social attention, sensory, and behavioural symptoms. A combination of symptomology across these domains correctly classified 100% of children with and without AD. The paper concludes that the ABII shows considerable promise as an instrument for the early identification of AD.

Class language

How social class factors into linguistic practices and use, language change and loss has been a major theme in postwar sociolinguistics and ethnography of communication, language planning and sociology of language. Key foci of linguistic and sociological research include the study of social class in everyday language use, media and institutional texts. A further concern is to understand the relationship between social class stratification, intergenerational social reproduction, and language variation. Bourdieu’s model of linguistic habitus and cultural capital offers a broad theoretical template for examining these relations, even as they are complicated by forces of economic and cultural globalization, new media and identity formations.

Hypercapitalism : an investigation into the relationship between language, new media, and social perceptions of value

Overall, this thesis purports to make two significant contributions to knowledge. The first is a foundational critique of political economy in the context of an emergent global knowledge economy. The second is a method for analysing evaluations in language. The relationships that give coherence to those two contributions are as follows. The widely-heralded emergence of a knowledge economy indicates that more intimate aspects of human activity have become exposed to commodification on a massive scale, specifically, activities associated with thought and language. Correspondingly, more abstract forms of value have developed as the products of thought and language have become dominant commodity forms. Historical investigation shows that value has moved from an objective category in political economy, pertaining to such substances as precious metals and land, to become situated today predominantly in “expert” expressions of language, or more precisely, their institutional contexts of production. These are now propagated and circulated on a global scale. Legal, political, and technological developments are key in the development of new, more abstract forms of labour and value, although the relationships connecting these are neither simple nor direct. They are, however, inseparably related in the trajectories that this thesis describes. Consequently they are dealt with inseparably throughout.

The language of neofeudal corporatism and the war on Iraq

In this paper we argue that the term “capitalism” is no longer useful for understanding the current system of political economic relations in which we live. Rather, we argue that the system can be more usefully characterised as neofeudal corporatism. Using examples drawn from a 300,000 word corpus of public utterances by three political leaders from the “coalition of the willing”— George W. Bush, Tony Blair, and John Howard—we show some defining characteristics of this relatively new system and how they are manifest in political language about the invasion of Iraq.

Luna Language Report

Luna is an object-oriented language. It does not, as do many other object-oriented languages, have a conventional procedural language as a base. It is strongly typed and modular. The elegance of Luna is that it is entirely reference based, there are no static objects. Luna is similar to Oberon in that inheritance and subtyping is based on type extension.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.

Bit-pattern based integral attack

Integral attacks are well-known to be effective against byte-based block ciphers. In this document, we outline how to launch integral attacks against bit-based block ciphers. This new type of integral attack traces the propagation of the plaintext structure at bit-level by incorporating bit-pattern based notations. The new notation gives the attacker more details about the properties of a structure of cipher blocks. The main difference from ordinary integral attacks is that we look at the pattern the bits in a specific position in the cipher block has through the structure. The bit-pattern based integral attack is applied to Noekeon, Serpent and present reduced up to 5, 6 and 7 rounds, respectively. This includes the first attacks on Noekeon and present using integral cryptanalysis. All attacks manage to recover the full subkey of the final round.