XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.

Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Selected new training documents to update user profile

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.

Compensation for compulsory acquisition of a licensed car parking space

The Acquisition of Land Act 1967 (Qld) (‘the Act’) deals with the acquisition of land by the State for public purposes and provides for compensation. The issue that arose for determination in Sorrento Medical Service Pty Ltd v Chief Executive, Dept of Main Roads [2007] QCA 73 was whether the appellant was entitled to claim compensation under the Act in respect of land resumed by the Main Roads Department over which the appellant had an exclusive contractual licence for car parking spaces for use in association with a medical centre leased by the appellant. At first instance, it was held by the Land Court that the appellant was not entitled to compensation for the resumption of the car parking spaces. The basis for this decision by the Land Court was that a right to compensation only exists where resumption has taken some proprietary interest of the claimant in the land. Following an appeal to the Land Appeal Court being dismissed, the appellant instituted the present appeal to the Queensland Court of Appeal (McMurdo P, Holmes JA and Chesterman J).

Negative Mood Regulation (NMR) expectancies, mood, and affect intensity among clients in substance disorder treatment facilities

Negative mood regulation (NMR) expectancies, stress, anxiety, depression and affect intensity were examined by means of self-report questionnaires in 158 volunteers, including 99 clients enrolled in addiction treatment programs. As expected, addicts reported significantly higher levels of stress, anxiety, depression and affect intensity and lower levels of NMR compared to non-addict controls. NMR was negatively correlated with stress, anxiety, depression and affect intensity. The findings indicate that mood self-regulation is impaired in addicts. Low NMR and high affect intensity may predispose to substance abuse and addiction, or alternatively may reflect chronic drug-induced affective dysregulation.

The embodied hybrid space : designing for digital encounters in physical environments

The emergence of mobile and ubiquitous computing has created what is referred to as a hybrid space – a virtual layer of digital information and interaction opportunities that sits on top and augments the physical environment. The increasing connectedness through such media, from anywhere to anybody at anytime, makes us less dependent on being physically present somewhere in particular. But, what is the role of ubiquitous computing in making physical presence at a particular place more attractive? Acknowledging historic context and identity as important attributes of place, this work embarks on a ‘global sense of place’ in which the cultural diversity, multiple identities, backgrounds, skills and experiences of people traversing a place are regarded as social assets of that place. The aim is to explore ways how physical architecture and infrastructure of a place can be mediated towards making invisible social assets visible, thus augmenting people’s situated social experience. Thereby, the focus is on embodied media, i.e. media that materialise digital information as observable and sometimes interactive parts of the physical environment hence amplify people’s real world experience, rather than substituting or moving it to virtual spaces.

Space : Irrealis objects and their role in a new political economy

In this paper, I show how new spaces are being prefigured for colonisation in the language of contemporary technology policy. Drawing on a corpus of 1.3 million words collected from technology policy centres throughout the world, I show the role of policy language in creating the foundations of an emergent form of political economy. The analysis is informed by principles from critical discourse analysis (CDA) and classical political economy. It foregrounds a functional aspect of language called process metaphor to show how aspects of human activity are prefigured for mass commodification by the manipulation of irrealis spaces. I also show how the fundamental element of any new political economy, the property element, is being largely ignored. The potential creation of a global space as concrete as landed property – electromagnetic spectrum – has significant ramifications for the future of social relations in any global “knowledge economy”.