A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

How quantum theory is developing the field of information retrieval

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Effect of topic domain and task type on Web image searching

Many user studies in Web information searching have found the significant effect of task types on search strategies. However, little attention was given to Web image searching strategies, especially the query reformulation activity despite that this is a crucial part in Web image searching. In this study, we investigated the effects of topic domains and task types on user’s image searching behavior and query reformulation strategies. Some significant differences in user’s tasks specificity and initial concepts were identified among the task domains. Task types are also found to influence participant’s result reviewing behavior and query reformulation strategies.

Performance and scalability assessment for non-certificate-based public key management in VANETS

Current research in secure messaging for Vehicular Ad hoc Networks (VANETs) appears to focus on employing a digital certificate-based Public Key Cryptosystem (PKC) to support security. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This paper proposes a non-certificate-based public key management for VANETs. A comprehensive evaluation of performance and scalability of the proposed public key management regime is presented, which is compared to a certificate-based PKC by employing a number of quantified analyses and simulations. Not only does this paper demonstrate that the proposal can maintain security, but it also asserts that it can improve overall performance and scalability at a lower cost, compared to the certificate-based PKC. It is believed that the proposed scheme will add a new dimension to the key management and verification services for VANETs.

Diversified service provisioning in global business networks

Companies and their services are being increasingly exposed to global business networks and Internet-based ondemand services. Much of the focus is on flexible orchestration and consumption of services, beyond ownership and operational boundaries of services. However, ways in which third-parties in the “global village” can seamlessly self-create new offers out of existing services remains open. This paper proposes a framework for service provisioning in global business networks that allows an open-ended set of techniques for extending services through a rich, multi-tooling environment. The Service Provisioning Management Framework, as such, supports different modeling techniques, through supportive tools, allowing different parts of services to be integrated into new contexts. Integration of service user interfaces, business processes, operational interfaces and business object are supported. The integration specifications that arise from service extensions are uniformly reflected through a kernel technique, the Service Integration Technique. Thus, the framework preserves coherence of service provisioning tasks without constraining the modeling techniques needed for extending different aspects of services.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Efficient and secure self-escrowed public-key infrastructures

A self-escrowed public key infrastructure (SE-PKI) combines the usual functionality of a public-key infrastructure with the ability to recover private keys given some trap-door information. We present an additively homomorphic variant of an existing SE-PKI for ElGamal encryption. We also propose a new efficient SE-PKI based on the ElGamal and Okamoto-Uchiyama cryptosystems that is more efficient than the previous SE-PKI. This is the first SE-PKI that does not suffer from a key doubling problem of previous SE-PKI proposals. Additionally, we present the first self-escrowed encryption schemes secure against chosen-ciphertext attack in the standard model. These schemes are also quite efficient and are based on the Cramer-Shoup cryptosystem, and the Kurosawa-Desmedt hybrid variant in different groups.

Semantics-based web service discovery using information retrieval techniques

This paper demonstrates an experimental study that examines the accuracy of various information retrieval techniques for Web service discovery. The main goal of this research is to evaluate algorithms for semantic web service discovery. The evaluation is comprehensively benchmarked using more than 1,700 real-world WSDL documents from INEX 2010 Web Service Discovery Track dataset. For automatic search, we successfully use Latent Semantic Analysis and BM25 to perform Web service discovery. Moreover, we provide linking analysis which automatically links possible atomic Web services to meet the complex requirements of users. Our fusion engine recommends a final result to users. Our experiments show that linking analysis can improve the overall performance of Web service discovery. We also find that keyword-based search can quickly return results but it has limitation of understanding users’ goals.

Quantum-like non-separability of concept combinations, emergent associates and abduction

Consider the concept combination ‘pet human’. In word association experiments, human subjects produce the associate ‘slave’ in relation to this combination. The striking aspect of this associate is that it is not produced as an associate of ‘pet’, or ‘human’ in isolation. In other words, the associate ‘slave’ seems to be emergent. Such emergent associations sometimes have a creative character and cognitive science is largely silent about how we produce them. Departing from a dimensional model of human conceptual space, this article will explore concept combinations, and will argue that emergent associations are a result of abductive reasoning within conceptual space, that is, below the symbolic level of cognition. A tensor-based approach is used to model concept combinations allowing such combinations to be formalized as interacting quantum systems. Free association norm data is used to motivate the underlying basis of the conceptual space. It is shown by analogy how some concept combinations may behave like quantum-entangled (non-separable) particles. Two methods of analysis were presented for empirically validating the presence of non-separable concept combinations in human cognition. One method is based on quantum theory and another based on comparing a joint (true theoretic) probability distribution with another distribution based on a separability assumption using a chi-square goodness-of-fit test. Although these methods were inconclusive in relation to an empirical study of bi-ambiguous concept combinations, avenues for further refinement of these methods are identified.

A quantum information retrieval approach to memory

As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Gardenfors’ Conceptual Space approach and Humphreys et al.’s matrix model of memory.

It’s okay to ask : development and pilot testing of a question prompt list for patients with brain tumours

Background: Previous research identified that primary brain tumour patients have significant psychological morbidity and unmet needs, particularly the need for more information and support. However, the utility of strategies to improve information provision in this setting is unknown. This study involved the development and piloting of a brain tumour specific question prompt list (QPL). A QPL is a list of questions patients may find useful to ask their health professionals, and is designed to facilitate communication and information exchange. Methods: Thematic analysis of QPLs developed for other chronic diseases and brain tumour specific patient resources informed a draft QPL. Subsequent refinement of the QPL involved an iterative process of interviews and review with 12 recently diagnosed patients and six caregivers. Final revisions were made following readability analyses and review by health professionals. Piloting of the QPL is underway using a non-randomised control group trial with patients undergoing treatment for a primary brain tumour in Brisbane, Queensland. Following baseline interviews, consenting participants are provided with the QPL or standard information materials. Follow-up interviews four to 6 weeks later allow assessment of the acceptability of the QPL, how it is used by patients, impact on information needs, and feasibility of recruitment, implementation and outcome assessment. Results: The final QPL was determined to be readable at the sixth grade level. It contains seven sections: diagnosis, prognosis, symptoms and changes, the health professional team, support, treatment and management, and post-treatment concerns. At this time, fourteen participants have been recruited for the pilot, and data collection completed for eleven. Data collection and preliminary analysis are expected to be completed by and presented at the conference. Conclusions: If acceptable to participants, the QPL may encourage patients, doctors and nurses to communicate more effectively, reducing unmet information needs and ultimately improving psychological wellbeing.

Exploring health information use by older Australians within everyday life

Exploring information use within everyday or community contexts is a recent area of interest for information literacy research endeavors. Within this domain, health information literacy (HIL) has emerged as a focus of interest due to identified synergies between information use and health status. However, while HIL has been acknowledged as a core ingredient that can assist people to take responsibility for managing and improving their own health, limited research has explored how HIL is experienced in everyday community life. This article will present the findings of ongoing research undertaken using phenomenography to explore how HIL is experienced among older Australians within everyday contexts. It will also discuss how these findings may be used to inform policy formulation in health communication and as an evidence base for the design and delivery of consumer health information resources and services.

Informed for healthy ageing : exploring how ageing Australians experience health information use

Exploring information use within everyday or community contexts is a recent area of interest for information literacy research endeavours. Within this domain, health information literacy (HIL) has emerged as a focus of interest due to identified synergies between information use and health status. However, while HIL has been acknowledged as a core ingredient that can assist people to take responsibility for managing and improving their own health, limited research has explored how HIL is experienced in everyday community life. This article will present the findings of ongoing research undertaken using phenomenography to explore how HIL is experienced among older Australians within everyday contexts. It will also discuss how these findings may be used to inform policy formulation in health communication and as an evidence base for the design and delivery of consumer health information resources and services.

Minimizing information leakage of tree-based RFID authentication protocols using alternate tree-walking

The privacy of efficient tree-based RFID authentication protocols is heavily dependent on the branching factor on the top layer. Indefinitely increasing the branching factor, however, is not a viable option. This paper proposes the alternate-tree walking scheme as well as two protocols to circumvent this problem. The privacy of the resulting protocols is shown to be comparable to that of linear-time protocols, where there is no leakage of information, whilst reducing the computational load of the database by one-third of what is required of tree-based protocols during authentication. We also identify and address a limitation in quantifying privacy in RFID protocols.