State convergence in bit-based stream ciphers

Well-designed initialisation and keystream generation processes for stream ciphers should ensure that each key-IV pair generates a distinct keystream. In this paper, we analyse some ciphers where this does not happen due to state convergence occurring either during initialisation, keystream generation or both. We show how state convergence occurs in each case and identify two mechanisms which can cause state convergence.

Regulating journalists? The Finkelstein Review, the Convergence Review, and News Media Regulation in Australia

This paper identifies two major forces driving change in media policy worldwide: media convergence, and renewed concerns about media ethics, with the latter seen in the U.K. Leveson Inquiry. It focuses on two major public inquiries in Australia during 2011-2012 – the Independent Media Inquiry (Finkelstein Review) and the Convergence Review – and the issues raised about future regulation of journalism and news standards. Drawing upon perspectives from media theory, it observes the strong influence of social responsibility theories of the media in the Finkelstein Review, and the adverse reaction these received from those arguing from Fourth Estate/free press perspectives, which were also consistent with the longstanding opposition of Australian newspaper proprietors to government regulation. It also discusses the approaches taken in the Convergence Review to regulating for news standards, in light of the complexities arising from media convergence. The paper concludes with consideration of the fast-changing environment in which such proposals to transform media regulation are being considered, including the crisis of news media organisation business models, as seen in Australia with major layoffs of journalists from the leading print media publications.

Using patterns co-occurrence matrix for cleaning closed sequential patterns for text mining

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

A computationally effective alternating direction method for the space and time fractional Bloch–Torrey equation in 3-D

The space and time fractional Bloch–Torrey equation (ST-FBTE) has been used to study anomalous diffusion in the human brain. Numerical methods for solving ST-FBTE in three-dimensions are computationally demanding. In this paper, we propose a computationally effective fractional alternating direction method (FADM) to overcome this problem. We consider ST-FBTE on a finite domain where the time and space derivatives are replaced by the Caputo–Djrbashian and the sequential Riesz fractional derivatives, respectively. The stability and convergence properties of the FADM are discussed. Finally, some numerical results for ST-FBTE are given to confirm our theoretical findings.

Convergence of corporate social responsibility and corporate governance in weak economies : the case of Bangladesh

The convergence of corporate social responsibility (CSR) and corporate governance (CG) has changed the corporate accountability mechanism. This has developed a socially responsible ‘corporate self-regulation’, a synthesis of governance and responsibility in the companies of strong economies. However, unlike in the strong economies, this convergence has not been visible in the companies of weak economies, where the civil society groups are unorganised, regulatory agencies are either ineffective or corrupt and the media and non-governmental organisations do not mirror the corporate conscience. Using the case of Bangladesh, this article investigates the convergence between CSR and CG in the self-regulation of companies in a less vigilant environment.

The Convergence Review and the principle of Australians' access to and opportunities for participation

The recent Australian Convergence Review’s second principle states: “Australians should have access to and opportunities for participation in a diverse mix of services, voices, views and information”. However, in failing to define its own use and understanding of the terms ‘access’ and ‘participation’ the Convergence Review exposes itself to criticism. These terms would no doubt be made unambiguously clear should the Review’s recommendations move towards policy, and this paper contributes to this discussion by framing access and participation, from the perspective of the ‘produser’ (Bruns, 2008), around three separate but related issues: the failure to frame the discussion that will be undertaken by the Australian Law Reform Commission’s 2012 2013 Copyright Inquiry; the prioritising of the market over and above media accountability and the health of the public sphere; and the missed opportunity to develop a national framework for digital literacy and advanced digital citizenry.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

Convergence in the gender wage gap in Australia over the 1980s : identifying the role of counteracting forces via the Juhn, Murphy and Pierce decomposition

The paper utilises the Juhn Murphy and Pierce (1991) decomposition to shed light on the pattern of slow male-female wage convergance in Australia over the 1980s. The analysis allows one to distinguish between the role of wage structure and genderspecific effects. The central question addressed is whether rising wage inequality counteracted the forces of increased female investment in labour market skills, i.e. education and experience. The conclusion is that in contrast to the US and the UK, Australian women do not appear to have been swimming against a tide of adverse wage structure changes.

Convergence and divergence during the adaptation to similar environments by an Australian Groundsel

Adaptation to replicate environments is often achieved through similar phenotypic solutions. Whether selection also produces convergent genomic changes in these situations remains largely unknown. The variable groundsel, Senecio lautus, is an excellent system to investigate the genetic underpinnings of convergent evolution, because morphologically similar forms of these plants have adapted to the same environments along the coast of Australia. We compared range-wide patterns of genomic divergence in natural populations of this plant and searched for regions putatively affected by natural selection. Our results indicate that environmental adaptation followed complex genetic trajectories, affecting multiple loci, implying both the parallel recruitment of the same alleles and the divergence of completely different genomic regions across geography. An analysis of the biological functions of candidate genes suggests that adaptation to coastal environments may have occurred through the recruitment of different genes participating in similar processes. The relatively low genetic convergence that characterizes the parallel evolution of S. lautus forms suggests that evolution is more constrained at higher levels of biological organization.

The gender wage gap in Australia - The path of future convergence

The paper attempts to project the future trend of the gender wage gap in Australia up to 2031. The empirical analysis utilises the Income Distribution Survey (1996) together with Australian Bureau of Statistics (ABS) demographic projections. The methodology combines the ABS projections with assumptions relating to the evolution of educational attainment in order to project the future distribution of human capital skills and consequently the future size of the gender wage gap. The analysis suggests that female relative pay will continue to rise up to 2031. However, gender wage convergence will be relatively slow, with a substantial gap remaining in 2031.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

A reference station-based GNSS computing mode to support unified precise point positioning and real-time kinematic services

Currently, the GNSS computing modes are of two classes: network-based data processing and user receiver-based processing. A GNSS reference receiver station essentially contributes raw measurement data in either the RINEX file format or as real-time data streams in the RTCM format. Very little computation is carried out by the reference station. The existing network-based processing modes, regardless of whether they are executed in real-time or post-processed modes, are centralised or sequential. This paper describes a distributed GNSS computing framework that incorporates three GNSS modes: reference station-based, user receiver-based and network-based data processing. Raw data streams from each GNSS reference receiver station are processed in a distributed manner, i.e., either at the station itself or at a hosting data server/processor, to generate station-based solutions, or reference receiver-specific parameters. These may include precise receiver clock, zenith tropospheric delay, differential code biases, ambiguity parameters, ionospheric delays, as well as line-of-sight information such as azimuth and elevation angles. Covariance information for estimated parameters may also be optionally provided. In such a mode the nearby precise point positioning (PPP) or real-time kinematic (RTK) users can directly use the corrections from all or some of the stations for real-time precise positioning via a data server. At the user receiver, PPP and RTK techniques are unified under the same observation models, and the distinction is how the user receiver software deals with corrections from the reference station solutions and the ambiguity estimation in the observation equations. Numerical tests demonstrate good convergence behaviour for differential code bias and ambiguity estimates derived individually with single reference stations. With station-based solutions from three reference stations within distances of 22–103 km the user receiver positioning results, with various schemes, show an accuracy improvement of the proposed station-augmented PPP and ambiguity-fixed PPP solutions with respect to the standard float PPP solutions without station augmentation and ambiguity resolutions. Overall, the proposed reference station-based GNSS computing mode can support PPP and RTK positioning services as a simpler alternative to the existing network-based RTK or regionally augmented PPP systems.

Detecting news topics from microblogs using sequential pattern mining

This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.

A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information

In this paper we present a unified sequential Monte Carlo (SMC) framework for performing sequential experimental design for discriminating between a set of models. The model discrimination utility that we advocate is fully Bayesian and based upon the mutual information. SMC provides a convenient way to estimate the mutual information. Our experience suggests that the approach works well on either a set of discrete or continuous models and outperforms other model discrimination approaches.