A quality driven adaptation scheme for DASH streaming

In this work we present a quality driven approach to DASH (Dynamic Adaptive Streaming over HTTP) for segment selection in varying network conditions. Current adaption algorithms focus largely on regulating data rates using network layer parameters by selecting the level of quality on offer that can eliminate buffer underrun without considering picture fidelity. In reality, viewers may accept a level of buffer underrun in order to achieve an improved level of picture fidelity. In this case, the conventional DASH algorithms can cause extreme degradation of the picture fidelity when attempting to eliminate buffer underrun with scarce bandwidth availability. Our work is concerned with a quality-aware rate adaption scheme that maximizes the client's quality of experience in terms of both continuity and fidelity (picture quality). Results show that the scheme proposed can maintain a high level of quality for streaming services, especially at low packet loss rates. It is also shown that by eliminating buffer underrun completely, the PSNR that reflects the picture quality of the video is greatly reduced. Our scheme offers the offset between continuity-based quality and resolution-based quality, which can be used to set threshold values for the level of quality desired by clients with different quality requirements. © 2013 IEEE.

Strong designated verifier signature in a multi-user setting

The security of strong designated verifier (SDV) signature schemes has thus far been analyzed only in a two-user setting. We observe that security in a two-user setting does not necessarily imply the same in a multi-user setting for SDV signatures. Moreover, we show that existing security notions do not adequately model the security of SDV signatures even in a two-user setting. We then propose revised notions of security in a multi-user setting and show that no existing scheme satisfies these notions. A new SDV signature scheme is then presented and proven secure under the revised notions in the standard model. For the purpose of constructing the SDV signature scheme, we propose a one-pass key establishment protocol in the standard model, which is of independent interest in itself.

Improved GMM-based speaker verification using SVM-driven impostor dataset selection

The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

Unsupervised speaker adaptation for telephone call transcription

The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper investigates how to best exploit this data for speaker-dependent speech recognition. Supervised and unsupervised experiments in acoustic model and language model adaptation are presented. Using one hour of automatically transcribed speech per speaker with a word error rate of 36.0%, unsupervised adaptation resulted in an absolute gain of 6.3%, equivalent to 70% of the gain from the supervised case, with additional adaptation data likely to yield further improvements. LM adaptation experiments suggested that although there seems to be a small degree of speaker idiolect, adaptation to the speaker alone, without considering the topic of the conversation, is in itself unlikely to improve transcription accuracy.

A role mining inspired approach to representing user behaviour in ERP systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the most predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Data-driven background dataset selection for SVM-based speaker verification

The recently proposed data-driven background dataset refinement technique provides a means of selecting an informative background for support vector machine (SVM)-based speaker verification systems. This paper investigates the characteristics of the impostor examples in such highly-informative background datasets. Data-driven dataset refinement individually evaluates the suitability of candidate impostor examples for the SVM background prior to selecting the highest-ranking examples as a refined background dataset. Further, the characteristics of the refined dataset were analysed to investigate the desired traits of an informative SVM background. The most informative examples of the refined dataset were found to consist of large amounts of active speech and distinctive language characteristics. The data-driven refinement technique was shown to filter the set of candidate impostor examples to produce a more disperse representation of the impostor population in the SVM kernel space, thereby reducing the number of redundant and less-informative examples in the background dataset. Furthermore, data-driven refinement was shown to provide performance gains when applied to the difficult task of refining a small candidate dataset that was mis-matched to the evaluation conditions.

Exploiting multiple feature sets in data-driven impostor dataset selection for speaker verification

This study assesses the recently proposed data-driven background dataset refinement technique for speaker verification using alternate SVM feature sets to the GMM supervector features for which it was originally designed. The performance improvements brought about in each trialled SVM configuration demonstrate the versatility of background dataset refinement. This work also extends on the originally proposed technique to exploit support vector coefficients as an impostor suitability metric in the data-driven selection process. Using support vector coefficients improved the performance of the refined datasets in the evaluation of unseen data. Further, attempts are made to exploit the differences in impostor example suitability measures from varying features spaces to provide added robustness.

Detection of anomalies from user profiles generated from system logs

We describe research into the identification of anomalous events and event patterns as manifested in computer system logs. Prototype software has been developed with a capability that identifies anomalous events based on usage patterns or user profiles, and alerts administrators when such events are identified. To reduce the number of false positive alerts we have investigated the use of different user profile training techniques and introduce the use of abstractions to group together applications which are related. Our results suggest that the number of false alerts that are generated is significantly reduced when a growing time window is used for user profile training and when abstraction into groups of applications is used.

Improving information security management in nonprofit organisations

All organisations, irrespective of size and type, need effective information security management (ISM) practices to protect vital organisational in- formation assets. However, little is known about the information security management practices of nonprofit organisations. Australian nonprofit organisations (NPOs) employed 889,900 people, managed 4.6 million volunteers and contributed $40,959 million to the economy during 2006-2007 (Australian Bureau of Statistics, 2009). This thesis describes the perceptions of information security management in two Australian NPOs and examines the appropriateness of the ISO 27002 information security management standard in an NPO context. The overall approach to the research is interpretive. A collective case study has been performed, consisting of two instrumental case studies with the researcher being embedded within two NPOs for extended periods of time. Data gathering and analysis was informed by grounded theory and action research, and the Technology Acceptance Model was utilised as a lens to explore the findings and provide limited generalisability to other contexts. The major findings include a distinct lack of information security management best practice in both organisations. ISM Governance and risk management was lacking and ISM policy was either outdated or non- existent. While some user focused ISM practices were evident, reference to standards, such as ISO 27002, were absent. The main factor that negatively impacted on ISM practices was the lack of resources available for ISM in the NPOs studied. Two novel aspects of information security dis- covered in this research were the importance of accuracy and consistency of information. The contribution of this research is a preliminary understanding of ISM practices and perceptions in NPOs. Recommendations for a new approach to managing information security management in nonprofit organisations have been proposed.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

Beyond difference : reconfiguring education for the user-led age

In recent years, various observers have pointed to the shifting paradigms of cultural and societal participation and economic production in developed nations. These changes are facilitated (although, importantly, not solely driven) by the emergence of new, participatory technologies of information access, knowledge exchange, and content production, many of whom are associated with Internet and new media technologies. In an online context, such technologies are now frequently described as social software, social media, or Web2.0, but their impact is no longer confined to cyberspace as an environment that is somehow different and separate from ‘real life’: user-led content and knowledge production is increasingly impacting on media, economy, law, social practices, and democracy itself.

ACE research briefing paper 002: learning and adaptation in the business creation process

This year marks the completion of data collection for year three (Wave 3) of the CAUSEE study. This report uses data from the first three years and focuses on the process of learning and adaptation in the business creation process. Most start-ups need to change their business model, their product, their marketing plan, their market or something else about the business to be successful. PayPal changed their product at least five times, moving from handheld security, to enterprise apps, to consumer apps, to a digital wallet, to payments between handhelds before finally stumbling on the model that made the a multi-billion dollar company revolving around email-based payments. PayPal is not alone and anecdotes abounds of start-ups changing direction: Sysmantec started as an artificial intelligence company, Apple started selling plans to build computers and Microsoft tried to peddle compilers before licensing an operating system out of New Mexico. To what extent do Australian new ventures change and adapt as their ideas and business develop? As a longitudinal study, CAUSEE was designed specifically to observe development in the venture creation process. In this research briefing paper, we compare development over time of randomly sampled Nascent Firms (NF) and Young Firms(YF), concentrating on the surviving cases. We also compare NFs with YFs at each yearly interval. The 'high potential' over sample is not used in this report.