YAWL : power through patterns

Workflow Management Systems (WfMSs) enable the development and maintenance of workflow specifications at design time and their execution and monitoring at runtime. The open source WfMS YAWL supports the YAWL language – a formally defined language based on Petri nets which offers comprehensive support for control-flow and resource patterns. In addition, the YAWL system provides extensive support for process flexibility, in particular for process configuration, exception handling, dynamic workflow and declarative workflow. Due to its formal foundation, sophisticated verification support can also be achieved. This paper presents the YAWL system and its main applications.

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.

Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.

Minimising speaker verification utterance length through confidence based early verification decisions

This paper presents a novel approach of estimating the confidence interval of speaker verification scores. This approach is utilised to minimise the utterance lengths required in order to produce a confident verification decision. The confidence estimation method is also extended to address both the problem of high correlation in consecutive frame scores, and robustness with very limited training samples. The proposed technique achieves a drastic reduction in the typical data requirements for producing confident decisions in an automatic speaker verification system. When evaluated on the NIST 2005 SRE, the early verification decision method demonstrates that an average of 5–10 seconds of speech is sufficient to produce verification rates approaching those achieved previously using an average in excess of 100 seconds of speech.

A novel nonrepudiable threshold multi-proxy multi-signature scheme with shared verification

Tzeng et al. proposed a new threshold multi-proxy multi-signature scheme with threshold verification. In their scheme, a subset of original signers authenticates a designated proxy group to sign on behalf of the original group. A message m has to be signed by a subset of proxy signers who can represent the proxy group. Then, the proxy signature is sent to the verifier group. A subset of verifiers in the verifier group can also represent the group to authenticate the proxy signature. Subsequently, there are two improved schemes to eliminate the security leak of Tzeng et al.’s scheme. In this paper, we have pointed out the security leakage of the three schemes and further proposed a novel threshold multi-proxy multi-signature scheme with threshold verification.

Treadmill adaptation and verification of self-selected walking speed : a protocol for children

The term self-selected (i.e., individual or comfortable walking pace or speed) is commonly used in the literature (Frost, Dowling, Bar-Or, & Dyson, 1997; Jeng, Liao, Lai, & Hou, 1997; Wergel-Kolmert & Wohlfart, 1999; Maltais, Bar-Or, Pienynowski, & Galea, 2003; Browning & Kram, 2005; Browning, Baker, Herron, & Kram, 2006; Hills, Byrne, Wearing, & Armstrong, 2006) and is identified as the most efficient walking speed, with increased efficiency defined by lower oxygen uptake (VO^sub 2^) per unit mechanical work (Hoyt & Taylor, 1981; Taylor, Heglund, & Maloiy, 1982; Hreljac, 1993). [...] assessing individual and group differences in metabolic energy expenditure using oxygen uptake requires individuals to be comfortable with, and able to accommodate to, the equipment.

The roles and importance of formal and informal sources of business support in young firms : evidence from the UK’s 2008 Federation of Small Business Survey

Objectives This research explores the relationship between young firms, their growth orientation-intention and a range of relationships which can be seen to provide business support. Prior-work Research indicates that networks impact the firm’s ability to secure resources (Sirmon and Hitt 2003; Liao and Welsch. 2004; Hanlon and Saunders 2007). Networks have been evaluated in a number of ways ranging from simple counts to characteristics of their composition (Davidsson and Honig 2003), strength of relationships (Granovetter 1973) and network diversity (Carter et al 2003). By providing access to resources and knowledge (from start-up assistance and raising capital, (e.g. Smallbone et al, 2003), networks may assist in enabling continued persistence during those times where firms may experience resource constraints owing to firm growth (Baker and Nelson 2005). Approach The data used in this research was generated in the 2008 UK Federation of Small Businesses (FSB) survey. Over 1,000 of the firms responding were found to fall into the category of “young”, ((defined as firms under 4 years old). Firms were considered the unit of analysis with the entrepreneur being the chief spokesperson for the firm. Preliminary data analysis considered key demographic characteristics and industry classifications, comparing the FSB data with that of the UK government’s own (BERR) Small Business Surveys of 2007 and 2008, to establish some degree of representativeness of the respondents. The analysis then examined networks with varying potential ability to provide support for young firms, the networks measured in terms of number, diversity, characteristic and strength in its relationship to young firm growth orientation. The diversity of business-support-related relationships ranged from friends and family, through professional services, customers and suppliers, and government business services, to trade associations and informal business networks. The characteristics of these formal and informal sources of support for new businesses are examined across a range of business support-type activities for new firms. The number of relationships and types of business support are also explored. Finally, the strength of these relationships is examined by analysis of the source of business support, type of business support, and links to the growth orientation-intention of the firm, after controlling for a number of key variables related to firm and industry status and owner characteristics. Results Preliminary analysis of the data by means of univariate analysis showed that average number of sources of advice was around 2.5 (from a potential total of 6). In terms of the diversity of relationships, universities had by far the smallest percentage of firms receiving beneficial advice from them. Government business services were beneficially used by 40% of young firms, the other relationship types being around the 50-55% mark. In terms of characteristics of the advice, the average number of areas in which benefit was achieved was around 5.5 of a maximum of 15. Start-up advice has by far the highest percentage of firms obtaining beneficial advice, with increasing sales, improving contacts and improving confidence being the other categories at or around the 50% mark. Other market-focused areas where benefits were also received were in the areas of new markets, existing product improvements and new product improvements, where around 40% of the young responding firms obtained benefit. Regression techniques evaluating the strength of these relationships in terms of the links between business support (by source of support, type of support, and range of support) and firm growth orientation-intention focus highlighted a number of significant relationships, even after controlling for a range of other explanatory variables identified in the literature. Specifically, there was found to be a positive relationship between receiving business advice generally (regardless of type or source) and growth orientation. This relationship was seen to be stronger, however, when looking at the number of types of beneficial advice received, and stronger again for the number of sources of this advice. In terms of individual sources of advice, customers and suppliers had the strongest relationship with growth, with Government business services also found to be significant. Combining these two sources was also seen to increase the strength of the relationship between these two sources of advice and growth orientation. In considering areas of support, growth was most strongly positively related to advice that benefited the development of new products and services, and also business confidence, but was negatively related to advice linked to business recovery. Finally, amalgamating the 4 key types and sources of advice to examine the impact of combinations of these types and sources of advice also improved the strength of the relationship. Implications The findings will assist in the understanding of young firms in general and growth more specifically, particularly the role and importance of specific sources, types and combinations of business support used more extensively by new young growth-oriented firms. Value This research may assist in processes designed to allow entrepreneurs to make better decisions; educators and support organizations to develop better advice and assistance, and Governments design better conditions for the creation of new growth-oriented businesses.

Attitudes to Formal Business Training and Learning amongst Entrepreneurs in the Cultural Industries: Situated Business Learning through "Doing with Others."

Presents arguments supporting a social model of learning linked to situated learning and cultural capital. Critiques training methods used in cultural industries (arts, publishing, broadcasting, design, fashion, restaurants). Uses case study evidence to demonstrates inadequacies of formal training in this sector. (Contains 49 references.)

Cascading appearance-based features for visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state of the art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the speech recognition application also provide similar improvements for speaker recognition. These results suggest that visual speaker recognition can improve considerable when conducted solely through a consideration of the dynamic speech information rather than the static appearance of the speaker's mouth region.

Correctness ensuring process configuration : an approach based on partner synthesis

A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions.} In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the ``operating guidelines'' used for partner synthesis. We view the configuration process as an external service, and compute a characterization of all such services which meet particular requirements using the notion of configuration guideline. As a result, we can characterize all feasible configurations (i.\,e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

QUT speaker identity verification system for EVALITA 2009

This document outlines the system submitted by the Speech and Audio Research Laboratory at the Queensland University of Technology (QUT) for the Speaker Identity Verication: Application task of EVALITA 2009. This submission consisted of a score-level fusion of three component systems, a joint-factor GMM system and two SVM systems using GLDS and GMM supervector kernels. Development and evaluation results are presented, demonstrating the effectiveness of this fused system approach.