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.

Indirect message injection for MAC generation

This paper presents a model for the generation of a MAC tag using a stream cipher. The input message is used indirectly to control segments of the keystream that form the MAC tag. Several recent proposals can be considered as instances of this general model, as they all perform message accumulation in this way. However, they use slightly different processes in the message preparation and finalisation phases. We examine the security of this model for different options and against different types of attack, and conclude that the indirect injection model can be used to generate MAC tags securely for certain combinations of options. Careful consideration is required at the design stage to avoid combinations of options that result in susceptibility to forgery attacks. Additionally, some implementations may be vulnerable to side-channel attacks if used in Authenticated Encryption (AE) algorithms. We give design recommendations to provide resistance to these attacks for proposals following this model.

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.

Novel sequential methods in dose-finding designs : extensions of the continual reassessment method

Dose-finding trials are a form of clinical data collection process in which the primary objective is to estimate an optimum dose of an investigational new drug when given to a patient. This thesis develops and explores three novel dose-finding design methodologies. All design methodologies presented in this thesis are pragmatic. They use statistical models, incorporate clinicians' prior knowledge efficiently, and prematurely stop a trial for safety or futility reasons. Designing actual dose-finding trials using these methodologies will minimize practical difficulties, improve efficiency of dose estimation, be flexible to stop early and reduce possible patient discomfort or harm.

Macromolecular engineering via RAFT chemistry : from sequential to modular design

Sequential Design Molecular Weight Range Functional Monomers: Possibilities, Limits, and Challenges Block Copolymers: Combinations, Block Lengths, and Purities Modular Design End-Group Chemistry Ligation Protocols Conclusions

Numerical modelling of porous injection in a radical farming scramjet

A numerical investigation of the behaviour of fuel injection through a porous surface in an inlet-fuelled, radial-farming scramjet is presented. The performance of porous fuel injection is compared to discrete port hole injection at an equivalence ratio of φ ≈ 0.4 for both cases. The comparison is performed at a Mach 6.5 flow condition with a total specific enthalpy of 4.3 MJ/kg. The numerical results are compared to experiments performed in the T4 shock tunnel where available. The presented results demonstrate for the first time, that porous fuel injection has the potential to outperform port hole injectors in scramjet engines in terms of fuel-air mixing, ignition delays and achievable combustion efficiencies despite reduced fuel penetration heights.

H2-O2 porous fuel injection in a radical farming scramjet

This paper reports on the experimental testing of oxygen compatible ceramic matrix composite porous injectors in a nominally two-dimensional hydrogen fuelled and oxygen enriched radical farming scramjet in the T4 shock tunnel facility. All experiments were performed at a dynamic pressure of 146 kPa, an equivalent flight Mach number of 9.7, a stagnation pressure and enthalpy of 40MPa and 4.3 MJ/kg respectively and at a fuelling condition that resulted in an average equivalence ratio of 0.472. Oxygen was pre-mixed with the fuel prior to injection to achieve enrichment percentages of approximately 13%, 15% and 17%. These levels ensured that the hydrogen-oxidiser mix injected into the engine always remained too fuel rich to sustain a flame without any additional mixing with the captured air. Addition of pre-mixed oxygen with the fuel was found to significantly alter the performance of the engine; enhancing both combustion and ignition and converting a previously observed limited combustion condition into one with sustained and noticeable combustion induced pressure rise. Increases in the enrichment percentage lead to further increases in combustion levels and acted to reduce ignition lengths within the engine. Suppressed combustion runs, where a nitrogen test gas was used, confirmed that the pressure rise observed in these experiments as attributed to the oxygen enrichment and not associated with the increased mass injected.

Sequential versus simultaneous viewing of an environment : effects of focal attention to individual object locations on visual spatial learning

We investigated memories of room-sized spatial layouts learned by sequentially or simultaneously viewing objects from a stationary position. In three experiments, sequential viewing (one or two objects at a time) yielded subsequent memory performance that was equivalent or superior to simultaneous viewing of all objects, even though sequential viewing lacked direct access to the entire layout. This finding was replicated by replacing sequential viewing with directed viewing in which all objects were presented simultaneously and participants’ attention was externally focused on each object sequentially, indicating that the advantage of sequential viewing over simultaneous viewing may have originated from focal attention to individual object locations. These results suggest that memory representation of object-to-object relations can be constructed efficiently by encoding each object location separately, when those locations are defined within a single spatial reference system. These findings highlight the importance of considering object presentation procedures when studying spatial learning mechanisms.

Sequential monitoring of hospital adverse events when control charts fail : the example of fall injuries in hospitals

Objective To evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation. Design Retrospective audit. Setting The Northern Hospital, Melbourne, Australia. Participants 171,059 patients admitted between January 2001 and December 2006. Measurements The analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit. Results The data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation. Conclusion Charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Intravenous injection of leconotide, an omega conotoxin : synergistic antihyperalgesic effects with morphine in a rat model of bone cancer pain

Objective.  Leconotide (CVID, AM336, CNSB004) is an omega conopeptide similar to ziconotide, which blocks voltage sensitive calcium channels. However, unlike ziconotide, which must be administered intrathecally, leconotide can be given intravenously because it is less toxic. This study investigated the antihyperalgesic potency of leconotide given intravenously alone and in combinations with morphine-administered intraperitoneally, in a rat model of bone cancer pain. Design.  Syngeneic rat prostate cancer cells AT3B-1 were injected into one tibia of male Wistar rats. The tumor expanded within the bone causing hyperalgesia to heat applied to the ipsilateral hind paw. Measurements were made of the maximum dose (MD) of morphine and leconotide given alone and in combinations that caused no effect in an open-field activity monitor, rotarod, and blood pressure and heart rate measurements. Paw withdrawal thresholds from noxious heat were measured. Dose response curves for morphine (0.312–5.0 mg/kg intraperitoneal) and leconotide (0.002–200 µg/kg intravenous) given alone were plotted and responses compared with those caused by morphine and leconotide in combinations. Results.  Leconotide caused minimal antihyperalgesic effects when administered alone. Morphine given alone intraperitoneally caused dose-related antihyperalgesic effects (ED50 = 2.40 ± 1.24 mg/kg), which were increased by coadministration of leconotide 20 µg/kg (morphine ED50 = 0.16 ± 1.30 mg/kg); 0.2 µg/kg (morphine ED50 = 0.39 ± 1.27 mg/kg); and 0.02 µg/kg (morphine ED50 = 1.24 ± 1.30 mg/kg). Conclusions.  Leconotide caused a significant increase in reversal by morphine of the bone cancer-induced hyperalgesia without increasing the side effect profile of either drug. Clinical Implication.  Translation into clinical practice of the method of analgesia described here will improve the quantity and quality of analgesia in patients with bone metastases. The use of an ordinary parenteral route for administration of the calcium channel blocker (leconotide) at low dose opens up the technique to large numbers of patients who could not have an intrathecal catheter for drug administration. Furthermore, the potentiating synergistic effect with morphine on hyperalgesia without increased side effects will lead to greater analgesia with improved quality of life.