A low-cost attack on branch-based software watermarking schemes

In 2005, Ginger Myles and Hongxia Jin proposed a software watermarking scheme based on converting jump instructions or unconditional branch statements (UBSs) by calls to a fingerprint branch function (FBF) that computes the correct target address of the UBS as a function of the generated fingerprint and integrity check. If the program is tampered with, the fingerprint and integrity checks change and the target address will not be computed correctly. In this paper, we present an attack based on tracking stack pointer modifications to break the scheme and provide implementation details. The key element of the attack is to remove the fingerprint and integrity check generating code from the program after disassociating the target address from the fingerprint and integrity value. Using the debugging tools that give vast control to the attacker to track stack pointer operations, we perform both subtractive and watermark replacement attacks. The major steps in the attack are automated resulting in a fast and low-cost attack.

Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

Assessment of fundamental movement skills in childhood cancer patients

Background The improved treatment protocols and subsequent improved survival rates amongst childhood cancer patients has shifted the focus towards the long-term consequences arising from cancer treatment. Children who have completed cancer treatment are at a greater risk of delayed development, diminished functioning, disability, compromised fundamental movement skill (FMS) attainment and long term chronic health conditions. The aim of the study was to compare FMS of childhood cancer patients with an aged matched healthy reference group. Methods Pediatric cancer patients aged 5-8 years of age (n=26; median age 6.91 years), who completed cancer treatment (<5 years) at the Sydney Children’s Hospital were assessed performing 7 key FMS; sprint, side-gallop, vertical-jump, catch, over-arm throw, kick and leap. Results were compared to the reference group (n=430; 6.56 years). Results Childhood cancer patients scored significantly lower on 3 out of 7 FMS tests when compared to the reference group. These results equated to a significantly lower overall score for FMS. Conclusion This study highlighted the significant deficits in FMS within pediatric patients having completed cancer treatment. In order to reduce the occurrence of significant FMS deficits in this population, FMS interventions maybe warranted to assist in recovery from childhood cancer, prevent late effects and improve the quality of life in survivors of childhood cancer.

Volatility transmission in global financial markets

As financial markets have become increasingly integrated internationally, the topic of volatility transmission across these markets has become more important. This thesis investigates how the volatility patterns of the world's main financial centres differ across foreign exchange, equity, and bond markets.

Effects of different instructional constraints on task performance and emergence of coordination in children

The objectives of this study were to determine the impact of different instructional constraints on standing board jump (sbj) performance in children and understand the underlying changes in emergent movement patterns. Two groups of novice participants were provided with either externally or internally focused attentional instructions during an intervention phase. Pre- and post-test sessions were undertaken to determine changes to performance and movement patterns. Thirty-six primary fourth-grade male students were recruited for this study and randomly assigned to either an external, internal focus or control group. Different instructional constraints with either an external focus (image of the achievement) or an internal focus (image of the act) were provided to the participants. Performance scores (jump distances), and data from key kinematic (joint range of motion, ROM) and kinetic variables (jump impulses) were collected. Instructional constraints with an emphasis on an external focus of attention were generally more effective in assisting learners to improve jump distances. Intra-individual analyses highlighted how enhanced jump distances for successful participants may be concomitant with specific changes to kinematic and kinetic variables. Larger joint ROM and adjustment to a comparatively larger horizontal impulse to a vertical impulse were observed for more successful participants at post-test performance. From a constraints-led perspective, the inclusion of instructional constraints encouraging self-adjustments in the control of movements (i.e., image of achievement) had a beneficial effect on individuals performing the standing broad jump task. However, the advantage of using an external focus of attentional instructions could be task- and individual-specific.

Estimating the parameters of stochastic volatility models using option price data

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

Induced smoothing for rank regression with censored survival times

Adaptions of weighted rank regression to the accelerated failure time model for censored survival data have been successful in yielding asymptotically normal estimates and flexible weighting schemes to increase statistical efficiencies. However, for only one simple weighting scheme, Gehan or Wilcoxon weights, are estimating equations guaranteed to be monotone in parameter components, and even in this case are step functions, requiring the equivalent of linear programming for computation. The lack of smoothness makes standard error or covariance matrix estimation even more difficult. An induced smoothing technique overcame these difficulties in various problems involving monotone but pure jump estimating equations, including conventional rank regression. The present paper applies induced smoothing to the Gehan-Wilcoxon weighted rank regression for the accelerated failure time model, for the more difficult case of survival time data subject to censoring, where the inapplicability of permutation arguments necessitates a new method of estimating null variance of estimating functions. Smooth monotone parameter estimation and rapid, reliable standard error or covariance matrix estimation is obtained.

Bayesian mixture model estimation of aerosol particle size distributions

In this paper, we examine approaches to estimate a Bayesian mixture model at both single and multiple time points for a sample of actual and simulated aerosol particle size distribution (PSD) data. For estimation of a mixture model at a single time point, we use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate mixture model parameters including the number of components which is assumed to be unknown. We compare the results of this approach to a commonly used estimation method in the aerosol physics literature. As PSD data is often measured over time, often at small time intervals, we also examine the use of an informative prior for estimation of the mixture parameters which takes into account the correlated nature of the parameters. The Bayesian mixture model offers a promising approach, providing advantages both in estimation and inference.

A geometric approach to stationary defect solutions in one space dimension

In this manuscript, we consider the impact of a small jump-type spatial heterogeneity on the existence of stationary localized patterns in a system of partial dierential equations in one spatial dimension...