Lattice-based threshold-changeability for standard Shamir secret-sharing schemes

We consider the problem of increasing the threshold parameter of a secret-sharing scheme after the setup (share distribution) phase, without further communication between the dealer and the shareholders. Previous solutions to this problem require one to start off with a non-standard scheme designed specifically for this purpose, or to have communication between shareholders. In contrast, we show how to increase the threshold parameter of the standard Shamir secret-sharing scheme without communication between the shareholders. Our technique can thus be applied to existing Shamir schemes even if they were set up without consideration to future threshold increases. Our method is a new positive cryptographic application for lattice reduction algorithms, inspired by recent work on lattice-based list decoding of Reed-Solomon codes with noise bounded in the Lee norm. We use fundamental results from the theory of lattices (Geometry of Numbers) to prove quantitative statements about the information-theoretic security of our construction. These lattice-based security proof techniques may be of independent interest.

Threshold MACs

The power of sharing computation in a cryptosystem is crucial in several real-life applications of cryptography. Cryptographic primitives and tasks to which threshold cryptosystems have been applied include variants of digital signature, identification, public-key encryption and block ciphers etc. It is desirable to extend the domain of cryptographic primitives which threshold cryptography can be applied to. This paper studies threshold message authentication codes (threshold MACs). Threshold cryptosystems usually use algebraically homomorphic properties of the underlying cryptographic primitives. A typical approach to construct a threshold cryptographic scheme is to combine a (linear) secret sharing scheme with an algebraically homomorphic cryptographic primitive. The lack of algebraic properties of MACs rules out such an approach to share MACs. In this paper, we propose a method of obtaining a threshold MAC using a combinatorial approach. Our method is generic in the sense that it is applicable to any secure conventional MAC by making use of certain combinatorial objects, such as cover-free families and their variants. We discuss the issues of anonymity in threshold cryptography, a subject that has not been addressed previously in the literature in the field, and we show that there are trade-offis between the anonymity and efficiency of threshold MACs.

An intentional class design model to engage first year students with threshold concepts using the academic discourse theories of Vygotsky and Laurillard

In the context of the first-year university classroom, this paper develops Vygotsky’s claim that ‘the relations between the higher mental functions were at one time real relations between people’. By taking the main horizontal and hierarchical levels of classroom discourse and dialogue (student-student, student-teacher, teacher-teacher) and marrying these with the possibilities opened up by Laurillard’s conversational framework, we argue that the learning challenge of a ‘troublesome’ threshold concept might be met by a carefully designed sequence of teaching events and experiences for first year students, and we provide a number of strategies that exploit each level of these ‘hierarchies of discourse’. We suggest that an analytical approach to classroom design that embodies these levels of discourse in sequenced dialogic methods could be used by teachers as a strategy to interrogate and adjust teaching-in-practice especially in the first year of university study.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

A new ambiguity acceptance test threshold determination method with controllable failure rate

The ambiguity acceptance test is an important quality control procedure in high precision GNSS data processing. Although the ambiguity acceptance test methods have been extensively investigated, its threshold determine method is still not well understood. Currently, the threshold is determined with the empirical approach or the fixed failure rate (FF-) approach. The empirical approach is simple but lacking in theoretical basis, while the FF-approach is theoretical rigorous but computationally demanding. Hence, the key of the threshold determination problem is how to efficiently determine the threshold in a reasonable way. In this study, a new threshold determination method named threshold function method is proposed to reduce the complexity of the FF-approach. The threshold function method simplifies the FF-approach by a modeling procedure and an approximation procedure. The modeling procedure uses a rational function model to describe the relationship between the FF-difference test threshold and the integer least-squares (ILS) success rate. The approximation procedure replaces the ILS success rate with the easy-to-calculate integer bootstrapping (IB) success rate. Corresponding modeling error and approximation error are analysed with simulation data to avoid nuisance biases and unrealistic stochastic model impact. The results indicate the proposed method can greatly simplify the FF-approach without introducing significant modeling error. The threshold function method makes the fixed failure rate threshold determination method feasible for real-time applications.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

Approximation of Bayesian predictive p-values with regression ABC

In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and it's well known that computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. The second problem considered is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several samples.

A novel ambiguity acceptance test threshold determination method with controllable failure rate

Ambiguity validation as an important procedure of integer ambiguity resolution is to test the correctness of the fixed integer ambiguity of phase measurements before being used for positioning computation. Most existing investigations on ambiguity validation focus on test statistic. How to determine the threshold more reasonably is less understood, although it is one of the most important topics in ambiguity validation. Currently, there are two threshold determination methods in the ambiguity validation procedure: the empirical approach and the fixed failure rate (FF-) approach. The empirical approach is simple but lacks of theoretical basis. The fixed failure rate approach has a rigorous probability theory basis, but it employs a more complicated procedure. This paper focuses on how to determine the threshold easily and reasonably. Both FF-ratio test and FF-difference test are investigated in this research and the extensive simulation results show that the FF-difference test can achieve comparable or even better performance than the well-known FF-ratio test. Another benefit of adopting the FF-difference test is that its threshold can be expressed as a function of integer least-squares (ILS) success rate with specified failure rate tolerance. Thus, a new threshold determination method named threshold function for the FF-difference test is proposed. The threshold function method preserves the fixed failure rate characteristic and is also easy-to-apply. The performance of the threshold function is validated with simulated data. The validation results show that with the threshold function method, the impact of the modelling error on the failure rate is less than 0.08%. Overall, the threshold function for the FF-difference test is a very promising threshold validation method and it makes the FF-approach applicable for the real-time GNSS positioning applications.

Implementing the self-management threshold learning outcome for law : some intentional design strategies from the current curriculum toolbox

There is a growing awareness of the high levels of psychological distress being experienced by law students and the practising profession in Australia. In this context, a Threshold Learning Outcome (TLO) on self-management has been included in the six TLOs recently articulated as minimum learning outcomes for all Australian graduates of the Bachelor of Laws degree (LLB). The TLOs were developed during 2010 as part of the Australian Learning and Teaching Council’s (ALTC’s) project funded by the Australian Government to articulate ‘Learning and Teaching Academic Standards’. The TLOs are the result of a comprehensive national consultation process led by the ALTC’s Discipline Scholars: Law, Professors Sally Kift and Mark Israel.1 The TLOs have been endorsed by the Council of Australian Law Deans (CALD) and have received broad support from members of the judiciary and practising profession, representative bodies of the legal profession, law students and recent graduates, Legal Services Commissioners and the Law Admissions Consultative Committee. At the time of writing, TLOs for the Juris Doctor (JD) are also being developed, utilising the TLOs articulated for the LLB as their starting point but restating the JD requirements as the higher order outcomes expected of graduates of a ‘Masters Degree (Extended)’, this being the award level designation for the JD now set out in the new Australian Qualifications Framework.2 As Australian law schools begin embedding the learning, teaching and assessment of the TLOs in their curricula, and seek to assure graduates’ achievement of them, guidance on the implementation of the self-management TLO is salient and timely.

The Threshold Learning Outcome on self-management for the Bachelor of Laws degree : a proposed focus for teaching strategies in the first year law curriculum

The Australian Learning and Teaching Council (ALTC) Discipline Scholars for Law, Professors Sally Kift and Mark Israel, articulated six Threshold Learning Outcomes (TLOs) for the Bachelor of Laws degree as part of the ALTC’s 2010 project on Learning and Teaching Academic Standards. One of these TLOs promotes the learning, teaching and assessment of self-management skills in Australian law schools. This paper explores the concept of self-management and how it can be relevantly applied in the first year of legal education. Recent literature from the United States (US) and Australia provides insights into the types of issues facing law students, as well as potential antidotes to these problems. Based on these findings, I argue that designing a pedagogical framework for the first year law curriculum that promotes students’ connection with their intrinsic interests, values, motivations and purposes will facilitate student success in terms of their personal well-being, ethical dispositions and academic engagement.