Multiple-time signature schemes against adaptive chosen message attacks

Multiple-time signatures are digital signature schemes where the signer is able to sign a predetermined number of messages. They are interesting cryptographic primitives because they allow to solve many important cryptographic problems, and at the same time offer substantial efficiency advantage over ordinary digital signature schemes like RSA. Multiple-time signature schemes have found numerous applications, in ordinary, on-line/off-line, forward-secure signatures, and multicast/stream authentication. We propose a multiple-time signature scheme with very efficient signing and verifying. Our construction is based on a combination of one-way functions and cover-free families, and it is secure against the adaptive chosen-message attack.

Bayesian imputation of non-chosen attribute values in revealed preference surveys

Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.

How to strengthen any weakly unforgeable signature into a strongly unforgeable signature

Standard signature schemes are usually designed only to achieve weak unforgeability – i.e. preventing forgery of signatures on new messages not previously signed. However, most signature schemes are randomised and allow many possible signatures for a single message. In this case, it may be possible to produce a new signature on a previously signed message. Some applications require that this type of forgery also be prevented – this requirement is called strong unforgeability. At PKC2006, Boneh Shen and Waters presented an efficient transform based on any randomised trapdoor hash function which converts a weakly unforgeable signature into a strongly unforgeable signature and applied it to construct a strongly unforgeable signature based on the CDH problem. However, the transform of Boneh et al only applies to a class of so-called partitioned signatures. Although many schemes fall in this class, some do not, for example the DSA signature. Hence it is natural to ask whether one can obtain a truly generic efficient transform based on any randomised trapdoor hash function which converts any weakly unforgeable signature into a strongly unforgeable one. We answer this question in the positive by presenting a simple modification of the Boneh-Shen-Waters transform. Our modified transform uses two randomised trapdoor hash functions.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Template-based circuit understanding

When verifying or reverse-engineering digital circuits, one often wants to identify and understand small components in a larger system. A possible approach is to show that the sub-circuit under investigation is functionally equivalent to a reference implementation. In many cases, this task is difficult as one may not have full information about the mapping between input and output of the two circuits, or because the equivalence depends on settings of control inputs. We propose a template-based approach that automates this process. It extracts a functional description for a low-level combinational circuit by showing it to be equivalent to a reference implementation, while synthesizing an appropriate mapping of input and output signals and setting of control signals. The method relies on solving an exists/forall problem using an SMT solver, and on a pruning technique based on signature computation.

Speed-based diagnostics of aerodynamic and mass imbalance in large wind turbines

Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.

Secondary attribute retrieval using tree data structures

Several techniques are known for searching an ordered collection of data. The techniques and analyses of retrieval methods based on primary attributes are straightforward. Retrieval using secondary attributes depends on several factors. For secondary attribute retrieval, the linear structures—inverted lists, multilists, doubly linked lists—and the recently proposed nonlinear tree structures—multiple attribute tree (MAT), K-d tree (kdT)—have their individual merits. It is shown in this paper that, of the two tree structures, MAT possesses several features of a systematic data structure for external file organisation which make it superior to kdT. Analytic estimates for the complexity of node searchers, in MAT and kdT for several types of queries, are developed and compared.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

Search for New Physics with a Dijet Plus Missing ET Signature in pp̅ Collisions at √s=1.96 TeV

We present results of a signature-based search for new physics using a dijet plus missing transverse energy data sample collected in 2 fb-1 of p-pbar collisions at sqrt(s) = 1.96 TeV with the CDF II detector at the Fermilab Tevatron. We observe no significant event excess with respect to the standard model prediction and extract a 95% C.L. upper limit on the cross section times acceptance for a potential contribution from a non-standard model process. Based on this limit the mass of a first or second generation scalar leptoquark is constrained to be above 187 GeV/c^2.

Incremental attribute evaluation through recursive procedures

Incremental semantic analysis in a programming environment based on Attribute Grammars is performed by an Incremental Attribute Evaluator (IAE). Current IAEs are either table-driven or make extensive use of graph structures to schedule reevaluation of attributes. A method of compiling an Ordered Attribute Grammar into mutually recursive procedures is proposed. These procedures form an optimal time Incremental Attribute Evaluator for the attribute grammar, which does not require any graphs or tables.

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio HR] = 2.4; 95% CI = 1.4-3.8; p < 0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1-2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4-2.8; p < 0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Off-line signature verification using genetically optimized weighted features

This paper is concerned with off-line signature verification. Four different types of pattern representation schemes have been implemented, viz., geometric features, moment-based representations, envelope characteristics and tree-structured Wavelet features. The individual feature components in a representation are weighed by their pattern characterization capability using Genetic Algorithms. The conclusions of the four subsystems teach depending on a representation scheme) are combined to form a final decision on the validity of signature. Threshold-based classifiers (including the traditional confidence-interval classifier), neighbourhood classifiers and their combinations were studied. Benefits of using forged signatures for training purposes have been assessed. Experimental results show that combination of the Feature-based classifiers increases verification accuracy. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Lamb wave based identification and parameter estimation of corrosion in metallic plate structure using a circular PWAS array

A circular array of Piezoelectric Wafer Active Sensor (PWAS) has been employed to detect surface damages like corrosion using lamb waves. The array consists of a number of small PWASs of 10 mm diameter and 1 mm thickness. The advantage of a circular array is its compact arrangement and large area of coverage for monitoring with small area of physical access. Growth of corrosion is monitored in a laboratory-scale set-up using the PWAS array and the nature of reflected and transmitted Lamb wave patterns due to corrosion is investigated. The wavelet time-frequency maps of the sensor signals are employed and a damage index is plotted against the damage parameters and varying frequency of the actuation signal (a windowed sine signal). The variation of wavelet coefficient for different growth of corrosion is studied. Wavelet coefficient as function of time gives an insight into the effect of corrosion in time-frequency scale. We present here a method to eliminate the time scale effect which helps in identifying easily the signature of damage in the measured signals. The proposed method becomes useful in determining the approximate location of the corrosion with respect to the location of three neighboring sensors in the circular array. A cumulative damage index is computed for varying damage sizes and the results appear promising.

Ultrasonic Lamb wave based monitoring of corrosion type of damage in plate using a circular array of piezoelectric transducers

In this paper we propose a concept and report experimental results based on a circular array of Piezoelectric Wafer Active Sensors (PWASs) for rapid localization and parametric identification of corrosion type damage in metallic plates. Implementation of this circular array of PWASs combines the use of ultrasonic Lamb wave propagation technique and an algorithm based on symmetry breaking in the signal pattern to locate and monitor the growth of a corrosion pit on a metallic plate. Wavelet time-frequency maps of the sensor signals are employed to obtain an insight regarding the effect of corrosion growth on the Lamb wave transmission in time-frequency scale. We present here a method to eliminate the time scale, which helps in identifying easily the signature of damage in the measured signals. The proposed method becomes useful in determining the approximate location of the damage with respect to the location of three neighboring sensors in the circular array. A cumulative damage index is computed from the wavelet coefficients for varying damage sizes and the results appear promising. Damage index is plotted against the damage parameters for frequency sweep of the excitation signal (a windowed sine signal). Results of corrosion damage are compared with circular holes of various sizes to demonstrate the applicability of present method to different types of damage. (C) 2011 Elsevier Ltd. All rights reserved.

Tuning the Sensitivity of a Metal-Based Piezoresistive Sensor Using Electromigration

We report a simple method to enhance the piezoresistive sensitivity of a gold film by more than 30 times and demonstrate it using a microcantilever resonator. Our method depends on controlled electromigration that we use to tune the resistance and sensitivity of the piezoresistive sensor. We attribute the enhancement in strain sensitivity to the creation of an inhomogeneous conduction medium at a predefined location by directed and controlled electromigration. We understand this phenomenon with tunneling-percolation model, which was originally hypothesized to explain nonuniversal percolation behavior of composite materials. 2012-0174]