Examining the predictive value of combining the theory of planned behaviour and the volunteer functions inventory

The functions of the volunteer functions inventory were combined with the constructs of the theory of planned behaviour (i.e., attitudes, subjective norms, and perceived behavioural control) to establish whether a stronger, single explanatory model prevailed. Undertaken in the context of episodic, skilled volunteering by individuals who were retired or approaching retirement (N = 186), the research advances on prior studies which either examined the predictive capacity of each model independently or compared their explanatory value. Using hierarchical regression analysis, the functions of the volunteer functions inventory (when controlling for demographic variables) explained an additional 7.0% of variability in individuals’ willingness to volunteer over and above that accounted for by the theory of planned behaviour. Significant predictors in the final model included attitudes, subjective norms and perceived behavioural control from the theory of planned behaviour and the understanding function from the volunteer functions inventory. It is proposed that the items comprising the understanding function may represent a deeper psychological construct (e.g., self-actualisation) not accounted for by the theory of planned behaviour. The findings highlight the potential benefit of combining these two prominent models in terms of improving understanding of volunteerism and providing a single parsimonious model for raising rates of this important behaviour.

Informal interactions between audit committees and internal audit functions : exploratory evidence and directions for future research

Purpose – This paper aims to recognise the importance of informal processes within corporate governance and complement existing research in this area by investigating factors associated with the existence of informal interactions between audit committees and internal audit functions and in providing directions for future research. Design/methodology/approach – To examine the existence and drivers of informal interactions between audit committees and internal audit functions, this paper relies on a questionnaire survey of chief audit executives (CAEs) in the UK from listed and non-listed, as well as financial and non-financial, companies. While prior qualitative research suggests that informal interactions do take place, most of the evidence is based on particular organisational setting or on a very small range of interviews. The use of a questionnaire enabled the examination of the existence of internal interactions across a relatively larger number of entities. Findings – The paper finds evidence of audit committees and internal audit functions engaging in informal interactions in addition to formal pre-scheduled regular meetings. Informal interactions complement formal meetings with the audit committee and as such represent additional opportunities for the audit committees to monitor internal audit functions. Audit committees’ informal interactions are significantly and positively associated with audit committee independence, audit chair’s knowledge and experience, and internal audit quality. Originality/value – The results demonstrate the importance of the background of the audit committee chair for the effectiveness of the governance process. This is possibly the first paper to examine the relationship between audit committee quality and internal audit, on the existence and driver of informal interactions. Policy makers should recognize that in addition to formal mechanisms, informal processes, such as communication outside of formal pre-scheduled meetings, play a significant role in corporate governance.

Building indifferentiable compression functions from the PGV compression functions

Preneel, Govaerts and Vandewalle (PGV) analysed the security of single-block-length block cipher based compression functions assuming that the underlying block cipher has no weaknesses. They showed that 12 out of 64 possible compression functions are collision and (second) preimage resistant. Black, Rogaway and Shrimpton formally proved this result in the ideal cipher model. However, in the indifferentiability security framework introduced by Maurer, Renner and Holenstein, all these 12 schemes are easily differentiable from a fixed input-length random oracle (FIL-RO) even when their underlying block cipher is ideal. We address the problem of building indifferentiable compression functions from the PGV compression functions. We consider a general form of 64 PGV compression functions and replace the linear feed-forward operation in this generic PGV compression function with an ideal block cipher independent of the one used in the generic PGV construction. This modified construction is called a generic modified PGV (MPGV). We analyse indifferentiability of the generic MPGV construction in the ideal cipher model and show that 12 out of 64 MPGV compression functions in this framework are indifferentiable from a FIL-RO. To our knowledge, this is the first result showing that two independent block ciphers are sufficient to design indifferentiable single-block-length compression functions.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Cryptographic hash functions

Cryptographic hash functions are an important tool of cryptography and play a fundamental role in efficient and secure information processing. A hash function processes an arbitrary finite length input message to a fixed length output referred to as the hash value. As a security requirement, a hash value should not serve as an image for two distinct input messages and it should be difficult to find the input message from a given hash value. Secure hash functions serve data integrity, non-repudiation and authenticity of the source in conjunction with the digital signature schemes. Keyed hash functions, also called message authentication codes (MACs) serve data integrity and data origin authentication in the secret key setting. The building blocks of hash functions can be designed using block ciphers, modular arithmetic or from scratch. The design principles of the popular Merkle–Damgård construction are followed in almost all widely used standard hash functions such as MD5 and SHA-1.

On hash functions using checksums

We analyse the security of iterated hash functions that compute an input dependent checksum which is processed as part of the hash computation. We show that a large class of such schemes, including those using non-linear or even one-way checksum functions, is not secure against the second preimage attack of Kelsey and Schneier, the herding attack of Kelsey and Kohno and the multicollision attack of Joux. Our attacks also apply to a large class of cascaded hash functions. Our second preimage attacks on the cascaded hash functions improve the results of Joux presented at Crypto’04. We also apply our attacks to the MD2 and GOST hash functions. Our second preimage attacks on the MD2 and GOST hash functions improve the previous best known short-cut second preimage attacks on these hash functions by factors of at least 226 and 254, respectively. Our herding and multicollision attacks on the hash functions based on generic checksum functions (e.g., one-way) are a special case of the attacks on the cascaded iterated hash functions previously analysed by Dunkelman and Preneel and are not better than their attacks. On hash functions with easily invertible checksums, our multicollision and herding attacks (if the hash value is short as in MD2) are more efficient than those of Dunkelman and Preneel.

On the Collision and Preimage Resistance of Certain Two-Call Hash Functions

In this paper we present concrete collision and preimage attacks on a large class of compression function constructions making two calls to the underlying ideal primitives. The complexity of the collision attack is above the theoretical lower bound for constructions of this type, but below the birthday complexity; the complexity of the preimage attack, however, is equal to the theoretical lower bound. We also present undesirable properties of some of Stam’s compression functions proposed at CRYPTO ’08. We show that when one of the n-bit to n-bit components of the proposed 2n-bit to n-bit compression function is replaced by a fixed-key cipher in the Davies-Meyer mode, the complexity of finding a preimage would be 2 n/3. We also show that the complexity of finding a collision in a variant of the 3n-bits to 2n-bits scheme with its output truncated to 3n/2 bits is 2 n/2. The complexity of our preimage attack on this hash function is about 2 n . Finally, we present a collision attack on a variant of the proposed m + s-bit to s-bit scheme, truncated to s − 1 bits, with a complexity of O(1). However, none of our results compromise Stam’s security claims.

On Randomizing Hash Functions to Strengthen the Security of Digital Signatures

Halevi and Krawczyk proposed a message randomization algorithm called RMX as a front-end tool to the hash-then-sign digital signature schemes such as DSS and RSA in order to free their reliance on the collision resistance property of the hash functions. They have shown that to forge a RMX-hash-then-sign signature scheme, one has to solve a cryptanalytical task which is related to finding second preimages for the hash function. In this article, we will show how to use Dean’s method of finding expandable messages for finding a second preimage in the Merkle-Damgård hash function to existentially forge a signature scheme based on a t-bit RMX-hash function which uses the Davies-Meyer compression functions (e.g., MD4, MD5, SHA family) in 2 t/2 chosen messages plus 2 t/2 + 1 off-line operations of the compression function and similar amount of memory. This forgery attack also works on the signature schemes that use Davies-Meyer schemes and a variant of RMX published by NIST in its Draft Special Publication (SP) 800-106. We discuss some important applications of our attack.

Cryptographic Hash Functions

In the modern era of information and communication technology, cryptographic hash functions play an important role in ensuring the authenticity, integrity, and nonrepudiation goals of information security as well as efficient information processing. This entry provides an overview of the role of hash functions in information security, popular hash function designs, some important analytical results, and recent advances in this field.

A new twist on mode indicator functions

Mode indicator functions (MIFs) are used in modal testing and analysis as a means of identifying modes of vibration, often as a precursor to modal parameter estimation. Various methods have been developed since the MIF was introduced four decades ago. These methods are quite useful in assisting the analyst to identify genuine modes and, in the case of the complex mode indicator function, have even been developed into modal parameter estimation techniques. Although the various MIFs are able to indicate the existence of a mode, they do not provide the analyst with any descriptive information about the mode. This paper uses the simple summation type of MIF to develop five averaged and normalised MIFs that will provide the analyst with enough information to identify whether a mode is longitudinal, vertical, lateral or torsional. The first three functions, termed directional MIFs, have been noted in the literature in one form or another; however, this paper introduces a new twist on the MIF by introducing two MIFs, termed torsional MIFs, that can be used by the analyst to identify torsional modes and, moreover, can assist in determining whether the mode is of a pure torsion or sway type (i.e., having a rigid cross-section) or a distorted twisting type. The directional and torsional MIFs are tested on a finite element model based simulation of an experimental modal test using an impact hammer. Results indicate that the directional and torsional MIFs are indeed useful in assisting the analyst to identify whether a mode is longitudinal, vertical, lateral, sway, or torsion.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Information-theoretic analysis of brain white matter fiber orientation distribution functions

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

How does angular resolution affect diffusion imaging measures?

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6 ≤ N ≤ 94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9.13 for GA and tGA, 10.57 for FA, 9.17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.