Predicting psychological and subjective well-being from personality: incremental prediction from 30 facets over the Big 5

This study investigated the relationship between the Big 5, measured at factor and facet levels, and dimensions of both psychological and subjective well-being. Three hundred and thirty-seven participants completed the 30 Facet International Personality Item Pool Scale, Satisfaction with Life Scale, Positive and Negative Affectivity Schedule, and Ryff’s Scales of Psychological Well-Being. Cross-correlation decomposition presented a parsimonious picture of how well-being is related to personality factors. Incremental facet prediction was examined using double-adjusted r2 confidence intervals and semi-partial correlations. Incremental prediction by facets over factors ranged from almost nothing to a third more variance explained, suggesting a more modest incremental prediction than presented in the literature previously. Examination of semi-partial correlations controlling for factors revealed a small number of important facet-well-being correlations. All data and R analysis scripts are made available in an online repository.

A five-gene hedgehog signature developed as a patient preselection tool for hedgehog inhibitor therapy in medulloblastoma

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Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.

Comprehensive analysis of big data variety landscape

Big data presents a remarkable opportunity for organisations to obtain critical intelligence to drive decisions and obtain insights as never before. However, big data generates high network traffic. Moreover, the continuous growth in the variety of network traffic due to big data variety has rendered the network to be one of the key big data challenges. In this article, we present a comprehensive analysis of big data variety and its adverse effects on the network performance. We present taxonomy of big data variety and discuss various dimensions of the big data variety features. We also discuss how the features influence the interconnection network requirements. Finally, we discuss some of the challenges each big data variety dimension presents and possible approach to address them.

Assessing outcomes for cost-utility analysis in depression : comparison of five multi-attribute utility instruments with two depression-specific outcome measures

Many mental health surveys and clinical studies do not include a multi-attribute utility instrument (MAUI) that produces quality-adjusted life-years (QALYs). There is also some question about the sensitivity of the existing utility instruments to mental health.

The five-parameter grain boundary character and energy distributions of a fully austenitic high-manganese steel using three dimensional data

The three-dimensional interfacial grain boundary network in a fully austenitic high-manganese steel was studied as a function of all five macroscopic crystallographic parameters (i.e. lattice misorientation and grain boundary plane normal) using electron backscattering diffraction mapping in conjunction with focused ion beam serial sectioning. The relative grain boundary area and energy distributions were strongly influenced by both the grain boundary plane orientation and the lattice misorientation. Grain boundaries terminated by (1 1 1) plane orientations revealed relatively higher populations and lower energies compared with other boundaries. The most frequently observed grain boundaries were {1 1 1} symmetric twist boundaries with the Σ3 misorientation, which also had the lowest energy. On average, the relative areas of different grain boundary types were inversely correlated to their energies. A comparison between the current result and previously reported observations (e.g. high-purity Ni) revealed that polycrystals with the same atomic structure (e.g. face-centered cubic) have very similar grain boundary character and energy distributions. © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Restrictions in pub closing times and lockouts in Newcastle, Australia five years on.

In 2008 pub closing times were restricted from 5 am to 3:30 am in the central business district (CBD) of Newcastle, Australia. A previous study showed a one-third reduction in assaults in the 18 months following the restriction. We assessed whether the assault rate remained lower over the following 3.5 years and whether the introduction of a 'lockout' in nearby Hamilton was associated with a reduction in assaults there.

Evolutionary optimization: a big data perspective

Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new computational challenges including very high dimensionality and sparseness of data. Evolutionary algorithms' superior exploration skills should make them promising candidates for handling optimization problems involving big data. High dimensional problems introduce added complexity to the search space. However, EAs need to be enhanced to ensure that majority of the potential winner solutions gets the chance to survive and mature. In this paper we present an evolutionary algorithm with enhanced ability to deal with the problems of high dimensionality and sparseness of data. In addition to an informed exploration of the solution space, this technique balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions. To achieve this, in addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance. The algorithm has also been successfully applied to a real world problem of financial portfolio management. Although the proposed method cannot be considered big data-ready, it is certainly a move in the right direction.

Five ways to hack and cheat with bring-your-own-device electronic examinations

Bring-your-own-device electronic examinations (BYOD e-exams) are a relatively new type of assessment where students sit an in-person exam under invigilated conditions with their own laptop. Special software restricts student access to prohibited computer functions and files, and provides access to any resources or software the examiner approves. In this study, the decades-old computer security principle that ‘software security depends on hardware security’ is applied to a range of BYOD e-exam tools. Five potential hacks are examined, four of which are confirmed to work against at least one BYOD e-exam tool. The consequences of these hacks are significant, ranging from removal of the exam paper from the venue through to receiving live assistance from an outside expert. Potential mitigation strategies are proposed; however, these are unlikely to completely protect the integrity of BYOD e-exams. Educational institutions are urged to balance the additional affordances of BYOD e-exams for examiners against the potential affordances for cheaters.

Obtain confidentiality or/and authenticity in Big Data by ID-based generalized signcryption

Recently, the Big Data paradigm has received considerable attention since it gives a great opportunity to mine knowledge from massive amounts of data. However, the new mined knowledge will be useless if data is fake, or sometimes the massive amounts of data cannot be collected due to the worry on the abuse of data. This situation asks for new security solutions. On the other hand, the biggest feature of Big Data is "massive", which requires that any security solution for Big Data should be "efficient". In this paper, we propose a new identity-based generalized signcryption scheme to solve the above problems. In particular, it has the following two properties to fit the efficiency requirement. (1) It can work as an encryption scheme, a signature scheme or a signcryption scheme as per need. (2) It does not have the heavy burden on the complicated certificate management as the traditional cryptographic schemes. Furthermore, our proposed scheme can be proven-secure in the standard model. © 2014 Elsevier Inc. All rights reserved.