A sociology of hackers

Illicit computer intruders, or hackers, are often thought of as pathological individuals rather than as members of a community. However, hackers exist within social groups that provide expertise, support, training, journals and conferences. This article outlines this community to establish the nature of hacking within ‘information societies’. To delineate a ‘sociology of hackers’, an introduction is provided to the nature of computer–mediated communication and the act of computer intrusion, the hack. Following this the hacking community is explored in three sections. First, a profile of the number of hackers and hacks is provided by exploring available demographics. Second, an outline of its culture is provided through a discussion of six different aspects of the hacking community. The six aspects are technology, secrecy, anonymity, membership fluidity, male dominance and motivations. Third, an exploration of the community's construction of a boundary, albeit fluid, between itself and its other, the computer security industry, is provided. This boundary is constructed through metaphors whose central role is to establish the ethical nature of hacking. Finally, a conclusion that rejects any pathologisation of hackers is offered.     

Social Work, Merit and Ethnic Diversity

This paper concentrates on the issues of merit and diversityin social work. The principal focus is ethnic diversity, usedas an example to illustrate a wider point about diversity. Itis clear that ethnic diversity in particular has become a majorpolitical objective across the public sector as part of the‘community cohesion’ initiative. Several benefitshave been identified and linked to the achievement of ethnicdiversity in the workforce; many of these have appeared in socialwork debate. The argument here is that while diversity has transformativepotential, it must be located within a more radical agenda ifit is to be successful. As a profession with a radical threadto its pedigree, social work is well placed to take this agendaforward.     

Nonparametric empirical Bayes for the Dirichlet process mixture model

The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data arrive. However, analyses based on the Dirichlet process prior are sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G ₀. Most previous applications have either fixed G ₀ as a member of a parametric family or treated G ₀ in a Bayesian fashion, using parametric prior specifications. In contrast, we have developed an adaptive nonparametric method for constructing smooth estimates of G ₀. We combine this method with a technique for estimating α, the other Dirichlet process parameter, that is inspired by an existing characterization of its maximum-likelihood estimator. Together, these estimation procedures yield a flexible empirical Bayes treatment of Dirichlet process mixtures. Such a treatment is useful in situations where smooth point estimates of G ₀ are of intrinsic interest, or where the structure of G ₀ cannot be conveniently modeled with the usual parametric prior families. Analysis of simulated and real-world datasets illustrates the robustness of this approach.     

Indiana bat roosting behavior differs between urban and rural landscapes

Urban Ecosystems - Urbanization may negatively affect forest obligate bat species. We compared the roosting behavior of federally endangered Indiana bats (Myotis sodalis) in a fragmented site,...     

Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelization and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the suggested estimators, and provide central limit theorems with expressions for asymptotic variances. We demonstrate how our method can make use of SMC in the state space models context, using Laplace approximations and time-discretized diffusions. Our experimental results are promising and show that the IS-type approach can provide substantial gains relative to an analogous DA scheme, and is often competitive even without parallelization.