Knowledge society arguments revisited in the semantic technologies era

In the light of high profile governmental and international efforts to realise the knowledge society, I review the arguments made for and against it from a technology standpoint. I focus on advanced knowledge technologies with applications on a large scale and in open- ended environments like the World Wide Web and its ambitious extension, the Semantic Web. I argue for a greater role of social networks in a knowledge society and I explore the recent developments in mechanised trust, knowledge certification, and speculate on their blending with traditional societal institutions. These form the basis of a sketched roadmap for enabling technologies for a knowledge society

The impact of using pair programming on system evolution a simulation-based study

In this paper we investigate the impact of pair--programming on the long term evolution of software systems. We use system dynamics to build simulation models which predict the trend in system growth with and without pair programming. Initial results suggest that the extra effort needed for two people to code together may generate sufficient benefit to justify pair programming.Peer reviewe

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

A direct method to compute the galaxy count angular correlation function including redshift-space distortions

In the near future, cosmology will enter the wide and deep galaxy survey area allowing high-precision studies of the large scale structure of the universe in three dimensions. To test cosmological models and determine their parameters accurately, it is natural to confront data with exact theoretical expectations expressed in the observational parameter space (angles and redshift). The data-driven galaxy number count fluctuations on redshift shells, can be used to build correlation functions $C(\theta; z_1, z_2)$ on and between shells which can probe the baryonic acoustic oscillations, the distance-redshift distortions as well as gravitational lensing and other relativistic effects. Transforming the model to the data space usually requires the computation of the angular power spectrum $C_\ell(z_1, z_2)$ but this appears as an artificial and inefficient step plagued by apodization issues. In this article we show that it is not necessary and present a compact expression for $C(\theta; z_1, z_2)$ that includes directly the leading density and redshift space distortions terms from the full linear theory. It can be evaluated using a fast integration method based on Clenshaw-Curtis quadrature and Chebyshev polynomial series. This new method to compute the correlation functions without any Limber approximation, allows us to produce and discuss maps of the correlation function directly in the observable space and is a significant step towards disentangling the data from the tested models

Open Source Integrated Library Systems in Public Libraries

One of the most fundamental decisions a library makes is choosing an integrated library system, or ILS. A public library can remove unwanted outside influence and save money by switching their ILS to free and open source software, or FOSS. This article is an examination of the progress made by FOSS ILSs to become not only contenders against proprietary systems, but also an appropriate choice for financial, functional, and philosophical reasons. Included is a timeline of published evaluations, the milestone of 14% adoption, a summary of the current landscape, and example implementation cases. A functional analysis shows why a public library can now safely make the switch. A philosophical analysis shows why they should do so. Finally, a proposal is made to “Buy Back America’s Libraries, and return ownership of the keystone of our public information infrastructure to the people

Network strategies for the new economy

In this paper we argue that the pace and scale of development in the information and communication technology industries (ICT) has had and continues to have major effects on the industry economics and competitive dynamics generally. We maintain that the size of changes in demand and supply conditions is forcing companies to make significant changes in the way they conceive and implement their strategies. We decompose the ICT industries into four levels, technology standards, supply chains, physical platforms, and consumer networks. The nature of these technologies and their cost characteristics coupled with higher degrees of knowledge specialisation is impelling companies to radical revisions of their attitudes towards cooperation and co-evolution with suppliers and customers. Where interdependencies between customers are particularly strong, we anticipate the possibility of winner-takes-all strategies. In these circumstances industry risks become very high and there will be significant consequences for competitive markets

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, sometimes by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We review, discuss and benchmark state-of-the-art representations and relations between them, including smooth overlap of atomic positions, many-body tensor representation, and symmetry functions. For this, we use a unified mathematical framework based on many-body functions, group averaging and tensor products, and compare energy predictions for organic molecules, binary alloys and Al-Ga-In sesquioxides in numerical experiments controlled for data distribution, regression method and hyper-parameter optimization