Managing the introduction of information security awareness programmes in organisations

Several studies explore information security awareness focusing on individual and/or organisational aspects. This paper argues that security awareness processes are associated with interrelated changes that occur at the organisational, the technological and the individual level. We introduce an integrated analytical framework that has been developed through action research in a public sector organisation, comprising actor-network theory (ANT), structuration theory and contextualism. We develop and use this framework to analyse and manage changes introduced by the implementation of a security awareness programme in the research setting. The paper illustrates the limitations of each theory (ANT, structuration theory and contextualism) to study multi-level changes when used individually, demonstrates the synergies of the three theories, and proposes how they can be used to study and manage awareness-related changes at the individual, organisational and technological level.     

Chemical investigation and antimicrobial properties of mastic water and its major constituents

Mastic water is a commercial flavouring obtained during the steam distillation of mastic resin (the resin of Pistacia lentiscus var. chia) for the production of mastic oil. The mastic water extracts were analysed by GC−MS. The major compounds identified were verbenone, α-terpineol, linalool and trans-pinocarveol. Overall the composition was found to be very different from that of mastic oil. Additional GC−MS revealed the enantiomeric ratio of the chiral constituents of mastic water. The antimicrobial activity of mastic water extract, as well as that of its major constituents, was examined against Escherichia coli, Staphylococcus aureus and Candida spp. including ATCC wild clinical and food-borne strains. Linalool and α-terpineol were found to be the most potent antimicrobial constituents. Finally the stability of mastic water at different temperatures was studied, showing no change in the GC−MS profile of the organic extract for a period of 4 months at storage temperatures up to 4 °C.     

A survey on the structure of approximation classes

The structure of approximability classes by the introduction of approximation preserving reductions has been one of the main research programmes in theoretical computer science during the last thirty years. This paper surveys the main results achieved in this domain.     

Towards a Drought Watch System based on Spatial SPI

Regional Drought can be assessed through various meaningfulprocedures mainly related to the expected consequences. However, a general knowledge of the occurrence of drought, thearea which is affected, its severity and its duration are ofgreat importance for a series of decisions, which may beappropriate for a variety of activities. From the existingsimple and popular indices used for the estimation of drought,the Standardised Precipitation Index, known as SPI, seems to winuniversal applicability. A method based on the estimation of SPIover a geographical area and its use for characterising drought,is presented in this paper. Applications of the method arepresented using a digital terrain model and a simple computercalculating routine. It is shown that the proposed procedurecan be easily applied and can support a Drought Watch System foran area of mesoscale dimensions.     

In-depth analysis and evaluation of diffusive glioma models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.     

Drought Severity Assessment Based on Bivariate Probability Analysis

Conventionally drought severity is assessed based on drought indices. Recently the Reconnaissance Drought Index (RDI) was proposed to assess drought severity based on the precipitation to potential evapotranspiration ratio (P/PET). In this paper RDI is studied as a bivariate index under a set of assumptions and simplifications. The paper presents a simple computational procedure for estimating the P/PET ratio for selected reference periods varying from 3 to 12 months, for any return period of drought. Alternatively, based on this procedure, the severity of any drought episode is rationally assessed. A bivariate probability analysis is employed based on the assumption that P and PET values are normally distributed and often negatively correlated. Examples for the application of the proposed procedure are presented using data from several meteorological stations in Greece. It is shown that the assumption of normality of both P and PET holds for long periods at all examined stations.     

On the discovery of subsumption relations for the alignment of ontologies

For the effective alignment of ontologies, the subsumption mappings between the elements of the source and target ontologies play a crucial role, as much as equivalence mappings do. This paper presents the “Classification-Based Learning of Subsumption Relations” (CSR) method for the alignment of ontologies. Given a pair of two ontologies, the objective of CSR is to learn patterns of features that provide evidence for the subsumption relation among concepts, and thus, decide whether a pair of concepts from these ontologies is related via a subsumption relation. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series of both artificially created and real world cases, and discusses the potential of the method.     

Named-Entity Recognition from Greek and English Texts

Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.