PaaSword: A Holistic Data Privacy and Security by Design Framework for Cloud Services

Enterprises increasingly recognize the compelling economic and operational benefits from virtualizing and pooling IT resources in the cloud. Nevertheless, the significant and valuable transformation of organizations that adopt cloud computing is accompanied by a number of security threats that should be considered. In this paper, we outline significant security challenges presented when migrating to a cloud environment and propose PaaSword – a novel holistic framework that aspires to alleviate these challenges. Specifically, the proposed framework involves a context-aware security model, the necessary policies enforcement mechanism along with a physical distribution, encryption and query middleware.     

Model reduction of multi-scale chemical Langevin equations

This paper addresses the model reduction problem for a class of stiff chemical Langevin equations that arise as models of biomolecular networks with fast and slow reactions and can be described as continuous Markov processes. Initially, a coordinate transformation is sought that allows the decoupling of fast and slow variables in the model equations. Necessary and sufficient conditions are derived for such a linear transformation to exist, along with an explicit change of variables which achieves the desired decoupling. For the systems for which this step is applicable, the method of adiabatic elimination is applied to determine a representation of the slow dynamics. Theoretical concepts and results are illustrated with simple examples.     

A sparse nonparametric hierarchical Bayesian approach towards inductive transfer for preference modeling

In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).     

Oligosaccharide model of the vascular endothelial glycocalyx in physiological flow

Experiments have consistently revealed the pivotal role of the endothelial glycocalyx layer in vasoregulation and the layer’s contribution to mechanotransduction pathways. However, the exact mechanism by which the glycocalyx mediates fluid shear stress remains elusive. This study employs atomic-scale molecular simulations with the aim of investigating the conformational and orientation properties of highly flexible oligosaccharide components of the glycocalyx and their suitability as transduction molecules under hydrodynamic loading. Fluid flow was shown to have nearly no effect on the conformation populations explored by the oligosaccharide, in comparison with static (diffusion) conditions. However, the glycan exhibited a significant orientation change, when compared to simple diffusion, aligning itself with the flow direction. It is the tethered end of the glycan, an asparagine amino acid, which experienced conformational changes as a result of this flow-induced bias. Our results suggest that shear flow through the layer can have an impact on the conformational properties of saccharide-decorated transmembrane proteins, thus acting as a mechanosensor.     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Porous,Sintered Glass‐Ceramics from Inorganic Polymers Based on Fayalite Slag

The present paper deals with the synthesis of porous, sintered glass‐ceramics obtained at temperatures below 1150°C, originating from inorganic polymers based on fayalite slag. Firing led to the evaporation of water, dehydroxylation, and oxidation of Fe²⁺ above 345°C. For heating >700°C, the Si–O stretching band shifted from the 1160 and 750 cm^?1 to the 1255 and 830 cm^?1 region, due to a structural reorganization of the amorphous phase, whereas Fe–O bands appeared at 550 cm^?1. The final microstructure consisted predominantly of an amorphous phase, hematite, and franklinite. The open porosity and compressive strength decreased and increased, respectively, as the firing temperature increased. The final values suggest properties comparable to that of structural lightweight concrete, still, the materials synthesized herein, are lighter, and made primarily from secondary resources.     

Impact of the solidification path of FeOx–SiO2 slags on the resultant inorganic polymers

Aiming to reduce the carbon dioxide emissions associated with cement production, alternative binders such as inorganic polymers currently receive substantial attention and slags from the non-ferrous metallurgy are promising precursors. However, studies  that correlate their chemistry and crystallinity with the newly formed binder remain limited. In this work, the effect of three different solidification methods on glass formation and reactivity of FeO_x–SiO₂ slags, as well as on the molecular structure of the resultant Fe-rich inorganic polymers, was investigated. The inorganic polymers were synthesized by mixing the slags (approximate molar ratio FeO/SiO₂ = 1.6) with an alkali silicate solution (molar ratios SiO₂/Na₂O = 1.6 and H₂O/Na₂O = 25). Results demonstrated that higher cooling rates promoted higher glass formation and faster reaction kinetics when the slags were activated. ⁵⁷Fe Mössbauer spectroscopy indicated that all the slags consisted predominantly of Fe²⁺ ions with a minor amount of Fe³⁺ ions, regardless of the variability in glass content. The binder phase of all inorganic polymers consisted of iron in both Fe²⁺ and Fe³⁺ states, after 28 days of curing. After pulverizing the inorganic polymer pastes and exposing the powder to air for 28 additional days, the Fe²⁺ state in the binder transformed to Fe³⁺. The compressive strength evolution of the three slags showed that the 2-day strength was higher for the samples with a higher amorphous fraction, while after 28 days, this influence was less pronounced.     

Inorganic polymers made of fayalite slag: On the microstructure and behavior of Fe

The microstructure of inorganic polymers (IP) formed from fayalite slag was investigated as a function of the composition of different activating solutions. The starting slag was 80 wt% amorphous, and after activation using sodium silicate solutions with varying SiO₂/Na₂O molar ratios, the amorphous phase dissolved and a binder phase was formed. The morphology of this binder, including the population and size of remnant particles and pores, was dependent on the particular activating solution used, and became denser as the level of silicate rose. ⁵⁷Fe Mössbauer spectroscopy revealed that the IP synthesis reaction is combined with the oxidation of Fe²⁺ from the fayalite slag to Fe³⁺ in the inorganic polymer binder. The reaction extent varied and could be quantified using the absorption areas of these ions. Data corroborate that the Fe²⁺ ions in the amorphous part of the fayalite slag and the Fe³⁺ ions in the new binder phase had an average oxygen‐coordination number of 5.