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.     

Structure from Motion: Beyond the Epipolar Constraint

The classic approach to structure from motion entails a clear separation between motion estimation and structure estimation and between two-dimensional (2D) and three-dimensional (3D) information. For the recovery of the rigid transformation between different views only 2D image measurements are used. To have available enough information, most existing techniques are based on the intermediate computation of optical flow which, however, poses a problem at the locations of depth discontinuities. If we knew where depth discontinuities were, we could (using a multitude of approaches based on smoothness constraints) accurately estimate flow values for image patches corresponding to smooth scene patches; but to know the discontinuities requires solving the structure from motion problem first. This paper introduces a novel approach to structure from motion which addresses the processes of smoothing, 3D motion and structure estimation in a synergistic manner. It provides an algorithm for estimating the transformation between two views obtained by either a calibrated or uncalibrated camera. The results of the estimation are then utilized to perform a reconstruction of the scene from a short sequence of images.The technique is based on constraints on image derivatives which involve the 3D motion and shape of the scene, leading to a geometric and statistical estimation problem. The interaction between 3D motion and shape allows us to estimate the 3D motion while at the same time segmenting the scene. If we use a wrong 3D motion estimate to compute depth, we obtain a distorted version of the depth function. The distortion, however, is such that the worse the motion estimate, the more likely we are to obtain depth estimates that vary locally more than the correct ones. Since local variability of depth is due either to the existence of a discontinuity or to a wrong 3D motion estimate, being able to differentiate between these two cases provides the correct motion, which yields the least varying estimated depth as well as the image locations of scene discontinuities. We analyze the new constraints, show their relationship to the minimization of the epipolar constraint, and present experimental results using real image sequences that indicate the robustness of the method.     

Free and immobilized Lactobacillus casei ATCC 393 on whey protein as starter cultures for probiotic Feta-type cheese production

The use of free and immobilized Lactobacillus casei ATCC 393 on whey protein as starter culture in probiotic Feta-type cheese production was evaluated. The probiotic cultures resulted in significantly higher acidity; lower pH; reduced counts of coliforms, enterobacteria, and staphylococci; and improved quality characteristics compared with cheese with no culture. Microbiological and strain-specific multiplex PCR analysis showed that both free and immobilized L. casei ATCC 393 were detected in the novel products at levels required for conferring a probiotic effect at the end of the ripening. The effect of starter culture on production of volatile compounds was investigated by the solid-phase microextraction gas chromatography-mass spectrometry analysis technique. The immobilized cells resulted in an improved profile of aroma-related compounds and the overall high quality of the novel products was ascertained by the preliminary sensory test. Finally, the high added value produced by exploitation of whey, which is an extremely polluting industrial waste, was highlighted and assessed.     

Detection and Identification of Probiotic Lactobacillus plantarum Strains by Multiplex PCR Using RAPD-Derived Primers

Lactobacillus plantarum 2035 and Lactobacillus plantarum ACA-DC 2640 are two lactic acid bacteria (LAB) strains that have been isolated from Feta cheese. Both display significant potential for the production of novel probiotic food products. The aim of the present study was the development of an accurate and efficient method for the molecular detection and identification of the above strains in a single reaction. A multiplex PCR assay was designed for each strain, based on specific primers derived from Random Amplified Polymorphic DNA (RAPD) Sequenced Characterized Amplified Region (SCAR) analysis. The specificity of the assay was tested with a total of 23 different LAB strains, for L. plantarum 2035 and L. plantarum ACA-DC 2640. The multiplex PCR assay was also successfully applied for the detection of the above cultures in yogurt samples prepared in our lab. The proposed methodology may be applied for monitoring the presence of these strains in food products, thus evaluating their probiotic character. Moreover, our strategy may be adapted for other novel LAB strains with probiotic potential, thus providing a powerful tool for molecular discrimination that could be invaluable to the food industry.     

Kinetics of pore formation and resulting properties of lightweight inorganic polymers

Inorganic polymer (IP) foams could be used as an alternative to commonly available cellular concretes. To do so, however, it is crucial to understand how the foaming kinetics and the final foam properties can be controlled and tailored to delivered the desired performance. The research reported in this article investigates the influence of the SiO₂/Na₂O ratio of the activating solution, addition time of foaming agent, and temperature, on the reaction kinetics and final properties of a porous IP. The IPs were formed by activating fayalite slag, a by-product of secondary copper production, with a sodium-silicate solution. Foaming was achieved by introducing metallic Al into the formulation, oxidizing it in the alkaline environment, liberating H₂ gas, and entrapping it. Reaction kinetics were assessed using isothermal calorimetry, rheology, and a dedicated setup to record the foaming. The mechanical and physical properties of the foam were assessed via compressive strength and water absorption measurements. Microstructural analysis was undertaken using electron microscopy and computed tomography. The results showed that by decreasing the SiO₂/Na₂O ratio and/or increasing the temperature, the reaction kinetics were accelerated. Regarding the properties, postponing the initiation of the foaming led to more uniform and smaller pores, resulting in an increased compressive strength-to-density ratio.     

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).