Enhancing data security in cloud using random pattern fragmentation and a distributed nosql database

© 2019 IEEE. The cloud computing model has become very popular among users, as it has proven to be a cost-effective solution to store and process data, thanks to recent advancements in virtualization and distributed computing. Nevertheless, in the cloud environment, the user entrusts the safekeeping of its data entirely to the provider, which introduces the problem of how secure such data is and whether its integrity has been maintained. This paper proposes an approach to the data security in cloud by utilizing a random pattern fragmentation algorithm and combining it with a distributed NoSQL database. This not only increases the security of the data by storing it in different nodes and scramble all the bytes, but also allows the user to implement an alternative method of securing data. The performance of the approach is compared to other approaches, along with AES 256 encryption. Results indicate a significant performance improvement over encryption, highlighting the capabilities of this method for cloud stored data, as it creates a layer of protection without additional overhead

A multivariate high-order markov model for the income estimation of a wind farm

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm

New Techniques in Diagnostic X-ray Imaging: A Simulation Tool and Experimental Findings

AbstractAbsorption X-ray imaging is a well-established technique. However it is still a challenging task in its search for a compromise between the need for high spatial resolution and high contrast and the request to keep the dose delivered to the patient within acceptable values. New imaging techniques are under investigation, like the use of new X-ray sources, phase contrast imaging or K-edge imaging. Monte Carlo or analytic simulations are often the best way to test and predict the effectiveness of these techniques. A new simulation tool for X-ray imaging will be presented together with some applications to the characterization of new X-ray sources, in-line phase contrast effect and angiographic K-edge imaging. Simulation results will be compared also with experimental dat

A comparison between the β-globin gene clusters of domestic sheep (Ovis aries) and Sardinian mouflon (Ovis gmelini musimon)

International audienc

ANNABELL, a cognitive system able to learn different languages

© 2018 The authors and IOS Press. All rights reserved. ANNABELL is a cognitive system entirely based on a large-scale neural architecture capable of learning to communicate through natural language starting from a tabula rasa condition. In order to shed light on the level of cognitive development required for language acquisition, in this work the model is used to study the acquisition of a new language, namely Albanian, in addition to English. The aim is to evaluate in a completely different and more complex language the ability of the model to acquire new information through several examples introduced in the new language and to process the acquired information, answering questions that require the use of different language patterns. The results show that the system is capable of learning cumulatively in either language and to develop a broad range of language processing functionalities in both languages

A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure

The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200