A New Distributed Chinese Wall Security Policy Model

The application of the Chinese wall security policy model (CWSPM) to control the information flows between two or more competing and/or conflicting companies in cloud computing (Multi-tenancy) or in the social network, is a very interesting solution. The main goal of the Chinese Wall Security Policy is to build a wall between the datasets of competing companies, and among the system subjects. This is done by the applying to the subjects mandatory rules, in order to control the information flow caused between them. This problem is one of the hottest topics in the area of cloud computing (as a distributed system) and has been attempted in the past; however the proposed solutions cannot deal with the composite information flows problem (e.g., a malicious Trojan horses problem), caused by the writing access rule imposed to the subject on the objects. In this article, we propose a new CWSP model, based on the access query type of the subject to the objects using the concepts of the CWSP. We have two types of walls placement, the first type consists of walls that are built around the subject, and the second around the object. We cannot find inside each once wall two competing objects\u27 data. We showed that this mechanism is a good alternative to deal with some previous models\u27 limitations. The model is easy to implement in a distributed system (as Cloud-Computing). It is based on the technique of Object Oriented Programming (Can be used in Cloud computing Software as a service SaaS ) or by using the capabilities as an access control in real distributed system

Semantic Segmentation of Medical Images with Deep Learning: Overview

Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem

PU-NET Deep Learning Architecture for Gliomas Brain Tumor Segmentation in Magnetic Resonance Images

Automatic medical image segmentation is one of the main tasks for many organs and pathology structures delineation. It is also a crucial technique in the posterior clinical examination of brain tumors, like applying radiotherapy or tumor restrictions. Various image segmentation techniques have been proposed and applied to different image types. Recently, it has been shown that the deep learning approach accurately segments images, and its implementation is usually straightforward. In this paper, we proposed a novel approach, called PU-NET, for automatic brain tumor segmentation in multi-modal magnetic resonance images (MRI). We introduced an input processing block to a customized fully convolutional network derived from the U-Net network to handle the multi-modal inputs. We performed experiments over the Brain Tumor Segmentation (BRATS) dataset collected in 2018 and achieved Dice scores of 90.5%, 82.7%, and 80.3% for the whole tumor, tumor core, and enhancing tumor classes, respectively. This study provides promising results compared to the deep learning methods used in this context

Green Approach for Joint Management of Geo-Distributed Data Centers and Interconnection Networks

Every time an Internet user downloads a video, shares a picture, or sends an email, his/her device addresses a data center and often several of them. These complex systems feed the web and all Internet applications with their computing power and information storage, but they are very energy hungry. The energy consumed by Information and Communication Technology (ICT) infrastructures is currently more than 4\% of the worldwide consumption and it is expected to double in the next few years. Data centers and communication networks are responsible for a large portion of the ICT energy consumption and this has stimulated in the last years a research effort to reduce or mitigate their environmental impact. Most of the approaches proposed tackle the problem by separately optimizing the power consumption of the servers in data centers and of the network. However, the Cloud computing infrastructure of most providers, which includes traditional telcos that are extending their offer, is rapidly evolving toward geographically distributed data centers strongly integrated with the network interconnecting them. Distributed data centers do not only bring services closer to users with better quality, but also provide opportunities to improve energy efficiency exploiting the variation of prices in different time zones, the locally generated green energy, and the storage systems that are becoming popular in energy networks. In this paper, we propose an energy aware joint management framework for geo-distributed data centers and their interconnection network. The model is based on virtual machine migration and formulated using mixed integer linear programming. It can be solved using state-of-the art solvers such as CPLEX in reasonable time. The proposed approach covers various aspects of Cloud computing systems. Alongside, it jointly manages the use of green and brown energies using energy storage technologies. The obtained results show that significant energy cost savings can be achieved compared to a baseline strategy, in which data centers do not collaborate to reduce energy and do not use the power coming from renewable resources