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Wednesday, 13 June 2012

A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts


ABSTRACT

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.

Independently Verifiable Decentralized Role-Based Delegation



Introduction:
                             Cloud Computing is a term that is often bandied about the web these days and often attributed to different things that -- on the surface -- don't seem to have that much in common. So just what is Cloud Computing? I've heard it called a service, a platform, and even an operating system. Some even link it to such concepts as grid computing -- which is a way of taking many different computers and linking them together to form one very big computer.
                                    In this cloud computing model the major role has been given to the service provider the admin person must be there because his authorization signature is required to provide a service to clients. Since pay was maintained the server side has concern about security.
                                         The main challenge addressed in this paper is the verification of role-based authorization chains in decentralized environments, which has not been much studied in the existing literature. We have presented the RBCD model and its associated cryptographic operations for convenient verification of delegation chains
.
                                                 RBCD enables a role member to create delegations based on the need of collaboration; in the meantime anyone can verify a delegation chain without the participation of role administrators. Our protocol is general and can be realized by any signature scheme. We have described a specific realization with a hierarchical certificate-based encryption scheme that gives delegation compact credentials.
                      In our RBCD, given a privilege, two types of entities can delegate the privilege to others: 1) the resource owner of the privilege and 2) a member of a role who is delegated the privilege Decentralized role-based delegation allows users from administratively
Independent domains to be dynamically joined according to the needs of the tasks. We have also explored the applications of RBCD for efficient and flexible trust establishment in decentralized and pervasive environments

A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability


INTRODUCTION
      Storing data in the cloud has become a trend. An increasing number of clients store their important data n Remote servers in the cloud, without leaving a copy in their local computers. Sometimes the data stored in the cloud is so important that the clients must ensure it is not lost or corrupted. While it is easy to check data integrity after completely downloading the data to be checked, downloading large amounts of data just for checking data integrity is a waste of communication bandwidth. Hence, a lot of works have been done on designing remote data integrity checking protocols, which allow data integrity to be checked without completely downloading the data. Remote data integrity checking is first introduced in which independently propose RSA-based methods for solving this problem. Propose a remote storage auditing method based on pre-computed challenge-response pairs. Recently many works focus on providing three advanced features for remote data integrity checking protocols: data dynamics public verifiability and privacy against verifiers the protocols is support data dynamics at the block level, including block insertion, block modification and block deletion. The protocol of supports data appends operation. In addition, can be easily adapted to support data dynamics can be adapted to support data dynamics by using the techniques of. On the other hand, protocols in support public verifiability, by which anyone (not just the client) can perform the integrity checking operation.
 The protocols support privacy against third party verifiers. We compare the proposed protocol with selected previous protocols. (See Table I.) In this paper, we have the following main contributions:We propose a remote data integrity checking protocol for cloud storage, which can be viewed as an adaptation of Seb´e et al.’s protocol  The proposed protocol inherits the support of data dynamics from and supports public verifiability and privacy against third party verifiers, while at the same time it doesn’t need to use a third-party auditor. We give a security analysis of the proposed protocol, which shows that it is secure against the untrusted server and private against third party verifiers.We have theoretically analyzed and experimentally tested the efficiency of the protocol. Both theoretical and experimental results demonstrate that our protocol is efficient. The rest of this paper is organized as follows. In Section II, technical preliminaries are presented. In Section, the proposed remote data integrity checking protocol is presented. In Section IV, a formal analysis of the proposed protocol is presented. In Section V, we describe the support of data dynamics of the proposed protocol. In Section VI, the protocol’s complexity is analyzed in the aspects of communication, computation and storage costs; furthermore, experimental results are presented for the efficiency of the protocol. And finally, conclusions and possible future work are presented in Section

Towards Secure and Dependable Storage Services in Cloud Computing





Introduction:

SEVERAL trends are opening up the era of Cloud Computing, which is an Internet-based development and use of computer technology. The ever cheaper and more powerful processors, together with the software as a service computing architecture, are transforming data centers into pools of computing service on a huge scale. The increasing network bandwidth and reliable yet flexible network connections make it even possible that users can now subscribe high quality services from data and software that reside solely on remote data centers. Moving data into the cloud offers great convenience to users since they don’t have to care about the complexities of direct hardware management. The pioneer of Cloud Computing vendors, Amazon Simple Storage Service (and Amazon Elastic Compute Cloud are both well known examples. While these internet-based online services do provide huge amounts of storage space and customizable computing resources, this computing platform shift, however, is eliminating the responsibility of local machines for data maintenance at the same time.
 As a result, users are at the mercy of their cloud service providers for the availability and integrity of their data. On the one hand, although the cloud infrastructures are much more powerful and reliable than personal computing devices, broad range of both internal and external threats for data integrity still exist. Examples of outages and data loss incidents of noteworthy cloud storage services appear from time to time . On the other hand, since users may not retain a local copy of outsourced data, there exist various incentives for cloud service providers (CSP) to behave unfaithfully towards the cloud users regarding the status of their outsourced data. For example, to increase the profit margin by reducing cost, it is possible for CSP to discard rarely accessed data without being detected in a timely fashion. Similarly, CSP may even attempt to hide data loss incidents so as to maintain a reputation.

Therefore, although outsourcing data into the cloud is economically attractive for the cost and complexity of long-term large-scale data storage, it’s lacking of offering strong assurance
of data integrity and availability may impede its wide adoption by both enterprise and individual cloud users. In order to achieve the assurances of cloud data integrity and availability and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. However, the fact that users no longer have physical possession of data in the cloud prohibits the direct adoption of traditional cryptographic primitives for the purpose of data integrity protection. Hence, the verification of cloud storage correctness must be conducted without explicit knowledge of the whole data files. Meanwhile, cloud storage is not just a third party data warehouse.
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Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud





About Project:

In recent years ad hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper, we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. Based on this new framework, we perform extended evaluations of MapReduce-inspired processing jobs on an IaaS cloud system and compare the results to the popular data processing framework Hadoop.

Today a growing number of companies have to process huge amounts of data in a cost-efficient manner. Classic representatives for these companies are operators of Internet search engines, like Google, Yahoo, or Micros ft. The vast amount of data they have to deal with every day has made traditional database solutions prohibitively expensive . Instead, these companies have popularized an architectural paradigm based on a large number of commodity servers. Problems like processing crawled documents or regenerating a web index are split into several independent subtasks, distributed among the available nodes, and computed in parallel. In order to simplify the development of distributed applications on top of such architectures, many of these companies have also built customized data processing frameworks. Examples are Google’s MapReduce , Microsoft’s Dryad , or Yahoo!’s Map-ReduceMerge . They can be classified by terms like high-throughput computing (HTC) or many-task computing (MTC), depending on the amount of data and the number of tasks involved in the computation . Although these systems differ in design, their programming models share similar objectives, namely hiding the hassle of parallel programming, fault tolerance,and execution optimizations from the developer. Developers can typically continue to write sequential programs. The processing framework then takes care of distributing the program among the available nodes and executes each instance of the program on the appropriate fragment of data. For companies that only have to process large amounts of data occasionally running their own data center is obviously not an option. Instead, Cloud computing has emerged as a promising approach to rent a large IT infrastructure on a short-term pay-per-usage basis. Operators of so-called IaaS clouds, like Amazon EC2 , let their customers allocate, access, and control a set of virtual machines (VMs) which run inside their data centers and only charge them for the period of time the machines are allocated. The VMs are typically offered in different types, each type with its own characteristics (number of CPU cores, amount of main memory, etc.) and cost.

Multicloud Deployment of Computing Clusters For Loosely Coupled MTC Applications


ABSTRACT:

Unlike traditional utilities where a single provider scheme is a common practice, the ubiquitous access to cloud resources easily enables the simultaneous use of different clouds. Here we explore this scenario to deploy a computing cluster on the top of a multicloud infrastructure, for solving loosely coupled Many-Task Computing (MTC) applications. In this way, the cluster nodes can be provisioned with resources from different clouds to improve the cost effectiveness of the deployment, or to implement high-availability strategies. We prove the viability of this kind of solutions by evaluating the scalability, performance, and cost of different configurations of a Sun Grid Engine cluster, deployed on a multicloud infrastructure spanning a local data center and three different cloud sites: Amazon EC2 Europe, Amazon EC2 US, and ElasticHosts.