The radius-stepping algorithm is an efficient, parallelixable algorithm for finding the shortest paths in graphs. It solved the problem in Δ-Stepping algorithm, which has no known theoretical bounds fur general graphs. 1" this paper, we describe a parallel privacy-preserving method for finding SingleSource Shortest Paths (SSSP). Our optimized method is based on the Radius-Stepping algorithm. The method is implemented on iop of the Secure Multiparty Computation (SMC) Sharemiiid platform. We have reshaped the radius-stepping algorithm to work on vectors representing the graph in a SIMD manner, in order to enable a fast execution using the secret-sharing based SMC protocol set of Sharemind. The results of the real implementation show an efficient method that reduced the execution time hundreds of times iii comparison with a standard case of the privacy-preserving radius-stepping and Δ-Stepping algorithms.

The aim of Elliptic Curve Cryptosystems (ECC) is to achieve the same security level as RSA but with shorter key size. The basic operation in the ECC is scalar multiplication which is an expensive operation. In this paper, we focus on optimizing ECC scalar multiplication based on Non-Adjacent Form (NAF). A new algorithm is introduced that combines an Add-Subtract Scalar Multiplication Algorithm with NAF representation to accelerate the performance of the ECC calculation. Parallelizing the new algorithm shows an efficient method to calculate ECC. The proposed method has speed up the calculation up to 60% compared with the standard method.

This article presents a mobility prediction method for mobile phone users based on an enhanced Markov Chain algorithm. The mobile phone data has a highly dynamic nature and a sparcely sampled aspect; therefore, the prediction of user's mobility location poses a challenge. Our enhancement approach can be summarized as an embedded association of rules applied to a Markov chain algorithm. The proposed solution is encouraging for the next generation of mobile networks and it can be used to optimize the existing mobile network infrastructure, road traffic, tracking systems and localization. Validation of our system was carried out using real data collected from the field.

We present a knowledge discovery tool Search Excavator (SE) developed for detecting similar words in web documents ranked by overall usage frequency in American English. The SE prototype application is a web browser add-on developed to assist users in acquiring new knowledge in unknown domains and to help in posing more specific search queries. The SE is designed to discover similar but generally infrequent words with surrounding texts in browser web documents and then suggest found words as possible query keywords. This technique allows users to discover unknown data intersections and use less ambiguous queries to target the required documents. The SE concept is motivated by a number of ideas. The similar infrequent words in the texts of the relevant documents can include field specific terms and facts that can be unknown to the user. Suggesting such keywords can decrease the overall search time encouraging early learning by directing users to the new unknown relevant terms and facts in a search session with an ambiguous query. Finally, we present four demonstration scenarios from our small-scale qualitative user study of the SE tool.

SciCloud is a project studying the scope of establishing private clouds at universities. With these clouds, researchers can efficiently use the already existing resources in solving computationally intensive scientific, mathematical, and academic problems. The project established a Eucalyptus based private cloud and developed several customized images that can be used in solving problems from mobile web services, distributed computing and bio-informatics domains. The poster demonstrates the SciCloud and reveals two applications that are benefiting from the setup along with our research scope and results in scientific computing.

Scientific computation applications often consist of very different components that produce (or take as an input) huge amounts of data. Wrapping such components into web services to form a combined service for the scientific community has an additional complexity to deal with: how to organize data transfer within the web service framework efficiently enough to form high quality services that do not suffer in performance. In this paper we propose a method for processing large amounts of data transferred between different components of a compound web service. For this we develop a full workflow consisting of three separate components around an existing open-source package DOUG, a parallel black-box solver for large sparse systems of linear equations. We demonstrate the feasibility of the concept on an example of solving the steady state heat equations within design problems. The workflow involves a full production cycle, starting with the design of the geometry of the underlying problem domain with appropriate boundary conditions and other parameters, formation of the discretized system to be solved by DOUG and visualization of the heat distribution as a result.

Nowadays, grids are very heavyweight and complex. They need huge administrative capacities, just to keep the grid infrastructure running. If only a small job should be submitted, this administrative overhead is not justified. The initial metaphor for the computational grid being as easy as a power grid is still unfulfilled. To facilitate a public usage of Grids, we simplify grid computing using peer-to-peer computing and instant messaging. Instant messaging is used by the vast majority of computer users. We assume that using instant messaging techniques for the administrative part of setting up the grid will open grid capabilities to the public and thereby enable a desktop grid. We call the here applied paradigm friend-to-friend computing (F2F Computing). F2F computing offers the opportunity to spontaneously setup desktop grids with friends or colleagues via instant messaging. There is no server installation or maintenance needed. We describe the F2F computing framework, its implementation, future steps, and present the first results of running applications on top of it.

Abstract We describe the construction of parallel iterative solvers for finite‐element approximations of the Navier–Stokes equations on unstructured grids using domain decomposition methods. The iterative method used is FGMRES, preconditioned by a parallel adaptation of a block preconditioner recently proposed by Kay et al. The parallelization is achieved by adapting the technology of our domain decomposition solver DOUG (previously used for scalar problems) to block‐systems. The iterative solver is applied to shifted linear systems that arise in eigenvalue calculations. To illustrate the performance of the solver, we compare several strategies both theoretically and practically for the calculation of the eigenvalues of large sparse non‐symmetric matrices arising in the assessment of the stability of flow past a cylinder. Copyright © 2003 John Wiley & Sons, Ltd.