Volume 2, Issue 4

Alignment is the fundamental operation in molecular biology for comparing biomolecular sequences. The most widely used method for aligning groups of alignments is based on the alignment of the profiles corresponding to the groups. We show that profile-profile alignment can be significantly speeded up by general purpose computing on a modern commodity graphics card. Wavefront and matrix-matrix product approaches for implementing profile-profile alignment onto graphics processor are analyzed. The average speed-up obtained is one order of magnitude even when overheads are considered. Thus the computational power of graphics cards can be exploited to develop improved solutions for multiple sequence alignment.

In this paper we introduce efficient algorithm for the multiplication of sedenions. The direct multiplication of two sedenions requires 256 real multiplications and 240 real additions. We show how to compute a sedenions product with 120 real multiplications and 344 real additions.

Success in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

The goal of this paper is to introduce a technique, called ‘OGR’, that allows anonymous transmission of IP datagrams of any kind. During the last decade numerous anonymizing solutions implementing the Chaumian ‘onion’ encryption have been proposed. Many of them are dedicated to specific services: either anonymous mail delivery or anonymous storage sharing, while the other ones, referred as low-latency systems, work real-time and are able to anonymize any TCP streams (with preference to http), but poorly deal with or even don’t support other transports. ‘OGR’ is strongly inspired by the original Chaumian Mix-net model and aims at universality (the ability to work at the level of IP datagrams rather than upper-layers protocols) and maximal simplicity (the KISS principle). It discards the idea of fixed circuits (or cascades) present in most low-latency solutions in favor of independent, intelligent packets. Although this approach may occur less efficient for huge data streams it’s able to handle any request-reply protocols including those operating over UDP as well as all applications where other transports or raw IP packets are to be used.

Discovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.

Dedicated methods for dealing with curve interpolation and curve smoothing have been developed. One such method, Hurwitz-Radon Matrices (MHR), can be used in reconstruction and interpolation of curves in the plane. The method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The of Hurwitz-Radon Operator (OHR), built from these matrices, is described. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of curve interpolation and modelling. The method needs suitable choice of nodes, i.e. points of the curve to be reconstructed: nodes should be settled at each local extremum and nodes should be monotonic in one of coordinates (for example equidistance). Application of MHR gives a very good interpolation accuracy in the process of modeling and reconstruction of the curve. Created from the family of N-1 HR matrices and completed with the identity matrix, the system of matrices is orthogonal only for vector spaces of dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very important and significant for stability and high precision of calculations. The MHR method models the curve point by point without using any formula or function. Main features of the MHR method are: the accuracy of curve reconstruction depends on the number of nodes and method of choosing nodes, interpolation of L points of the curve has a computational cost of rank O(L), and the smoothing of the curve depends on the number of OHR operators used to build the average matrix operator. The problem of curve length estimation is also considered. Algorithms and numerical results are presented.