Search Results

Modern botnets leverage TLS encryption to mask C&C server communications. TLS certificates used by botnets could exhibit subtle characteristics that facilitate detection. In this paper we investigate whether text features from TLS certificates can be ...

By "checking into'' various points-of-interest (POIs), users create a rich source of location-based social network data that can be used in expressive spatio-social queries. This paper studies the use of popularity as a means to diversify results of top-...

This paper addresses reliable and efficient calculation of the mode of a multivariate sample, which is a classical fusion function. In particular, we focus on the inputs given on the unit simplex, when aggregating elements of Atanassov ...

In memory nearest neighbour computation is a typical approach for collaborative filtering (CF) due to its high recommendation accuracy. However, this approach fails on scalability; which is the declined performance of the same due to the rapid increase in ...

We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all ...

We present vote estimation results on the largely unexplored Reddit voting dataset that contains 23M votes from 43k users on 3.4M links. This problem is approached using Variational Bayesian Principal Component Analysis VBPCA and a novel algorithm for k-...

We present a unified framework for processing and representing images using a feature space related to local similarity. The visual data is represented by the versatile multiscale local jet feature space, possibly reduced by vector quantisation and/or ...

K-nearest neighbours algorithms are among the most popular existing classification methods, due to their simplicity and good performances. Over the years, several extensions of the initial method have been proposed. In this paper, we propose a K-nearest ...

The k-nearest neighbour (KNN) technique is a simple yet effective method for classification. In this paper, we propose an efficient weighted nearest neighbour classification algorithm, called PINE, using vertical data representation. A metric called ...

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in ...

This paper presents an unsupervised segmentation method applicable to both 2D and 3D images. The segmentation is achieved by a bottom-up hierarchical analysis to progressively agglomerate pixels/voxels in the image into non-overlapped homogeneous ...

Abstract: We present a new method for the global registration of several overlapping 3D surfaces sampled on an object. The method is based on the ICP (iterative closest point) algorithm and on a segmentation of the sampled points in an optimized set of ...

We present a new method for the global registration of several overlapping 3D surfaces sampled on an object. The method is based on the ICP (iterative closest point) algorithm and on a segmentation of the sampled points in an optimized set of z-buffers. ...

The following problem is discussed: given n points in the plane (the sites) and an arbitrary query point q, find the site that is closest to q. This problem can be solved by constructing the Voronoi diagram of the griven sites and then locating the query ...

A classical problem of Pattern Recognition consists in looking for an operator of classification (a 'classifier') induced from a learning set on which classes are known. A problem frequently encountered in practice is that of looking for an operator of ...