In this paper, we address a new type of coverage problem in mobile sensor network, named Line K-Coverage. It guarantees that any line cutting across a region of interest will be detected by at least K sensors. We aim to schedule an efficient sensor movement to satisfy the line K-coverage while minimize the total sensor movements for energy efficiency, which is named as LK-MinMovs problem. We pr...

The Named Entity Recognition Task is one of the most common steps used in natural language applications. Linked Data datasets have been presented as promising background knowledge for Named Entity Recognition algorithms due to the amount of data available and the high variety of knowledge domains they cover. However, the discovery of names in Linked Data datasets is still a costly task if we co...

Modularity is a popular measure for the quality of a cluster partition. Primarily, its popularity originates from its suitability for community identification through maximization. A lot of algorithms to maximize modularity have been proposed in recent years. Especially agglomerative hierarchical algorithms showed to be fast and find clusterings with high modularity. In this paper we present se...

The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve cont...

We consider a version of pattern matching useful in processing large musical data: matching, which consists in finding matches which are -approximate in the sense of the distance measured as maximum difference between symbols. The alphabet is an interval of integers, and the distance between two symbols , is measured as . We also consider -matching, where is a bound on the total sum of the diff...

The Named Entity Recognition (NER) is very important task revolving around many natural language processing applications. However, most Named Entity Recognition (NER) systems have been developed using either of two approaches: a rule-based or Machine Learning (ML) based approach, with their effectiveness and weaknesses. In this paper, the problem of Amazighe NER is tackled through using the two...

Clustering ensemble, also referred to as consensus clustering, has emerged a method of combining an ensemble different clusterings derive final clustering that is better quality and robust than any single in the ensemble. Normally algorithms literature combine all without learning But by one can define merit or even cluster it, forming consensus. In this work, we propose cluster-level surprisal...

Semantic annotation of named entities for enriching unstructured content is a critical step in development of Semantic Web and many Natural Language Processing applications. To this end, this paper addresses the named entity disambiguation problem that aims at detecting entity mentions in a text and then linking them to entries in a knowledge base. In this paper, we propose a hybrid method, com...

We study models of opinion formation on static as well as dynamic networks where interaction among individuals is governed by widely accepted social theories. In particular, three models of competing opinions based on distinct interaction mechanisms are studied. A common feature in all of these models is the existence of a tipping point in terms of a model parameter beyond which a rapid consens...

We consider the problem of gravitational clustering in a D-dimensional expanding universe and derive scaling relations connecting the exact mean two-point correlation function with the linear mean correlation function, in the quasilinear and nonlinear regimes, using the standard paradigms of scale-invariant radial collapse and stable clustering. We show that the existence of scaling laws is a g...