Detection of concealed information using event-related potentials

Existe un interés considerable en hallar métodos que nos ayuden a saber cuándo una persona miente y cuándo dice la verdad desde un punto de vista forense. Actualmente, una de las líneas de investigación se inclina hacia el uso de potenciales relacionados con eventos. Se pretende hacer una revisión de los artículos que estudian estos procedimientos mediante distintos métodos: propiedades, fiabilidad, validez y limitaciones. Los resultados indican tasas de acierto en la discriminación de culpables en un rango de 7 al 100 por ciento, y en la de inocentes de 31 a 100 por ciento. La gran variabilidad y la posibilidad de “falsear” las respuestas llevan a cuestionar la inexactitud utilizada en algunos círculos mediáticos respecto a las cualidades y finalidades de dicha prueba. Se concluye la necesidad de profundizar más la posibilidad de que esta prueba sea utilizada con fines forenses.

A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions

One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions.