Intelligence-led crime scene processing. Part I : Forensic intelligence.

Forensic science is generally defined as the application of science to address questions related to the law. Too often, this view restricts the contribution of science to one single process which eventually aims at bringing individuals to court while minimising risk of miscarriage of justice. In order to go beyond this paradigm, we propose to refocus the attention towards traces themselves, as remnants of a criminal activity, and their information content. We postulate that traces contribute effectively to a wide variety of other informational processes that support decision making inmany situations. In particular, they inform actors of new policing strategies who place the treatment of information and intelligence at the centre of their systems. This contribution of forensic science to these security oriented models is still not well identified and captured. In order to create the best condition for the development of forensic intelligence, we suggest a framework that connects forensic science to intelligence-led policing (part I). Crime scene attendance and processing can be envisaged within this view. This approach gives indications abouthowto structure knowledge used by crime scene examiners in their effective practice (part II).

Assessment of emotional reactivity produced by exposure to virtual environments in patients with eating disorders

The aim of this study was to assess the usefulness of virtual environments representing situations that are emotionally significant to subjects with eating disorders (ED). These environments may be applied with both evaluative and therapeutic aims and in simulation procedures to carry out a range of experimental studies. This paper is part of a wider research project analyzing the influence of the situation to which subjects are exposed on their performance on body image estimation tasks. Thirty female patients with eating disorders were exposed to six virtual environments: a living-room (neutral situation), a kitchen with highcalorie food, a kitchen with low-calorie food, a restaurant with high-calorie food, a restaurant with low-calorie food, and a swimming-pool. After exposure to each environment the STAI-S (a measurement of state anxiety) and the CDB (a measurement of depression) were administered to all subjects. The results show that virtual reality instruments are particularly useful for simulating everyday situations that may provoke emotional reactions such as anxiety and depression, in patients with ED. Virtual environments in which subjects are obliged to ingest high-calorie food provoke the highest levels of state anxiety and depression.

Let's get emotional!

Artikkelissa kerrotaan luennoista, jotka brittiläinen professori Rob Briner piti Suomessa toukokuussa 2003

De la Analítica al Social Media Intelligence : Una nueva oportunidad de liderar el mercado mediante una importante ventaja competitiva : saber identificar las necesidades del cliente en el Social Media : La reinvención de las investigaciones de mercado

La revolución que significó Internet a finales del siglo XX supone una oportunidad inmensa para el mundo corporativo. Con la aparición del Social Media y, en concreto, de las redes sociales, se han abierto un sinfín de nuevas oportunidades.No existe ningún otro invento que haya penetrado con tanta facilidad como lo han hecho estas plataformas; es el momento de que las empresas saquen provecho de ello.El objetivo principal de este proyecto consiste en identificar los beneficios potenciales para las empresas que conlleva actuar a través del Social Media. No contentos con eso, nos disponemos a mostrar las grandes razones por las que actuar: la posibilidad deidentificar las necesidades de tu público objetivo, huecos en el mercado y, por consiguiente, conseguir una ventaja competitiva clave en tu sector. ¿Cómo? La clave está en saber gestionar grandes volúmenes de información aplicando soluciones de inteligencia competitiva.Teniendo en cuenta que el proyecto gira entorno al Social Media, la estructura del mismo está dividida en dos grandes partes: en primer lugar estudiamos las claves de la investigación de mercados actual y, seguidamente, utilizamos una perspectiva más corporativa para mostrar las principales inquietudes que surgen en las empresas.Mediante ejemplos de casos prácticos muy relevantes, estudios que aporten datos clave y, sobre todo, las opiniones de profesionales del sector dentro y fuera de España, seremos capaces de deducir la importancia que tiene este terreno, así como el largo camino que nos queda a todos por delante.

Diagnóstico de las dificultades de lectura y escritura y de la dislexia basado en la Teoría PASS de lainteligencia utilizando la batería DN-CAS. Origen cognitivo de la dislexia

Las dificultades de lectura y escritura se pueden detectar desde el momento en que los niños y niñas inician el aprendizaje de la lectoescritura en la etapa de Educación Infantil. En algunos casos estos alumnos reciben un apoyo escolar que en muchas ocasiones no conlleva las mejoras esperadas, siendo las técnicas y metodologías de refuerzo aplicadas ineficaces. El problema, desde nuestro punto de vista, empieza con el diagnóstico que se realiza a estos jóvenes, que determina las directrices de la intervención idónea en cada caso. La Teoría PASS de la inteligencia nos permite conocer qué procesos están implicados cuando el niño lee o escribe, y parte de la premisa de que si conocemos el perfil cognitivo de un alumno que presenta dificultades podremos entender como estas se originan. Para conocer este perfil cognitivo (los cuatro procesos cognitivos que describe esta teoría: Planificación, Atención, Simultaneo y Secuencial) utilizamos la batería DN-CAS (Das & Naglieri: Cognitive Assessment System). El perfil obtenido al aplicar el DN-CAS nos permitirá conocer el origen de las dificultades de lectura y escritura, saber cuando está justificada una dislexia, descartar problemas emocionales o la presencia de los mismos y diseñar la intervención más adecuada en cada situación

Implantación de una plataforma corporativa de business intelligence

La Universidad UPROMA ha identificado una línea de actuación inicial caracterizada por la implantación de una plataforma de reporting y análisis de la información corporativa.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Differences of Functional Connectivity Brain Network in Emotional Judgment

Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli).

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Correlations between negative-induced cerebral dynamics and idiopathic intellectual disability: an EEG study

Intellectual disability has long been associated with deficits in socio-emotional processing. However, studies investigating brain dynamics of maladaptive socio-emotional skills associated with intellectual disability are scarce. Here, we compared differences in brain activity between low intelligence quotient (I.Q.<75, N=13) and normal controls (N=15) while evaluating their subjective emotions. Positive (P) and negative (N) valenced pictures were presented one at a time to participants of both groups, at a rate of ¾. The task required that each participant evaluate their subjective emotion and press a predefined push-button when done, alternatively P and N. Electroencephalographic (EEG) signals were continuously recorded, and the 1000ms time window following each picture was analyzed offline for power in frequency domain. Alpha low (8-10Hz) and upper (10-13Hz) frequency bands were then compared for both groups and for both P and N emotions in 12 distributed scalp electrodes. The qualitative evaluation of emotions was similar between both groups, with constant longer reaction times for the low IQ participants. The EEG signal comparison shows marked power decrease in upper alpha frequency range for N emotions in low intelligence group. Otherwise no significant difference was noticed between low and normal IQ. Main findings of the present study are (1) results do not support the hypothesis that impairment in developmental intelligence roots in maladaptive emotional processing; (2) the strong alpha power suppression during negative-induced emotions suggests the involvement of an extended neural network and more effortful inhibition processes than positive ones. We call for further studies with a larger sample.