Extended phone log-likelihood ratio features and acoustic-based I-vectors for language recognition

This paper presents new techniques with relevant improvements added to the primary system presented by our group to the Albayzin 2012 LRE competition, where the use of any additional corpora for training or optimizing the models was forbidden. In this work, we present the incorporation of an additional phonotactic subsystem based on the use of phone log-likelihood ratio features (PLLR) extracted from different phonotactic recognizers that contributes to improve the accuracy of the system in a 21.4% in terms of Cavg (we also present results for the official metric during the evaluation, Fact). We will present how using these features at the phone state level provides significant improvements, when used together with dimensionality reduction techniques, especially PCA. We have also experimented with applying alternative SDC-like configurations on these PLLR features with additional improvements. Also, we will describe some modifications to the MFCC-based acoustic i-vector system which have also contributed to additional improvements. The final fused system outperformed the baseline in 27.4% in Cavg.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Selection, training and validation process of a sensory panel for bread analysis: Influence of cultivar on the quality of breads made from common wheat and spelta wheat

A protocol of selection, training and validation of the members of the panel for bread sensory analysis is proposed to assess the influence of wheat cultivar on the sensory quality of bread. Three cultivars of bread wheat and two cultivars of spelt wheat organically-grown under the same edaphoclimatic conditions were milled and baked using the same milling and baking procedure. Through the use of triangle tests, differences were identified between the five breads. Significant differences were found between the spelt breads and those made with bread wheat for the attributes ?crumb cell homogeneity? and ?crumb elasticity?. Significant differences were also found for the odor and flavor attributes, with the bread made with ?Espelta Navarra? being the most complex, from a sensory point of view. Based on the results of this study, we propose that sensory properties should be considered as breeding criteria for future work on genetic improvement.

Ontology Evaluation: a pitfall-based approach to ontology diagnosis

La evaluación de ontologías, incluyendo diagnóstico y reparación de las mismas, es una compleja actividad que debe llevarse a cabo en cualquier proyecto de desarrollo ontológico para comprobar la calidad técnica de las ontologías. Sin embargo, existe una gran brecha entre los enfoques metodológicos sobre la evaluación de ontologías y las herramientas que le dan soporte. En particular, no existen enfoques que proporcionen guías concretas sobre cómo diagnosticar y, en consecuencia, reparar ontologías. Esta tesis pretende avanzar en el área de la evaluación de ontologías, concretamente en la actividad de diagnóstico. Los principales objetivos de esta tesis son (a) ayudar a los desarrolladores en el diagnóstico de ontologías para encontrar errores comunes y (b) facilitar dicho diagnóstico reduciendo el esfuerzo empleado proporcionando el soporte tecnológico adecuado. Esta tesis presenta las siguientes contribuciones: • Catálogo de 41 errores comunes que los ingenieros ontológicos pueden cometer durante el desarrollo de ontologías. • Modelo de calidad para el diagnóstico de ontologías alineando el catálogo de errores comunes con modelos de calidad existentes. • Diseño e implementación de 48 métodos para detectar 33 de los 41 errores comunes en el catálogo. • Soporte tecnológico OOPS!, que permite el diagnstico de ontologías de forma (semi)automática. De acuerdo con los comentarios recibidos y los resultados de los test de satisfacción realizados, se puede afirmar que el enfoque desarrollado y presentado en esta tesis ayuda de forma efectiva a los usuarios a mejorar la calidad de sus ontologías. OOPS! ha sido ampliamente aceptado por un gran número de usuarios de formal global y ha sido utilizado alrededor de 3000 veces desde 60 países diferentes. OOPS! se ha integrado en software desarrollado por terceros y ha sido instalado en empresas para ser utilizado tanto durante el desarrollo de ontologías como en actividades de formación. Abstract Ontology evaluation, which includes ontology diagnosis and repair, is a complex activity that should be carried out in every ontology development project, because it checks for the technical quality of the ontology. However, there is an important gap between the methodological work about ontology evaluation and the tools that support such an activity. More precisely, not many approaches provide clear guidance about how to diagnose ontologies and how to repair them accordingly. This thesis aims to advance the current state of the art of ontology evaluation, specifically in the ontology diagnosis activity. The main goals of this thesis are (a) to help ontology engineers to diagnose their ontologies in order to find common pitfalls and (b) to lessen the effort required from them by providing the suitable technological support. This thesis presents the following main contributions: • A catalogue that describes 41 pitfalls that ontology developers might include in their ontologies. • A quality model for ontology diagnose that aligns the pitfall catalogue to existing quality models for semantic technologies. • The design and implementation of 48 methods for detecting 33 out of the 41 pitfalls defined in the catalogue. • A system called OOPS! (OntOlogy Pitfall Scanner!) that allows ontology engineers to (semi)automatically diagnose their ontologies. According to the feedback gathered and satisfaction tests carried out, the approach developed and presented in this thesis effectively helps users to increase the quality of their ontologies. At the time of writing this thesis, OOPS! has been broadly accepted by a high number of users worldwide and has been used around 3000 times from 60 different countries. OOPS! is integrated with third-party software and is locally installed in private enterprises being used both for ontology development activities and training courses.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

KDIGO-based acute kidney injury criteria operate differently in hospitals and the community—findings from a large population cohort

ACKNOWLEDGEMENTS We acknowledge the data management support of Grampian Data Safe Haven (DaSH) and the associated financial support of NHS Research Scotland, through NHS Grampian investment in the Grampian DaSH. S.S. is supported by a Clinical Research Training Fellowship from the Wellcome Trust (Ref 102729/Z/13/Z). We also acknowledge the support from The Farr Institute of Health Informatics Research. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome Trust (MRC Grant Nos: Scotland MR/K007017/1).

A two-year experience teaching computer literacy to first-year medical students using skill-based cohorts

Because it is widely accepted that providing information online will play a major role in both the teaching and practice of medicine in the near future, a short formal course of instruction in computer skills was proposed for the incoming class of students entering medical school at the State University of New York at Stony Brook. The syllabus was developed on the basis of a set of expected outcomes, which was accepted by the dean of medicine and the curriculum committee for classes beginning in the fall of 1997. Prior to their arrival, students were asked to complete a self-assessment survey designed to elucidate their initial skill base; the returned surveys showed students to have computer skills ranging from complete novice to that of a systems engineer. The classes were taught during the first three weeks of the semester to groups of students separated on the basis of their knowledge of and comfort with computers. Areas covered included computer basics, e-mail management, MEDLINE, and Internet search tools. Each student received seven hours of hands-on training followed by a test. The syllabus and emphasis of the classes were tailored to the initial skill base but the final test was given at the same level to all students. Student participation, test scores, and course evaluations indicated that this noncredit program was successful in achieving an acceptable level of comfort in using a computer for almost all of the student body.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

L’abilità di perspective taking : un training per bambini in età prescolare in differenti contesti

Elemento centrale della presente tesi dottorale è il costrutto di perspective taking, definibile come l’abilità, emergente nei bambini intorno a 4-5 anni, di assumere la prospettiva altrui secondo tre differenti dimensioni: emotiva, cognitiva e percettiva (Bonino, Lo Coco, Tani, 1998; Moll e Meltzoff, 2011). Dalla letteratura emerge come il perspective taking, in quanto abilità di comprensione sociale, rivesta un ruolo adattivo e sia fondamentale per lo sviluppo, non solo intellettivo, ma anche per la formazione di adeguate capacità relazionali e sociali (Jenkins e Astington, 2000; Weil et al., 2011). Sulla base di tali considerazioni, alcuni ricercatori si sono interrogati sulla possibilità di insegnare questa abilità, elaborando specifiche e differenti procedure di intervento finalizzate ad incrementare l’abilità di perspective taking sia in bambini a sviluppo normativo (Cigala e Mori, 2015), sia in gruppi di bambini a sviluppo atipico (Fisher e Happé, 2005; Heagle e Rehfeldt, 2006; Paynter e Peterson, 2012). A partire da una prospettiva teorica socio-costruzionista, secondo cui l’acquisizione del perspective taking si configura come un’impresa di co-costruzione continua, all’interno di interazioni quotidiane con figure significative per il bambino, si è deciso di analizzare il perspective taking non solo in relazione a variabili individuali (genere, età del bambino, regolazione emotiva, abilità sociali) ma anche e soprattutto a variabili contestuali quali le caratteristiche del contesto familiare (caratteristiche disposizionali e stili genitoriali di socializzazione emotiva, presenza di fratelli). Sono stati in particolare indagati un contesto familiare normativo ed uno caratterizzato da maltrattamento psicologico, contrassegnato dalla reiterazione di comportamenti inadeguati (critiche svalutanti, denigrazione, umiliazione, minacce verbali, indifferenza) nei confronti del minore, che convogliano sul bambino l’idea di non essere amato e di avere poco valore. Con i termini “a sviluppo tipico” si intendono i bambini per i quali non sussista una diagnosi clinica e con quelli di “famiglie normative” ci si riferisce a nuclei per i quali non ci siano state segnalazioni da parte dei Servizi Educativi e Sociali di riferimento, indipendentemente dalle caratteristiche della composizione del nucleo familiare (nucleare, estesa, multipla, ricostituita o ricomposta). Tale studio rientra in un ampio progetto di ricerca e formazione che ha coinvolto più di 250 prescolari frequentanti 8 scuole dell’infanzia e 15 comunità terapeutiche e di accoglienza mamma-bambino, situate in differenti province del Nord Italia. Il gruppo dei partecipanti alla ricerca si è composto di 256 bambini in età prescolare, compresa quindi tra 3 e 5 anni (M=54,39; DS=5,705): 128 maschi (M=54,08; DS=5,551) e 128 femmine (M=54,70; DS=5,860). In particolare, 213 bambini appartenevano a famiglie normative e 43 a nuclei familiari caratterizzati dalla presenza di maltrattamento psicologico. Oltre ai bambini, la ricerca ha previsto il coinvolgimento di 155 coppie di genitori, 43 madri ospitate in comunità, 18 insegnanti e 30 operatori. Obiettivo centrale è stato l’indagine della possibilità di poter promuovere il perspective taking in bambini di età prescolare a sviluppo tipico appartenenti a due differenti tipologie di contesto familiare (normativo e psicologicamente maltrattante), attraverso l’applicazione di uno specifico percorso di training di natura “ecologica” all’interno della scuola dell’infanzia e della comunità, assimilabile a quelli di tipo evidence based. In particolare è stata prevista una procedura quasi sperimentale di tipo pre-test, training, post-test e follow-up. Dopo una preliminare valutazione dello sviluppo del perspective taking nelle sue tre componenti, in bambini appartenenti ad entrambi i contesti, si è voluto verificare l’esistenza di eventuali relazioni tra questa abilità ed alcune capacità socio-emotive dei bambini, con particolare riferimento alla disposizione prosociale, rilevate nel contesto scolastico attraverso differenti metodologie (osservazioni dirette non partecipanti, questionari self report compilati dalle insegnanti). Inoltre, data l’importanza del contesto familiare per lo sviluppo di tale abilità, la ricerca ha avuto lo scopo di verificare l’esistenza di eventuali relazioni tra le abilità di perspective taking mostrate dai bambini e gli stili di socializzazione emotiva delle figure familiari, caratteristiche di entrambi i contesti (maltrattante e non maltrattante). È stato inoltre previsto uno studio di confronto tra i due campioni rispetto alle dimensioni indagate. I risultati ottenuti sono stati particolarmente interessanti. Innanzitutto, le esperienze di training hanno determinato, in entrambi i contesti, miglioramenti nell’abilità dei prescolari di mettersi nei panni altrui. Tale training ha inoltre dimostrato effetti positivi sulla competenza sociale dei bambini, che, a seguito del percorso, hanno manifestato un incremento dei comportamenti prosociali ed una diminuzione di quelli aggressivi. Per lo studio in contesto normativo, è stato inoltre dimostrato un mantenimento delle abilità acquisite a seguito del training attraverso un follow-up a distanza di 4 mesi dal termine dell’intervento. Il positivo esito di tale percorso sembra quindi rappresentare un’importante risorsa per i prescolari, soprattutto in caso di situazioni in cui l’abilità di perspective taking risulti deficitaria. Il confronto dei due gruppi a seguito del training ha evidenziato come non siano emerse differenze significative, rispetto al perspective taking, ad eccezione della dimensione emotiva, in cui le prestazioni dei prescolari maltrattati sono risultate inferiori, come già evidenziato prima del training. Tali risultati non giungono però inaspettati, poiché, sebbene il percorso abbia agito significativamente sull’abilità di comprensione delle emozioni altrui di questi bambini, non si configura come sufficiente a ristrutturare così profondamente le problematiche presentate. Interessanti sono stati altresì i risultati ottenuti dall’analisi degli stili di socializzazione emotiva, dei genitori (madri e padri) dei prescolari non maltrattati e delle mamme dei bambini residenti in comunità. In particolare è emerso come, stili accettanti e di tipo coaching nei confronti delle emozioni negative dei bambini, siano positivamente correlati con il perspective taking dei figli, e come all’opposto, stili rifiutanti rispetto alle espressioni emotive negative dei propri bambini, mostrino correlazioni negative con le abilità di perspective taking dei figli. Oltre ad interessi di ordine teorico e metodologico, è possibile quindi affermare come, il presente lavoro di tesi, sia stato guidato da fini applicativi, affinché la ricerca scientifica possa tradursi in pratiche educative quotidiane da applicare ai contesti di vita significativi per i bambini.

Contemplative Education CenterStage: Training the Mindful Performer

In the late 20th century and early 21st century, contemplative education/studies courses, concentrations, and initiatives have emerged in the academy. Although there has been significant discussion of postsecondary courses and programs that have integrated contemplative views and practices in the literature, there have been few studies of contemplative curricula and pedagogy in higher education. Additionally, there have been even fewer inquiries of the influence of contemplative education on performing arts training within conservatories and college and university departments. The aim of this qualitative study was two-fold: (1) to describe, interpret, and appraise the impact of contemplative education on the curricula of an interdisciplinary conservatory level performing arts program, MFA Contemporary Performance, at Naropa University; and (2) to disclose, compare, and analyze MFA student perceptions of the influence of contemplative education on their professional and personal development. The following questions guided this study: (1) How do faculty and students characterize contemplative education within the MFA in Theater: Contemporary Performance Program? (2) How does contemplative education impact the intended and operational curricula of courses within the MFA Contemporary Performance Program? (3) How do graduate students perceive the effects of contemplative education, offered by the MFA Contemporary Performance Program, on the development of their communication abilities, presence-in-performance, sociolinguistic perspectives, and aesthetic perspectives? Based on the research methodology of educational criticism and connoisseurship, this investigation provides a vivid description and interpretation of the intended and operational curricula of three core courses within the MFA program. These curricula were examined through five dimensions: intentional, curricular, pedagogical, structural, and evaluative. In order to shape our understanding of the contemplative and performative nature of the curricula, the significant and subtle qualities of the courses were further captured by preparation, context-building, reflective, showing, and closing conventions. Since the courses were grounded in postmodern view, they were evaluated according to Doll's criteria of richness, recursion, relations, and rigor for the evaluation of postmodern curricula. MFA first- and second-year students primarily characterized contemplative education as body/mind training for performance and personal development, sitting meditation, and cultivation of mindfulness and awareness. Student perceptions of the impact of contemplative education on the development of their communication abilities, presence-in-performance, sociolinguistic perspectives, and aesthetic perspectives, throughout the course of their two-year training, are presented in a dimensional analysis. The research reveals eight different themes that intersect the three core curricula and interviews with MFA students and faculty. These thematics include inclusivity, nowness, silence, improvisation, goodness, heart, training, and space. The beginning letter of each theme combines to form the acronym, insights. The framework of insights connects and illuminates the most potent aspects of MFA Contemporary Performance values and training.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

A Framework for Addressing Psychological Inflexibility Among Act Therapists-In-Training.

Novice therapists training in Acceptance and Commitment Therapy (ACT) may encounter challenges in therapy in which their own personal history functions as a barrier to flexible modes of therapeutic engagement with the therapist. From the ACT perspective, counter-therapeutic interpersonal responses may be examined relative to six behavioral sub-processes. It is suggested that the most vulnerable moments for the therapist will involve those in which certain contextual features of therapy pull historical awareness of a painful personal past into relation with the psychological present. This paper hypothesizes that utilizing approaches based in ACT will assist therapists in overcoming these challenges and will illustrate how to approach case formulation and intervention with therapists in training from a functional contextualistic perspective. To begin, the philosophical and theoretical underpinnings of ACT will be outlined in sufficient depth to intellectually ground the model and its therapeutic project. This conceptual foundation will then be brought to applied focus using hypothetical case material, followed by ACT interventions designed to increase clinical flexibility in the given therapeutic scenario. Future research that systematically examines the effectiveness of such methods among therapists is encouraged.

Utilizing Evidence-Based Psychology Practice in the University of Denver's Professional Psychology Center: A Proposal

As acceptance of the Evidence-based Psychology Practice (EBPP) model continues to grow (Pagoto, Spring, Coups, Mulvaney, Coutu, & Ozakinci, 2007), it seems pertinent to explore how this model can be applied in different settings. This topic is timely as practitioners in the field are being held ever more accountable for the efficacy of the treatments they employ (Pagoto et al., 2007). Increased scrutiny has resulted in a need to integrate research into practice in order to ensure continued relevance in the ever-changing realm of American health care (Luebbe, Radcliffe, Callands, Green & Thorn, 2007; Collins, Leffingwell & Belar, 2007; Chwalisz, 2003). This paper explores how the requirements set forth by the American Psychological Association Presidential Task Force on Evidence-Based Practice (2006) can be implemented at the University of Denver's (DU) Professional Psychology Center (PPC), a training clinic for students enrolled in the Psy.D. program at DU's Graduate School of Professional Psychology (GSPP). In doing so, the methods employed by Collins et al. (2007) at Oklahoma State University (OSU) are used as a template and modified to accommodate differences between these two institutions.

A Meta-Analytic Review of Mindfulness-Based Interventions on Symptom Reduction: A Critique and Guide for Future Investigation.

Over the past decade, mindfulness practices have been used with increasing frequency as therapeutic components within cognitive behavioral treatment regimens. As is standard practice, prescriptive uses of mindfulness intervention are incorporated to improve end-state functioning by ameliorating problematic symptoms and conditions. Common change-targets include the control of cognitive and emotional content for purposes of enhancing psychological self-regulation and physical well-being. The term mindfulness applies to a heterogeneous range of practices, methods, and techniques. While there is no singular agreed upon definition for mindfulness, as a process concept, the term connotes an immediate, non-thetic access to events, wherein each occasioning event is experienced in toto within the broader contextual event-field, and distinct from intervening conceptual themes being noticed. Training in mindfulness practices may be conducted using individual, group, or small class formats. The current paper provides a meta-analytic review of 44 treatment outcome studies (extracted 1982 through 2006), which examines the clinical utility of mindfulness as the primary therapeutic approach. Results indicated that average effect sizes for mindfulness based interventions fell within the medium range for construct category variables examined (d = .56). These findings suggest that mindfulness training is a cost-effective treatment for a wide array of contemporary psychological problems and diagnoses, in addition to fostering positive psychology attributes such as quality and satisfaction with life. A critique of the research and recommendations for future research, including a need to examine the role of mindfulness as a tool for cultivating increased psychological acceptance and life satisfaction, is presented.

Development of the DORI-TBI: An Investigation to Develop a New Screening Measure to Determine Consultation with a School-Based Brain Injury Resource Team

Brain injury is the leading cause of disability and death in children in the United States. Student re-entry into the school setting following a traumatic brain injury is crucial to student success. Multidisciplinary teams within the school district comprised of individuals with expertise in brain injury are ideal in implementing student specific treatment plans given their specialized training and wide range of expertise addressing student needs. Therefore, the purpose of this study is to develop and initially validate a quantitative instrument that school personnel can use to determine if a student, identified as having a traumatic brain injury, will benefit from district-level consultation from a brain injury team. Three studies were designed to investigate the research questions. In study one, the planning and construction of the DORI-TBI was completed. Study two addressed the content validity of the DORI-TBI through a comparison analysis with other referral forms, content review with experts in the field of TBI, and cognitive interviews with professionals to test the usability of the new screening tool. In study three, a field administration was conducted using vignettes to measure construct validity. Results produced a valid and reliable new screening instrument that can aid school-based teams to more efficiently utilize district level consultation with a brain injury support team.