USING DEEP LEARNING-BASED FRAMEWORK FOR CHILD SPEECH EMOTION RECOGNITION

Biological languages of the body through which human emotion can be detected abound including heart rate, facial expressions, movement of the eyelids and dilation of the eyes, body postures, skin conductance, and even the speech we make. Speech emotion recognition research started some three decades ago, and the popular Interspeech Emotion Challenge has helped to propagate this research area. However, most speech recognition research is focused on adults and there is very little research on child speech. This dissertation is a description of the development and evaluation of a child speech emotion recognition framework. The higher-level components of the framework are designed to sort and separate speech based on the speaker’s age, ensuring that focus is only on speeches made by children. The framework uses Baddeley’s Theory of Working Memory to model a Working Memory Recurrent Network that can process and recognize emotions from speech. Baddeley’s Theory of Working Memory offers one of the best explanations on how the human brain holds and manipulates temporary information which is very crucial in the development of neural networks that learns effectively. Experiments were designed and performed to provide answers to the research questions, evaluate the proposed framework, and benchmark the performance of the framework with other methods. Satisfactory results were obtained from the experiments and in many cases, our framework was able to outperform other popular approaches. This study has implications for various applications of child speech emotion recognition such as child abuse detection and child learning robots

Intelligent system to support micro injection process through artificial intelligent techniques and cae model integration

Trabajo de investigaciónIn this project a propose of integration of CAE Modeling and artificial intelligence systems to support the process in the production of micro plastic parts is presented. Based on analysis provided by CAE systems, studies will be carried out for diverse parts, to be analyses and throw to artificial intelligent techniques give recommendations of optimal values of plastic micro injection process.1. INTRODUCTION 2. PROBLEM STATEMENT 3. OBJECTIVES 4. CONCEPTUAL FRAMEWORK 5. THEORETICAL FRAMEWORK 6. STATE OF THE ART 7. METHODOLOGY 8. DESCRIPCION OF PROJECT 9. RESULTS 10. VALIDATION OF PROJECT 11. CONCLUSIONS AND FUTURE WORKS 12. REFERENCES 13. ANNEXESMaestríaMagister en Ingeniería y Gestión de la Innovació

Automatic production and integration of knowledge to the support of the decision and planning activities in medical-clinical diagnosis, treatment and prognosis.

El concepto de procedimiento médico se refiere al conjunto de actividades seguidas por los profesionales de la salud para solucionar o mitigar el problema de salud que afecta a un paciente. La toma de decisiones dentro del procedimiento médico ha sido, por largo tiempo, uno de las áreas más interesantes de investigación en la informática médica y el contexto de investigación de esta tesis. La motivación para desarrollar este trabajo de investigación se basa en tres aspectos fundamentales: no hay modelos de conocimiento para todas las actividades médico-clínicas que puedan ser inducidas a partir de datos médicos, no hay soluciones de aprendizaje inductivo para todas las actividades de la asistencia médica y no hay un modelo integral que formalice el concepto de procedimiento médico. Por tanto, nuestro objetivo principal es desarrollar un modelo computable basado en conocimiento que integre todas las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos. Para alcanzar el objetivo principal, en primer lugar, explicamos el problema de investigación. En segundo lugar, describimos los antecedentes del problema de investigación desde los contextos médico e informático. En tercer lugar, explicamos el desarrollo de la propuesta de investigación, basada en cuatro contribuciones principales: un nuevo modelo, basado en datos y conocimiento, para la actividad de planificación en el diagnóstico y tratamiento médico-clínicos; una novedosa metodología de aprendizaje inductivo para la actividad de planificación en el diagnóstico y tratamiento médico-clínico; una novedosa metodología de aprendizaje inductivo para la actividad de decisión en el pronóstico médico-clínico, y finalmente, un nuevo modelo computable, basado en datos y conocimiento, que integra las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos.The concept of medical procedure refers to the set of activities carried out by the health care professionals to solve or mitigate the health problems that affect a patient. Decisions making within a medical procedure has been, for a long time, one of the most interesting research areas in medical informatics and the research context of this thesis. The motivation to develop this research work is based on three main aspects: Nowadays there are not knowledge models for all the medical-clinical activities that can be induced from medical data, there are not inductive learning solutions for all the medical-clinical activities, and there is not an integral model that formalizes the concept of medical procedure. Therefore, our main objective is to develop a computable model based in knowledge that integrates all the decision and planning activities for the medical-clinical diagnosis, treatment and prognosis. To achieve this main objective: first, we explain the research problem. Second, we describe the background of the work from both the medical and the informatics contexts. Third, we explain the development of the research proposal based on four main contributions: a novel knowledge representation model, based in data, to the planning activity in medical-clinical diagnosis and treatment; a novel inductive learning methodology to the planning activity in diagnosis and medical-clinical treatment; a novel inductive learning methodology to the decision activity in medical-clinical prognosis, and finally, a novel computable model, based on data and knowledge, which integrates the decision and planning activities of medical-clinical diagnosis, treatment and prognosis

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 261)

This bibliography lists 281 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1984

Supporting the design of sequences of cumulative activities impacting on multiple areas through a data mining approach : application to design of cognitive rehabilitation programs for traumatic brain injury patients

Traumatic brain injury (TBI) is a leading cause of disability worldwide. It is the most common cause of death and disability during the first three decades of life and accounts for more productive years of life lost than cancer, cardiovascular disease and HIV/AIDS combined. Cognitive Rehabilitation (CR), as part of Neurorehabilitation, aims to reduce the cognitive deficits caused by TBI. CR treatment consists of sequentially organized tasks that require repetitive use of impaired cognitive functions. While task repetition is not the only important feature, it is becoming clear that neuroplastic change and functional improvement only occur after a number of specific tasks are performed in a certain order and repetitions and does not occur otherwise. Until now, there has been an important lack of well-established criteria and on-field experience by which to identify the right number and order of tasks to propose to each individual patient. This thesis proposes the CMIS methodology to support health professionals to compose CR programs by selecting the most promising tasks in the right order. Two contributions to this topic were developed for specific steps of CMIS through innovative data mining techniques SAIMAP and NRRMR methodologies. SAIMAP (Sequence of Activities Improving Multi-Area Performance) proposes an innovative combination of data mining techniques in a hybrid generic methodological framework to find sequential patterns of a predefined set of activities and to associate them with multi-criteria improvement indicators regarding a predefined set of areas targeted by the activities. It combines data and prior knowledge with preprocessing, clustering, motif discovery and classes` post-processing to understand the effects of a sequence of activities on targeted areas, provided that these activities have high interactions and cumulative effects. Furthermore, this work introduces and defines the Neurorehabilitation Range (NRR) concept to determine the degree of performance expected for a CR task and the number of repetitions required to produce maximum rehabilitation effects on the individual. An operationalization of NRR is proposed by means of a visualization tool called SAP. SAP (Sectorized and Annotated Plane) is introduced to identify areas where there is a high probability of a target event occurring. Three approaches to SAP are defined, implemented, applied, and validated to a real case: Vis-SAP, DT-SAP and FT-SAP. Finally, the NRRMR (Neurorehabilitation Range Maximal Regions) problem is introduced as a generalization of the Maximal Empty Rectangle problem (MER) to identify maximal NRR over a FT-SAP. These contributions combined together in the CMIS methodology permit to identify a convenient pattern for a CR program (by means of a regular expression) and to instantiate by a real sequence of tasks in NRR by maximizing expected improvement of patients, thus provide support for the creation of CR plans. First of all, SAIMAP provides the general structure of successful CR sequences providing the length of the sequence and the kind of task recommended at every position (attention tasks, memory task or executive function task). Next, NRRMR provides specific tasks information to help decide which particular task is placed at each position in the sequence, the number of repetitions, and the expected range of results to maximize improvement after treatment. From the Artificial Intelligence point of view the proposed methodologies are general enough to be applied in similar problems where a sequence of interconnected activities with cumulative effects are used to impact on a set of areas of interest, for example spinal cord injury patients following physical rehabilitation program or elderly patients facing cognitive decline due to aging by cognitive stimulation programs or on educational settings to find the best way to combine mathematical drills in a program for a specific Mathematics course.El traumatismo craneoencefálico (TCE) es una de las principales causas de morbilidad y discapacidad a nivel mundial. Es la causa más común de muerte y discapacidad en personas menores de 30 años y es responsable de la pérdida de más años de vida productiva que el cáncer, las enfermedades cardiovasculares y el SIDA sumados. La Rehabilitación Cognitiva (RC) como parte de la Neurorehabilitación, tiene como objetivo reducir el impacto de las condiciones de discapacidad y disminuir los déficits cognitivos causados (por ejemplo) por un TCE. Un tratamiento de RC está formado por un conjunto de tareas organizadas de forma secuencial que requieren un uso repetitivo de las funciones cognitivas afectadas. Mientras que el número de ejecuciones de una tarea no es la única característica importante, es cada vez más evidente que las transformaciones neuroplásticas ocurren cuando se ejecutan un número específico de tareas en un cierto orden y no ocurren en caso contrario. Esta tesis propone la metodología CMIS para dar soporte a los profesionales de la salud en la composición de programas de RC, seleccionando las tareas más prometedoras en el orden correcto. Se han desarrollado dos contribuciones para CMIS mediante las metodologías SAMDMA y RNRRM basadas en técnicas innovadoras de minería de datos. SAMDMA (Secuencias de Actividades que Mejoran el Desempeño en Múltiples Áreas) propone una combinación de técnicas de minería de datos y un marco de trabajo genérico híbrido para encontrar patrones secuenciales en un conjunto de actividades y asociarlos con indicadores de mejora multi-criterio en relación a un conjunto de áreas hacia las cuales las actividades están dirigidas. Combina el uso de datos y conocimiento experto con técnicas de pre-procesamiento, clustering, descubrimiento de motifs y post procesamiento de clases. Además, se introduce y define el concepto de Rango de NeuroRehabilitación (RNR) para determinar el grado de performance esperado para una tarea de RC y el número de repeticiones que debe ejecutarse para producir mayores efectos rehabilitadores. Se propone una operacionalización del RNR por medio de una herramienta de visualización llamada Plano Sectorizado Anotado (PAS). PAS permite identificar áreas en las que hay una alta probabilidad de que ocurra un evento. Tres enfoques diferentes al PAS se definen, implementan, aplican y validan en un caso real : Vis-PAS, DT-PAS y FT-PAS. Finalmente, el problema RNRRM (Rango de NeuroRehabilitación de Regiones Máximas) se presenta como una generalización del problema del Máximo Rectángulo Vacío para identificar RNR máximos sobre un FT-PAS. La combinación de estas dos contribuciones en la metodología CMIS permite identificar un patrón conveniente para un programa de RC (por medio de una expresión regular) e instanciarlo en una secuencia real de tareas en RNR maximizando las mejoras esperadas de los pacientes, proporcionando soporte a la creación de planes de RC. Inicialmente, SAMDMA proporciona la estructura general de secuencias de RC exitosas para cada paciente, proporcionando el largo de la secuencia y el tipo de tarea recomendada en cada posición. RNRRM proporciona información específica de tareas para ayudar a decidir cuál se debe ejecutar en cada posición de la secuencia, el número de veces que debe ser repetida y el rango esperado de resultados para maximizar la mejora. Desde el punto de vista de la Inteligencia Artificial, ambas metodologías propuestas, son suficientemente generales como para ser aplicadas a otros problemas de estructura análoga en que una secuencia de actividades interconectadas con efectos acumulativos se utilizan para impactar en un conjunto de áreas de interés. Por ejemplo pacientes lesionados medulares en tratamiento de rehabilitación física, personas mayores con deterioro cognitivo debido al envejecimiento y utilizan programas de estimulación cognitiva, o entornos educacionales para combinar ejercicios de cálculo en un programa específico de Matemáticas

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse