188 research outputs found
The pragmatics of monologue: interaction in video blogs
This study reports an in-depth pragmatic analysis of spoke monologues as they appear in video blogs (vlogs). Vlogs are videos of a person talking into the camera, which are edited and subsequently uploaded to video sharing websites such as YouTube, where they appear in a highly multimodal environment. Using methods situated in the fields of Conversation Analysis, Interactive Sociolinguistics and multimodality studies, the research presented investigates a wide range of speaker strategies. These strategies are studied with regard to their form, frequency of occurrence, how they are adapted to the specific context of language use and, where possible, what effect this has on vlog viewers. The strategies and phenomena under investigation are the openings and closings of monologues; repetition and involvement strategies; pointing gestures and video-comment coherence in the virtual online space. Comparison with another monologic genre, the TED Talk, reveals that the monologue setting itself is an influential variable that naturally shapes a vlogger’s or lecturer’s way of speaking compared to conversational settings. However, more specifically, the comparison also shows that the contextual differences between a stage monologue at a TED conference and a camera monologue as part of a vlog, can be significant in terms of their influence on the interaction that takes place.Diese Forschungsarbeit untersucht gesprochene Monologe wie sie in Videoblogs (Vlogs) vorkommen aus linguistisch-pragmatischer Sicht. Vlogs sind Videos von Menschen, die in die Kamera sprechen. Diese Videos werden nach der Aufnahme bearbeitet und dann auf Videoseiten im Internet, wie zum Beispiel YouTube, hochgeladen, wo sie im Rahmen einer multimodal komplexen Webseite einer breiten Öffentlichkeit zugänglich sind. Die Vlogmonologe werden mit Hilfe von Methoden aus der Konversationsanalyse, der Interaktionalen Soziolinguistik und Studien zur Multimodalität auf die Strategien der Sprecher beleuchtet. Diese Strategien werden untersucht im Hinblick auf ihre Form, Häufigkeit, wie sie an den spezifischen Sprechkontext angepasst sind und, wo möglich, was für einen Effekt sie auf die Zuschauer haben. Die Strategien und Phänomene, die hier untersucht werden sind Anfangs- und Schlusssequenzen; Wiederholungen und Involvement-Strategien; Zeigegesten und die Kohärenz zwischen Video und Zuschauerkommentaren im virtuellen Raum. Der Vergleich mit einer anderen monologischen Gattung, dem TED-Talk, zeigt, dass die Sprechsituation im Monolog eine einflussreiche Variable ist verglichen mit konversationellen Situationen. Der Vergleich zeigt aber weiterhin, dass die kontextuellen Unterschiede zwischen Vlogs, die vor der Kamera entstehen, und TED Talks, die auf einer Bühne vor Publikum vorgetragen werden, signifikant sein können hinsichtlich ihres Einflusses auf die stattfindende Interaktion
Adaptive Cognitive Interaction Systems
Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems
Human-Robot Interaction architecture for interactive and lively social robots
Mención Internacional en el título de doctorLa sociedad está experimentando un proceso de envejecimiento que puede provocar un desequilibrio
entre la población en edad de trabajar y aquella fuera del mercado de trabajo. Una de las soluciones
a este problema que se están considerando hoy en día es la introducción de robots en multiples
sectores, incluyendo el de servicios. Sin embargo, para que esto sea una solución viable, estos robots
necesitan ser capaces de interactuar con personas de manera satisfactoria, entre otras habilidades. En
el contexto de la aplicación de robots sociales al cuidado de mayores, esta tesis busca proporcionar
a un robot social las habilidades necesarias para crear interacciones entre humanos y robots que
sean naturales. En concreto, esta tesis se centra en tres problemas que deben ser solucionados: (i) el
modelado de interacciones entre humanos y robots; (ii) equipar a un robot social con las capacidades
expresivas necesarias para una comunicación satisfactoria; y (iii) darle al robot una apariencia vivaz.
La solución al problema de modelado de diálogos presentada en esta tesis propone diseñar estos
diálogos como una secuencia de elementos atómicos llamados Actos Comunicativos (CAs, por sus
siglas en inglés). Se pueden parametrizar en tiempo de ejecución para completar diferentes objetivos
comunicativos, y están equipados con mecanismos para manejar algunas de las imprecisiones que
pueden aparecer durante interacciones. Estos CAs han sido identificados a partir de la combinación
de dos dimensiones: iniciativa (si la tiene el robot o el usuario) e intención (si se pretende obtener o
proporcionar información). Estos CAs pueden ser combinados siguiendo una estructura jerárquica
para crear estructuras mas complejas que sean reutilizables. Esto simplifica el proceso para crear
nuevas interacciones, permitiendo a los desarrolladores centrarse exclusivamente en diseñar el flujo
del diálogo, sin tener que preocuparse de reimplementar otras funcionalidades que tienen que estar
presentes en todas las interacciones (como el manejo de errores, por ejemplo).
La expresividad del robot está basada en el uso de una librería de gestos, o expresiones,
multimodales predefinidos, modelados como estructuras similares a máquinas de estados. El
módulo que controla la expresividad recibe peticiones para realizar dichas expresiones, planifica
su ejecución para evitar cualquier conflicto que pueda aparecer, las carga, y comprueba que su
ejecución se complete sin problemas. El sistema es capaz también de generar estas expresiones en
tiempo de ejecución a partir de una lista de acciones unimodales (como decir una frase, o mover una
articulación). Una de las características más importantes de la arquitectura de expresividad propuesta
es la integración de una serie de métodos de modulación que pueden ser usados para modificar los
gestos del robot en tiempo de ejecución. Esto permite al robot adaptar estas expresiones en base
a circunstancias particulares (aumentando al mismo tiempo la variabilidad de la expresividad del robot), y usar un número limitado de gestos para mostrar diferentes estados internos (como el estado
emocional).
Teniendo en cuenta que ser reconocido como un ser vivo es un requisito para poder participar en
interacciones sociales, que un robot social muestre una apariencia de vivacidad es un factor clave
en interacciones entre humanos y robots. Para ello, esta tesis propone dos soluciones. El primer
método genera acciones a través de las diferentes interfaces del robot a intervalos. La frecuencia e
intensidad de estas acciones están definidas en base a una señal que representa el pulso del robot.
Dicha señal puede adaptarse al contexto de la interacción o al estado interno del robot. El segundo
método enriquece las interacciones verbales entre el robot y el usuario prediciendo los gestos no
verbales más apropiados en base al contenido del diálogo y a la intención comunicativa del robot.
Un modelo basado en aprendizaje automático recibe la transcripción del mensaje verbal del robot,
predice los gestos que deberían acompañarlo, y los sincroniza para que cada gesto empiece en el
momento preciso. Este modelo se ha desarrollado usando una combinación de un encoder diseñado
con una red neuronal Long-Short Term Memory, y un Conditional Random Field para predecir la
secuencia de gestos que deben acompañar a la frase del robot.
Todos los elementos presentados conforman el núcleo de una arquitectura de interacción
humano-robot modular que ha sido integrada en múltiples plataformas, y probada bajo diferentes
condiciones. El objetivo central de esta tesis es contribuir al área de interacción humano-robot
con una nueva solución que es modular e independiente de la plataforma robótica, y que se centra
en proporcionar a los desarrolladores las herramientas necesarias para desarrollar aplicaciones que
requieran interacciones con personas.Society is experiencing a series of demographic changes that can result in an unbalance between
the active working and non-working age populations. One of the solutions considered to mitigate
this problem is the inclusion of robots in multiple sectors, including the service sector. But for
this to be a viable solution, among other features, robots need to be able to interact with humans
successfully. This thesis seeks to endow a social robot with the abilities required for a natural
human-robot interactions. The main objective is to contribute to the body of knowledge on the area
of Human-Robot Interaction with a new, platform-independent, modular approach that focuses on
giving roboticists the tools required to develop applications that involve interactions with humans. In
particular, this thesis focuses on three problems that need to be addressed: (i) modelling interactions
between a robot and an user; (ii) endow the robot with the expressive capabilities required for a
successful communication; and (iii) endow the robot with a lively appearance.
The approach to dialogue modelling presented in this thesis proposes to model dialogues as a
sequence of atomic interaction units, called Communicative Acts, or CAs. They can be parametrized
in runtime to achieve different communicative goals, and are endowed with mechanisms oriented to
solve some of the uncertainties related to interaction. Two dimensions have been used to identify the
required CAs: initiative (the robot or the user), and intention (either retrieve information or to convey
it). These basic CAs can be combined in a hierarchical manner to create more re-usable complex
structures. This approach simplifies the creation of new interactions, by allowing developers to focus
exclusively on designing the flow of the dialogue, without having to re-implement functionalities
that are common to all dialogues (like error handling, for example).
The expressiveness of the robot is based on the use of a library of predefined multimodal gestures,
or expressions, modelled as state machines. The module managing the expressiveness receives requests
for performing gestures, schedules their execution in order to avoid any possible conflict that might
arise, loads them, and ensures that their execution goes without problems. The proposed approach
is also able to generate expressions in runtime based on a list of unimodal actions (an utterance,
the motion of a limb, etc...). One of the key features of the proposed expressiveness management
approach is the integration of a series of modulation techniques that can be used to modify the
robot’s expressions in runtime. This would allow the robot to adapt them to the particularities of a
given situation (which would also increase the variability of the robot expressiveness), and to display
different internal states with the same expressions. Considering that being recognized as a living being is a requirement for engaging in social
encounters, the perception of a social robot as a living entity is a key requirement to foster
human-robot interactions. In this dissertation, two approaches have been proposed. The first
method generates actions for the different interfaces of the robot at certain intervals. The frequency
and intensity of these actions are defined by a signal that represents the pulse of the robot, which can
be adapted to the context of the interaction or the internal state of the robot. The second method
enhances the robot’s utterance by predicting the appropriate non-verbal expressions that should
accompany them, according to the content of the robot’s message, as well as its communicative
intention. A deep learning model receives the transcription of the robot’s utterances, predicts
which expressions should accompany it, and synchronizes them, so each gesture selected starts at
the appropriate time. The model has been developed using a combination of a Long-Short Term
Memory network-based encoder and a Conditional Random Field for generating a sequence of
gestures that are combined with the robot’s utterance.
All the elements presented above conform the core of a modular Human-Robot Interaction
architecture that has been integrated in multiple platforms, and tested under different conditions.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fernando Torres Medina.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Amirabdollahian Farshi
Time- and value-continuous explainable affect estimation in-the-wild
Today, the relevance of Affective Computing, i.e., of making computers recognise and simulate human emotions, cannot be overstated. All technology giants (from manufacturers of laptops to mobile phones to smart speakers) are in a fierce competition to make their devices understand not only what is being said, but also how it is being said to recognise user’s emotions. The goals have evolved from predicting the basic emotions (e.g., happy, sad) to now the more nuanced affective states (e.g., relaxed, bored) real-time. The databases used in such research too have evolved, from earlier featuring the acted behaviours to now spontaneous behaviours. There is a more powerful shift lately, called in-the-wild affect recognition, i.e., taking the research out of the laboratory, into the uncontrolled real-world.
This thesis discusses, for the very first time, affect recognition for two unique in-the-wild audiovisual databases, GRAS2 and SEWA. The GRAS2 is the only database till date with time- and value-continuous affect annotations for Labov effect-free affective behaviours, i.e., without the participant’s awareness of being recorded (which otherwise is known to affect the naturalness of one’s affective behaviour). The SEWA features participants from six different cultural backgrounds, conversing using a video-calling platform. Thus, SEWA features in-the-wild recordings further corrupted by unpredictable artifacts, such as the network-induced delays, frame-freezing and echoes. The two databases present a unique opportunity to study time- and value-continuous affect estimation that is truly in-the-wild.
A novel ‘Evaluator Weighted Estimation’ formulation is proposed to generate a gold standard sequence from several annotations. An illustration is presented demonstrating that the moving bag-of-words (BoW) representation better preserves the temporal context of the features, yet remaining more robust against the outliers compared to other statistical summaries, e.g., moving average. A novel, data-independent randomised codebook is proposed for the BoW representation; especially useful for cross-corpus model generalisation testing when the feature-spaces of the databases differ drastically. Various deep learning models and support vector regressors are used to predict affect dimensions time- and value-continuously. Better generalisability of the models trained on GRAS2 , despite the smaller training size, makes a strong case for the collection and use of Labov effect-free data.
A further foundational contribution is the discovery of the missing many-to-many mapping between the mean square error (MSE) and the concordance correlation coefficient (CCC), i.e., between two of the most popular utility functions till date. The newly invented cost function |MSE_{XY}/σ_{XY}| has been evaluated in the experiments aimed at demystifying the inner workings of a well-performing, simple, low-cost neural network effectively utilising the BoW text features. Also proposed herein is the shallowest-possible convolutional neural network (CNN) that uses the facial action unit (FAU) features. The CNN exploits sequential context, but unlike RNNs, also inherently allows data- and process-parallelism. Interestingly, for the most part, these white-box AI models have shown to utilise the provided features consistent with the human perception of emotion expression
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
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