Depression and anxiety in the postnatal period : an examination of mother–infant interactions and infants’ language development

Infancy is a time period associated with significant and rapid social-emotional and cognitive development. Environmental influences, particularly the quality of the mother–infant interaction, assist in shaping these early capacities. Maternal factors such as depression and anxiety can have a negative impact on a mother’s sensitivity towards her infant and indirectly compromise child developmental outcomes. However, little is known about the impact of depression and anxiety on communicative interactions and language outcomes in young infants. This thesis reports a longitudinal study, which primary objective was to examine the mechanisms through which maternal depression and anxiety influence infant language development via the quantity and quality of mother–infant interactions. The second objective was to evaluate the effectiveness of a video feedback intervention aimed at promoting maternal responsiveness, a construct that captures the quality of early mother–infant interactions. To address these objectives this longitudinal study followed a sample of mother–infant dyads in which the mothers were or were not affected by anxiety and depression symptoms, between the infants’ ages of 6 to 18 months. The study included four components that measured the quantity and quality of the mother–infant interactions and infant developmental outcomes between groups and across time. The first component of the longitudinal study involved home recordings examining the quantity of maternal speech input to the infants at 6 and 12 months of age. The second component involved the assessment of infants’ lexical abilities at 18 months of age. The third component consisted of assessments of the quality of mother–infant interactions at 9 and 12 months. The final component involved the evaluation of a short intervention aimed at promoting maternal responsiveness within mother–infant interactions. Findings demonstrated that maternal depression and anxiety have an effect on infants’ early lexical abilities via both the quantity and quality of mother–infant interactions. These results suggest that variability in mothers’ emotional health influences infants’ home language experience, the concurrent frequency of vocalisations, and their later vocabulary size and lexical processing efficiency at 18 months. Maternal responsiveness, a measure of the quality of mother–infant interactions, emerged as the strongest predictor of infant vocabulary size

Use of automated coding methods to assess motivational behaviour in education

Teachers’ motivational behaviour is related to important student outcomes. Assessing teachers’ motivational behaviour has been helpful to improve teaching quality and enhance student outcomes. However, researchers in educational psychology have relied on self-report or observer ratings. These methods face limitations on accurately and reliably assessing teachers’ motivational behaviour; thus restricting the pace and scale of conducting research. One potential method to overcome these restrictions is automated coding methods. These methods are capable of analysing behaviour at a large scale with less time and at low costs. In this thesis, I conducted three studies to examine the applications of an automated coding method to assess teacher motivational behaviours. First, I systematically reviewed the applications of automated coding methods used to analyse helping professionals’ interpersonal interactions using their verbal behaviour. The findings showed that automated coding methods were used in psychotherapy to predict the codes of a well-developed behavioural coding measure, in medical settings to predict conversation patterns or topics, and in education to predict simple concepts, such as the number of open/closed questions or class activity type (e.g., group work or teacher lecturing). In certain circumstances, these models achieved near human level performance. However, few studies adhered to best-practice machine learning guidelines. Second, I developed a dictionary of teachers’ motivational phrases and used it to automatically assess teachers’ motivating and de-motivating behaviours. Results showed that the dictionary ratings of teacher need support achieved a strong correlation with observer ratings of need support (rfull dictionary = .73). Third, I developed a classification of teachers’ motivational behaviour that would enable more advanced automated coding of teacher behaviours at each utterance level. In this study, I created a classification that includes 57 teacher motivating and de-motivating behaviours that are consistent with self-determination theory. Automatically assessing teachers’ motivational behaviour with automatic coding methods can provide accurate, fast pace, and large scale analysis of teacher motivational behaviour. This could allow for immediate feedback and also development of theoretical frameworks. The findings in this thesis can lead to the improvement of student motivation and other consequent student outcomes

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Mobile service robots are going to play an increasing role in the society of humans. Voice-enabled interaction with service robots becomes very important, if such robots are to be deployed in real-world environments and accepted by the vast majority of potential human users. The research presented in this thesis addresses the problem of speech recognition integration in an interactive voice-enabled interface of a service robot, in particular a tour-guide robot. The task of a tour-guide robot is to engage visitors to mass exhibitions (users) in dialogue providing the services it is designed for (e.g. exhibit presentations) within a limited time. In managing tour-guide dialogues, extracting the user goal (intention) for requesting a particular service at each dialogue state is the key issue. In mass exhibition conditions speech recognition errors are inevitable because of noisy speech and uncooperative users of robots with no prior experience in robotics. They can jeopardize the user goal identification. Wrongly identified user goals can lead to communication failures. Therefore, to reduce the risk of such failures, methods for detecting and compensating for communication failures in human-robot dialogue are needed. During the short-term interaction with visitors, the interpretation of the user goal at each dialogue state can be improved by combining speech recognition in the speech modality with information from other available robot modalities. The methods presented in this thesis exploit probabilistic models for fusing information from speech and auxiliary modalities of the robot for user goal identification and communication failure detection. To compensate for the detected communication failures we investigate multimodal methods for recovery from communication failures. To model the process of modality fusion, taking into account the uncertainties in the information extracted from each input modality during human-robot interaction, we use the probabilistic framework of Bayesian networks. Bayesian networks are graphical models that represent a joint probability function over a set of random variables. They are used to model the dependencies among variables associated with the user goals, modality related events (e.g. the event of user presence that is inferred from the laser scanner modality of the robot), and observed modality features providing evidence in favor of these modality events. Bayesian networks are used to calculate posterior probabilities over the possible user goals at each dialogue state. These probabilities serve as a base in deciding if the user goal is valid, i.e. if it can be mapped into a tour-guide service (e.g. exhibit presentation) or is undefined – signaling a possible communication failure. The Bayesian network can be also used to elicit probabilities over the modality events revealing information about the possible cause for a communication failure. Introducing new user goal aspects (e.g. new modality events and related features) that provide auxiliary information for detecting communication failures makes the design process cumbersome, calling for a systematic approach in the Bayesian network modelling. Generally, introducing new variables for user goal identification in the Bayesian networks can lead to complex and computationally expensive models. In order to make the design process more systematic and modular, we adapt principles from the theory of grounding in human communication. When people communicate, they resolve understanding problems in a collaborative joint effort of providing evidence of common shared knowledge (grounding). We use Bayesian network topologies, tailored to limited computational resources, to model a state-based grounding model fusing information from three different input modalities (laser, video and speech) to infer possible grounding states. These grounding states are associated with modality events showing if the user is present in range for communication, if the user is attending to the interaction, whether the speech modality is reliable, and if the user goal is valid. The state-based grounding model is used to compute probabilities that intermediary grounding states have been reached. This serves as a base for detecting if the the user has reached the final grounding state, or wether a repair dialogue sequence is needed. In the case of a repair dialogue sequence, the tour-guide robot can exploit the multiple available modalities along with speech. For example, if the user has failed to reach the grounding state related to her/his presence in range for communication, the robot can use its move modality to search and attract the attention of the visitors. In the case when speech recognition is detected to be unreliable, the robot can offer the alternative use of the buttons modality in the repair sequence. Given the probability of each grounding state, and the dialogue sequence that can be executed in the next dialogue state, a tour-guide robot has different preferences on the possible dialogue continuation. If the possible dialogue sequences at each dialogue state are defined as actions, the introduced principle of maximum expected utility (MEU) provides an explicit way of action selection, based on the action utility, given the evidence about the user goal at each dialogue state. Decision networks, constructed as graphical models based on Bayesian networks are proposed to perform MEU-based decisions, incorporating the utility of the actions to be chosen at each dialogue state by the tour-guide robot. These action utilities are defined taking into account the tour-guide task requirements. The proposed graphical models for user goal identification and dialogue error handling in human-robot dialogue are evaluated in experiments with multimodal data. These data were collected during the operation of the tour-guide robot RoboX at the Autonomous System Lab of EPFL and at the Swiss National Exhibition in 2002 (Expo.02). The evaluation experiments use component and system level metrics for technical (objective) and user-based (subjective) evaluation. On the component level, the technical evaluation is done by calculating accuracies, as objective measures of the performance of the grounding model, and the resulting performance of the user goal identification in dialogue. The benefit of the proposed error handling framework is demonstrated comparing the accuracy of a baseline interactive system, employing only speech recognition for user goal identification, and a system equipped with multimodal grounding models for error handling

An Exploratory Critical Study of Questioning Strategies Posed by Early Childhood Teachers During Literacy Blocks

The purpose of this study was to examine the cognitive types and functions of questions orally posed by early childhood teachers in kindergarten through 3rd grade during a 90-minute literacy block. The cognitive types of questions were determined by the criteria established using Hess’ Cognitive Rigor Matrix (Hess, Jones, Carlock, & Walkup, 2009). The functions of the posed questions were determined by criteria based on the work of Costa (2001), Hughes (as cited in Fusco, 2012), and Lowery (as cited in Fusco, 2012). This study examined questioning strategies used by 12 early childhood teachers from a Northeast Tennessee School District. The 12 teachers orally posed questions were recorded, scripted, and coded by the researcher to determine each question’s type, frequency, and function and how these indicators serve to increase student engagement during the literacy block. Results from the study show that the majority of questions posed are low in cognitive level requiring students to perform primarily at the basic level of remembering and understanding. The primary function of the recorded posed questions called for students to verify their understanding and many closed questions were asked during the documented lessons. The time teachers gave students to answer a question was minimal and a single student generated response was the predominant vehicle used to glean an answer to a presented question. While the teachers in this study appeared to understand the importance of posing high level cognitive questions in order to increase Common Core Standards instruction, results from this study showed that there seems to be a disconnect between what teachers think they do and their actual practice in regard to posing effective questions as a strategy for active student engagement and learning

The SEMAINE API : a component integration framework for a naturally interacting and emotionally competent embodied conversational agent

The present thesis addresses the topic area of Embodied Conversational Agents (ECAs) with capabilities for natural interaction with a human user and emotional competence with respect to the perception and generation of emotional expressivity. The focus is on the technological underpinnings that facilitate the implementation of a real-time system with these capabilities, built from re-usable components. The thesis comprises three main contributions. First, it describes a new component integration framework, the SEMAINE API, which makes it easy to build emotion-oriented systems from components which interact with one another using standard and pre-standard XML representations. Second, it presents a prepare-and-trigger system architecture which substantially speeds up the time to animation for system utterances that can be pre-planned. Third, it reports on the W3C Emotion Markup Language, an upcoming web standard for representing emotions in technological systems. We assess critical aspects of system performance, showing that the framework provides a good basis for implementing real-time interactive ECA systems, and illustrate by means of three examples that the SEMAINE API makes it is easy to build new emotion-oriented systems from new and existing components.Die vorliegende Dissertation behandelt das Thema der virtuellen Agenten mit Fähigkeiten zur natürlichen Benutzer-Interaktion sowie emotionaler Kompetenz bzgl. der Wahrnehmung und Generierung emotionalen Ausdrucks. Der Schwerpunkt der Arbeit liegt auf den technologischen Grundlagen für die Implementierung eines echtzeitfähigen Systems mit diesen Fähigkeiten, das aus wiederverwendbaren Komponenten erstellt werden kann. Die Arbeit umfasst drei Kernaspekte. Zum Einen beschreibt sie ein neues Framework zur Komponenten-Integration, die SEMAINE API: Diese erleichtert die Erstellung von Emotions-orientierten Systemen aus Komponenten, die untereinander mittels Standard- oder Prä-Standard-Repräsentationen kommunizieren. Zweitens wird eine Systemarchitektur vorgestellt, welche Vorbereitung und Auslösung von Systemverhalten entkoppelt und so zu einer substanziellen Beschleunigung der Generierungszeit führt, wenn Systemäußerungen im Voraus geplant werden können. Drittens beschreibt die Arbeit die W3C Emotion Markup Language, einen werdenden Web-Standard zur Repräsentation von Emotionen in technologischen Systemen. Es werden kritische Aspekte der Systemperformanz untersucht, wodurch gezeigt wird, dass das Framework eine gute Basis für die Implementierung echtzeitfähiger interaktiver Agentensysteme darstellt. Anhand von drei Beispielen wird illustriert, dass mit der SEMAINE API leicht neue Emotions-orientierte Systeme aus neuen und existierenden Komponenten erstellt werden können

Sustainable development under the conditions of European integration. Part I

This collective monograph offers the description of sustainable development in the condition of European integration. The authors of individual chapters have chosen such point of view for the topic which they considered as the most important and specific for their field of study using the methods of logical and semantic analysis of concepts, the method of reflection, textual reconstruction and comparative analysis. The theoretical and applied problems of sustainable development in the condition of European integration are investigated in the context of economics, education, cultural, politics and law

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Sandspur, Vol 102 No 05, September 21, 1995

Rollins College student newspaper, written by the students and published at Rollins College. The Sandspur started as a literary journal.https://stars.library.ucf.edu/cfm-sandspur/1049/thumbnail.jp

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas