Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.

/pdf/iemocap-interactive-emotional-dyadic-motion-capture-database-2ffyt1sb5i.pdf

IEMOCAP: interactive emotional dyadic motion capture database

This research investigates the meaning of “human-computer relationship” and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality We especially consider the problem of designing for long-term interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationship-building skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers

/pdf/establishing-and-maintaining-long-term-human-computer-32fopmq28i.pdf

Establishing and maintaining long-term human-computer relationships

Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speaking "Robovie" robots interacted with first- and sixth-grade pupils at the perimeter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition. The children were given a brief picture-word matching English test at the start of the trial, after 1 week and after 2 weeks. Interactions were counted using the tags, and video and audio were recorded. In the majority of cases, a child's friends were present during the interactions.

Interaction with the robot was frequent in the 1st week, and then it fell off sharply by the 2nd week. Nonetheless, some children continued to interact with the robot. Interaction time during the 2nd week predicted improvements in English skill at the posttest, controlling for pretest scores. Further analyses indicate that the robots may have been more successful in establishing common ground and influence when the children already had some initial proficiency or interest in English. These results suggest that interactive robots should be designed to have something in common with their users, providing a social as well as technical challenge.

Interactive robots as social partners and peer tutors for children: a field trial

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM

https://www.morganclaypool.com/doi/suppl/10.2200/S00509ED1V01Y201305HLT023/suppl_file/Dagan_Ch1.pdf

Synthesis Lectures on Human Language Technologies

In this paper, we argue for embodied corrversational characters as the logical extension of the metaphor of human - computer interaction as a conversation. We argue that the only way to fully model the richness of human I&+ to-face communication is to rely on conversational analysis that describes sets of conversational behaviors as fi~lfilling conversational functions, both interactional and propositional. We demonstrate how to implement this approach in Rea, an embodied conversational agent that is capable of both multimodal input understanding and output generation in a limited application domain. Rea supports both social and task-oriented dialogue. We discuss issues that need to be addressed in creating embodied conversational agents, and describe the architecture of the Rea interface.

Embodiment in conversational interfaces: Rea

In this paper we describe the computational and architectural requirements for systems which support real-time multimodal interaction with an embodied conversational character. We argue that the three primary design drivers are real-time multithreaded entrainment, processing of both interactional and propositional information, and an approach based on a functional understanding of human face-to-face conversation. We then present an architecture which meets these requirements and an initial conversational character that we have developed who is capable of increasingly sophisticated multimodal input and output in a limited application domain.

/pdf/requirements-for-an-architecture-for-embodied-conversational-5dbb0q1z5f.pdf

Requirements for an Architecture for Embodied Conversational Characters

In this paper we describe the computational and architectural requirements for systems which support real-time multimodal interaction with an embodied conversational character. We argue that the three primary design drivers are real-time multithreaded entrainment, processing of both interactional and propositional information, and an approach based on a functional understanding of human face-to-face conversation. We then present an architecture which meets these requirements and an initial conversational character that we have developed who is capable of increasingly sophisticated multimodal input and output in a limited

/pdf/an-architecture-for-embodied-conversational-characters-1tqwc76hdy.pdf

An Architecture for Embodied Conversational Characters

Embodiment in conversational characters: Rea

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Embodiment in conversational interfaces: Rea

Requirements for an Architecture for Embodied Conversational Characters

An Architecture for Embodied Conversational Characters

Embodiment in conversational characters: Rea