Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

Data Mining Practical Machine Learning Tools and Techniques

The power of feedback.

Emotion and Adaptation

Supervised recognition of emotions from physiological signals has been widely accomplished to measure affective interactions. Less attention is, however, placed upon learning descriptive models to characterize physiological responses. In this work we delve on why and how to learn discriminative, complete and usable descriptive models based on physiological signals from emotion-evocative stimuli. By satisfying these three properties, we guarantee that the target descriptors can be expressively adopted to understand the physiological behavior underlying multiple emotions. In particular, we explain why classification and unsupervised learning models do not address these properties, and point new directions on how to adapt existing learners to met them based on theoretical and empirical evidence.

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Descriptive Models of Emotion - Learning Useful Abstractions from Physiological Responses during Affective Interactions

Humans have the remarkable ability to interact with their environment through rich and diverse actions. In order to actuate on shared environments with humans, artificial agents should have the ability to recognize the human's actions. However the creation of an artificial agent that is capable of recognizing all possible human actions from prior training is unrealistic, given the diversity of potential actions and ways to perform them. If we aim at deploying competent artificial agents in such scenarios, this hurdle should be overcome.

Online Motion Concept Learning: A Novel Algorithm for Sample-Efficient Learning and Recognition of Human Actions

Social robots are being used to create better educational scenarios, boosting children's motivation and engagement. The focus of the research is to explore new ways to support children in acquisition of their handwriting skills with the help of a social robot. With this perspective, three studies are discussed to investigate aspects related to the learning modes of child-robot interaction, children's impression of a social robot and classification of children's common handwriting difficulties.

/pdf/developing-learning-scenarios-to-foster-children-s-3kqjq96r7z.pdf

Developing Learning Scenarios to Foster Children's Handwriting Skills with the Help of Social Robots

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this paper, we describe FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, FAtiMA Toolkit's library based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.

FAtiMA Toolkit - Toward an effective and accessible tool for the development of intelligent virtual agents and social robots.

/pdf/serious-game-based-psychosocial-intervention-to-foster-1bjp44um.pdf

Ana Paiva

Papers

Descriptive Models of Emotion - Learning Useful Abstractions from Physiological Responses during Affective Interactions

Online Motion Concept Learning: A Novel Algorithm for Sample-Efficient Learning and Recognition of Human Actions

Developing Learning Scenarios to Foster Children's Handwriting Skills with the Help of Social Robots

FAtiMA Toolkit - Toward an effective and accessible tool for the development of intelligent virtual agents and social robots.

Serious Game-based Psychosocial Intervention to Foster Prosociality in Cyberbullying Bystanders