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

This work explores the use of a social robot as an assistive agent during therapy sessions, in order to assist children with Autism Spectrum Disorder (ASD), through a Tangram game. This experiment has two conditions: the Tutor Mode - the robot gives help whenever the child needs; and the Peer Mode - the robot plays with the child in turn-taking. The results showed that, in the TM, the robot was capable of stimulating children’s attention towards the game and to assist them most of the times. In the PM, the robot also stimulated children’s attention to the game and was able to establish turns for most participants.

An Interactive Tangram Game for Children with Autism

This paper presents a study on collaborative manipulation between an autonomous robot and multiple users. We investigate how different motion types impact people's ability to understand the robot's goals in a multi-user scenario. We propose an approach based on Collaborative Probabilistic Movement Primitives to generate the robot's movements, exploiting predictability and legibility of movement to express intentions through motion. We compare the impact on the interaction of using only either predictable or legible movements, and propose a third approach — hybrid motion — that selects, in each situation, whether to execute a predictable motion or a legible motion, depending on what the robot perceives as more efficient for the multi-user collaboration effort. To test the impact of the three motion types in the context of a collaborative task, we run a user study using a Baxter robot that autonomously serves cups of water to three users upon request. Our results show that, in the particular case where all users simultaneously request water, the hybrid motion performs better than the other two.

“Me and you together” movement impact in multi-user collaboration tasks

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the {PT} method - A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

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Classification of Children's Handwriting Errors for the Design of an Educational Co-writer Robotic Peer

Learner Modeling is a dynamic process. Creating and maintaining models of learners by an interactive system is not a precise task, and it requires guesses and abductive generation of explanation of the learners’ actions. In addition, learners change their behavior while interacting with the system. Therefore, these systems need some mechanisms to maintain the rationality of the learner models created. For this maintenance to be rational, we enunciate two basic principles: system consistency and learner accuracy. We present a system AMMS (Agent Model Maintenance System) to maintain learner models in accordance with these two principles, which were followed by introducing reasons (endorsements) for the hypotheses created about the learner. These reasons (which are based on the acquisition rules) are kept in the AMMS, so it is possible to choose the most trustable learner model by using a "trust" function.

A learner model reason maintenance system

This paper proposes a framework for cognitive-based affective user modeling. Such framework relies on the idea that, to model affect in user models, one can use the situations experienced by the user as well as the observable behaviour of the user. These situations and behaviours, when interpreted under the light of a cognitive theory of emotions, will allow us to make inferences on the possible affective states of the user. Such type of approach will be here called cognitive-based affective user modeling. To describe the framework we will use a cognitive theory of emotions (the Ortony, Clore and Collins theory of emotions) as the basis for the representation of the affective states of the user. We will also illustrate the use of this modelling in the context of a virtual theatre, where children engage in collaborative story creation.

Ana Paiva

Papers

An Interactive Tangram Game for Children with Autism

“Me and you together” movement impact in multi-user collaboration tasks

Classification of Children's Handwriting Errors for the Design of an Educational Co-writer Robotic Peer

A learner model reason maintenance system

A cognitive approach to affective user modeling