Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Mapping experience in organizations: A learning process for strategic technology planning

Today's fast-paced, dynamic environments mean that for organizations to keep "ahead of the game", engineering managers need to maximize current opportunities and avoid repeating past mistakes. This article describes the development study of a collaborative strategic management tool - the Experience Scan to capture past experience and apply learning from this to present and future situations. Experience Scan workshops were held in a number of different technology organizations, developing and refining the tool until its format stabilized. From participants' feedback, the workshop-based tool was judged to be a useful and efficient mechanism for communication and knowledge management, contributing to organizational learning.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Meng, Q., & Lee, M. (2005). Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics. Wermter, S., Palm, G., & Elshaw, M. (Eds.), In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. (pp. 315-332). (Lecture Notes in Computer Science). Springer Berlin Heidelberg.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Q. Meng and M. H. Lee, Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics, AI-Workshop on NeuroBotics, University of Ulm, Germany. September 2004.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Understanding teacher collaboration in disadvantaged urban primary schools: uncovering practices that foster professional learning communities

This study is set in the context of disadvantaged urban primary schools in Ireland. It inquires into the collaborative practices of primary teachers exploring how class teachers and support teachers develop ways of working together in an effort to improve the literacy and numeracy levels of their student. Traditionally teachers have worked in isolation and therefore ‘collaboration’ as a practice has been slow to permeate the historically embedded assumption of how a teacher should work. This study aims to answer the following questions. 1). What are the dynamics of teacher collaboration in disadvantaged urban primary schools? 2). In what ways are teacher collaboration and teacher learning related? 3). In what ways does teacher collaboration influence students’ opportunities for learning? In answering these research questions, this study aims to contribute to the body of knowledge pertaining to teacher learning through collaboration. Though current policy and literature advocate and make a case for the development of collaborative teaching practices, key studies have identified gaps in the research literature in relation to the impact of teacher collaboration in schools. This study seeks to address some of those gaps by establishing how schools develop a collaborative environment and how teaching practices are enacted in such a setting. It seeks to determine what skills, relationships, structures and conditions are most important in developing collaborative environments that foster the development of professional learning communities (PLCs). This study uses a mixed method research design involving a postal survey, four snap-shot case studies and one in depth case study in an effort to establish if collaborative practice is a feasible practice resulting in worthwhile benefits for both teachers and students.

Enhancing social work students’ learning experience and readiness to undertake practice learning.

This project set out to evaluate the effectiveness of social work education by analysing student perceptions of the strengths and limitations of their education and training on the Bachelor of Social Work, Queen’s University, Belfast (QUB) at different stages of their ‘learning journey’ through the programme.
The author’s primary aim in undertaking this study was to contribute evidence-based understanding of the challenges and opportunities students identified themselves within contemporary practice environments. A secondary aim was to test the effectiveness of key approaches, theories and learning tools in common usage in social work education. The authors believe the outcomes generated by the project demonstrate the value of systematically researching student perceptions of their learning experience and feel the study provides important lessons which should help to inform the future development of social work education not only locally but in other parts of the UK.

‘Preparing Social Workers for Practice: Re-evaluating Student Learning Needs’

Among the key developmental priorities that have been identified in the current process of reform taking place in social work in the UK is the need to improve social work students' preparedness to meet the challenges they will encounter in practice. This paper contributes to the current debate about this issue by reporting a research study that focused on final year undergraduates' experience of academic and practice learning and considered the impact of demographic factors, including age, gender, disability, previous experience and qualifications, on their perceptions of preparedness. The results indicate that students were satisfied with most aspects of preparatory teaching and learning. However, the findings also highlight areas in which students' preparation could be further enhanced, including their skills in dealing with conflict and managing risk. The results suggest that social work programmes should not overly depend on practice learning to prepare students to address the challenges presented by increasingly complex working environments and that educators need to work closely in collaboration with employing partners to ensure that the curriculum keeps up to date with the changing learning needs of practitioners.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Navigation in dynamic environments taking advantage of moving agents

Esta tese propõe uma forma diferente de navegação de robôs em ambientes dinâmicos, onde o robô tira partido do movimento de pedestres, com o objetivo de melhorar as suas capacidades de navegação. A ideia principal é que, ao invés de tratar as pessoas como obstáculos dinâmicos que devem ser evitados, elas devem ser tratadas como agentes especiais com conhecimento avançado em navegação em ambientes dinâmicos. Para se beneficiar do movimento de pedestres, este trabalho propõe que um robô os selecione e siga, de modo que possa mover-se por caminhos ótimos, desviar-se de obstáculos não detetados, melhorar a navegação em ambientes densamente populados e aumentar a sua aceitação por outros humanos. Para atingir estes objetivos, novos métodos são desenvolvidos na área da seleção de líderes, onde duas técnicas são exploradas. A primeira usa métodos de previsão de movimento, enquanto a segunda usa técnicas de aprendizagem por máquina, para avaliar a qualidade de candidatos a líder, onde o treino é feito com exemplos reais. Os métodos de seleção de líder são integrados com algoritmos de planeamento de movimento e experiências são realizadas para validar as técnicas propostas.

Metacognição em práticas colaborativas numa comunidade de b-learning: um estudo de caso

Nesta tese apresenta-se um estudo sobre a metacognição em práticas colaborativas numa comunidade de b-learning no ensino superior. O referencial teórico adotado desenvolveu-se com o intuito de reunir as discussões sobre a metacognição, o b-learning, a comunidade de aprendizagem colaborativa e as comunidades de práticas no ensino superior. Com base nesse referencial teórico desenvolveu-se um estudo empírico sobre a metacognição analisada numa comunidade de b-learning tendo como estudo de caso o Programa Doutoral em Multimédia em Educação da Universidade de Aveiro (edições 2009 e 2010). O estudo, que envolveu 7 docentes e 17 alunos das referidas edições do programa doutoral, teve como uma das suas finalidades verificar a perspetiva do aluno sobre o processo de aprendizagem vivido, das posturas que assume, das estratégias a que recorre para interagir nas comunidades de aprendizagem no âmbito do Programa Doutoral em Multimédia em Educação (PDMMEDU), que funciona na modalidade blearning. Os dados foram obtidos através de inquérito por questionário realizado com os alunos e de sessões de focus group com alunos e docentes. A partir da análise dos resultados obtidos sob o referencial teórico que lhes serviu de enquadramento, procurou dar-se resposta às questões orientadoras do estudo: i) Encontramos evidências de pensamento ou processos metacognitivos nas comunidades de b-learning que se instalaram nas Unidades Curriculares (UC) do PDMMEDU? ii) Que perceções da aprendizagem nas comunidades de b-learning têm os estudantes? iii) Que sentimentos afluem da interação com o grupo de trabalho e com toda a turma? Qual a perceção que os docentes têm sobre a temática e que evidências poderão fornecer sobre a possibilidade de desenvolvimento de processos metacognitivos no âmbito das respetivas UCs? iv) Que relação pode ser observada entre os elementos motivadores da aprendizagem dos alunos e a participação nas UC? Do resultado da análise dos dados obtidos através do inquérito por questionário realizado com os alunos e das sessões de focus group com alunos e docentes identificou-se o perfil metacognitivo dos alunos, uma avaliação da experiência no programa doutoral, as novas posturas assumidas e alterações nos cenários profissionais. Por fim, são feitas sugestões para investigação futura com a proposta de sessões de trabalho onde se desenvolvem competências metacognitivas por parte de alunos envolvidos em cursos de pós-graduação em ambientes online.

Seamless integration of heterogeneous networks in multicast environments

Nowadays, communication environments are already characterized by a myriad of competing and complementary technologies that aim to provide an ubiquitous connectivity service. Next Generation Networks need to hide this heterogeneity by providing a new abstraction level, while simultaneously be aware of the underlying technologies to deliver richer service experiences to the end-user. Moreover, the increasing interest for group-based multimedia services followed by their ever growing resource demands and network dynamics, has been boosting the research towards more scalable and exible network control approaches. The work developed in this Thesis enables such abstraction and exploits the prevailing heterogeneity in favor of a context-aware network management and adaptation. In this scope, we introduce a novel hierarchical control framework with self-management capabilities that enables the concept of Abstract Multiparty Trees (AMTs) to ease the control of multiparty content distribution throughout heterogeneous networks. A thorough evaluation of the proposed multiparty transport control framework was performed in the scope of this Thesis, assessing its bene ts in terms of network selection, delivery tree recon guration and resource savings. Moreover, we developed an analytical study to highlight the scalability of the AMT concept as well as its exibility in large scale networks and group sizes. To prove the feasibility and easy deployment characteristic of the proposed control framework, we implemented a proof-of-concept demonstrator that comprehends the main control procedures conceptually introduced. Its outcomes highlight a good performance of the multiparty content distribution tree control, including its local and global recon guration. In order to endow the AMT concept with the ability to guarantee the best service experience by the end-user, we integrate in the control framework two additional QoE enhancement approaches. The rst employs the concept of Network Coding to improve the robustness of the multiparty content delivery, aiming at mitigating the impact of possible packet losses in the end-user service perception. The second approach relies on a machine learning scheme to autonomously determine at each node the expected QoE towards a certain destination. This knowledge is then used by di erent QoE-aware network management schemes that, jointly, maximize the overall users' QoE. The performance and scalability of the control procedures developed, aided by the context and QoE-aware mechanisms, show the advantages of the AMT concept and the proposed hierarchical control strategy for the multiparty content distribution with enhanced service experience. Moreover we also prove the feasibility of the solution in a practical environment, and provide future research directions that bene t the evolved control framework and make it commercially feasible.

Using Workload Measurement Tools in Diverse Care Contexts: the Experience of Staff in Mental Health and Learning Disability Inpatient Settings

Introduction The critical challenge of determining the correct level and skill-mix of nursing staff required to deliver safe and effective healthcare has become an international concern. It is recommended that evidence-based staffing decisions are central to the development of future workforce plans. Workforce planning in mental health and learning disability nursing is largely under-researched with few tools available to aid the development of evidence-based staffing levels in these environments. Aim It was the aim of this study to explore the experience of staff using the Safer Nursing Care Tool (SNCT) and the Mental Health and Learning Disability Workload Tool (MHLDWT) in mental health and learning disability environments. Method Following a 4-week trial period of both tools a survey was distributed via Qualtrics on-line survey software to staff members who used the tools during this time. Results The results of the survey revealed that the tools were considered a useful resource to aid staffing decisions; however specific criticisms were highlighted regarding their suitability to psychiatric intensive care units (PICU) and learning disability wards. Discussion This study highlights that further development of workload measurement tools is required to support the implementation of effective workforce planning strategies within mental health and learning disability services. Implications for Practice With increasing fiscal pressures the need to provide cost-effective care is paramount within NHS services. Evidence-based workforce planning is therefore necessary to ensure that appropriate levels of staff are determined. This is of particular importance within mental health and learning disability services due to the reduction in the number of available beds and an increasing focus on purposeful admission and discharge.