Conformity, deformity and reformity

In any given field of artistic practice, practitioners position themselves—or find themselves positioned—according to interests and allegiances with specific movements, genres, and traditions. Selecting particular frameworks through which to approach the development of new ideas, patterns and expressions, balance is invariably maintained between the desire to contribute towards and connect with a particular set of domain conventions, whilst at the same time developing distinction and recognition as a creative individual. Creativity through the constraints of artistic domain, discipline and style provides a basis for consideration of notions of originality in the context of activity primarily associated with reconfiguration, manipulation and reorganisation of existing elements and ideas. Drawing from postmodern and post-structuralist perspectives in the analysis of modern hybrid art forms and the emergence of virtual creative environments, the transition from traditional artistic practice and notions of craft and creation, to creative spaces in which elements are manipulated, mutated, combined and distorted with often frivolous or subversive intent are considered. This paper presents an educational and musically focused perspective of the relationship between the individual and domain-based creative practice. Drawing primarily from musical and audio-visual examples with particular interest in creative disruption of pre-existing elements, creative strategies of appropriation and recycling are explored in the context of music composition and production. Conclusions focus on the interpretation of creativity as essentially a process of recombination and manipulation and highlight how the relationship between artist and field of practice creates unique creative spaces through which new ideas emerge

Transition expertise: Cognitive factors and developmental processes that contribute to repeated successful career transitions amongst elite athletes, musicians and business people

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines the nature of transition expertise which enables individuals to make repeated successful transitions over the course of their career. It addressed four areas that contribute to transition expertise: 1) cognitive flexibility that enables the generalisation of expert knowledge and processes; 2) inferential and inductive cognitive mechanisms that enable expertise to be generalised; 3) personal intelligences that are used to support transitions; and 4) practical intelligence as it supports performance contextually during transitions. The study used retrospective interviews to gather data from elite performers in three fields who had made successful career transitions: sports people who become national coaches or heads of national bodies; successful musicians who become heads of faculty or principals of a conservatoire; successful business people who become senior vice presidents or CEOs. Participants were able to generalise expert knowledge and processes beyond their primary domains, contrary to widely held views about the domain specificity of expertise. Cognitive flexibility enabled this generalisation and was developed through broad based training, early exposure to multiple domains and the early use of generative cognitive processes during the development of primary domain expertise. Inductive, inferential and analogical cognitive mechanisms were the main tools through which expertise was generalised during transitions. Personal intelligence contributed to transition expertise. Intrapersonal intelligence enabled individuals to understand how their abilities, values and motivations shaped their career progression. Interpersonal intelligence enabled individuals to respond effectively to the requirements of their peers, direct reports, stakeholders and organisational context. Contrary to expectations, self regulatory processes did not play a central role in the management of transitions. Practical intelligence enabled transition expertise. It involved more than applying subject-area and tacit knowledge. It encompassed the abilities to: identify and resolve problems; manipulate environmental objects in the form of administrative tasks, schedules and plans; utilise resources in terms of people and materials; and shape their environment, corporate structures and culture. Transition expertise develops and evolves over the course of a career as it uses convergent and divergent cognitive processes, inductive mechanisms, personal awareness and cognitive pragmatics to address issues of increasing scope and implication. While motivational factors, self belief and personality resiliency are important contributors to transition expertise they did not form part of this study

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance

Societal issues in machine learning: When learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues

Societal issues in machine learning: when learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues.Peer ReviewedPostprint (published version