Learning Appropriate Contexts

Genetic Programming is extended so that the solutions being evolved do so in the context of local domains within the total problem domain. This produces a situation where different “species” of solution develop to exploit different “niches” of the problem – indicating exploitable solutions. It is argued that for context to be fully learnable a further step of abstraction is necessary. Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify when it is the content of a model is wrong and when it is the context. Some principles of learning to identify useful contexts are proposed

Using Localised ‘Gossip’ to Structure Distributed Learning

The idea of a “memetic” spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlappingt localities in a space and solutions are then evolved in those localites. Good solutions are not only crossed with others to search for better solutions but also they propogate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occcurence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process occurs can effect its efficiency

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Personalized information retrieval based on context and ontological knowledge

The article has been accepted for publication and appeared in a revised form, subsequent to peer review and/or editorial input by Cambridge University PressExtended papers from C&O-2006, the second International Workshop on Contexts and Ontologies, Theory, Practice and Applications1 collocated with the seventeenth European Conference on Artificial Intelligence (ECAI)Context modeling has been long acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are a) the explicit distinction between historic user context and live user context, b) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition of effective means to relate the three of them, and c) the introduction of fuzzy representations as an instrument to properly handle the uncertainty and imprecision involved in the automatic interpretation of meanings, user attention, and user wishes. Based on a formal grounding at the representational level, we propose methods for the automatic extraction of persistent semantic user preferences, and live, ad-hoc user interests, which are combined in order to improve the accuracy and reliability of personalization for retrieval.This research was partially supported by the European Commission under contracts FP6-001765 aceMedia and FP6-027685 MESH. The expressed content is the view of the authors but not necessarily the view of the aceMedia or MESH projects as a whole

Transitions through lifelong learning: Implications for learning analytics

The ability to develop new skills and competencies is a central concept of lifelong learning. Research to date has largely focused on the processes and support individuals require to engage in upskilling, re-learning or training. However, there has been limited attention examining the types of support that are necessary to assist a learner's transition from “old” workplace contexts to “new”. Professionals often undergo significant restructuring of their knowledge, skills, and identities as they transition between career roles, industries, and sectors. Domains such as learning analytics (LA) have the potential to support learners as they use the analysis of fine-grained data collected from education technologies. However, we argue that to support transitions throughout lifelong learning, LA needs fundamentally new analytical and methodological approaches. To enable insights, research needs to capture and explain variability, dynamics, and causal interactions between different levels of individual development, at varying time scales. Scholarly conceptions of the context in which transitions occur are also required. Our interdisciplinary argument builds on the synthesis of literature about transitions in the range of disciplinary and thematic domains such as conceptual change, shifts between educational systems, and changing roles during life course. We highlight specific areas in research designs and current analytical methods that hinder insight into transformational changes during transitions. The paper concludes with starting points and frameworks that can advance research in this area

Audience-generated traces: audience experience in performance documentation

This thesis explores whether and how audience-generated content produced from and about audiences’ experience and during and as part of a live performance might become part of a theatre and performance work’s archive. It sets out to examine both the challenges as well as the documentational opportunities that this material might afford. The thesis is influenced by Gabriella Giannachi’s articulation of digital technologies as archival interfaces and Sarah Bay-Cheng’s convergence of live performance and documentation. It examines the function of audience-generated content during three case studies and postulates that audiences can be regarded as co-producers of performance documents. To do so, it analyses how Speak Bitterness by Forced Entertainment, Karen by Blast Theory, and Flatland by Extant request that their audiences activate the live performance or augment its experience by using a digital technology, and how by doing so they leave digital traces behind. Building upon this condition the thesis interrogates how the three company casestudies archive these works’ audience-generated traces. In addition, it investigates how digital traces are perceived by institutional theatre and performance collections. Through interviews with the case-study practitioners, the curator of the British Library Sound Archive and the archivists of the National Theatre and Victoria and Albert Museum the thesis reveals a set of technical and organisational challenges involved in this process. Although audience-generated traces are considered valuable marketing and research material they also unsettle established notions and structures of performance documentation and its archive. Rethinking the established notion of the performance document and the form of files through which it conveys knowledge, the thesis returns to Ricoeur’s theory of the trace so as to expand ideas of how performance documentation enables ways of knowing a past performance. It argues that, as direct remnants of the live performance moment originating in the participant, audiencegenerated content offers solutions to ‘presencing’ the audience in documentation and novel ways for revisiting a past performance work from within its unfolding

Leveraging lessons learned in organizations through implementing practice-based organizational learning and performance improvement - An opportunity for context-based intelligent assistant support (CIAS)

Organizations that leverage lessons learned from their experience in the practice of complex real-world activities are faced with five difficult problems. First, how to represent the learning situation in a recognizable way. Second, how to represent what was actually done in terms of repeatable actions. Third, how to assess performance taking account of the particular circumstances. Fourth, how to abstract lessons learned that are re-usable on future occasions. Fifth, how to determine whether to pursue practice maturity or strategic relevance of activities. Here, organizational learning and performance improvement are investigated in a field study using the Context-based Intelligent Assistant Support (CIAS) approach. A new conceptual framework for practice-based organizational learning and performance improvement is presented that supports researchers and practitioners address the problems evoked and contributes to a practice-based approach to activity management. The novelty of the research lies in the simultaneous study of the different levels involved in the activity. Route selection in light rail infrastructure projects involves practices at both the strategic and operational levels; it is part managerial/political and part engineering. Aspectual comparison of practices represented in Contextual Graphs constitutes a new approach to the selection of Key Performance Indicators (KPIs). This approach is free from causality assumptions and forms the basis of a new approach to practice-based organizational learning and performance improvement. The evolution of practices in contextual graphs is shown to be an objective and measurable expression of organizational learning. This diachronic representation is interpreted using a practice-based organizational learning novelty typology. This dissertation shows how lessons learned when effectively leveraged by an organization lead to practice maturity. The practice maturity level of an activity in combination with an assessment of an activity’s strategic relevance can be used by management to prioritize improvement effort

Contribution à la spécification et à la vérification des logiciels à base de composants : enrichissement du langage de données de Kmelia et vérication de contrats

With Model Driven Engineering models are the heart of software development. Thesemodels evolve through transformations. In this thesis our interest was the validationfor these model transformations by testing, and more precisely the test oracles. Wepropose two approaches to assist the tester to create these oracles. With the first ap-proach this assistance is passive; we provide the tester with a new oracle function.The test oracles created with this new oracle function control only part of the modelproduced by the transformation under test. We defined the notion of partial verdict,described the situations where having a partial verdict is beneficial for the tester andhow to test a transformation in this context. We developed a tool implementing thisproposal, and ran experiments with it. With the second approach, we provide a moreactive assistance about test oracles’ quality. We study the quality of a set of modeltransformation test oracles. We consider that the quality of a set of oracles is linkedto its ability to detect faults in the transformation under test. We show the limits ofmutation analysis which is used for this purpose, then we propose a new approach thatcorrects part of these drawbacks. We measure the coverage of the output meta-modelby the set of oracles we consider. Our approach does not depend on the language usedfor the transformation under test’s implementation. It also provides the tester withhints on how to improve her oracles. We defined a process to evaluate meta-modelcoverage and qualify test oracles. We developed a tool implementing our approach tovalidate it through experimentations.L'utilisation croissante des composants et des services logiciels dans les différents secteursd'activité (télécommunications, transports, énergie, finance, santé, etc.) exige desmoyens (modèles, méthodes, outils, etc.) rigoureux afin de maîtriser leur production etd'évaluer leur qualité. En particulier, il est crucial de pouvoir garantir leur bon fonctionnementen amont de leur déploiement lors du développement modulaire de systèmes logiciels.Kmelia est un modèle à composants multi-services développé dans le but de construiredes composants logiciels et des assemblages prouvés corrects. Trois objectifs principauxsont visés dans cette thèse. Le premier consiste à enrichir le pouvoir d'expression du modèle Kmelia avec un langage de données afin de satisfaire le double besoin de spécificationet de vérification. Le deuxième vise l'élaboration d'un cadre de développement fondé sur lanotion de contrats multi-niveaux. L'intérêt de tels contrats est de maîtriser la constructionprogressive des systèmes à base de composants et d'automatiser le processus de leur véri-fication. Nous nous focalisons dans cette thèse sur la vérification des contrats fonctionnelsen utilisant la méthode B. Le troisième objectif est l'instrumentation de notre approchedans la plate-forme COSTO/Kmelia. Nous avons implanté un prototype permettant deconnecter COSTO aux différents outils associés à la méthode B. Ce prototype permet deconstruire les machines B à partir des spécifications Kmelia en fonction des propriétés à vé-rifier. Nous montrons que la preuve des spécifications B générées garantit la cohérence desspécifications Kmelia de départ. Les illustrations basées sur l'exemple CoCoME confortentnos propositions