This paper presents an approach for modeling location-based profiles of social image media based on tagging information and collaborative geo-reference annotations. We utilize pattern mining techniques for obtaining s...
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Efficiently locating learning content and web services for adaptive and personalized online courses is a demanding research area. This paper identifies the key challenges that need to be addressed. An approach is then...
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Efficiently locating learning content and web services for adaptive and personalized online courses is a demanding research area. This paper identifies the key challenges that need to be addressed. An approach is then presented that empowers educators to locate relevant learning resources and services through domain expertise and semantics that are meaningful to them. This approach utilizes the existing SARA, SABer and Adaptive Engine systems, in order to support an application that educators can effectively engage with to form adaptive courses. A compelling case study in the Technology Enhanced Learning domain is detailed, as well as an overview of the architecture that the AMAS project uses for supporting such a scenario.
In order to support a software development team in its day-to-day operations, different data sources can be exploited. In this paper, we focus on CVS logs and communication profiles between developers provided by RFID...
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In order to support a software development team in its day-to-day operations, different data sources can be exploited. In this paper, we focus on CVS logs and communication profiles between developers provided by RFID-proximity information. We provide a novel approach for combining the data sources into a graph, and apply the page rank algorithm for capturing interesting knowledge about resource and developer profiles. Additionally, we discuss the application in the software developer setting, and also for project management. The proposed approach is evaluated in the context of a real-world developer setting.
We describe a framework for Adaptive Multilingual Information Retrieval (AMIR) which allows multilingual resource discovery and delivery using on-the-fly machine translation of documents and queries. Result documents ...
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This paper describes the SimCon (Simulated Context) Generator which combines data on the state of a Virtual Reality building with the SimCon Model to generate interactive location context for the rapid evaluation of S...
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This paper describes the SimCon (Simulated Context) Generator which combines data on the state of a Virtual Reality building with the SimCon Model to generate interactive location context for the rapid evaluation of Smart Building Applications. The paper evaluates the simulated context against physical readings. The SimCon Model does not set out to replace deterministic models, but rather allow for rapid configuration of simulated context early in the building design cycle to evaluate the impact of uncertainty in context on application behavior.
The aims of the workshop on Personalised Multilingual Hypertext Retri eval (PMHR) are twofold: to set the scene in this challenging area, allowing the different communities engaged in related research topics to meet a...
Currently the user's web search is disjoint from the resources which is subsequently browsed. Specifically the related instances of the search are not displayed on the following pages. This lack of continuity betw...
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Currently the user's web search is disjoint from the resources which is subsequently browsed. Specifically the related instances of the search are not displayed on the following pages. This lack of continuity between the actual search and the web sites displayed may lead to skimming by the user to identify what is relevant on the pages. This paper presents an approach to the continuous modeling of a user's interests through a browser based plug-in that may be used to inform the intelligent highlighting of content on the browsed pages. Specifically the paper presents two related systems, one for capturing and modeling the user's browsing behavior and the second for leveraging the power of linked data to highlight information relevant to the user's interests. The design, implementation and evaluation of both systems are presented as well as a conclusion and outlook for further work.
Some popular algorithms used in Music Information Retrieval (MIR) such as Self-Organizing Maps (SOMs) require the objects they process to be represented as vectors, i.e. elements of a vector space. This is a rather se...
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Some popular algorithms used in Music Information Retrieval (MIR) such as Self-Organizing Maps (SOMs) require the objects they process to be represented as vectors, i.e. elements of a vector space. This is a rather severe restriction and if the data does not adhere to it, some means of vectorization is required. As a common practice, the full distance matrix is computed and each row of the matrix interpreted as an artificial feature vector. This paper empirically investigates the impact of this transformation. Further, an alternative approach for vectorization based on Multidimensional Scaling is pro posed that is able to better preserve the actual distance relations of the objects which is essential for obtaining a good retrieval performance.
Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automati...
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Drawing on the experiences and context of others who have already used a particular resource can greatly facilitate that resource's reuse. Such reuse is essential when the resources in question are digital learnin...
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Drawing on the experiences and context of others who have already used a particular resource can greatly facilitate that resource's reuse. Such reuse is essential when the resources in question are digital learning assets, services and models which are expensive both in terms of time and monetary expenditure to develop and use. When these resources are deployed in personalised settings, where each user may be delivered a tailored sequence of resources that uniquely suits their particular needs, gathering and federating a rich view of how these resources are being used becomes important. In this article we describe an approach to facilitating the federating of contextual usage data, which is compiled over the life cycle of a resource. Given that this data is likely to come from a range of different sources, our approach will need to be able to cope with the high level of heterogeneity expected in terms of its structure, syntax and semantics. We describe how such data may be used to support users in assessing the value of learning resources and facilitating their appropriate reuse.
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