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As e-learning gradually evolved many specialized and disparate systems appeared to fulfil the needs of teachers and students, such as repositories of learning objects, authoring tools, intelligent tutors and automatic evaluators. This heterogeneity raises interoperability issues giving the standardization of content an important role in e-learning. This article presents a survey on current e-learning content aggregation standards focusing on their internal organization and packaging. This study is part of an effort to choose the most suitable specifications and standards for an e-learning framework called Ensemble defined as a conceptual tool to organize a network of e-learning systems and services for domains with complex evaluation.

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The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms that compute a proximity given as input a pair of concept labels. The algorithms depends on a semantic graph, provided as RDF data, and on a particular set of weights assigned to the properties of RDF statements (types of arcs in the RDF graph). The current research objective is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by the method are compared with those on the benchmark using the Spearman's rank coefficient. This methodology works the other way round and uses this coefficient to tune the proximity weights. The tuning process is controlled by a genetic algorithm using the Spearman's rank coefficient as the fitness function. The genetic algorithm has its own set of parameters which also need to be tuned. Bootstrapping is based on a statistical method for generating samples that is used in this methodology to enable a large number of repetitions of the genetic algorithm, exploring the results of alternative parameter settings. This approach raises several technical challenges due to its computational complexity. This paper provides details on the techniques used to speedup this process. The proposed approach was validated with the WordNet 2.0 and the WordSim-353 data set. Several ranges of parameters values were tested and the obtained results are better than the state of the art methods for computing semantic relatedness using the WordNet 2.0, with the advantage of not requiring any domain knowledge of the ontological graph. © José Paulo Leal and Teresa Costa.

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We present a new and versatile sensor platform to readout the response of sensitive colorimetric films. The platform is fully self-contained and based on a switched dual-wavelength scheme. After filtering and signal processing, the system is able to provide self-referenced measures of color intensity changes in the film, while being immune to noise sources such as ambient light and fluctuations in the power source and in the optical path. By controlling the power and the switching frequency between the two wavelengths it is possible to fine tune the output gain as well as the operational range of the sensor for a particular application, thus improving the signal conditioning. The platform uses a micro-controller that complements the analog circuit used to acquire the signal. The latter pre-amplifies, filters and conditions the signal, leaving the micro-controller free to perform sensor linearization and unit conversion. By changing the sensitive film and the wavelength of the light source it is possible to use this platform for a wide range of sensing applications.