Learning Object Repositories with Federated Searcher over the Cloud

The education sector is a significant generator, consumer and depository for educational content. Educators and Learners have access to technologies that allow them to obtain information ubiquitously on demand. The problems arising from the integration of educational content are usually caused by the vast amount of educational content distributed among several repositories. This work presents a proposal for an architecture based on a cloud computing paradigm that will permit the evolution of current learning resource repositories by means of cloud computing paradigm and the integration of federated search system

Retrieving Learning Resources over the Cloud

Reusing resources has been on the rise recently in the ICT sector. In fact, this trend is expanding into other areas such as the educational sector. Learning objects have made it possible to create digital resources that can be reused in various didactic units. These resources are stored in repositories, and thus require a search process that allows them to be located and retrieved. The present study proposes the AIREH tool, which was deployed into a cloud environment and facilitates the retrieval of learning objects by integrating virtual organizations and agents with CBR systems that implement collaborative filtering techniques

Multi-agent System for Tracking and Classification of Moving Objects

In the past, computational barriers have limited the complexity of video and image processing applications but recently, faster computers have enabled researchers to consider more complex algorithms which can deal successfully with vehicle and pedestrian detection technologies. However, much of the work only pays attention to the accuracy of the final results provided by the systems, leaving aside the computational efficiency. Therefore, this paper describes a system using a paradigm of multi-agent system capable of regulating itself dynamically taking into account certain parameters pertaining to detection, tracking and classification, to reduce the computational burden as low as possible at all times without this in any way compromise the reliability of the result

A review of k-NN algorithm based on classical and Quantum Machine Learning

[EN] Artificial intelligence algorithms, developed for traditional computing, based on Von Neumann’s architecture, are slow and expen- sive in terms of computational resources. Quantum mechanics has opened up a new world of possibilities within this field, since, thanks to the basic properties of a quantum computer, a great degree of parallelism can be achieved in the execution of the quantum version of machine learning algorithms. In this paper, a study has been carried out on these proper- ties and on the design of their quantum computing versions. More specif- ically, the study has been focused on the quantum version of the k-NN algorithm that allows to understand the fundamentals when transcribing classical machine learning algorithms into its quantum versions

Personalization on E-Content Retrieval Based on Semantic Web Services

Abstract: In the current educational context there has been a significant increase in learning object repositories (LOR), which are found in large databases available on the hidden web. All these information is described in any metadata labeling standard (LOM, Dublin Core, etc). It is necessary to work and develop solutions that provide efficiency in searching for heterogeneous content and finding distributed context. Distributed information retrieval, or federated search, attempts to respond to the problem of information retrieval in the hidden Web. Multi-agent systems are known for their ability to adapt quickly and effectively to changes in their environment. This study presents a model for the development of digital content retrieval based on the paradigm of virtual organizations of agents using

A Network-Oriented Modeling Approach to Voting Behavior During the 2016 US Presidential Election

In this paper a network-oriented computational model is presented for voting intentions over time specifically for the race between Donald Trump and Hillary Clinton in the 2016 US presidential election. The focus was on the role of social and mass communication media and the statements made by Donald Trump or Hillary Clinton during their speeches. The aim was to investigate the influence on the voting intentions and the final voting. Sentiment analysis was performed to check whether the statements were high or low in language intensity. Simulation experiments using parameter tuning were compared to real world data (3 election polls until the 8th of November)

Workshop on Learning Technology for Education in Cloud (LTEC'12)

XII, 220 p. 44 illus.online resource