A semi-automatic strategy for achieving learning content and practice repurposing

The development of instructional content using Information Technologies is an expensive, time-consuming and complex process that requires new methodologies. It was in this context that the concept of Learning Objects (LOs) was proposed in order to promote reuse. However, this goal is not yet fully attained and new contributions to increase reuse are still welcome. Besides, if content is conveyed in LOs that are easier to reuse, they must be combined and sequenced in order to build more elaborated and complex content. This paper presents a strategy to deal with these problems based on the definition of small LOs here called Component Objects (COs). These COs are structured and combined according to a conceptual metamodel, which is the basis for the definition of conceptual schemas representing the existing material, including not only content but also practice. This strategy for searching, extracting, and sequencing COs, supports a teacher to better control the implementation of complex content, reducing errors in the authoring process. This approach includes a specification language and an algorithm for semi-automatic sequencing learning content and practice. Finally, a case study that shows the proposed approach and some results of using the algorithm are presented.     

Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper, we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests.     

Error Analysis for hp-FEM Semi-Lagrangian Second Order BDF Method for Convection-Dominated Diffusion Problems

We present in this paper an analysis of a semi-Lagrangian second order Backward Difference Formula combined with hp-finite element method to calculate the numerical solution of convection diffusion equations in ℝ². Using mesh dependent norms, we prove that the a priori error estimate has two components: one corresponds to the approximation of the exact solution along the characteristic curves, which is O(Dt²+h^m+1(1+\frac\mathopen|logh|Dt))O(\Delta t^{2}+h^{m+1}(1+\frac{\mathopen{|}\log h|}{\Delta t})); and the second, which is O(Dt^p+|| [(u)\vec]-[(u)\vec]_h||_L^￥)O(\Delta t^{p}+\| \vec{u}-\vec{u}_{h}\|_{L^{\infty}}), represents the error committed in the calculation of the characteristic curves. Here, m is the degree of the polynomials in the finite element space, [(u)\vec]\vec{u} is the velocity vector, [(u)\vec]_h\vec{u}_{h} is the finite element approximation of [(u)\vec]\vec{u} and p denotes the order of the method employed to calculate the characteristics curves. Numerical examples support the validity of our estimates.     

The efficiency of identifying timed automata and the power of clocks

We develop theory on the efficiency of identifying (learning) timed automata. In particular, we show that: (i) deterministic timed automata cannot be identified efficiently in the limit from labeled data and (ii) that one-clock deterministic timed automata can be identified efficiently in the limit from labeled data. We prove these results based on the distinguishability of these classes of timed automata. More specifically, we prove that the languages of deterministic timed automata cannot, and that one-clock deterministic timed automata can be distinguished from each other using strings in length bounded by a polynomial. In addition, we provide an algorithm that identifies one-clock deterministic timed automata efficiently from labeled data.Our results have interesting consequences for the power of clocks that are interesting also out of the scope of the identification problem.     

IEEE 802.21: Media independence beyond handover

The IEEE 802.21 standard facilitates media independent handovers by providing higher layer mobility management functions with common service primitives for all technologies. Right after the base specification was published, several voices rose up in the working group advocating to broaden the scope of IEEE 802.21 beyond handovers. This paper aims at updating the reader with the main challenges and functionalities required to create a Media Independence Service Layer, through the analysis of scenarios which are being discussed within the working group: 1) Wireless Coexistence, and 2) Heterogeneous Wireless Multihop Backhaul Networks.     

Time series prediction evolving Voronoi regions

Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.     

MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks

In this paper, we propose a methodology for training a new model of artificial neural network called the generalized radial basis function (GRBF) neural network. This model is based on generalized Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter τ. The generalized radial basis function allows different radial basis functions to be represented by updating the new parameter τ. For example, when GRBF takes a value of τ=2, it represents the standard Gaussian radial basis function. The model parameters are optimized through a modified version of the extreme learning machine (ELM) algorithm. In the methodology proposed (MELM-GRBF), the centers of each GRBF were taken randomly from the patterns of the training set and the radius and τ values were determined analytically, taking into account that the model must fulfil two constraints: locality and coverage. An thorough experimental study is presented to test its overall performance. Fifteen datasets were considered, including binary and multi-class problems, all of them taken from the UCI repository. The MELM-GRBF was compared to ELM with sigmoidal, hard-limit, triangular basis and radial basis functions in the hidden layer and to the ELM-RBF methodology proposed by Huang et al. (2004) [1]. The MELM-GRBF obtained better results in accuracy than the corresponding sigmoidal, hard-limit, triangular basis and radial basis functions for almost all datasets, producing the highest mean accuracy rank when compared with these other basis functions for all datasets.     

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg–Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN’s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.