Hernane Borges de Barros Pereira

Learning computer programming: Implementing a fractal in a Turing Machine

It is common to start a course on computer programming logic by teaching the algorithm concept from the point of view of natural languages, but in a schematic way. In this sense we note that the students have difficulties in understanding and implementation of the problems proposed by the teacher. The main idea of this paper is to show that the logical reasoning of computer programming students can be efficiently developed by using at the same time Turing Machine, cellular automata (Wolfram rule) and fractals theory via Problem-Based Learning (PBL). The results indicate that this approach is useful, but the teacher needs introducing, in an interdisciplinary context, the simple theory of cellular automata and the fractals before the problem implementation.

COMPLEX SEMANTIC NETWORKS

Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

Uma ontologia para a Gestão do Conhecimento no Processo de Desenvolvimento de Produto

Organizations need to effectively manage the knowledge used in their processes for promoting organizational learning and preserving their intellectual capital. Knowledge Management plays a key role in product development as a dissemination agent of information to the actors involved in this process. Product Development Process (PDP) has an interdisciplinary nature and is characterized by the high amount of information generated and manipulated. In PDP, knowledge sharing and the integration between human resources are necessary for problem solving. Ontology as a knowledge representation system is important to support the organization, classification, representation, retrieval, and dissemination of knowledge in the PDP. This paper describes the preparation of a proposal for Knowledge Management using ontologies, which support the representation, retrieval, and dissemination of knowledge in the PDP. Hence, the proposed method to formalize and build ontologies of PDP sub-processes is described. In addition, an application carried out at SENAI CIMATEC - Center for Integrated Manufacturing and Technology is presented.

A Model for Improving the Learning Curves of Artificial Neural Networks

In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.

Mathematics education semantic networks

Abstract Continuous technological advances have resulted in analysis techniques that can be used in network theory. Topological characterisation, study of cohesion, and analysis of prominence are relevant techniques through which vertices (e.g., words) and their relationships are considered. The goal of this paper is to produce a comparative study on semantic networks based on titles of scientific papers in the field of mathematics education in Portuguese (Brazil) and English to ascertain the topological structure of these semantic networks and to present reflections thereafter concerning the diffusion of mathematics education. The vertices of proposed semantic networks are words with intrinsic meaning belonging to the titles of scientific papers and two words are connected if both belong to the same title. Methods and metrics from social and complex network analysis have been used to develop a diagnosis of the characterisation of this type of semantic network. Within this context, this study could be used to offer support to facilitate the process of diffusing knowledge in specific areas.

Community detection in visibility networks: an approach to categorize percussive influence on audio musical signals

The feature extraction is a very important step in the music audio classification. This task has been performed by renowned descriptors using, in most cases, the time-frequency approach. In this article we propose a descriptor that performs the feature extraction in a set of music audio files labeled in symphonic and percussive music, using parameters calculated within the Euclidean domain. First we calculate the variance fluctuation series of music signal, after we map this series into visibility graphs [13]. At the end each audio track will correspond to a network, where the links are defined by the visibility of variance fluctuations of their respective audio signal. Then, we measure the strength of the partitions of each network in clusters, using calculation of modularity. The results of computation of this parameter in sixty networks showed that percussive and symphonic music can be distinguished and hierarchized on a growing rang, following a direct correlation with modularity.

A computational model to textual extraction and construction of social and complex networks

This work aims presenting a computational modeling to extract specific data from textual repository, in order to build social and complex networks. These networks structures are implicit in texts. This paper presents the model process, which involves text mining by regular expressions, and the construction of networks. To validate the model, an experimental procedure was applied to build scientific collaboration networks in the context of post-graduation programs.

Towards Indicators of Sustainable Product Design

This paper aims in presenting a proposal of a set of indicators of sustainable and responsible product design. The main idea is to develop a conceptual model of indicators, which will be presented and applied to design process in general. The proposed set of indicators is discussed and suggested as basis for the development of tools that offer a broader sense of sustainability in order to support the main decisions during the product development.

Study of the Impact of the Topology of Artificial Neural Networks for the Prediction of Meteorological Data

In this chapter, we present an analysis of the capacity of six topologies of neural networks—multilayer perceptron (MLP), complete (CP), random (RD), scale-free (SF), small world (SW), and hybrid (HY)—to perform next-step predictions of temperature and solar radiation. For this purpose, 100 networks of each complex topology, including an MLP network and a complete network, were created, and each network contained twenty-six neurons (five entries, twenty processing units, and one output). The networks were trained for 2000 epochs and used for the next-step prediction of the selected climate variables. The MLP and Complete networks were trained 100 times, and the averages were taken for comparison. The parameters of comparison were the root mean square error (RMSE), the mean time to learn (ETL), and the mean time to predict the next step after training (ETP), using 1000 h of input data obtained from INMET (National Institute of Meteorology), a station on the coast of Bahia. The set of 1000 h of data was divided into two parts: 700 h for network training and 300 h to verify the effectiveness of learning.

Categorisation of polyphonic musical signals by using modularity community detection in audio-associated visibility network

This article proposes a method to numerically characterise the homogeneity of polyphonic musical signals through community detection in audio-associated visibility networks and to detect patterns that allow the categorisation of these signals into two types of grouping based on this numerical characterization. To implement this methodology, we first calculate the variance fluctuation series in fixed-size windows of an audio stretch. Next we map this series onto a visibility graph, where the nodes are the points of the series, and the edges are defined by the visibility between each pair of points. Then, we measure the quality of the partitions of the network using the modularity and Louvain optimisation. We observed that a greater or lesser homogeneity of the magnitudes of the signal transients is related to a higher or lower modularity of the audio-associated visibility network. We also note that these differences are related to musical choices that can establish important differences between musical styles. In this article, we show that the modularity is able to give relevant information to allow the categorisation of 120 musical signs labelled in percussive and symphonic music.