Thiago Christiano Silva

Network-Based Stochastic Semisupervised Learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Stochastic Competitive Learning in Complex Networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particles walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..

Network-Based High Level Data Classification

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configurations complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model.

Machine Learning in Complex Networks

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Application of an active attachment model as a high-throughput demineralization biofilm model

OBJECTIVES To investigate the potential of an active attachment biofilm model as a high-throughput demineralization biofilm model for the evaluation of caries-preventive agents. METHODS Streptococcus mutans UA159 biofilms were grown on bovine dentine discs in a high-throughput active attachment model. Biofilms were first formed in a medium with high buffer capacity for 24h and then subjected to various photodynamic therapies (PACT) using the combination of Light Emitting Diodes (LEDs, Biotable(®)) and Photogem(®). Viability of the biofilms was evaluated by plate counts. To investigate treatment effects on dentine lesion formation, the treated biofilms were grown in a medium with low buffer capacity for an additional 24h. Integrated mineral loss (IML) and lesion depth (LD) were assessed by transversal microradiography. Calcium release in the biofilm medium was measured by atomic absorption spectroscopy. RESULTS Compared to the water treated control group, significant reduction in viability of S. mutans biofilms was observed when the combination of LEDs and Photogem(®) was applied. LEDs or Photogem(®) only did not result in biofilm viability changes. Similar outcomes were also found for dentine lesion formation. Significant lower IML and LD values were only found in the group subjected to the combined treatment of LEDs and Photogem(®). There was a good correlation between the calcium release data and the IML or LD values. CONCLUSIONS The high-throughput active attachment biofilm model is applicable for evaluating novel caries-preventive agents on both biofilm and demineralization inhibition. PACT had a killing effect on 24h S. mutans biofilms and could inhibit the demineralization process.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

KNOWLEDGE AND ATTITUDE OF PARENTS OR CARETAKERS REGARDING TRANSMISSIBILITY OF CARIES DISEASE

Dental caries is a transmissible infectious disease in which mutans streptococci are generally considered to be the main etiological agents. Although the transmissibility of dental caries is relatively well established in the literature, little is known whether information regarding this issue is correctly provided to the population. The present study aimed at evaluating, by means of a questionnaire, the knowledge and usual attitude of 640 parents and caretakers regarding the transmissibility of caries disease. Most interviewed adults did not know the concept of dental caries being an infectious and transmissible disease, and reported the habit of blowing and tasting food, sharing utensils and kissing the children on their mouth. 372 (58.1%) adults reported that their children had already been seen by a dentist, 264 (41.3%) answered that their children had never gone to a dentist, and 4 (0.6%) did not know. When the adults were asked whether their children had already had dental caries, 107 (16.7%) answered yes, 489 (76.4%) answered no, and 44 (6.9%) did not know. Taken together, these data reinforce the need to provide the population with some important information regarding the transmission of dental caries in order to facilitate a more comprehensive approach towards the prevention of the disease.

In Vivo Effects on the Expression of Vascular Endothelial Growth Factor-A165 Messenger Ribonucleic Acid of an Infrared Diode Laser Associated or Not with a Visible Red Diode Laser

OBJECTIVE This study investigated and correlated the kinetic expression of vascular endothelial growth factor (VEGF)-A(165) messenger ribonucleic acid (mRNA) with the associated use or not of an infrared laser and a visible red laser during the wound healing in rats. BACKGROUND DATA There is a lack of scientific evidence demonstrating the influence of low-level laser therapy (LLLT) on the expression of VEGF mRNA in vivo. MATERIALS AND METHODS Forty-five Wistar rats were randomly allocated to one of three groups: I (n = 5, nonoperated animals), II (n = 25, operated animals), and III (n = 25, animals operated and subjected to laser irradiation). A surgical wound was performed using a scalpel in the right side of the tongue of operated animals. In group III, two sessions of laser irradiation were performed, one right after the surgical procedure (infrared laser, 780 nm, 70 mW, 35 J/cm(2)) and the other 48 h later (visible red laser, 660 nm, 40 mW, 5 J/cm(2)). Five animals each were sacrificed 1, 3, 5, and 7 days postoperatively in groups II and III, and samples of tongue tissue were obtained. The animals of group I were sacrificed on day 7. Total RNA was extracted using guanidine-isothiocyanate-phenol-chloroform method. The results of horizontal electrophoresis after reverse transcription polymerase chain reaction permitted the ratio of VEGF-A(165) mRNA and glyceraldehyde 3-phosphate dehydrogenase mRNA expression for groups I, II, and III to be assessed (two-way analysis of variance and Tukey test, p < 0.05). RESULTS The expression of VEGF-A(165) mRNA in group II (0.770 +/- 0.098) was statistically greater than that observed in groups I (0.523 +/- 0.164) and III (0.504 +/- 0.069) in the first day after surgery (p < 0.05). Significant differences between the groups were not observed in other time periods. CONCLUSION LLLT influenced the expression of VEGF-A(165) mRNA during wound healing after a surgical procedure on the tongue of Wistar rats.

High-level pattern-based classification via tourist walks in networks

Abstract In this paper, we present a hybrid classification technique, which combines the decisions of low- and high-level classifiers. The low-level term realizes the classification task considering only the input data’s physical features, such as geometrical or statistical characteristics. In contrast, the high-level classification process checks the compliance of the new test instances against the pattern formations of each class that composes the training data. For this end, we extract suitable organizational and topological descriptors of a network that is constructed from the input data. With these descriptors, we show that the high-level term has the ability of detecting data patterns with semantic and global meanings. Here, the input data’s pattern formations are extracted by utilizing the dynamical information generated from several tourist walk processes , which are performed on the resulting network. Specifically, weighted combinations of transient and cycle lengths, which are derived variables from the tourist walks, are employed. Moreover, we show an effective method for calibrating the learning weights of these terms by using a statistical approach. Furthermore, we show that the tourist’s memory size is related to what extent one may capture organizational and complex features of the network. This means that local, quasi-local, and global features can be extracted, depending on the value of memory size parameter. Still in this work, we uncover the existence of a critical memory length, here denominated complex saturation , where any values larger than this critical point make no changes in the behaviors of the transient and cycle lengths. We also investigate several artificial and real-world situations where the low-level term alone fails to identify intrinsic data patterns, but the high-level term is able to perform well. Our investigation suggests that the proposed technique is able to improve the already optimized performance of traditional classification techniques. Finally, we apply the proposed technique in recognizing handwritten digits images and interesting results are obtained.