Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Digital proficiency and digital inclusion: Comparison between students of computer science, public relations and engineering

New information and communication technologies may be useful for providing more in-depth knowledge to students in many ways, whether through online multimedia educational material, or through online debates with colleagues, teachers and other area professionals in a synchronous or asynchronous manner. This paper focuses on participation in online discussion in e-learning courses for promoting learning. Although an important theoretical aspect, an analysis of literature reveals there are few studies evaluating the personal and social aspects of online course users in a quantitative manner. This paper aims to introduce a method for diagnosing inclusion and digital proficiency and other personal aspects of the student through a case study comparing Information System, Public Relations and Engineering students at a public university in Brazil. Statistical analysis and analysis of variances (ANOVA) were used as the methodology for data analysis in order to understand existing relations between the components of the proposed method. The survey methodology was also used, in its online format, as a research instrument. The method is based on using online questionnaires that diagnose digital proficiency and time management, level of extroversion and social skills of the students. According to the sample studied, there is no strong correlation between digital proficiency and individual characteristics tied to the use of time, level of extroversion and social skills of students. The differences in course grades for some components are partly due to subject 'Introduction to Economics' being offered to freshmen in Public Relations, whereas subject 'Economics in Engineering' is offered in the final semesters of Engineering and Information Systems courses. Therefore, the difference could be more tied to the respondent's age than to the course. Information Systems students were observed to be older, with access to computers and Internet at the workplace, compared to the other students who access the Internet more often from home. This paper presents a pilot study aimed at conducting a diagnosis that permits proposing actions for information and communication technology to contribute towards student education. Three levels of digital inclusion are described as a scale to measure whether information technology increases personal performance and professional knowledge and skills. This study may be useful for other readers interested in themes related to education in engineering. © 2013 IEEE.

Autenticação biométrica de usuários em sistemas de E-learning baseada em reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

Deep learning architecture for gesture recognition

Pós-graduação em Engenharia Mecânica - FEG

Practising Arithmetic Using Educational Video Games with an Interpersonal Computer

Studies show the positive effects that video games can have on student performance and attitude towards learning. In the past few years, strategies have been generated to optimize the use of technological resources with the aim of facilitating widespread adoption of technology in the classroom. Given its low acquisition and maintenance costs, the interpersonal computer allows individual interaction and simultaneous learning with large groups of students. The purpose of this work was to compare arithmetical knowledge acquired by third-grade students through the use of game-based activities and non-game-based activities using an interpersonal computer, with knowledge acquired through the use of traditional paper-and-pencil activities, and to analyze their impact in various socio-cultural contexts. To do this, a quasi-experimental study was conducted with 271 students in three different countries (Brazil, Chile, and Costa Rica), in both rural and urban schools. A set of educational games for practising arithmetic was developed and tested in six schools within these three countries. Results show that there were no significant differences (ANCOVA) in the learning acquired from game-based vs. non-game-based activities. However, both showed a significant difference when compared with the traditional method. Additionally, both groups using the interpersonal computer showed higher levels of student interest than the traditional method group, and these technological methods were seen to be especially effective in increasing learning among weaker students.

Desenvolvimento de uma aplicação para suporte ao t-learning

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

Particle competition and cooperation for semi-supervised learning with label noise

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)