Introduction: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifi er is proposed for face recognition: the novelty classifi er. Methods:The performance of a novelty classifi er is compared with the performance of the nearest neighbor classifi er. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. Results:In identifi cation mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verifi cation mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identifi cation mode is equal to 98.5%, and in the verifi cation mode, 88%. Conclusion: For face recognition, the novelty classifi er performs comparable to the best results already published in the literature, which further confi rms the novelty classifi er as an important pattern recognition method in biometry.

In a world rich in interconnected and complex data, the non-relational database paradigm can better handle large volumes of data at high speed with a scale-out architecture, which are two essential requirements for large industries and world-class applications. This article presents AMANDA, a flexible middleware for automatic migration between relational and non-relational databases based on a user-defined schema that offers support for multiple sources and target databases. We evaluate the performance of AMANDA by assessing the migration speed, query execution, query performance, and migration correctness, from two Relational Database Management Systems (RBMSs), i.e., Postgres and MySQL, to a non-relational database (NoSQL), i.e., DGpraph. The results show that AMANDA successfully migrates data 26 times faster than previous approaches, when considering Northwind. Regarding the IMDB database, it took 7 days to migrate 5.5 GB of data.

In past decades, the requirements that database management systems (DBMSs) must achieve have become increasingly stringent (speed, data volume). This increase in complexity led to the development of a wide range of non-relational databases strategies, each one suited for specific scenarios. In this context, Graph Database Management Systems (GDBMSs) became popular to represent social networks and other domains that can be intuitively represented as graph-like structures. In this paper, we represent Version Control System data, specifically Git, from a large software project in a graph structure and compared three popular GDBMSs: Neo4j, Janus Graph and Dgraph. We evaluated read/write operations performance for common activities, such as inserting new commits into the graph and retrieving the complete commit history of a specific project. With this contribution, researches and engineers may choose, assertively, the better solution for their needs.

Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring system, robotic and human machine interaction. In this paper, a new classifier is proposed for face recognition. The performance of this new classifier is compared with the performance of the KNN classifier. The face image database used was the ORL. For feature extractions the following methods were employed: PCA, 2DPCA and (2D)2PCA. The performance tests of both classifiers were done both in verification and identification mode. In identification mode, the recognition rate with the leave-one-out strategy is equal to 100% with PCA, 2DPCA and (2D)2PCA. In the verification mode, the recognition rate is 100% with PCA and 2DPCA and 97.5% for (2D)2PCA. For the half-half strategy, the best recognition rate in the identification mode was obtained with (2D)2PCA, 98.5%, and in the verification mode, with PCA, 88%.

This paper proposes using the novelty classifier to face recognition. This classifier is based on novelty filters, proposed by Kohonen. The performance of the new classifier is compared with nearest neighbor classifier, using Euclidian distance. The face data base used for this comparison was the ORL. The face data is extracted using PCA and 2DPCA strategies. Some results are presented: recognition rate versus number of auto vectors, for identification and verification mode and equivalent error rate for verification mode. The results shown that the proposed classifier has a performance better than others previously published, when the leave-one-out method is employed as a test strategy. Best recognition rate of 100% is achieved with this test methodology. Keywords    Pattern recognition, face recognition, novelty classifier, principal components analysis. Resumo    Este artigo propõe a utilização do classificado de novidade para reconhecimento de face. Esse classificador é baseado na utilização do filtro de novidade, proposto por Kohonen. O desempenho do novo classificador é comparado com o desempenho do classificador vizinho mais próximo, usando distância euclidiana. A base de dados utilizada para essa comparação foi a base ORL. A informação da face é extraída utilizando PCA e 2DPCA. Os seguintes resultados são apresentados: taxa de reconhecimento versus número de autovetores, no modo de identificação e verificação e taxa de erro equivalente para o modo de verificação. Os resultados obtidos mostraram que o classificador proposto tem um desempenho melhor do que o desempenho do vizinho mais próximo e do que outros classificadores anteriormente publicados usando a mesma base, quando a estratégia Deixa-um-fora (Leave-one-out). A melhor taxa de reconhecimento, 100%, foi obtida com essa metodologia de teste. Palavras-chave    Reconhecimento de padrões, reconhecimento facial, filtro de novidades, Análise de Componentes Principais.