Generalização da periodicidade: um estudo sobre Apostrophe et six réflexions de Henri Posseur

Pós-graduação em Música - IA

Constelações ciclotômicas

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

A organização do conhecimento e a representação do conhecimento no domínio da arquivística

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aspectos éticos em representação do conhecimento em temáticas relativas à homossexualidade masculina: uma análise da precisão em linguagens de indexação brasileiras

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A study of the dynamics of quantum correlations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Generalização da periodicidade: um estudo sobre Apostrophe et Six Réflexions de Henri Pousseur

Neste livro, Rodolfo Augusto Daniel Vaz Valente debruça-se sobre a obra do compositor belga Henri Pousseur (1929-2009), um dos principais expoentes da música contemporânea ao lado de Luciano Berio, Karlheinz Stockhausen e Pierre Boulez. Valente discute, principalmente, a contribuição de Pousseur para a revisão teórica do serialismo, já que este recupera, no âmbito da música serial, a noção de periodicidade (ou fraseologia regular), que havia sido banida das experiências iniciais do serialismo em nome do princípio basilar da não repetição. Pousseur estava convencido de que era necessário retomar em alguma medida a periodicidade das estruturas. Chegou mesmo a experimentar a sua teoria na música eletrônica e a recorrer à teoria ondulatória proveniente da Acústica para repensar o discurso musical, especialmente no terreno da organização das durações. Valente também aborda a periodicidade conforme os preceitos de Abraham Moles, um dos principais teóricos da Teoria da Informação e que teve grande influência sobre o trabalho de Pousseur. Analisa ainda a obra Apostrophe et six réflexions, escrita pelo compositor na época em este que formulou o conceito de periodicidade generalizada, como chamou a fraseologia do serialismo revisado que já não recorria às estruturas musicais essencialmente não periódicas.

Bias na Representação de Assunto: Uma Discussão de Oposições Binárias nos Functional Requirements for Subject Authority Data (FRSAD)

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

Influências multi-escala da paisagem e limiar da fragmentação em morcegos no cerrado

Pós-graduação em Ciências Biológicas (Zoologia) - IBRC

O processo de institucionalização sociocognitiva do domínio de Organização do Conhecimento a partir dos trabalhos científicos dos congressos da ISKO

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Anéis de inteiros de corpos de números e aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extensões multidimensionais para correntropia e suas aplicações em estimativas robustas

This present work uses a generalized similarity measure called correntropy to develop a new method to estimate a linear relation between variables given their samples. Towards this goal, the concept of correntropy is extended from two variables to any two vectors (even with different dimensions) using a statistical framework. With this multidimensionals extensions of Correntropy the regression problem can be formulated in a different manner by seeking the hyperplane that has maximum probability density with the target data. Experiments show that the new algorithm has a nice fixed point update for the parameters and robust performs in the presence of outlier noise.

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Uso de correntropia na generalização de funções cicloestacionárias e aplicações para a extração de características de sinais modulados

Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and the Spectral Cyclic Density (SCD) can be used to extract second-order cyclostationary information. However, second-order cyclostationary information is poor in nongaussian signals, as the cyclostationary analysis in this case should comprise higher-order statistical information. This paper proposes a new mathematical tool for the higher-order cyclostationary analysis based on the correntropy function. Specifically, the cyclostationary analysis is revisited focusing on the information theory, while the Cyclic Correntropy Function (CCF) and Cyclic Correntropy Spectral Density (CCSD) are also defined. Besides, it is analytically proven that the CCF contains information regarding second- and higher-order cyclostationary moments, being a generalization of the CAF. The performance of the aforementioned new functions in the extraction of higher-order cyclostationary characteristics is analyzed in a wireless communication system where nongaussian noise exists.

Context-awareness privacy in data communications

Internet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.

Sistemas agroindustriais integrados: uma análise por meio da entropia de informação

Utilizando a teoria matemática da informação, este artigo analisa quatro sistemas agroindustriais de produção de bioenergia: a indústria de álcool de cana-de-açúcar do Brasil, a indústria de álcool de milho e a indústria de biodiesel dos Estados Unidos, além da indústria de farinha de mandioca da Amazônia. Com base em um importante conjunto de informações empíricas, são feitas avaliações de eficiência energética para diferentes níveis de organização dos sistemas.