Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Moreno, Marcio"'
Autor:
Azevedo, Leonardo Guerreiro, Souza, Renan Francisco Santos, Soares, Elton F. de S., Thiago, Raphael M., Tesolin, Julio Cesar Cardoso, Oliveira, Ann C., Moreno, Marcio Ferreira
Modern applications commonly need to manage dataset types composed of heterogeneous data and schemas, making it difficult to access them in an integrated way. A single data store to manage heterogeneous data using a common data model is not effective
Externí odkaz:
http://arxiv.org/abs/2308.03584
The Polkadot ecosystem is a disruptive and highly complex multi-chain architecture that poses challenges in terms of data analysis and communicability. Currently, there is a lack of standardized and holistic approaches to retrieve and analyze data ac
Externí odkaz:
http://arxiv.org/abs/2308.00735
Autor:
Souza, Renan, Azevedo, Leonardo G., Lourenço, Vítor, Soares, Elton, Thiago, Raphael, Brandão, Rafael, Civitarese, Daniel, Brazil, Emilio Vital, Moreno, Marcio, Valduriez, Patrick, Mattoso, Marta, Cerqueira, Renato, Netto, Marco A. S.
Publikováno v:
Concurrency Computation Practice Experience. 2021;e6544
Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains
Externí odkaz:
http://arxiv.org/abs/2010.00330
In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description differs from others fundamentally, since its entities aim at represent
Externí odkaz:
http://arxiv.org/abs/1912.08740
Autor:
Moreno, Marcio, Lourenço, Vítor, Fiorini, Sandro Rama, Costa, Polyana, Brandão, Rafael, Civitarese, Daniel, Cerqueira, Renato
Machine Learning Workflows (MLWfs) have become essential and a disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complicated, hard to achieve, time-consuming, and error-prone. To handle t
Externí odkaz:
http://arxiv.org/abs/1912.05665
Autor:
Moreno, Marcio Ferreira, Lima, Guilherme, Santos, Rodrigo Costa Mesquita, Azevedo, Roberto, Endler, Markus
In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and d
Externí odkaz:
http://arxiv.org/abs/1911.11631
In this chapter, we give an introduction to symbolic artificial intelligence (AI) and discuss its relation and application to multimedia. We begin by defining what symbolic AI is, what distinguishes it from non-symbolic approaches, such as machine le
Externí odkaz:
http://arxiv.org/abs/1911.09606
Autor:
Moreno, Marcio Ferreira, Santos, Rodrigo Costa Mesquita, Santos, Wallas Henrique Sousa dos, Fiorini, Sandro Rama, Silva, Reinaldo Mozart da Gama
Properly modelling dynamic information that changes over time still is an open issue. Most modern knowledge bases are unable to represent relationships that are valid only during a given time interval. In this work, we revisit a previous extension to
Externí odkaz:
http://arxiv.org/abs/1911.08225
Autor:
Souza, Renan, Azevedo, Leonardo, Lourenço, Vítor, Soares, Elton, Thiago, Raphael, Brandão, Rafael, Civitarese, Daniel, Brazil, Emilio Vital, Moreno, Marcio, Valduriez, Patrick, Mattoso, Marta, Cerqueira, Renato, Netto, Marco A. S.
Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tr
Externí odkaz:
http://arxiv.org/abs/1910.04223
Autor:
Fiorini, Sandro Rama, Santos, Wallas Sousa dos, Mesquita, Rodrigo Costa, Lima, Guilherme Ferreira, Moreno, Marcio F.
The use of semantic descriptions in data intensive domains require a systematic model for linking semantic descriptions with their manifestations in fragments of heterogeneous information and data objects. Such information heterogeneity requires a fr
Externí odkaz:
http://arxiv.org/abs/1909.04117