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Sandro Rigo

Researcher at Universidade do Vale do Rio dos Sinos

Publications -  162
Citations -  1234

Sandro Rigo is an academic researcher from Universidade do Vale do Rio dos Sinos. The author has contributed to research in topics: Computer science & SystemC. The author has an hindex of 15, co-authored 146 publications receiving 918 citations. Previous affiliations of Sandro Rigo include State University of Campinas & Rutgers University.

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The ArchC architecture description language and tools

TL;DR: This paper has used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.
Proceedings ArticleDOI

ArchC: a systemC-based architecture description language

TL;DR: This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description that has a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description.
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The impact of gamification on students’ learning, engagement and behavior based on their personality traits

TL;DR: Investigating the effects of gamification on students’ learning, behavior, and engagement based on their personality traits in a web-based programming learning environment finds evidence that the effect ofgamification depends on the specific characteristics of users.
Journal ArticleDOI

Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach

TL;DR: A Learning Analytics system developed to deal with dropout problem in Distance Education courses on university-level education, with results from experiments carried out with courses in a Brazilian university show the dropout prediction with an average of 87% precision.
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Toward a Model for Personal Health Record Interoperability

TL;DR: OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP) with the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.