Sérgio Manuel Serra da Cruz

WAVE: an architecture for predicting dropout in undergraduate courses using EDM

Predicting the academic progress of student is an issue faced by many public universities in emerging countries. Although, those institutions stores large amounts of educational data, they fail to recognize the students that are in danger to leave the system. This paper presents a novel architecture that uses EDM techniques to predict and to identify those who are at dropout risk. This approach allows academic managers to monitor the progress of the students in each academic semester, identifying the ones in difficult to fulfill their academic requirements. This paper shows initial experimental results using real world data about of three undergraduate engineering courses of one the largest Brazilian public university. According to the experiments, the classifier Naïve Bayes presented the highest true positive rate for all datasets used in the experiments.

LOP: capturing and linking open provenance on LOD cycle

The Web of Data has emerged as a means to expose, share, reuse, and connect information on the Web identified by URIs using RDF as a data model, following Linked Data Principles. However, the reuse of third party data can be compromised without proper data quality assessments. In this context, important questions emerge: how can one trust on published data and links? Which manipulation, modification and integration operations have been applied to the data before its publication? What is the nature of comparisons or transformations applied to data during the interlinking process? In this scenario, provenance becomes a fundamental element. In this paper, we describe an approach for generating and capturing Linked Open Provenance (LOP) to support data quality and trustworthiness assessments, which covers preparation and format transformation of traditional data sources, up to dataset publication and interlinking. The proposed architecture takes advantage of provenance agents, orchestrated by an ETL workflow approach, to collect provenance at any specified level and also link it with its corresponding data. We also describe a real use case scenario where the architecture was implemented to evaluate the proposal.

SisGExp: Rethinking Long-Tail Agronomic Experiments

Reproducibility is a major feature of Science. Even agronomic research of exemplary quality may have irreproducible empirical findings because of random or systematic error. This work presents SisGExp, a provenance-based approach that aid researchers to manage, share, and enact the computational scientific workflows that encapsulate legacy R scripts. SisGExp transparently captures provenance of R scripts and endows experiments reproducibility. SisGExp is non-intrusive, does not require users to change their working way, it wrap agronomic experiments as a scientific workflow system.

Enhancing Provenance Representation with Knowledge Based on NFR Conceptual Modeling: A Softgoal Catalog Approach

This work explores the organization of the provenance as a catalog of non-functional requirement NFR. It considers provenance as a quality factor that should be incorporated since the early stages of software development as softgoals. The aim of this research is to introduce a systematic approach to design a provenance catalog using consolidated software engineering techniques. The study is an effort to depict provenance as patterns supported by Softgoal Interdependency Graphs SIG and Goal-Question-Operationalization method GQO, a reusable framework that makes explicit characterization, decomposition, relationships and operationalization of elements that can be satisfied during the software design.

Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops

The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.

Cataloguing provenance-awareness with patterns

This paper explores the organization of provenance as a catalogue of non-functional requirement. The aim of this paper is to introduce a systematic approach to design a provenance catalogue for reuse using consolidated software engineering techniques. Provenance captures a derivation history of data products and is essential to the long-term preservation, to reuse, and to determine data quality. We propose the provenance catalogue that took into account NFR patterns and provenance taxonomies and specifications to define its softgoals. This work depicts a novel approach on provenance describing it as a Softgoal Interdependency Graph, a reusable framework that makes explicit characterization, decomposition, relationships and operationalization of elements that can be satisfied with the software. We exemplify the approach in a real usage scenario based on scientific software development.

Using Well-Founded Provenance Ontologies to Query Meteorological Data

The analysis of increasing flow of data about Tropical rainfall is a big challenge faced by meteorologists. This work presents an approach to pre-process, organize and query high quality meteorological data. Thus, we present a semantic approach that uses well-founded ontologies that help meteorologists to develop SPARQL queries that navigate over high quality data and provenance metadata collected during the execution meteorological in silico experiments.

Enriching Agronomic Experiments with Data Provenance

Reproducibility is a major feature of Science. Even agronomic research of exemplary quality may have irreproducible empirical findings because of random or systematic error. The ability to reproduce agronomic experiments based on statistical data and legacy scripts are not easily achieved. We propose RFlow, a tool that aid researchers to manage, share, and enact the scientific experiments that encapsulate legacy R scripts. RFlow transparently captures provenance of scripts and endows experiments reproducibility. Unlike existing computational approaches, RFlow is non-intrusive, does not require users to change their working way, it wraps agronomic experiments in a scientific workflow system. Our computational experiments show that the tool can collect different types of provenance metadata of real experiments and enrich agronomic data with provenance metadata. This study shows the potential of RFlow to serve as the primary integration platform for legacy R scripts, with implications for other data- and compute-intensive agronomic projects.

Computational Neuroscience

Understanding the core function of the brain is one the major challenges of our times. In the areas of neuroscience and education, several new studies try to correlate the learning difficulties faced by children and youth with behavioral and social problems. This work aims to present the challenges and opportunities of computational neuroscience research, with the aim of detecting people with learning disorders. We present a line of investigation based on the key areas: neuroscience, cognitive sciences and computer science, which considers young people between nine and eighteen years of age, with or without a learning disorder. The adoption of neural networks reveals consistency in dealing with pattern recognition problems and they are shown to be effective for early detection in patients with these disorders. We argue that computational neuroscience can be used for identifying and analyzing young Brazilian people with several cognitive disorders.