A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments

Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Generating intelligent tutoring systems for teaching reading: combining phonological awareness and thematic approaches

The objective of this thesis is to investigate the use of computers with artificial intelligence methods for the teaching of basic literacy skills to be applied eventually to the teaching of illiterate adults in Brazil.In its development many issues related to adult education have been considered, and two very significant approaches to the teaching of reading were focused on in detail: Phonological Awareness (PA) and Generative Themes. After being excluded from literacy curricula for a long time during the ascendancy of the "Whole Word" approaches, activities for the development of phonological awareness are currently being accepted as fundamental for teaching reading, and are being incorporated in most English literacy programmes. Generative Themes, in turn, were first introduced in Brazil in a massive programme for teaching reading to adults, and have since then been used successfully in a number of developing countries for the same purpose. However, these two approaches are apparently conflicting in their principles and emphasis, for the first (PA) is generally centred on the technical aspects of phonology, based on well controlled experiments and research, whereas the second is socially inspired and focused mainly on meaning and social relationships.The main question addressed in this research, consequently, is whether these two apparently conflicting approaches could be combined to create a method that would be technically PA oriented but at the same time could concentrate on meaning by using thematic vocabularies as stimuli for teaching. Would it be possible to find words to illustrate all the phonological features with which a PA method deals using a thematic vocabulary?To answer this question diverse concepts, languages and tools have been developed as part of this research, in order to allow the selection of thematic vocabularies, the description of PA curricula, the distribution of thematic words across PA curricula, the description of teaching activities and the definition of the teaching strategy rules to orient the teaching sequence.The resultant vocabularies have been evaluated and the outcomes of the research have been assessed by literacy experts. A prototype system for delivering experimental teaching activities through the Internet has also been developed and demonstrated