Search results for "Software engineering"
showing 10 items of 1151 documents
Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers’ satisfaction
2018
[EN] Any knowledge generation process involves raw data comprehension, evaluation and inferential reasoning. These practices, common to different disciplines, are known as data analysis, and represent the most important set of activities in research contexts. Researchers use data analysis software methods and tools for generating new knowledge in their daily data analysis. In recent years, data analysis software has been incorporating explicit references in modelling of cognitive processes, in order to improve the assistance offered in data analysis tasks. However, data analysis software commercial suites are still resisting this inclusion, and there is little empirical work done in knowing…
Global emergence of the widespread Pseudomonas aeruginosa ST235 clone
2018
Abstract Objectives Despite the non-clonal epidemic population structure of Pseudomonas aeruginosa , several multi-locus sequence types are distributed worldwide and are frequently associated with epidemics where multidrug resistance confounds treatment. ST235 is the most prevalent of these widespread clones. In this study we aimed to understand the origin of ST235 and the molecular basis for its success. Methods The genomes of 79 P. aeruginosa ST235 isolates collected worldwide over a 27-year period were examined. A phylogenetic network was built, using a Bayesian approach to find the Most Recent Common Ancestor, and we identified antibiotic resistance determinants and ST235-specific genes…
A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.
2018
International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…
panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.
2018
Abstract Motivation The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic spec…
Simulation-based estimation of branching models for LTR retrotransposons
2017
Abstract Motivation LTR retrotransposons are mobile elements that are able, like retroviruses, to copy and move inside eukaryotic genomes. In the present work, we propose a branching model for studying the propagation of LTR retrotransposons in these genomes. This model allows us to take into account both the positions and the degradation level of LTR retrotransposons copies. In our model, the duplication rate is also allowed to vary with the degradation level. Results Various functions have been implemented in order to simulate their spread and visualization tools are proposed. Based on these simulation tools, we have developed a first method to evaluate the parameters of this propagation …
Towards Self-explanatory Ontology Visualization with Contextual Verbalization
2016
Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) e…
An Industrial Automation Course: Common Infrastructure for Physical, Virtual and Remote Laboratories for PLC Programming
2018
<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">This work describes the development of a teaching strategy to leverage current simulation tools and promote learning of industrial automation systems. Specifically, Programmable Logic Controller (PLC) programming in an industrial automation course. We propose an infrastructure where it is possible to work with physical, virtual and mixed laboratories</span>
GPU-Based Optimisation of 3D Sensor Placement Considering Redundancy, Range and Field of View
2020
This paper presents a novel and efficient solution for the 3D sensor placement problem based on GPU programming and massive parallelisation. Compared to prior art using gradient-search and mixed-integer based approaches, the method presented in this paper returns optimal or good results in a fraction of the time compared to previous approaches. The presented method allows for redundancy, i.e. requiring selected sub-volumes to be covered by at least n sensors. The presented results are for 3D sensors which have a visible volume represented by cones, but the method can easily be extended to work with sensors having other range and field of view shapes, such as 2D cameras and lidars.
An exploration of digital ride-hailing multisided platforms' market dynamics: empirical evidence from the Uber case study
2020
PurposeThe purpose of this paper is to experiment a dynamic performance management (DPM) approach to explore and assess the business dynamics of digital ride-hailing platforms with a focus on both supply and demand sides, and related interplays.Design/methodology/approachThe research adopts the DPM framework supported by simulation-based experimentations for developing a systemic case interpretation of Uber Inc. and its specific business complexity.FindingsThe emerging scenario analysis reveals that changes in the commission percentage for drivers and cutting prices for customers (car hailers) by competitors have significant impacts on the car-hailing industry.Originality/valueDPM and assoc…
How to Write Ethical User Stories? : Impacts of the ECCOLA Method
2021
AbstractArtificial Intelligence (AI) systems are increasing in significance within software services. Unfortunately, these systems are not flawless. Their faults, failures and other systemic issues have emphasized the urgency for consideration of ethical standards and practices in AI engineering. Despite the growing number of studies in AI ethics, comparatively little attention has been placed on how ethical issues can be mitigated in software engineering (SE) practice. Currently understanding is lacking regarding the provision of useful tools that can help companies transform high-level ethical guidelines for AI ethics into the actual workflow of developers. In this paper, we explore the i…