2021
Authors
Rodrigues, R; Matos, T; de Carvalho, AV; Barbosa, JG; Assaf, R; Nóbrega, R; Coelho, A; de Sousa, AA;
Publication
Graph. Vis. Comput.
Abstract
2021
Authors
Garrido, D; Rodrigues, R; de Sousa, AA; Jacob, J; Silva, DC;
Publication
AIVR 2021: The 5th International Conference on Artificial Intelligence and Virtual Reality, Kumamoto, Japan, July 23 - 25, 2021
Abstract
The use of virtual reality technologies for data visualization and analysis has been an emerging topic of research in the past years. However, one type of data has been left neglected, the point cloud. While some strides have been made in the visualization and analysis of point clouds in immersive environments, these have yet to be used for direct manipulation interactions. It is hypothesized that as with other types of data, bringing direct interactions and 3D visualization to point clouds may increase the ease of performing basic handling tasks. An immersive application for virtual reality HMDs was developed in Unity to help research this hypothesis. It is capable of parsing classified point cloud files with extracted objects and representing them in a virtual environment. Several editing tools were also developed, designed with the HMD controllers in mind. The end result allows the user to perform basic transformative tasks to the point cloud with an ease of use and intuitive feeling unmatched by the traditional desktop-based tools. © 2021 Owner/Author.
2021
Authors
de Sousa, AA; Havran, V; Braz, J; Bouatouch, K;
Publication
VISIGRAPP (1: GRAPP)
Abstract
2021
Authors
Karimova, Y; Ribeiro, C; David, G;
Publication
Int. J. Metadata Semant. Ontologies
Abstract
Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community. © 2021 Inderscience Enterprises Ltd.. All rights reserved.
2021
Authors
Lopes, CT; Ribeiro, C; Niccolucci, F; Rodrigues, IP; Antunes Freire, NM;
Publication
SIGIR Forum
Abstract
2021
Authors
Miranda, P; Faria, JP; Correia, FF; Fares, A; Graça, R; Moreira, JM;
Publication
SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021
Abstract
Forecasts of the effort or delivery date can play an important role in managing software projects, but the estimates provided by development teams are often inaccurate and time-consuming to produce. This is not surprising given the uncertainty that underlies this activity. This work studies the use of Monte Carlo simulations for generating forecasts based on project historical data. We have designed and run experiments comparing these forecasts against what happened in practice and to estimates provided by developers, when available. Comparisons were made based on the mean magnitude of relative error (MMRE). We did also analyze how the forecasting accuracy varies with the amount of work to be forecasted and the amount of historical data used. To minimize the requirements on input data, delivery date forecasts for a set of user stories were computed based on takt time of past stories (time elapsed between the completion of consecutive stories); effort forecasts were computed based on full-time equivalent (FTE) hours allocated to the implementation of past stories. The MMRE of delivery date forecasting was 32% in a set of 10 runs (for different projects) of Monte Carlo simulation based on takt time. The MMRE of effort forecasting was 20% in a set of 5 runs of Monte Carlo simulation based on FTE allocation, much smaller than the MMRE of 134% of developers' estimates. A better forecasting accuracy was obtained when the number of historical data points was 20 or higher. These results suggest that Monte Carlo simulations may be used in practice for delivery date and effort forecasting in agile projects, after a few initial sprints. © 2021 ACM.
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