Fire detection from social media images by means of instance-based learning

Social media can provide valuable information to support decision making in crisis management, such as in accidents, explosions, and fires. However, much of the data from social media are images, which are uploaded at a rate that makes it impossible for human beings to analyze them. To cope with that problem, we design and implement a database-driven architecture for fast and accurate fire detection named FFireDt. The design of FFireDt uses the instance-based learning through indexed similarity queries expressed as an extension of the relational Structured Query Language. Our contributions are: (i) the design of the Fast-Fire Detection (FFireDt), which achieves efficiency and efficacy rates that rival to the state-of-the-art techniques; (ii) the sound evaluation of 36 image descriptors, for the task of image classification in social media; (iii) the evaluation of content-based indexing with respect to the construction of instance-based classification systems; and (iv) the curation of a ground-truth annotated dataset of fire images from social media. Using real data from Flickr, the experiments showed that system FFireDt was able to achieve a precision for fire detection comparable to that of human annotators. Our results are promising for the engineering of systems to monitor images uploaded to social media services.FAPESPCNPqCAPESSTIC-AmSudRESCUER project, funded by the European Commission (Grant: 614154) and by the CNPq/MCTI (Grant: 490084/2013-3)International Conference on Enterprise Information Systems - ICEIS (17. 2015 Barcelona

Fire detection from social media images by means of instance-based learning

Social media can provide valuable information to support decision making in crisis management, such as in accidents, explosions, and fires. However, much of the data from social media are images, which are uploaded at a rate that makes it impossible for human beings to analyze them. To cope with that problem, we design and implement a database-driven architecture for fast and accurate fire detection named FFireDt. The design of FFireDt uses the instance-based learning through indexed similarity queries expressed as an extension of the relational Structured Query Language. Our contributions are: (i) the design of the Fast-Fire Detection (FFireDt), which achieves efficiency and efficacy rates that rival to the state-of-the-art techniques; (ii) the sound evaluation of 36 image descriptors, for the task of image classification in social media; (iii) the evaluation of content-based indexing with respect to the construction of instance-based classification systems; and (iv) the curation of a ground-truth annotated dataset of fire images from social media. Using real data from Flickr, the experiments showed that system FFireDt was able to achieve a precision for fire detection comparable to that of human annotators. Our results are promising for the engineering of systems to monitor images uploaded to social media services.FAPESPCNPqCAPESSTIC-AmSudRESCUER project, funded by the European Commission (Grant: 614154) and by the CNPq/MCTI (Grant: 490084/2013-3)International Conference on Enterprise Information Systems - ICEIS (17. 2015 Barcelona

Investigation of Two Immiscible Liquids Wetting at Elevated Temperature: Interaction Between Liquid FeMn Alloy and Liquid Slag

The goal of the current work is to develop a methodology to study the wetting behaviour of two immiscible liquids at high temperatures, and to investigate the parameters which influence the wetting properties. The wetting behaviour between synthetic FeMn alloy and synthetic slag has been investigated using the sessile drop technique. Two experimental procedures were implemented under both Ar and CO atmospheres: (a) FeMn alloy and slag placed next to each other on a graphite substrate; and (b) one droplet dropped on top of the other. FactSage is applied to calculate reactions and their equilibrium. The current work presents and demonstrates the suggested methodologies. The results indicate that the wetting between slag and FeMn alloy is relatively stable at temperatures up to 100 K above their melting points, regardless of the droplet size and atmosphere. MnO reduction is accelerated at higher temperature, especially in CO, thus increasing the wetting between FeMn alloy and slag, eventually fusing together. At even higher temperature, slag separates from FeMn alloy due to changing chemical composition during non-equilibrium MnO reduction