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

Wetting behavior of Si-13.5B alloy on polycrystalline h-BN-based substrates

In this work, for the first time the results of an experimental evaluation of the high temperature behavior of molten Si-B alloy in contact with refractory materials at temperatures up to 1750°C, under static argon atmosphere (p = 850– 900 mbar), is shown. The material investigated, having a nominal chemical composition of Si-13.5B (at. %), was fabricated by using the crucible-less electric arc-melting process assisted by the levitation drop method. The wettability of the molten alloy in contact with commercial hexagonal boron nitride (h-BN) substrates was evaluated by means of especially developed sessile drop technique combined with a contact heating procedure. It was found that both couples show a lack of wettability in the whole tested temperature range (the measured contact angle was θ > 130°). The more stable behavior in contact with molten Si-13.B alloy, evidenced by higher θ values and a lack of drop vibration during the high temperature exposition, was observed for the h-BN based composite substrate