Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it

Deconstructing Controversies to design a trusted AI future

<p>This article proposes a new methodology to integrate social controversies into foresight scenarios as a means to enhance the trustworthiness, inclusivity and effectiveness of policymaking processes around the emerging technologies of Artificial Intelligence. Foresight exercises play a key role in anticipating future tech challenges and informing policy development. However, the integration of social controversies within these exercises remains an unexplored area. This article aims to bridge this gap by providing insights and guidelines on why and how we should incorporate social controversies into designing foresight scenarios. We emphasize the importance of considering social controversies, as they allow to re-balance power dynamics, de-black box technologies and have trustworthy policymaking processes that are open to listening social needs. Building on empirical research, we present a step by step method that involves identifying the key policy challenges and relevant controversies related to an emerging technology, deconstructing the identified controversies, and mapping them onto future scenarios to test policy options and build policy road mapping. Furthermore, we discuss the importance of strategically engaging involved stakeholders, including affected communities, civil society organizations, and experts, to ensure a comprehensive and inclusive perspective. Finally, we showcase the application of the proposed method to popAI project, an EU-funded project on AI use in law enforcement.</p&gt

Chapter Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it