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Deep learning for situational understanding

Chakraborty, Supriyo, Preece, Alun David, Alzantot, Moustafa, Xing, Tianwei, Braines, David and Srivastava, Mani 2017. Deep learning for situational understanding. Presented at: IEEE International Conference on Information Fusion, Xi'an, China, 10-13 July 2017. Information Fusion (Fusion), 2017 20th International Conference on. IEEE, 10.23919/ICIF.2017.8009785

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Abstract

Situational understanding (SU) requires a combination of insight — the ability to accurately perceive an existing situation — and foresight — the ability to anticipate how an existing situation may develop in the future. SU involves information fusion as well as model representation and inference. Commonly, heterogenous data sources must be exploited in the fusion process: often including both hard and soft data products. In a coalition context, data and processing resources will also be distributed and subjected to restrictions on information sharing. It will often be necessary for a human to be in the loop in SU processes, to provide key input and guidance, and to interpret outputs in a way that necessitates a degree of transparency in the processing: systems cannot be “black boxes”. In this paper, we characterize the Coalition Situational Understanding (CSU) problem in terms of fusion, temporal, distributed, and human requirements. There is currently significant interest in deep learning (DL) approaches for processing both hard and soft data. We analyze the state-of-the-art in DL in relation to these requirements for CSU, and identify areas where there is currently considerable promise, and key gaps.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 9781509045822
Last Modified: 22 Aug 2017 09:58
URI: http://orca.cf.ac.uk/id/eprint/101346

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