The US and nations of the NATO Alliance are increasingly threatened by the global spread of terrorism, humanitarian crises/disaster response, and public health emergencies. These threats are influenced by the unprecedented rise of information sharing technologies and practices, where mobile access to social networking sites is ubiquitous. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. US science and technology investments in Artificial Intelligence and Machine Learning (AI/ML) and Human Agent Teaming (HAT) are increasingly focused on developing capabilities toward that end. A critical foundation of these technologies is the awareness of the underlying context to accurately interpret machine-processed warnings and recommendations. In this sense, context can be a dynamic characteristic of the operating environment and demands a multi-analytic approach. In this paper, we describe US doctrine that formulates capability requirements for operations in the information environment. We then describe a promising social computing approach that brings together information retrieval strategies using multimedia sources that include text, video, and imagery. Social computing is used in this case to increase awareness of societal dynamics at various scales that influence and impact military operations in both the physical and information domains. Our focus, content based information retrieval and multimedia analytics, involves the exploitation of multiple data sources to deliver timely and accurate synopses of data that can be combined with human intuition and understanding to develop a comprehensive worldview.
Elizabeth K. Bowman, Gertjan Burghouts, Lasse Overlier, Sue E. Kase, Randal J. Zimmerman, and Serena Oggero, "Content-based multimedia analytics: US and NATO research," Proc. SPIE 10653, Next-Generation Analyst VI, 106530H (Presented at SPIE Defense + Security: April 16, 2018; Published: 27 April 2018); https://doi.org/10.1117/12.2307756.
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