Search

During Fusion 2019 Conference (https://www.fusion2019.org/program.html), leading experts presented ideas on the historical, contemporary, and future coordination of artificial intelligence/machine learning (AI/ML) with sensor data fusion (SDF). While AI/ML and SDF concepts have had a rich history since the early 1900s—emerging from philosophy and psychology—it was not until the dawn of computers that both AI/ML and SDF researchers initiated discussions on how mathematical techniques could be implemented for real-time analysis. ML, and in particular deep learning, has demonstrated tremendous success in computer vision, natural language understanding, and data analytics. As a result, ML has been proposed as the solution to many problems that inherently include multi-modal data. For example, success in autonomous vehicles has validated the promise of ML with SDF, but additional research is needed to explain, understand, and coordinate heterogeneous data analytics for situation awareness. The panel identified opportunities for merging AI/ML and SDF such as computational efficiency, improved decision making, expanding knowledge, and providing security; while highlighting challenges for multi-domain operations, human-machine teaming, and ethical deployment strategies.

The current research examines how the efficiency/security tradeoff shapes the evolution of dynamic terrorist networks by focusing on the structural properties of these collectives. Some scholars argue that terrorist groups develop as chain-like, decentralized structures, while others maintain that terrorist networks form patterns of redundant ties and organize around a few highly connected individuals, or central hubs. We investigate these structural properties and consider whether patterns vary at different phases of a terrorist network’s formation. Using a variety of descriptive network measures and Separable Temporal Exponential Random Graph Models, we consider patterns of tie formation across eleven multi-wave terrorism networks from the John Jay & ARTIS Transnational Terrorism database. This dataset includes networks from prominent attacks and bombings that occurred in the last 3 decades (e.g., the 2002 Bali Bombings), where nodes represent individual terrorists and ties represent social relationships. We find that terrorist groups navigate the efficiency/security tradeoff by developing increasingly well-connected networks as they prepare for a violent incident. Our results also show that highly central nodes acquire even more ties in the years directly preceding an attack, signifying that the evolution of terrorist networks tends to be structured around a few key actors. Our findings have the potential to inform counterterrorism efforts by suggesting which actors in the network make the most influential targets for law enforcement. We discuss how these strategies should vary as extremist networks evolve over time.

Social network analysis is well-known as a fundamental tool for structural assessment of relationships at scale. The overwhelming convention in this field is to model relations based on positive factors, often related to commonality between participants, such as mutual friendship, or other characteristics that may positively bind participants. However, this leaves undefined the meaning of absent links between agents, leaving open scope for alternative interpretations. For example, an absent link may be due to individuals being unfamiliar and irrelevant to each other, but it could also be due to negative relations. This context is particularly relevant to adversarial scenarios, where actors, or the groups that they represent, are the focus of analysis. Accordingly, in this paper we consider the importance of explicitly modelling so-called negative ties as a means to provide insight into conflict and adversarial interaction. This subtle redefinition of social network analysis provides an opportunity to gain new perspectives on the threats that can subvert traditional social network analysis due to negativity not being explicitly represented. In this paper we focus on the fundamental issue of how to define negative ties. Generally, these invoke or are the consequence of cognitive dissonance between conflicting parties. We distinguish further between these three domains of negative, network connections and provide illustrations of each type in the context of social conflict. We discuss implications of these various genres of negativity regarding their potential to be used as tools by adversaries and their ability to provide a deeper understanding of human conflict.

Knowledge graphs (KGs) provide a useful representation format for capturing complex knowledge about an information domain, with rich logical descriptions available for defining the relationships between entities. Separately, semantic vector spaces (SVSs) capture the relative meanings of terms based on their actual usage within a dataset and allow useful operations for exploring the relationships between these terms. Combining KGs and SVSs via knowledge graph embedding (KGE) enables further analysis tasks to leverage learned semantic vectors to gain additional insights. Therefore, KGE represents an interesting and potentially powerful tool for identifying emergent or unexpected behavior, or for seeking previously unaccounted for relationships, event, and groups. In this work, we report on the state-of-the-art in KGE. We describe the operational benefits that can be gained from this approach and the considerations that apply for observational ontologies that describe a complex, untrusted, time-sensitive, and rapidly-evolving environment. We suggest several promising avenues for future research in this context.