Phil Tinn

Weitung Chen, Phil Tinn, Per Gunnar Auran et al.

Underwater robots typically use both cameras and sonar for perception to leverage the rich semantic details of vision and the robust range measurements of acoustics. However, learning to map between these modalities via cross-modal prediction remains underexplored due to limited sonar-visual paired datasets. We present SOVIS, a sonar-visual dataset for cross-modal underwater perception. SOVIS comprises over 76,000 paired frames collected across 17 dives at six sites in the Trondheimfjord, supported by an end-to-end pipeline that cleans and synchronizes the cross-modal sensor data. We also introduce an interactive annotation tool designed to accelerate the labeling process for this paired data. Finally, we demonstrate a proof-of-concept cross-modal fish detection task using a small subset of labeled data, achieving a 7x improvement in mAP@0.10 over a monocular camera baseline. SOVIS serves as the first step toward advancing cross-modal underwater perception research, enabling research directions such as dense sonar prediction from monocular images.

Kevin Tseng, Juan Carlos Toledano, Bart De Clerck et al.

The interoperability of data and intelligence across allied partners and their respective end-user groups is considered a foundational enabler to the collective defense capability--both conventional and hybrid--of NATO countries. Foreign Information Manipulation and Interference (FIMI) and related hybrid activities are conducted across various societal dimensions and infospheres, posing an ever greater challenge to the characterization of threats, sustaining situational awareness, and response coordination. Recent advances in AI have further led to the decreasing cost of AI-augmented trolling and interference activities, such as through the generation and amplification of manipulative content. Despite the introduction of the DISARM framework as a standardized metadata and analytical framework for FIMI, operationalizing it at the scale of social media remains a challenge. We propose a framework-agnostic agent-based operationalization of DISARM to investigate FIMI on social media. We develop a multi-agent pipeline in which specialized agentic AI components collaboratively (1) detect candidate manipulative behaviors, and (2) map these behaviors onto standard DISARM taxonomies in a transparent manner. We evaluated the approach on two real-world datasets annotated by domain practitioners. We demonstrate that our approach is effective in scaling the predominantly manual and heavily interpretive work of FIMI analysis, providing a direct contribution to enhancing the situational awareness and data interoperability in the context of operating in media and information-rich settings.