Representative Pat Fallon (TX-04) will make a push for creating a US Cyber Force tomorrow at CSIS
https://www.csis.org/events/implementing-us-cyber-force-conversation-representative-pat-fallon
The collapse of response time is leaving defenders with less and less time to respond during incidents
That's one big reason why proactive cyber -- or offensive cyber or active defense -- is now garnering the spotlight.
Check out my latest CSO piece on why leading cyber defenders think defense is no longer enough.
Many thanks to Glenn Gerstell, John Hultquist, Cynthia Kaiser, and Adam Maruyama for their insight.
Series C, Episode 13 - Terminal
TARRANT: Some sort of shaft cover. Could explain where Avon vanished to. Damn. Electrostatic lock. It needs a sonal key.
CALLY: [Nods towards the bodies] They might have it.
https://blake.torpidity.net/m/313/305 B7B4
NFL betting picks, player props: Here's why Denver will cover https://www.nytimes.com/athletic/6996000/2026/01/24/nfl-betting-picks-props-patriots-broncos-rams-seahawks/
Nett, mein Fedi Circle ist voll.
#CyberCircleCreator #FediCircle
This is the definition of an unnecessary cover, surely.
Only seems like a few weeks ago we had Johnnie & Denise.
I guess they're relying on the Boyzone fans to raise money for Comic Relief.
But can't we just edit in Billy Ocean now? #TOTP
Structural-Ambiguity-Aware Translation from Natural Language to Signal Temporal Logic
Kosei Fushimi, Kazunobu Serizawa, Junya Ikemoto, Kazumune Hashimoto
https://arxiv.org/abs/2603.28426 https://arxiv.org/pdf/2603.28426 https://arxiv.org/html/2603.28426
arXiv:2603.28426v1 Announce Type: new
Abstract: Signal Temporal Logic (STL) is widely used to specify timed and safety-critical tasks for cyber-physical systems, but writing STL formulas directly is difficult for non-expert users. Natural language (NL) provides a convenient interface, yet its inherent structural ambiguity makes one-to-one translation into STL unreliable. In this paper, we propose an \textit{ambiguity-preserving} method for translating NL task descriptions into STL candidate formulas. The key idea is to retain multiple plausible syntactic analyses instead of forcing a single interpretation at the parsing stage. To this end, we develop a three-stage pipeline based on Combinatory Categorial Grammar (CCG): ambiguity-preserving $n$-best parsing, STL-oriented template-based semantic composition, and canonicalization with score aggregation. The proposed method outputs a deduplicated set of STL candidates with plausibility scores, thereby explicitly representing multiple possible formal interpretations of an ambiguous instruction. In contrast to existing one-best NL-to-logic translation methods, the proposed approach is designed to preserve attachment and scope ambiguity. Case studies on representative task descriptions demonstrate that the method generates multiple STL candidates for genuinely ambiguous inputs while collapsing unambiguous or canonically equivalent derivations to a single STL formula.
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