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Abstract The use of metaphor in cybersecurity discourse has become a topic of interest because of its ability to aid communication about abstract security concepts. In this paper, we borrow from existing metaphor identification algorithms and general theories to create a lightweight metaphor identification algorithm, which uses only one external source of knowledge. The algorithm also introduces a real time corpus builder for extracting collocates; this is, identifying words that appear together more frequently than chance. We implement several variations of the introduced algorithm and empirically evaluate the output using the TroFi dataset, a de facto evaluation dataset in metaphor research. We find first, contrary to our expectation, that adding word sense disambiguation to our metaphor identification algorithm decreases its performance. Second, we find, that our lightweight algorithms perform comparably to their existing, more complex, counterparts. Finally, we present the results of several case studies to observe the utility of the algorithm for future research in linguistic metaphor identification in text related to cybersecurity texts and threats.
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This content will become publicly available on June 1, 2026
Searching for differential addition chains
The literature sometimes uses slow algorithms to find minimum-length continued-fraction differential addition chains to speed up subsequent computations of multiples of points on elliptic curves. This paper introduces two faster algorithms to find these chains. The first algorithm prunes more effectively than previous algorithms. The second algorithm uses a meet-in-the-middle approach and appears to have a limiting cost exponent below 1
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- Award ID(s):
- 2401305
- PAR ID:
- 10626967
- Publisher / Repository:
- SpringerNature
- Date Published:
- Journal Name:
- Research in Number Theory
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 2522-0160
- Page Range / eLocation ID:
- 45
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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