Pith Number
pith:JCM54EO5
pith:2024:JCM54EO5PWGLIL3K46FNL2XMEQ
not attested
not anchored
not stored
refs pending
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation
arxiv:2411.02403 v2 · 2024-10-18 · cs.SI · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{JCM54EO5PWGLIL3K46FNL2XMEQ}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-05T09:31:51.701197Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4899de11dd7d8cb42f6ae78ad5eaec24220c9baae0fee6fb17db6b88a2e81d1c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/JCM54EO5PWGLIL3K46FNL2XMEQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4899de11dd7d8cb42f6ae78ad5eaec24220c9baae0fee6fb17db6b88a2e81d1c
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "f7580b343b25d8620899aad6f1c45af7893e5be606ae1245d0c3bb8848a29a4a",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.SI",
"submitted_at": "2024-10-18T04:20:02Z",
"title_canon_sha256": "1256815b4221dfbaeba2d90ff7fb43f105195250504053c3039e008add950caf"
},
"schema_version": "1.0",
"source": {
"id": "2411.02403",
"kind": "arxiv",
"version": 2
}
}