{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BYASBXBZ76TRG7OXFTPFBXL5TV","short_pith_number":"pith:BYASBXBZ","schema_version":"1.0","canonical_sha256":"0e0120dc39ffa7137dd72cde50dd7d9d75ff44013ef269dba3490568e34bb279","source":{"kind":"arxiv","id":"2312.13023","version":2},"attestation_state":"computed","paper":{"title":"Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jiabin Liu, Mengtao Zhu, Shafei Wang, Yunjie Li, Ziwei Zhang","submitted_at":"2023-12-20T13:44:18Z","abstract_excerpt":"Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into known classes, leading to serious consequences for situation awareness and threat assessment. To address this issue, Automatic Modulation Open-set Recognition (AMOSR) defines two tasks as Known Class Classification (KCC) and Unknown Class Identification (UCI). However, AMOSR faces core challenges in terms of inappropriate decision boundaries and sparse feature distributions. To o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.13023","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-12-20T13:44:18Z","cross_cats_sorted":[],"title_canon_sha256":"1d33fc7e1150875746f536afb3ee4a4afab31bc5a8a12bc29dde7ed5a2a6e443","abstract_canon_sha256":"25896bb9a07ea7037a5030666162656e65834a707b2a374fc8b952e3c30739c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:31.837078Z","signature_b64":"tXVcsJ0lrGWyZt/SOst3kC/fh3wz/4QWi5cn/1wub98vrPnnz+wEi918EDnLXZXgKyPA5tRaC1WLaMxIeomMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e0120dc39ffa7137dd72cde50dd7d9d75ff44013ef269dba3490568e34bb279","last_reissued_at":"2026-07-05T08:07:31.836615Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:31.836615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jiabin Liu, Mengtao Zhu, Shafei Wang, Yunjie Li, Ziwei Zhang","submitted_at":"2023-12-20T13:44:18Z","abstract_excerpt":"Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into known classes, leading to serious consequences for situation awareness and threat assessment. To address this issue, Automatic Modulation Open-set Recognition (AMOSR) defines two tasks as Known Class Classification (KCC) and Unknown Class Identification (UCI). However, AMOSR faces core challenges in terms of inappropriate decision boundaries and sparse feature distributions. To o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.13023","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.13023/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.13023","created_at":"2026-07-05T08:07:31.836676+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.13023v2","created_at":"2026-07-05T08:07:31.836676+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.13023","created_at":"2026-07-05T08:07:31.836676+00:00"},{"alias_kind":"pith_short_12","alias_value":"BYASBXBZ76TR","created_at":"2026-07-05T08:07:31.836676+00:00"},{"alias_kind":"pith_short_16","alias_value":"BYASBXBZ76TRG7OX","created_at":"2026-07-05T08:07:31.836676+00:00"},{"alias_kind":"pith_short_8","alias_value":"BYASBXBZ","created_at":"2026-07-05T08:07:31.836676+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00796","citing_title":"An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV","json":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV.json","graph_json":"https://pith.science/api/pith-number/BYASBXBZ76TRG7OXFTPFBXL5TV/graph.json","events_json":"https://pith.science/api/pith-number/BYASBXBZ76TRG7OXFTPFBXL5TV/events.json","paper":"https://pith.science/paper/BYASBXBZ"},"agent_actions":{"view_html":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV","download_json":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV.json","view_paper":"https://pith.science/paper/BYASBXBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.13023&json=true","fetch_graph":"https://pith.science/api/pith-number/BYASBXBZ76TRG7OXFTPFBXL5TV/graph.json","fetch_events":"https://pith.science/api/pith-number/BYASBXBZ76TRG7OXFTPFBXL5TV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV/action/storage_attestation","attest_author":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV/action/author_attestation","sign_citation":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV/action/citation_signature","submit_replication":"https://pith.science/pith/BYASBXBZ76TRG7OXFTPFBXL5TV/action/replication_record"}},"created_at":"2026-07-05T08:07:31.836676+00:00","updated_at":"2026-07-05T08:07:31.836676+00:00"}