{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CUMNY6XDZZK7H64XNKOPPFJFVP","short_pith_number":"pith:CUMNY6XD","schema_version":"1.0","canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","source":{"kind":"arxiv","id":"1712.03987","version":1},"attestation_state":"computed","paper":{"title":"End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Eli de Poorter, Ingrid Moerman, Merima Kulin, Tarik Kazaz","submitted_at":"2017-12-11T19:02:51Z","abstract_excerpt":"This paper presents end-to-end learning from spectrum data - an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article "},"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":"1712.03987","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2017-12-11T19:02:51Z","cross_cats_sorted":[],"title_canon_sha256":"907c168aa8c1ef38ff5d857676aaa42cf10a0d201fd5c0c03803f7020b805134","abstract_canon_sha256":"9149768db1b39f8f108814c27ed83bf62e040a4a33b31246b2ab766eef2f48d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:09.723833Z","signature_b64":"gl1eXCupQ4L9FHwRtzaJNtSXvMEQVSwp48i8U3pCmdn5YFgo5dc+oRVxsmdi7nsnWS9uSZ3hJVxm6mshm29OBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","last_reissued_at":"2026-05-18T00:28:09.723148Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:09.723148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Eli de Poorter, Ingrid Moerman, Merima Kulin, Tarik Kazaz","submitted_at":"2017-12-11T19:02:51Z","abstract_excerpt":"This paper presents end-to-end learning from spectrum data - an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03987","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1712.03987","created_at":"2026-05-18T00:28:09.723242+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.03987v1","created_at":"2026-05-18T00:28:09.723242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03987","created_at":"2026-05-18T00:28:09.723242+00:00"},{"alias_kind":"pith_short_12","alias_value":"CUMNY6XDZZK7","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CUMNY6XDZZK7H64X","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CUMNY6XD","created_at":"2026-05-18T12:31:10.602751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP","json":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP.json","graph_json":"https://pith.science/api/pith-number/CUMNY6XDZZK7H64XNKOPPFJFVP/graph.json","events_json":"https://pith.science/api/pith-number/CUMNY6XDZZK7H64XNKOPPFJFVP/events.json","paper":"https://pith.science/paper/CUMNY6XD"},"agent_actions":{"view_html":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP","download_json":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP.json","view_paper":"https://pith.science/paper/CUMNY6XD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.03987&json=true","fetch_graph":"https://pith.science/api/pith-number/CUMNY6XDZZK7H64XNKOPPFJFVP/graph.json","fetch_events":"https://pith.science/api/pith-number/CUMNY6XDZZK7H64XNKOPPFJFVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/action/storage_attestation","attest_author":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/action/author_attestation","sign_citation":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/action/citation_signature","submit_replication":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/action/replication_record"}},"created_at":"2026-05-18T00:28:09.723242+00:00","updated_at":"2026-05-18T00:28:09.723242+00:00"}