{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CUMNY6XDZZK7H64XNKOPPFJFVP","short_pith_number":"pith:CUMNY6XD","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"},"canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","source":{"kind":"arxiv","id":"1712.03987","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.03987","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"arxiv_version","alias_value":"1712.03987v1","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03987","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"pith_short_12","alias_value":"CUMNY6XDZZK7","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CUMNY6XDZZK7H64X","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CUMNY6XD","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CUMNY6XDZZK7H64XNKOPPFJFVP","target":"record","payload":{"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"},"canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","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"},"source_kind":"arxiv","source_id":"1712.03987","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8/vEXvqBTDAbKOYCICBwJiXGe2ZoKtGL9Cf6iBEB7NkeSATgEqB83x4rT5HrG3/WKp369lRTK4zuls41+1/RBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:13:00.807134Z"},"content_sha256":"bdea23e36cef64c33bf9d28624f6fe7ec678ded274f624ec6b3fceb2b6f7a40c","schema_version":"1.0","event_id":"sha256:bdea23e36cef64c33bf9d28624f6fe7ec678ded274f624ec6b3fceb2b6f7a40c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CUMNY6XDZZK7H64XNKOPPFJFVP","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6NuHWPMWKy1StYU3U+DU9XF2//QFa5symqQ6ZkHVw/94wTTpVlAXiP/EwBKaGQKQDgeOj6bdtsZnsoEVVzunAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:13:00.807796Z"},"content_sha256":"d338339c91dedbdbd839b4c80a28b161fe362953f17e3142d9c36cd6ce099c40","schema_version":"1.0","event_id":"sha256:d338339c91dedbdbd839b4c80a28b161fe362953f17e3142d9c36cd6ce099c40"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/bundle.json","state_url":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T18:13:00Z","links":{"resolver":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP","bundle":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/bundle.json","state":"https://pith.science/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CUMNY6XDZZK7H64XNKOPPFJFVP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CUMNY6XDZZK7H64XNKOPPFJFVP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9149768db1b39f8f108814c27ed83bf62e040a4a33b31246b2ab766eef2f48d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2017-12-11T19:02:51Z","title_canon_sha256":"907c168aa8c1ef38ff5d857676aaa42cf10a0d201fd5c0c03803f7020b805134"},"schema_version":"1.0","source":{"id":"1712.03987","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.03987","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"arxiv_version","alias_value":"1712.03987v1","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03987","created_at":"2026-05-18T00:28:09Z"},{"alias_kind":"pith_short_12","alias_value":"CUMNY6XDZZK7","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CUMNY6XDZZK7H64X","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CUMNY6XD","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:d338339c91dedbdbd839b4c80a28b161fe362953f17e3142d9c36cd6ce099c40","target":"graph","created_at":"2026-05-18T00:28:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"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 ","authors_text":"Eli de Poorter, Ingrid Moerman, Merima Kulin, Tarik Kazaz","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2017-12-11T19:02:51Z","title":"End-to-end Learning from Spectrum Data: A Deep Learning approach for Wireless Signal Identification in Spectrum Monitoring applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03987","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bdea23e36cef64c33bf9d28624f6fe7ec678ded274f624ec6b3fceb2b6f7a40c","target":"record","created_at":"2026-05-18T00:28:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9149768db1b39f8f108814c27ed83bf62e040a4a33b31246b2ab766eef2f48d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2017-12-11T19:02:51Z","title_canon_sha256":"907c168aa8c1ef38ff5d857676aaa42cf10a0d201fd5c0c03803f7020b805134"},"schema_version":"1.0","source":{"id":"1712.03987","kind":"arxiv","version":1}},"canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1518dc7ae3ce55f3fb976a9cf79525abf776968cead16c336742db4b729e1dab","first_computed_at":"2026-05-18T00:28:09.723148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:09.723148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gl1eXCupQ4L9FHwRtzaJNtSXvMEQVSwp48i8U3pCmdn5YFgo5dc+oRVxsmdi7nsnWS9uSZ3hJVxm6mshm29OBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:09.723833Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.03987","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bdea23e36cef64c33bf9d28624f6fe7ec678ded274f624ec6b3fceb2b6f7a40c","sha256:d338339c91dedbdbd839b4c80a28b161fe362953f17e3142d9c36cd6ce099c40"],"state_sha256":"3b0af9557164d6ea854375366c7aeb858e3d238b3d2640ec7ddb8f0c25ca1574"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JYHXzl7S4LmDMIjZnsj5K/f1pYjENCodSNRy6ROETdLilv1uqV8WWVb+I4PPry2GUS2lplyONcyul8Q8WTxKBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T18:13:00.811245Z","bundle_sha256":"3e664e0ff6ed446b8a37e31ca103594a75fe64237d86b0cc41853b980d77cd3e"}}