{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:PIQJVNQ3PLTB7RSGVO5H2IZTK4","short_pith_number":"pith:PIQJVNQ3","canonical_record":{"source":{"id":"1703.10729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-31T01:42:46Z","cross_cats_sorted":[],"title_canon_sha256":"a1dac970aeb3a6c00ac2cbf37a77e500059a77453dc760492277115143eafc27","abstract_canon_sha256":"bd77842cabdd4fac5d8b289c85927955bce517669a0d67255d797249bdf97cf0"},"schema_version":"1.0"},"canonical_sha256":"7a209ab61b7ae61fc646abba7d2333571cf3a5f723a5f59e6151cd28a16fb16b","source":{"kind":"arxiv","id":"1703.10729","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.10729","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"arxiv_version","alias_value":"1703.10729v1","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.10729","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"pith_short_12","alias_value":"PIQJVNQ3PLTB","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PIQJVNQ3PLTB7RSG","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PIQJVNQ3","created_at":"2026-05-18T12:31:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:PIQJVNQ3PLTB7RSGVO5H2IZTK4","target":"record","payload":{"canonical_record":{"source":{"id":"1703.10729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-31T01:42:46Z","cross_cats_sorted":[],"title_canon_sha256":"a1dac970aeb3a6c00ac2cbf37a77e500059a77453dc760492277115143eafc27","abstract_canon_sha256":"bd77842cabdd4fac5d8b289c85927955bce517669a0d67255d797249bdf97cf0"},"schema_version":"1.0"},"canonical_sha256":"7a209ab61b7ae61fc646abba7d2333571cf3a5f723a5f59e6151cd28a16fb16b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:31.035261Z","signature_b64":"DRo2AUzqoJyoK1VcmZ/6dBYa4V32S8pe/XQ/tOOWq1oB5GIiTBWRUL6UGBIzkm6LxarqDvVNLk/+UDrxoAM5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a209ab61b7ae61fc646abba7d2333571cf3a5f723a5f59e6151cd28a16fb16b","last_reissued_at":"2026-05-18T00:34:31.034618Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:31.034618Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.10729","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":"2026-05-18T00:34:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xphV4HWWVtmXsiFtsIBalkAPRuLSeq1sPSeiTDZ7qQI7k4myJsaMX5Ro3cu/6tu9/UX26EAXN5R6AGfjvchoDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:19:50.468525Z"},"content_sha256":"5eb9092f08d1fc16b0ebfeb0a5fd71cee79bb65105ca5e70ef8ffad695067191","schema_version":"1.0","event_id":"sha256:5eb9092f08d1fc16b0ebfeb0a5fd71cee79bb65105ca5e70ef8ffad695067191"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:PIQJVNQ3PLTB7RSGVO5H2IZTK4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gaohua Lin, Gao Xu, Jinjun Wang, Qixing Zhang, Yongming Zhang","submitted_at":"2017-03-31T01:42:46Z","abstract_excerpt":"In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically produced adequate synthetic smoke images with a wide variation in the smoke shape, background and lighting conditions. Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.10729","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":"2026-05-18T00:34:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ocIISafAsvyhf5H9U7aB3SkaocShPdNrHMkrgyWZUj+upEbw/7UFHntlI2C8flXQyBJ4k5PPxLBQsU1adtABg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:19:50.469100Z"},"content_sha256":"e140199d4eab7e7cd0868c92c74e7664602b7c6eb3634ba811d9e02643132fda","schema_version":"1.0","event_id":"sha256:e140199d4eab7e7cd0868c92c74e7664602b7c6eb3634ba811d9e02643132fda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/bundle.json","state_url":"https://pith.science/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/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":"2026-05-25T20:19:50Z","links":{"resolver":"https://pith.science/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4","bundle":"https://pith.science/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/bundle.json","state":"https://pith.science/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PIQJVNQ3PLTB7RSGVO5H2IZTK4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:PIQJVNQ3PLTB7RSGVO5H2IZTK4","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":"bd77842cabdd4fac5d8b289c85927955bce517669a0d67255d797249bdf97cf0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-31T01:42:46Z","title_canon_sha256":"a1dac970aeb3a6c00ac2cbf37a77e500059a77453dc760492277115143eafc27"},"schema_version":"1.0","source":{"id":"1703.10729","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.10729","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"arxiv_version","alias_value":"1703.10729v1","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.10729","created_at":"2026-05-18T00:34:31Z"},{"alias_kind":"pith_short_12","alias_value":"PIQJVNQ3PLTB","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PIQJVNQ3PLTB7RSG","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PIQJVNQ3","created_at":"2026-05-18T12:31:37Z"}],"graph_snapshots":[{"event_id":"sha256:e140199d4eab7e7cd0868c92c74e7664602b7c6eb3634ba811d9e02643132fda","target":"graph","created_at":"2026-05-18T00:34:31Z","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":"In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically produced adequate synthetic smoke images with a wide variation in the smoke shape, background and lighting conditions. Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based ","authors_text":"Gaohua Lin, Gao Xu, Jinjun Wang, Qixing Zhang, Yongming Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-31T01:42:46Z","title":"Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.10729","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":"sha256:5eb9092f08d1fc16b0ebfeb0a5fd71cee79bb65105ca5e70ef8ffad695067191","target":"record","created_at":"2026-05-18T00:34:31Z","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":"bd77842cabdd4fac5d8b289c85927955bce517669a0d67255d797249bdf97cf0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-31T01:42:46Z","title_canon_sha256":"a1dac970aeb3a6c00ac2cbf37a77e500059a77453dc760492277115143eafc27"},"schema_version":"1.0","source":{"id":"1703.10729","kind":"arxiv","version":1}},"canonical_sha256":"7a209ab61b7ae61fc646abba7d2333571cf3a5f723a5f59e6151cd28a16fb16b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a209ab61b7ae61fc646abba7d2333571cf3a5f723a5f59e6151cd28a16fb16b","first_computed_at":"2026-05-18T00:34:31.034618Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:34:31.034618Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DRo2AUzqoJyoK1VcmZ/6dBYa4V32S8pe/XQ/tOOWq1oB5GIiTBWRUL6UGBIzkm6LxarqDvVNLk/+UDrxoAM5Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:34:31.035261Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.10729","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5eb9092f08d1fc16b0ebfeb0a5fd71cee79bb65105ca5e70ef8ffad695067191","sha256:e140199d4eab7e7cd0868c92c74e7664602b7c6eb3634ba811d9e02643132fda"],"state_sha256":"912f5f1ec4f4f99a8ef9064d6928dc1abe1abff37fad782b4396d85a8b2b821d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e5vrDLLRfUuISYAvw0RTT/cHDR0p+Dl3K1bf8nZgS3/y55qaH4ByoBjj0zncgg6gmi9mjlZlmh1GQnGET8mjCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:19:50.472595Z","bundle_sha256":"a5a99a36d252a1f9523234ea8791527828d596e44cf73cc81b74c737bef26f53"}}