{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SLCKQKCT6BZWUHAKIS2PHKAISA","short_pith_number":"pith:SLCKQKCT","canonical_record":{"source":{"id":"1811.07485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T03:51:19Z","cross_cats_sorted":["cs.CL","cs.MM"],"title_canon_sha256":"bfe9a5ce338ff1623de530f388967e0571914de52b1a4d785c053d9a396bac1d","abstract_canon_sha256":"1e6bcaa1a9154eb290bd6fb41746625631a6087c0bf5058e734c6902c280a5ee"},"schema_version":"1.0"},"canonical_sha256":"92c4a82853f0736a1c0a44b4f3a80890220d22085d6fc7f1d66ec2e7d0e83781","source":{"kind":"arxiv","id":"1811.07485","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07485","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07485v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07485","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"SLCKQKCT6BZW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SLCKQKCT6BZWUHAK","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SLCKQKCT","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SLCKQKCT6BZWUHAKIS2PHKAISA","target":"record","payload":{"canonical_record":{"source":{"id":"1811.07485","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T03:51:19Z","cross_cats_sorted":["cs.CL","cs.MM"],"title_canon_sha256":"bfe9a5ce338ff1623de530f388967e0571914de52b1a4d785c053d9a396bac1d","abstract_canon_sha256":"1e6bcaa1a9154eb290bd6fb41746625631a6087c0bf5058e734c6902c280a5ee"},"schema_version":"1.0"},"canonical_sha256":"92c4a82853f0736a1c0a44b4f3a80890220d22085d6fc7f1d66ec2e7d0e83781","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:24.351770Z","signature_b64":"71jKMTDTaQipwNBGpmWrtaCqYy1nizDZc7w7xTSvWrfMlYQ0v6pKL16CCnmHkGvauTEIbF4+86eZ0X8GZiM7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92c4a82853f0736a1c0a44b4f3a80890220d22085d6fc7f1d66ec2e7d0e83781","last_reissued_at":"2026-05-18T00:00:24.351220Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:24.351220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.07485","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":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RZYSFBsU2+vGPDBTdPvS7bOQQorSD2albbmucqVpJ5YGYbOSdyiEFq436RI2VyDZpJ52/B6pkDWtJ096HEcoAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:56:25.583371Z"},"content_sha256":"b8fb83c7ad51380b8d3a337a8b5bb8db5b0515913641c782694a0e6a32caf058","schema_version":"1.0","event_id":"sha256:b8fb83c7ad51380b8d3a337a8b5bb8db5b0515913641c782694a0e6a32caf058"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SLCKQKCT6BZWUHAKIS2PHKAISA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MM"],"primary_cat":"cs.CV","authors_text":"Chenchen Li, Hongwei Wang, Jialin Wang, Miao Zhao, Wenjie Li, Xiaotie Deng","submitted_at":"2018-11-19T03:51:19Z","abstract_excerpt":"User emotion analysis toward videos is to automatically recognize the general emotional status of viewers from the multimedia content embedded in the online video stream. Existing works fall in two categories: 1) visual-based methods, which focus on visual content and extract a specific set of features of videos. However, it is generally hard to learn a mapping function from low-level video pixels to high-level emotion space due to great intra-class variance. 2) textual-based methods, which focus on the investigation of user-generated comments associated with videos. The learned word represent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07485","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":"2026-05-18T00:00:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K004idlFn5UUzMyU7vHHRH+VHsNsfC6IEfP1fobjYnExmsDxSeFm4uQS9biJx2C1smB8ntMuJq7JhWrDnBRBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T22:56:25.584021Z"},"content_sha256":"e80a75b8b52f7f21117b867e5636536d42da44da68a02d7dc16e6c6f1e6ef64e","schema_version":"1.0","event_id":"sha256:e80a75b8b52f7f21117b867e5636536d42da44da68a02d7dc16e6c6f1e6ef64e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/bundle.json","state_url":"https://pith.science/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/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":"2026-05-27T22:56:25Z","links":{"resolver":"https://pith.science/pith/SLCKQKCT6BZWUHAKIS2PHKAISA","bundle":"https://pith.science/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/bundle.json","state":"https://pith.science/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SLCKQKCT6BZWUHAKIS2PHKAISA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SLCKQKCT6BZWUHAKIS2PHKAISA","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":"1e6bcaa1a9154eb290bd6fb41746625631a6087c0bf5058e734c6902c280a5ee","cross_cats_sorted":["cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T03:51:19Z","title_canon_sha256":"bfe9a5ce338ff1623de530f388967e0571914de52b1a4d785c053d9a396bac1d"},"schema_version":"1.0","source":{"id":"1811.07485","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.07485","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.07485v1","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07485","created_at":"2026-05-18T00:00:24Z"},{"alias_kind":"pith_short_12","alias_value":"SLCKQKCT6BZW","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SLCKQKCT6BZWUHAK","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SLCKQKCT","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:e80a75b8b52f7f21117b867e5636536d42da44da68a02d7dc16e6c6f1e6ef64e","target":"graph","created_at":"2026-05-18T00:00:24Z","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":"User emotion analysis toward videos is to automatically recognize the general emotional status of viewers from the multimedia content embedded in the online video stream. Existing works fall in two categories: 1) visual-based methods, which focus on visual content and extract a specific set of features of videos. However, it is generally hard to learn a mapping function from low-level video pixels to high-level emotion space due to great intra-class variance. 2) textual-based methods, which focus on the investigation of user-generated comments associated with videos. The learned word represent","authors_text":"Chenchen Li, Hongwei Wang, Jialin Wang, Miao Zhao, Wenjie Li, Xiaotie Deng","cross_cats":["cs.CL","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T03:51:19Z","title":"Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07485","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:b8fb83c7ad51380b8d3a337a8b5bb8db5b0515913641c782694a0e6a32caf058","target":"record","created_at":"2026-05-18T00:00:24Z","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":"1e6bcaa1a9154eb290bd6fb41746625631a6087c0bf5058e734c6902c280a5ee","cross_cats_sorted":["cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T03:51:19Z","title_canon_sha256":"bfe9a5ce338ff1623de530f388967e0571914de52b1a4d785c053d9a396bac1d"},"schema_version":"1.0","source":{"id":"1811.07485","kind":"arxiv","version":1}},"canonical_sha256":"92c4a82853f0736a1c0a44b4f3a80890220d22085d6fc7f1d66ec2e7d0e83781","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92c4a82853f0736a1c0a44b4f3a80890220d22085d6fc7f1d66ec2e7d0e83781","first_computed_at":"2026-05-18T00:00:24.351220Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:24.351220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"71jKMTDTaQipwNBGpmWrtaCqYy1nizDZc7w7xTSvWrfMlYQ0v6pKL16CCnmHkGvauTEIbF4+86eZ0X8GZiM7Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:24.351770Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.07485","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b8fb83c7ad51380b8d3a337a8b5bb8db5b0515913641c782694a0e6a32caf058","sha256:e80a75b8b52f7f21117b867e5636536d42da44da68a02d7dc16e6c6f1e6ef64e"],"state_sha256":"5884034f43ab7fca7dd618cbf31721bf3e72e490421ae06954e65e0672170391"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FdeNauu1xHk70w1tCVS36C/FH8Gn2CLVhEAIS7biKBspC3eyeRdLn/pmFJtDgHDO3EQjOFjGPkWunmyKPB7oBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T22:56:25.587419Z","bundle_sha256":"67fbb6e817547114189b171d6666243f191574d33bf1410c51df74bba93cb8e8"}}