{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:72QVMCVCPYCYNDQ4R5RCXMCZYX","short_pith_number":"pith:72QVMCVC","canonical_record":{"source":{"id":"1907.03422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T06:54:47Z","cross_cats_sorted":[],"title_canon_sha256":"1a9a1caef472aa9b843b66ab49b7e70980bffdfd3f1d112d5898a6fa60f57052","abstract_canon_sha256":"b25268e239d9e636ca2ff309b5c9c3e5685a7ec78bbc8b1b0d9f50782cc470e5"},"schema_version":"1.0"},"canonical_sha256":"fea1560aa27e05868e1c8f622bb059c5c55d8c6e7587a294375718d3f2ae5781","source":{"kind":"arxiv","id":"1907.03422","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.03422","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"arxiv_version","alias_value":"1907.03422v1","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03422","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"pith_short_12","alias_value":"72QVMCVCPYCY","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"72QVMCVCPYCYNDQ4","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"72QVMCVC","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:72QVMCVCPYCYNDQ4R5RCXMCZYX","target":"record","payload":{"canonical_record":{"source":{"id":"1907.03422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T06:54:47Z","cross_cats_sorted":[],"title_canon_sha256":"1a9a1caef472aa9b843b66ab49b7e70980bffdfd3f1d112d5898a6fa60f57052","abstract_canon_sha256":"b25268e239d9e636ca2ff309b5c9c3e5685a7ec78bbc8b1b0d9f50782cc470e5"},"schema_version":"1.0"},"canonical_sha256":"fea1560aa27e05868e1c8f622bb059c5c55d8c6e7587a294375718d3f2ae5781","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:16.264101Z","signature_b64":"0E6zGPP9FrWIIB2J5KRfCjQ5zFdNbOzcOtLwHJt6AFlWaaTmlrk/4xb/93SFbjzmkW4k/ZXIMswanx5I8yD/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fea1560aa27e05868e1c8f622bb059c5c55d8c6e7587a294375718d3f2ae5781","last_reissued_at":"2026-05-17T23:41:16.263457Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:16.263457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.03422","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":"2026-05-17T23:41:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r6+MyWUQXBz8ECm4ga29XnCEqSVzsPQp9/t8+KXp7X4MwtJV/bWIdgf2IKgKHc7VqKJLlGzD2h8VeNK0gAfKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:44:15.390029Z"},"content_sha256":"f817c3bebeed5fffeaf2192cfa4b2314713e0464119b7521be97d4379f5942b6","schema_version":"1.0","event_id":"sha256:f817c3bebeed5fffeaf2192cfa4b2314713e0464119b7521be97d4379f5942b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:72QVMCVCPYCYNDQ4R5RCXMCZYX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Da Guo, Jianfei Yang, Kaipeng Zhang, Kai Wang, Xiaojiang Peng, Yu Qiao","submitted_at":"2019-07-08T06:54:47Z","abstract_excerpt":"This paper presents our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is watching an online MOOCs video in various conditions. Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper. We maintain the framework of multi-instance learning with long short-term memory (LSTM) network, and make three contributions. First, besides of the gaze and head pose features, we explore facial landmark fe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03422","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":"2026-05-17T23:41:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O3RA4cEZe3txhEoOt45ohxQQzDH+1Y7jNHv27bnHjNw3Vi3+Pw0x0wI2awdjJndkvVt4rfr2UOzhYyG2aehkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:44:15.390735Z"},"content_sha256":"eba63dc210549d52ada13b2a0593d2864eebf094e04fb4042a1827d4eb8469f3","schema_version":"1.0","event_id":"sha256:eba63dc210549d52ada13b2a0593d2864eebf094e04fb4042a1827d4eb8469f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/bundle.json","state_url":"https://pith.science/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/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":"2026-05-26T01:44:15Z","links":{"resolver":"https://pith.science/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX","bundle":"https://pith.science/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/bundle.json","state":"https://pith.science/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/72QVMCVCPYCYNDQ4R5RCXMCZYX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:72QVMCVCPYCYNDQ4R5RCXMCZYX","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":"b25268e239d9e636ca2ff309b5c9c3e5685a7ec78bbc8b1b0d9f50782cc470e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T06:54:47Z","title_canon_sha256":"1a9a1caef472aa9b843b66ab49b7e70980bffdfd3f1d112d5898a6fa60f57052"},"schema_version":"1.0","source":{"id":"1907.03422","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.03422","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"arxiv_version","alias_value":"1907.03422v1","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03422","created_at":"2026-05-17T23:41:16Z"},{"alias_kind":"pith_short_12","alias_value":"72QVMCVCPYCY","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"72QVMCVCPYCYNDQ4","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"72QVMCVC","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:eba63dc210549d52ada13b2a0593d2864eebf094e04fb4042a1827d4eb8469f3","target":"graph","created_at":"2026-05-17T23:41:16Z","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 our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is watching an online MOOCs video in various conditions. Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper. We maintain the framework of multi-instance learning with long short-term memory (LSTM) network, and make three contributions. First, besides of the gaze and head pose features, we explore facial landmark fe","authors_text":"Da Guo, Jianfei Yang, Kaipeng Zhang, Kai Wang, Xiaojiang Peng, Yu Qiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T06:54:47Z","title":"Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03422","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:f817c3bebeed5fffeaf2192cfa4b2314713e0464119b7521be97d4379f5942b6","target":"record","created_at":"2026-05-17T23:41:16Z","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":"b25268e239d9e636ca2ff309b5c9c3e5685a7ec78bbc8b1b0d9f50782cc470e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T06:54:47Z","title_canon_sha256":"1a9a1caef472aa9b843b66ab49b7e70980bffdfd3f1d112d5898a6fa60f57052"},"schema_version":"1.0","source":{"id":"1907.03422","kind":"arxiv","version":1}},"canonical_sha256":"fea1560aa27e05868e1c8f622bb059c5c55d8c6e7587a294375718d3f2ae5781","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fea1560aa27e05868e1c8f622bb059c5c55d8c6e7587a294375718d3f2ae5781","first_computed_at":"2026-05-17T23:41:16.263457Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:16.263457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0E6zGPP9FrWIIB2J5KRfCjQ5zFdNbOzcOtLwHJt6AFlWaaTmlrk/4xb/93SFbjzmkW4k/ZXIMswanx5I8yD/DQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:16.264101Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.03422","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f817c3bebeed5fffeaf2192cfa4b2314713e0464119b7521be97d4379f5942b6","sha256:eba63dc210549d52ada13b2a0593d2864eebf094e04fb4042a1827d4eb8469f3"],"state_sha256":"bb57abac065f7d6aa74c52e44db979205b045b3d2a5045bce62c4a085d93b4ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"giZEkh/8adcFgAZ+hXK2X5SMxUSIJFt1Lu/3u8LNYUHqm5rwttPHS0qdXdaxdOZrxQ6Dj/kejrPAq71ONQSzAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T01:44:15.394505Z","bundle_sha256":"2bffecaa7edfa578b36bd0b516faec061cae16f92b751c3556228221eee1e950"}}