{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AZHSTJF25HR5HYLMRMKQ5CVW6Y","short_pith_number":"pith:AZHSTJF2","canonical_record":{"source":{"id":"1906.02728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T17:49:41Z","cross_cats_sorted":[],"title_canon_sha256":"71d1e01111677fe7cecd0ad8c70fcb8a1352a8e67688f7ee5c1a0f9bb66fecb6","abstract_canon_sha256":"90f4551be3a88f5c8be0918235dd31e218ae1f5072378215f9c91c986df5e138"},"schema_version":"1.0"},"canonical_sha256":"064f29a4bae9e3d3e16c8b150e8ab6f60c2e298382673d9c94284f19047f9df4","source":{"kind":"arxiv","id":"1906.02728","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02728","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02728v1","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02728","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"pith_short_12","alias_value":"AZHSTJF25HR5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AZHSTJF25HR5HYLM","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AZHSTJF2","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AZHSTJF25HR5HYLMRMKQ5CVW6Y","target":"record","payload":{"canonical_record":{"source":{"id":"1906.02728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T17:49:41Z","cross_cats_sorted":[],"title_canon_sha256":"71d1e01111677fe7cecd0ad8c70fcb8a1352a8e67688f7ee5c1a0f9bb66fecb6","abstract_canon_sha256":"90f4551be3a88f5c8be0918235dd31e218ae1f5072378215f9c91c986df5e138"},"schema_version":"1.0"},"canonical_sha256":"064f29a4bae9e3d3e16c8b150e8ab6f60c2e298382673d9c94284f19047f9df4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:00.379155Z","signature_b64":"1h1UcaDoSL4FOUtYxuOhHmw/Kd/gU3Xh6pMX7zW5IHhTibfPt5t90KFYWrmcn4ujcm2swFYrIbCufBjXSTS4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"064f29a4bae9e3d3e16c8b150e8ab6f60c2e298382673d9c94284f19047f9df4","last_reissued_at":"2026-05-17T23:44:00.378676Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:00.378676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.02728","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":"2026-05-17T23:44:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pk/Onpy2czWy6WEJXJtcKMrgCJKB11p0SzrwoumSGy1KGzGQjBL4qhUsaDXnbASMZ/6c2pEdnG+Bt+FynlHmDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T16:23:13.112596Z"},"content_sha256":"10afbb6218037e6c3d584ae7aa9d442e270570a0f264b7457997b88aca82b225","schema_version":"1.0","event_id":"sha256:10afbb6218037e6c3d584ae7aa9d442e270570a0f264b7457997b88aca82b225"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AZHSTJF25HR5HYLMRMKQ5CVW6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Shehab Khan, James O'Reilly, Jie Cai, Min Chen, Ping Liu, Shizhong Han, Yan Tong, Zhiyuan Li, Zibo Meng","submitted_at":"2019-06-06T17:49:41Z","abstract_excerpt":"Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in \"close-to-real-world\" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02728","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":"2026-05-17T23:44:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TWhmiIrWaa5IhvP27YGFMO4ekFOebK4lLt7Rukl0domOboA1q3V9J5g6qNWygV8DJdwQtYLMtJp52R4Tc5tLAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T16:23:13.112955Z"},"content_sha256":"537c12d8ac3668b154a157e2b12dcb581630f81526dbb6065e25541019930892","schema_version":"1.0","event_id":"sha256:537c12d8ac3668b154a157e2b12dcb581630f81526dbb6065e25541019930892"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/bundle.json","state_url":"https://pith.science/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/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":"2026-07-04T16:23:13Z","links":{"resolver":"https://pith.science/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y","bundle":"https://pith.science/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/bundle.json","state":"https://pith.science/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AZHSTJF25HR5HYLMRMKQ5CVW6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AZHSTJF25HR5HYLMRMKQ5CVW6Y","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":"90f4551be3a88f5c8be0918235dd31e218ae1f5072378215f9c91c986df5e138","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T17:49:41Z","title_canon_sha256":"71d1e01111677fe7cecd0ad8c70fcb8a1352a8e67688f7ee5c1a0f9bb66fecb6"},"schema_version":"1.0","source":{"id":"1906.02728","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02728","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02728v1","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02728","created_at":"2026-05-17T23:44:00Z"},{"alias_kind":"pith_short_12","alias_value":"AZHSTJF25HR5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AZHSTJF25HR5HYLM","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AZHSTJF2","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:537c12d8ac3668b154a157e2b12dcb581630f81526dbb6065e25541019930892","target":"graph","created_at":"2026-05-17T23:44:00Z","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":"Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in \"close-to-real-world\" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the ","authors_text":"Ahmed Shehab Khan, James O'Reilly, Jie Cai, Min Chen, Ping Liu, Shizhong Han, Yan Tong, Zhiyuan Li, Zibo Meng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T17:49:41Z","title":"Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02728","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":"sha256:10afbb6218037e6c3d584ae7aa9d442e270570a0f264b7457997b88aca82b225","target":"record","created_at":"2026-05-17T23:44:00Z","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":"90f4551be3a88f5c8be0918235dd31e218ae1f5072378215f9c91c986df5e138","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T17:49:41Z","title_canon_sha256":"71d1e01111677fe7cecd0ad8c70fcb8a1352a8e67688f7ee5c1a0f9bb66fecb6"},"schema_version":"1.0","source":{"id":"1906.02728","kind":"arxiv","version":1}},"canonical_sha256":"064f29a4bae9e3d3e16c8b150e8ab6f60c2e298382673d9c94284f19047f9df4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"064f29a4bae9e3d3e16c8b150e8ab6f60c2e298382673d9c94284f19047f9df4","first_computed_at":"2026-05-17T23:44:00.378676Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:00.378676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1h1UcaDoSL4FOUtYxuOhHmw/Kd/gU3Xh6pMX7zW5IHhTibfPt5t90KFYWrmcn4ujcm2swFYrIbCufBjXSTS4BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:00.379155Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.02728","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:10afbb6218037e6c3d584ae7aa9d442e270570a0f264b7457997b88aca82b225","sha256:537c12d8ac3668b154a157e2b12dcb581630f81526dbb6065e25541019930892"],"state_sha256":"67e5ea679a95fe0e099e71236c6ea8163b8569a69257e5cba17ef10e2c59b32b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SZJKydmnbxpMOjR69TXGrAnDw1WcXN0Fp4IfGYnEZkcqEvmKxI5VcxiPrd+ahwsSyYqDrn1n5DGzQVLiyINODA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T16:23:13.114826Z","bundle_sha256":"302adb8220b205a74a9e6f0672e26158d2ea77d195937831364e98532f53ce85"}}