{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:LK6QGFZOWDCODZN6FFSW3LVGO2","short_pith_number":"pith:LK6QGFZO","canonical_record":{"source":{"id":"2005.10915","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T21:29:44Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"257ced813edf55f363e12de9f567e4db620725bd24c5bd9fa72be90dfeb7b23a","abstract_canon_sha256":"94c12d5ca567a3f62efa55c66b9f3a3bdb4085114e5e926f72e09eb8656e0e35"},"schema_version":"1.0"},"canonical_sha256":"5abd03172eb0c4e1e5be29656daea676b680e0a9934e56e0fd677ceb7ad775f2","source":{"kind":"arxiv","id":"2005.10915","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.10915","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2005.10915v1","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.10915","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"LK6QGFZOWDCO","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"LK6QGFZOWDCODZN6","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"LK6QGFZO","created_at":"2026-07-05T01:05:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:LK6QGFZOWDCODZN6FFSW3LVGO2","target":"record","payload":{"canonical_record":{"source":{"id":"2005.10915","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T21:29:44Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"257ced813edf55f363e12de9f567e4db620725bd24c5bd9fa72be90dfeb7b23a","abstract_canon_sha256":"94c12d5ca567a3f62efa55c66b9f3a3bdb4085114e5e926f72e09eb8656e0e35"},"schema_version":"1.0"},"canonical_sha256":"5abd03172eb0c4e1e5be29656daea676b680e0a9934e56e0fd677ceb7ad775f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:05:03.579713Z","signature_b64":"aDlmAm/9Qy2Afwgjx/QWvEbI9kFL3y3RL7No9YN95EwZZ80D1HxWRtQmeb20uC6PKAWLomdjauXT0uB4aohZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5abd03172eb0c4e1e5be29656daea676b680e0a9934e56e0fd677ceb7ad775f2","last_reissued_at":"2026-07-05T01:05:03.579308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:05:03.579308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.10915","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":"2026-07-05T01:05:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BwRrb9lnJcclNQG0U3hgeHc/xMAx6nPp+uG1lnTVRBapzPsgGqUL6CUfiubZAYENsHaJPxPqODUPFVL7yz/xCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T05:38:19.433482Z"},"content_sha256":"37b8ed28931c4299512ce3bcccf98d71c3115eaef3c699146fb64f5a8dd539d3","schema_version":"1.0","event_id":"sha256:37b8ed28931c4299512ce3bcccf98d71c3115eaef3c699146fb64f5a8dd539d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:LK6QGFZOWDCODZN6FFSW3LVGO2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Soumil Mandal, Sourya Dipta Das","submitted_at":"2020-05-21T21:29:44Z","abstract_excerpt":"In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020. This challenge had three subtasks where affect based sentiment classification of the memes was required along with intensities. The system we proposed combines the three tasks into a single one by representing it as multi-label hierarchical classification problem.Here,Multi-Task learning or Joint learning Procedure is used to train our model.We have used dual channels to extract text and image based features from separate Deep Neural Network Backbone and aggregate them to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.10915","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.10915/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:05:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hqA/1jJ+/wU8P/qaeD8czo2VuZDPrXkJhe3B3+a2EehNyvTeJCrcw3V4qmTppy+eOhpGW13wvLyzftLMocgWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T05:38:19.433853Z"},"content_sha256":"7c5d190ca1412529dbd22d3fced9478c770b1b8363e596561cc187df03d671bf","schema_version":"1.0","event_id":"sha256:7c5d190ca1412529dbd22d3fced9478c770b1b8363e596561cc187df03d671bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/bundle.json","state_url":"https://pith.science/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/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":"2026-07-16T05:38:19Z","links":{"resolver":"https://pith.science/pith/LK6QGFZOWDCODZN6FFSW3LVGO2","bundle":"https://pith.science/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/bundle.json","state":"https://pith.science/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LK6QGFZOWDCODZN6FFSW3LVGO2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:LK6QGFZOWDCODZN6FFSW3LVGO2","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":"94c12d5ca567a3f62efa55c66b9f3a3bdb4085114e5e926f72e09eb8656e0e35","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T21:29:44Z","title_canon_sha256":"257ced813edf55f363e12de9f567e4db620725bd24c5bd9fa72be90dfeb7b23a"},"schema_version":"1.0","source":{"id":"2005.10915","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.10915","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2005.10915v1","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.10915","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"LK6QGFZOWDCO","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"LK6QGFZOWDCODZN6","created_at":"2026-07-05T01:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"LK6QGFZO","created_at":"2026-07-05T01:05:03Z"}],"graph_snapshots":[{"event_id":"sha256:7c5d190ca1412529dbd22d3fced9478c770b1b8363e596561cc187df03d671bf","target":"graph","created_at":"2026-07-05T01:05:03Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2005.10915/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020. This challenge had three subtasks where affect based sentiment classification of the memes was required along with intensities. The system we proposed combines the three tasks into a single one by representing it as multi-label hierarchical classification problem.Here,Multi-Task learning or Joint learning Procedure is used to train our model.We have used dual channels to extract text and image based features from separate Deep Neural Network Backbone and aggregate them to ","authors_text":"Soumil Mandal, Sourya Dipta Das","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T21:29:44Z","title":"Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.10915","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":"sha256:37b8ed28931c4299512ce3bcccf98d71c3115eaef3c699146fb64f5a8dd539d3","target":"record","created_at":"2026-07-05T01:05:03Z","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":"94c12d5ca567a3f62efa55c66b9f3a3bdb4085114e5e926f72e09eb8656e0e35","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T21:29:44Z","title_canon_sha256":"257ced813edf55f363e12de9f567e4db620725bd24c5bd9fa72be90dfeb7b23a"},"schema_version":"1.0","source":{"id":"2005.10915","kind":"arxiv","version":1}},"canonical_sha256":"5abd03172eb0c4e1e5be29656daea676b680e0a9934e56e0fd677ceb7ad775f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5abd03172eb0c4e1e5be29656daea676b680e0a9934e56e0fd677ceb7ad775f2","first_computed_at":"2026-07-05T01:05:03.579308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:05:03.579308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aDlmAm/9Qy2Afwgjx/QWvEbI9kFL3y3RL7No9YN95EwZZ80D1HxWRtQmeb20uC6PKAWLomdjauXT0uB4aohZCw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:05:03.579713Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.10915","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37b8ed28931c4299512ce3bcccf98d71c3115eaef3c699146fb64f5a8dd539d3","sha256:7c5d190ca1412529dbd22d3fced9478c770b1b8363e596561cc187df03d671bf"],"state_sha256":"31b73953e3b7b3e319c7f35c63f27a93e6f6913c32b826da39070991e4859602"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nxnRhC54HIepwJeFZrygxzEGohUXtMECPSDtZdkIc3cE7R6pR08+iIPc8XW4DY248J+nGOP4WdMM/kYN0FRuBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-16T05:38:19.436044Z","bundle_sha256":"5c5668e02c21ea4e51374a29a45ea9365b7dec7576c375dd29a0917c1f8a880b"}}