{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:7AG3EMOF2MLV6PZ3645WME67YZ","short_pith_number":"pith:7AG3EMOF","canonical_record":{"source":{"id":"2012.03711","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T13:37:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9e9b49928f39367b2745d3309dbf00d0b199581ee65ea3ea550141c349dd2c5a","abstract_canon_sha256":"876f71b27a15c4f708f32dc17c78474483325e37b014a9ec8afd405ad68d3c5f"},"schema_version":"1.0"},"canonical_sha256":"f80db231c5d3175f3f3bf73b6613dfc66ad5287da47d2fcb8e1ac1b9e0bb5390","source":{"kind":"arxiv","id":"2012.03711","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.03711","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"arxiv_version","alias_value":"2012.03711v1","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.03711","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_12","alias_value":"7AG3EMOF2MLV","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_16","alias_value":"7AG3EMOF2MLV6PZ3","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_8","alias_value":"7AG3EMOF","created_at":"2026-07-05T01:57:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:7AG3EMOF2MLV6PZ3645WME67YZ","target":"record","payload":{"canonical_record":{"source":{"id":"2012.03711","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T13:37:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9e9b49928f39367b2745d3309dbf00d0b199581ee65ea3ea550141c349dd2c5a","abstract_canon_sha256":"876f71b27a15c4f708f32dc17c78474483325e37b014a9ec8afd405ad68d3c5f"},"schema_version":"1.0"},"canonical_sha256":"f80db231c5d3175f3f3bf73b6613dfc66ad5287da47d2fcb8e1ac1b9e0bb5390","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:57:33.104254Z","signature_b64":"BqqRdqvnFiQUAt2AEeFF0KzeF9CUceyrb/KCIqFNvH87BLHEG86uXxEbFjTA7e12LaLUmJ4sYUt+H0OsTZ47BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f80db231c5d3175f3f3bf73b6613dfc66ad5287da47d2fcb8e1ac1b9e0bb5390","last_reissued_at":"2026-07-05T01:57:33.103852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:57:33.103852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.03711","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":"2026-07-05T01:57:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9csz5DmfWbzQLG9F7NrSkjVbvsgdbPGrCsjL0hvug+50UYTUAvbRVQUtQVTpy/ZkUj399/gJ5KrgokdH9KIlDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:47:10.171180Z"},"content_sha256":"fe374c933460fd33e1cac54c9943ebe22eb8aab5596325a098be44b5bf892b26","schema_version":"1.0","event_id":"sha256:fe374c933460fd33e1cac54c9943ebe22eb8aab5596325a098be44b5bf892b26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:7AG3EMOF2MLV6PZ3645WME67YZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Athanasios Tsanas, Eiman Kanjo, Kieran Woodward","submitted_at":"2020-11-20T13:37:23Z","abstract_excerpt":"The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.03711","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/2012.03711/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":"2026-07-05T01:57:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RddvV4Fn0mIFKA1gvEh08mFjqblqwySs+JW7rIsBjtSN9kVDCPMs86RFdeIuXxVacBQg/WCEISpqAFYaew18Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:47:10.171960Z"},"content_sha256":"cb1255b0c07f11385030d20c7edf8dcc42885300e3c7765af2b903426aff67f4","schema_version":"1.0","event_id":"sha256:cb1255b0c07f11385030d20c7edf8dcc42885300e3c7765af2b903426aff67f4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7AG3EMOF2MLV6PZ3645WME67YZ/bundle.json","state_url":"https://pith.science/pith/7AG3EMOF2MLV6PZ3645WME67YZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7AG3EMOF2MLV6PZ3645WME67YZ/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":"2026-08-04T17:47:10Z","links":{"resolver":"https://pith.science/pith/7AG3EMOF2MLV6PZ3645WME67YZ","bundle":"https://pith.science/pith/7AG3EMOF2MLV6PZ3645WME67YZ/bundle.json","state":"https://pith.science/pith/7AG3EMOF2MLV6PZ3645WME67YZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7AG3EMOF2MLV6PZ3645WME67YZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:7AG3EMOF2MLV6PZ3645WME67YZ","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":"876f71b27a15c4f708f32dc17c78474483325e37b014a9ec8afd405ad68d3c5f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T13:37:23Z","title_canon_sha256":"9e9b49928f39367b2745d3309dbf00d0b199581ee65ea3ea550141c349dd2c5a"},"schema_version":"1.0","source":{"id":"2012.03711","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.03711","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"arxiv_version","alias_value":"2012.03711v1","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.03711","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_12","alias_value":"7AG3EMOF2MLV","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_16","alias_value":"7AG3EMOF2MLV6PZ3","created_at":"2026-07-05T01:57:33Z"},{"alias_kind":"pith_short_8","alias_value":"7AG3EMOF","created_at":"2026-07-05T01:57:33Z"}],"graph_snapshots":[{"event_id":"sha256:cb1255b0c07f11385030d20c7edf8dcc42885300e3c7765af2b903426aff67f4","target":"graph","created_at":"2026-07-05T01:57:33Z","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/2012.03711/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition","authors_text":"Athanasios Tsanas, Eiman Kanjo, Kieran Woodward","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T13:37:23Z","title":"Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.03711","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:fe374c933460fd33e1cac54c9943ebe22eb8aab5596325a098be44b5bf892b26","target":"record","created_at":"2026-07-05T01:57:33Z","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":"876f71b27a15c4f708f32dc17c78474483325e37b014a9ec8afd405ad68d3c5f","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T13:37:23Z","title_canon_sha256":"9e9b49928f39367b2745d3309dbf00d0b199581ee65ea3ea550141c349dd2c5a"},"schema_version":"1.0","source":{"id":"2012.03711","kind":"arxiv","version":1}},"canonical_sha256":"f80db231c5d3175f3f3bf73b6613dfc66ad5287da47d2fcb8e1ac1b9e0bb5390","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f80db231c5d3175f3f3bf73b6613dfc66ad5287da47d2fcb8e1ac1b9e0bb5390","first_computed_at":"2026-07-05T01:57:33.103852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:57:33.103852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BqqRdqvnFiQUAt2AEeFF0KzeF9CUceyrb/KCIqFNvH87BLHEG86uXxEbFjTA7e12LaLUmJ4sYUt+H0OsTZ47BA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:57:33.104254Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.03711","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fe374c933460fd33e1cac54c9943ebe22eb8aab5596325a098be44b5bf892b26","sha256:cb1255b0c07f11385030d20c7edf8dcc42885300e3c7765af2b903426aff67f4"],"state_sha256":"12f21b9702b31411af997188b14f8ebf11ee2edc20662612a93aa1f49ccc34c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Okg3ixXBe2LM1WolrV53ZR/yvcsywvo8kvS9P9r9rZbqpEyvLV1+aFOPur1hZKyqlYGU01tgAJGxOyRW6McjAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T17:47:10.177465Z","bundle_sha256":"b807d7460df0cae6aa5cd3679d415ef5db858ab3e256f1d17da5f372bd6b49d8"}}