{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SLM4ZTQQ35VCMNMBWJCGKWGZMW","short_pith_number":"pith:SLM4ZTQQ","schema_version":"1.0","canonical_sha256":"92d9ccce10df6a263581b2446558d965ba0b913e2c73cb3a796f9c4c0a28b765","source":{"kind":"arxiv","id":"2308.10869","version":1},"attestation_state":"computed","paper":{"title":"A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Mahrukh Tauseef, Nibraas Khan, Nilanjan Sarkar, Ritam Ghosh","submitted_at":"2023-08-17T01:15:26Z","abstract_excerpt":"Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. Thus, the ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces. The correlation between patterns in physiological data and affective states has allowed for the utilization of deep learning techniques which can accurately detect the affective states of a person. However, the generalisability of existing models is often limited "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.10869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-08-17T01:15:26Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"139afb7507ff769cc13ddffc0b4d6e0c4c8b0a271bf68b9f6d82b74be5a8ddec","abstract_canon_sha256":"a86f8b96b0766b98952cdf824f207463fe29d0a377178a7bb8e5bc2962fa536b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:43:15.647326Z","signature_b64":"OHO9T+K6FhZaGkGLYOBWZJP5BPY2YXFA6pxbvNjMxB5vgimTt7ZG4w8jaNUPhEUwaclKfedaRhbJUBiNI/1uCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92d9ccce10df6a263581b2446558d965ba0b913e2c73cb3a796f9c4c0a28b765","last_reissued_at":"2026-07-05T06:43:15.646845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:43:15.646845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Mahrukh Tauseef, Nibraas Khan, Nilanjan Sarkar, Ritam Ghosh","submitted_at":"2023-08-17T01:15:26Z","abstract_excerpt":"Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. Thus, the ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces. The correlation between patterns in physiological data and affective states has allowed for the utilization of deep learning techniques which can accurately detect the affective states of a person. However, the generalisability of existing models is often limited "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10869","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/2308.10869/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.10869","created_at":"2026-07-05T06:43:15.646902+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.10869v1","created_at":"2026-07-05T06:43:15.646902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10869","created_at":"2026-07-05T06:43:15.646902+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLM4ZTQQ35VC","created_at":"2026-07-05T06:43:15.646902+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLM4ZTQQ35VCMNMB","created_at":"2026-07-05T06:43:15.646902+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLM4ZTQQ","created_at":"2026-07-05T06:43:15.646902+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW","json":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW.json","graph_json":"https://pith.science/api/pith-number/SLM4ZTQQ35VCMNMBWJCGKWGZMW/graph.json","events_json":"https://pith.science/api/pith-number/SLM4ZTQQ35VCMNMBWJCGKWGZMW/events.json","paper":"https://pith.science/paper/SLM4ZTQQ"},"agent_actions":{"view_html":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW","download_json":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW.json","view_paper":"https://pith.science/paper/SLM4ZTQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.10869&json=true","fetch_graph":"https://pith.science/api/pith-number/SLM4ZTQQ35VCMNMBWJCGKWGZMW/graph.json","fetch_events":"https://pith.science/api/pith-number/SLM4ZTQQ35VCMNMBWJCGKWGZMW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW/action/storage_attestation","attest_author":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW/action/author_attestation","sign_citation":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW/action/citation_signature","submit_replication":"https://pith.science/pith/SLM4ZTQQ35VCMNMBWJCGKWGZMW/action/replication_record"}},"created_at":"2026-07-05T06:43:15.646902+00:00","updated_at":"2026-07-05T06:43:15.646902+00:00"}