{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2N3HFUNPRXDXRT3YSTFO25NUEZ","short_pith_number":"pith:2N3HFUNP","schema_version":"1.0","canonical_sha256":"d37672d1af8dc778cf7894caed75b42661bffcb3c99b3e67c2e75539d2a160d9","source":{"kind":"arxiv","id":"2608.07132","version":1},"attestation_state":"computed","paper":{"title":"Tabular Image: a method to convert tabular data to images for convolutional neural networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Barbara Summers, Junhao Liang, Xingjie Wei","submitted_at":"2026-08-07T11:46:55Z","abstract_excerpt":"Improving the predictive capability of credit scoring models is always an active area of research in the financial sector. Recognising the impressive effectiveness of neural networks in different domains (such as computer vision and natural language processing), various neural networks have been tested to potentially improve loan default prediction on credit data. Nevertheless, a significant challenge emerges due to the predominantly tabular nature of credit data, which is not well-suited to the structure and strengths of neural networks, hindering their ability to surpass traditional machine "},"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":"2608.07132","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CE","submitted_at":"2026-08-07T11:46:55Z","cross_cats_sorted":[],"title_canon_sha256":"2274ecc6afe422f860574c7a2c9ce47f09111b371f464332feee3a70907ab477","abstract_canon_sha256":"f5ab2505fe329bd10946a43409f8e2a58c6d5dd451771891416c493e7acc0803"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-10T01:13:23.977529Z","signature_b64":"H0/GgoTrLe3TJIn1WG/gBlD7XyiEKYF1s+D7QjH6H4pc1WzJH7cGfuiIhc/rrf1UdZdrSXbBKKyeLKLMnqs+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d37672d1af8dc778cf7894caed75b42661bffcb3c99b3e67c2e75539d2a160d9","last_reissued_at":"2026-08-10T01:13:23.974559Z","signature_status":"signed_v1","first_computed_at":"2026-08-10T01:13:23.974559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tabular Image: a method to convert tabular data to images for convolutional neural networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Barbara Summers, Junhao Liang, Xingjie Wei","submitted_at":"2026-08-07T11:46:55Z","abstract_excerpt":"Improving the predictive capability of credit scoring models is always an active area of research in the financial sector. Recognising the impressive effectiveness of neural networks in different domains (such as computer vision and natural language processing), various neural networks have been tested to potentially improve loan default prediction on credit data. Nevertheless, a significant challenge emerges due to the predominantly tabular nature of credit data, which is not well-suited to the structure and strengths of neural networks, hindering their ability to surpass traditional machine "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.07132","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/2608.07132/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":"2608.07132","created_at":"2026-08-10T01:13:23.975896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.07132v1","created_at":"2026-08-10T01:13:23.975896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.07132","created_at":"2026-08-10T01:13:23.975896+00:00"},{"alias_kind":"pith_short_12","alias_value":"2N3HFUNPRXDX","created_at":"2026-08-10T01:13:23.975896+00:00"},{"alias_kind":"pith_short_16","alias_value":"2N3HFUNPRXDXRT3Y","created_at":"2026-08-10T01:13:23.975896+00:00"},{"alias_kind":"pith_short_8","alias_value":"2N3HFUNP","created_at":"2026-08-10T01:13:23.975896+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/2N3HFUNPRXDXRT3YSTFO25NUEZ","json":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ.json","graph_json":"https://pith.science/api/pith-number/2N3HFUNPRXDXRT3YSTFO25NUEZ/graph.json","events_json":"https://pith.science/api/pith-number/2N3HFUNPRXDXRT3YSTFO25NUEZ/events.json","paper":"https://pith.science/paper/2N3HFUNP"},"agent_actions":{"view_html":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ","download_json":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ.json","view_paper":"https://pith.science/paper/2N3HFUNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.07132&json=true","fetch_graph":"https://pith.science/api/pith-number/2N3HFUNPRXDXRT3YSTFO25NUEZ/graph.json","fetch_events":"https://pith.science/api/pith-number/2N3HFUNPRXDXRT3YSTFO25NUEZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ/action/storage_attestation","attest_author":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ/action/author_attestation","sign_citation":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ/action/citation_signature","submit_replication":"https://pith.science/pith/2N3HFUNPRXDXRT3YSTFO25NUEZ/action/replication_record"}},"created_at":"2026-08-10T01:13:23.975896+00:00","updated_at":"2026-08-10T01:13:23.975896+00:00"}