{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:SDZ64RUT6ZUTFDFXZP5EBKXMZZ","short_pith_number":"pith:SDZ64RUT","canonical_record":{"source":{"id":"2207.02163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-05T16:38:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d9bf6f5c1a1fdb0f2f8b5e222f8316fb0c149ca825a4275cddc381e574de3aa2","abstract_canon_sha256":"f453ccde555e49af7da6c2474ab3e0b348269f7c9566894043db6206dd50e988"},"schema_version":"1.0"},"canonical_sha256":"90f3ee4693f669328cb7cbfa40aaecce6bdc3ad051d8e3a25d086a7b5d781433","source":{"kind":"arxiv","id":"2207.02163","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.02163","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"arxiv_version","alias_value":"2207.02163v1","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.02163","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_12","alias_value":"SDZ64RUT6ZUT","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_16","alias_value":"SDZ64RUT6ZUTFDFX","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_8","alias_value":"SDZ64RUT","created_at":"2026-07-05T04:37:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:SDZ64RUT6ZUTFDFXZP5EBKXMZZ","target":"record","payload":{"canonical_record":{"source":{"id":"2207.02163","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-05T16:38:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d9bf6f5c1a1fdb0f2f8b5e222f8316fb0c149ca825a4275cddc381e574de3aa2","abstract_canon_sha256":"f453ccde555e49af7da6c2474ab3e0b348269f7c9566894043db6206dd50e988"},"schema_version":"1.0"},"canonical_sha256":"90f3ee4693f669328cb7cbfa40aaecce6bdc3ad051d8e3a25d086a7b5d781433","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:47.947596Z","signature_b64":"nforfq/kL2MDGC7Hte+07Qf4rhO5bUKzzcixFpFjkWFXi8yY4o+tmCK0Ga058HWbO/4mXe0RE1P4oXR9CMpaAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90f3ee4693f669328cb7cbfa40aaecce6bdc3ad051d8e3a25d086a7b5d781433","last_reissued_at":"2026-07-05T04:37:47.947108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:47.947108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.02163","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":"2026-07-05T04:37:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6RpAoa1NxLT1Q7K2qaUIOm/J//b8mo6UyZwi3TJRYXXFgaPMQaPUfMCmny/X3J+2a6VoX/io5enB6Sy3zepQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T21:23:07.182812Z"},"content_sha256":"6d6ae4180a4bffbfda8c39109b98299af57cc861b6b32690d882f43ec6203c3f","schema_version":"1.0","event_id":"sha256:6d6ae4180a4bffbfda8c39109b98299af57cc861b6b32690d882f43ec6203c3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:SDZ64RUT6ZUTFDFXZP5EBKXMZZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Anastasios Doulamis, Athanasios Voulodimos, Ioannis N. Tzortzis, Ioannis Rallis, Konstantinos Makantasis, Nikolaos Doulamis","submitted_at":"2022-07-05T16:38:27Z","abstract_excerpt":"In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.02163","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/2207.02163/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":"2026-07-05T04:37:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7aILG99GeKHsDuLSYSelAKFbbQrlFsQSo3vnENvKNY47fd4sgIIPQkCs3Y3BvWpzKdOGHHZv94C7Xru4UY1aDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T21:23:07.183322Z"},"content_sha256":"d6132af775cc4705a5b25a1991d6093ebeddc29ee4e02ef749985439beddc893","schema_version":"1.0","event_id":"sha256:d6132af775cc4705a5b25a1991d6093ebeddc29ee4e02ef749985439beddc893"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/bundle.json","state_url":"https://pith.science/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/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":"2026-08-22T21:23:07Z","links":{"resolver":"https://pith.science/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ","bundle":"https://pith.science/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/bundle.json","state":"https://pith.science/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SDZ64RUT6ZUTFDFXZP5EBKXMZZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SDZ64RUT6ZUTFDFXZP5EBKXMZZ","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":"f453ccde555e49af7da6c2474ab3e0b348269f7c9566894043db6206dd50e988","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-05T16:38:27Z","title_canon_sha256":"d9bf6f5c1a1fdb0f2f8b5e222f8316fb0c149ca825a4275cddc381e574de3aa2"},"schema_version":"1.0","source":{"id":"2207.02163","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.02163","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"arxiv_version","alias_value":"2207.02163v1","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.02163","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_12","alias_value":"SDZ64RUT6ZUT","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_16","alias_value":"SDZ64RUT6ZUTFDFX","created_at":"2026-07-05T04:37:47Z"},{"alias_kind":"pith_short_8","alias_value":"SDZ64RUT","created_at":"2026-07-05T04:37:47Z"}],"graph_snapshots":[{"event_id":"sha256:d6132af775cc4705a5b25a1991d6093ebeddc29ee4e02ef749985439beddc893","target":"graph","created_at":"2026-07-05T04:37:47Z","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/2207.02163/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimen","authors_text":"Anastasios Doulamis, Athanasios Voulodimos, Ioannis N. Tzortzis, Ioannis Rallis, Konstantinos Makantasis, Nikolaos Doulamis","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-05T16:38:27Z","title":"Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.02163","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":"sha256:6d6ae4180a4bffbfda8c39109b98299af57cc861b6b32690d882f43ec6203c3f","target":"record","created_at":"2026-07-05T04:37:47Z","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":"f453ccde555e49af7da6c2474ab3e0b348269f7c9566894043db6206dd50e988","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-05T16:38:27Z","title_canon_sha256":"d9bf6f5c1a1fdb0f2f8b5e222f8316fb0c149ca825a4275cddc381e574de3aa2"},"schema_version":"1.0","source":{"id":"2207.02163","kind":"arxiv","version":1}},"canonical_sha256":"90f3ee4693f669328cb7cbfa40aaecce6bdc3ad051d8e3a25d086a7b5d781433","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"90f3ee4693f669328cb7cbfa40aaecce6bdc3ad051d8e3a25d086a7b5d781433","first_computed_at":"2026-07-05T04:37:47.947108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:37:47.947108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nforfq/kL2MDGC7Hte+07Qf4rhO5bUKzzcixFpFjkWFXi8yY4o+tmCK0Ga058HWbO/4mXe0RE1P4oXR9CMpaAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:37:47.947596Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.02163","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d6ae4180a4bffbfda8c39109b98299af57cc861b6b32690d882f43ec6203c3f","sha256:d6132af775cc4705a5b25a1991d6093ebeddc29ee4e02ef749985439beddc893"],"state_sha256":"c550f7f7190e524868d2e4f57d1f7f6ed967a99826193da8ab1dd70899957f54"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wt/VENGtOtJOpnghJ6Lqx1bvsGezkM+qd/6aB/J3GZT5s/cw4opOlpoKBlp3P/0+QoOr9HrhGENLy6j0lEw4DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T21:23:07.188035Z","bundle_sha256":"68fd3318fc78fa3a15d7748f2f90d8a35a57bd4f7217e194911cc0f6850edbe2"}}