{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UCWWSJOULL23NTREXQMJ3GFTK3","short_pith_number":"pith:UCWWSJOU","canonical_record":{"source":{"id":"1705.01217","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-03T01:26:24Z","cross_cats_sorted":[],"title_canon_sha256":"1fb2905438235f8f040ab46c548848e793e1abd726f46af028a767872d551cb9","abstract_canon_sha256":"6010629ce6ab721dfbbcf5bc9c681d27e62f9a54f348f8712e4711d3719ec707"},"schema_version":"1.0"},"canonical_sha256":"a0ad6925d45af5b6ce24bc189d98b356caedb504bd33054121026564b29fbd3d","source":{"kind":"arxiv","id":"1705.01217","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.01217","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"arxiv_version","alias_value":"1705.01217v1","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.01217","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"pith_short_12","alias_value":"UCWWSJOULL23","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UCWWSJOULL23NTRE","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UCWWSJOU","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UCWWSJOULL23NTREXQMJ3GFTK3","target":"record","payload":{"canonical_record":{"source":{"id":"1705.01217","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-03T01:26:24Z","cross_cats_sorted":[],"title_canon_sha256":"1fb2905438235f8f040ab46c548848e793e1abd726f46af028a767872d551cb9","abstract_canon_sha256":"6010629ce6ab721dfbbcf5bc9c681d27e62f9a54f348f8712e4711d3719ec707"},"schema_version":"1.0"},"canonical_sha256":"a0ad6925d45af5b6ce24bc189d98b356caedb504bd33054121026564b29fbd3d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:05.656722Z","signature_b64":"y1ZFAtZWxog0qKi/HeYMhmGq/X24nc6dtpu3KAeaCcaiwc8k2QaFbezPYEEBXvEwXPAVBd5ojSWZoDWj/yZQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0ad6925d45af5b6ce24bc189d98b356caedb504bd33054121026564b29fbd3d","last_reissued_at":"2026-05-18T00:45:05.656194Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:05.656194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.01217","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":"2026-05-18T00:45:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6o+l41kWSmrGXXn8gbpLeyw4zCRhcOZ0rUn9n9cWfDZl7hEbVnaApjhvUSrS+e3aeyr3j7FGzAl0C/tmSerIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T03:34:26.372302Z"},"content_sha256":"70631eecd301873e6aebe9efe7d2b1f76cfb1a08cfbe662fcf76a600200433e4","schema_version":"1.0","event_id":"sha256:70631eecd301873e6aebe9efe7d2b1f76cfb1a08cfbe662fcf76a600200433e4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UCWWSJOULL23NTREXQMJ3GFTK3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Marine Animal Classification with Correntropy Loss Based Multi-view Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anni Vuorenkoski, Bing Ouyang, Fraser Dalgleish, Gabriel Alsenas, Jose Principe, Shujian Yu, Zheng Cao","submitted_at":"2017-05-03T01:26:24Z","abstract_excerpt":"To analyze marine animals behavior, seasonal distribution and abundance, digital imagery can be acquired by visual or Lidar camera. Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.), or dissimilarity matrices derived from different shape analysis methods (shape context, internal distance shape context, etc.). For both cases, multi-view learning is critical in integrating more than one set of feature or dissimilarity matrix for higher classification accuracy. This paper adopts correntropy loss as cost fun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.01217","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":""},"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":"2026-05-18T00:45:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e8V0zkdTpVzBGWn9cPBUl/lmTRSIZ+FthrkSwg7wiMZdTe9+HP9dZUDEH7KVvGYxj6bjAOH8O8w4E3dOvOV1Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T03:34:26.372983Z"},"content_sha256":"5b8d57beaefa9c1a518a7e572ddf49deaaf434ca25046fbc8937dd776a01467e","schema_version":"1.0","event_id":"sha256:5b8d57beaefa9c1a518a7e572ddf49deaaf434ca25046fbc8937dd776a01467e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UCWWSJOULL23NTREXQMJ3GFTK3/bundle.json","state_url":"https://pith.science/pith/UCWWSJOULL23NTREXQMJ3GFTK3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UCWWSJOULL23NTREXQMJ3GFTK3/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":"2026-05-22T03:34:26Z","links":{"resolver":"https://pith.science/pith/UCWWSJOULL23NTREXQMJ3GFTK3","bundle":"https://pith.science/pith/UCWWSJOULL23NTREXQMJ3GFTK3/bundle.json","state":"https://pith.science/pith/UCWWSJOULL23NTREXQMJ3GFTK3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UCWWSJOULL23NTREXQMJ3GFTK3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UCWWSJOULL23NTREXQMJ3GFTK3","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":"6010629ce6ab721dfbbcf5bc9c681d27e62f9a54f348f8712e4711d3719ec707","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-03T01:26:24Z","title_canon_sha256":"1fb2905438235f8f040ab46c548848e793e1abd726f46af028a767872d551cb9"},"schema_version":"1.0","source":{"id":"1705.01217","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.01217","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"arxiv_version","alias_value":"1705.01217v1","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.01217","created_at":"2026-05-18T00:45:05Z"},{"alias_kind":"pith_short_12","alias_value":"UCWWSJOULL23","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"UCWWSJOULL23NTRE","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"UCWWSJOU","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:5b8d57beaefa9c1a518a7e572ddf49deaaf434ca25046fbc8937dd776a01467e","target":"graph","created_at":"2026-05-18T00:45:05Z","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"},"paper":{"abstract_excerpt":"To analyze marine animals behavior, seasonal distribution and abundance, digital imagery can be acquired by visual or Lidar camera. Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.), or dissimilarity matrices derived from different shape analysis methods (shape context, internal distance shape context, etc.). For both cases, multi-view learning is critical in integrating more than one set of feature or dissimilarity matrix for higher classification accuracy. This paper adopts correntropy loss as cost fun","authors_text":"Anni Vuorenkoski, Bing Ouyang, Fraser Dalgleish, Gabriel Alsenas, Jose Principe, Shujian Yu, Zheng Cao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-03T01:26:24Z","title":"Marine Animal Classification with Correntropy Loss Based Multi-view Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.01217","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":"sha256:70631eecd301873e6aebe9efe7d2b1f76cfb1a08cfbe662fcf76a600200433e4","target":"record","created_at":"2026-05-18T00:45:05Z","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":"6010629ce6ab721dfbbcf5bc9c681d27e62f9a54f348f8712e4711d3719ec707","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-03T01:26:24Z","title_canon_sha256":"1fb2905438235f8f040ab46c548848e793e1abd726f46af028a767872d551cb9"},"schema_version":"1.0","source":{"id":"1705.01217","kind":"arxiv","version":1}},"canonical_sha256":"a0ad6925d45af5b6ce24bc189d98b356caedb504bd33054121026564b29fbd3d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0ad6925d45af5b6ce24bc189d98b356caedb504bd33054121026564b29fbd3d","first_computed_at":"2026-05-18T00:45:05.656194Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:05.656194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y1ZFAtZWxog0qKi/HeYMhmGq/X24nc6dtpu3KAeaCcaiwc8k2QaFbezPYEEBXvEwXPAVBd5ojSWZoDWj/yZQCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:05.656722Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.01217","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70631eecd301873e6aebe9efe7d2b1f76cfb1a08cfbe662fcf76a600200433e4","sha256:5b8d57beaefa9c1a518a7e572ddf49deaaf434ca25046fbc8937dd776a01467e"],"state_sha256":"511c7b64c6a16e86381a0ec63de4649a4d4dcfb00ac8ee1ea01cd0b33053df01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qgTiHtEY7Y/A5nXNi74HbEeYRkA6mQZLi4PmEK5wQjfGLOBR+lRd91UthNhL2hjTN56E/BwekTVO4cGg7eW7AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T03:34:26.376500Z","bundle_sha256":"ab9826ea44161222a5617ff18e2120e712ad04b9228ad070798001e2618ea8fe"}}