{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:5NVIYFJCQX7YEZHGSCDPYDOI5Z","short_pith_number":"pith:5NVIYFJC","canonical_record":{"source":{"id":"2007.05942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T09:01:57Z","cross_cats_sorted":[],"title_canon_sha256":"f39cf0a8e83d5ad9504b398be5a6a37c4321fc1025c211bc667f66150ab37eaa","abstract_canon_sha256":"edb5de1b0e7e2f3fd7ffa1ca731b0639f77f28b11578b7d32ce0412c09dcd569"},"schema_version":"1.0"},"canonical_sha256":"eb6a8c152285ff8264e69086fc0dc8ee70a1dcd86441d8a596407c7a10920c3b","source":{"kind":"arxiv","id":"2007.05942","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.05942","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"arxiv_version","alias_value":"2007.05942v1","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.05942","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_12","alias_value":"5NVIYFJCQX7Y","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_16","alias_value":"5NVIYFJCQX7YEZHG","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_8","alias_value":"5NVIYFJC","created_at":"2026-07-05T03:39:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:5NVIYFJCQX7YEZHGSCDPYDOI5Z","target":"record","payload":{"canonical_record":{"source":{"id":"2007.05942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T09:01:57Z","cross_cats_sorted":[],"title_canon_sha256":"f39cf0a8e83d5ad9504b398be5a6a37c4321fc1025c211bc667f66150ab37eaa","abstract_canon_sha256":"edb5de1b0e7e2f3fd7ffa1ca731b0639f77f28b11578b7d32ce0412c09dcd569"},"schema_version":"1.0"},"canonical_sha256":"eb6a8c152285ff8264e69086fc0dc8ee70a1dcd86441d8a596407c7a10920c3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:39:54.732017Z","signature_b64":"EyBdQiQgaCLc5Lxd5Lh8CNsK0ZSpFP0GtVR12eKlLh6uU8SOqGoU4jKcYSifGoNzxCYjGGIeSiqK1xxaipjTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb6a8c152285ff8264e69086fc0dc8ee70a1dcd86441d8a596407c7a10920c3b","last_reissued_at":"2026-07-05T03:39:54.731639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:39:54.731639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.05942","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":"2026-07-05T03:39:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IfYtzF3Bv1TF2rBTOYG6cAtH9m4nAR18nDxdzCV/KIsjAmNb2kY5OxKBf5vhIrMF8UCWOTPt99YDl9cK4GdZCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T13:54:11.476192Z"},"content_sha256":"0e265a6059ed5aa0cee9fa3881e48f39a50202163565f8645da027f8e82d3ad8","schema_version":"1.0","event_id":"sha256:0e265a6059ed5aa0cee9fa3881e48f39a50202163565f8645da027f8e82d3ad8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:5NVIYFJCQX7YEZHGSCDPYDOI5Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fruit classification using deep feature maps in the presence of deceptive similar classes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohit Dandekar, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal","submitted_at":"2020-07-12T09:01:57Z","abstract_excerpt":"Autonomous detection and classification of objects are admired area of research in many industrial applications. Though, humans can distinguish objects with high multi-granular similarities very easily; but for the machines, it is a very challenging task. The convolution neural networks (CNN) have illustrated efficient performance in multi-level representations of objects for classification. Conventionally, the existing deep learning models utilize the transformed features generated by the rearmost layer for training and testing. However, it is evident that this does not work well with multi-g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.05942","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/2007.05942/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":"2026-07-05T03:39:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZvjytnaxkTw2mRtjjomADWEYqCOV6HqJO6RPJCxr9N35DO9P2nmu+hmyvRTgozNN4SCKNHf83TND46FiviKjCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T13:54:11.476867Z"},"content_sha256":"0713d7b70cf728003b2bfd47899f04f70ecaac83d15b9180be5b289519db624c","schema_version":"1.0","event_id":"sha256:0713d7b70cf728003b2bfd47899f04f70ecaac83d15b9180be5b289519db624c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/bundle.json","state_url":"https://pith.science/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/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":"2026-07-05T13:54:11Z","links":{"resolver":"https://pith.science/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z","bundle":"https://pith.science/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/bundle.json","state":"https://pith.science/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5NVIYFJCQX7YEZHGSCDPYDOI5Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:5NVIYFJCQX7YEZHGSCDPYDOI5Z","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":"edb5de1b0e7e2f3fd7ffa1ca731b0639f77f28b11578b7d32ce0412c09dcd569","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T09:01:57Z","title_canon_sha256":"f39cf0a8e83d5ad9504b398be5a6a37c4321fc1025c211bc667f66150ab37eaa"},"schema_version":"1.0","source":{"id":"2007.05942","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.05942","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"arxiv_version","alias_value":"2007.05942v1","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.05942","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_12","alias_value":"5NVIYFJCQX7Y","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_16","alias_value":"5NVIYFJCQX7YEZHG","created_at":"2026-07-05T03:39:54Z"},{"alias_kind":"pith_short_8","alias_value":"5NVIYFJC","created_at":"2026-07-05T03:39:54Z"}],"graph_snapshots":[{"event_id":"sha256:0713d7b70cf728003b2bfd47899f04f70ecaac83d15b9180be5b289519db624c","target":"graph","created_at":"2026-07-05T03:39:54Z","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/2007.05942/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autonomous detection and classification of objects are admired area of research in many industrial applications. Though, humans can distinguish objects with high multi-granular similarities very easily; but for the machines, it is a very challenging task. The convolution neural networks (CNN) have illustrated efficient performance in multi-level representations of objects for classification. Conventionally, the existing deep learning models utilize the transformed features generated by the rearmost layer for training and testing. However, it is evident that this does not work well with multi-g","authors_text":"Mohit Dandekar, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T09:01:57Z","title":"Fruit classification using deep feature maps in the presence of deceptive similar classes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.05942","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":"sha256:0e265a6059ed5aa0cee9fa3881e48f39a50202163565f8645da027f8e82d3ad8","target":"record","created_at":"2026-07-05T03:39:54Z","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":"edb5de1b0e7e2f3fd7ffa1ca731b0639f77f28b11578b7d32ce0412c09dcd569","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-07-12T09:01:57Z","title_canon_sha256":"f39cf0a8e83d5ad9504b398be5a6a37c4321fc1025c211bc667f66150ab37eaa"},"schema_version":"1.0","source":{"id":"2007.05942","kind":"arxiv","version":1}},"canonical_sha256":"eb6a8c152285ff8264e69086fc0dc8ee70a1dcd86441d8a596407c7a10920c3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb6a8c152285ff8264e69086fc0dc8ee70a1dcd86441d8a596407c7a10920c3b","first_computed_at":"2026-07-05T03:39:54.731639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:39:54.731639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EyBdQiQgaCLc5Lxd5Lh8CNsK0ZSpFP0GtVR12eKlLh6uU8SOqGoU4jKcYSifGoNzxCYjGGIeSiqK1xxaipjTAA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:39:54.732017Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.05942","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0e265a6059ed5aa0cee9fa3881e48f39a50202163565f8645da027f8e82d3ad8","sha256:0713d7b70cf728003b2bfd47899f04f70ecaac83d15b9180be5b289519db624c"],"state_sha256":"59f622d4a18658ede58bdfcc920eb2a7e330e4cc5532e4548cf273f1e6ffc137"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mDmEwpYVaWChzieWnNWTSBlegMNoO9Kk/j/mwakvbqhkmbW2YvJPtYKjl7tkoRve6RmcVhlVdu+HWb0XebbiBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T13:54:11.479720Z","bundle_sha256":"e338784b4a12dadae9160bed2b285baff198304ba1c638963272c48583c22edf"}}