{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BBK5SC4IPT4WKVE46IA7CIOI7Q","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":"06188754b346090481e47d7b685a0705803aa0120892d610bec54c44ad09d8fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T10:15:10Z","title_canon_sha256":"ec4a50ab4578fe13c9f1a023d053e46be365065451163c68478af5b7ed60d38b"},"schema_version":"1.0","source":{"id":"1806.05886","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05886","created_at":"2026-07-05T02:36:05Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05886v2","created_at":"2026-07-05T02:36:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05886","created_at":"2026-07-05T02:36:05Z"},{"alias_kind":"pith_short_12","alias_value":"BBK5SC4IPT4W","created_at":"2026-07-05T02:36:05Z"},{"alias_kind":"pith_short_16","alias_value":"BBK5SC4IPT4WKVE4","created_at":"2026-07-05T02:36:05Z"},{"alias_kind":"pith_short_8","alias_value":"BBK5SC4I","created_at":"2026-07-05T02:36:05Z"}],"graph_snapshots":[{"event_id":"sha256:5ac42bb28babf691e963386e0366dcbda3f3efb3e869cdbe2c4f0b770e6da789","target":"graph","created_at":"2026-07-05T02:36:05Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1806.05886/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data preparation, i.e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. For image data, preprocessing typically involves a sequence of basic transformations such as cropping, filtering, rotating or flipping images. Currently, data scientists decide manually based on their experience which transformations to apply in which particular order to a given image data set. Besides constituting a bottleneck in real-world data science projects, manual image data preprocessing may yield suboptimal res","authors_text":"Hoang Thanh Lam, Martin Wistuba, Mathieu Sinn, Tran Ngoc Minh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T10:15:10Z","title":"Automated Image Data Preprocessing with Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05886","kind":"arxiv","version":2},"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":"sha256:43c334e350e6ca6fed0b4701dda301e104ed4a91cf8b368c9787c35bce252481","target":"record","created_at":"2026-07-05T02:36:05Z","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":"06188754b346090481e47d7b685a0705803aa0120892d610bec54c44ad09d8fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T10:15:10Z","title_canon_sha256":"ec4a50ab4578fe13c9f1a023d053e46be365065451163c68478af5b7ed60d38b"},"schema_version":"1.0","source":{"id":"1806.05886","kind":"arxiv","version":2}},"canonical_sha256":"0855d90b887cf965549cf201f121c8fc1b0ef3a06e0aaba4cc8d6635f630cb01","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0855d90b887cf965549cf201f121c8fc1b0ef3a06e0aaba4cc8d6635f630cb01","first_computed_at":"2026-07-05T02:36:05.695255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:36:05.695255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QZqMaxsFTVaMLTOE7P8nCjBZqUazS5okCrcflK51DqoYG9790VHBGIEWYakJAnIsXD1+niw4AeoVOxaGoSqeAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:36:05.695741Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05886","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43c334e350e6ca6fed0b4701dda301e104ed4a91cf8b368c9787c35bce252481","sha256:5ac42bb28babf691e963386e0366dcbda3f3efb3e869cdbe2c4f0b770e6da789"],"state_sha256":"51c96154f6f93fce239c144e3af8a752fe16c7256870699e1cdfba3a6202913b"}