{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:32EFBRZDVUOEA5V7UVSMMNAWRR","short_pith_number":"pith:32EFBRZD","schema_version":"1.0","canonical_sha256":"de8850c723ad1c4076bfa564c634168c6c6ce11b6d0e218a4978f0ef77ab8ea3","source":{"kind":"arxiv","id":"1909.03036","version":1},"attestation_state":"computed","paper":{"title":"Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Andreas Jedlitschka, Daniel Schneider, Lisa J\\\"ockel, Michael Kl\\\"as, Mohammed Naveed Akram, Pascal Bauer, Pascal Gerber, Patrik Feth, Rasmus Adler","submitted_at":"2019-09-02T09:36:24Z","abstract_excerpt":"Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastrophic consequences. However, the development of such neural networks is still immature and good engineering practices are missing. With that, ANNs are in the same position as software was several decades ago. Today, standards for functional safety, such as ISO 26262 in the automotive domain, require the application of a collection of proven engineering principles and methods in the creation of software to increase its"},"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":"1909.03036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2019-09-02T09:36:24Z","cross_cats_sorted":[],"title_canon_sha256":"4bfe21796a7db63a129cb342353dc7f2335020f179da0a7e3f91c909c18ca98d","abstract_canon_sha256":"843b028c8f97f802c8e3debb207a9e29bee40e0d75424a4b4c4efdf4a93791a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:51.715137Z","signature_b64":"NrnIwASCWTSvNsVj/ZvnzZVg+ltdB8tKdOuB8CrY7b5dWG1Aqk2/iAMRmsSsWqFOj4XMOTnPG+TsN36+xhTXCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de8850c723ad1c4076bfa564c634168c6c6ce11b6d0e218a4978f0ef77ab8ea3","last_reissued_at":"2026-07-05T00:02:51.714627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:51.714627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hardening of Artificial Neural Networks for Use in Safety-Critical Applications -- A Mapping Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Andreas Jedlitschka, Daniel Schneider, Lisa J\\\"ockel, Michael Kl\\\"as, Mohammed Naveed Akram, Pascal Bauer, Pascal Gerber, Patrik Feth, Rasmus Adler","submitted_at":"2019-09-02T09:36:24Z","abstract_excerpt":"Context: Across different domains, Artificial Neural Networks (ANNs) are used more and more in safety-critical applications in which erroneous outputs of such ANN can have catastrophic consequences. However, the development of such neural networks is still immature and good engineering practices are missing. With that, ANNs are in the same position as software was several decades ago. Today, standards for functional safety, such as ISO 26262 in the automotive domain, require the application of a collection of proven engineering principles and methods in the creation of software to increase its"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03036","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/1909.03036/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":"1909.03036","created_at":"2026-07-05T00:02:51.714689+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.03036v1","created_at":"2026-07-05T00:02:51.714689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03036","created_at":"2026-07-05T00:02:51.714689+00:00"},{"alias_kind":"pith_short_12","alias_value":"32EFBRZDVUOE","created_at":"2026-07-05T00:02:51.714689+00:00"},{"alias_kind":"pith_short_16","alias_value":"32EFBRZDVUOEA5V7","created_at":"2026-07-05T00:02:51.714689+00:00"},{"alias_kind":"pith_short_8","alias_value":"32EFBRZD","created_at":"2026-07-05T00:02:51.714689+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/32EFBRZDVUOEA5V7UVSMMNAWRR","json":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR.json","graph_json":"https://pith.science/api/pith-number/32EFBRZDVUOEA5V7UVSMMNAWRR/graph.json","events_json":"https://pith.science/api/pith-number/32EFBRZDVUOEA5V7UVSMMNAWRR/events.json","paper":"https://pith.science/paper/32EFBRZD"},"agent_actions":{"view_html":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR","download_json":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR.json","view_paper":"https://pith.science/paper/32EFBRZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.03036&json=true","fetch_graph":"https://pith.science/api/pith-number/32EFBRZDVUOEA5V7UVSMMNAWRR/graph.json","fetch_events":"https://pith.science/api/pith-number/32EFBRZDVUOEA5V7UVSMMNAWRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR/action/storage_attestation","attest_author":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR/action/author_attestation","sign_citation":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR/action/citation_signature","submit_replication":"https://pith.science/pith/32EFBRZDVUOEA5V7UVSMMNAWRR/action/replication_record"}},"created_at":"2026-07-05T00:02:51.714689+00:00","updated_at":"2026-07-05T00:02:51.714689+00:00"}