{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:E3SXOSVJHY77IVZZUQVJTIZTD4","short_pith_number":"pith:E3SXOSVJ","canonical_record":{"source":{"id":"2211.00277","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T05:01:34Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"53e5ebca934ac5d208002708d2cd451abd51dce9aa78bb97796e0e9bf508bdde","abstract_canon_sha256":"73c54b9aad0ddc5a4be5854c5750ff3dc5068017eb816f182604e89016f111c4"},"schema_version":"1.0"},"canonical_sha256":"26e5774aa93e3ff45739a42a99a3331f29f166a9b07a331077f7ea6d55220fdc","source":{"kind":"arxiv","id":"2211.00277","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00277","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00277v2","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00277","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"E3SXOSVJHY77","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_16","alias_value":"E3SXOSVJHY77IVZZ","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_8","alias_value":"E3SXOSVJ","created_at":"2026-07-05T05:12:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:E3SXOSVJHY77IVZZUQVJTIZTD4","target":"record","payload":{"canonical_record":{"source":{"id":"2211.00277","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T05:01:34Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"53e5ebca934ac5d208002708d2cd451abd51dce9aa78bb97796e0e9bf508bdde","abstract_canon_sha256":"73c54b9aad0ddc5a4be5854c5750ff3dc5068017eb816f182604e89016f111c4"},"schema_version":"1.0"},"canonical_sha256":"26e5774aa93e3ff45739a42a99a3331f29f166a9b07a331077f7ea6d55220fdc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:32.381455Z","signature_b64":"QqLbjiPL0F3uQsVJ8BehzB5JVTPxaOVJNq/t0XdDX/AnDaF/vgD45VjxFfCiXXbsNPsa2usnE97cUS31lWozBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26e5774aa93e3ff45739a42a99a3331f29f166a9b07a331077f7ea6d55220fdc","last_reissued_at":"2026-07-05T05:12:32.381039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:32.381039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.00277","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qMosjbJAfiXqYuy5acb3qjuZxw60fNoTWIvQVBgo3Pz8IbYrUmTA43lyaUnPza38pK6MkE7r7wQX/wWjJdv7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:29:47.896247Z"},"content_sha256":"2cb78d64621bb19928360dce780a39e98a838b87d4650ab3a63f8693f3ae597f","schema_version":"1.0","event_id":"sha256:2cb78d64621bb19928360dce780a39e98a838b87d4650ab3a63f8693f3ae597f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:E3SXOSVJHY77IVZZUQVJTIZTD4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Canqun Yang, Chengkun Wu, Jun Zhan, Qiucheng Miao, Xiandong Ma","submitted_at":"2022-11-01T05:01:34Z","abstract_excerpt":"Network or physical attacks on industrial equipment or computer systems may cause massive losses. Therefore, a quick and accurate anomaly detection (AD) based on monitoring data, especially the multivariate time-series (MTS) data, is of great significance. As the key step of anomaly detection for MTS data, learning the relations among different variables has been explored by many approaches. However, most of the existing approaches do not consider the heterogeneity between variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00277","kind":"arxiv","version":2},"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/2211.00277/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":"2026-07-05T05:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+/Q3ISTRmBM5Re2hUKI3swfs5lilWBCG+EEBlXILNHEOvTuFelCqM72zScfGW8ysn/RBDd3VS8XGjwZWTeMkAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:29:47.896758Z"},"content_sha256":"34d6b0bfecf4965b1958516b00e212ee20d679e970944b046fd60c227029b52f","schema_version":"1.0","event_id":"sha256:34d6b0bfecf4965b1958516b00e212ee20d679e970944b046fd60c227029b52f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/bundle.json","state_url":"https://pith.science/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/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":"2026-08-18T23:29:47Z","links":{"resolver":"https://pith.science/pith/E3SXOSVJHY77IVZZUQVJTIZTD4","bundle":"https://pith.science/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/bundle.json","state":"https://pith.science/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E3SXOSVJHY77IVZZUQVJTIZTD4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:E3SXOSVJHY77IVZZUQVJTIZTD4","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":"73c54b9aad0ddc5a4be5854c5750ff3dc5068017eb816f182604e89016f111c4","cross_cats_sorted":["cs.AI","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T05:01:34Z","title_canon_sha256":"53e5ebca934ac5d208002708d2cd451abd51dce9aa78bb97796e0e9bf508bdde"},"schema_version":"1.0","source":{"id":"2211.00277","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00277","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00277v2","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00277","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"E3SXOSVJHY77","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_16","alias_value":"E3SXOSVJHY77IVZZ","created_at":"2026-07-05T05:12:32Z"},{"alias_kind":"pith_short_8","alias_value":"E3SXOSVJ","created_at":"2026-07-05T05:12:32Z"}],"graph_snapshots":[{"event_id":"sha256:34d6b0bfecf4965b1958516b00e212ee20d679e970944b046fd60c227029b52f","target":"graph","created_at":"2026-07-05T05:12:32Z","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/2211.00277/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Network or physical attacks on industrial equipment or computer systems may cause massive losses. Therefore, a quick and accurate anomaly detection (AD) based on monitoring data, especially the multivariate time-series (MTS) data, is of great significance. As the key step of anomaly detection for MTS data, learning the relations among different variables has been explored by many approaches. However, most of the existing approaches do not consider the heterogeneity between variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid","authors_text":"Canqun Yang, Chengkun Wu, Jun Zhan, Qiucheng Miao, Xiandong Ma","cross_cats":["cs.AI","cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T05:01:34Z","title":"HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00277","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":"sha256:2cb78d64621bb19928360dce780a39e98a838b87d4650ab3a63f8693f3ae597f","target":"record","created_at":"2026-07-05T05:12:32Z","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":"73c54b9aad0ddc5a4be5854c5750ff3dc5068017eb816f182604e89016f111c4","cross_cats_sorted":["cs.AI","cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-01T05:01:34Z","title_canon_sha256":"53e5ebca934ac5d208002708d2cd451abd51dce9aa78bb97796e0e9bf508bdde"},"schema_version":"1.0","source":{"id":"2211.00277","kind":"arxiv","version":2}},"canonical_sha256":"26e5774aa93e3ff45739a42a99a3331f29f166a9b07a331077f7ea6d55220fdc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26e5774aa93e3ff45739a42a99a3331f29f166a9b07a331077f7ea6d55220fdc","first_computed_at":"2026-07-05T05:12:32.381039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:32.381039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QqLbjiPL0F3uQsVJ8BehzB5JVTPxaOVJNq/t0XdDX/AnDaF/vgD45VjxFfCiXXbsNPsa2usnE97cUS31lWozBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:32.381455Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.00277","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2cb78d64621bb19928360dce780a39e98a838b87d4650ab3a63f8693f3ae597f","sha256:34d6b0bfecf4965b1958516b00e212ee20d679e970944b046fd60c227029b52f"],"state_sha256":"14ce5b50fe73d44bf0a63b2c203a1db2dcd1b60ed0cc05988c8042e545bb67dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JQgSpNxfFe6EAe/MgDycItXqMnri0VcO3tX25MuBbM1u2rfKBipUQNLCSJxtkdRn3lcvuGXV0xrFLFxEnIFZBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T23:29:47.901400Z","bundle_sha256":"c0f805ed8d6bd15344f29bba682d13ca311a58d632f73c43b1740e686c32651b"}}