{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OYNZ3VVTHHGM5PSAGWR2K7FCLD","short_pith_number":"pith:OYNZ3VVT","schema_version":"1.0","canonical_sha256":"761b9dd6b339cccebe4035a3a57ca258db36ea91b102c74e1c6f4e8d7cbcefe1","source":{"kind":"arxiv","id":"2004.06896","version":1},"attestation_state":"computed","paper":{"title":"Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.NI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hakima Chaouchi, Mao V. Ngo, Tie Luo, Tony Q.S. Quek","submitted_at":"2020-04-15T06:13:33Z","abstract_excerpt":"Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, w"},"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":"2004.06896","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-15T06:13:33Z","cross_cats_sorted":["cs.DC","cs.NI","stat.ML"],"title_canon_sha256":"703bda8c9cf4dc0d9b329a43432c82780c02e310513b76997d0108708ca36c99","abstract_canon_sha256":"3eaabcd3e77cd351c9c08cd155dfef63cb0336ab9236bf6821a0eb43f11b61c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:55:22.911881Z","signature_b64":"NST/+1ZHYILWHp4Ay5i3dSo7I0PaUVWHpJ+fOGZELGZ4EWc7IqvX8WY+GLsOnnD65PsNamgWMaG2rYFKiPAIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"761b9dd6b339cccebe4035a3a57ca258db36ea91b102c74e1c6f4e8d7cbcefe1","last_reissued_at":"2026-07-05T00:55:22.911532Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:55:22.911532Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.NI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hakima Chaouchi, Mao V. Ngo, Tie Luo, Tony Q.S. Quek","submitted_at":"2020-04-15T06:13:33Z","abstract_excerpt":"Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.06896","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/2004.06896/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":"2004.06896","created_at":"2026-07-05T00:55:22.911585+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.06896v1","created_at":"2026-07-05T00:55:22.911585+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.06896","created_at":"2026-07-05T00:55:22.911585+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYNZ3VVTHHGM","created_at":"2026-07-05T00:55:22.911585+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYNZ3VVTHHGM5PSA","created_at":"2026-07-05T00:55:22.911585+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYNZ3VVT","created_at":"2026-07-05T00:55:22.911585+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/OYNZ3VVTHHGM5PSAGWR2K7FCLD","json":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD.json","graph_json":"https://pith.science/api/pith-number/OYNZ3VVTHHGM5PSAGWR2K7FCLD/graph.json","events_json":"https://pith.science/api/pith-number/OYNZ3VVTHHGM5PSAGWR2K7FCLD/events.json","paper":"https://pith.science/paper/OYNZ3VVT"},"agent_actions":{"view_html":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD","download_json":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD.json","view_paper":"https://pith.science/paper/OYNZ3VVT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.06896&json=true","fetch_graph":"https://pith.science/api/pith-number/OYNZ3VVTHHGM5PSAGWR2K7FCLD/graph.json","fetch_events":"https://pith.science/api/pith-number/OYNZ3VVTHHGM5PSAGWR2K7FCLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD/action/storage_attestation","attest_author":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD/action/author_attestation","sign_citation":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD/action/citation_signature","submit_replication":"https://pith.science/pith/OYNZ3VVTHHGM5PSAGWR2K7FCLD/action/replication_record"}},"created_at":"2026-07-05T00:55:22.911585+00:00","updated_at":"2026-07-05T00:55:22.911585+00:00"}