{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:BS2Q75EHPJMMNE65JSXYVTM6UL","short_pith_number":"pith:BS2Q75EH","schema_version":"1.0","canonical_sha256":"0cb50ff4877a58c693dd4caf8acd9ea2f8e78c6acae27822932217b53459905a","source":{"kind":"arxiv","id":"2105.10302","version":1},"attestation_state":"computed","paper":{"title":"Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrea Acquaviva, Davide Brunelli, Enrico Tabanelli, Luca Benini","submitted_at":"2021-05-21T12:08:16Z","abstract_excerpt":"Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spac"},"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":"2105.10302","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-21T12:08:16Z","cross_cats_sorted":[],"title_canon_sha256":"d9cb131d26377ecf414930204cd643a141ad4d1393dc02b9e71f0185e091d354","abstract_canon_sha256":"04e8818d8dbc7408bac34354b17cff4e9d33f6d2a3744de7a096bdac46123a1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:42:09.936205Z","signature_b64":"qsNNDnhdBk/DDWy3MecB5waloOs8S3wEd2pRlqM/vMq91d20xtiKy8VUQUGl1RRDT4YwKCtxwzeos2Rxi7HiDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cb50ff4877a58c693dd4caf8acd9ea2f8e78c6acae27822932217b53459905a","last_reissued_at":"2026-07-05T02:42:09.935845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:42:09.935845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andrea Acquaviva, Davide Brunelli, Enrico Tabanelli, Luca Benini","submitted_at":"2021-05-21T12:08:16Z","abstract_excerpt":"Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10302","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/2105.10302/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":"2105.10302","created_at":"2026-07-05T02:42:09.935906+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.10302v1","created_at":"2026-07-05T02:42:09.935906+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10302","created_at":"2026-07-05T02:42:09.935906+00:00"},{"alias_kind":"pith_short_12","alias_value":"BS2Q75EHPJMM","created_at":"2026-07-05T02:42:09.935906+00:00"},{"alias_kind":"pith_short_16","alias_value":"BS2Q75EHPJMMNE65","created_at":"2026-07-05T02:42:09.935906+00:00"},{"alias_kind":"pith_short_8","alias_value":"BS2Q75EH","created_at":"2026-07-05T02:42:09.935906+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/BS2Q75EHPJMMNE65JSXYVTM6UL","json":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL.json","graph_json":"https://pith.science/api/pith-number/BS2Q75EHPJMMNE65JSXYVTM6UL/graph.json","events_json":"https://pith.science/api/pith-number/BS2Q75EHPJMMNE65JSXYVTM6UL/events.json","paper":"https://pith.science/paper/BS2Q75EH"},"agent_actions":{"view_html":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL","download_json":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL.json","view_paper":"https://pith.science/paper/BS2Q75EH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.10302&json=true","fetch_graph":"https://pith.science/api/pith-number/BS2Q75EHPJMMNE65JSXYVTM6UL/graph.json","fetch_events":"https://pith.science/api/pith-number/BS2Q75EHPJMMNE65JSXYVTM6UL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL/action/storage_attestation","attest_author":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL/action/author_attestation","sign_citation":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL/action/citation_signature","submit_replication":"https://pith.science/pith/BS2Q75EHPJMMNE65JSXYVTM6UL/action/replication_record"}},"created_at":"2026-07-05T02:42:09.935906+00:00","updated_at":"2026-07-05T02:42:09.935906+00:00"}