{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:KXVK6U5QS4N5QSHDUEL3L7WO77","short_pith_number":"pith:KXVK6U5Q","canonical_record":{"source":{"id":"1811.08127","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T08:54:31Z","cross_cats_sorted":["cs.HC","stat.ML"],"title_canon_sha256":"e7f86aa214374fd8653f6c47c2675067da2f647cffcde3b3567229b3bb79d7e5","abstract_canon_sha256":"ad5d33863e25a0410a5324760f4bd6d2e87b1879007201942a0811a57b6f951e"},"schema_version":"1.0"},"canonical_sha256":"55eaaf53b0971bd848e3a117b5feceffe134ad224520941f268e6fa9ea8d25ff","source":{"kind":"arxiv","id":"1811.08127","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08127","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08127v1","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08127","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"pith_short_12","alias_value":"KXVK6U5QS4N5","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KXVK6U5QS4N5QSHD","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KXVK6U5Q","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:KXVK6U5QS4N5QSHDUEL3L7WO77","target":"record","payload":{"canonical_record":{"source":{"id":"1811.08127","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T08:54:31Z","cross_cats_sorted":["cs.HC","stat.ML"],"title_canon_sha256":"e7f86aa214374fd8653f6c47c2675067da2f647cffcde3b3567229b3bb79d7e5","abstract_canon_sha256":"ad5d33863e25a0410a5324760f4bd6d2e87b1879007201942a0811a57b6f951e"},"schema_version":"1.0"},"canonical_sha256":"55eaaf53b0971bd848e3a117b5feceffe134ad224520941f268e6fa9ea8d25ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:16.810191Z","signature_b64":"N9Ui23ezsLhOBBHcTV1Bdse7cAJoOceQlsStBCyO0yQa3/c5NIAgSn9T3OMFWcPFqebH5NsijmxmFxgTr3Y7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55eaaf53b0971bd848e3a117b5feceffe134ad224520941f268e6fa9ea8d25ff","last_reissued_at":"2026-05-18T00:00:16.809378Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:16.809378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.08127","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+IpHUFUUKDW5784k/KI9Nnv7JrH62g12WvqXoOghT4iwobAq096+VZWKC3tvR8EmmAtjzLjSz3oP1n0nkO1YAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T16:17:19.793691Z"},"content_sha256":"34ebe61d4f7c20f4ec0326ce01fe900f916352b2039ef44b3ecdf0da93426210","schema_version":"1.0","event_id":"sha256:34ebe61d4f7c20f4ec0326ce01fe900f916352b2039ef44b3ecdf0da93426210"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:KXVK6U5QS4N5QSHDUEL3L7WO77","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alireza Abedin Varamin, Damith Ranasinghe, Ehsan Abbasnejad, Hamid Rezatofighi, Qinfeng Shi","submitted_at":"2018-11-20T08:54:31Z","abstract_excerpt":"Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-class classification problem where detecting a single activity class within the duration of a sensory data segment suffices. However, due to the innate diversity of human activities and their corresponding duration, no data segment is guaranteed to contain sensor recordings of a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08127","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":""},"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":"2026-05-18T00:00:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IByCRnn6+/DqlEaQj4Xdis8/9ingEcrr9og74g/TdQBgUkWdcOohGiErC3BfrypOhX3NeHDbWQ0JAxIYjbCLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T16:17:19.794036Z"},"content_sha256":"0b8d5b97bf471405764cd9023d2eea94c585c32f0e66b57917f363651cd68e75","schema_version":"1.0","event_id":"sha256:0b8d5b97bf471405764cd9023d2eea94c585c32f0e66b57917f363651cd68e75"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/bundle.json","state_url":"https://pith.science/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/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":"2026-06-03T16:17:19Z","links":{"resolver":"https://pith.science/pith/KXVK6U5QS4N5QSHDUEL3L7WO77","bundle":"https://pith.science/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/bundle.json","state":"https://pith.science/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KXVK6U5QS4N5QSHDUEL3L7WO77/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:KXVK6U5QS4N5QSHDUEL3L7WO77","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":"ad5d33863e25a0410a5324760f4bd6d2e87b1879007201942a0811a57b6f951e","cross_cats_sorted":["cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T08:54:31Z","title_canon_sha256":"e7f86aa214374fd8653f6c47c2675067da2f647cffcde3b3567229b3bb79d7e5"},"schema_version":"1.0","source":{"id":"1811.08127","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08127","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08127v1","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08127","created_at":"2026-05-18T00:00:16Z"},{"alias_kind":"pith_short_12","alias_value":"KXVK6U5QS4N5","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KXVK6U5QS4N5QSHD","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KXVK6U5Q","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:0b8d5b97bf471405764cd9023d2eea94c585c32f0e66b57917f363651cd68e75","target":"graph","created_at":"2026-05-18T00:00:16Z","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"},"paper":{"abstract_excerpt":"Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-class classification problem where detecting a single activity class within the duration of a sensory data segment suffices. However, due to the innate diversity of human activities and their corresponding duration, no data segment is guaranteed to contain sensor recordings of a ","authors_text":"Alireza Abedin Varamin, Damith Ranasinghe, Ehsan Abbasnejad, Hamid Rezatofighi, Qinfeng Shi","cross_cats":["cs.HC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T08:54:31Z","title":"Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08127","kind":"arxiv","version":1},"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":"sha256:34ebe61d4f7c20f4ec0326ce01fe900f916352b2039ef44b3ecdf0da93426210","target":"record","created_at":"2026-05-18T00:00:16Z","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":"ad5d33863e25a0410a5324760f4bd6d2e87b1879007201942a0811a57b6f951e","cross_cats_sorted":["cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-20T08:54:31Z","title_canon_sha256":"e7f86aa214374fd8653f6c47c2675067da2f647cffcde3b3567229b3bb79d7e5"},"schema_version":"1.0","source":{"id":"1811.08127","kind":"arxiv","version":1}},"canonical_sha256":"55eaaf53b0971bd848e3a117b5feceffe134ad224520941f268e6fa9ea8d25ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55eaaf53b0971bd848e3a117b5feceffe134ad224520941f268e6fa9ea8d25ff","first_computed_at":"2026-05-18T00:00:16.809378Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:16.809378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N9Ui23ezsLhOBBHcTV1Bdse7cAJoOceQlsStBCyO0yQa3/c5NIAgSn9T3OMFWcPFqebH5NsijmxmFxgTr3Y7DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:16.810191Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.08127","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34ebe61d4f7c20f4ec0326ce01fe900f916352b2039ef44b3ecdf0da93426210","sha256:0b8d5b97bf471405764cd9023d2eea94c585c32f0e66b57917f363651cd68e75"],"state_sha256":"92159e59a9f9d32b0b51a926f8602d4d71d3af573e33435191e159ecec8f6a75"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ICWZfkDCvYaKNLvGlnVamE6yuPh9QDyfaCrL/2CQmBMQp6og0jMP5pl/QX8JAjEqu+Q+Bgs8diRBMHYTdc/SAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T16:17:19.795995Z","bundle_sha256":"dc06e867bdc9c8baacc8f54a8085294da1419dc3192d26ae73d4dedd19ab16c7"}}