{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:D3ZYXHUFSQ673EEHEJ5VQ5FH5W","short_pith_number":"pith:D3ZYXHUF","canonical_record":{"source":{"id":"1705.06224","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-17T15:55:53Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"19cf90a01a761f36988fc860bfe2b24536245566ff029e81301942a12394f5ab","abstract_canon_sha256":"e8398663f0f9495ae903474cf4e5ae166486dba8ce129f758a2c299ce57cf9a0"},"schema_version":"1.0"},"canonical_sha256":"1ef38b9e85943dfd9087227b5874a7ed866745ac8e79bf826d1c0c7cbd13b43e","source":{"kind":"arxiv","id":"1705.06224","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06224","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06224v1","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06224","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"pith_short_12","alias_value":"D3ZYXHUFSQ67","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D3ZYXHUFSQ673EEH","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D3ZYXHUF","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:D3ZYXHUFSQ673EEHEJ5VQ5FH5W","target":"record","payload":{"canonical_record":{"source":{"id":"1705.06224","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-17T15:55:53Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"19cf90a01a761f36988fc860bfe2b24536245566ff029e81301942a12394f5ab","abstract_canon_sha256":"e8398663f0f9495ae903474cf4e5ae166486dba8ce129f758a2c299ce57cf9a0"},"schema_version":"1.0"},"canonical_sha256":"1ef38b9e85943dfd9087227b5874a7ed866745ac8e79bf826d1c0c7cbd13b43e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:13.140932Z","signature_b64":"rpKal45iPzkVnEAjvd0VaRtf31YDZO+0gD8hswhYWGcYFq4YI0H9P2yVbFLB0G3k3+ozQ/73rE14wjBjcBoRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ef38b9e85943dfd9087227b5874a7ed866745ac8e79bf826d1c0c7cbd13b43e","last_reissued_at":"2026-05-18T00:44:13.140483Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:13.140483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.06224","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":"2026-05-18T00:44:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EgvX2OcbFUk3PKdOq4NDdztRh9XXEs2U/+kNUjuvrkt3wHyYsdwMI26EWK7HJSiXYFTMWXPpw8md6k7go/t6DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:09:27.581130Z"},"content_sha256":"fb2d527f22474f15ec607165373073a8d8a5ed9c8d19e688c784732fdb580449","schema_version":"1.0","event_id":"sha256:fb2d527f22474f15ec607165373073a8d8a5ed9c8d19e688c784732fdb580449"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:D3ZYXHUFSQ673EEHEJ5VQ5FH5W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Ilias Leontiadis, Joan Serr\\`a, Kleomenis Katevas, Martin Pielot","submitted_at":"2017-05-17T15:55:53Z","abstract_excerpt":"We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06224","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":"2026-05-18T00:44:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vb2NGT6vDErnKuc2W4J5HqptD048hL/GA6veGXwPClF0J6njevxtxSHLrMDOiehymqfo2S6R08dHjAAf6QAwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:09:27.581698Z"},"content_sha256":"7fcfcc5b0820ab79f7a9b270dce24d6d7d4ad50c8fb44096a7bc0d362ef72345","schema_version":"1.0","event_id":"sha256:7fcfcc5b0820ab79f7a9b270dce24d6d7d4ad50c8fb44096a7bc0d362ef72345"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/bundle.json","state_url":"https://pith.science/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/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":"2026-05-31T19:09:27Z","links":{"resolver":"https://pith.science/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W","bundle":"https://pith.science/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/bundle.json","state":"https://pith.science/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D3ZYXHUFSQ673EEHEJ5VQ5FH5W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:D3ZYXHUFSQ673EEHEJ5VQ5FH5W","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":"e8398663f0f9495ae903474cf4e5ae166486dba8ce129f758a2c299ce57cf9a0","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-17T15:55:53Z","title_canon_sha256":"19cf90a01a761f36988fc860bfe2b24536245566ff029e81301942a12394f5ab"},"schema_version":"1.0","source":{"id":"1705.06224","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06224","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06224v1","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06224","created_at":"2026-05-18T00:44:13Z"},{"alias_kind":"pith_short_12","alias_value":"D3ZYXHUFSQ67","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D3ZYXHUFSQ673EEH","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D3ZYXHUF","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:7fcfcc5b0820ab79f7a9b270dce24d6d7d4ad50c8fb44096a7bc0d362ef72345","target":"graph","created_at":"2026-05-18T00:44:13Z","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":"We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a","authors_text":"Ilias Leontiadis, Joan Serr\\`a, Kleomenis Katevas, Martin Pielot","cross_cats":["cs.HC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-17T15:55:53Z","title":"Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06224","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:fb2d527f22474f15ec607165373073a8d8a5ed9c8d19e688c784732fdb580449","target":"record","created_at":"2026-05-18T00:44:13Z","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":"e8398663f0f9495ae903474cf4e5ae166486dba8ce129f758a2c299ce57cf9a0","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-17T15:55:53Z","title_canon_sha256":"19cf90a01a761f36988fc860bfe2b24536245566ff029e81301942a12394f5ab"},"schema_version":"1.0","source":{"id":"1705.06224","kind":"arxiv","version":1}},"canonical_sha256":"1ef38b9e85943dfd9087227b5874a7ed866745ac8e79bf826d1c0c7cbd13b43e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ef38b9e85943dfd9087227b5874a7ed866745ac8e79bf826d1c0c7cbd13b43e","first_computed_at":"2026-05-18T00:44:13.140483Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:13.140483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rpKal45iPzkVnEAjvd0VaRtf31YDZO+0gD8hswhYWGcYFq4YI0H9P2yVbFLB0G3k3+ozQ/73rE14wjBjcBoRBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:13.140932Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.06224","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb2d527f22474f15ec607165373073a8d8a5ed9c8d19e688c784732fdb580449","sha256:7fcfcc5b0820ab79f7a9b270dce24d6d7d4ad50c8fb44096a7bc0d362ef72345"],"state_sha256":"759e67e2977af1d52268e9958ee472f3e03b54c8dc0eb028315a3fb67620bc04"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+bILgqQ8nAL/IPC5aONQmvzJTRgyHe9EGTPyDOLopZc5TK4UYfdPF7AlxL0/pEx4WgWOFiSr8nRaGcCfOOCOBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T19:09:27.585364Z","bundle_sha256":"72e15a3d9cde7f0866ec1092fbafbfba99f790e1913007312166cead637f3ab1"}}