{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4SHKYZ43JIJUO3CAISMHMWTZYB","short_pith_number":"pith:4SHKYZ43","schema_version":"1.0","canonical_sha256":"e48eac679b4a13476c404498765a79c069709727acc3e1e69a59472ba081a4de","source":{"kind":"arxiv","id":"1803.04240","version":1},"attestation_state":"computed","paper":{"title":"Discovering demographic data of users from the evolution of their spatio-temporal entropy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Arielle Moro, Beno\\^it Garbinato, Val\\'erie Chavez-Demoulin","submitted_at":"2018-02-16T22:54:38Z","abstract_excerpt":"Inferring information related to users enables to highly improve the quality of many mobile services. For example, knowing the demographic characteristics of a user allows a service to display more accurate information. According to the literature, various works present models to detect them but, to the best of our knowledge, no one is based on the use of the spatio-temporal entropy and introduces Generalized Additive models (GAMs) in this context to reach this goal. In this preliminary work, we present a new approach including these two key elements. The spatio-temporal entropy enables to cap"},"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":"1803.04240","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-02-16T22:54:38Z","cross_cats_sorted":[],"title_canon_sha256":"c86dbe8b2a504f27aee6185273de299984c56e5400eb6def7c6064e113c49363","abstract_canon_sha256":"1aeb76bc81f5ea493bd9c63125dc4c5f3dc78041c042282e82832f99fd7a98c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:32.177766Z","signature_b64":"TdQx2XlHqZqJ0g8EGTVuPxfPYattGSpJ1J2lJsROnjdMioERx0+LHldfNuZtn14mLRPKGPGUGYedc5zruqRfDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e48eac679b4a13476c404498765a79c069709727acc3e1e69a59472ba081a4de","last_reissued_at":"2026-05-18T00:21:32.177053Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:32.177053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discovering demographic data of users from the evolution of their spatio-temporal entropy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Arielle Moro, Beno\\^it Garbinato, Val\\'erie Chavez-Demoulin","submitted_at":"2018-02-16T22:54:38Z","abstract_excerpt":"Inferring information related to users enables to highly improve the quality of many mobile services. For example, knowing the demographic characteristics of a user allows a service to display more accurate information. According to the literature, various works present models to detect them but, to the best of our knowledge, no one is based on the use of the spatio-temporal entropy and introduces Generalized Additive models (GAMs) in this context to reach this goal. In this preliminary work, we present a new approach including these two key elements. The spatio-temporal entropy enables to cap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.04240","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1803.04240","created_at":"2026-05-18T00:21:32.177159+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.04240v1","created_at":"2026-05-18T00:21:32.177159+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.04240","created_at":"2026-05-18T00:21:32.177159+00:00"},{"alias_kind":"pith_short_12","alias_value":"4SHKYZ43JIJU","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4SHKYZ43JIJUO3CA","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4SHKYZ43","created_at":"2026-05-18T12:32:05.422762+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/4SHKYZ43JIJUO3CAISMHMWTZYB","json":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB.json","graph_json":"https://pith.science/api/pith-number/4SHKYZ43JIJUO3CAISMHMWTZYB/graph.json","events_json":"https://pith.science/api/pith-number/4SHKYZ43JIJUO3CAISMHMWTZYB/events.json","paper":"https://pith.science/paper/4SHKYZ43"},"agent_actions":{"view_html":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB","download_json":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB.json","view_paper":"https://pith.science/paper/4SHKYZ43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.04240&json=true","fetch_graph":"https://pith.science/api/pith-number/4SHKYZ43JIJUO3CAISMHMWTZYB/graph.json","fetch_events":"https://pith.science/api/pith-number/4SHKYZ43JIJUO3CAISMHMWTZYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB/action/storage_attestation","attest_author":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB/action/author_attestation","sign_citation":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB/action/citation_signature","submit_replication":"https://pith.science/pith/4SHKYZ43JIJUO3CAISMHMWTZYB/action/replication_record"}},"created_at":"2026-05-18T00:21:32.177159+00:00","updated_at":"2026-05-18T00:21:32.177159+00:00"}