{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:F2Y2PRVLO2P5U2HZCRPF4TYO2Z","short_pith_number":"pith:F2Y2PRVL","schema_version":"1.0","canonical_sha256":"2eb1a7c6ab769fda68f9145e5e4f0ed64ee6cb3a1fa21e1938b6394ef17c0277","source":{"kind":"arxiv","id":"1901.03404","version":1},"attestation_state":"computed","paper":{"title":"Handcrafted vs Deep Learning Classification for Scalable Video QoE Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.MM","authors_text":"Christina Vlachou, Dasari Mallesham, Kyu-Han Kim, Pranjal Sahu, Samir R. Das, Shruti Sanadhya, Yang Qiu","submitted_at":"2019-01-10T21:33:19Z","abstract_excerpt":"Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of diverse applications, network administrators face the challenge to provide high QoE in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map QoS to QoE by training machine learning models without requiring user feedback, these techniques are limited to only few applications, due to insufficient QoE ground-truth annotation for "},"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":"1901.03404","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-01-10T21:33:19Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"64ff254bb050f57ccfa7a6d386711a0f1b24e8efa7a2007b2f037edca4a866f7","abstract_canon_sha256":"43fe7fc5c6eca4e3143cfedbe93c004df95cec4b3289ff131d6f2680a40990ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:31.307519Z","signature_b64":"T3YpAE4ltEuFOMxp0n30DRPGIplbRU73aRuuSEqEFnl+M7eXw80ZFYJ62qAn/O76r4W0VTSiaBLV1Ba1SQMZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2eb1a7c6ab769fda68f9145e5e4f0ed64ee6cb3a1fa21e1938b6394ef17c0277","last_reissued_at":"2026-05-17T23:56:31.307012Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:31.307012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Handcrafted vs Deep Learning Classification for Scalable Video QoE Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.MM","authors_text":"Christina Vlachou, Dasari Mallesham, Kyu-Han Kim, Pranjal Sahu, Samir R. Das, Shruti Sanadhya, Yang Qiu","submitted_at":"2019-01-10T21:33:19Z","abstract_excerpt":"Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of diverse applications, network administrators face the challenge to provide high QoE in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map QoS to QoE by training machine learning models without requiring user feedback, these techniques are limited to only few applications, due to insufficient QoE ground-truth annotation for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03404","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":"1901.03404","created_at":"2026-05-17T23:56:31.307106+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03404v1","created_at":"2026-05-17T23:56:31.307106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03404","created_at":"2026-05-17T23:56:31.307106+00:00"},{"alias_kind":"pith_short_12","alias_value":"F2Y2PRVLO2P5","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"F2Y2PRVLO2P5U2HZ","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"F2Y2PRVL","created_at":"2026-05-18T12:33:15.570797+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/F2Y2PRVLO2P5U2HZCRPF4TYO2Z","json":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z.json","graph_json":"https://pith.science/api/pith-number/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/graph.json","events_json":"https://pith.science/api/pith-number/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/events.json","paper":"https://pith.science/paper/F2Y2PRVL"},"agent_actions":{"view_html":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z","download_json":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z.json","view_paper":"https://pith.science/paper/F2Y2PRVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03404&json=true","fetch_graph":"https://pith.science/api/pith-number/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/graph.json","fetch_events":"https://pith.science/api/pith-number/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/action/storage_attestation","attest_author":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/action/author_attestation","sign_citation":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/action/citation_signature","submit_replication":"https://pith.science/pith/F2Y2PRVLO2P5U2HZCRPF4TYO2Z/action/replication_record"}},"created_at":"2026-05-17T23:56:31.307106+00:00","updated_at":"2026-05-17T23:56:31.307106+00:00"}