{"as_of":"2026-08-13T04:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fd3f9d3f4c7a7f79294fcbade2049a288b41ebe6ed62ff8d4b1e4896d977227c","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:37:51.188771Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.18060/citation-record","integrity":"/paper/2411.18060/integrity","json":"/paper/2411.18060/citation-record.json","paper":"/paper/2411.18060"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.933960Z","title":"Castillo, Big crisis data: social media in disasters and time-critical situations","venue":null,"work_id":"cc0d0363-a9fd-462d-af99-156c244c9416","year":2016},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.033099Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:218ca1a3da5b9a4d921d19c26d3da053e2df6da78937a0ee58780c08536dbeee","observation_id":"6e9e6819-49bb-4f00-8439-043c783b5991","resolution":{"observed_at":"2026-08-12T11:37:51.938620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.038262Z","title":"A survey on concept drift adaptation,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.038262Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:a3aa8c6d448f1ae3a404a0d6de2bcd70a623bb49154d1f30dbc397c1006c57e6","observation_id":"b551649d-1d81-4eb8-835d-789e8980c33b","resolution":{"observed_at":"2026-08-12T11:37:51.038262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.043334Z","title":"A Survey of Deep Active Learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.043334Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:c71abc4371c9bf940d9013ec98f4f8e205961cdb5be62983f357762788664cac","observation_id":"f79c4bc1-dd8c-4187-9751-d23e1d0cd35c","resolution":{"observed_at":"2026-08-12T11:37:51.043334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1310.5463","last_updated":"2014-08-04T08:54:40Z","snapshot_observed_at":"2026-08-01T04:28:07.657387Z","submitted_at":"2013-10-21T08:46:29Z","title":"Engineering Crowdsourced Stream Processing Systems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1310.5463","snapshot_observed_at":"2026-08-12T11:37:51.048074Z","title":"Engineering Crowdsourced Stream Processing Systems,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.048074Z"},"links":{"cited_paper":"/paper/1310.5463","citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:41bfb318597ce8c992e7a7be0fc7ade1dbb3fc154a02aa3821d01c823f8d82dc","observation_id":"5a23fc5b-78fa-44a5-9b77-49fbc35b73a7","resolution":{"observed_at":"2026-08-12T11:37:51.048074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.920041Z","title":"Design Patterns for Hybrid Algorithmic- Crowdsourcing Workflows,","venue":null,"work_id":"a975202e-661a-4a4b-8626-34f0375890c1","year":2014},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.053371Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:6e36cfc4fc4f855744a423a46e33e9a89bc7e7846103140e95a5c538fdcdb0e7","observation_id":"0aab1c09-c552-4579-84d0-2a237d8814f1","resolution":{"observed_at":"2026-08-12T11:37:51.924743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.905826Z","title":"Modeling human annotation errors to design bias-aware systems for social stream processing,","venue":null,"work_id":"1b551656-2013-4067-8fa8-8d80aa5208b5","year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.057923Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:d2e51df17af704b9da045b1d0a8c0552040e3ad5024c2b9074081e791567c945","observation_id":"9d52f1fb-1ac5-4255-adf5-f066489700e5","resolution":{"observed_at":"2026-08-12T11:37:51.910483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.891390Z","title":"Modeling and mitigating human annotation errors to design efficient stream processing systems with human-in-the-loop machine learning,","venue":null,"work_id":"a3ee5303-e1e7-4951-ba60-d3549a08b65d","year":2022},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.062865Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:7409608c8572f90eb0d06f43b6a582edb75a18c01faeedc32baec18b8e223423","observation_id":"b74991ae-dd19-43e1-8f65-a33e5ada335f","resolution":{"observed_at":"2026-08-12T11:37:51.896225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.877163Z","title":"Human error: models and management,","venue":null,"work_id":"bc8babc1-f676-4f80-8906-ac57ee546f98","year":2000},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.067465Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:eeb0d22a988a700b9591420144b0c7521140eafeb1bc9697798113c09eb4d0f8","observation_id":"a33f8e89-582d-4735-a877-6ad437a1cc27","resolution":{"observed_at":"2026-08-12T11:37:51.881806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.862743Z","title":"Human-level control through deep reinforcement learning,","venue":null,"work_id":"9c5deade-cbb0-4fc4-bff9-c31f96db743f","year":2015},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.071787Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:e22056db39e4eb4c3bd150511bdc8cf41a09854801893a373bd33130fce81464","observation_id":"4ff455e4-2d28-45bb-b674-2334326eef15","resolution":{"observed_at":"2026-08-12T11:37:51.867573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.076090Z","title":"Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.076090Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:71cb88492d45fe842b299e293ac9daa3bb0e9c04b41be5edbf14b3b3ad0ad06c","observation_id":"c613f452-56c0-498e-b4a8-c76e84f0172b","resolution":{"observed_at":"2026-08-12T11:37:51.076090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.848649Z","title":"Cold-start Active Learning through Self-supervised Language Modeling,","venue":null,"work_id":"1f46390c-9fec-4efa-aca6-6b08fa0819d6","year":2020},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.080422Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:264ff33d0ca7e058973ac31d61d4b2ed6925358f3708464dfa101ccfa8aa2963","observation_id":"890de3af-842c-4a76-b4c3-7924bdd3c531","resolution":{"observed_at":"2026-08-12T11:37:51.853211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.834525Z","title":"Feedback-driven multiclass active learning for data streams,","venue":null,"work_id":"d432fd02-fc74-49e3-99e6-912bf138d9d0","year":2013},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.084514Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:b8311fcc5d57cb34f0b4975efc8285f6ba712212755aba360aa4f7914d6ffb2f","observation_id":"b7e7a8e7-82a2-4ac8-885c-8c054497676b","resolution":{"observed_at":"2026-08-12T11:37:51.838995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.819905Z","title":"Online active learning with expert advice,","venue":null,"work_id":"33724e3d-c882-49b7-8af5-9aecd763641d","year":2018},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.093305Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:fcd805a8835fe6ba7072eb433fd36da9d31002b7d3866b2996580ba690b2191d","observation_id":"995821f0-637b-4d92-9d91-bb553b3f4013","resolution":{"observed_at":"2026-08-12T11:37:51.824312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.806614Z","title":"Active Learning With Drifting Streaming Data,","venue":null,"work_id":"709d6732-2e01-4678-9cd2-2cb00ae98ad7","year":2014},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.097803Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:f88b09c0264aa1b18c83e94dac60400518f74753777939d8ff042a52376b0269","observation_id":"13a4da32-2b5f-4e95-9b74-ec20562df7ca","resolution":{"observed_at":"2026-08-12T11:37:51.810956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.792141Z","title":"Learning how to Active Learn: A Deep Reinforcement Learning Approach,","venue":null,"work_id":"c108d41e-4c02-4d6a-9adf-734bbbe5bfd4","year":2017},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.102080Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:2a81677f1b4a433722ca532d5123f6dab6e15dd3f55527acc3927e36cb996212","observation_id":"0a29a89b-5c96-4c58-9b44-e8b6527acc99","resolution":{"observed_at":"2026-08-12T11:37:51.796897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.777885Z","title":"Deep Reinforcement Active Learning for Human-in-the- Loop Person Re-Identification,","venue":null,"work_id":"28e0f8b7-44bd-489b-af3b-20f3746ea409","year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.106354Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:4b3ccf5b805054d7215949a962f4bb62a9a980eee0eb36698e59ca71c2d57232","observation_id":"2539b7d8-d76e-4594-8c70-b80524c319ff","resolution":{"observed_at":"2026-08-12T11:37:51.782533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.763397Z","title":"Reinforced active learning for image segmentation,","venue":null,"work_id":"20de7603-2f33-465b-9683-13e5992eb400","year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.110745Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:5bfc4c0d7e8bd9f81dd16e8a3008b00b7762b8c33c7f0348ce694991c6ac33a0","observation_id":"189e4c70-816b-4139-b23b-eb08ba53dc1f","resolution":{"observed_at":"2026-08-12T11:37:51.767991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.748055Z","title":"AI-Based Request Augmentation to Increase Crowdsourcing Participation,","venue":null,"work_id":"0e0c6012-474d-4854-95a7-12352a6c2a41","year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.115078Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:106449ded6f324dc4324316e7694e4feb1f93f9d91f770949822d54505d32e33","observation_id":"46a4796e-8767-4050-84c4-fc12e8641a12","resolution":{"observed_at":"2026-08-12T11:37:51.753972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.733335Z","title":"Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research,","venue":null,"work_id":"4b4d9a35-1a62-4720-a4e1-2d88785dea2a","year":2018},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.119447Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:fd01cc172ac45ff7403cb7e5d8c4913c829d702d76dfcadd960d3e4ba4731116","observation_id":"dc60a1f0-54ed-4083-9c60-ee544833480a","resolution":{"observed_at":"2026-08-12T11:37:51.738695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.719935Z","title":"Some tests of the decay theory of immediate memory,","venue":null,"work_id":"5b323aa5-c057-47e2-b92c-434a961d8c3b","year":1958},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.124138Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:0b75e73e62b7cb4a6c96a3a0b4f5c621c1d27ed0defece8df70de29ac2e50e95","observation_id":"47e5182a-e8ca-455d-8b2b-344311f9b396","resolution":{"observed_at":"2026-08-12T11:37:51.724351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.705928Z","title":"Ebbinghaus, Memory: A Contribution to Experimental Psychology","venue":null,"work_id":"6c5206bc-b793-4a31-8919-19c1756b0d4d","year":1913},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.128469Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:c633975a2a23973ab9d6a629309be9e089f0e136efc1e8f2d8013a2b35a165ae","observation_id":"d3ca7279-0b43-45b3-9d6a-2ef3bb291a0a","resolution":{"observed_at":"2026-08-12T11:37:51.710611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.692547Z","title":"Reflections of the environment in memory,","venue":null,"work_id":"d113b735-91ae-4d27-95a8-0dc0107b168c","year":1991},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.132713Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:ea67e06507ebfd44fc4c81097e7c0115866f32dc2abd49096ceffc4f8ac628ac","observation_id":"341df050-8659-4087-8f21-ae50423e738d","resolution":{"observed_at":"2026-08-12T11:37:51.696859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.679022Z","title":"Evaluating forgetting curves,","venue":null,"work_id":"d984a446-8a12-48ef-89da-18d29ba5902c","year":1985},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.136929Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:764de8bb366a958b65c5949a64dd78f06fbe212703d074d7524b37af7788f3a3","observation_id":"da08b2fc-9fe7-4bb8-9b49-cd385ff797ae","resolution":{"observed_at":"2026-08-12T11:37:51.683286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.665109Z","title":"A markovian decision process,","venue":null,"work_id":"ee511fdb-a35c-47f2-bafe-076c748926ad","year":1957},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.141418Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:f3afe2405b21c3f5c1acbca0c27f97516ac22da587747132f3bc428365b3b92c","observation_id":"72cf2f4c-9903-461c-8c26-ce9a29449a54","resolution":{"observed_at":"2026-08-12T11:37:51.669428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.145805Z","title":"A mathematical theory of communication,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.145805Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:9a98021387579b0d6463336ef398fea208afbbd612196d60edda44a0a7690758","observation_id":"21af01fa-eac8-427f-95ce-7b754a9972eb","resolution":{"observed_at":"2026-08-12T11:37:51.145805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.650757Z","title":"CARER: Contextualized affect representations for emotion recognition,","venue":null,"work_id":"df41f9b6-9723-4d16-91b8-3cc8277a630b","year":2018},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.150069Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:c1d3ab17790d2662514802cf3d78af60b6fb74e8aeeb6ddbe0c93d9b0e179346","observation_id":"ddb70804-0ab9-469e-83e1-19d4d1b9971d","resolution":{"observed_at":"2026-08-12T11:37:51.655369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.636079Z","title":"GoEmotions: A dataset of fine-grained emotions,","venue":null,"work_id":"765a3e43-9e43-4700-a71d-961a158d1446","year":2020},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.154330Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:9400f83a08dd8a2f65e3dcd4a8d6a7e9716dd68b218cfe11fcd0771a809ecc2c","observation_id":"27f2f1ac-0adc-4fb0-8c00-90c489be04fb","resolution":{"observed_at":"2026-08-12T11:37:51.640989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.621354Z","title":"Enriching word vectors with subword information,","venue":null,"work_id":"2daef922-265e-4a67-ba24-d089f756abc2","year":2017},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.159011Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:b044bab3360df2a3e915d219b3b2e9c0c545766f7a5efa9e00318ae246c6f9a8","observation_id":"77a08ebd-a180-4e21-a896-7090e31e34b0","resolution":{"observed_at":"2026-08-12T11:37:51.625785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.607337Z","title":"Loss in translation: Learning bilingual word mapping with a retrieval criterion,","venue":null,"work_id":"0e360338-a373-4f43-9ed6-007d3e930740","year":2018},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.163316Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:70bddcc1741251008dbc7de1ca28f71a36d52b343660e2a6bc1a0f024dccc7a6","observation_id":"f4b3ffbd-54cc-4aff-b48f-cc280fbdf5b5","resolution":{"observed_at":"2026-08-12T11:37:51.611839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.591935Z","title":"Generalization in nli: Ways (not) to go beyond simple heuristics,","venue":null,"work_id":"b3aa3b72-cf0e-4c9c-94b6-e7b9e6c873db","year":2021},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.167580Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:2d254133aa92e209da07efb035efa3dd1a4829c253f7d66f9ac34ef7e5e8f1e7","observation_id":"dd93e3c8-e6bc-4205-9064-8d63277d3812","resolution":{"observed_at":"2026-08-12T11:37:51.597019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.576628Z","title":"Well-read students learn better: On the importance of pre-training compact models,","venue":null,"work_id":"1eb06a49-c10d-46c5-8459-2731e4b65853","year":2020},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.171958Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:c030063db05607ee78dfc58ccb346e9c8e8d11253f79e5163eef89d82a93323d","observation_id":"081ea3e9-1d7e-4a56-9b23-b7124ff6a61d","resolution":{"observed_at":"2026-08-12T11:37:51.581316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.561686Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,","venue":null,"work_id":"02cc136b-a47b-442a-9809-aaff7409cc90","year":2019},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.176160Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:561c7d2a122c1461b85ff6b12c1a731fff1545f6585352d61c2c39e24dc5bf1c","observation_id":"2f991918-da03-4677-93c3-8150cfd99d49","resolution":{"observed_at":"2026-08-12T11:37:51.566628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.547150Z","title":"Active Learning with Clustering,","venue":null,"work_id":"41633f61-2a3d-4454-8292-51340e248e4b","year":2010},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.180396Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:a96f725eebaa053446ca642f3d2dbdd83ce121dae2339c9769c24fea731de241","observation_id":"22bbf50a-238d-46ea-a200-b8b66acc1463","resolution":{"observed_at":"2026-08-12T11:37:51.551627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.533310Z","title":"Off to a good start: Using clustering to select the initial training set in active learning,","venue":null,"work_id":"c293189a-a3ba-410a-8702-1a5cd388a67e","year":2010},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.184586Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:1f76a0509ccdd4872b5ead2a5aac11ec1e249ae4f6dc3c6f0493b5681db4d631","observation_id":"6f36e0e3-1b24-4083-b5c6-5cadf8e80850","resolution":{"observed_at":"2026-08-12T11:37:51.537874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.517686Z","title":"Cost-effective active learning for deep image classifica- tion,","venue":null,"work_id":"03684316-0d67-48d4-a629-e6d2b8fae10c","year":2016},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.188771Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:c781dac71204972194ce9ff3f42e3e17c9be4f0cb57e76c58f96f0a54811084c","observation_id":"2f7b3ce0-fd67-4d55-9c77-905e38a33757","resolution":{"observed_at":"2026-08-12T11:37:51.523957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T11:37:51.088811Z","title":"Available: https://doi.org/10.1145/2505515.2505528","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","version":1},"reference_index":1320,"source":"pdf_text","source_observed_at":"2026-08-12T11:37:51.088811Z"},"links":{"citing_paper":"/paper/2411.18060"},"observation_digest":"sha256:0943c7e01428f1c4539b98273c92c2779f8a42afcfee522d965d5092438121fe","observation_id":"d7e7b1cc-3a01-41ea-9c5e-01766ebb0fff","resolution":{"observed_at":"2026-08-12T11:37:51.088811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.18060","last_updated":"2024-11-27T05:11:37Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T11:31:12.927120Z","submitted_at":"2024-11-27T05:11:37Z","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.18060."}