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Paper Citation Record · LEDGER

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction

As of 12 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2501.05936.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.05936 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:09:24.593662Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d075735-ab8a-4065-9305-034111c61106 · outbound

This paper cites A 3d-cnns approach to classify users’ emotion through eeg-based topographical maps in hri,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction A 3d-cnns approach to classify users’ emotion through eeg-based topographical maps in hri,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 525fcbd2-368b-49b1-a9f3-30fd15feb1ac · outbound

This paper cites What we learn on the streets: Situated human-robot interactions from an industry perspective,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction What we learn on the streets: Situated human-robot interactions from an industry perspective,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:25.093084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation e3dad706-b49c-4544-89ed-297dfa38400b · outbound

This paper cites Getting closer to real-world: Monitoring humans working with collaborative industrial robots,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Getting closer to real-world: Monitoring humans working with collaborative industrial robots,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:25.065745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation a9e75a96-59bc-483c-9a66-6b7f30442a0f · outbound

This paper cites Iar-net: A human-object context guided action recognition network for industrial environment monitoring,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Iar-net: A human-object context guided action recognition network for industrial environment monitoring,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:25.037182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b6f0b9d3-ebc6-42cb-987d-e173320bbd28 · outbound

This paper cites Df sampler: A self-supervised method for adaptive keyframe sampling,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Df sampler: A self-supervised method for adaptive keyframe sampling,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:25.013769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 904f2ea0-7722-4700-8197-3a114979a462 · outbound

This paper cites Meccano: A multimodal egocentric dataset for humans behavior understanding in the industrial- like domain,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Meccano: A multimodal egocentric dataset for humans behavior understanding in the industrial- like domain,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.985778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 43c4bf17-5040-4962-bb5c-a6177ed2fb28 · outbound

This paper cites Hri30: An action recognition dataset for industrial human-robot interaction,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Hri30: An action recognition dataset for industrial human-robot interaction,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.955731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 6a58ebd2-ce81-4740-9169-4aae5fee85cd · outbound

This paper cites The ha4m dataset: Multi-modal monitoring of an assembly task for human action recognition in manufacturing,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction The ha4m dataset: Multi-modal monitoring of an assembly task for human action recognition in manufacturing,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.930842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d3bf1691-fbe2-4167-b409-0068d60277cb · outbound

This paper cites Enigma-51: Towards a fine-grained understanding of human behavior in industrial scenarios,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Enigma-51: Towards a fine-grained understanding of human behavior in industrial scenarios,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.906288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 91410af7-7c9a-4e75-a914-517bd6c62757 · outbound

This paper cites Multimodal engagement prediction in multiperson human–robot interaction,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Multimodal engagement prediction in multiperson human–robot interaction,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.884698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2a0c7d5b-9444-4dd4-ada1-d51aae434160 · outbound

This paper cites A survey on dialogue management in human-robot interaction,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction A survey on dialogue management in human-robot interaction,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T21:09:24.516281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 397d6a18-3c0c-4d83-ac71-cb5a3fcdcf53 · outbound

This paper cites Engagement in human-agent interaction: An overview,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Engagement in human-agent interaction: An overview,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T21:09:24.525169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b568e2e8-b074-406e-a146-a9fba4e59b04 · outbound

This paper cites From the definition to the automatic assessment of engagement in human–robot interaction: A systematic review,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction From the definition to the automatic assessment of engagement in human–robot interaction: A systematic review,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.825065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3e139c15-63cb-4bfa-adc0-0e659e95ce75 · outbound

This paper cites Enhancing human-machine interactions: a novel framework for ar-based digital twin systems in industrial environments,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Enhancing human-machine interactions: a novel framework for ar-based digital twin systems in industrial environments,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.804771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 55a153dd-359c-45b6-8f12-0e5fbe358fe0 · outbound

This paper cites Unlocking human-robot dynamics: Introducing sensec- obot, a novel multimodal dataset on industry 4.0,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Unlocking human-robot dynamics: Introducing sensec- obot, a novel multimodal dataset on industry 4.0,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.782224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 9b066c98-61e4-485f-9848-dc8dc12f4196 · outbound

This paper cites The via annotation software for images, audio and video,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction The via annotation software for images, audio and video,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:09:24.757149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 407f0a51-e324-422c-87f0-fc60bfd256f8 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 17

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no resolver link, observed 2026-08-10T21:09:24.565202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 52026dd1-d33e-497e-bf8e-46f3fb44f197 · outbound

This paper cites The Kinetics Human Action Video Dataset.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction The Kinetics Human Action Video Dataset

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T21:09:24.570842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bf2bd7a0-a6c2-488d-aac2-bc1610d260df · outbound

This paper cites Deep residual learning for image recognition,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Deep residual learning for image recognition,

Reference 19

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unresolved
no resolver link, observed 2026-08-10T21:09:24.576923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:09:24.576923Z digest=sha256:3cff3c17319ed580bced6dcdc24b17bb0cc4812a401f7eca979a47c3d67d1cff

Observation 88092a0a-c2e4-4902-a45b-27a3052e59ad · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction Imagenet: A large-scale hierarchical image database,

Reference 20

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unresolved
no resolver link, observed 2026-08-10T21:09:24.582952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:09:24.582952Z digest=sha256:9f7e586d44a0f9a8cf5b53a11aa4e0eb151fd73d95d7b97d1b3a4273994730b5

Observation a7b82c53-e14c-4b42-9b38-8449d89ffa85 · outbound

This paper cites MediaPipe: A Framework for Building Perception Pipelines.

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction MediaPipe: A Framework for Building Perception Pipelines

Reference 21

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unresolved
no resolver link, observed 2026-08-10T21:09:24.593662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:09:24.593662Z digest=sha256:6c27b9afe0030b3798fb4bd0c6d1f3587b8dbb9c26bd12d7bdfb4b531765a354

Pith citing papers

No inbound Pith citation observations are available.