Paper Citation Record · LEDGER

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.21918.

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

pith.paper-citation-record.v1
2505.21918 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:53.640345Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

36 of 36 outbound references displayed

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verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5c4aefc-cf49-4ed9-93ee-34421b8d84f3 · outbound

This paper cites A random forest guided tour.Test, 25:197–227, 2016.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing A random forest guided tour.Test, 25:197–227, 2016

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.566266Z

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Observation 72a3ada0-b9b0-4470-9c6e-4e16a229e14e · outbound

This paper cites Dataset for ADL Recognition with Wrist-worn Ac- celerometer.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Dataset for ADL Recognition with Wrist-worn Ac- celerometer

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:50.006930Z

Source-reported events for the cited work

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Observation a9eac092-9d09-4005-9c5a-18ac75443bd7 · outbound

This paper cites Chan Chang, R.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Chan Chang, R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.423132Z

Source-reported events for the cited work

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Observation bb63b965-f016-4fdd-8ed2-f310be4e0556 · outbound

This paper cites Deep learning for sensor-based human activity recog- nition: Overview, challenges, and opportunities.ACM Computing Surveys (CSUR), 54(4):1–40, 2021.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Deep learning for sensor-based human activity recog- nition: Overview, challenges, and opportunities.ACM Computing Surveys (CSUR), 54(4):1–40, 2021

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.246536Z

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Observation 8d7694c2-dda9-4dfc-ae43-3170f4c02a65 · outbound

This paper cites Learning to rotate: Quaternion transformer for complicated periodical time series forecasting.KDD, 2022.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Learning to rotate: Quaternion transformer for complicated periodical time series forecasting.KDD, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:59.016975Z

Source-reported events for the cited work

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Observation f1c26906-4663-4c01-aa4d-88aabdc4de83 · outbound

This paper cites Leveraging large language models for activity recognition in smart environments.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Leveraging large language models for activity recognition in smart environments

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.758076Z

Source-reported events for the cited work

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Observation 34884dca-92f4-4fdb-a8cb-7441a0e7469b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Observation 7d03e33c-15b7-4675-8351-5d645c7972b3 · outbound

This paper cites Deep residual learning for image recognition.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Deep residual learning for image recognition

Reference 8

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Observation 279a6f95-0e57-44a2-9294-34206144b59c · outbound

This paper cites Hargpt: Are llms zero-shot human activity recognizers?, 2024.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Hargpt: Are llms zero-shot human activity recognizers?, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.492953Z

Source-reported events for the cited work

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Observation 297a7045-5c13-486b-a856-5eff420f2185 · outbound

This paper cites A review of privacy-preserving human and human activ- ity recognition.International Journal on Smart Sensing and Intelli- gent Systems, 13(1):1–13, 2020.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing A review of privacy-preserving human and human activ- ity recognition.International Journal on Smart Sensing and Intelli- gent Systems, 13(1):1–13, 2020

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.266639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b567db4-cc1a-46a5-8593-3f22b354c6cb · outbound

This paper cites Activity recognition using cell phone accelerometers.ACM SigKDD Explo- rations Newsletter, 12(2):74–82, 2011.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Activity recognition using cell phone accelerometers.ACM SigKDD Explo- rations Newsletter, 12(2):74–82, 2011

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:58.031914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d0c3d2c0-bd85-4ffe-b424-0dd877273d9b · outbound

This paper cites A survey on human activity recognition using wearable sensors.IEEE communications surveys & tutorials, 15(3):1192–1209, 2012.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing A survey on human activity recognition using wearable sensors.IEEE communications surveys & tutorials, 15(3):1192–1209, 2012

Reference 12

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Source-reported events for the cited work

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Observation 4eeb5902-987c-4865-bfcf-d2be2f2728dd · outbound

This paper cites Enhancing the locality and break- ing the memory bottleneck of transformer on time series forecasting.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Enhancing the locality and break- ing the memory bottleneck of transformer on time series forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.772646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b1886f0-6e13-41a9-a8a5-9b65ee1a4714 · outbound

This paper cites Liu, and Schahram Dustdar.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Liu, and Schahram Dustdar

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.567732Z

Source-reported events for the cited work

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Observation eaf917dc-aa1f-4d2d-8d4d-5a70cd1a1931 · outbound

This paper cites Non- stationary transformers: Exploring the stationarity in time series forecasting.NeurIPS, 2022.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Non- stationary transformers: Exploring the stationarity in time series forecasting.NeurIPS, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.388245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 69414c0a-b9a5-40f3-ba1b-db06bd8a422e · outbound

This paper cites Decoupled Weight Decay Regularization.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Decoupled Weight Decay Regularization

Reference 16

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Observation 5ab37f0f-8e33-410a-894b-dfbea66001f2 · outbound

This paper cites Chapter 14 - a study on smartphone sensor-based human activity recognition using deep learning approaches.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Chapter 14 - a study on smartphone sensor-based human activity recognition using deep learning approaches

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:57.183194Z

Source-reported events for the cited work

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Observation 7443685e-3bde-4689-8b1d-bb5a10fd1ef7 · outbound

This paper cites Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.965814Z

Source-reported events for the cited work

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Observation 4ad18461-bd57-4e86-874d-b4b059206f98 · outbound

This paper cites Towards llms for sensor data: Multi-task self-supervised learning.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Towards llms for sensor data: Multi-task self-supervised learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.784838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 064d2ce7-af78-49c7-bcfc-f1f29494aef0 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 20

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Source-reported events for the cited work

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Observation 2ebb9334-07b6-40bc-8f71-668f5f6c67ca · outbound

This paper cites Introducing a new benchmarked dataset for activity monitoring.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Introducing a new benchmarked dataset for activity monitoring

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.592935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2e6c135f-f6b1-42e0-a86b-bd7d069f5e7f · outbound

This paper cites Mill` an.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Mill` an

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.369117Z

Source-reported events for the cited work

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Observation bbfb1565-361a-4aef-ada1-f19669108933 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:52.484166Z

Source-reported events for the cited work

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Observation 0c9ce49b-763f-4035-8d44-d55471e144bd · outbound

This paper cites On-body localization of wearable devices: An investigation of position-aware activity recog- nition.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing On-body localization of wearable devices: An investigation of position-aware activity recog- nition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.160441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c128674a-c814-442d-92c8-c23bdbfe3b3c · outbound

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Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:55.875282Z

Source-reported events for the cited work

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Observation 7d82fb02-dc30-4957-bd43-1ee1ba2f9144 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Attention is all you need.Advances in Neural Information Processing Systems, 2017

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:52.747853Z

Source-reported events for the cited work

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Observation 0d6c9a5d-0baf-4fae-8f23-49f8b8a042d5 · outbound

This paper cites Advancing human activity recognition us- ing ultra-wideband channel impulse response snapshots.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Advancing human activity recognition us- ing ultra-wideband channel impulse response snapshots

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.655039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6be89174-d572-49af-94cb-bc97577af126 · outbound

This paper cites Auto- former: Decomposition transformers with auto-correlation for long- term series forecasting.NeurIPS, 2021.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Auto- former: Decomposition transformers with auto-correlation for long- term series forecasting.NeurIPS, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.445685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4f25ed14-f44b-43f8-84a7-0d0568855190 · outbound

This paper cites Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:52.988060Z

Source-reported events for the cited work

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Observation 2b52e205-348c-4bd3-8e90-5ee221c7c95c · outbound

This paper cites Adversarial sparse transformer for time series fore- casting.NeurIPS, 2020.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Adversarial sparse transformer for time series fore- casting.NeurIPS, 2020

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.227456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb01a976-87e5-45b0-b227-349b7ed535e0 · outbound

This paper cites Soft sensing transformer: hun- dreds of sensors are worth a single word.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Soft sensing transformer: hun- dreds of sensors are worth a single word

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.017204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b621f595-3af6-4363-a7bd-63de5585353d · outbound

This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:54.786837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 624b39dc-7583-4f34-85bd-1be41def1f89 · outbound

This paper cites Informer: Beyond efficient trans- former for long sequence time-series forecasting.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Informer: Beyond efficient trans- former for long sequence time-series forecasting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:54.567602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b2888d81-6027-4776-977e-9891eb614441 · outbound

This paper cites Informer: Beyond efficient trans- former for long sequence timeseries forecasting.AAAI, 2021.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Informer: Beyond efficient trans- former for long sequence timeseries forecasting.AAAI, 2021

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:54.428262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23e3fc14-c1ba-4f6e-9772-b4b3838575b7 · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.ICML, 2022.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.ICML, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:54.236964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f32f8bcc-7718-465c-b001-be5e34ad3801 · outbound

This paper cites Sensor data augmentation from skeleton pose sequences for improving human activity recognition.

Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing Sensor data augmentation from skeleton pose sequences for improving human activity recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:54.023594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.