Paper Citation Record · LEDGER

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning

As of 10 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 2 inbound Pith citation observations for arXiv:2507.12011.

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

pith.paper-citation-record.v1
2507.12011 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:02:28.603266Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:53:14.914429Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:53:05.813136Z

Reference resolution

64 of 64 outbound references displayed

verified exact1
verified fuzzy49
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 566b08f1-f867-4117-a5fe-8426fece6d1b · outbound

This paper cites Towards automated 3d evaluation of water leakage on a tunnel face via improved gan and self-attention dl model,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Towards automated 3d evaluation of water leakage on a tunnel face via improved gan and self-attention dl model,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:32.032132Z

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Observation ecfa0b22-cb50-48b9-b7d8-938cd92d16a1 · outbound

This paper cites Feature map distillation of thin nets for low-resolution object recognition,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Feature map distillation of thin nets for low-resolution object recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:32.017096Z

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Observation a7fd90de-6921-4719-af79-323d4fe9b2b1 · outbound

This paper cites Fine-grained learning behavior-oriented knowledge distillation for graph neural net- works,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Fine-grained learning behavior-oriented knowledge distillation for graph neural net- works,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:32.002466Z

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Observation 63ba73c8-ce5d-4f69-8876-013af5818ba6 · outbound

This paper cites Type of modulation identification using wavelet transform and neural network,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Type of modulation identification using wavelet transform and neural network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.987549Z

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Observation 55ac2db9-2818-47fe-bb32-38059300a980 · outbound

This paper cites Wavelet transform based modula- tion classification for 5g and uav communication in multipath fading channel,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Wavelet transform based modula- tion classification for 5g and uav communication in multipath fading channel,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.970986Z

Source-reported events for the cited work

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Observation 2407343a-db0b-409a-92c3-7e5a50d9f345 · outbound

This paper cites Phasma: An automatic modulation classification system based on random forest,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Phasma: An automatic modulation classification system based on random forest,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.955670Z

Source-reported events for the cited work

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Observation 2700d553-3b32-4d8a-ab18-90a14415a638 · outbound

This paper cites Cyclic spectral analysis of ofdm/oqam signals,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Cyclic spectral analysis of ofdm/oqam signals,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.940682Z

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Observation 96600fef-0117-4bc6-9687-24cf2b2169e9 · outbound

This paper cites Automatic mod- ulation classification based on high order cumulants and hierarchical polynomial classifiers,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Automatic mod- ulation classification based on high order cumulants and hierarchical polynomial classifiers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.926313Z

Source-reported events for the cited work

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Observation e1055182-5ae2-4fe9-ab78-23cb52ac5ae4 · outbound

This paper cites Minimizing long- term energy consumption in ris-assisted aav-enabled mec network,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Minimizing long- term energy consumption in ris-assisted aav-enabled mec network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.911577Z

Source-reported events for the cited work

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Observation e2b948d2-10fb-4a39-97df-c1059c758104 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Over-the-air deep learning based radio signal classification,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:02:28.219900Z

Source-reported events for the cited work

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Observation 577e794b-35fb-4fa6-afec-d0b4a88dff3b · outbound

This paper cites Convolutional radio mod- ulation recognition networks,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Convolutional radio mod- ulation recognition networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.886097Z

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Observation 5ae4e4aa-99de-4c4c-b343-d1b17ee1ba87 · outbound

This paper cites An improved neural network pruning technology for automatic modulation classification in edge devices,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning An improved neural network pruning technology for automatic modulation classification in edge devices,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.872303Z

Source-reported events for the cited work

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Observation 528a4a1e-8b6f-4351-94ef-ebb9a3cf33e0 · outbound

This paper cites Signet: A novel deep learning framework for radio signal classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Signet: A novel deep learning framework for radio signal classification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.857265Z

Source-reported events for the cited work

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Observation 8eb498d7-c1a9-4d49-be7e-39e8380f40cf · outbound

This paper cites Contour stella image and deep learning for signal recognition in the physical layer,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Contour stella image and deep learning for signal recognition in the physical layer,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.841359Z

Source-reported events for the cited work

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Observation 4ef00116-42b4-4731-a292-13f8db7ee5e3 · outbound

This paper cites Complex-valued networks for automatic modulation classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Complex-valued networks for automatic modulation classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.826955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e9da0a8-bce9-4017-b6d1-7e2855b573a3 · outbound

This paper cites Adversarial attacks in modulation recognition with convolutional neural networks,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Adversarial attacks in modulation recognition with convolutional neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.812062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 28da311f-f07c-46dc-97eb-419e70475876 · outbound

This paper cites Lightweight automatic modulation classification via progres- sive differentiable architecture search,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Lightweight automatic modulation classification via progres- sive differentiable architecture search,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.796739Z

Source-reported events for the cited work

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Observation c996d9a9-bbff-4649-855e-cfcb58eff511 · outbound

This paper cites Multi-view discriminant framework for automatic modulation open set recognition,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Multi-view discriminant framework for automatic modulation open set recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.781880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 684d4c29-0fb9-4e19-9716-b46d5ecd39b9 · outbound

This paper cites MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition

Reference 19

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Source-reported events for the cited work

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Observation b20c43bb-d855-495f-938f-9a25d85662ea · outbound

This paper cites FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

Reference 20

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Observation 01269625-6ea5-488a-ab24-06e6f23172f0 · outbound

This paper cites A generic layer pruning method for signal modulation recognition deep learning models,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A generic layer pruning method for signal modulation recognition deep learning models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:02:28.276452Z

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Observation 359ebfc3-f667-4e47-b849-d58482d4c87e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 22

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Source-reported events for the cited work

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Observation 24423a16-7914-44fd-bd91-131141eb6777 · outbound

This paper cites Amc-net: An effective network for automatic modulation classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Amc-net: An effective network for automatic modulation classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.757317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f142ccd3-e576-4449-8ab6-8812ca147991 · outbound

This paper cites Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting,

Reference 24

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Observation a85b3618-bba3-4080-b108-c36ad2c6228b · outbound

This paper cites Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.732965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34925464-4c4c-4da9-8b8b-18c1f7bd72b6 · outbound

This paper cites A simple data augmentation method for automatic modulation recognition via mixing signals,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A simple data augmentation method for automatic modulation recognition via mixing signals,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.719038Z

Source-reported events for the cited work

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Observation eb7e0519-a55e-4552-b513-2ba2c7df850e · outbound

This paper cites An efficient data augmentation method for automatic modulation recognition from low- data imbalanced-class regime,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning An efficient data augmentation method for automatic modulation recognition from low- data imbalanced-class regime,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.705387Z

Source-reported events for the cited work

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Observation 89a5e32c-b6f6-42b7-a97f-6c2716c9be32 · outbound

This paper cites Data augmentation with conditional gan for automatic modulation classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Data augmentation with conditional gan for automatic modulation classification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.691326Z

Source-reported events for the cited work

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Observation 4bf4d455-c4a6-463f-b03b-8ff750ce8206 · outbound

This paper cites Spectrum interference- based two-level data augmentation method in deep learning for auto- matic modulation classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Spectrum interference- based two-level data augmentation method in deep learning for auto- matic modulation classification,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.677776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 55c84d11-afe0-4a15-af52-cf5797aa7814 · outbound

This paper cites Data augmentation aided automatic modulation recognition using diffusion model,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Data augmentation aided automatic modulation recognition using diffusion model,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.664057Z

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Observation e48a8fe7-bf15-4d38-927b-f68619637c05 · outbound

This paper cites Diffusion model empowered data augmentation for automatic modulation recognition,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Diffusion model empowered data augmentation for automatic modulation recognition,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.650512Z

Source-reported events for the cited work

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Observation 12c88fd7-b398-43a0-812e-b416d6242f6c · outbound

This paper cites Radio machine learning dataset generation with gnu radio,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Radio machine learning dataset generation with gnu radio,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.636369Z

Source-reported events for the cited work

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Observation 8a306b65-c4d9-488b-bd7e-30493150bb93 · outbound

This paper cites Smaller coresets for k-median and k- means clustering,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Smaller coresets for k-median and k- means clustering,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.621310Z

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Observation 77c268ac-f0d2-49be-b487-88cbfb374229 · outbound

This paper cites A unified framework for approximating and clustering data,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A unified framework for approximating and clustering data,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.607904Z

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Observation eb7f66e4-2b92-4f8e-b3db-e420f5ebd557 · outbound

This paper cites Core vector machines: Fast svm training on very large data sets.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Core vector machines: Fast svm training on very large data sets

Reference 35

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Observation 446272c7-f64d-4f4d-959b-752774f98a50 · outbound

This paper cites Coresets for scalable bayesian logistic regression,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Coresets for scalable bayesian logistic regression,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.519393Z

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Observation 26dc12ae-0778-42cb-8a15-02e8ec9ced7c · outbound

This paper cites Training gaussian mixture models at scale via coresets,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Training gaussian mixture models at scale via coresets,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.360774Z

Source-reported events for the cited work

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Observation 1d397468-96ab-43b0-a9df-6222733c48c4 · outbound

This paper cites Scalable training of mix- ture models via coresets,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Scalable training of mix- ture models via coresets,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.200031Z

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Observation 00e34c4f-d4b6-4c69-a006-f682da756fc1 · outbound

This paper cites Efficient coreset selection with cluster-based methods,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Efficient coreset selection with cluster-based methods,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:31.026727Z

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Observation 8eae5215-d299-40a0-a6a3-99c7b81e4755 · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Deepcore: A comprehensive library for coreset selection in deep learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.886452Z

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Observation 36ef52b2-2c40-44bc-8c45-dddc2639a2bb · outbound

This paper cites A coreset selection of coreset selection literature: Introduction and recent advances,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A coreset selection of coreset selection literature: Introduction and recent advances,

Reference 41

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This paper cites Rk-core: An established methodology for exploring the hierarchical structure within datasets,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Rk-core: An established methodology for exploring the hierarchical structure within datasets,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.774386Z

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Observation d6e32a5f-d4bc-4d6b-964c-5d6bf1a67822 · outbound

This paper cites Coreset selection for object detection,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Coreset selection for object detection,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.673844Z

Source-reported events for the cited work

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Observation 1cb6ed88-9ac7-496a-824d-ba024add121c · outbound

This paper cites Coreset selection via reducible loss in continual learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Coreset selection via reducible loss in continual learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.591219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e22402b7-1b32-4a96-b305-b4ef39c3bd08 · outbound

This paper cites Fedcs: Coreset selection for federated learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Fedcs: Coreset selection for federated learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.485997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8634a023-456f-41e7-9a3a-e12005f62a26 · outbound

This paper cites Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.394832Z

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Observation c877d16d-f257-4795-b660-7eb0173ed114 · outbound

This paper cites Active learning literature survey,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Active learning literature survey,

Reference 47

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Observation 9ca61f51-e6ab-46a4-83db-4fbda8d043a5 · outbound

This paper cites Learning from human educational wisdom: A student-centered knowledge distillation method,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Learning from human educational wisdom: A student-centered knowledge distillation method,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.275947Z

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Observation 63ce9f69-625e-465d-b61e-1094937d9766 · outbound

This paper cites A cost-sensitive active learning algorithm: toward imbalanced time series forecasting,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A cost-sensitive active learning algorithm: toward imbalanced time series forecasting,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.186829Z

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Observation 8029b154-e75d-4cb3-9380-7c03d1c2a72a · outbound

This paper cites Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:30.064258Z

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Observation f295e248-a2ae-485f-8b6c-61dd048693b3 · outbound

This paper cites Task-aware variational adversarial active learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Task-aware variational adversarial active learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.969813Z

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Observation 9169e160-51e1-43f2-b45a-c841232722d8 · outbound

This paper cites Active transfer learning network: A unified deep joint spectral–spatial feature learning model for hyperspectral image classification,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Active transfer learning network: A unified deep joint spectral–spatial feature learning model for hyperspectral image classification,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.861837Z

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Observation 25f8ef4f-c1a1-4d56-8676-2650622f4918 · outbound

This paper cites Modulation and signal class labelling with active learning and classification using ma- chine learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Modulation and signal class labelling with active learning and classification using ma- chine learning,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.775097Z

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Observation b8edb042-6f9b-4ebd-8846-6aa50e2044d7 · outbound

This paper cites Gnu radio: tools for exploring the radio frequency spec- trum,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Gnu radio: tools for exploring the radio frequency spec- trum,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.616025Z

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Observation e9e144a8-9359-4393-9527-a05d51740d96 · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 55

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Observation 802f3446-bcaf-47a5-b209-c97cbfcb82ec · outbound

This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T17:02:28.518818Z

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Observation 595d3888-c075-46a3-a40c-506947db461c · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 57

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Observation 98131ffc-a9c5-42f7-9290-1e7d9c7ed396 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Glister: Generalization based data subset selection for efficient and robust learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.470360Z

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Observation 6f1e2b81-5017-4770-bfb9-5cb98e59e7cc · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Deep learning on a data diet: Finding important examples early in training,

Reference 59

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Observation abef7ae6-9440-40a9-92f6-4a2dc5f7a63e · outbound

This paper cites Herding dynamical weights to learn,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Herding dynamical weights to learn,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:02:28.558854Z

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Observation e33da321-4395-4506-8fe4-eccb2304634f · outbound

This paper cites Super-Samples from Kernel Herding.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Super-Samples from Kernel Herding

Reference 61

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Observation 7200b729-e147-43fb-956c-3bd477183f24 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Imagenet classification with deep convolutional neural networks,

Reference 62

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Observation f1d4accf-1c13-4b51-aadc-1a5d6ae5419d · outbound

This paper cites A spatiotemporal multi-channel learning framework for automatic modulation recognition,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning A spatiotemporal multi-channel learning framework for automatic modulation recognition,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.314336Z

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Observation 3255e18a-dbd0-4140-b62a-ec4d7e29f12c · outbound

This paper cites Stochastic neighbor embedding,.

DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning Stochastic neighbor embedding,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:29.159986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 65f2c58c-90ab-41cc-8d96-d289ac5b6a25 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:53:05.818166Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c6fb92f-778d-4a6c-8ede-120812224e05 · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning

Reference 60

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