ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI | Paper Citation Record

Observation 5ec2d1a6-f885-4432-8547-45d1d0c4f603 · outbound

This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.Information fusion, 58:82–115, 2020.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai.Information fusion, 58:82–115, 2020

Reference 1

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Observation 4545718e-498c-4120-b5e7-7a824f6e6a4f · outbound

This paper cites A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018

Reference 2

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Observation 777948e0-5d88-4119-98c7-0abc5c217c9f · outbound

This paper cites why should i trust you?.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI why should i trust you?

Reference 3

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Observation c72c5445-19cc-4441-9562-256c9926c98a · outbound

This paper cites A unified approach to interpreting model predictions.Advances in neural information processing systems, 30, 2017.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI A unified approach to interpreting model predictions.Advances in neural information processing systems, 30, 2017

Reference 4

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Observation b7633758-bfe4-4aeb-b5bf-d8f1efc0865a · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 5

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Observation 497c83dc-36ff-49a1-b803-819969fe550f · outbound

This paper cites Sanity checks for saliency maps.Advances in neural information processing systems, 31, 2018.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Sanity checks for saliency maps.Advances in neural information processing systems, 31, 2018

Reference 6

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Observation 1ad4c152-0733-4532-95ce-356000728907 · outbound

This paper cites Towards automatic concept-based explanations.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Towards automatic concept-based explanations

Reference 7

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Observation 53429dce-f46e-4b88-8c62-0d064718890c · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5):206–215, 2019.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5):206–215, 2019

Reference 8

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Observation 6aa63ac0-7ade-437d-8aff-f7070c0e9201 · outbound

This paper cites Concept-based explainable artificial intelligence: A survey.ACM Computing Surveys, 2023.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Concept-based explainable artificial intelligence: A survey.ACM Computing Surveys, 2023

Reference 9

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Observation ab82edb8-e48b-42a5-887c-bfac129f365b · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 10

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Observation db732e11-39c4-4bd1-8ff6-20c6fd965bd7 · outbound

This paper cites Concept bottleneck models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Concept bottleneck models

Reference 11

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Observation 54512541-bcd1-48f0-b418-31eeb348902f · outbound

This paper cites This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019

Reference 12

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Observation 769547ae-7ae5-4ed0-92c4-0f5ca8f5b449 · outbound

This paper cites Language in a bottle: Language model guided concept bottlenecks for interpretable image classification.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Reference 13

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Observation 8f0f0eb6-51fc-4808-bcd2-a203644c8782 · outbound

This paper cites Do Concept Bottleneck Models Learn as Intended?.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Do Concept Bottleneck Models Learn as Intended?

Reference 14

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Observation 20222170-2e0d-411e-8249-5e6624a9bf8f · outbound

This paper cites Addressing leakage in concept bottleneck models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Addressing leakage in concept bottleneck models

Reference 15

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Observation e3239da1-3e9a-44dc-860d-adff913e5e98 · outbound

This paper cites Glancenets: Interpretable, leak-proof concept-based models.Advances in Neural Information Processing Systems, 35:21212–21227, 2022.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Glancenets: Interpretable, leak-proof concept-based models.Advances in Neural Information Processing Systems, 35:21212–21227, 2022

Reference 16

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Observation 96b6d559-e8eb-470f-924c-65abc3eab4e3 · outbound

This paper cites Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

Reference 17

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Observation 5e928f7f-f7ec-41ba-a34a-7e9163703108 · outbound

This paper cites Network dissection: Quantifying interpretability of deep visual representations.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Network dissection: Quantifying interpretability of deep visual representations

Reference 18

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Observation 388f28e2-382c-41b3-b71c-84ee765bf8a4 · outbound

This paper cites Bayesian Reward Models for LLM Alignment.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Bayesian Reward Models for LLM Alignment

Reference 19

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Observation 00ebe53f-3b93-42ef-a5c3-7ee480db7178 · outbound

This paper cites Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability

Reference 20

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Observation ceac0c2f-a539-4fd9-96ea-2cde96186082 · outbound

This paper cites Making corgis important for honeycomb classification: Adversarial attacks on concept-based explainability tools.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Making corgis important for honeycomb classification: Adversarial attacks on concept-based explainability tools

Reference 21

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Observation 82c09e5b-7390-4f12-9867-25e5312f2a97 · outbound

This paper cites Promises and Pitfalls of Black-Box Concept Learning Models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Promises and Pitfalls of Black-Box Concept Learning Models

Reference 22

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Observation 065a306f-e177-404d-934d-872eb844e9e9 · outbound

This paper cites Interpretation of neural networks is fragile.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Interpretation of neural networks is fragile

Reference 23

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Observation 33d69b0f-719b-415f-b517-c7f8c508a2b6 · outbound

This paper cites Probabilistic Concept Bottleneck Models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Probabilistic Concept Bottleneck Models

Reference 24

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Observation 4530f772-83da-4efa-8acd-147ba9d52dee · outbound

This paper cites On completeness- aware concept-based explanations in deep neural networks.Advances in neural information processing systems, 33:20554–20565, 2020.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI On completeness- aware concept-based explanations in deep neural networks.Advances in neural information processing systems, 33:20554–20565, 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation 6b351347-a3e3-4ae2-b634-93f8fff8b945 · outbound

This paper cites Anchors: High-precision model-agnostic explanations.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Anchors: High-precision model-agnostic explanations

Reference 26

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Observation c7fa0122-5b0e-430f-a7e4-a20ff6987cb7 · outbound

This paper cites S-lime: Stabilized-lime for model explanation.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI S-lime: Stabilized-lime for model explanation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation 83ba9176-f063-410c-b44a-5840a126d4c7 · outbound

This paper cites Us-lime: Increasing fidelity in lime using uncertainty sampling on tabular data.Neurocomputing, 597:127969, 2024.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Us-lime: Increasing fidelity in lime using uncertainty sampling on tabular data.Neurocomputing, 597:127969, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation e2fcbf54-c899-4b01-9db9-949bdfccf26e · outbound

This paper cites Guided-lime: Structured sampling based hybrid approach towards explaining blackbox machine learning models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Guided-lime: Structured sampling based hybrid approach towards explaining blackbox machine learning models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation 227d69ec-828a-423f-b944-65908013a1a0 · outbound

This paper cites Deterministic local interpretable model-agnostic explanations for stable explainability.Machine Learning and Knowledge Extraction, 3(3):525–541, 2021.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Deterministic local interpretable model-agnostic explanations for stable explainability.Machine Learning and Knowledge Extraction, 3(3):525–541, 2021

Reference 30

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Observation 5f1d247f-ef46-43a3-8d1b-ed15a07e3919 · outbound

This paper cites Defining Locality for Surrogates in Post-hoc Interpretablity.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Defining Locality for Surrogates in Post-hoc Interpretablity

Reference 31

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Observation d9afd55e-5909-4b88-8fe2-3d78a2672428 · outbound

This paper cites Qlime-a quadratic local interpretable model- agnostic explanation approach.SMU Data Science Review, 3(1):4, 2020.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Qlime-a quadratic local interpretable model- agnostic explanation approach.SMU Data Science Review, 3(1):4, 2020

Reference 32

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Observation aa725fb0-944c-4393-b386-7740ae5e7d77 · outbound

This paper cites Baylime: Bayesian local interpretable model-agnostic explanations.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Baylime: Bayesian local interpretable model-agnostic explanations

Reference 33

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Observation 50a3239e-901a-4fd4-8abf-681e950f0140 · outbound

This paper cites Alime: Autoencoder based approach for local interpretability.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Alime: Autoencoder based approach for local interpretability

Reference 34

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Observation 1fc81710-b227-4727-b8ac-4e97f1fd2404 · outbound

This paper cites OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms

Reference 35

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Observation ebc844b8-f70d-4663-ad14-5f7095f32fce · outbound

This paper cites Glime: general, stable and local lime explanation.Advances in neural information processing systems, 36:36250–36277, 2023.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Glime: general, stable and local lime explanation.Advances in neural information processing systems, 36:36250–36277, 2023

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation d7bb03bb-2853-42e1-b348-b08f7fe66620 · outbound

This paper cites Graphlime: Local interpretable model explanations for graph neural networks.IEEE Transactions on Knowledge and Data Engineering, 35(7):6968– 6972, 2022.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Graphlime: Local interpretable model explanations for graph neural networks.IEEE Transactions on Knowledge and Data Engineering, 35(7):6968– 6972, 2022

Reference 37

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Observation ce4c84cd-3083-48b9-9f00-915729005ef2 · outbound

This paper cites Ts-mule: Local interpretable model-agnostic explanations for time series forecast models.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Ts-mule: Local interpretable model-agnostic explanations for time series forecast models

Reference 38

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Observation 33f064e1-43db-4bee-850a-b3160db53fcf · outbound

This paper cites Local interpretable model-agnostic explanations for music content analysis.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Local interpretable model-agnostic explanations for music content analysis

Reference 39

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Observation 648e182b-9ad2-44fd-b053-88a4f6d25c8d · outbound

This paper cites B-lime: An improvement of lime for interpretable deep learning classification of cardiac arrhythmia from ecg signals.Processes, 11(2):595, 2023.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI B-lime: An improvement of lime for interpretable deep learning classification of cardiac arrhythmia from ecg signals.Processes, 11(2):595, 2023

Reference 40

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Observation e391ab98-99cc-4f5f-93ba-3ea31121b650 · outbound

This paper cites Which lime should i trust? concepts, challenges, and solutions.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Which lime should i trust? concepts, challenges, and solutions

Reference 41

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Observation 590e38cb-f923-45c4-98e5-87a9a49d0fb9 · outbound

This paper cites Dseg-lime: Improving image explanation by hierarchical data-driven segmentation.arXiv preprint arXiv:2403.07733, 2024.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Dseg-lime: Improving image explanation by hierarchical data-driven segmentation.arXiv preprint arXiv:2403.07733, 2024

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Observation 237bc2cb-799d-4373-a19e-12565e3f7da9 · outbound

This paper cites Slice: Stabilized lime for consistent explanations for image classification.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Slice: Stabilized lime for consistent explanations for image classification

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Observation b047f5fd-162c-4710-b2fe-b9c8a3844b06 · outbound

This paper cites Which LIME should I trust? Concepts, Challenges, and Solutions.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Which LIME should I trust? Concepts, Challenges, and Solutions

Reference 44

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Observation d0320705-f7e6-43c9-8762-7c260eaac0d4 · outbound

This paper cites Mapping the Mind of an Instruction-based Image Editing using SMILE.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Mapping the Mind of an Instruction-based Image Editing using SMILE

Reference 45

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Observation 8ea01c8f-f14a-4b8c-a682-d2054c14cda2 · outbound

This paper cites Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE

Reference 46

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Observation cbd5d2a5-c477-48e7-abff-c73e1848cb37 · outbound

This paper cites Explainability of large language models using smile: Statistical model-agnostic interpretability with local explanations.arXiv preprint arXiv:2505.21657, 2025.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Explainability of large language models using smile: Statistical model-agnostic interpretability with local explanations.arXiv preprint arXiv:2505.21657, 2025

Reference 47

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Observation 2d10c3f1-919b-43a0-a07f-1dc6d6adf72f · outbound

This paper cites Interpreting black-box large language models with sentence-level energy landscapes.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Interpreting black-box large language models with sentence-level energy landscapes

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Observation 6b626757-8d6d-4441-81ce-8b9c680ea44a · outbound

This paper cites Computational optimal transport: With applications to data science.Founda- tions and Trends in Machine Learning, 11(5–6):355–607, 2019.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Computational optimal transport: With applications to data science.Founda- tions and Trends in Machine Learning, 11(5–6):355–607, 2019

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This paper cites Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al

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This paper cites Abràmoff, Philip T.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Abràmoff, Philip T

Reference 51

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Observation c4f8b3bb-c608-44cb-af00-1c88888d43e9 · outbound

This paper cites Chia, Siegfried K.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Chia, Siegfried K

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Observation 857d93e6-7929-44ed-8408-74621dff147c · outbound

This paper cites Kinahan, and Yu Qiao.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Kinahan, and Yu Qiao

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Observation c3597091-0d36-4815-b0a8-0351468b1a26 · outbound

This paper cites Segment anything in medical images.Nature communications, 15(1):654, 2024.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Segment anything in medical images.Nature communications, 15(1):654, 2024

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ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Qwen3-VL Technical Report

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