{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:15:02Z","timestamp":1768414502065,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:00:00Z","timestamp":1604793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,11,8]]},"DOI":"10.1145\/3368089.3417051","type":"proceedings-article","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T00:45:01Z","timestamp":1607647501000},"page":"1320-1330","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Enhancing the interoperability between deep learning frameworks by model conversion"],"prefix":"10.1145","author":[{"given":"Yu","family":"Liu","sequence":"first","affiliation":[{"name":"Microsoft Research, China \/ National University of Singapore, Singapore"}]},{"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"ByteDance, China"}]},{"given":"Ru","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Tingting","family":"Qin","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Xiang","family":"Ji","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Haoxiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Mao","family":"Yang","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]}],"member":"320","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (Savannah, GA, USA) ( OSDI '16). USENIX Association, USA, 265-283","author":"Abadi Mart\u00edn"},{"key":"e_1_3_2_2_2_1","unstructured":"Apple. 2017. Apple Core ML. https:\/\/developer.apple.com\/documentation\/ coreml.  Apple. 2017. Apple Core ML. https:\/\/developer.apple.com\/documentation\/ coreml."},{"key":"e_1_3_2_2_3_1","unstructured":"Apple. 2017. Core ML Tools. https:\/\/coremltools.readme.io\/docs.  Apple. 2017. Core ML Tools. https:\/\/coremltools.readme.io\/docs."},{"key":"e_1_3_2_2_4_1","unstructured":"Baidu. 2016. PaddlePaddle: PArallel Distributed Deep LEarning. https:\/\/github. com\/paddlepaddle\/paddle.  Baidu. 2016. PaddlePaddle: PArallel Distributed Deep LEarning. https:\/\/github. com\/paddlepaddle\/paddle."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/141000671"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2967117"},{"key":"e_1_3_2_2_7_1","unstructured":"Cafe. 2019. Cafe ResNet-152. prototxt: http:\/\/data.mxnet.io\/models\/ imagenet\/test\/cafe\/ResNet-152-deploy.prototxt params: http:\/\/data.mxnet.io\/ models\/imagenet\/test\/cafe\/ResNet-152-deploy.prototxt.  Cafe. 2019. Cafe ResNet-152. prototxt: http:\/\/data.mxnet.io\/models\/ imagenet\/test\/cafe\/ResNet-152-deploy.prototxt params: http:\/\/data.mxnet.io\/ models\/imagenet\/test\/cafe\/ResNet-152-deploy.prototxt."},{"key":"e_1_3_2_2_8_1","unstructured":"Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang and Zheng Zhang. 2015. MXNet: A Flexible and Eficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 ( 2015 ). arXiv: 1512.01274 http:\/\/arxiv.org\/abs\/1512.01274  Tianqi Chen Mu Li Yutian Li Min Lin Naiyan Wang Minjie Wang Tianjun Xiao Bing Xu Chiyuan Zhang and Zheng Zhang. 2015. MXNet: A Flexible and Eficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 ( 2015 ). arXiv: 1512.01274 http:\/\/arxiv.org\/abs\/1512.01274"},{"key":"e_1_3_2_2_9_1","volume-title":"Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (Carlsbad, CA, USA) ( OSDI '18). USENIX Association, USA, 579-594","author":"Chen Tianqi"},{"key":"e_1_3_2_2_10_1","volume-title":"Xception: Deep Learning with Depthwise Separable Convolutions. CoRR abs\/1610.02357 ( 2016 ). arXiv: 1610.02357 http:\/\/arxiv.org\/ abs\/1610.02357","author":"Chollet Fran\u00e7ois","year":"2016"},{"key":"e_1_3_2_2_11_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io.  Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_2_12_1","unstructured":"Junyoung Chung \u00c7aglar G\u00fcl\u00e7ehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs\/1412.3555 ( 2014 ). arXiv: 1412.3555 http:\/\/arxiv.org\/abs\/1412.3555  Junyoung Chung \u00c7aglar G\u00fcl\u00e7ehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs\/1412.3555 ( 2014 ). arXiv: 1412.3555 http:\/\/arxiv.org\/abs\/1412.3555"},{"key":"e_1_3_2_2_13_1","unstructured":"CNTK. 2019. CNTK Inception-V3. https:\/\/www.cntk.ai\/Models\/CNTK_Pretrained\/InceptionV3_ImageNet_CNTK.model.  CNTK. 2019. CNTK Inception-V3. https:\/\/www.cntk.ai\/Models\/CNTK_Pretrained\/InceptionV3_ImageNet_CNTK.model."},{"key":"e_1_3_2_2_14_1","unstructured":"CNTK. 2019. CNTK ResNet-152. https:\/\/www.cntk.ai\/Models\/CNTK_Pretrained\/ ResNet152_ImageNet_CNTK.model.  CNTK. 2019. CNTK ResNet-152. https:\/\/www.cntk.ai\/Models\/CNTK_Pretrained\/ ResNet152_ImageNet_CNTK.model."},{"key":"e_1_3_2_2_15_1","unstructured":"Saumitro Dasgupta. 2015. Cafe to TensorFlow. https:\/\/github.com\/ethereon\/cafetensorflow.  Saumitro Dasgupta. 2015. Cafe to TensorFlow. https:\/\/github.com\/ethereon\/cafetensorflow."},{"key":"e_1_3_2_2_16_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs\/","author":"Devlin Jacob","year":"2018"},{"key":"e_1_3_2_2_17_1","unstructured":"Khronos Group. 2017. NNEF-Tools. https:\/\/github.com\/KhronosGroup\/NNEFTools.  Khronos Group. 2017. NNEF-Tools. https:\/\/github.com\/KhronosGroup\/NNEFTools."},{"key":"e_1_3_2_2_18_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385 ( 2015 ). arXiv: 1512.03385 http:\/\/arxiv.org\/abs\/1512.03385  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. CoRR abs\/1512.03385 ( 2015 ). arXiv: 1512.03385 http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_20_1","unstructured":"Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Eficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 ( 2017 ). arXiv: 1704.04861 http:\/\/arxiv.org\/abs\/1704.04861  Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Eficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 ( 2017 ). arXiv: 1704.04861 http:\/\/arxiv.org\/abs\/1704.04861"},{"key":"e_1_3_2_2_21_1","unstructured":"Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR abs\/1602.07360 ( 2016 ). arXiv: 1602.07360 http:\/\/arxiv.org\/abs\/1602.07360  Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR abs\/1602.07360 ( 2016 ). arXiv: 1602.07360 http:\/\/arxiv.org\/abs\/1602.07360"},{"key":"e_1_3_2_2_22_1","volume-title":"Neethu Mariya Joy, Tejan Karmali, Avik Pal, and Viral Shah.","author":"Innes Michael","year":"2018"},{"key":"e_1_3_2_2_23_1","unstructured":"Intel. 2019. Understanding Memory Formats. https:\/\/intel.github.io\/mkl-dnn\/ understanding_memory_formats.html.  Intel. 2019. Understanding Memory Formats. https:\/\/intel.github.io\/mkl-dnn\/ understanding_memory_formats.html."},{"key":"e_1_3_2_2_24_1","first-page":"448","volume-title":"Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37","author":"Iofe Sergey","year":"2015"},{"key":"e_1_3_2_2_25_1","volume-title":"Cafe: Convolutional Architecture for Fast Feature Embedding. CoRR abs\/1408.5093 ( 2014 ). arXiv: 1408.5093 http:\/\/arxiv.org\/abs\/1408.5093","author":"Jia Yangqing","year":"2014"},{"key":"e_1_3_2_2_26_1","first-page":"1097","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems","author":"Krizhevsky Alex"},{"key":"e_1_3_2_2_27_1","volume-title":"MLIR: A Compiler Infrastructure for the End of Moore's Law. arXiv","author":"Lattner Chris","year":"2020"},{"key":"e_1_3_2_2_28_1","first-page":"2278","volume-title":"Proc. IEEE 86","author":"Lecun Y.","year":"1998"},{"key":"e_1_3_2_2_29_1","unstructured":"Tsung-Yi Lin Michael Maire Serge J. Belongie Lubomir D. Bourdev Ross B. Girshick James Hays Pietro Perona Deva Ramanan Piotr Doll\u00e1r and C. Lawrence Zitnick. 2014. Microsoft COCO : Common Objects in Context. CoRR abs\/1405.0312 ( 2014 ). arXiv: 1405.0312 http:\/\/arxiv.org\/abs\/1405.0312  Tsung-Yi Lin Michael Maire Serge J. Belongie Lubomir D. Bourdev Ross B. Girshick James Hays Pietro Perona Deva Ramanan Piotr Doll\u00e1r and C. Lawrence Zitnick. 2014. Microsoft COCO : Common Objects in Context. CoRR abs\/1405.0312 ( 2014 ). arXiv: 1405.0312 http:\/\/arxiv.org\/abs\/1405.0312"},{"key":"e_1_3_2_2_30_1","unstructured":"Microsoft. 2018. Convert ML models to ONNX with WinMLTools. https:\/\/docs. microsoft.com\/en-us\/windows\/ai\/windows-ml\/convert-model-winmltools.  Microsoft. 2018. Convert ML models to ONNX with WinMLTools. https:\/\/docs. microsoft.com\/en-us\/windows\/ai\/windows-ml\/convert-model-winmltools."},{"key":"e_1_3_2_2_31_1","unstructured":"MMdnn. 2018. MMdnn: Model Management for deep neural networks. https: \/\/github.com\/microsoft\/MMdnn.  MMdnn. 2018. MMdnn: Model Management for deep neural networks. https: \/\/github.com\/microsoft\/MMdnn."},{"key":"e_1_3_2_2_32_1","unstructured":"MXNet. 2019. MXNet imagenet1k-resnet-152. symbol: http:\/\/data.mxnet.io\/ models\/imagenet\/resnet\/152-layers\/resnet-152-symbol.json params: http:\/\/data. mxnet.io\/models\/imagenet\/resnet\/152-layers\/resnet-152-0000.params.  MXNet. 2019. MXNet imagenet1k-resnet-152. symbol: http:\/\/data.mxnet.io\/ models\/imagenet\/resnet\/152-layers\/resnet-152-symbol.json params: http:\/\/data. mxnet.io\/models\/imagenet\/resnet\/152-layers\/resnet-152-0000.params."},{"key":"e_1_3_2_2_33_1","unstructured":"MXNet. 2019. MXNet Inception-V3. symbol: http:\/\/data.mxnet.io\/models\/ imagenet\/inception-bn\/ Inception-BN-symbol.json params: http:\/\/data.mxnet. io\/models\/imagenet\/inception-bn\/Inception-BN-0126.params.  MXNet. 2019. MXNet Inception-V3. symbol: http:\/\/data.mxnet.io\/models\/ imagenet\/inception-bn\/ Inception-BN-symbol.json params: http:\/\/data.mxnet. io\/models\/imagenet\/inception-bn\/Inception-BN-0126.params."},{"key":"e_1_3_2_2_34_1","unstructured":"MXNet. 2020. The Slice Symbol API of MXNet. https:\/\/mxnet.incubator. apache. org\/versions\/1.6\/api\/python\/docs\/api\/symbol\/symbol.html#mxnet.symbol.slice.  MXNet. 2020. The Slice Symbol API of MXNet. https:\/\/mxnet.incubator. apache. org\/versions\/1.6\/api\/python\/docs\/api\/symbol\/symbol.html#mxnet.symbol.slice."},{"key":"e_1_3_2_2_35_1","unstructured":"NNEF. 2017. Neural Network Exchange Format. https:\/\/www.khronos.org\/nnef.  NNEF. 2017. Neural Network Exchange Format. https:\/\/www.khronos.org\/nnef."},{"key":"e_1_3_2_2_36_1","volume-title":"NumPy: A guide to NumPy","author":"Oliphant Travis"},{"key":"e_1_3_2_2_37_1","unstructured":"ONNX. 2017. Open Neural Network Exchange. https:\/\/onnx.ai\/.  ONNX. 2017. Open Neural Network Exchange. https:\/\/onnx.ai\/."},{"key":"e_1_3_2_2_38_1","unstructured":"ONNX. 2018. ONNXMLTools. https:\/\/github.com\/onnx\/onnxmltools.  ONNX. 2018. ONNXMLTools. https:\/\/github.com\/onnx\/onnxmltools."},{"key":"e_1_3_2_2_39_1","unstructured":"ONNX. 2018. tf2onnx-Convert TensorFlow models to ONNX. https:\/\/github. com\/onnx\/tensorflow-onnx.  ONNX. 2018. tf2onnx-Convert TensorFlow models to ONNX. https:\/\/github. com\/onnx\/tensorflow-onnx."},{"key":"e_1_3_2_2_40_1","unstructured":"Ran Pan. 2014. Tensor Transpose and Its Properties. CoRR abs\/1411.1503 ( 2014 ). arXiv: 1411.1503 http:\/\/arxiv.org\/abs\/1411.1503  Ran Pan. 2014. Tensor Transpose and Its Properties. CoRR abs\/1411.1503 ( 2014 ). arXiv: 1411.1503 http:\/\/arxiv.org\/abs\/1411.1503"},{"key":"e_1_3_2_2_41_1","first-page":"8024","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. Curran Associates","author":"Paszke Adam","year":"2019","journal-title":"Inc."},{"key":"e_1_3_2_2_42_1","unstructured":"PyTorch. 2019. PyTorch Inception-V3. https:\/\/download.pytorch.org\/models\/ inception_v3_google-1a9a5a14.pth.  PyTorch. 2019. PyTorch Inception-V3. https:\/\/download.pytorch.org\/models\/ inception_v3_google-1a9a5a14.pth."},{"key":"e_1_3_2_2_43_1","unstructured":"PyTorch. 2019. PyTorch ResNet-152. resnet152 of torchvision 0.2.1 https:\/\/ download.pytorch.org\/models\/resnet152-b121ed2d.pth.  PyTorch. 2019. PyTorch ResNet-152. resnet152 of torchvision 0.2.1 https:\/\/ download.pytorch.org\/models\/resnet152-b121ed2d.pth."},{"key":"e_1_3_2_2_44_1","unstructured":"Joseph Redmon. 2013-2016. Darknet: Open Source Neural Networks in C. http:\/\/pjreddie.com\/darknet\/.  Joseph Redmon. 2013-2016. Darknet: Open Source Neural Networks in C. http:\/\/pjreddie.com\/darknet\/."},{"key":"e_1_3_2_2_45_1","volume-title":"Ross B. Girshick, and Ali Farhadi.","author":"Redmon Joseph","year":"2015"},{"key":"e_1_3_2_2_46_1","volume-title":"Relay: A High-Level IR for Deep Learning. CoRR abs\/","author":"Roesch Jared","year":"2019"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Grzegorz Rozenberg (Ed.). 1997. Handbook of Graph Grammars and Computing by Graph Transformations. World Scientific.  Grzegorz Rozenberg (Ed.). 1997. Handbook of Graph Grammars and Computing by Graph Transformations. World Scientific.","DOI":"10.1142\/3303"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_49_1","unstructured":"Florian Schrof Dmitry Kalenichenko and James Philbin. 2015. FaceNet: A Unified Embedding for Face Recognition and Clustering. CoRR abs\/1503.03832 ( 2015 ). arXiv: 1503.03832 http:\/\/arxiv.org\/abs\/1503.03832  Florian Schrof Dmitry Kalenichenko and James Philbin. 2015. FaceNet: A Unified Embedding for Face Recognition and Clustering. CoRR abs\/1503.03832 ( 2015 ). arXiv: 1503.03832 http:\/\/arxiv.org\/abs\/1503.03832"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2945397"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6404"},{"key":"e_1_3_2_2_52_1","volume-title":"3rd International Conference on Learning Representations, ICLR","author":"Simonyan Karen","year":"2015"},{"key":"e_1_3_2_2_53_1","volume-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1-9.","author":"Szegedy C."},{"key":"e_1_3_2_2_54_1","volume-title":"Data for the ImageNet ILSVRC 2012 Dataset plus some bounding boxes. https:\/\/github.com\/tensorflow\/models\/blob\/v1.13","year":"2019"},{"key":"e_1_3_2_2_55_1","unstructured":"TensorFlow. 2019. The Matrix Multiplication of TensorFlow. https:\/\/github.com\/ tensorflow\/docs\/blob\/r1.13\/site\/en\/api_docs\/python\/tf\/linalg\/matmul.md.  TensorFlow. 2019. The Matrix Multiplication of TensorFlow. https:\/\/github.com\/ tensorflow\/docs\/blob\/r1.13\/site\/en\/api_docs\/python\/tf\/linalg\/matmul.md."},{"key":"e_1_3_2_2_56_1","unstructured":"TensorFlow. 2019. TensorFLow Inception-V3. http:\/\/download.tensorflow.org\/ models\/inception_v3_2016_08_28.tar.gz.  TensorFlow. 2019. TensorFLow Inception-V3. http:\/\/download.tensorflow.org\/ models\/inception_v3_2016_08_28.tar.gz."},{"key":"e_1_3_2_2_57_1","volume-title":"pre-trained model : http:\/\/download.tensorflow.org\/models\/resnet_v1_152_2016_08_28.tar.gz","author":"Low","year":"2012"},{"key":"e_1_3_2_2_58_1","unstructured":"TensorFlow. 2020. Save and load models. https:\/\/www.tensorflow.org\/tutorials\/ keras\/save_and_load#save_checkpoints_during_training.  TensorFlow. 2020. Save and load models. https:\/\/www.tensorflow.org\/tutorials\/ keras\/save_and_load#save_checkpoints_during_training."},{"key":"e_1_3_2_2_59_1","volume-title":"Protocol Bufers: Google's Data Interchange Format. Technical Report. Google","author":"Varda Kenton","year":"2008"},{"key":"e_1_3_2_2_60_1","volume-title":"DLVM: A modern compiler infrastructure for deep learning systems. CoRR abs\/1711.03016 ( 2017 ). arXiv: 1711.03016 http:\/\/arxiv.org\/abs\/1711.03016","author":"Wei Richard","year":"2017"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"e_1_3_2_2_62_1","volume-title":"The Free Encyclopedia"},{"key":"e_1_3_2_2_63_1","volume-title":"The Free Encyclopedia"},{"key":"e_1_3_2_2_64_1","volume-title":"The Free Encyclopedia"},{"key":"e_1_3_2_2_65_1","unstructured":"Saining Xie Ross B. Girshick Piotr Doll\u00e1r Zhuowen Tu and Kaiming He. 2016. Aggregated Residual Transformations for Deep Neural Networks. CoRR abs\/1611.05431 ( 2016 ). arXiv: 1611.05431 http:\/\/arxiv.org\/abs\/1611.05431  Saining Xie Ross B. Girshick Piotr Doll\u00e1r Zhuowen Tu and Kaiming He. 2016. Aggregated Residual Transformations for Deep Neural Networks. CoRR abs\/1611.05431 ( 2016 ). arXiv: 1611.05431 http:\/\/arxiv.org\/abs\/1611.05431"},{"key":"e_1_3_2_2_66_1","volume-title":"Le","author":"Zoph Barret","year":"2017"}],"event":{"name":"ESEC\/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","location":"Virtual Event USA","acronym":"ESEC\/FSE '20","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"]},"container-title":["Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368089.3417051","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3368089.3417051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:58Z","timestamp":1750197718000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368089.3417051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,8]]},"references-count":66,"alternative-id":["10.1145\/3368089.3417051","10.1145\/3368089"],"URL":"https:\/\/doi.org\/10.1145\/3368089.3417051","relation":{},"subject":[],"published":{"date-parts":[[2020,11,8]]},"assertion":[{"value":"2020-11-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}