{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:29:24Z","timestamp":1772119764903,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"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":[[2021,7,11]]},"DOI":"10.1145\/3404835.3462894","type":"proceedings-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T02:41:48Z","timestamp":1626057708000},"page":"1544-1553","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Do Affective Cues Validate Behavioural Metrics for Search?"],"prefix":"10.1145","author":[{"given":"Daniel","family":"McDuff","sequence":"first","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"given":"Paul","family":"Thomas","sequence":"additional","affiliation":[{"name":"Microsoft, Canberra, Australia"}]},{"given":"Nick","family":"Craswell","sequence":"additional","affiliation":[{"name":"Microsoft, Bellevue, WA, USA"}]},{"given":"Kael","family":"Rowan","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"given":"Mary","family":"Czerwinski","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_2_2_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25:1097--1105","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25:1097--1105, 2012."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_2_5_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_2_6_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.668"},{"key":"e_1_3_2_2_9_1","volume-title":"Fractional max-pooling. arXiv preprint arXiv:1412.6071","author":"Graham Benjamin","year":"2014","unstructured":"Benjamin Graham. Fractional max-pooling. arXiv preprint arXiv:1412.6071, 2014."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"e_1_3_2_2_11_1","volume-title":"Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501","author":"Cubuk Ekin D","year":"2018","unstructured":"Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501, 2018."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2014.6974121"},{"key":"e_1_3_2_2_13_1","volume-title":"Analysis of cnn-based remote-ppg to understand limitations and sensitivities. arXiv preprint arXiv:1911.02736","author":"Zhan Qi","year":"2019","unstructured":"Qi Zhan, Wenjin Wang, and Gerard de Haan. Analysis of cnn-based remote-ppg to understand limitations and sensitivities. arXiv preprint arXiv:1911.02736, 2019."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00174"},{"key":"e_1_3_2_2_16_1","volume-title":"Improving deep learning using generic data augmentation. arXiv preprint arXiv:1708.06020","author":"Taylor Luke","year":"2017","unstructured":"Luke Taylor and Geoff Nitschke. Improving deep learning using generic data augmentation. arXiv preprint arXiv:1708.06020, 2017."},{"key":"e_1_3_2_2_17_1","volume-title":"Network in network. arXiv preprint arXiv:1312.4400","author":"Lin Min","year":"2013","unstructured":"Min Lin, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400, 2013."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354694"},{"key":"e_1_3_2_2_20_1","first-page":"1058","volume-title":"International conference on machine learning","author":"Wan Li","year":"2013","unstructured":"Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus. Regularization of neural networks using dropconnect. In International conference on machine learning, pages 1058--1066. PMLR, 2013."},{"key":"e_1_3_2_2_21_1","volume-title":"Apac: Augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229","author":"Sato Ikuro","year":"2015","unstructured":"Ikuro Sato, Hiroki Nishimura, and Kensuke Yokoi. Apac: Augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229, 2015."},{"key":"e_1_3_2_2_22_1","first-page":"4119","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Radosavovic Ilija","year":"2018","unstructured":"Ilija Radosavovic, Piotr Doll\u00e1r, Ross Girshick, Georgia Gkioxari, and Kaiming He. Data distillation: Towards omni-supervised learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4119--4128, 2018."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"e_1_3_2_2_24_1","volume-title":"Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552","author":"DeVries Terrance","year":"2017","unstructured":"Terrance DeVries and Graham W Taylor. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2018.8628917"},{"key":"e_1_3_2_2_27_1","volume-title":"Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199","author":"Szegedy Christian","year":"2013","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013."},{"key":"e_1_3_2_2_28_1","volume-title":"Jonathon Shlens, Ekin D Cubuk, and Justin Gilmer. A fourier perspective on model robustness in computer vision. arXiv preprint arXiv:1906.08988","author":"Yin Dong","year":"2019","unstructured":"Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D Cubuk, and Justin Gilmer. A fourier perspective on model robustness in computer vision. arXiv preprint arXiv:1906.08988, 2019."},{"key":"e_1_3_2_2_29_1","volume-title":"Adversarial examples are a natural consequence of test error in noise. arXiv preprint arXiv:1901.10513","author":"Ford Nic","year":"2019","unstructured":"Nic Ford, Justin Gilmer, Nicolas Carlini, and Dogus Cubuk. Adversarial examples are a natural consequence of test error in noise. arXiv preprint arXiv:1901.10513, 2019."},{"key":"e_1_3_2_2_30_1","volume-title":"Improving robustness without sacrificing accuracy with patch gaussian augmentation. arXiv preprint arXiv:1906.02611","author":"Lopes Raphael Gontijo","year":"2019","unstructured":"Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk. Improving robustness without sacrificing accuracy with patch gaussian augmentation. arXiv preprint arXiv:1906.02611, 2019."},{"key":"e_1_3_2_2_31_1","volume-title":"Patchshuffle regularization. arXiv preprint arXiv:1707.07103","author":"Kang Guoliang","year":"2017","unstructured":"Guoliang Kang, Xuanyi Dong, Liang Zheng, and Yi Yang. Patchshuffle regularization. arXiv preprint arXiv:1707.07103, 2017."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-14058-7_55"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2935128"},{"key":"e_1_3_2_2_34_1","volume-title":"Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929","author":"Inoue Hiroshi","year":"2018","unstructured":"Hiroshi Inoue. Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929, 2018."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1080\/24751839.2018.1479932"},{"key":"e_1_3_2_2_36_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Magdalini Paschali Walter Simson Abhijit Guha Roy Muhammad Ferjad Naeem R\u00fcdiger G\u00f6bl Christian Wachinger and Nassir Navab. Data augmentation with manifold exploring geometric transformations for increased performance and robustness. arXiv preprint arXiv:1901.04420 2019.","DOI":"10.1007\/978-3-030-20351-1_40"},{"key":"e_1_3_2_2_38_1","volume-title":"A bayesian data augmentation approach for learning deep models. arXiv preprint arXiv:1710.10564","author":"Tran Toan","year":"2017","unstructured":"Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, and Ian Reid. A bayesian data augmentation approach for learning deep models. arXiv preprint arXiv:1710.10564, 2017."},{"key":"e_1_3_2_2_39_1","volume-title":"Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340","author":"Antoniou Antreas","year":"2017","unstructured":"Antreas Antoniou, Amos Storkey, and Harrison Edwards. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340, 2017."},{"key":"e_1_3_2_2_40_1","volume-title":"Learning to compose domain-specific transformations for data augmentation. Advances in neural information processing systems, 30:3239","author":"Ratner Alexander J","year":"2017","unstructured":"Alexander J Ratner, Henry R Ehrenberg, Zeshan Hussain, Jared Dunnmon, and Christopher R\u00e9. Learning to compose domain-specific transformations for data augmentation. Advances in neural information processing systems, 30:3239, 2017."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696121"},{"key":"e_1_3_2_2_42_1","volume-title":"Dataset augmentation in feature space. arXiv preprint arXiv:1702.05538","author":"DeVries Terrance","year":"2017","unstructured":"Terrance DeVries and Graham W Taylor. Dataset augmentation in feature space. arXiv preprint arXiv:1702.05538, 2017."},{"key":"e_1_3_2_2_43_1","volume-title":"Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848","author":"Xie Qizhe","year":"2019","unstructured":"Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. Unsupervised data augmentation for consistency training. arXiv preprint arXiv:1904.12848, 2019."},{"key":"e_1_3_2_2_44_1","first-page":"2731","volume-title":"International Conference on Machine Learning","author":"Ho Daniel","year":"2019","unstructured":"Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, and Pieter Abbeel. Population based augmentation: Efficient learning of augmentation policy schedules. In International Conference on Machine Learning, pages 2731--2741. PMLR, 2019."},{"key":"e_1_3_2_2_45_1","volume-title":"A novel algorithm for remote photoplethysmography: Spatial subspace rotation","author":"Wang Wenjin","year":"1974","unstructured":"Wenjin Wang, Sander Stuijk, and Gerard De Haan. A novel algorithm for remote photoplethysmography: Spatial subspace rotation. IEEE transactions on biomedical engineering, 63(9):1974--1984, 2015."},{"key":"e_1_3_2_2_46_1","volume-title":"David MH Foo, and Ulf R Borg. Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge. Journal of clinical monitoring and computing, 32(5):871--880","author":"Addison Paul S","year":"2018","unstructured":"Paul S Addison, Dominique Jacquel, David MH Foo, and Ulf R Borg. Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge. Journal of clinical monitoring and computing, 32(5):871--880, 2018."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2585109"},{"key":"e_1_3_2_2_48_1","volume-title":"Influence of skin type and wavelength on light wave reflectance. Journal of clinical monitoring and computing, 27(3):313--317","author":"Fallow Bennett A","year":"2013","unstructured":"Bennett A Fallow, Takashi Tarumi, and Hirofumi Tanaka. Influence of skin type and wavelength on light wave reflectance. Journal of clinical monitoring and computing, 27(3):313--317, 2013."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2609282"}],"event":{"name":"SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Virtual Event Canada","acronym":"SIGIR '21","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3462894","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3462894","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:18Z","timestamp":1750193238000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3462894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":48,"alternative-id":["10.1145\/3404835.3462894","10.1145\/3404835"],"URL":"https:\/\/doi.org\/10.1145\/3404835.3462894","relation":{},"subject":[],"published":{"date-parts":[[2021,7,11]]},"assertion":[{"value":"2021-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}