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Impact of Indoor-Outdoor Context on Crowdsourcing based Mobile Coverage Analysis., , and . AllThingsCellular@SIGCOMM, page 45-50. ACM, (2015)Mapping parallelism in a functional IR through constraint satisfaction: a case study on convolution for mobile GPUs., , , and . CC, page 218-230. ACM, (2022)HiMLoc: Indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting., and . IPIN, page 1-10. IEEE, (2013)Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking., and . IPIN, page 1-8. IEEE, (2019)A semi-supervised learning approach for robust indoor-outdoor detection with smartphones., , , and . SenSys, page 280-294. ACM, (2014)Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients., and . EdgeSys@EuroSys, page 61-66. ACM, (2021)Multimodal Deep Learning for Activity and Context Recognition., , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1 (4): 157:1-157:27 (2017)Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs., , , , , , , , and . CoRR, (2020)POSTER: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming., , , , and . PACT, page 489-490. IEEE, (2019)Localization Systems Repository: A Platform for Open-source Localization Systems and Datasets., , and . IPIN, page 1-8. IEEE, (2019)