Paper:
Published as a conference paper at ICLR 2018 PREDICTING FLOOR LEVEL FOR 911 CALLS WITH NEURAL NETWORKS AND SMARTPHONE SENSOR DATA
In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.

Review:
Update: Based on the discussions and the revisions, I have improved my rating. However I still feel like the novelty is somewhat limited, hence the recommendation.
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The paper introduces a system to estimate a floor-level via their mobile device's sensor data using an LSTM to determine when a smartphone enters or exits a building, then using the change in barometric pressure from the entrance of the building to indoor location. Overall the methodology is a fairly simple application of existing methods to a problem, and  there remain some methodological issues (see below).
General Comments
- The claim that the bmp280 device is in most smartphones today doesn’t seem to be backed up by the “comScore” reference (a simple ranking of manufacturers).  Please provide the original source for this information.
- Almost all exciting results based on RNNs are achieved with LSTMs, so calling an RNN with LSTM hidden units a new name IOLSTM seems rather strange - this is simply an LSTM.
- There exist models for modelling multiple levels of abstraction, such as the contextual LSTM of [1]. This would be much more satisfying that the two level approach taken here, would likely perform better, would replace the need for the clustering method, and would solve issues such as the user being on the roof.  The only caveat is that it may require an encoding of the building (through a one-hot encoding) to ensure that the relationship between the floor height and barometric pressure is learnt. For unseen buildings a background class could be used, the estimators as used before, or aggregation of the other buildings by turning the whole vector on.
- It’s not clear if a bias of 1 was added to the forget gate of the LSTM or not. This has been shown to improve results [2].
- Overall the whole pipeline feels very ad-hoc, with many hand-tuned parameters.  Notwithstanding the network architecture, here I’m referring to the window for the barometric pressure, the Jaccard distance threshold, the binary mask lengths, and the time window for selecting p0.
- Are there plans to release the data and/or the code for the experiments? Currently the results would be impossible to reproduce.
- The typo of accuracy given by the authors is somewhat worrying, given that the result is repeated several times in the paper.
Typographical Issues
- Page 1:  ”floor-level accuracy” back ticks
- Page  4:   Figure  4.1→Figure  1;  Nawarathne  et  al  Nawarathne  et  al.→Nawarathne et al.
- Page 6:  ”carpet to carpet” back ticks
- Table 2:  What does -4+ mean?
- References.  The references should have capitalisation where appropriate.For example,  Iodetector→IODetector,  wi-fi→Wi-Fi,  apple→Apple, iphone→iPhone, i→I etc.
[1]  Shalini Ghosh, Oriol Vinyals, Brian Strope, Scott Roy, Tom Dean, and LarryHeck. Contextual LSTM (CLSTM) models for large scale NLP tasks. arXivpreprint arXiv:1602.06291, 2016.
[2]  Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever.  An empirical exploration of recurrent network architectures.  InProceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 2342–2350,2015