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  2. If I build it, will they come? Predicting new venue visitation…

    https://mobile-systems.cl.cam.ac.uk/papers/sigspatial17poster.pdf
    10 Oct 2017: If I build it, will they come? Predicting new venue visitationpaerns through mobility data. ... beused to provide insight into the temporal prole of a new venue.Baselines.
  3. aaai17 format (9 March)

    https://mobile-systems.cl.cam.ac.uk/papers/icwsm17poster.pdf
    10 Mar 2017: With an aim to overcome this limitation, researchers have recently started to mine low-cost, real-time, and fine-grained new data sources for socio-economic deprivation study. ... By applying new cultural metrics and traditional network met-rics to the
  4. A Study of Bluetooth Low Energy Performance forHuman Proximity ...

    https://mobile-systems.cl.cam.ac.uk/papers/percom17.pdf
    11 Jan 2017: Given that each device records thedata on a new file every day, we use the number of correctlyrecorded files as a measure of robustness of our prototype.
  5. SIGCHI Conference Proceedings Format

    https://mobile-systems.cl.cam.ac.uk/papers/www17.pdf
    17 Feb 2017: We found thatmost of the respondents consider themselves very open to new ex-periences, but not many of them reported to be neither extravertednor emotionally stable.
  6. Accelerating Mobile Audio Sensing Algorithmsthrough On-Chip GPU…

    https://mobile-systems.cl.cam.ac.uk/papers/mobisys17.pdf
    10 May 2017: The DNN feed forwarding is performed in a slid-ing window with every new frame, resulting in 100 propagationsper second (once every 10 ms). ... In a 1-second inference window there are a total ofn = 100 network propagations (one per new frame every
  7. UbiComp17_camera

    https://mobile-systems.cl.cam.ac.uk/papers/Ubicomp17-Georgiev.pdf
    1 Aug 2017: propose a new search-mechanism (Sec.
  8. Understanding the Role of Places and Activities on Mobile Phone…

    https://mobile-systems.cl.cam.ac.uk/papers/Ubicomp17-Abhinav.pdf
    1 Aug 2017: rough our analysis we uncover various new insights about the phone usage and notication interactionbehaviors of users and also, quite importantly, conrm some ndings of previous studies. ... erefore, we created anothercategory for chores and mapped the
  9. Multimodal Deep Learning for Activity and Context Recognition

    https://mobile-systems.cl.cam.ac.uk/papers/ubicomp2018-radu.pdf
    21 Dec 2017: One key attractionis that available classifiers for each sensor type (tested and verified with other applications) can be readily adoptedto undertake a new task on same sensing modality. ... Convolution layers are typically followed by a Pooling layer,

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