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  2. Where Online Friends Meet: Social Communities in Location-based…

    https://mobile-systems.cl.cam.ac.uk/papers/icwsm12poster.pdf
    13 Mar 2012: A new generation of online ser-vices has recently enabled such analysis: location-based so-cial networks are mobile social services built around loca-tion sharing. ... New York,NY, USA: ACM.Backstrom, L.; Sun, E.; and Marlow, C. 2010.
  3. Exploring On-Device Learning Using Few Shots forAudio Classification…

    https://mobile-systems.cl.cam.ac.uk/papers/euspico22.pdf
    24 Aug 2022: due to its high accuracy and efficiency.This is akin to a scenario where a pretrained model (trainedwith a few shot learning method) can be used to identify new. ... Through a large number of experiments, we show that thesemethods can adapt to new
  4. Towards Adversarial Robustness with Early Exit Ensembles Lorena…

    https://mobile-systems.cl.cam.ac.uk/papers/embc22-qendro.pdf
    22 Apr 2022: Current mitigation techniques often rely onexpensive re-training procedures as new attacks emerge. ... In a real world deployment, earlyexit ensembles have the potential to have a high level ofrobustness provided by adversarial training of known
  5. Experience in deploying wearable devices for office analytics

    https://mobile-systems.cl.cam.ac.uk/papers/cscw16.pdf
    29 Mar 2016: Sharing serendipityin the workplace. In Proceedings of the Third Inter-national Conference on Collaborative Virtual Environ-ments (New York, NY, USA, 2000), CVE ’00, ACM. ... 8] PENTLAND, A. The new science of building greatteams. Harvard Business
  6. IMChew: Chewing Analysis using Earphone Inertial Measurement Units

    https://mobile-systems.cl.cam.ac.uk/papers/bodysys24-yang.pdf
    2 May 2024: Meanwhile, LOSO CV better reflectsthe real-world application since all new users are unseen bythe system. ... This is inaccuratesince, during sessions of eating activities, participants pauseand stop chewing every now and then to put more foodin their
  7. An Empirical Study of Geographic User Activity Patterns in ...

    https://mobile-systems.cl.cam.ac.uk/papers/icwsm11poster.pdf
    5 Apr 2011: Switching our view to the 100 to 500 minuteinterval, the analysis reveals a new class of place/activitytransitions. ... The eighth transition with a distance of 3973.3 kmis that between LAX and JFK Airport in New York.
  8. Efficient Node Discovery in Mobile WirelessSensor Networks Vladimir…

    https://mobile-systems.cl.cam.ac.uk/papers/dcoss08.pdf
    4 Apr 2008: As it discovers new nodes it dynamically reajustsits budget according to the following steps. ... In: SenSys’06: Proceedings of the 4th international conference on Embeddednetworked sensor systems, New York, NY, USA, ACM Press (2006) 321–334.
  9. Uncertainty Estimation with Data Augmentationfor Active Learning…

    https://mobile-systems.cl.cam.ac.uk/papers/embc23-vavaroutas.pdf
    19 May 2023: AL interactively queries the oracle to label new –previouslyunlabelled– signals at each training pass [3], making it idealfor clinical tasks where labelled samples are scarce. ... The samples inthe newly-labelled batch then enter the labelled pool.
  10. Trust and Mobility Aware Service Provision forPervasive Computing…

    https://mobile-systems.cl.cam.ac.uk/papers/trustmob.pdf
    29 Feb 2008: News headlines, stock market levels: someone able to access the Internetthrough a GPRS phone could forward updates to others. – ... Gaming: devices could participate in a shared game during their trip.– Software components: new
  11. Enabling On-Device Smartphone GPU basedTraining: Lessons Learned…

    https://mobile-systems.cl.cam.ac.uk/papers/perfail22.pdf
    4 Feb 2022: e., forward pass) [1], [2].To name a few, weight quantization [1], [3], pruning [2], vectorquantization [4], and new neural architectures [5], [6] havebeen proposed to make inference more efficient. ... Instead of finding a new dataset to meet our input

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