Global attestation of location in mobile devices
Saritha Arunkumar, Mudhakar Srivatsa, et al.
MILCOM 2015
Vector space representations of text have increased in popularity and are used in various text classification problems. We present Doc2Img, a new approach to create document vectors that improves upon existing approaches such as Word2Vec and Doc2Vec in capturing similarities between words within a document and the differences across documents. We apply this new vector space representation to the problem of deriving the sensor requirements of apps (for smartphones and IoT devices) by learning a classification model using document vectors. We show that this learned model outperforms existing vector space representations (Word2Vec and Doc2Vec) by more than 10%. Further, this model can predict with an average accuracy of 75% and greater than 85% on the top-20 sensor requirements for 300 different applications.
Saritha Arunkumar, Mudhakar Srivatsa, et al.
MILCOM 2015
Dong-Anh Nguyen, Tarek Abdelzaher, et al.
MILCOM 2014
Yeonsup Lim, Mudhakar Srivatsa, et al.
Big Data 2018
Shouling Ji, Weiqing Li, et al.
IEEE/ACM TON