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Picpick install switches
Picpick install switches









picpick install switches

Yan T, Kumar V, Ganesan D (2010) Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. In: Proceeding of ACM SIGIR’06, pp 429–436 IEEE Commun Mag 52(8):144–152Ĭhen H, Karger DR (2006) Less is more: probabilistic models for retrieving fewer relevant documents. Guo B, Yu Z, Zhang D et al (2014) Cross-community sensing and mining. Guo B, Chen C, Yu Z et al (2015) Building human-machine intelligence in mobile crowd sensing. Zhang D, Wang L, Xiong H et al (2014) 4W1H in mobile crowd sensing. Ma H, Zhao D, Yuan P (2014) Opportunities in mobile crowd sensing. Ryong L, Shoko W, Kazutoshi S (2011) Discovery of unusual regional social activities using geo-tagged microblogs. In: Proceedings of ACM international conference on mobile computing and networking (MobiCom’14), pp 249–260 Gao R, Zhao M, Ye T et al (2014) Jigsaw: indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of IEEE sensing, communication, and networking (SECON’14), pp 546–554 In: Proceedings of the 9th ACM/IEEE international conference on information processing in sensor networks (IPSN’10), New York, USA, pp 105–116Īly H, Basalamah A, Youssef M (2014) Map++: a crowd-sensing system for automatic map semantics identification.

picpick install switches

(2010) Ear-phone: an end-to-end participatory urban noise mapping system.

picpick install switches

In: Proceeding of ACM MobiSys’11, pp 127–140 Koukoumidis E, Peh L S, Martonosi MR (2011) SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. Guo B, Chen H, Yu Z, Xie X, Huangfu S, Zhang D (2015) FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. In: Foresight & Governance Project, White Paper, 2009, pp 1–15 Goldman J, Shilton K, Burke J et al (2009) Participatory sensing: a citizen-powered approach to illuminating the patterns that shape our world. In: Proceedings of the ACM MobiHoc’14, pp 113–122 Wang Y, Hu W, Wu Y et al (2014) SmartPhoto: a resource-aware crowdsourcing approach for image sensing with smartphones. In: Proceedings of IEEE RTSS’11, pp 317–326 Uddin MYS, Wang H, Saremi F et al (2011) PhotoNet: a similarity-aware picture delivery service for situation awareness. Kim S, Robson C, Zimmerman T, Pierce J, Haber EM (2011) Creekwatch: pairing usefulness and usability for successful citizen science. Guo B, Wang Z, Yu Z et al (2015) Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. Experimental results on two real-world datasets indicate that PTree can effectively reduce data redundancy while maintaining the coverage requests, and the overall framework is flexible. A pyramid tree (PTree) method is further proposed to select an optimal set of pictures from picture streams based on multi-dimensional constraints. It first presents a multifaceted task model that allows for varied MCP task specification. To address this requirement, we propose a generic data collection framework called PicPick. This issue has little been investigated in existing studies. To meet diverse constraints (e.g., spatiotemporal contexts, single or multiple shooting angles) on the data to be collected in MCP tasks, a data selection process is needed to eliminate data redundancy and reduce network overhead.

picpick install switches

Pictures contributed later in the stream may be semantically or visually relevant to previous ones, which can result in data redundancy. In MCP, a picture stream is generated when delivering intermittently to the backend server by participants. Mobile crowd photography (MCP) is a widely used technique in crowd sensing.











Picpick install switches