Browsing by Subject "Smartphones"

Now showing 1 - 5 of 5

Open Access
Children's mobile communicative practices and locational privacy
(Oxford University Press, 2022-09-01) Özkul, Didem
Children start using smartphones increasingly from early ages. This makes it more difficult for them to develop an understanding of online privacy and managing their personal data. Many parents monitor and regulate children's online media use. However, they also encourage using smartphones to ensure the safety and security of their children. This study explores how children use smartphones in relation to their understanding of privacy of communication, content, data, and location. It examines data from 7 focus groups with arts-based methods conducted with 37 children in UK. The findings suggest that children think of their smartphones as a private communication technology and a private place, and they manage their locational privacy based on the necessity of using a mobile app and through adjusting the location settings on their phones. The findings also suggest that privacy of mobile data and user content are dependent on where mobile communication takes place. © 2022 The Author(s). Published by Oxford University Press on behalf of International Communication Association.
Open Access
Efficient community identification and maintenance at multiple resolutions on distributed datastores
(Elsevier BV, 2015) Aksu, H.; Canim, M.; Chang, Yuan-Chi; Korpeoglu, I.; Ulusoy, Özgür
The topic of network community identification at multiple resolutions is of great interest in practice to learn high cohesive subnetworks about different subjects in a network. For instance, one might examine the interconnections among web pages, blogs and social content to identify pockets of influencers on subjects like 'Big Data', 'smart phone' or 'global warming'. With dynamic changes to its graph representation and content, the incremental maintenance of a community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k-values, so that multiple community resolutions are represented with multiple k-core graphs. Recognizing that large graphs and their even larger attributed content cannot be stored and managed by a single server, we then propose distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable Big Data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on multiple subjects in rich network content simultaneously.
Open Access
Lineking: coffee shop wait-time monitoring using smartphones
(Institute of Electrical and Electronics Engineers, 2015) Bulut, M. F.; Demirbas, M.; Ferhatosmanoglu, H.
This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes.
Open Access
LineKing: Crowdsourced line wait-time estimation using smartphones
(Springer, 2013) Bulut, M. F.; Yilmaz, Y. S.; Demirbaş, M.; Ferhatosmanoğlu, N.; Ferhatosmanoğlu, Hakan
This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes. © 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
Open Access
A smartphone based surface plasmon resonance imaging (SPRi) platform for on-site biodetection
(Elsevier, 2017) Guner, H.; Ozgur, E.; Kokturk, G.; Celik, M.; Esen, E.; Topal, A. E.; Ayas, S.; Uludag, Y.; Elbuken, C.; Dana, A.
We demonstrate a surface plasmon resonance imaging platform integrated with a smartphone to be used in the field with high-throughput biodetection. Inexpensive and disposable SPR substrates are produced by metal coating of commercial Blu-ray discs. A compact imaging apparatus is fabricated using a 3D printer which allows taking SPR measurements from more than 20.000 individual pixels. Real-time bulk refractive index change measurements yield noise equivalent refractive index changes as low as 4.12 × 10−5 RIU which is comparable with the detection performance of commercial instruments. As a demonstration of a biological assay, we have shown capture of mouse IgG antibodies by immobilized layer of rabbit anti-mouse (RAM) IgG antibody with nanomolar level limit of detection. Our approach in miniaturization of SPR biosensing in a cost-effective manner could enable realization of portable SPR measurement systems and kits for point-of-care applications.