Rendering large datasets of georeferenced markers in mobile devices

2016 ◽  
Author(s):  
Sebastián Ortega ◽  
Agustín Trujillo ◽  
José M. Santana ◽  
José P. Suárez ◽  
Diego Gómez-Deck
Keyword(s):  
Mobile Devices ◽  
Large Datasets

scholarly journals Distributed Machine Learning Using Data Parallelism on Mobile Platform

2020 ◽  
Author(s):  
Máté Szabó
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Large Datasets ◽  
Software Implementation ◽  
Data Parallelism ◽  
Client Server ◽  
Distributed Training ◽  
Using Data ◽  
Server Architecture ◽  
Distributed Machine Learning

Machine learning has many challenges, and one of them is to deal with large datasets, because the size of them grows continuously year by year. One solution to this problem is data parallelism. This paper investigates the expansion of data parallelism to mobile, which became the most popular platform. Special client-server architecture was created for this purpose. The software implementation of this problem measures the mobile devices training capabilities and the efficiency of the whole system. The results show that doing distributed training on mobile cluster is possible and safe, but its performance depends on the algorithm’s implementation.

FaceBlit

2021 ◽  
Vol 4 (1) ◽  
pp. 1-17
Author(s):  
Aneta Texler ◽  
Ondřej Texler ◽  
Michal Kučera ◽  
Menglei Chai ◽  
Daniel Sýkora
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Large Datasets ◽  
Training Phase ◽  
Target Image ◽  
Identity Preservation ◽  
Synthesis Algorithm ◽  
Artistic Style ◽  
Target Subject

We present FaceBlit---a system for real-time example-based face video stylization that retains textural details of the style in a semantically meaningful manner, i.e., strokes used to depict specific features in the style are present at the appropriate locations in the target image. As compared to previous techniques, our system preserves the identity of the target subject and runs in real-time without the need for large datasets nor lengthy training phase. To achieve this, we modify the existing face stylization pipeline of Fišer et al. [2017] so that it can quickly generate a set of guiding channels that handle identity preservation of the target subject while are still compatible with a faster variant of patch-based synthesis algorithm of Sýkora et al. [2019]. Thanks to these improvements we demonstrate a first face stylization pipeline that can instantly transfer artistic style from a single portrait to the target video at interactive rates even on mobile devices.

scholarly journals Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices

2018 ◽  
Vol 8 (11) ◽  
pp. 2015 ◽  
Author(s):  
Jongwook Jeong ◽  
Neunghoe Kim ◽  
Hoh In
Keyword(s):  
User Interface ◽  
Mobile Devices ◽  
Graphical User Interface ◽  
User Study ◽  
Large Datasets ◽  
Target Information ◽  
Large Dataset ◽  
Time And Energy

Scrolling is a frequently used Graphical User Interface widget that enables users to interact with a large amount of data using a limited viewport. However, if excessive data is included in the scroll, users are required to spend a substantial amount of time and effort to find the required information. In this paper, we present adaptive kinetic scrolling (AKS), a technique based on kinetic scrolling by which users can access target information more rapidly on mobile devices. Based on the user’s behavior, AKS detects situations when the user intends to access certain information that may be distant from the current viewport. At this point, AKS amplifies the speed of kinetic scrolling. Furthermore, the scrolling speed adapts according to the size of the remaining data to be scrolled. The more data that the scrolling widget contains, the more rapidly it scrolls so that the user can quickly reach the target. Kinetic scrolling is frequently used in scrolling widgets, and with AKS, users can save time and energy wasted on repetitive meaningless scrolling. We conducted a user study and verified that the proposed scrolling technique enables users to access target information more rapidly, particularly when there is a large dataset to navigate.

Phishing in Mobile Devices: Survey and Prevention Mechanism

2012 ◽  
Vol 2 (3) ◽  
pp. 86-88
Author(s):  
Dr. Kuntal Patel ◽  
◽  
Prof. Parimal Patel
Keyword(s):  
Mobile Devices
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