Topic: mobile-devices Goto Github
Some thing interesting about mobile-devices
Some thing interesting about mobile-devices
mobile-devices,This project explores user authentication on mobile devices through typing patterns, leveraging touch and motion data. Using machine learning models, particularly LSTM, the research demonstrates superior user classification accuracy compared to traditional RNN models, enhancing security against ATO attacks.
User: aliabduljabbar
mobile-devices,🏄 A Lightweight Web Browser-based Machine Learning Framework
User: charmve
Home Page: https://github.com/Charmve
mobile-devices,
User: chirantar7004
mobile-devices,Public scenarios to crowdsource experiments (such as DNN crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework across diverse mobile devices provided by volunteers. Results are continuously aggregated at the open repository of knowledge:
Organization: ctuning
Home Page: http://cKnowledge.org/ai
mobile-devices,Collective Knowledge crowd-tuning extension to let users crowdsource their experiments (using portable Collective Knowledge workflows) such as performance benchmarking, auto tuning and machine learning across diverse platforms with Linux, Windows, MacOS and Android provided by volunteers. Demo of DNN crowd-benchmarking and crowd-tuning:
Organization: ctuning
Home Page: http://cKnowledge.org/repo
mobile-devices,CK repository with components and automation actions to enable portable workflows across diverse platforms including Linux, Windows, MacOS and Android. It includes software detection plugins and meta packages (code, data sets, models, scripts, etc) with the possibility of multiple versions to co-exist in a user or system environment.
Organization: ctuning
Home Page: https://github.com/mlcommons/ck
mobile-devices,Collective Knowledge workflow for ARM's workload automation tool: an open framework for gathering and sharing knowledge about system design and optimization using real-world workloads.
Organization: ctuning
mobile-devices,Android application to participate in experiment crowdsourcing (such as workload crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework and open repositories of knowledge:
Organization: ctuning
Home Page: http://cKnowledge.org/repo
mobile-devices,Detect device is iOS or Android. 检测是iOS设备还是安卓设备。
User: dev-coco
mobile-devices,Crowdsourcing video experiments (such as collaborative benchmarking and optimization of DNN algorithms) using Collective Knowledge Framework across diverse Android devices provided by volunteers. Results are continuously aggregated in the open repository:
Organization: dividiti
Home Page: http://cKnowledge.org/repo
mobile-devices,A static website of a mobile devices store
User: hamzjerdouj
mobile-devices,Development of applications using the React Native framework on mobile operating systems: user interface, microservices and database for automation, testing and debugging purposes.
User: hceresetti
Home Page: https://github.com/hceresetti/development-for-mobile-devices-ii
mobile-devices,An NPM package written in React used to preview mobile devices. Especially helpful when used in combination with a CMS like Sanity.
User: jesperpaulsen
Home Page: https://jesperpaulsen.github.io/sanity-mobile-preview/
mobile-devices,Utility for fetching mobile device(s) name(s). It uses DeviceSpecifications and a Google Programmable Search Engine to fetch the information based on provided model code(s).
User: joaocjesus
mobile-devices,Efficient offline route navigation with multi-criteria objectives on mobile devices
User: mikkelangeles
mobile-devices,This learning path provides system administrators with a comprehensive method for studying the skills tested in the CompTIA A+ (220-1001 and 220-1002) exams. It includes in-depth courses teaching skills from each exam domain and provides insights into resources you can use to prepare for the exam.
User: nancyalaswad90
mobile-devices,Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.
User: osamam0
mobile-devices,This standard outlines the security requirements required to protect your organisational data and financial assets
Organization: osodevops
mobile-devices,TransferCL: an open framework for transfer learning on mobile device
User: ovalery16
mobile-devices,A collection of awesome framework, libraries, learning tutorials, videos, webcasts, technical resources and cool stuff about iOS Security.
User: paulveillard
mobile-devices,Bespoke OTA service for Planet Computers Ltd, supported by cloud-native technology.
Organization: planet-community
mobile-devices,PeleeNet: An efficient DenseNet architecture for mobile devices
User: robert-junwang
mobile-devices,EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
Organization: snap-research
Home Page: https://arxiv.org/abs/2212.08059
mobile-devices,Official code (Tensorflow) for paper "Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks"
User: thangvubk
mobile-devices,PeleeNet in PyTorch
User: wpf535236337
mobile-devices,开源移动端快速视频人脸跟踪-移动端150FPS+
Organization: zeusees
Home Page: http://www.zeusee.com
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