A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

A Framework for Providing Mobile Web Services

Web services are moving towards mobile world as a new emerging technology for applications communication. Mobile devices can operate as service consumers or service providers. Most approaches nowadays support consuming web services from mobile devices. This paper introduces a new framework called “Mobile Web Server” for providing mobile web services for not only mobile devices but also web appl...

A Framework for Developing Mobile Network Services

Service mobility is a highly desirable feature of future networks and will be a key factor in supporting mobile users and meeting the increasing demand for service resiliency and efficient resource management. Currently the main approaches for developing mobile services are active capsules and mobile agents but these are limited by design and fail to deliver sufficiently generic and broadly sco...

A framework for spatiotemporal prediction with small and heterogeneous data

A Probabilistic Framework for Deep Learning

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first ...

XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework

Predicting the next activity of a running process is an important aspect of process management. Recently, artificial neural networks, so called deep-learning approaches, have been proposed to address this challenge. This demo paper describes a software application that applies the Tensorflow deep-learning framework to process prediction. The software application reads industry-standard XES file...