Your iCloud storage is almost full.

Apple recently sends the following emails to make us upgrade! As you see in my screenshot I have nothing to backup, but my iPhone fills the space with something!!! It is very strange and weird that Apple wants to make us backup on the only option (5GB small space!).

Dear ... ...,

Your iCloud storage is almost full. You have 5 MB remaining of 5 GB total storage.

Upgrade to 50 GB for $0.99 per month

Your iCloud storage is used for iCloud Mail and to keep the most important things on your iPhone, iPad, and iPod touch safe and available, even if you lose your device. iCloud Drive and apps like Keynote, Pages, and Numbers also use iCloud storage to keep your files up-to-date everywhere.
To continue to use iCloud and to back up your photos, documents, contacts, mail, and more, you need to upgrade your iCloud storage plan or reduce the amount of storage you are using.

The iCloud Team

Note: If you exceed your storage plan, your devices will stop backing up to iCloud. iCloud Drive and iCloud-enabled apps will no longer update across your devices, and you will not be able to send or receive messages with your iCloud email address,

iCloud is a service provided by Apple. Apple ID | Support | Terms and Conditions | Privacy Policy
Copyright © 2017 Apple Inc. 1 Infinite Loop, Cupertino, CA 95014, United States. All rights reserved.

Smart Aesthetic Scoring for Better Photography

Image Aesthetics Scoring Engine

The developed engine gets the image and outputs a score showing the amount of the beauty inside the image! The code is working on Android/iOS platform,
The engine speed is about 100-200 ms which makes it suitable for a real-time engine. The core exploits GPU to compute aesthetic features from the image, and estimate the aesthetic score.

Mirzakhani, Maryam: The only women who won Fields Medal in math

Maryam Mirzakhani was first women to win maths' Fields Medal also a mother and professor at Stanford university. She would be in our mind forever.

in Farshid Farhat 's Twitter

Deep Learning in Penn State

Integrating Deep-learned Models and Photography Idea Retrieval

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspectiveness, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

A SPAM like wordpress

How WORDPRESS treats customers:


> I cannot verify the breach as I don't have access to the content.
> But it is OK to empty the blog content and give me the permission.

Upon further review, the offer to empty your sites no longer stands. You've continued to publish spam content across a large number of sites. As such, all of the blogs owned by your account have been suspended and will not be returned.


Sal P. | Community Guardian |

Networking, Security, Big Data, and Computer Vision Endeavors

Academic Endeavors at Pennsylvania State University



MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This thesis analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

Stochastic processes, Computational model, Delayed Tailed Distribution, Optimal scheduling, Cloud computing, Synchronization, Queuing Theory, MapReduce, Stochastic Modeling, Performance Evaluation, Fork-Join Queue.

Your iCloud storage is almost full.

Apple recently sends the following emails to make us upgrade! As you see in my screenshot I have nothing to backup, but my iPhone fills the...