'123456' Is Once Again the Most Commonly-Used Password in 2016: Study

HIGHLIGHTS

• The other two most used passwords are - '123456789' and 'qwerty'
• The study also found that four of the top 10 passwords are 6-characters
• Nearly 17% of users are safeguarding their accounts with '123456'

Numeric combination of '123456' was the most common password of 2016, followed by '123456789' and 'qwerty', according to researchers who reviewed over 10 million security codes that became public following data breaches.

The study also found that four of the top 10 passwords on the list are six characters or shorter. Passwords '12345678', '111111', '1234567890', '1234567', 'password', '123123', '987654321' were among the top ten list.

This is stunning in light of the fact that, as we have reported, today's brute-force cracking software and hardware can unscramble those passwords in seconds," according to the US-based password management company Keeper Security .

"Website operators that permit such flimsy protection are either reckless or lazy," the company said.

"Nearly 17 percent of users are safeguarding their accounts with '123456'. What really perplexed us is that so many website operators are not enforcing password security best practices," it said.

The study found that the list of most-frequently used passwords has changed little over the past few years, which means that user education has limits.

While it is important for users to be aware of risks, a sizable minority are never going to take the time or effort to protect themselves. IT administrators and website operators must do the job for them, the company said.

The presence of passwords like '1q2w3e4r' and '123qwe' indicates that some users attempt to use unpredictable patterns to secure passwords, but their efforts are weak.

Dictionary-based password crackers know how to look for sequential key variations. At best, it sets them back only a few seconds. Email providers do not appear to be working all that hard to prevent the use of their services for spam, they added.

Google AMP Lite Brings More Optimisations for Low-RAM Devices on Slow Internet Connections

•                                       HIGHLIGHTS
• The search giant removes image data that is not visible to users
• Image size reduced using technique without noticeable impact on quality
• Rolling out for low-RAM devices as well as Vietnam and Malaysia

• Google's Accelerated Mobile Pages (AMP) technique has been beneficial to mobile users AMP Lite, which Google claims causes a combined 45 percent reduction in data across all optimisations, essentially allows the pages to load faster even on devices with low-RAM or that have weak Internet connections, as pointed out in a report by 9to5Google. In order to achieve these results, the search giant has made several optimisations to AMP, Google said in its blog post.
• Google points out that on an average, images form around 64 percent of the data of a webpage. This is why the company decided to remove that part of the image data which is not visible to the users, including thumbnail and geolocation data. The search giant has further reduced the size of JPEG images as well as quality to a point where the difference does not become noticeable.
• The image data is then compressed "exhaustively" and as per the company, these optimisations reduce data by around 40 percent while the differences are not noticeable. The company says that it further converts JPEG images to WebP format for supported browsers, which leads to further 25 percent data reduction to JPEG images.
• since its introduction as it helps devices with weak Internet connection to access webpages using minimum amount of data. Now, the search giant has tried to further improve upon its existing technology as it has announced AMP Lite, a stripped down version of its earlier technique.

Powerful 'Cosmic Double Whammy' Discovered

Astronomers, using data from India's Giant Metrewave Radio Telescope (GMRT), have
discovered two of the most powerful phenomena in the universe - a super massive black hole
and the collision of giant galaxy clusters about two billion light years from Earth.

The two phenomenon have combined to create a stupendous cosmic particle accelerator,
researchers said.

By combining data from NASA's Chandra X-ray Observatory, the Giant Metrewave Radio

Telescope (GMRT) in Pune and other telescopes, researchers found what happens when matter
ejected by a giant black hole is swept up in the merger of two enormous galaxy clusters.

"We have seen each of these spectacular phenomena separately in many places," said Reinout van Weeren of the Harvard-Smithsonian Centre for Astrophysics (CfA) in the US, who led the study.

"This is the first time, however, that we see them clearly linked together in the same system,"
said van Weeren.

This cosmic double whammy is found in a pair of colliding galaxy clusters called Abell 3411 and
Abell 3412 located about two billion light years from Earth.

The two clusters are both very massive, each weighing about a quadrillion - or a million billion -times the mass of the Sun. The comet-shaped appearance of the X-rays detected by Chandra is produced by hot gas from one cluster plowing through the hot gas of the other cluster.

Optical data from the Keck Observatory and Japan's Subaru telescope, both on Mauna Kea,Hawaii, detected the galaxies in each cluster.

First, at least one spinning, supermassive black hole in one of the galaxy clusters produced a rotating, tightly-wound magnetic funnel. The powerful electromagnetic fields associated with this structure have accelerated some of the inflowing gas away from the vicinity of the black hole in the form of an energetic, high-speed jet.

These accelerated particles in the jet were accelerated again when they encountered colossal
shock waves - cosmic versions of sonic booms generated by supersonic aircraft - produced by the collision of the massive gas clouds associated with the galaxy clusters. "It's almost like launching a rocket into low-Earth orbit and then getting shot out of the Solar System.

These particles are among the most energetic particles observed in the universe, thanks to the
double injection of energy," said Andrade-Santos.

This discovery solves a long-standing mystery in galaxy cluster research about the origin of
beautiful swirls of radio emission stretching for millions of light years, detected in Abell 3411 
and Abell 3412 with the GMRT.

The team determined that as the shock waves travel across the cluster for hundreds of millions of  years, the doubly accelerated particles produce giant swirls of radio emission.
The study appears in the journal Nature Astronomy.

The Data Modeling process

Data modeling in software engineering is the process of creating a data model for an information system by applying formal data modeling techniques.

Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. The data requirements are initially recorded as a conceptual data model which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The conceptual model is then translated into a logical data model, which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them.

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:

• to assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand and use an agreed semi-formal model the concepts of the organization and how they relate to one another
• to manage data as a resource
• for the integration of information systems
• for designing databases/data warehouses (aka data repositories)

Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten et al. (2004) determined two types of data modeling:

• Strategic data modeling: This is part of the creation of an information systems strategy, which defines an overall vision and architecture for information systems is defined.Information engineering is a methodology that embraces this approach.
• Data modeling during systems analysis: In systems analysis logical data models are created as part of the development of new databases.

Data modeling is also used as a technique for detailing business requirements for specific databases. It is sometimes called database modeling because a data model is eventually implemented in a database

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