Lovely professional Univ develops Algorithm to Prevent Financial Frauds- Tempemail – Blog – 10 minute

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A team of professors, Dr. G. Geetha, Dr. Rahul Saha and Dr. Gulshan Kumar at Lovely Professional University (LPU) have developed a new digital security algorithm to prevent financial frauds called BaReNPI, the algorithm uses a software-based random number generator making it even more secure to withstand cyber-attacks than AES 256 (Advanced Encryption Standard 256), which is the current  gold standard for electronic cryptographic encryption.
Security Algorithms like BaReNPI are used by messaging apps like WhatsApp and Signal programs like VeraCrypt and WinZip as well as a range of hardware and a variety of other technologies to transmit data securely. The research team has filed a US-patent and has also bagged funding from the Department of Science and Technology (DST) for the hardware implementation of the algorithm.
LPU Professors have used properties of random numbers to develop a solution in the form of Symmetric Random Function Generator (SRFG) which brings randomness in the key generation process in AES, thereby making their solution more efficient. The ability of SRFG to offer 3 times better confusion property (degree of ambiguity) and 53.7% better avalanche effect (a small change in input leading to a big change in output) makes BaReNPI better than AES on key parameters like nonlinearity, resiliency, balancedness, propagation characteristics and immunity.
“In the current digital economy, computer system and network security are intended to achieve many purposes including confidentiality, authentication, non-repudiation and access control by use of various security algorithms. BaReNPI has opened the path for further improvement of these security algorithms which eventually leads to better digital security and more secure social and economic encounters”, said Dr. G. Geetha, Professor and Head of Division of Research and Development, Lovely Professional University.
BaReNPI can work as an enabler for the parametric Monte Carlo experimentation with randomization, effective digital display, machine learning and statistical learning environment, CAPTCHA technology and data structure searching techniques.It will also help security professionals and technical analysts in OTP generation for next-generation technology of IoTs. Furthermore, browser security algorithms can also be strengthened with BaReNPI.

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AI Algorithm Discovers One of World’s Most Powerful Antibiotics – Tempemail – Blog – 10 minute

Sourced from Science History Images

An AI algorithm created at MIT has just discovered a powerful new antibiotic that kills some of the world’s most dangerous drug-resistant bacteria.
“In terms of antibiotic discovery, this is absolutely a first,” said Regina Barzilay, a senior researcher on the project and specialist in machine learning at MIT
The drug itself works in a way that is different from any other existing antibacterials and is the first-ever drug of its kind to be discovered by setting an AI upon a vast digital library of pharmaceutical compounds, reports The Guardian.
The new AI-found drug was tested and seen to have completely wiped out an entire range of antibiotic-resistant strains, including Acinetobacter baumannii and Enterobacteriaceae. Who make up two of the three highest priority “critical” bacteria that urgently need antibiotics.
“I think this is one of the more powerful antibiotics that has been discovered to date,” added James Collins, a bioengineer on the team at MIT. “It has remarkable activity against a broad range of antibiotic-resistant pathogens.”
In their quest to find new antibiotics, a “deep learning” algorithm was trained to identify the sorts of molecules that kill bacteria – the program was therefore given information on the atomic and molecular features of about 2500 drugs and natural compound using E-Coli as a benchmark to test how well or not the substances blocked the spread of bacteria.
Once the algorithm was well-versed in what makes a good antibiotic good, the team then began feeding the programme information from a library of over 6000 compounds under investigation for treating various human diseases, with a focus on compounds, unlike any existing antibiotics. This would boost the chances that any drugs found would bypass any resistance developed by bugs in a radical way.
It took only a matter of hours before the algorithm came up with a few promises antibiotics – a particularly potent compound was named ‘Halicin” by the researchers after the AI super-computer HAL from Ridley Scott’s 2001: A Space Odyssey.
The researchers began tests on halicin, and found that the new antibiotic killed Mycobacterium tuberculosis and other strains of bacteria known to be resistant to powerful antibiotics like Enterobacteriaceae. Halicin also cleared C-difficile and multi-drug resistant Acinetobacter baumannii infections in mice.
“The work really is remarkable,” said Jacob Durrant, who works on computer-aided drug design at the University of Pittsburgh. “Their approach highlights the power of computer-aided drug discovery. It would be impossible to physically test over 100m compounds for antibiotic activity.”
“Given typical drug-development costs, in terms of both time and money, any method that can speed early-stage drug discovery has the potential to make a big impact,” he added.
How do bacteria become resistant to antibiotics?
The antibiotic resistance of bacteria arises when they mutate and evolve an ability to bypass the mechanisms within antibiotics that kill them.
New antibiotics need to be discovered and tested constantly as without them more than 10 million lives around the world would be put at risk from new infections by 2050, a review on antimicrobial resistance warns.
Edited by Luis Monzon
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Toshiba says it created an algorithm that beats quantum computers using standard hardware – Blog – 10 minute

Something to look forward to: Some of the biggest problems that need solving in the enterprise world require sifting through vast amounts of data and finding the best possible solution given a number of factors and requirements, some of which are at times unknown. For years, quantum computing has been touted as the most promising jump in computational speed for certain kind of problems, but Toshiba says revisiting classical algorithms helped it develop a new one that can leverage existing silicon-based hardware to get a faster result.
Toshiba’s announcement this week claims a new algorithm it’s been perfecting for years is capable of analyzing market data much more quickly and efficiently than those used in some of the world’s fastest supercomputers.
The algorithm is called the “Simulated Bifurcation Algorithm,” and is supposedly good enough to be used in finding accurate approximate solutions for large-scale combinatorial optimization problems. In simpler terms, it can come up with a solution out of many possible ones for a particularly complex problem.
According to its inventor, Hayato Goto, it draws inspiration from the way quantum computers can efficiently comb through many possibilities. Work on SBA started in 2015, and Goto noticed that adding new inputs to a complex system with 100,000 variables makes it easy to solve it in a matter of seconds with a relatively small computational cost.

This essentially means that Toshiba’s new algorithm could be used on standard desktop computers. To give you an idea how important this development is, Toshiba demonstrated last year that SBA can get highly accurate solutions for an optimization problem with 2,000 connected variables in 50 microseconds, or 10 times faster than laser-based quantum computers.
SBA is also highly scalable, meaning it can be made to work on clusters of CPUs or FPGAs, all thanks to the contributions of Kosuke Tatsumura, another one of Toshiba’s senior researchers that specializes in semiconductors.
Companies like Microsoft, Google, IBM, and many others are racing to be the first with a truly viable quantum commercial system, but so far their approaches have produced limited results that live inside their labs.
Meanwhile, scientists like Goto and Kosuke are going back to the roots by exploring ways to improve on classical algorithms. Toshiba hopes to use SBA to optimize financial operations like currency trading and rapid-fire portfolio adjustments, but this could very well be used to calculate efficient routes for delivery services and molecular precision drug development.

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New algorithm helps find treatment for brain cancer: Study- Tempemail – Blog – 10 minute

Researchers have developed an algorithm using which they found a new treatment for neuroblastoma — a potentially deadly type of cancer in children, which occurs in specialised nerve cells controlling the body’s response to dangerous or stressful situations. The new treatment, described in the journal Nature Communications is based on activating a protein called CNR2 (cannabinoid receptor 2) in the nervous system.
As part of the study, the researchers, including those from Sweden’s Uppsala University, developed a new computer algorithm which combines massive quantities of genetic and drug data from European and American hospitals and universities. The algorithm then suggested new treatments that could influence the basic mechanisms of the disease, the researchers said.
“We were astonished when the algorithm came up with completely new ideas for treatment, such as CNR2, that no one has ever discussed in this context. So we decided to investigate this further in the lab,” said study co-author Sven Nelander from Uppsala University.
The scientists tested the potential drugs on cell samples from patients and in animal models, where they proved effective. According to the study, the cancer cells’ survival rate declined, and tumour growth in zebrafish decreased, following treatment with a substance which stimulates CNR2.
“Smart algorithms will be increasingly important in cancer research in the years ahead, since they can help us scientists to find unexpected angles,” Nelander said.
The researchers have also engineered the algorithm so that it can be applied to other forms of cancer.

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Apple tweaks its App Store algorithm as antitrust investigations loom – gpgmail


That Apple has used its App Store to offer itself a competitive advantage is nothing new. gpgmail and others have been reporting on this problem for years, including those times when Apple chose to display its apps in the No. 1 position on the Top Charts, for example, or when it stole some of the App Store’s best ideas for its own, banned apps that competed with iOS features, or positioned its apps higher than competitors in search. Now, in the wake of antitrust investigations in the U.S. and abroad as well as various anticompetitive lawsuits, Apple has adjusted the App Store’s algorithm so fewer of its own apps would appear at the top of the search results.

The change was reported by The New York Times on Monday, who presented Apple with a lengthy analysis of app rankings.

It even found that some searches for various terms would display as many as 14 Apple-owned apps before showing any results from rivals. Competitors could only rank higher if they paid for an App Store search ad, the report noted.

That’s a bad look for Apple which has recently been trying to distance itself and its App Store from any anti-competitive accusations.

In May, for example, Apple launched a new App Store website designed to demonstrate how it welcomes competition from third-party apps. The site showed that for every Apple built-in app, there were competitors available throughout the App Store.

But availability in the store and discoverability by consumers are two different things.

Apple admitted to The NYT that for over a year many common searches on the App Store would return Apple’s own apps, even when the Apple apps were less popular or relevant at times. The company explained the algorithm wasn’t manipulated to do so. For the most part, Apple said its own apps ranked higher because they’re more popular and because they come up in search results for many common terms. The company additionally said that one feature of the app’s algorithm would sometimes group apps by their maker, which gave Apple’s own apps better rankings than expected.

Above: via The NYT, the average number of Apple apps that returned at the top of the search results by month

Apple said it adjusted the algorithm in July to make it seem like Apple’s own apps weren’t receiving special treatment. According to the NYT, both Apple VP Philip Schiller, who oversees the App Store, and SVP Eddy Cue, who oversees many of Apple’s apps, confirmed that these changes have not fully fixed the problem.

The issue, as Apple explains it, is that its own apps are so popular that it had to tweak its algorithm to pretend they are not. Whether or not this is true can’t be independently verified, however, as Apple doesn’t allow any visibility into metrics like searches, downloads, or active users.

Maybe it’s time for Apple’s apps to exit the App Store?

The report, along with the supposed ineffectiveness of the algorithm’s changes, begs the question as to whether Apple’s apps should show up in the App Store’s charts and search results at all, and if so, how.

To be fair, this is a question that’s not limited to Apple. Google today is facing the same problem. Recently, the CEO of a popular software program, Basecamp, called Google’s paid search ads a “shakedown,” arguing that the only way his otherwise No. 1 search result can rank at the top of the search results page is to buy an ad. Meanwhile, his competitors can do so — even using his brand name as the keyword to bid against.

The same holds true for the App Store, but on a smaller scale than the entirety of the web. That also makes Apple’s problem easier to solve.

For example, Apple could simply choose to offer a dedicated section for its own software downloads, and leave the App Store as the home for third-party software alone.

This sort of change could help to eliminate concerns over Apple’s anti-competitive behavior in the search results and chart rankings. Apple might balk against this solution, saying that users should have an easy way to locate and download its own apps, and the App Store is the place to do that. But the actual marketplace itself could be left to the third-party software while the larger App Store app — which today includes a variety of app-related content including app reviews, interviews with developers, app tips, and a subscription gaming service, Apple Arcade — could still be used to showcase Apple-produced software.

It could just do so outside the actual marketplace.

Here’s how this could work. If users wanted to re-install an Apple app they had deleted or download one that didn’t come pre-installed on their device, they could be directed to a special Apple software download page. Pointers to this page could be in the App Store app itself as well as in the iOS Settings.

An ideal spot for this section could even be on the existing Search page of the App Store.

With a redesign, Apple could offer a modified search screen where users could optionally check a box to return a list of apps results that would come only from Apple. This would indicate intentional behavior on the consumer’s part. That is, they are directly seeking an Apple software download — as opposed to the current situation where a user searches for “Music” and sees Apple’s own music app appear above all the others from rivals, like Spotify and Pandora.

Alternately, Apple could just list its own apps on this page or offer a link to this dedicated page from the search screen.

And these are just a few variations on a single idea. There are plenty of other ways the App Store could be adjusted to be less anti-competitive, too.

As another example, Apple could also include the “You Might Also Like” section in its own apps’ App Store listings, as it does for all third-party apps.

Image from iOS 1Above: Apple Music’s App Store Listing

This section directs users to other apps that match the same search query right within the app’s detail page. Apple’s own apps, however, only include a “More by Apple” section. That means its keeping all the search traffic and consumer interest for itself.

Image from iOS

Above: Spotify’s App Store Listing

Or it could reduce the screen space dedicated to its own apps in the search results — even if they rank higher — in order to give more attention to apps from competitors while still being able to cater to users who were truly in search of Apple’s software.

But ultimately, how Apple will have to behave with regard to its App Store may be left to the regulators to decide, given Apple’s failure to bake this sort of anti-competitive thinking into its App Store design.

 


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This hand-tracking algorithm could lead to sign language recognition – gpgmail


Millions of people communicate using sign language, but so far projects to capture its complex gestures and translate them to verbal speech have had limited success. A new advance in real-time hand tracking from Google’s AI labs, however, could be the breakthrough some have been waiting for.

The new technique uses a few clever shortcuts and of course the increasing general efficiency of machine learning systems to produce, in real time, a highly accurate map of the hand and all its fingers, using nothing but a smartphone and its camera.

“Whereas current state-of-the-art approaches rely primarily on powerful desktop environments for inference, our method achieves real-time performance on a mobile phone, and even scales to multiple hands,” write Google researchers Valentin Bazarevsky and Fan Zhang in a blog post. “Robust real-time hand perception is a decidedly challenging computer vision task, as hands often occlude themselves or each other (e.g. finger/palm occlusions and hand shakes) and lack high contrast patterns.”

Not only that, but hand movements are often quick, subtle, or both — not necessarily the kind of thing that computers are good at catching in real time. Basically it’s just super hard to do right, and doing it right is hard to do fast. Even with multi-camera, depth-sensing rigs like those used by SignAll have trouble tracking every movement. (But that isn’t stopping them.)

The researchers’ aim in this case, at least partly, was to cut down on the amount of data that the algorithms needed to sift through. Less data means quicker turnaround.

For one thing, they abandoned the idea of having a system detect the position and size of the whole hand. Instead, they only have the system find the palm, which is not only the most distinctive and reliably shaped part of the hand, but is square to boot, meaning they didn’t have to worry about the system being able to handle tall rectangular images, short ones, and so on.

Once the palm is recognized, of course, the fingers sprout out of one end of it and can be analyzed separately. A separate algorithm looks at the image and assigns 21 coordinates, roughly coordinating to knuckles and fingertips, to it, including how far away they likely are (it can guess based on the size and angle of the palm, among other things).

To do this finger recognition part, they first had to manually add those 21 points to some 30,000 images of hands in various poses and lighting situations, for the machine learning system to ingest and learn from. As usual, artificial intelligence relies on hard human work to get going.

Once the pose of the hand is determined, that pose is compared to a bunch of known gestures, from sign language symbols for letters and numbers to things like “peace” and “metal.”

The result is a hand-tracking algorithm that’s both fast and accurate, and runs on a normal smartphone rather than a tricked-out desktop or the cloud (i.e. someone else’s tricked-out desktop). It all runs within the MediaPipe framework, which multimedia tech people may already know something about.

With luck other researchers will be able to take this and run with it, perhaps improving existing systems that needed beefier hardware to do the kind of hand recognition they needed to recognize gestures. It’s a long way from here to really understanding sign language, though, which uses both hands, facial expressions, and other cues to produce a rich mode of communication unlike any other.

This isn’t being used in any Google products yet, so the researchers were free to give their work away for free. The source code is here for anyone to take and build on.

“We hope that providing this hand perception functionality to the wider research and development community will result in an emergence of creative use cases, stimulating new applications and new research avenues,” they write.


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Racial bias observed in hate speech detection algorithm from Google – gpgmail


Understanding what makes something offensive or hurtful is difficult enough that many people can’t figure it out, let alone AI systems. And people of color are frequently left out of AI training sets. So it’s little surprise that Alphabet/Google -spawned Jigsaw manages to trip over both of these issues at once, flagging slang used by black Americans as toxic.

To be clear, the study was not specifically about evaluating the company’s hate speech detection algorithm, which has faced issues before. Instead it is cited as a contemporary attempt to computationally dissect speech and assign a “toxicity score” — and that it appears to fail in a way indicative of bias against black American speech patterns.

The researchers, at the University of Washington, were interested in the idea that databases of hate speech currently available might have racial biases baked in — like many other datasets that suffered from a lack of inclusive practices during formation.

They looked at a handful of such databases, essentially thousands of tweets annotated by people as being “hateful,” “offensive,” “abusive,” and so on. These databases were also analyzed to find language strongly associated with African American English or white-aligned English.

Combining these two sets basically let them see whether white or black vernacular had a higher or lower chance of being labeled offensive. Lo and behold, black-aligned English was much more likely to be labeled offensive.

For both datasets, we uncover strong associations between inferred AAE dialect and various hate speech categories, specifically the “offensive” label from DWMW 17 (r = 0.42) and the “abusive” label from FDCL 18 (r = 0.35), providing evidence that dialect-based bias is present in these corpora.

The experiment continued with the researchers sourcing their own annotations for tweets, and found that similar biases appeared. But by “priming” annotators with the knowledge that the person tweeting was likely black or using black-aligned English, the likelihood that they would label a tweet offensive dropped considerably.

Examples of control, dialect priming, and race priming for annotators.

This isn’t to say necessarily that annotators are all racist or anything like that. But the job of determining what is and isn’t offensive is a complex one socially and linguistically, and obviously awareness of the speaker’s identity is important in some cases, especially in cases where terms once used derisively to refer to that identity have been reclaimed.

What’s all this got to do with Alphabet, or Jigsaw, or Google? Well, Jigsaw is a company built out of Alphabet — which we all really just think of as Google by another name — with the intention of helping moderate online discussion by automatically detecting (among other things) offensive speech. Its PerspectiveAPI lets people input a snippet of text and receive a “toxicity score.”

As part of the experiment, the researchers fed a bunch of the tweets in question to Perspective. What they got saw was “correlations between dialects/groups in our datasets and the Perspective toxicity scores. All correlations are significant, which indicates potential racial bias for all datasets.”

chart perspe

Chart showing that African American English (AAE) was more likely to be labeled toxic by Alphabet’s Perspective API.

So basically, they found that Perspective was way more likely to label black speech as toxic, and white speech otherwise. Remember, this isn’t a model thrown together on the back of a few thousand tweets — it’s an attempt at a commercial moderation product.

As this comparison wasn’t the primary goal of the research, but rather a byproduct, it should not be taken as some kind of massive takedown of Jigsaw’s work. On the other hand, the differences shown are very significant and quite in keeping with the rest of the team’s findings. At the very least it is, as with the other datasets evaluated, a signal that the processes involved in their creation need to be reevaluated.

I’ve asked the researchers for a bit more information on the paper and will update this post if I hear back. In the meantime you can read the full paper, which was presented at the Proceedings of the Association for Computational Linguistics in Florence, below:

The Risk of Racial Bias in Hate Speech Detection by gpgmail on Scribd


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New Algorithm Could Make VR Sound More Realistic


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You’re probably familiar with the way good sound design can bring a game or video to life. It can take huge teams of creators hour upon hour to make the audio just right, but almost no amount of time is enough to craft the perfect audio for a virtual reality experience. Sound design has been vastly simplified because of the innate unscripted nature of VR simulations, but a new algorithm from researchers at Stanford could finally change that. 

In scripted media like a pre-rendered 2D video, you always know where sound should come from — the audio levels for each channel never change from one viewing to the next. Even a 3D game has a workable level of complexity thanks to the predetermined parameters of the environment. With VR, there are simply too many variables to create perfect, realistic sound from every perspective. 

Currently, the algorithms for creating sound models come from work done more than a century ago by scientist Hermann von Helmholtz. In the late 19th century, Helmholtz devised some of the theoretical underpinnings of wave propagation. The so-called Helmholtz Equation has since become a major component of audio modeling along with the boundary element method (BEM). 

That’s all well and good if you’re dealing with an environment without too many variables. Virtual reality ratchets up the possible audio models to previously unheard of levels. To make VR sound authentic, engineers would need to create sound models based on where the viewer is standing in the virtual world and what they’re looking at. Doing that with the Helmholtz Equation and BEM would take powerful computers multiple hours. So, far from practical. 

The potential solution comes from Stanford professor Doug James and graduate student Jui-Hsien Wang. The new GPU-accelerated algorithm calculates sound models thousands of times faster by completely avoiding the Helmholtz Equation and BEM. We’re talking seconds of processing instead of hours. 

The pair’s approach borrows from 20th-century Austrian composer Fritz Heinrich Klein, who found a way to generate the “Mother Chord” from multiple piano notes. They call their algorithm KleinPAT in recognition of his posthumous contribution. The video above includes some comparisons between Helmholtz-generated sound models and KleinPAT. They sound very similar, which is the point. You can get almost identical sound from KleinPAT with much less computing time. The researchers believe this algorithm could be a game-changer for simulating audio in dynamic 3D environments.

Now read:




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Google Algorithm Updates Impacting your Search Results


More than 90% of web searches happen on Google. With Google being a search giant claiming all those searches, business owners must find interest in investing in SEO in light of Google algorithm updates.

The trick in every update is that they lean toward bettering the user experience to give the users of Google the best possible results for their searches.

Without utmost consideration for Google updates, you may realize a drop in the position of your site on SERP. That said, consider also the fact that most of your target audience are probably using Google search engine.

Your digital marketing team must learn all there is to Google algorithmic updates. Here are several of the updates that always have an impact on your search results:

Google Panda

Do you ever wonder what would happen if you started sharing low-quality material? Obviously, you can start expecting a slower traffic flow than usual, along with low rankings for targeted keywords.

Google Panda is an update that evaluates all websites based on the quality of the material they share. The web pages with very high-quality material get a reward of a higher rank position than others. In the same way, low quality is penalized with low ranking positions.

To trigger Google panda, the checkpoints
are usually:

  • Thin content – thin content does not necessarily provide useful and
    explicit responses to address the needs of the searcher. Ideally, when your
    material does not conclusively respond to the query in hand, then it is thin
    content.
  • Valueless material – when your content does not inform, entertain, provoke
    emotion or thought, then, is it really relevant to the audience? When users
    cannot trust your site as a helpful source of information, then you can
    anticipate a very low-rank
    position on Google’s SERP.
  • Duplicate content – Google Panda regards duplicate content as copied material
    that only scatters the internet with chunks of copied text. With images and
    videos, you can maneuver without Google panda terming your work as duplicate.
    With text, however, you need to be cautious. Any material that can be found
    anywhere on the internet, even within your web pages, will attract you a lower
    ranking based on Google’s algorithm.
  • Article spinning – like with duplicate content, article spinning will reward
    you with a lower ranking. Some website owners try to avoid duplication of
    material by spinning the articles in a different way. Unfortunately, part of
    having a high-quality piece of the article includes originality, which lacks in
    article spinning.

Google Penguin

If you have escaped Google Panda just alright, watch out for Google Penguin. This update evaluates websites based on their link-building profiles. Technically, backlinks should affect your search rankings positively. However, it only depends on how you do it.

Website owners get caught up in the idea of adding several links on their web pages, quickly forgetting that quality matters quite as much as quantity does. For one, all the backlinks on your site must be related to the content on your web pages. Other than that, legit backlinks need to point your website to trustworthy sources, not dubious ones.

If Google penguin finds that your sources are nothing related to your niche industry, topic or content, then you will attract a hit for your search rankings.

Some of the triggers from Google Penguin
include:

  • Buying links – this is in violation of the guidelines of Google Webmaster.
    The good thing is that there are better ways to earn links other than buying
    them. For one, you can engage in a lot of guest blogging. Target websites in
    the same industry as you. You can also co-host events and partner with other
    business owners to merit link exchanges.
  • Lack of anchor text diversity – the anchor text diversity in your material is
    about having different texts to which you embed your links. Technically, having
    the same text all through for your anchors is what makes Google feel like it is
    manipulated to rank your website higher. Instead of a proper ranking, you get
    penalized.
  • Low-quality links – be keen and a little picky with where you get your links
    from. Much as you do not have a lot of control over the quality of other
    people’s site, you can control what gets to your site. Audit and verify that
    the websites you are seeking to get links from share valuable material that
    your visitors will also stand to benefit from.
  • Keyword stuffing – strange as it may seem. Keyword stuffing is scooped out by
    Google Penguin, along with
    poorly-written anchor texts. As you do your keyword research, the attempts you
    make in finding the best words to befit your text should align with the needs
    for diversity. It is why most SEO experts recommend the use of Google suggest
    to come up with other related terms that can help with having different
    keywords to use. Ensure that all the keywords and anchor texts you use in your
    copy have a natural flow to encourage readability. Remember that this remains a
    primary concern even for coming up with quality content for your target
    audience.

Google pigeon

Surprisingly, local SEO matters to Google more than website owners may know. Google ranks a website respective of the location of the business, and the distance from the user. Technically, you cannot do much to bring your target audience closer to the location of your business. However, you can be more deliberate with targeting people in your location.

Even with paid SEO and ads, there is a way to schedule them for an audience that is locally close to your business’s location. The best way possible is by using keywords that are locally targeted. For example, if you are talking about buying sunglasses, ensure you include the location of your business in the primary keywords as in the example below:

Summer sunglasses in New York.

Other than that, your content creation strategy should incorporate your location. The texts, images, and videos you use should strongly associate with your region. If you use memes, for example, make the humor in them relevant to the audience in your location.

Get your target local audience highly
engaged with your material. Ask for positive reviews from your local customers,
along with testimonials that you can host on your website. All these are areas
that Google bots will crawl in determining your rank position for matters of
local SEO.

If you have not done it yet, make sure you have submitted your business to local listings. For this one, be consistent with the information you provide so that it is uniform across the board, and people can find your business with ease. (NAP – name, address, phone number).

Google Hummingbird

The Google hummingbird is technically one to analyze your website based on artificial intelligence. It differs from penguin and panda because it does not directly change how your website gets ranked. However, it does check to ensure that your site is as relevant to the user as possible. This means that your site is analyzed in regard to user intent. For every query searched on Google search, there is a literal meaning and the intended meaning that is behind the queries.

Google hummingbird specializes in finding sites that are most qualified for the searched terms. This means that your keyword research strategy should nail it right. Thoroughly research in the subject you want to write about. Make sure you understand what the audience really wants to know. It helps to use long-tail keywords because they are more descriptive and will be more valuable to the user. Ensure that you find semantic search friendly keyword and phrases.

Pro tip

Google suggest will give you the best search terms. Just type in the seed keyword, and check out the ‘related results’ to find the keywords that most people are looking for online.

Google mobile-friendly update

The user experience involved in using a small screen matter a lot to Google. The mobile users accessing the internet today have far much surpassed the desktop audience. Since this realization, Google has been firm on the user experience for mobile users.

An ideal site should have prime consideration for the mobile audience first, and the desktop users second. According to this algorithmic update, the speed, responsiveness, theme, and navigability of your site matters. Since the mobile users have a way smaller screen to use than desktop users, the adjustments you make must make the usability of your site better than before. This means that you may have to give into getting an entirely different website theme for your mobile site.

Remember that mobile-first indexing is also a ranking factor. It does not mean that Google will only index your mobile site for the SERP, but rather that the mobile results appear first before the desktop results. Among the issues to consider include:

  • Large font – visitors
    should not struggle reading content from their small screens.
  • Non-intrusive site – with intrusive pop-up ads and prompts, you quickly decrease
    the user experience for your site.
  • Loading speed
    – mobile users are the least tolerant with slow loading sites than those using
    a PC. Aim for at most 3 seconds as the load time for your web pages.

A lot of what you do in SEO must be intentional, more especially when dealing with frequent Google algorithm updates. Visit this page and learn how you can align your keyword strategy with these updates for a better ranking.


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