10 data-driven strategies to spark conversions in 2022
Conversion begins with a click. And clicks come after you have successfully
grabbed your user’s attention. A headline is often the first thing your users
come across, and hence an excellent tool to use for grabbing their attention.
Therefore, using attention-grabbing headlines (paired with other factors) can
lead to better conversions. This is not your pass to creating controversial
and low-value titles. Grab attention while delivering value and maintaining
class. Again, tap into website analytics to find out which headlines have
worked the best for you. If you are entirely new to the website world, know
that headlines with numbers have shown to have 30% higher conversions than
those without numbers. Additionally, short and concise headlines, which have a
negative superlative (like x number of things you have never seen before or x
killer Instagram profiles you need to follow), have a higher tendency to earn
more clicks. A/B testing or split testing reveals incredibly insightful data
that can work wonders on your bottom line.
TechScape: can AI really predict crime?
The LAPD is working with a company called Voyager Analytics on a trial
basis. Documents the Guardian reviewed and wrote about in November show that
Voyager Analytics claimed it could use AI to analyse social media profiles to
detect emerging threats based on a person’s friends, groups, posts and more.
It was essentially Operation Laser for the digital world. Instead of focusing
on physical places or people, Voyager looked at the digital worlds of people
of interest to determine whether they were involved in crime rings or planned
to commit future crimes, based on who they interacted with, things they’ve
posted, and even their friends of friends. “It’s a ‘guilt by association’
system,” said Meredith Broussard, a New York University data journalism
professor. Voyager claims all of this information on individuals, groups and
pages allows its software to conduct real-time “sentiment analysis” and find
new leads when investigating “ideological solidarity”. “We don’t just connect
existing dots,” a Voyager promotional document read. “We create new dots. What
seem like random and inconsequential interactions, behaviours or interests,
suddenly become clear and comprehensible.”
Privacy and Confidentiality in Security Testing
Now when we understand the difference between privacy and confidentiality and
how it can affect a person, we can talk about keeping these privacy and
confidentiality safe while testing. The increasing number of malware bots
makes business owners concerned about keeping data confidential. It also makes
implementing security testing vital for any software development, and
especially for web applications. Knowing how to test software to prevent any
personal data from being compromised from their site is essential. For this,
let’s go through the steps QA testers can take to implement security testing.
To illustrate our suggestions we'll use the interface of aqua ALM that is
popular among QA teams for test management in security testing. ... The main
goal of security testing is to prevent applications from malware penetrations
and others access and also protect the confidentiality and privacy of a
person.
An introduction to the magic of machine learning
We hear about machine learning a lot these days, and in fact it’s all around
us. It can sound kind of mysterious, or even scary, but it turns out that
machine learning is just math. And to prove that it’s just math, I will
write this article the old-school way, with hand-written equations instead
of code. If you prefer to learn by… To explain what machine learning is and
how math makes it work, we will do a full walk-through of logistic
regression, a fairly simple but fundamental model that is in some sense the
building block of more complex models like neural networks. If I had to pick
one machine learning model to understand really well, this would be it. Most
often, we use logistic regression for a task called binary classification.
In binary classification, we want to learn how to predict whether a data
point belongs to one of two groups or classes, labeled 0 and 1. ... These
training data allow us to learn the optimal theta parameters. What does
optimal mean? Well, one reasonable and quite common definition is to say
that the optimal theta is the set of parameters that maximizes the
probability of obtaining our training data.
Alternative Feature Selection Methods in Machine Learning
The "Wrapper Methods" category includes greedy algorithms that will try
every possible feature combination based on a step forward, step backward,
or exhaustive search. For each feature combination, these methods will train
a machine learning model, usually with cross-validation, and determine its
performance. Thus, wrapper methods are very computationally expensive, and
often, impossible to carry out. The "Embedded Methods," on the other hand,
train a single machine learning model and select features based on the
feature importance returned by that model. They tend to work very well in
practice and are faster to compute. On the downside, we can’t derive feature
importance values from all machine learning models. For example, we can’t
derive importance values from nearest neighbours. In addition, co-linearity
will affect the coefficient values returned by linear models, or the
importance values returned by decision tree based algorithms, which may mask
their real importance. Finally, decision tree based algorithms may not
perform well in very big feature spaces, and thus, the importance values
might be unreliable.
Diversity in cybersecurity: Barriers and opportunities for women and minorities
Our world is getting increasingly digitized, and cybercrime continues to
break new records. As cyber risks intensify, organizations are beefing up
defenses and adding more outside consultants and resources to their teams.
But to their sad misfortune, they are getting hit by a major roadblock—a
long-standing shortage of qualified cybersecurity talent. A closer look at
the numbers reveal an even more startling statistic: women comprise only 25%
of the cybersecurity workforce, according to research from ISC2, despite
outpacing men in overall college enrollment. There are a number of reasons
why women and minorities pursuing cybersecurity careers can be significantly
beneficial to the overall industry. Here are two: People from different
genders, ethnicities and backgrounds can provide a fresh perspective to
solving highly complex security problems. And then there’s the simple fact
that leaving cybersecurity jobs unfilled puts businesses at risk. As the
cybersecurity skills gap continues to grow, that risk only increases.
Half-Billion Compromised Credentials Lurking on Open Cloud Server
“Through analysis, it became clear that these credentials were an
accumulation of breached datasets known and unknown,” the NCA said in a
statement provided to Hunt. “The fact that they had been placed on a U.K.
business’s cloud storage facility by unknown criminal actors meant the
credentials now existed in the public domain, and could be accessed by other
third parties to commit further fraud or cyber-offenses.” The passwords have
been added to HIBP, which means they’re searchable by individuals and
companies worldwide seeking to verify the security risk of a password before
usage. Previously unseen passwords include flamingo228, Alexei2005,
91177700, 123Tests and aganesq, Hunt said in a blog posting Monday. “It is a
both unfortunate and mind boggling that over 200 million of the passwords
that were shared by U.K. NCA were brand new to the HIBP service,” Baber
Amin, COO at Veridium, said via email. “It points to the sheer size of the
problem, the problem being passwords, an archaic method of proving one’s
bonafides. If there was ever a call to action to work towards eliminating
passwords and finding alternates, then this has to be it.”
A cybersecurity expert explains Log4Shell – the new vulnerability that affects computers worldwide
Log4Shell works by abusing a feature in Log4j that allows users to specify
custom code for formatting a log message. This feature allows Log4j to, for
example, log not only the username associated with each attempt to log in to
the server but also the person’s real name, if a separate server holds a
directory linking user names and real names. To do so, the Log4j server has
to communicate with the server holding the real names. Unfortunately, this
kind of code can be used for more than just formatting log messages. Log4j
allows third-party servers to submit software code that can perform all
kinds of actions on the targeted computer. This opens the door for nefarious
activities such as stealing sensitive information, taking control of the
targeted system and slipping malicious content to other users communicating
with the affected server. It is relatively simple to exploit Log4Shell. I
was able to reproduce the problem in my copy of Ghidra, a
reverse-engineering framework for security researchers, in just a couple of
minutes.
The Metaverse is Overhyped; But by 2050, AI Will Make It Real
The metaverse today is not a place to go so much as a collection of
technologies surrounding tools like NVIDIA’s Omniverse that can create
simulations used to train robots and autonomous cars. It is an easier-to-use
and more comprehensive tool set, like what architects have used to create
virtual building, but with far more realistic results, including lighting
effects, reflections, and a limited application of physics. For point
simulation, the metaverse concept is workable, but it really is just a
better simulation platform for point projects today, and nowhere near the
full virtual world we expect. By the end of the decade, NVIDIA’S Earth-2
project should be viable. This is currently the most aggressive public
project in process, and Earth 2 could well become the foundation of a far
broader use of the concept. Initially, Earth 2 will be limited by the
technology available at the time, but once it is workable, it will be able
to predict weather events more accurately and model potential climate change
remedies better than the simulations we currently have.
Eliminating artificial intelligence bias is everyone's job
As new tools are provided around the auditability of AI, we'll see a lot
more companies regularly reviewing their AI results. Today, many companies
either buy a product that has an AI feature or capability embedded or it's
part of the proprietary feature of that product, which doesn't expose the
auditability. Companies may also stand up the basic AI capabilities for a
specific use case, usually in that AI discover level of usage. However, in
each of these cases the auditing is usually limited. Where auditing really
becomes important is in "recommend" and "action" levels of AI. In these two
phases, it's important to use an auditing tool to not introduce bias and
skew the results. One of the best ways to help with auditing AI is to use
one of the bigger cloud service providers' AI and ML services. Many of those
vendors have tools and tech stacks that allow you to track this
information. Also key is for identifying bias or bias-like behavior to be
part of the training for data scientists and AI and ML developers. The more
people are educated on what to look out for, the more prepared companies
will be to identify and mitigate AI bias.
Quote for the day:
“Hard times are sometimes blessings in disguise. We do have to suffer but
in the end it makes us strong, better and wise.” --
Anurag Prakash Ray
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