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.
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.”
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.
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.
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.
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.
“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.”
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 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.
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