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What Is Font Family?

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Font families are like your favorite TV show: they're all about the same, but they have their unique flair. Do you know what's better than one font? Two! and you know what's better than two fonts? Three! But that's not all! You also have to consider the weight of your font and whether it's a roman, italic, bold or bold italic. You see, there are four versions of each of the above options—so now we're up to sixteen different choices! That's why we call them font families: they're like people who share a common ancestor but have varying characteristics. If you're looking for a typeface that is both light and bold but has an oblique slant, then Times New Roman is your best bet! Hundreds of font families are available, including serif, sans-serif, script, condensed, extended, decorative, etc. Choosing a font family will significantly impact the look of your content; you may want to select a font family with options for every situation, such as a roman font for your main content and an italic version for quotes; fonts are the lifeblood of any design. You can have the best color palette, the most exciting layout, and all the right content—but if your fonts aren't legible, your audience will get lost in the shuffle. That's where the font family comes in. Font family is a property of an element consisting of a list of font family names and generic family names applied to the component. The first item in the list is the default; the rest are used as fallbacks. To make your text look pretty, you need to choose a font. But what if you could choose from a hundred fonts? Or more? Well, with HTML and XHTML, you can! With the HTML5 Font family attribute, you can declare that your element should use a particular font. You can even specify that it should use a specific font size, weight and style. Now all you have to do is sit back and wait for people to start using your font for their purposes.

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Related Terms by Software Development

Sentiment Analysis

Sentiment analysis is a lot like having the ability to discern minds, except it's done with computers. Opinion mining is a data mining subfield that utilizes unstructured text analysis to gauge consumer sentiment toward a brand, individual, or concept. Sentiment analysis is a technique for gleaning emotional data from online sources using NLP, computational linguistics, and text analysis. Social media sites and other online forums where users post their thoughts and observations on various subjects are familiar places to find this data. Sentiment analysis uses complex algorithms and machine learning methods to identify a person's opinion's positive, negative, or neutral nature. As a bonus, it can determine whether the text is joyful, sad, angry, or anxious, as well as other emotions. The results of this analysis can be used to calculate the extent to which the public approves or disapproves of various brands, individuals, and concepts. Knowing the thoughts and preferences of customers can be invaluable to companies and organizations. A business may employ mood analysis to monitor customer feedback via social media and use the results to improve its offerings. The material's polarity in its context can also be revealed through sentiment analysis. It can tell you how people feel about a subject or entity and what it is about that subject or entity that people like or dislike. Sentiment analysis can show, for instance, that consumers have a generally positive attitude toward a given brand but a negative attitude toward its customer service. To sum up, sentiment analysis is a subfield of data mining that assesses consumer reaction to a brand, individual, or concept by examining written language. It's like having the ability to read thoughts, only this time, and it's accomplished through complex mathematical formulas stored in a computer. Sentiment analysis, or opinion mining, is a method for gleaning and analyzing biased data from online sources, such as social media and blogs. Data analysis can reveal the contextual polarity of information and provide quantitative estimates of the public's feelings or responses to specific goods, people, or ideas.

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Self-Provisioning

If you're like most people, you're always looking for ways to get out of work. So when we heard about self-provisioning—the ability to set up services and applications by yourself without the help of a dedicated IT specialist or service provider—we were all over it. It's like having your server, except that instead of having to buy your server, pay for its maintenance, and hire an IT person to manage it when things go wrong, you sign up with a cloud provider who has already done everything for you. Moreover, they'll even let you use their servers for free! So if you have ever wanted to launch your website but didn't want to take on the burden of managing it yourself, or if you've been dreaming of starting an online business but didn't want to spend all that money on servers and software licenses well, now's your chance! Self-provisioning is excellent, but the self-de-provisioning part is even more significant. Provisioning is like getting a massage—you know what you want and are in charge of getting it. Deprovisioning is like getting a haircut—it's a little more complicated than telling someone what to do. It requires much attention to detail and technical skill to ensure you're not cutting off any substantial parts of yourself in your zeal to be smooth and sleek. We don't want you to be soft and elegant! We want you to be well-groomed! So here are some tips for taking care of yourself by taking care of your resources. Always deprovision after using a resource so that others can use it when they need it later. Only do something once you've found another that does what that other one did for you (and then de-provision the old one).

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Secure Hash Algorithm (SHA)

Secure Hash Algorithm is a set of algorithms developed by the National Institutes of Standards and Technology and other government and private parties. Cryptographic hashes (or checksums) have been used for electronic signatures and file integrity for decades. However, these functions have evolved to address some of the cybersecurity challenges of the 21st century. The NIST has developed a set of secure hashing algorithms that act as a global framework for encryption and data management systems. The initial instance of the Secure hash Algorithm (SHA) was in 1993. It was a 16-bit hashing algorithm and is known as SHA-0. The successor to SHA-0, SHA-1, was released in 1995 and featured 32-bit hashing. Eventually, the next version of SHA was developed in 2002, and it is known as SHA-2. SHA-2 differs from its predecessors because it can generate hashes of different sizes. The whole family of secure hash algorithms goes by the name SHA. SHA-3, or Keccak or KECCAK, is a family of cryptographic hash functions designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. SHA-3 competition to develop a new secure hash algorithm was held by the United States National Security Agency (NSA) in 2007. To be a super safe and fast hashing algorithm, SHA3 was developed from this contest. The evolution of cybersecurity has led to the development of several "secure hash algorithms." Security is a crucial concern for businesses and individuals in today's digital world. As a result, many types of encryption have been developed to protect data in various scenarios. One of these is hash algorithms. All secure hash algorithms are part of new encryption standards to keep sensitive data safe and prevent different types of attacks. These algorithms use advanced mathematical formulas so that anyone who tries to decode them will get an error message that they aren't expected in regular operation.

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Trending Definitions

Data Management Platform (DMP)

Data management platforms (DMPs) are the new hotness for marketers. They're like a data-collecting heap of candy you can reach into and pull out exactly what you need to make your campaign work. DMPs are typically made up of lots of different kinds of data sources. They can be online, offline or mobile. You can use them to bring in all sorts of information about your customers' behavior: their browsing history, what they buy online, where they are at any given time, etc. that's not all! Once you have the data, the platform will also typically allow you to do things with it, like accessing it in real-time or using it as part of your marketing efforts. The concept in the back of a data management platform is that corporations should be capable of taking gain of various styles of facts from numerous reassets and gathering that points in a single significant vicinity to achieve the commercial enterprise intelligence that simply blessings the enterprise. This is often done by connecting disparate systems via an API, allowing data to move seamlessly between systems. This enables businesses to make better decisions by looking at all available information, not just what's stored in one system. It also helps companies avoid duplicate work, so they can focus on other projects instead of recreating work already done elsewhere in the organization. A data management platform (DMP) is a lot of things. It's a repository for your business's information, sure. It's also a cloud-based resource that can help you manage and process your disparate data types. In some aspects of, it makes it more than just a repository or processor: the DMP should be capable of extracting data for use on live platforms or environments, which means it needs to be able to handle the real-time analysis of incoming information. In other words, it's like an internal database, but not quite.

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Hairpin

So what is a hairpin? Well, it's not a hair accessory, so don't get too excited. A hairpin is also not a comb you use to style your hair. It's not a type of fish—though it does sound like one. It means something that helps two things come together and work together more efficiently. The name refers to the pin looping back on itself in a U-shape (which is also entirely appropriate). Hairpins are used in telecommunication to help direct calls between different providers or operators within a network. In other words, they let you send your phone back in the direction of its point of origin—like when you try to call your friend, and his phone isn't working. You can wait for him to text you back later or attempt to contact him again. Hairpins are pretty cool. You know how you go to her office when you're looking for a hairpin and can't find one in your mom's bathroom? She's got one in there, but she doesn't know where it came from? Well, that's what happens with hairpins: they appear from nowhere and then disappear into thin air. This is the same thing that happens with telephone calls! In telephony, a single call may be turned into a two-legged call. The two legs intersect at the destination number. The first leg is the call from the source to the destination number, while the second is the call redirected back from the call. The term hairpin was later used in digital data networks to denote an existing feature in some network address translations (NATs) where more than one host is placed behind a NAT. The NAT assigns each host a particular external address and port. So next time someone tells you they're "doing their hair," remember that they could be using an actual hairpin instead of just getting ready for work or school!

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Autonomous Things

Autonomous Things, or #AutonomousThings & AuT for short, are like robots that can think for themselves. They're machines that can sense their environment, make decisions, and take action without human intervention. Like how you can tell your robot vacuum cleaner to clean your room, Autonomous Things can be programmed to perform specific tasks. Some examples of Autonomous Things include self-driving cars, drones, and industrial robots. These machines are equipped with sensors, cameras, and other technologies that allow them to understand and navigate their surroundings. They also have rules or algorithms that dictate how they should behave in different situations. One of the main advantages of Autonomous Things is that they can operate 24/7 without getting tired or making mistakes. This makes them ideal for tasks that are dangerous or tedious for humans to perform, such as inspecting pipelines or exploring the deep sea. Additionally, Autonomous Things can collect and analyze large amounts of data, which can help decision-making and improve overall efficiency. However, they also present some concerns, including safety and security difficulties, privacy concerns, and ethical questions about their influence on jobs and society. These are only some of the problems that can be raised. For instance, a self-driving car needs to be able to make split-second judgments in the event of an emergency to guarantee the well-being of all its passengers and the safety of pedestrians and other motorists on the road. When autonomous items are connected to the internet, there is a potential for hacking and other cyberattacks, which raises concerns over data security. In short, #AutonomousThings are machines that can think for themselves, sense their environment, make decisions, and take actions without human intervention, such as self-driving cars, drones, and industrial robots. They have advantages such as operating 24/7, collecting and analyzing large amounts of data and being useful for dangerous or tedious tasks. However, they also raise some safety, security and ethical questions. #Autonomousvehicles #drones #industrialRobots

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