The Fifth Estate is a socio-cultural reference to groupings of outlier viewpoints in contemporary society, and is most associated with bloggers, journalists publishing in non-mainstream media outlets, and online social networks. The "Fifth" Estate extends the sequence of the three classical estates (clergy (first), nobility (second), commoners (third)) and the preceding Fourth Estate, essentially the common press. The use of "fifth estate" dates to the 1960s counterculture, and in particular the influential Fifth Estate, an underground newspaper first published in Detroit in 1965. Web-based technologies have enhanced the scope and power of the Fifth Estate far beyond the modest and boutique conditions of its beginnings. Nimmo and Combs asserted in 1992 that political pundits constitute a Fifth Estate. Media researcher Stephen D. Cooper argued in 2006 that bloggers are the Fifth Estate. In 2009, William Dutton argued that the Fifth Estate is not just the blogging community, nor an extension of the media, but "networked individuals" enabled by the Internet, e.g. social media, in ways that can hold the other estates accountable.
Image scaling
In computer graphics and digital imaging, image scaling is the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement. When scaling a vector graphic image, the graphic primitives that make up the image can be rendered using geometric transformations at any resolution with no loss of image quality. When scaling a raster graphics image, a new image with a higher or lower number of pixels must be generated. In the case of decreasing the pixel number (scaling down), this usually results in a visible quality loss. From the standpoint of digital signal processing, the scaling of raster graphics is a two-dimensional example of sample-rate conversion, the conversion of a discrete signal from a sampling rate (in this case, the local sampling rate) to another. == Mathematical == Image scaling can be interpreted as a form of image resampling or image reconstruction from the view of the Nyquist sampling theorem. According to the theorem, downsampling to a smaller image from a higher-resolution original can only be carried out after applying a suitable 2D anti-aliasing filter to prevent aliasing artifacts. The image is reduced to the information that can be carried by the smaller image. In the case of up sampling, a reconstruction filter takes the place of the anti-aliasing filter. A more sophisticated approach to upscaling treats the problem as an inverse problem, solving the question of generating a plausible image that, when scaled down, would look like the input image. A variety of techniques have been applied for this, including optimization techniques with regularization terms and the use of machine learning from examples. == Algorithms == An image size can be changed in several ways. === Nearest-neighbor interpolation === One of the simpler ways of increasing image size is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output; for upscaling, this means multiple pixels of the same color will be present. This can preserve sharp details but also introduce jaggedness in previously smooth images. 'Nearest' in nearest-neighbor does not have to be the mathematical nearest. One common implementation is to always round toward zero. Rounding this way produces fewer artifacts and is faster to calculate. This algorithm is often preferred for images which have little to no smooth edges. A common application of this can be found in pixel art. === Bilinear and bicubic interpolation === Bilinear interpolation works by interpolating pixel color values, introducing a continuous transition into the output even where the original material has discrete transitions. Although this is desirable for continuous-tone images, this algorithm reduces contrast (sharp edges) in a way that may be undesirable for line art. Bicubic interpolation yields substantially better results, with an increase in computational cost. === Sinc and Lanczos resampling === Sinc resampling, in theory, provides the best possible reconstruction for a perfectly bandlimited signal. In practice, the assumptions behind sinc resampling are not completely met by real-world digital images. Lanczos resampling, an approximation to the sinc method, yields better results. Bicubic interpolation can be regarded as a computationally efficient approximation to Lanczos resampling. === Box sampling === One weakness of bilinear, bicubic, and related algorithms is that they sample a specific number of pixels. When downscaling below a certain threshold, such as more than twice for all bi-sampling algorithms, the algorithms will sample non-adjacent pixels, which results in both losing data and rough results. The trivial solution to this issue is box sampling, which is to consider the target pixel a box on the original image and sample all pixels inside the box. This ensures that all input pixels contribute to the output. The major weakness of this algorithm is that it is hard to optimize. === Mipmap === Another solution to the downscale problem of bi-sampling scaling is mipmaps. A mipmap is a prescaled set of downscaled copies. When downscaling, the nearest larger mipmap is used as the origin to ensure no scaling below the useful threshold of bilinear scaling. This algorithm is fast and easy to optimize. It is standard in many frameworks, such as OpenGL. The cost is using more image memory, exactly one-third more in the standard implementation. === Fourier-transform methods === Simple interpolation based on the Fourier transform pads the frequency domain with zero components (a smooth window-based approach would reduce the ringing). Besides the good conservation (or recovery) of details, notable are the ringing and the circular bleeding of content from the left border to the right border (and the other way around). === Edge-directed interpolation === Edge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms, which can introduce staircase artifacts. Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI), Edge-Guided Image Interpolation (EGGI), Iterative Curvature-Based Interpolation (ICBI), and Directional Cubic Convolution Interpolation (DCCI). A 2013 analysis found that DCCI had the best scores in peak signal-to-noise ratio and structural similarity on a series of test images. === hqx === For magnifying computer graphics with low resolution and/or few colors (usually from 2 to 256 colors), better results can be achieved by hqx or other pixel-art scaling algorithms. These produce sharp edges and maintain a high level of detail. === Vectorization === Vector extraction, or vectorization, offers another approach. Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. The resulting SVG vector file can then be exported and rendered at any required resolution without quality loss, serving directly as production-ready artwork for scalable display & printing. This technique is used by Adobe Illustrator, Live Trace, and Inkscape. Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity. === Deep convolutional neural networks === This method uses machine learning for more detailed images, such as photographs and complex artwork. Programs that use this method include waifu2x, Imglarger and Neural Enhance. Demonstration of conventional vs. waifu2x upscaling with noise reduction, using a detail of Phosphorus and Hesperus by Evelyn De Morgan. [Click image for full size] AI-driven upscaling software allows detail and sharpness to be added to historical photographs, where it is not present in the original. The availability of AI upscaling tools has led to confusion where a person believes that the upscaled version of a blurry image is genuinely showing them the subject of the original photograph. In 2025 a user of the social media site X posted an AI-upscaled version of a low resolution photo of Donald Trump that they had zoomed in on, and asked if anyone could "explain what the hell is happening to his forehead". Experts noted that the image had been distorted by the upscaling process, and that such tools "inevitably have to invent, or at least recreate, details that were or were not there". == Applications == === General === Image scaling is used in, among other applications, web browsers, image editors, image and file viewers, software magnifiers, digital zoom, the process of generating thumbnail images, and when outputting images through screens or printers. === Video === This application is the magnification of images for home theaters for HDTV-ready output devices from PAL-Resolution content, for example, from a DVD player. Upscaling is performed in real time, and the output signal is not saved. === Pixel-art scaling === As pixel-art graphics are usually low-resolution, they rely on careful placement of individual pixels, often with a limited palette of colors. This results in graphics that rely on stylized visual cues to define complex shapes with little resolution, down to individual pixels. This makes scaling pixel art a particularly difficult problem. Specialized algorithms were developed to handle pixel-art graphics, as the traditional scaling algorithms do not take perceptual cues into account. Since a typical application is to improve the appearance of fourth-generation and earlier video games on arcade and console emulators, many are designed to run in real time for small input images at 60 frames per second. On fast hardware, these algorithms are suitable for gaming and other real-time image processing. These algorithms provide sharp, crisp graphics, while minimizing blur. Scaling art algorithms have been implemented in a wide range of emulators such as HqMAME and DOSBox, as well as 2D game engines and game engine recreations such as ScummVM. They gained recognition with game
Secure environment
In computing, a secure environment is any system which implements the controlled storage and use of information. In the event of computing data loss, a secure environment is used to protect personal or confidential data. It may also be known as a trusted execution environment (TEE). Often, secure environments employ cryptography as a means to protect information. This is typically used for processing confidential or restricted information. Some secure environments employ cryptographic hashing, simply to verify that the information has not been altered since it was last modified.
Database
In computing, a database is an organized collection of data or a type of data store based on the use of a database management system (DBMS), the software that interacts with end users, applications, and the database itself to capture and analyze the data. The DBMS additionally encompasses the core facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications can be referred to as a database system. Often the term "database" is also used loosely to refer to any of the DBMS, the database system or an application associated with the database. Before digital storage and retrieval of data became widespread, index cards were used for data storage in a wide range of applications and environments: in the home to record and store recipes, shopping lists, contact information and other organizational data; in business to record presentation notes, project research and notes, and contact information; in schools as flash cards or other visual aids; and in academic research to hold data such as bibliographical citations or notes in a card file. Professional book indexers used index cards in the creation of book indexes until they were replaced by indexing software in the 1980s and 1990s. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spans formal techniques and practical considerations, including data modeling, efficient data representation and storage, query languages, security and privacy of sensitive data, and distributed computing issues, including supporting concurrent access and fault tolerance. Computer scientists may classify database management systems according to the database models that they support. Relational databases became dominant in the 1980s. These model data as rows and columns in a series of tables, and the vast majority use SQL for writing and querying data. In the 2000s, non-relational databases became popular, collectively referred to as NoSQL, because they use different query languages. == Terminology and overview == Formally, a "database" refers to a set of related data accessed through the use of a "database management system" (DBMS), which is an integrated set of computer software that allows users to interact with one or more databases and provides access to all of the data contained in the database (although restrictions may exist that limit access to particular data). The DBMS provides various functions that allow entry, storage and retrieval of large quantities of information and provides ways to manage how that information is organized. Because of the close relationship between them, the term "database" is often used casually to refer to both a database and the DBMS used to manipulate it. Outside the world of professional information technology, the term database is often used to refer to any collection of related data (such as a spreadsheet or a card index) as size and usage requirements typically necessitate use of a database management system. Existing DBMSs provide various functions that allow management of a database and its data which can be classified into four main functional groups: Data definition – Creation, modification and removal of definitions that detail how the data is to be organized. Update – Insertion, modification, and deletion of the data itself. Retrieval – Selecting data according to specified criteria (e.g., a query, a position in a hierarchy, or a position in relation to other data) and providing that data either directly to the user, or making it available for further processing by the database itself or by other applications. The retrieved data may be made available in a more or less direct form without modification, as it is stored in the database, or in a new form obtained by altering it or combining it with existing data from the database. Administration – Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information that has been corrupted by some event such as an unexpected system failure. Both a database and its DBMS conform to the principles of a particular database model. "Database system" refers collectively to the database model, database management system, and database. Physically, database servers are dedicated computers that hold the actual databases and run only the DBMS and related software. Database servers are usually multiprocessor computers, with generous memory and RAID disk arrays used for stable storage. Hardware database accelerators, connected to one or more servers via a high-speed channel, are also used in large-volume transaction processing environments. DBMSs are found at the heart of most database applications. DBMSs may be built around a custom multitasking kernel with built-in networking support, but modern DBMSs typically rely on a standard operating system to provide these functions. Since DBMSs comprise a significant market, computer and storage vendors often take into account DBMS requirements in their own development plans. Databases and DBMSs can be categorized according to the database model(s) that they support (such as relational or XML), the type(s) of computer they run on (from a server cluster to a mobile phone), the query language(s) used to access the database (such as SQL or XQuery), and their internal engineering, which affects performance, scalability, resilience, and security. == History == The sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitude. These performance increases were enabled by the technology progress in the areas of processors, computer memory, computer storage, and computer networks. The concept of a database was made possible by the emergence of direct access storage media such as magnetic disks, which became widely available in the mid-1960s; earlier systems relied on sequential storage of data on magnetic tape. The subsequent development of database technology can be divided into three eras based on data model or structure: navigational, SQL/relational, and post-relational. The two main early navigational data models were the hierarchical model and the CODASYL model (network model). These were characterized by the use of pointers (often physical disk addresses) to follow relationships from one record to another. The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model employs sets of ledger-style tables, each used for a different type of entity. Only in the mid-1980s did computing hardware become powerful enough to allow the wide deployment of relational systems (DBMSs plus applications). By the early 1990s, however, relational systems dominated in all large-scale data processing applications, and as of 2018 they remain dominant: IBM Db2, Oracle, MySQL, and Microsoft SQL Server are the most searched DBMS. The dominant database language, standardized SQL for the relational model, has influenced database languages for other data models. Object databases were developed in the 1980s to overcome the inconvenience of object–relational impedance mismatch, which led to the coining of the term "post-relational" and also the development of hybrid object–relational databases. The next generation of post-relational databases in the late 2000s became known as NoSQL databases, introducing fast key–value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs. === 1960s, navigational DBMS === The introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English Dictionary cites a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense. As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s a number of such systems had come into commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the Database Task Group within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971, the Database Task Group delivered their standard, which generally became known as the CODASYL approach, and soon a number of commercial products based on this approach entered the market. The CODASYL approach of
Secure coding
Secure coding is the practice of developing computer software in such a way that guards against the accidental introduction of security vulnerabilities. Defects, bugs and logic flaws are consistently the primary cause of commonly exploited software vulnerabilities. Through the analysis of thousands of reported vulnerabilities, security professionals have discovered that most vulnerabilities stem from a relatively small number of common software programming errors. By identifying the insecure coding practices that lead to these errors and educating developers on secure alternatives, organizations can take proactive steps to help significantly reduce or eliminate vulnerabilities in software before deployment. Some scholars have suggested that in order to effectively confront threats related to cybersecurity, proper security should be coded or "baked in" to the systems. With security being designed into the software, this ensures that there will be protection against insider attacks and reduces the threat to application security. Implementing secure coding practices is part of the secure by design approach to security engineering. == Buffer-overflow prevention == Buffer overflows, a common software security vulnerability, happen when a process tries to store data beyond a fixed-length buffer. For example, if there are 8 slots to store items in, there will be a problem if there is an attempt to store 9 items. In computer memory the overflowed data may overwrite data in the next location which can result in a security vulnerability (stack smashing) or program termination (segmentation fault). An example of a C program prone to a buffer overflow is If the user input is larger than the destination buffer, a buffer overflow will occur. To fix this unsafe program, use strncpy to prevent a possible buffer overflow. Another secure alternative is to dynamically allocate memory on the heap using malloc. In the above code snippet, the program attempts to copy the contents of src into dst, while also checking the return value of malloc() to ensure that enough memory was able to be allocated for the destination buffer. == Format-string attack prevention == A Format String Attack is when a malicious user supplies specific inputs that will eventually be entered as an argument to a function that performs formatting, such as printf(). The attack involves the adversary reading from or writing to the stack. The C printf function writes output to stdout. If the parameter of the printf function is not properly formatted, several security bugs can be introduced. Below is a program that is vulnerable to a format string attack. A malicious argument passed to the program could be "%s%s%s%s%s%s%s", which can crash the program from improper memory reads. == Integer-overflow prevention == Integer overflow occurs when an arithmetic operation results in an integer too large to be represented within the available space. A program which does not properly check for integer overflow introduces potential software bugs and exploits. Below is a function in C++ which attempts to confirm that the sum of x and y is less than or equal to a defined value MAX: The problem with the code is it does not check for integer overflow on the addition operation. If the sum of x and y is greater than the maximum possible value of an unsigned int, the addition operation will overflow and perhaps result in a value less than or equal to MAX, even though the sum of x and y is greater than MAX. Below is a function which checks for overflow by confirming the sum is greater than or equal to both x and y. If the sum did overflow, the sum would be less than x or less than y. == Path traversal prevention == Path traversal is a vulnerability whereby paths provided from an untrusted source are interpreted in such a way that unauthorised file access is possible. For example, consider a script that fetches an article by taking a filename, which is then read by the script and parsed. Such a script might use the following hypothetical URL to retrieve an article about dog food: https://www.example.net/cgi-bin/article.sh?name=dogfood.html If the script has no input checking, instead trusting that the filename is always valid, a malicious user could forge a URL to retrieve configuration files from the web server: https://www.example.net/cgi-bin/article.sh?name=../../../../../etc/passwd Depending on the script, this may expose the /etc/passwd file, which on Unix-like systems contains (among others) user IDs, their login names, home directory paths and shells. (See SQL injection for a similar attack.) == Regulatory drivers == Secure coding practices are increasingly mandated by regulatory frameworks governing the development and maintenance of software systems that process sensitive data. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect the integrity of protected health information through technical safeguards under 45 CFR 164.312(c)(1) and to implement mechanisms to authenticate electronic protected health information under 45 CFR 164.312(c)(2). The Payment Card Industry Data Security Standard (PCI DSS) version 4.0 Requirement 6.2 mandates that custom software is developed securely, including training developers in secure coding techniques (6.2.2), reviewing custom code for vulnerabilities before release (6.2.3), and addressing common software attacks in development practices (6.2.4).
ISSCO Graphics
Integrated Software Systems Corporation (ISSCO), doing business as ISSCO Graphics, was an American software developer and publisher based in San Diego, California, and active from 1970 to 1986. They were best known for their enterprise graphics software packages, including Tellagraf, CueChart and Disspla. == History == ISSCO Graphics had considered acquiring Breakthrough Software, whose software focus involved PC DOS, as a means of getting into the PC arena, but backed off when Computer Associates made an offer to acquire ISSCO. By early 1987 it was reported that "Issco users breathe sigh of relief" that all was well. The ISSCO User's Group was founded in 1976. ISSCO, which was founded in 1970 by Peter Preuss, was acquired by Computer Associates in 1986. == Notable products == === Tellagraf === ISSCO's Tellagraf is an early software package designed to allow end-users to "turn out full color, professional quality charts" with initial results displayed on a screen, modified as needed, and then "a final 'hard-copy' can be made .. or made into 35mm color transparencies for projection onto a screen." Users of Tellagraf often had access to CueChart and Disspla software. Often computer sites having one had all three. Terminals with varying degrees of graphics, such as the DEC's VT100 and Tektronix's Tektronix 4xxx family of text and graphics terminals. were supported, and the software ran on popular computing platforms. Four years are important to Tellagraf's early history: 1978: ease of use 1980: graphic-artist quality 1982: introduction of CueChart, and recognition by IEEE. 1983: "quality graphics enters the mainstream of data processing with ..." Tellegraf was eventually acquired by Computer Associates and renamed CA-Tellegraf. SAS users found it helpful. Universities, research institutes and financial services firms were among early users. === Disspla === Disspla is a package of data plotting subroutines that can be used from high level languages. It was also acquired by Computer Associates. === Tellaplan === In 1983 ISSCO introduced Tellaplan, "a project planning, report and schedule charting system for Tell-A- Graf users in IBM MVS or CMS or Digital Equipment Corp. VAX computers" atop which they built "two visual project management software packages" three years later.
Unspent transaction output
In cryptocurrencies, an unspent transaction output (UTXO, often capitalized as UTxO) is a distinctive element in a subset of digital currency models. A UTXO represents a certain amount of cryptocurrency that has been authorized by a sender and is available to be spent by a recipient. The utilization of UTXOs in transaction processes is a key feature of many cryptocurrencies, but it primarily characterizes those implementing the UTXO model. UTXOs employ public key cryptography to ascertain and transfer ownership. More specifically, the recipient's public key is formatted into the UTXO, thereby limiting the capability to spend the UTXO to the account that can demonstrate ownership of the corresponding private key. A valid digital signature associated with the public key must be included for the UTXO to be spent. In the UTXO model, each unit of currency is treated as a discrete object. The history of a UTXO is documented only within the blocks where it is transferred. To ascertain the total balance of an account, one must scan each block to find the latest UTXOs linked to that account. While all nodes within a blockchain network must consent to the block history, the blocks relevant to an account's balance are unique to that account. UTXOs constitute a chain of ownership depicted as a series of digital signatures dating back to the coin's inception, regardless of whether the coin was minted via mining, staking, or another procedure determined by the cryptocurrency protocol. The UTXO model was invented for Bitcoin. Cardano uses an extended version of the UTXO model known as EUTXO. == Origins == The conceptual framework of the UTXO model can be traced back to Hal Finney's Reusable Proofs of Work proposal, which itself was based on Adam Back's 1997 Hashcash proposal. Bitcoin, released in 2009, was the first widespread implementation of the UTXO model in practice. == UTXO model vs. account Model == Cryptocurrencies that utilize the UTXO model function differently compared to those using the account model. In the UTXO model, individual units of cryptocurrency, termed as unspent transaction outputs (UTXOs), are transferred between users, analogous to the exchange of physical cash. This model impacts how transactions and ownership are recorded and verified within the blockchain network. The account model preserves a record of each account and its corresponding balance for every block added to the network. This setup enables quicker balance verification without the need to scan historical blocks, but it increases the raw size of each block (though data compression techniques can be utilized to alleviate this). However, both models necessitate the inspection of past blocks to fully authenticate the origin of coins. In the UTXO model, each object is immutable - units of coins cannot be 'edited' in the same way an account balance is modified when a transaction occurs. Rather, the balance is computed from the transaction history dating back to when the coins were first minted. This simplicity enhances security, as a UTXO either exists in its anticipated form or it does not. In contrast, the account model requires meticulous verification of the account's status during transactions, which can lead to oversights if not conducted correctly. In valid blockchain transactions, only unspent outputs (UTXOs) are permissible for funding subsequent transactions. This requirement is critical to prevent double-spending and fraud. Accordingly, inputs in a transaction are removed from the UTXO set, while outputs create new UTXOs that are added to the set. The holders of private keys, such as those with cryptocurrency wallets, can utilize these UTXOs for future transactions.