AI Data Center Financing Surge

AI Data Center Financing Surge — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Rake (software)

Rake is a software task management and a build automation tool created by Jim Weirich. It allows the user to specify tasks and to describe dependencies as well as to group tasks into namespaces. It is similar to SCons and Make. Rake was written in Ruby and has been part of the standard library of Ruby since version 1.9. == Examples == The tasks that should be executed need to be defined in a configuration file called Rakefile. A Rakefile has no special syntax and contains executable Ruby code. === Tasks === The basic unit in Rake is the task. A task has a name and an action block, that defines its functionality. The following code defines a task called greet that will output the text "Hello, Rake!" to the console. When defining a task, you can optionally add dependencies, that is one task can depend on the successful completion of another task. Calling the "seed" task from the following example will first execute the "migrate" task and only then proceed with the execution of the "seed" task.Tasks can also be made more versatile by accepting arguments. For example, the "generate_report" task will take a date as argument. If no argument is supplied the current date is used.A special type of task is the file task, which can be used to specify file creation tasks. The following task, for example, is given two object files, i.e. "a.o" and "b.o", to create an executable program.Another useful tool is the directory convenience method, that can be used to create directories upon demand. === Rules === When a file is named as a prerequisite but it does not have a file task defined for it, Rake will attempt to synthesize a task by looking at a list of rules supplied in the Rakefile. For example, suppose we were trying to invoke task "mycode.o" with no tasks defined for it. If the Rakefile has a rule that looks like this: This rule will synthesize any task that ends in ".o". It has as a prerequisite that a source file with an extension of ".c" must exist. If Rake is able to find a file named "mycode.c", it will automatically create a task that builds "mycode.o" from "mycode.c". If the file "mycode.c" does not exist, Rake will attempt to recursively synthesize a rule for it. When a task is synthesized from a rule, the source attribute of the task is set to the matching source file. This allows users to write rules with actions that reference the source file. === Advanced rules === Any regular expression may be used as the rule pattern. Additionally, a proc may be used to calculate the name of the source file. This allows for complex patterns and sources. The following rule is equivalent to the example above: NOTE: Because of a quirk in Ruby syntax, parentheses are required around a rule when the first argument is a regular expression. The following rule might be used for Java files: === Namespaces === To better organize big Rakefiles, tasks can be grouped into namespaces. Below is an example of a simple Rake recipe:
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Color gradient

In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme. In computer graphics, the term swatch has come to mean a palette of active colors. == Definitions == Color gradient is a set of colors arranged in a linear order (ordered) A continuous colormap is a curve through a colorspace === Strict definition === A colormap is a function which associate a real value r with point c in color space C {\displaystyle C} f : [ r m i n , r m a x ] ⊂ R → C {\displaystyle f:[r_{min},r_{max}]\subset \mathbf {R} \to C} which is defined by: a colorspace C an increasing sequence of sampling points r 0 < . . . < r m ∈ [ r m i n , r m a x ] {\displaystyle r_{0}<... Read more →
Immediate mode (computer graphics)

Immediate mode is an API design pattern in computer graphics libraries, in which the client calls directly cause rendering of graphics objects to the display, or in which the data to describe rendering primitives is inserted frame by frame directly from the client into a command list (in the case of immediate mode primitive rendering), without the use of extensive indirection – thus immediate – to retained resources. It does not preclude the use of double-buffering. Retained mode is an alternative approach. Historically, retained mode has been the dominant style in GUI libraries; however, both can coexist in the same library and are not necessarily exclusive in practice. == Overview == In immediate mode, the scene (complete object model of the rendering primitives) is retained in the memory space of the client, instead of the graphics library. This implies that in an immediate mode application, the lists of graphical objects to be rendered are kept by the client and are not saved by the graphics library API. The application must re-issue all drawing commands required to describe the entire scene each time a new frame is required, regardless of actual changes. This method provides on the one hand a maximum of control and flexibility to the application program, but on the other hand it also generates continuous work load on the CPU. Examples of immediate mode rendering systems include Direct2D, OpenGL and Quartz. There are some immediate mode GUIs that are particularly suitable when used in conjunction with immediate mode rendering systems. == Immediate mode primitive rendering == Primitive vertex attribute data may be inserted frame by frame into a command buffer by a rendering API. This involves significant bandwidth and processor time (especially if the graphics processing unit is on a separate bus), but may be advantageous for data generated dynamically by the CPU. It is less common since the advent of increasingly versatile shaders, with which a graphics processing unit may generate increasingly complex effects without the need for CPU intervention. == Immediate mode rendering with vertex buffers == Although drawing commands have to be re-issued for each new frame, modern systems using this method are generally able to avoid the unnecessary duplication of more memory-intensive display data by referring to that unchanging data (via indirection) (e.g. textures and vertex buffers) in the drawing commands. == Immediate mode GUI == Graphical user interfaces traditionally use retained mode-style API design, but immediate mode GUIs instead use an immediate mode-style API design, in which user code directly specifies the GUI elements to draw in the user input loop. For example, rather than having a CreateButton() function that a user would call once to instantiate a button, an immediate-mode GUI API may have a DoButton() function which should be called whenever the button should be on screen. The technique was developed by Casey Muratori in 2002. Prominent implementations include Omar Cornut's Dear ImGui in C++, Nic Barker's Clay in C and Micha Mettke's Nuklear in C.
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Whitelist

A whitelist or allowlist is a list or register of entities that are being provided a particular privilege, service, mobility, access or recognition. Entities on the list will be accepted, approved and/or recognized. Whitelisting is the reverse of blacklisting, the practice of identifying entities that are denied, unrecognized, or ostracized. == Email whitelists == Spam filters often include the ability to "whitelist" certain sender IP addresses, email addresses or domain names to protect their email from being rejected or sent to a junk mail folder. These can be manually maintained by the user or system administrator - but can also refer to externally maintained whitelist services. === Non-commercial whitelists === Non-commercial whitelists are operated by various non-profit organizations, ISPs, and others interested in blocking spam. Rather than paying fees, the sender must pass a series of tests; for example, their email server must not be an open relay and have a static IP address. The operator of the whitelist may remove a server from the list if complaints are received. === Commercial whitelists === Commercial whitelists are a system by which an Internet service provider allows someone to bypass spam filters when sending email messages to its subscribers, in return for a pre-paid fee, either an annual or a per-message fee. A sender can then be more confident that their messages have reached recipients without being blocked, or having links or images stripped out of them, by spam filters. The purpose of commercial whitelists is to allow companies to reliably reach their customers by email. == Advertising whitelist == Many websites rely on ads as a source of revenue, but the use of ad blockers is increasingly common. Websites that detect an adblocker in use often ask for it to be disabled - or their site to be "added to the whitelist" - a standard feature of most adblockers. == Network whitelists == === LAN whitelists === A use for whitelists is in local area network (LAN) security. Many network admins set up MAC address whitelists, or a MAC address filter, to control who is allowed on their networks. This is used when encryption is not a practical solution or in tandem with encryption. However, it's sometimes ineffective because a MAC address can be faked. === IP whitelist === Firewalls can usually be configured to only allow data-traffic from/to certain (ranges of) IP-addresses. === Application whitelists === One approach in combating viruses and malware is to whitelist software which is considered safe to run, blocking all others. This is particularly attractive in a corporate environment, where there are typically already restrictions on what software is approved. Leading providers of application whitelisting technology include Bit9, Velox, McAfee, Lumension, ThreatLocker, Airlock Digital and SMAC. On Microsoft Windows, recent versions include AppLocker, which allows administrators to control which executable files are denied or allowed to execute. With AppLocker, administrators are able to create rules based on file names, publishers or file location that will allow certain files to execute. Rules can apply to individuals or groups. Policies are used to group users into different enforcement levels. For example, some users can be added to a report-only policy that will allow administrators to understand the impact before moving that user to a higher enforcement level. Linux systems typically have AppArmor and SE Linux features available which can be used to effectively block all applications which are not explicitly whitelisted, and commercial products are also available. On HP-UX introduced a feature called "HP-UX Whitelisting" on 11iv3 version. == Controversy regarding name == In 2018, a journal commentary on a report on predatory publishing was released making claims that "white" and "black" are racially charged terms that need to be avoided in instances such as "whitelist" and "blacklist". The premise of the journal is that "black" and "white" have negative and positive connotations respectively. It states that since "blacklisting" was first referred to during "the time of mass enslavement and forced deportation of Africans to work in European-held colonies in the Americas," the word is therefore related to race. There is no mention of "whitelist" and its origin or relation to race. This issue is most widely disputed in computing industries where "whitelist" and "blacklist" are prevalent (e.g. IP whitelisting). Despite the commentary nature of the journal, some companies and individuals in others have taken to replacing "whitelist" and "blacklist" with new alternatives such as "allow list" and "deny list". Those adopting this change consider using the "whitelist"/"blacklist" names as a code smell. Those that oppose these changes question its attribution to race, citing the same etymology quote that the 2018 journal uses. According to the remark, the term "blacklist" evolved from the term "black book" about a century ago. The term "black book" does not appear to have any etymology or sources that support racial associations, instead originating in the 1400s as a reference to "a list of people who had committed crimes or fallen out of favor with leaders", and popularized by King Henry VIII's literal use of a black book. Others also note the prevalence of positive and negative connotations to "white" and "black" in the Bible, predating attributions to skin tone and slavery. It wasn't until the 1960s Black Power movement that "Black" became a widespread word to refer to one's race as a person of color in America (alternate to African-American) lending itself to the argument that the negative connotation behind "black" and "blacklist" both predate attribution to race.
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Foveated rendering

Foveated rendering is a rendering technique which uses an eye tracker integrated with a virtual reality headset to reduce the rendering workload by greatly reducing the image quality in the peripheral vision (outside of the zone gazed by the fovea). A less sophisticated variant called fixed foveated rendering doesn't utilise eye tracking and instead assumes a fixed focal point. == History == Research into foveated rendering dates back at least to 1991. At Tech Crunch Disrupt SF 2014, Fove unveiled a headset featuring foveated rendering. This was followed by a successful kickstarter in May 2015. At CES 2016, SensoMotoric Instruments (SMI) demoed a new 250 Hz eye tracking system and a working foveated rendering solution. It resulted from a partnership with camera sensor manufacturer Omnivision who provided the camera hardware for the new system. In July 2016, Nvidia demonstrated during SIGGRAPH a new method of foveated rendering claimed to be invisible to users. In February 2017, Qualcomm announced their Snapdragon 835 Virtual Reality Development Kit (VRDK) which includes foveated rendering support called Adreno Foveation. == Use == According to chief scientist Michael Abrash at Oculus, utilising foveated rendering in conjunction with sparse rendering and deep learning image reconstruction has the potential to require an order of magnitude fewer pixels to be rendered in comparison to a full image. Later, these results have been demonstrated and published. In December 2019, fixed foveated rendering support was added to the Oculus Quest SDK. A number of VR headsets have included on-board eye tracking to provide support for foveated rendering, including HTC's Vive Pro Eye (2019), Meta Quest Pro (2022), PlayStation VR2 (2023), and Apple Vision Pro (2024). In 2025, Valve announced the upcoming Steam Frame headset, which applies a variation of the technique known as "foveated streaming" for wireless streaming from a PC to the headset; the method similarly uses variance in bit rate, and is performed at the encoder level rather than the software level.
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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
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Random (software)

Random was an iOS mobile app that used algorithms and human-curation to create an adaptive interface to the Internet. The app served a remix of relevance and serendipity that allowed people to find diverse topics and interesting content that they might not have encountered otherwise. Random did not require a login or sign-up - the use of the app was anonymous. The app was powered by an artificial intelligence that learned from direct and indirect user interactions inside the app. While learning and adapting to a person, Random created a unique anonymous choice profile that was then used for recommending topics and content. The app didn't recommend the same content twice. == User interface == Random's user interface was made of ever-changing topic blocks that contained keywords and images. By choosing any of the blocks, the user would see related web content. By closing the web content, the user could access new related topics. The user interface allowed people to get more information about a specific topic area or then just leap freely from topic to topic. The content recommended by Random could be any type of web content, varying from news articles to long-form stories and from photographs to videos. Every user of the Random was curating content for other users by using the app. == History == Random was launched in March 2014. The startup was backed by Skype co-founder Janus Friis. The Random app received a strong reception from the likes of The New York Times, TechCrunch, New Scientist, Vice, and other leading publications. The app went on to gain traction with an active and loyal user community of several hundreds of thousands. This was not enough to support the free app model the team strongly believed in, and the service was terminated in December 2015. == Reception == Various reviews in media have emphasized that Random enables people to break their filter bubble and find diverse content they might not find elsewhere. Alan Henry of Lifehacker wrote: "Random... breaks you out by intentionally guiding you to new topics and interesting articles at sites you may not otherwise read." Vice Motherboard's Claire Evans says that: "Random never turns into a filter bubble, because it perpetually injects the irrational into my experience… in a cocktail of relevancy and serendipity." The app has been said to have a unique, minimalistic user experience. Kit Eaton of The New York Times commented that Random "let's you browse the news in a different way to all the other news sites you've probably ever used." Mashable reviewed Random by concluding that the "app may be one of the most simple content-discovery apps on the market."
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SMBGhost

SMBGhost (or SMBleedingGhost or CoronaBlue) is a type of security vulnerability, with wormlike features, that affects Windows 10 computers and was first reported publicly on 10 March 2020. == Security vulnerability == A proof of concept (PoC) exploit code was published 1 June 2020 on GitHub by a security researcher. The code could possibly spread to millions of unpatched computers, resulting in as much as tens of billions of dollars in losses. Microsoft recommends all users of Windows 10 versions 1903 and 1909 and Windows Server versions 1903 and 1909 to install patches, and states, "We recommend customers install updates as soon as possible as publicly disclosed vulnerabilities have the potential to be leveraged by bad actors ... An update for this vulnerability was released in March [2020], and customers who have installed the updates, or have automatic updates enabled, are already protected." Workarounds, according to Microsoft, such as disabling SMB compression and blocking port 445, may help but may not be sufficient. According to the advisory division of Homeland Security, "Malicious cyber actors are targeting unpatched systems with the new [threat], ... [and] strongly recommends using a firewall to block server message block ports from the internet and to apply patches to critical- and high-severity vulnerabilities as soon as possible."
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Supermind AI

Supermind is a state-funded Chinese artificial intelligence platform that tracks scientists and researchers internationally. The platform is the flagship project of Shenzhen's International Science and Technology Information Center. It mines data from science and technology databases such as Springer, Wiley, Clarivate and Elsevier. It is intended to detect technological breakthroughs and to identify possible sources of talent as part of China's efforts to advance technologically. The platform also uses government data security and security intelligence organizations such as Peng Cheng Laboratory, the China National GeneBank, BGI Group and the Key Laboratory of New Technologies of Security Intelligence. According to Hong Kong-based Asia Times, the platform, "While not an overt espionage tool...may be used to identify key personnel who could be bribed, deceived or manipulated into divulging classified information". The Organisation for Economic Co-operation and Development (OECD) flagged the project as an incident, meaning it may be of interest to policymakers and other stakeholders. US technology group American Edge Project criticized the project as a global risk of China's security services using the platform to place agents in jobs with access to important information, recruit technical personnel, and identify targets for hacking operations.
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Data classification (data management)

Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information, enabling organizations to manage, protect, and govern their data more effectively. Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. == Approaches == Classification techniques might be used for reports generated by ERP systems or where the data includes specific personal information that is identified. Many organizations also employ context-based classification that considers factors such as data source, user identity, and application context. == Regulatory frameworks == Data classification schemes are mandated or implied by numerous regulatory frameworks that require organizations to identify, categorize, and protect sensitive information according to its level of sensitivity. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to conduct an accurate and thorough assessment of potential risks and vulnerabilities to the confidentiality, integrity, and availability of protected health information under 45 CFR 164.308(a)(1)(ii)(A), which necessitates classification of data to distinguish protected health information from other organizational data."Security Standards: Administrative Safeguards". U.S. Department of Health and Human Services. Retrieved April 1, 2026. The December 2024 HIPAA Security Rule notice of proposed rulemaking (90 FR 898) would mandate comprehensive technology asset inventories and require mapping of how electronic protected health information moves through an organization, formalizing data classification as an explicit compliance obligation."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. NIST Special Publication 800-60 provides guidelines for mapping information types to security categories, establishing a structured methodology for federal agencies to classify data and apply appropriate security controls based on the potential impact of a security breach."NIST SP 800-60 Vol. 1 Rev. 1: Guide for Mapping Types of Information and Information Systems to Security Categories". National Institute of Standards and Technology. August 2008. Retrieved April 1, 2026.
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Data administration

Data administration or data resource management is an organizational function working in the areas of information systems and computer science that plans, organizes, describes and controls data resources. Data resources are usually stored in databases under a database management system or other software such as electronic spreadsheets. In many smaller organizations, data administration is performed occasionally, or is a small component of the database administrator’s work. In the context of information systems development, data administration ideally begins at system conception, ensuring there is a data dictionary to help maintain consistency, avoid redundancy, and model the database so as to make it logical and usable, by means of data modeling, including database normalization techniques. == Data resource management == According to the Data Management Association (DAMA), data resource management is "the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise". Data Resource management may be thought of as a managerial activity that applies information system and other data management tools to the task of managing an organization’s data resource to meet a company’s business needs, and the information they provide to their shareholders. From the perspective of database design, it refers to the development and maintenance of data models to facilitate data sharing between different systems, particularly in a corporate context. Data Resource Management is also concerned with both data quality and compatibility between data models. Since the beginning of the information age, businesses need all types of data on their business activity. With each data created, when a business transaction is made, need data is created. With these data, new direction is needed that focuses on managing data as a critical resource of the organization to directly support its business activities. The data resource must be managed with the same intensity and formality that other critical resources are managed. Organizations must emphasize the information aspect of information technology, determine the data needed to support the business, and then use appropriate technology to build and maintain a high-quality data resource that provides that support. Data resource quality is a measure of how well the organization's data resource supports the current and the future business information demand of the organization. The data resource cannot support just the current business information demand while sacrificing the future business information demand. It must support both the current and the future business information demand. The ultimate data resource quality is stability across changing business needs and changing technology. A corporate data resource must be developed within single, organization-wide common data architecture. A data architecture is the science and method of designing and constructing a data resource that is business driven, based on real-world objects and events as perceived by the organization, and implemented into appropriate operating environments. It is the overall structure of a data resource that provides a consistent foundation across organizational boundaries to provide easily identifiable, readily available, high-quality data to support the business information demand. The common data architecture is a formal, comprehensive data architecture that provides a common context within which all data at an organization's disposal are understood and integrated. It is subject oriented, meaning that it is built from data subjects that represent business objects and business events in the real world that are of interest to the organization and about which data are captured and maintained.
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Subpixel rendering

Subpixel rendering is a method used to increase the effective resolution of a color display device. It utilizes the composition of each pixel, which consists of three subpixels of which are red, green, and blue that can each be individually addressable on the display matrix. Subpixel rendering is primarily used for text rendering on standard DPI displays. Despite the inherent color anomalies, it can also be used to render general graphics. == History == The origin of subpixel rendering as used today remains controversial. Apple Inc., IBM, and Microsoft patented various implementations that differed in technical details owing to the different purposes for which their technologies were intended. Microsoft held several patents in the United States for subpixel rendering technology used in text rendering on RGB Stripe layouts. The patents 6,219,025; 6,239,783; 6,307,566; 6,225,973; 6,243,070; 6,393,145; 6,421,054; 6,282,327; and 6,624,828 were filed between October 7, 1998, and October 7, 1999, and expired on July 30, 2019. Analysis of the patent by FreeType indicates that the patent does not cover the idea of subpixel rendering, but rather the actual filter used as a last step to balance the color. Microsoft's patent describes the smallest possible filter that distributes each subpixel value equally among the R, G, and B pixels. Any other filter will either be blurrier or will introduce color artifacts. Apple was able to use it in Mac OS X due to a patent cross-licensing agreement. == Characteristics == A single pixel on a color display is made of several subpixels, typically three arranged left-to-right as red, green, and blue (RGB). The components are readily visible with a small magnifying glass, such as a loupe. These pixel components appear as a single color to the human eye because of blurring by optics and spatial integration by nerve cells in the eye. However, the eye is much more sensitive to the location. Therefore, turning on the G and B of one pixel and the R of the next pixel to the right will produce a white dot, but it will appear to be 1/3 of a pixel to the right of the white dot that would be seen from the RGB of only the first pixel. Subpixel rendering leverages this to provide three times the horizontal resolution of the rendered image. However, it has to blur this image to produce the correct color by ensuring the same amount of red, green, and blue are turned on as when no subpixel rendering is being done. Subpixel rendering does not necessitate the use of antialiasing. It gives a smoother result regardless of whether antialiasing is used or not since it artificially increases the resolution. However, it introduces color aliasing since subpixels are colored. Subsequent filtering applied to remove the color artifacts is a form of antialiasing, although its purpose is not smoothing jagged shapes as in conventional antialiasing. Subpixel rendering requires the software to know the layout of the subpixels. The most common reason it is wrong is monitors that can be rotated 90 (or 180) degrees, though monitors are manufactured with other arrangements of the subpixels, such as BGR or in triangles, or with 4 colors like RGBW squares. On any such display the result of incorrect subpixel rendering will be worse than if no subpixel rendering was done at all (it will not produce color artifacts, but it will produce noisy edges). == Implementations == === Apple II === Steve Gibson has claimed that the Apple II, introduced in 1977, supports an early form of subpixel rendering in its high-resolution (280×192) graphics mode. The Wozniak patent only used 2 "sub-pixels". The bytes that comprise the Apple II high-resolution screen buffer contain seven visible bits (each corresponding directly to a pixel) and a flag bit used to select between purple/green or blue/orange color sets. Each pixel, since it is represented by a single bit, is either on or off; there are no bits within the pixel itself for specifying color or brightness. Color is instead created as an artifact of the NTSC color encoding scheme, determined by horizontal position: pixels with even horizontal coordinates are always purple (or blue, if the flag bit is set), and odd pixels are always green (or orange). Two lit pixels next to each other are always white, regardless of whether the pair is even/odd or odd/even, and irrespective of the value of the flag bit. This is an approximation, but it is what most programmers of the time would have in mind while working with the Apple's high-resolution mode. Gibson's example claims that because two adjacent bits form a white block, there are, in fact, two bits per pixel: one that activates the pixel's purple left half and the other that activates its green right half. If the programmer instead activates the green right half of a pixel and the purple left half of the next pixel, the result is a white block 1/2 pixel to the right, which is indeed an instance of subpixel rendering. However, it is not clear whether any programmers of the Apple II have considered the pairs of bits as pixels—instead calling each bit a pixel. The flag bit in each byte affects color by shifting pixels half a pixel-width to the right. This half-pixel shift was exploited by some graphics software, such as HRCG (High-Resolution Character Generator), an Apple utility that displayed text using the high-resolution graphics mode, to smooth diagonals. === ClearType === Microsoft announced its subpixel rendering technology, called ClearType, at COMDEX in 1998. Microsoft published a paper in May 2000, Displaced Filtering for Patterned Displays, describing the filtering behind ClearType. It was then made available in Windows XP. Still, it was not activated by default until Windows Vista, while Windows XP OEMs could and did change the default setting. === FreeType === FreeType, the library used by most current software on the X Window System, contains two open source implementations. The original implementation uses the ClearType antialiasing filters and carries the following notice: "The colour filtering algorithm of Microsoft's ClearType technology for subpixel rendering is covered by patents; for this reason, the corresponding code in FreeType is disabled by default. Note that subpixel rendering per se is prior art; using a different colour filter thus easily circumvents Microsoft's patent claims." FreeType offers a variety of color filters. Since version 2.6.2, the default filter is light, a filter that is both normalized (value sums up to 1) and color-balanced (eliminate color fringes at the cost of resolution). Since version 2.8.1, a second implementation exists, called Harmony, that "offers high quality LCD-optimized output without resorting to ClearType techniques of resolution tripling and filtering". This is the method enabled by default. When using this method, "each color channel is generated separately after shifting the glyph outline, capitalizing on the fact that the color grids on LCD panels are shifted by a third of a pixel. This output is indistinguishable from ClearType with a light 3-tap filter." Since the Harmony method does not require additional filtering, it is not covered by the ClearType patents. === CoolType === Adobe created their own subpixel renderer called CoolType, allowing them to display documents the same way across various operating systems: Windows, MacOS, Linux etc. When it was launched around the year 2001, CoolType supported a wider range of fonts than Microsoft's ClearType, which at the time was limited to TrueType fonts. In contrast, Adobe's CoolType also supported PostScript fonts (and their OpenType equivalents). === macOS === Mac OS X (later OS X, now macOS) also used subpixel rendering, as part of Quartz 2D. However, it was removed after the introduction of Retina displays. Unlike Microsoft's implementation, which favors a tight fit to the grid (font hinting) to maximize legibility, Apple's implementation prioritizes the shape of the glyphs as set out by their designer.
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Wadhwani Institute for Artificial Intelligence

Wadhwani AI, based in Mumbai, Maharashtra, is an independent, non-profit institute. Founded in 2018, it is dedicated to developing Artificial intelligence solutions for social good. Their mission is to build AI-based innovations and solutions for underserved communities in developing countries, for a wide range of domains including agriculture, education, financial inclusion, healthcare, and infrastructure. == History and funding == The institute was founded with a $30 million philanthropic effort by the Wadhwani brothers, Romesh Wadhwani and Sunil Wadhwani. The institute was inaugurated and dedicated to the nation by Narendra Modi, the 14th Prime Minister of India. In 2019, the institute received a $2 million grant from Google.org to create technologies to help reduce crop losses in cotton farming, through integrated pest management. The United States Agency for International Development awarded $2 million to the institute in 2020 to develop tools, using mathematical modeling techniques and digital technologies such as artificial intelligence and machine learning, to forecast COVID-19 disease patterns, estimate resources needed, and plan interventions. == Collaboration == With assistance from Google, the Ministry of Agriculture and Farmers' Welfare and the Wadhwani AI developed Krishi 24/7, the first AI-powered automated agricultural news monitoring and analysis tool. Through better decision-making, Krishi 24/7 will support the identification of valuable news, provide timely notifications, and respond quickly to safeguard farmers' interests and advance sustainable agricultural growth. The application converts news articles into English after scanning them in several languages. It ensures that the ministry is informed in a timely manner about pertinent occurrences that are published online by extracting key information from news items, including the headline, crop name, event type, date, location, severity, summary, and source link. The National Center for Disease Control has effectively implemented a comparable automated surveillance and analysis tool for disease outbreaks.
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Play Integrity API

Play Integrity API (formerly known as SafetyNet) consists of several application programming interfaces (APIs) offered by the Google Play Services to support security sensitive applications and enforce DRM. Currently, these APIs include device integrity verification, app verification, recaptcha and web address verification. It uses an environment called DroidGuard to perform the attestation. == Attestation == The SafetyNet Attestation API, one of the APIs under the SafetyNet umbrella, provides verification that the integrity of the device is not compromised. In practice, non-official ROMs such as LineageOS fail the hardware attestation and thus prevent the user from using a non-compliant ROM with third-party apps (mainly banking) that require the API. Due to this, some consider this a monopolistic practice deterring the entrance of competing mobile operating systems in the market. It requires a network connection to Google servers and validates the hardware signatures. Amongst the checks, the API looks for bootloader unlock status, ROM signatures, kernel strings, it also uses AVB2.0 and dm-verity attestations. Upon successful checks, Google Play will mark the device as Certified. The attestation runs in an environment called DroidGuard (com.google.android.gms.unstable). The SafetyNet Attestation API (one of the four APIs under the SafetyNet umbrella) has been deprecated. As of 6 October 2023, Google planned to replace it with the Play Integrity API by the end of January 2025. The transition ended on 20 May 2025, breaking applications which hadn't been updated. These attestations are offered by Google Play Services and thus are not available on free Android environments, like AOSP. Therefore, developers can require the API to be available and may refuse to execute on AOSP builds. == Google Play Protect == Under the same umbrella, Play Protect is a mechanism to find and remove "vulnerable" apps from one's Android device as well as store apps. Although it's meant to scan for malware-containing apps, it also looks for non-DRM compliant apps. == Criticism == Multiple groups have criticised SafetyNet and the Play Integrity API. Criticisms include that it offers weaker protection compared to alternatives such as Android's hardware attestation API, which provides a stronger form of verification while having the ability to remain compatible with more secure Android operating systems like GrapheneOS. Critics argued it undermines competition by effectively requiring developers to rely on Google's proprietary services, strengthening its monopoly over the Android ecosystem and disadvantaging alternative, privacy-focused operating systems. Users have also developed tools, such as the Play Integrity Fix module for Magisk/KernelSU/APatch, which tricks the attestation using leaked fingerprints of vulnerable devices. Furthermore, some have questioned the effectiveness of the attestation, claiming it does not deliver the level of security promised by Google and instead serves more as a form of vendor lock-in than a meaningful security measure. Activists have also raised concerns that it may violate antitrust and competition laws, like the Digital Markets Act.
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Depth peeling

In computer graphics, depth peeling is an exact multipass method of order-independent transparency that extracts transparent fragments into depth layers and composites those layers in depth order. Depth peeling has the advantage of being able to generate correct results even for complex images containing intersecting transparent objects. == Method == Depth peeling works by rendering the image multiple times. Depth peeling uses two Z buffers, one that works conventionally, and one that is not modified, and sets the minimum distance at which a fragment can be drawn without being discarded. For each pass, the previous pass' conventional Z-buffer is used as the minimal Z-buffer, so each pass removes already-captured nearer fragments and draws the next depth layer behind them. The resulting images can then be composited in depth order to form a single image. A major drawback of classical depth peeling is performance: it requires one geometry pass per peeled layer, so scenes with high depth complexity require many passes that each re-rasterize the transparent geometry. Later variants reduce the number of passes by peeling multiple layers or both front and back layers in a pass. Dual depth peeling reduces the geometry-pass count from N to N/2+1 by peeling one layer from the front and one from the back in each pass, while multi-layer depth peeling peels several layers per pass and reported up to an 8x speed-up in RGBA8 settings.
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