Scene statistics is a discipline within the field of perception. It is concerned with the statistical regularities related to scenes. It is based on the premise that a perceptual system is designed to interpret scenes. Biological perceptual systems have evolved in response to physical properties of natural environments. Therefore natural scenes receive a great deal of attention. Natural scene statistics are useful for defining the behavior of an ideal observer in a natural task, typically by incorporating signal detection theory, information theory or estimation theory. == Within-domain versus across-domain == Geisler (2008) distinguishes between four kinds of domains: (1) Physical environments (2) Images/Scenes (3) Neural responses and (4) Behavior. Within the domain of images/scenes one can study the characteristics of information related to redundancy and efficient coding. Across-domain statistics determine how an autonomous system should make inferences about its environment, process information and control its behavior. To study these statistics it is necessary to sample or register information in multiple domains simultaneously. == Applications == === Prediction of picture and video quality === One of the most successful applications of Natural Scenes Statistics Models has been perceptual picture and video quality prediction. For example, the Visual Information Fidelity (VIF) algorithm, which is used to measure the degree of distortion of pictures and videos, is used extensively by the image and video processing communities to assess perceptual quality. This is often after processing, such as compression, which can degrade the appearance of a visual signal. The premise is that the scene statistics are changed by distortion and that the visual system is sensitive to the changes in the scene statistics. VIF is heavily used in the streaming television industry. Other popular picture quality models that use natural scene statistics include BRISQUE and NIQE, both of which are no-reference since they do not require any reference picture to measure quality against.
IruSoft
IruSoft (Arabic: آيروسوفت) is an insurance regulatory platform designated for licensing, supervision and inspection of the insurance sector within a country. The platform introduced unique supervision-technology (suptech), insurance-technology (insurtech) and regulatory-technology (regtech) automated modules by which a regulator requires less resources to ensure fairness, transparency and competition and to prevent conflicts of interest in the sector. IruSoft was founded by Abdullah Al-Salloum and owned by the Insurance Regulatory Unit in Kuwait. The Insurance Regulatory Unit optimized processing insurance-sector's customer complaints by issuing Resolution No. (1) of 2022 that introduced IruSoft's complaints public module; an automated resolution center, by which the process of receiving submitted complaints, passing them on to the platforms of licensed insurance companies, tracking matter-related discussions and updates and getting them escalated if unresolved to be discussed by a committee assigned by the unit is integrally automated and analyzed for better key performance indicators.
Social trading
Social trading is a form of investing that allows investors to observe the trading behavior of their peers and expert traders. The primary objective is to follow their investment strategies using copy trading or mirror trading. Social trading requires little or no knowledge about financial markets. == History == One of the first social trading platforms was Collective2] which began offering a social trading functionality to retail traders as early as 2003 (preceding ZuluTrade by four years). In 2010, social trading started to achieve a greater degree of mainstream appeal with eToro, followed by Wikifolio in 2012. Europe-based NAGA, listed on Frankfurt Stock Exchange since 2017, claims more than EUR 27 billion was traded on its platform in the second half of 2019. Some of the other contemporary social trading platforms and tech providers are Trading Motion, Brokeree Solutions, iSystems, and FX Junction, among others. === Research === MIT Computer Scientist and researcher Yaniv Altshuler described social trading networks as complex adaptive systems, and in his 2014 research on eToro's OpenBook, wrote that "Having the inherent ability to share ideas and information between each others, OpenBook's users are given a new source of information they can use in order to enhance their trading performance. As the users are not playing against each other but rather – against the market, this situation becomes a non zero-sum game, hence incentivizing the users to share as much information as possible." His paper concludes that "social trading provides much better opportunities for profiting compared with individual trading," but that users make "excellent but sometimes not optimal decisions in selecting experts when they can see others' choices." A 2015 World Economic Forum report described social trading networks as disruptors, which "have emerged to provide low-cost, sophisticated alternatives to traditional wealth managers. These solutions cater to a broader customer base and empower customers to have more control of their wealth management," and "pose a tangible threat to the traditional practices of the wealth management industry". Economist Nouriel Roubini's thinktank predicted in 2016 that "newer forms of investment, such as socially responsible investments and social trading will bring some of the largest industry growth in the coming years." A 2017 St. John's University study found that 'leader' traders, or those with followers, are more susceptible to the disposition effect than investors that are not being followed by any other traders, with the authors suggesting the observation may be explained by "leaders feeling responsible towards their followers and an urge to not let them down, by fear of losing followers when admitting a bad investment decision and signaling confidence in their initial investment choice, or by an attempt of newly appointed leaders to manage their self-image." Social trading may potentially also change how much risk investors take. A recent experimental study argues that merely providing information on the success of others may lead to a significant increase in risk taking. This increase in risk taking may even be larger when subjects are provided with the option to directly copy others. == Characteristics == Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing and copying their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Social trading platforms or networks can be considered a subcategory of social networking services. Social trading allows traders to trade online with the help of others and some have claimed shortens the learning curve from novice to experienced trader. Traders can interact with others, watch others take trades, then duplicate their trades and learn what prompted the top performer to take a trade in the first place. By copying trades, traders can learn which strategies work and which do not work. Social trading is used to do speculation; in the moral context speculative practices are considered negatively and to be avoided by each individual. who conversely should maintain a long-term horizon avoiding any types of short term speculation. Social Media has permeated the trading world such that two main types of trading has evolved: Traditional Trades Single (or non-social) trade: Trader A places a normal trade by himself or herself; This can by manual or automated Social Trading There are two main types of social trading: Copy trade: Trader A places exactly the same trade as trader B's one single trade; (iii) Mirror trade: Trader A automatically executes trader B's every single trade, i.e., trader A follows exactly trader B's trading activities. Other variations offered on some platforms allow users to copy another trader's portfolio (copy portfolio), and follow a trader's dividends (copy dividends), where whenever a followed trader withdraws money from his or her account, a proportional amount of money will be withdrawn from the balance of their follower, in real time. === Key features === Information flow: Unencumbered access to information is important in financial markets and that makes the free exchange of information of interest to small scale as well as individual investors. Cooperative trading: Social trading offers traders the opportunity to work together in trading teams which can trade the markets collaboratively, whether by pooling funds, dividing research or through sharing information. Monetization: As with social networks in the broader sense, monetization strategies are not always clear. As with social networks in general, it is possible, however, that the long-term worth of such websites may come from the variety and depth of data about their users which their active communities are likely to generate. Transparency: Social trading platforms reveal traders' performance stats, open and past positions, and market sentiment, giving members complete information to assess the credibility of the contributors they follow on the platform.
Embedded analytics
Embedded analytics enables organisations to integrate analytics capabilities into their own, often software as a service, applications, portals, or websites. This differs from embedded software and web analytics (also commonly known as product analytics). This integration typically provides contextual insights, quickly, easily and conveniently accessible since these insights should be present on the web page right next to the other, operational, parts of the host application. Insights are provided through interactive data visualisations, such as charts, diagrams, filters, gauges, maps and tables often in combination as dashboards embedded within the system. This setup enables easier, in-depth data analysis without the need to switch and log in between multiple applications. Embedded analytics is also known as customer facing analytics. Embedded analytics is the integration of analytic capabilities into a host, typically browser-based, business-to-business, software as a service, application. These analytic capabilities would typically be relevant and contextual to the use-case of the host application. == History == The term "embedded analytics" was first used by Howard Dresner: consultant, author, former Gartner analyst and inventor of the term "business intelligence" said Howard Dresner while he was working for Hyperion Solutions, a company that Oracle bought in 2007. Oracle started then to use the term "embedded analytics" at their press release for Oracle Rapid Planning on 2009 . == Considerations with embedded analytics == When evaluating embedding analytics, consideration would normally be given to integration at various levels, these would likely include: security integration, data integration, application logic integration, business rules integration, and user experience integration. This is in contrast to traditional BI, which expects users to leave their workflow applications to look at data insights in a separate set of tools. This immediacy makes embedded analytics much more intuitive and likely to be valued by users. A December 2016 report from Nucleus Research found that using BI tools, which require toggling between applications, can take up as much as 1–2 hours of an employee's time each week, whereas embedded analytics eliminate the need to toggle between apps.
Strategic Air Command Digital Information Network
The Strategic Air Command DIgital Network (SACDIN) was a United States military computer network that provided computerized record communications, replacing the Data Transmission Subsystem and part of the Data Display Subsystem of the SAC Automated Command and Control System. SACDIN enabled a rapid flow of communications from headquarters SAC to its fielded forces, such as B-52 bases and ICBM Launch Control Centers. == Logistics == Major portions of SACDIN were developed, engineered and installed by the International Telephone and Telegraph (ITT) company, under contract to the Electronic Systems Center. == Chronology == 1969 - Headquarters SAC submits a request to the Joint Chiefs of Staff to study an expanded communications system, known as the SAC Total Information Network (SATIN). It would interconnect Air Force Satellite Communications (AFSATCOM), Advanced Airborne Command Post (AABNCP), Airborne Command Post (ABNCP), high frequency/single sideband radio HF/SSB radio, SAC Automated Command and Control System (SACCS), Automatic Digital Information Network (AUTODIN), Survivable Low Frequency Communications System (SLFCS) and Command Data Buffer (CDB) 1977 1 November - SATIN IV was effectively terminated by Congress. The restructured program was renamed SAC Digital Network (SACDIN), and was formulated to meet SAC's minimum essential data communications requirements, but also had the capability to grow in a modular fashion. 1986 ?? ??? - SACDIN replaces much of the SAC Automated Command and Control System (SACCS) and the SAC Automated Total Information Network (SATIN)
Clapper (service)
Clapper is an American short-form video-hosting service headquartered in Dallas, Texas. It was founded in 2020 by Edison Chen as an alternative for TikTok for mature audiences. The app is functionally similar to TikTok and includes tipping and e-commerce features. Following an influx of far-right content in early 2021, Clapper strengthened its moderation practices. It achieved 2 million monthly active users by 2023, and the number of downloads increased after a U.S. bill that would potentially ban TikTok in the country was signed in 2024. == History == With its offices in Dallas, Texas, Clapper was founded in July 2020 by Chinese-American entrepreneur Edison Chen. Chen considered that most online platforms, such as TikTok, were being targeted to young generations, such as Generation Z. He then concepted Clapper as a service with short-form content for mature audiences among Generation X and millennials, while not intending to compete directly with TikTok. Clapper averaged fewer than ten thousand daily active users during 2020, reaching 500 thousand downloads in the next year. Initially without paying for external advertising, the company raised about $3 million during a 2021 seed funding round. In 2023, the app reportedly reached about 300 to 400 thousand daily active users and 2 million monthly active users. The average user was between the ages of 35 and 55. Following the April 2024 signing of the Protecting Americans from Foreign Adversary Controlled Applications Act, which would potentially enact a ban on TikTok in the U.S. in January 2025, Clapper averaged 200 thousand weekly downloads. In 2025, before the day scheduled for the ban (January 19), TikTok users migrated to other apps. As a result, Clapper received 1.4 million new downloads in a week preceding the date. It was listed as the third most-downloaded free app on Apple's App Store on January 14, behind Xiaohongshu and Lemon8, and the term "TikTok refugee" became a trending term. == Features == Clapper presents similarities with TikTok in its layout, including "Following" and "For You" tabs with videos up to three minutes long that can be liked, commented on or shared. A "Clapback" feature allows users to create responses to videos from others. Users can create livestreams and chat rooms in the app. Users can tip Clapper creators through its Clapper Fam monetization feature, in place of in-app advertisements. The Clapper Shop allows for e-commerce between users. The service had distributed $10 million to its users in total by 2023, according to Clapper CEO Chen. == Content == Clapper includes a policy requiring users to be at least 17 years of age, although Clapper CEO Chen described that "there is no adult content" on the platform. Lindsay Dodgson of Business Insider described the content as generally outdated and "reminiscent of 'getting owned' compilations of the earlier internet." The Washington Post's Tatum Hunter characterized Clapper as including sexual or engagement baiting content more prevalently than TikTok. === Moderation === Clapper's team, which had fifteen employees in early 2021, initially stated it would not moderate content as strictly as TikTok and would mostly rely on user reports. Following that year's January 6 United States Capitol attack, far-right conservative videos promoting QAnon and anti-vaccine conspiracy theories appeared on Clapper's "For You" page to a substantial degree for weeks. The videos were made in protest against decisions by platforms, particularly TikTok, to ban such content. Clapper's team stated in January 10 that its rules prohibiting incitements to violence would be strictly enforced. By February, videos and accounts promoting the conspiracy theories had been removed, and QAnon-related content was banned permanently. Clapper's team hired more content auditors and implemented moderation by artificial intelligence for further community guideline violations.
Change data capture
In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed (the "deltas") so that action can be taken using the changed data. The result is a delta-driven dataset. CDC is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources. For instance it can be used for incremental update of data loading. CDC occurs often in data warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system. == Methodology == System developers can set up CDC mechanisms in a number of ways and in any one or a combination of system layers from application logic down to physical storage. In a simplified CDC context, one computer system has data believed to have changed from a previous point in time, and a second computer system needs to take action based on that changed data. The former is the source, the latter is the target. It is possible that the source and target are the same system physically, but that would not change the design pattern logically. Multiple CDC solutions can exist in a single system. === Timestamps on rows === Tables whose changes must be captured may have a column that represents the time of last change. Names such as LAST_UPDATE, LAST_MODIFIED, etc. are common. Any row in any table that has a timestamp in that column that is more recent than the last time data was captured is considered to have changed. Timestamps on rows are also frequently used for optimistic locking so this column is often available. === Version numbers on rows === Database designers give tables whose changes must be captured a column that contains a version number. Names such as VERSION_NUMBER, etc. are common. One technique is to mark each changed row with a version number. A current version is maintained for the table, or possibly a group of tables. This is stored in a supporting construct such as a reference table. When a change capture occurs, all data with the latest version number is considered to have changed. Once the change capture is complete, the reference table is updated with a new version number. (Do not confuse this technique with row-level versioning used for optimistic locking. For optimistic locking each row has an independent version number, typically a sequential counter. This allows a process to atomically update a row and increment its counter only if another process has not incremented the counter. But CDC cannot use row-level versions to find all changes unless it knows the original "starting" version of every row. This is impractical to maintain.) === Status indicators on rows === This technique can either supplement or complement timestamps and versioning. It can configure an alternative if, for example, a status column is set up on a table row indicating that the row has changed (e.g., a boolean column that, when set to true, indicates that the row has changed). Otherwise, it can act as a complement to the previous methods, indicating that a row, despite having a new version number or a later date, still shouldn't be updated on the target (for example, the data may require human validation). === Time/version/status on rows === This approach combines the three previously discussed methods. As noted, it is not uncommon to see multiple CDC solutions at work in a single system, however, the combination of time, version, and status provides a particularly powerful mechanism and programmers should utilize them as a trio where possible. The three elements are not redundant or superfluous. Using them together allows for such logic as, "Capture all data for version 2.1 that changed between 2005-06-01 00:00 and 2005-07-01 00:00 where the status code indicates it is ready for production." === Triggers on tables === May include a publish/subscribe pattern to communicate the changed data to multiple targets. In this approach, triggers log events that happen to the transactional table into another queue table that can later be "played back". For example, imagine an Accounts table, when transactions are taken against this table, triggers would fire that would then store a history of the event or even the deltas into a separate queue table. The queue table might have schema with the following fields: Id, TableName, RowId, Timestamp, Operation. The data inserted for our Account sample might be: 1, Accounts, 76, 2008-11-02 00:15, Update. More complicated designs might log the actual data that changed. This queue table could then be "played back" to replicate the data from the source system to a target. Data capture offers a challenge in that the structure, contents and use of a transaction log is specific to a database management system. Unlike data access, no standard exists for transaction logs. Most database management systems do not document the internal format of their transaction logs, although some provide programmatic interfaces to their transaction logs (for example: Oracle, DB2, SQL/MP, SQL/MX and SQL Server 2008). Other challenges in using transaction logs for change data capture include: Coordinating the reading of the transaction logs and the archiving of log files (database management software typically archives log files off-line on a regular basis). Translation between physical storage formats that are recorded in the transaction logs and the logical formats typically expected by database users (e.g., some transaction logs save only minimal buffer differences that are not directly useful for change consumers). Dealing with changes to the format of the transaction logs between versions of the database management system. Eliminating uncommitted changes that the database wrote to the transaction log and later rolled back. Dealing with changes to the metadata of tables in the database. CDC solutions based on transaction log files have distinct advantages that include: minimal impact on the database (even more so if one uses log shipping to process the logs on a dedicated host). no need for programmatic changes to the applications that use the database. low latency in acquiring changes. transactional integrity: log scanning can produce a change stream that replays the original transactions in the order they were committed. Such a change stream include changes made to all tables participating in the captured transaction. no need to change the database schema == Confounding factors == As often occurs in complex domains, the final solution to a CDC problem may have to balance many competing concerns. === Unsuitable source systems === Change data capture both increases in complexity and reduces in value if the source system saves metadata changes when the data itself is not modified. For example, some Data models track the user who last looked at but did not change the data in the same structure as the data. This results in noise in the Change Data Capture. === Tracking the capture === Actually tracking the changes depends on the data source. If the data is being persisted in a modern database then Change Data Capture is a simple matter of permissions. Two techniques are in common use: Tracking changes using database triggers Reading the transaction log as, or shortly after, it is written. If the data is not in a modern database, CDC becomes a programming challenge. === Push versus pull === Push: the source process creates a snapshot of changes within its own process and delivers rows downstream. The downstream process uses the snapshot, creates its own subset and delivers them to the next process. Pull: the target that is immediately downstream from the source, prepares a request for data from the source. The downstream target delivers the snapshot to the next target, as in the push model. === Alternatives === Sometimes the slowly changing dimension is used as an alternative method. CDC and SCD are similar in that both methods can detect changes in a data set. The most common forms of SCD are type 1 (overwrite), type 2 (maintain history) or 3 (only previous and current value). SCD 2 can be useful if history is needed in the target system. CDC overwrites in the target system (akin to SCD1), and is ideal when only the changed data needs to arrive at the target, i.e. a delta-driven dataset.