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  RagTime

  RagTime is a frame-oriented business publishing software which combines word processing, spreadsheets, simple drawings, image processing, and charts, in a single document/program, integrated software. It is often used to create forms, reports, documentation, desktop publishing, and in office environments. Typical users are business clients, educational institutions, administrations, architects, and also private users. Ragtime includes the following modules: Page layout (forms, templates etc.) Word processing Image processing Spreadsheets, similar to Microsoft Excel Formulas and functions which can be used throughout, in text, graphics, and spreadsheets Charts in different types of diagrams Drawings in vector graphics including lines, polygons, Bézier curves and more Slide show (presentation of RagTime documents) Audio/video Buttons (pop-up menus, switches, and more) that can be used within RagTime documents Import/export of various file formats Support of the AppleScript scripting language available system-wide under macOS == Principle == RagTime differs from most other comparable programs or software packages in its strict frame-oriented design: all content is contained within frames on each page. The content can have a fixed position within its frame or, if it is text or a spreadsheet, flow into another frame that is connected to the first frame via a so-called “pipeline”. RagTime has no different document types for different types of data; all content is stored in a single compound document type. Thus, a RagTime document not only can contain multiple pages, but also multiple layouts within the same document; e.g. spreadsheets in addition to text and images. The RagTime filename extension is .rtd (RagTime document); for templates the extension is .rtt (RagTime template). The current version is RagTime 6.6.5. It is available for OS X (10.6-10.14) and Windows (XP/Vista/7/8/10). == Extensions == FileTime – allows accessing “FileMaker Pro” databases from RagTime documents under OS X RagTime Connect – ODBC database connection for RagTime 6 (Mac and Windows) Johannes – print extension for the simple creation of stapled or folded brochures, booklets etc. PowerFunctions – additional functions for a more effective creation of intelligent documents for exchanging data and for use in mixed Mac/Windows environments MetaFormula – SYLK-based extension that allows calculating text as formula == History == RagTime has been developed since 1985 for the Macintosh – originally named MacFrame – and was published in 1986. When released, it already had the present name, which was chosen following the then-available software package Lotus Jazz. In the European Macintosh market, RagTime quickly gained a prominent position that continues to this day, even though the market share has decreased. Despite repeated attempts, the program could not gain acceptance in the North American market due to its high cost ($395 in 1990). The North American sales office closed in 1991, shortly after Claris Corporation released ClarisWorks which duplicated much of the functionality of RagTime for a lower price. After the manufacturer – first Brüning & Everth, followed by B&E Software and today RagTime.de Development – had focused on the Macintosh only for a very long time, it also released a Windows version, RagTime 5.0, in 1999. However, the program could not assume great significance against established competitors, especially Microsoft Office. Until mid-2006 RagTime was, in addition to the commercial version, also available as a free version (RagTime Solo) for personal use. RagTime Solo included the same features and performance (except for spelling and Syllabification) dictionaries), but was not allowed for use in commercial environments. In other languages RagTime Solo was distributed as RagTime Privat. In a press release from July 5, 2006, RagTime announced the discontinuation of RagTime Solo: “… the RagTime Solo license conditions were often misinterpreted or deliberately flouted. Therefore we discontinued RagTime Solo, there will be no private version of RagTime 6 anymore.” After a successful start of the RagTime 6.0 software, sales edged significantly lower in the following years. Disagreements arose among the shareholders about the continuation of the company, which filed for bankruptcy in July 2007. As a result, the rights to RagTime were taken over by the newly established company RagTime.de Development GmbH, which was responsible for the development. The sales partner RagTime.de Sales GmbH distributed the RagTime products until October 2015. Today RagTime.de Development GmbH is also responsible for sales. The last level of development is the extensively revamped version RagTime 6.6 of 8 October 2015, which also includes new OS X features (e.g. high-resolution “Retina” displays) and supports Windows 10. == Programming == RagTime 1-3 were developed in Pascal, since version 4 the development is completely coded in C++. External programming and automation can be implemented via AppleScript on a Mac, and via OLE/COM-API (e.g. Visual Basic) under Windows. On a Mac, RagTime provides a comprehensive AppleScript library, for the automation of almost any task, from automatic document creation to the export of PDF documents. RagTime also supports “recordings” by use of the “AppleScript Editor”, which allows recording the interactive RagTime operation as an AppleScript program sequence. AppleScripts can be saved in the RagTime document and called via menu or shortcut keys. On Windows, RagTime (since version 6) disposes over an OLE/COM API, which allows automating many RagTime components via external programming. For that purpose there is a type library that installs the available RagTime OLE/COM object catalogue. Programming can be realized in all programming languages supported by Microsoft.

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  Social media use by businesses

  Social media use by businesses includes a range of applications. Although social media accessed via desktop computers offer an online shopping variety of opportunities for companies in a wide range of business sectors, mobile social media, which users can access when they are "on the go" via tablet computers or smartphones, benefit companies because of the location- and time-sensitive awareness of their users. Mobile social media tools can be used for marketing research, communication, sales promotions/discounts, informal employee learning/organizational development, relationship development/loyalty programs, and e-commerce. Marketing research: Mobile social media applications provide companies data about offline consumer movements at a level of detail that was previously accessible to online companies only. These applications allow any business to know the exact time a customer who uses social media entered one of its locations, as well as know the social media comments made during the visit. Communication: Mobile social media communication takes two forms: company-to-consumer (in which a company may establish a connection to a consumer based on its location and provide reviews about locations nearby) and user-generated content. For example, McDonald's offered $5 and $10 gift-cards to 100 users randomly selected among those checking in at one of its restaurants. This promotion increased check-ins by 33% (from 2,146 to 2,865), resulted in over 50 articles and blog posts, and prompted several hundred thousand news feeds and Twitter messages. Sales promotions and discounts: Although customers have had to use printed coupons in the past, mobile social media allows companies to tailor promotions to specific users at specific times. For example, when launching its California-Cancun service, Virgin America offered users who checked in through Loopt at one of three designated taco trucks in San Francisco or Los Angeles between 11 a.m. and 3 p.m. on 31 August 2010, two tacos for $1 and two flights to Cancun or Cabo for the price of one. This special promotion was only available to people who were at a certain location at a certain time. Relationship development and loyalty programs: In order to increase long-term relationships with customers, companies can develop loyalty programs that allow customers who check-in via social media regularly at a location to earn discounts or perks. For example, American Eagle Outfitters remunerates such customers with a tiered 10%, 15%, or 20% discount on their total purchase. Informal employee learning/organizational development is facilitated by social media. Technologies such as blogs, wiki pages, web forums, social networks and other social media act as technology enhanced learning (TEL) tools, and their users perceive change in organizational structure, culture and knowledge management. The prerequisite for the successful use of social media are motivated employees who want to use the new technologies. It is central for companies to understand the factors that determine the willingness to use social media. Customer service and support: A company can gain cost savings and increase revenue and customer satisfaction by using social media platforms in customer service and support. By using social media tools, company's have easy and widescale contact to its customers and simultaneously increase their brand knowledge. E-commerce: Social media sites are increasingly implementing marketing-friendly strategies, creating platforms that are mutually beneficial for users, businesses, and the networks themselves in the popularity and accessibility of e-commerce, or online purchases. The user who posts their comments about a company's product or service benefits because they are able to share their views with their online friends and acquaintances. The company benefits because it obtains insight (positive or negative) about how their product or service is viewed by consumers. Mobile social media applications such as Amazon.com and Pinterest have started to influence an upward trend in the popularity and accessibility of e-commerce. E-commerce businesses may refer to social media as consumer-generated media (CGM). A common thread running through all definitions of social media is a blending of technology and social interaction for the co-creation of value for the business or organization that is using it. People obtain valuable information, education, news, and other data from electronic and print media. Social media are distinct from industrial and traditional media such as newspapers, magazines, television, and film as they are comparatively inexpensive marketing tools and are highly accessible. They enable anyone, including private individuals, to publish or access information easily. Industrial media generally require significant resources to publish information, and in most cases the articles go through many revisions before being published. This process adds to the cost and the resulting market price. Originally social media was only used by individuals, but now it is used by both businesses and nonprofit organizations and also in government and politics. One characteristic shared by both social and industrial media is the capability to reach small or large audiences; for example, either a blog post or a television show may reach no people or millions of people. Some of the properties that help describe the differences between social and industrial media are: Quality: In industrial (traditional) publishing—mediated by a publisher—the typical range of quality is substantially narrower (skewing to the high quality side) than in niche, unmediated markets like user-generated social media posts. The main challenge posed by the content in social media sites is the fact that the distribution of quality has high variance: from very high-quality items to low-quality, sometimes even abusive or inappropriate content. Reach: Both industrial and social media technologies provide scale and are capable of reaching a global audience. Industrial media, however, typically use a centralized framework for organization, production, and dissemination, whereas social media are by their very nature more decentralized, less hierarchical, and distinguished by multiple points of production and utility. Frequency: The number of times users access a type of media per day. Heavy social media users, such as young people, check their social media account numerous times throughout the day. Accessibility: The means of production for industrial media are typically government or corporate (privately owned); social media tools are generally available to the public at little or no cost, or they are supported by advertising revenue. While social media tools are available to anyone with access to Internet and a computer or mobile device, due to the digital divide, the poorest segment of the population lacks access to the Internet and computer. Low-income people may have more access to traditional media (TV, radio, etc.), as an inexpensive TV and aerial or radio costs much less than an inexpensive computer or mobile device. Moreover, in many regions, TV or radio owners can tune into free over the air programming; computer or mobile device owners need Internet access to go to social media sites. Usability: Industrial media production typically requires specialized skills and training. For example, in the 1970s, to record a pop song, an aspiring singer would have to rent time in an expensive professional recording studio and hire an audio engineer. Conversely, most social media activities, such as posting a video of oneself singing a song require only modest reinterpretation of existing skills (assuming a person understands Web 2.0 technologies); in theory, anyone with access to the Internet can operate the means of social media production, and post digital pictures, videos or text online. Immediacy: The time lag between communications produced by industrial media can be long (days, weeks, or even months, by the time the content has been reviewed by various editors and fact checkers) compared to social media (which can be capable of virtually instantaneous responses). The immediacy of social media can be seen as a strength, in that it enables regular people to instantly communicate their opinions and information. At the same time, the immediacy of social media can also be seen as a weakness, as the lack of fact checking and editorial "gatekeepers" facilitates the circulation of hoaxes and fake news. Permanence: Industrial media, once created, cannot be altered (e.g., once a magazine article or paper book is printed and distributed, changes cannot be made to that same article in that print run) whereas social media posts can be altered almost instantaneously, when the user decides to edit their post or due to comments from other readers. Community media constitute a hybrid of industrial and social media. Though community-owned, some community radio,

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  Sharenting

  "Sharenting" is a portmanteau of "sharing" and "parenting", describing the practice of parents publicizing a large amount of potentially sensitive content about their children on internet platforms, most notably on social media. While the term was coined as recently as 2010, sharenting has become an international phenomenon with widespread presence in the United States, Spain, France, and the United Kingdom. Proponents of sharenting frame the practice as a natural expression of parental pride in their children and argue that critics take sharenting-related posts out of context. Detractors find that it violates child privacy and hurts a parent–child relationship. Academic research has been conducted over the potential social motivations for sharenting and legal frameworks to balance child privacy with this parental practice. Researchers have conducted several psychological surveys, outlining social media accessibility, parental self-identification with children, and social pressure as potential causes for sharenting. Legal scholars have identified international human rights laws, labor protections, and recent online child privacy statutes as potential legal standards to check sharenting abuses. == History == The origins of the term "sharenting" have been attributed to the Wall Street Journal, where they called it "oversharenting," a portmanteau of "oversharing" and "parenting." Priya Kumar suggests that recording life moments of children rearing is not a new practice: people have been using diaries, scrapbooks and baby log books as the media of documentation for centuries. Scholars assert that sharenting has become popular as a result of social media, which has made many people more comfortable with sharing their lives and those of their children online. The trend of oversharing on social media has raised public attention in the 2010s and become the focus of a number of editorials and academic research projects. It was also added to Times Word of the Day in February 2013 and Collins English Dictionary in 2016 given its influence. == Popularity == Several studies describe sharenting as an international phenomenon with widespread prevalence across households. In the United States, researchers at the University of Michigan C.S. Mott Children's Hospital found that almost 75% of American parents were familiar with someone who over-shared information about their child on social media, and an AVG survey determined that 92% of all American two-year-olds had some presence on the internet. In Australia, Fisher-Price conducted a survey which revealed that 90% of Australian parents admitted to over-sharing. In Spain and Czech Republic, a survey of approximately 1,500 parents found that 70-80% participated in sharenting. In the United Kingdom, France, Germany, and Italy, a Research Now report revealed that almost three-quarters of surveyed parents said that they were "willing to share images of their infants". Some claim that sharenting presents a violation of child privacy, and this backlash includes anti-sharenting sites and apps that block baby pictures. One particular outlet of protest was the blog STFU Parents, founded in 2009 to criticize parental oversharing on social media. Some parents felt that these criticisms of sharenting often took posts out of context and neglected some positive aspects of the practice, including advancing a stronger sense of online community. Others, while acknowledging the potential privacy violations of sharenting, suggested a more tailored approach that would only permit posting under certain conditions, notwithstanding audience and identification restrictions for social media posts. == Motivations == Research has suggested that sharenting is associated with a mix of parent self-identification with children, mothering pressures, and the accessibility of social media. Conducting 17 interviews with mothers in the United Kingdom, a London School of Economics study found that parent bloggers often re-explained their sharing practices in terms of expressing their own personal identity, representing their own child as part of themselves. In particular, the report surveyed the use of blogs as a networking vehicle to connect parents with similar family situations and found that sharenting parents, by filtering self-presentation through their parent-child relationship, adopted a more relational identity on social media websites. This included identifying oneself in terms of parental circumstances, whether it be raising a child with a disability or being a single mother. Alternatively, some have suggested that these online expressions indicate the infiltration of individual pride into the sphere of parenting, as family photography becomes a means to "show off" one's children to the others and strengthens a parent's sense of individuated self. Addressing the prevalence of mothers engaging in sharenting, those who purport this view argue that the rise of digital communication has pressured mothers into performing the role of a "good" parent on social media platforms. They claim that these developments may reinforce a dominant vision of a "normal" family, as sharenting posts could be motivated by the need to converge to a normative interpretation of family. == Controversy == While some people assert that online platforms enable parents to establish a community and seek parenting support, others are concerned about the children's data privacy and their lack of informed consent. Sharing content may not only embarrass children but also creates an initial digital footprint, a history of online activity, that the children themselves have no control over. This might bring some negative consequences, such as being ridiculed at school or leaving a negative impression on future employers. === Parental benefits === Many parents use social media to seek parenting advice and share information about their children. With the convenience of online platforms, parent bloggers can easily connect with other people in similar situations as well as those who are willing to contribute meaningful advice. By forming a community, parents can receive encouragement from empathetic peers and assistance from experts in children rearing. Parents whose children need special educational accommodations or have disabilities often found themselves detached from the mainstream parenting style. Therefore, they regard online blogs as a means to gain support from others and support back. Online blogging enables parents of children with disabilities and special needs to connect with other parents. The advice from similarly situated families can open up new possibilities that help the parents "negotiate the complexities of social services, health care, and schools". However, in some cases, posting online about a parent's struggles can cause a backlash, as advocates may accuse the parent of presenting people with that condition in a bad light, or wonder how the child will feel, if they later read these posts and see how much their parents struggled to care for them. Such advantages of social media are not limited to particular groups of parents. In general, most parents benefit from exchanging parenting experience. Statistically speaking, 72% of parents rate social media useful for emotional connection and affirmations, and 74% of them receive support about parenting from friends on social media. Sharenting also plays a role in fostering interpersonal relationships. As the images and words about children's lives initiate conversations, parents use sharenting to stay connected with distant friends and relatives. In particular, mothers, as a research study reveals, are willing to engage in sharenting since they believe that the positive contents can help avoid digital conflicts and maintain close relations with those in their social circles. Researchers also found that female participants in this study carefully chose photos and phrases to express love and present laudable behaviors of children in their updates, which indicates their intention to convey positive messages. These messages also promote a close social network for a child as the parents invites supportive family members and friends into daily life. === Children's privacy === Given the potential misuse of digital data, people are critical about sharenting, and the majority of parents are cautious about the wrongdoing with online posts. The disclosure of minors' personal information, such as geographic location, name, date of birth, pictures, and the schools they attend, might expose them to illegal practices by recipients with malicious intentions. Sharented information is often abused for "identity theft", when imposters manage to track, stalk, commit fraud against children, or even blackmail the family. According to Barclays, online fraud targeting the young generation will contribute to a loss of £670 million (approximately $790 million) by 2030, and two-thirds of identity fraud will be related to s

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  IWARP

  iWARP is a computer networking protocol that implements remote direct memory access (RDMA) for efficient data transfer over Internet Protocol networks. Contrary to some accounts, iWARP is not an acronym. Because iWARP is layered on Internet Engineering Task Force (IETF)-standard congestion-aware protocols such as Transmission Control Protocol (TCP) and Stream Control Transmission Protocol (SCTP), it makes few requirements on the network, and can be successfully deployed in a broad range of environments. == History == In 2007, the IETF published five Request for Comments (RFCs) that define iWARP: RFC 5040 A Remote Direct Memory Access Protocol Specification is layered over Direct Data Placement Protocol (DDP). It defines how RDMA Send, Read, and Write operations are encoded using DDP into headers on the network. RFC 5041 Direct Data Placement over Reliable Transports is layered over MPA/TCP or SCTP. It defines how received data can be directly placed into an upper layer protocols receive buffer without intermediate buffers. RFC 5042 Direct Data Placement Protocol (DDP) / Remote Direct Memory Access Protocol (RDMAP) Security analyzes security issues related to iWARP DDP and RDMAP protocol layers. RFC 5043 Stream Control Transmission Protocol (SCTP) Direct Data Placement (DDP) Adaptation defines an adaptation layer that enables DDP over SCTP. RFC 5044 Marker PDU Aligned Framing for TCP Specification defines an adaptation layer that enables preservation of DDP-level protocol record boundaries layered over the TCP reliable connected byte stream. These RFCs are based on the RDMA Consortium's specifications for RDMA over TCP. The RDMA Consortium's specifications are influenced by earlier RDMA standards, including Virtual Interface Architecture (VIA) and InfiniBand (IB). Since 2007, the IETF has published three additional RFCs that maintain and extend iWARP: RFC 6580 IANA Registries for the Remote Direct Data Placement (RDDP) Protocols published in 2012 defines IANA registries for Remote Direct Data Placement (RDDP) error codes, operation codes, and function codes. RFC 6581 Enhanced Remote Direct Memory Access (RDMA) Connection Establishment published in 2011 fixes shortcomings with iWARP connection setup. RFC 7306 Remote Direct Memory Access (RDMA) Protocol Extensions published in 2014 extends RFC 5040 with atomic operations and RDMA Write with Immediate Data. == Protocol == The main component in the iWARP protocol is the Direct Data Placement Protocol (DDP), which permits the actual zero-copy transmission. DDP itself does not perform the transmission; the underlying protocol (TCP or SCTP) does. However, TCP does not respect message boundaries; it sends data as a sequence of bytes without regard to protocol data units (PDU). In this regard, DDP itself may be better suited for SCTP, and indeed the IETF proposed a standard RDMA over SCTP. To run DDP over TCP requires a tweak known as marker PDU aligned (MPA) framing to guarantee boundaries of messages. Furthermore, DDP is not intended to be accessed directly. Instead, a separate RDMA protocol (RDMAP) provides the services to read and write data. Therefore, the entire RDMA over TCP specification is really RDMAP over DDP over either MPA/TCP or SCTP. All of these protocols can be implemented in hardware. Unlike IB, iWARP only has reliable connected communication, as this is the only service that TCP and SCTP provide. The iWARP specification omits other features of IB, such as Send with Immediate Data operations. With RFC 7306, the IETF is working to reduce these omissions. == Implementation == Because a kernel implementation of the TCP stack can be seen as a bottleneck, the protocol is typically implemented in hardware RDMA network interface controllers (rNICs). As simple data losses are rare in tightly coupled network environments, the error-correction mechanisms of TCP may be performed by software while the more frequently performed communications are handled strictly by logic embedded on the rNIC. Similarly, connections are often established entirely by software and then handed off to the hardware. Furthermore, the handling of iWARP specific protocol details is typically isolated from the TCP implementation, allowing rNICs to be used for both as RDMA offload and TCP offload (in support of traditional sockets based TCP/IP applications). The portion of the hardware implementation used for implementing the TCP protocol is known as the TCP Offload Engine (TOE). TOE itself does not prevent copying on the reception side, and must be combined with RDMA hardware for zero-copy results. The RDMA / TCP specification is a set of different wire protocols intended to be implemented in hardware (though it seems feasible to emulate it in software for compatibility but without the performance benefits). == Interfaces == iWARP is a protocol, not an implementation, but defines protocol behavior in terms of the operations that are legal for the protocol, known as Verbs. As such, iWARP does not have any single standard programming interface. However, programming interfaces tend to very closely correspond to the Verbs. Several programmatic interfaces have been proposed, including OpenFabrics Verbs, Network Direct, uDAPL, kDAPL, IT-API, and RNICPI. Implementations of some of these interfaces are available for different platforms, including Windows and Linux. == Services available == Networking services implemented over iWARP include those offered in the OpenFabrics Enterprise Distribution (OFED) by the OpenFabrics Alliance for Linux operating systems, and by Microsoft Windows via Network Direct. NVMe over Fabrics (NVMEoF) iSCSI Extensions for RDMA (iSER) Server Message Block Direct (SMB Direct) Sockets Direct Protocol (SDP) SCSI RDMA Protocol (SRP) Network File System over RDMA (NFS over RDMA) GPUDirect

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  Business process automation

  Business process automation (BPA), also known as business automation, refers to the technology-enabled automation of business processes. == Development approaches == There are three main approaches to developing BPA: traditional business process automation involves developing BPA software in a programming language for integrating relevant applications in the digital ecosystem to execute a given process; robotic process automation uses software robots (also called agents, bots, or workers) to emulate human-computer interaction for executing a combination of processes, activities, transactions, and tasks in one or more unrelated software systems; hyperautomation (also called intelligent automation (IA), intelligent process automation (IPA), integrated automation platform (IAP), and cognitive automation (CA) combines business process automation, artificial intelligence (AI), and machine learning (ML) to discover, validate, and execute organizational processes automatically with no or minimal human intervention. == Deployment == BPA toolsets vary in capability. With the increasing adoption of artificial intelligence (AI), organizations are implementing AI-driven technologies that can process natural language, interpret unstructured datasets, and interact with users. These systems are designed to adapt to new types of problems with reduced reliance on human intervention. == Business process management implementation == A business process management system differs from BPA. However, it is possible to implement automation based on a BPM implementation. The methods to achieve this vary, from writing custom application code to using specialist BPA tools. == Robotic process automation == Robotic process automation (RPA) involves the deployment of attended or unattended software agents in an organization's environment. These software agents, or robots, are programmed to perform predefined structured and repetitive sets of business tasks or processes. Robotic process automation is designed to streamline workflows by delegating repetitive tasks to software agents, allowing human workers to focus on more complex and strategic activities. BPA providers typically focus on different industry sectors, but the underlying approach is generally similar in that they aim to provide the shortest route to automation by interacting with the user interface rather than modifying the application code or database behind it. == Use of artificial intelligence == Artificial intelligence software robots are used to handle unstructured data sets (like images, texts, audios) and are often deployed after implementing robotic process automation. They can, for instance, generate an automatic transcript from a video. The combination of automation and artificial intelligence (AI) enables autonomy for robots, along with the capability to perform cognitive tasks. At this stage, robots can learn and improve processes by analyzing and adapting them.

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  ACTS Gigabit Satellite Network

  The ACTS Gigabit Satellite Network was a pioneering, high-speed communications satellite network in the years 1993-2004, created as a prototype system to explore high-speed networking of digital endpoints. The system was jointly sponsored by NASA and ARPA, implemented by BBN Technologies and Motorola, and was inducted into the Space Technology Hall of Fame in April 1997. The Advanced Communications Technology Satellite (ACTS) network was designed to provide fiber-compatible SONET service to remote nodes and networks through a wideband satellite system, and provided long-haul, point-to-point and point-to-multipoint full-duplex SONET services, at rates up to 622 Mbit/s, over NASA's Advanced Communication Technology Satellite (ACTS). The Advanced Communications Technology Satellite itself, built and operated by Lockheed Martin, was launched on STS-51 on September 12, 1993, by the Space Shuttle Discovery, and occupied a geostationary orbit at 100° west longitude. It was the first communication satellite to operate in the 20–30 GHz frequency band (Ka band), with 30 GHz uplink and 20 GHz downlink signals. The satellite incorporated advanced on-board switching and multiple dynamically-hopping spot-beam antennas for selected areas of the United States including Hawaii. Up to 3 uplink and 3 downlink antenna beams could be active simultaneously. The ACTS network ground terminals were transportable Gigabit Earth Stations (GES) with fiber-optic SONET interfaces (OC-3 and OC-12), which also supported the Asynchronous Transfer Mode (ATM) protocol suite. The network control and management functions are distributed in the various Gigabit Earth Stations, with the operator's interface being centralized in a Network Management Terminal (NMT), which could be collocated at a GES, or anywhere in the Internet. The system was operational and used for experiments for 127 months, instead of the originally planned 24–48 months. In all, 53 terminals were built and used by more than 100 experimenters to test ACTS abilities. In Nov. 1997 a record data rate of 520 Mbit/s TCP/IP throughput was achieved using ATM between several ground stations via ACTS. On May 31, 2000 the ACTS experiments program officially came to a close, but the system continued to support experiments until it was deactivated on April 28, 2004.

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  Data room

  Data rooms are secure spaces used for housing data, usually of a privileged or confidential nature. They can be physical data rooms, virtual data rooms (VDRs), or data centers. They are primarily used for a variety of corporate purposes, including data storage, document exchange, file sharing, financial transactions, and legal proceedings. Today, data rooms are central to workflows in mergers and acquisitions, venture capital, and corporate restructuring, increasingly utilizing artificial intelligence to securely manage and review large datasets. Historically, data rooms were strictly physical locations heavily guarded and monitored. Today, the vast majority of corporate data rooms are hosted virtually on secure cloud platforms, though physical rooms are still occasionally used for highly sensitive government or proprietary intelligence. == Physical Data Rooms == In mergers and acquisitions (M&A), the traditional data room genuinely consists of a physically secured and continually monitored room, normally in the vendor's offices or those of their legal counsel. Bidders and their advisers visit this room in order to inspect and report on various documents, legal contracts, and financial statements made available during the due diligence process. Historically, physical data rooms presented significant logistical challenges. Often, only one bidder at a time was allowed to enter to maintain document integrity and confidentiality. If new documents or new versions of documents were required, they had to be brought in by courier as hardcopies. Teams involved in large due diligence processes typically had to be flown in from many regions or countries and remain available throughout the process. Because these teams comprised a number of experts in different fields—such as legal counsel, forensic accountants, and industry specialists—the overall cost of keeping such groups on call near the physical data room was often extremely high. == Virtual Data Rooms (VDRs) == To address the costs and logistical bottlenecks of physical data rooms, virtual data rooms (VDRs) were developed to provide secure, online dissemination of confidential information. A VDR is essentially a secure cloud repository with strictly controlled access. Access is managed through secure log-ons supplied by the vendor or authority, which can be disabled at any time if a bidder withdraws from a transaction. Because much of the information released during corporate transactions is highly confidential, VDRs utilize digital rights management (DRM) to control information. Restrictions are applied to the viewers' ability to release data to third parties by disabling forwarding, copying, or printing capabilities. Modern VDRs also employ dynamic watermarking and detailed auditing capabilities. Detailed auditing is required for legal reasons so that a precise digital footprint is kept of who has viewed which version of each document, and for how long. Furthermore, modern VDR platforms are typically built to comply with stringent information security standards such as ISO 27001 and SOC 2. Transitioning from sequential physical data rooms to parallel virtual data rooms has been shown to significantly reduce the duration of M&A transactions while allowing sellers to field multiple bidders simultaneously. == Key Applications == Data rooms are commonly used by legal, accounting, investment banking, and private equity firms. Primary applications include: Mergers and Acquisitions (M&A): VDRs are central to the sell-side M&A process. After potential buyers sign a Non-Disclosure Agreement (NDA) and review a Confidential Information Memorandum (CIM), they are granted data room access to perform deep financial due diligence, such as Quality of Earnings (QoE) analysis and legal liability assessments. Venture Capital and Startups: Startups use data rooms as a centralized location for key operational data, capitalization tables, and financial projections to streamline due diligence for angel investors and venture capital firms during fundraising rounds. Initial Public Offerings (IPOs): Taking a company public requires intense regulatory scrutiny. Data rooms are used to securely share company histories and financial audits with investment bankers, legal teams, and regulatory bodies. Corporate Restructuring and Insolvency: During bankruptcies or corporate carve-outs, data rooms are used to organize outstanding debt profiles, creditor agreements, and operational liabilities. == Emerging Technologies == In recent years, the management of virtual data rooms has increasingly incorporated Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Natural Language Processing (NLP) tools are now integrated into VDRs to automatically index thousands of documents, perform auto-redaction of personally identifiable information (PII), and assist buy-side analysts in identifying hidden liabilities within unstructured text data during the due diligence phase. Modern AI algorithms can extract line items from financial statements to instantly populate structured databases.

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  Protecting Our Kids from Social Media Addiction Act

  Protecting Our Kids from Social Media Addiction Act also known as California SB 976 is a law that was enacted in September 2024 that is meant to address problematic social media usage among minors. The law prohibitions minors to have "addictive feeds" unless they have verifiable parental consent, minor's notifications are also restricted between 12 am to 6 am and during school hours between 8 am and 3 pm it also well requires minors to have default privacies settings and have social media companies to publicly disclose certain metrics about their users. The law was set to take effect in two steps the first being the restrictions on social media feeds, notifications, disclosures from social media companies and default settings which would have taken effect on January 1, 2025, and the age verification provision which would have taken effect on January 1, 2027. However, has faced legal challenges since its enactment delaying its enactment. == Legal Challenges == In November 2024 NetChoice a trade association representing many of the biggest social media companies such as YouTube, Facebook and Instagram sued the attorney general of California Rob Bonta hoping to get an injunction before the first set of the law's provisions would take effect in January of the next year. However, judge Edward Davila would only grant Netchoice's request as to the restrictions on notifications and public disclosures and would deny their request as to the rest of the law. The law was later fully enjoined temporarily by the District Court and Appellant Court pending appeal, and the case is now in the Ninth Circuit Court of Appeals and is pending a decision. === Social media platforms challenges to law === In November 2025 Meta, Google and TikTok filed lawsuits against the law arguing it violates the first amendment.

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  Large language model

  A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable. As of 2026, the most capable LLMs are based on transformer architectures, which, according to the 2017 paper "Attention Is All You Need", can be more efficient and parallelizable than earlier statistical and recurrent neural network models. Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety. == History == Before the emergence of transformer-based models in 2017, some language models were considered large relative to the computational and data constraints of their time. In the early 1990s, IBM's statistical models pioneered word alignment techniques for machine translation, laying the groundwork for corpus-based language modeling. In 2001, a smoothed n-gram model, such as those employing Kneser–Ney smoothing, trained on 300 million words, achieved state-of-the-art perplexity on benchmark tests. During the 2000s, with the rise of widespread internet access, researchers began compiling massive text datasets from the web ("web as corpus") to train statistical language models. Moving beyond n-gram models, researchers started in 2000 to use neural networks as language models. Following the breakthrough of deep neural networks in image classification around 2012, similar architectures were adapted for language tasks. This shift was marked by the development of word embeddings (e.g., Word2Vec by Mikolov in 2013) and sequence-to-sequence (seq2seq) models using LSTM. In 2016, Google transitioned its translation service to neural machine translation (NMT), replacing statistical phrase-based models with deep recurrent neural networks. These early NMT systems used LSTM-based encoder-decoder architectures, as they preceded the invention of transformers. At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their landmark paper "Attention Is All You Need". This paper's goal was to improve upon 2014 seq2seq technology, and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. The following year in 2018, BERT was introduced and quickly became "ubiquitous". Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model. Academic and research usage of BERT began to decline in 2023, following rapid improvements in the abilities of decoder-only models (such as GPT) to solve tasks via prompting. Although decoder-only GPT-1 was introduced in 2018, it was GPT-2 in 2019 that caught widespread attention because OpenAI claimed to have initially deemed it too powerful to release publicly, out of fear of malicious use. GPT-3 in 2020 went a step further and as of 2025 is available only via API with no offering of downloading the model to execute locally. But it was the consumer-facing chatbot ChatGPT in late 2022 that received extensive media coverage and public attention by 2023. The 2023 GPT-4 was praised for its increased accuracy and as a "holy grail" for its multimodal capabilities. OpenAI did not reveal the high-level architecture and the number of parameters of GPT-4. The release of ChatGPT led to an uptick in LLM usage across several research subfields of computer science, including robotics, software engineering, and societal impact work. In 2024, OpenAI released the reasoning model OpenAI o1, which generates long chains of thought before returning a final answer. Many LLMs with parameter counts comparable to those of OpenAI's GPT series have been developed. Since 2022, weights-available models have been gaining popularity, especially at first with BLOOM and LLaMA, though both have restrictions on usage and deployment. Mistral AI's open-weight models Mistral 7B and Mixtral 8x7B have a more permissive Apache License. In January 2025, DeepSeek released DeepSeek R1, a 671-billion-parameter open-weight model that performs comparably to OpenAI o1 but at a much lower price per token for users. Since 2023, many LLMs have been trained to be multimodal, having the ability to also process or generate other types of data, such as images, audio, or 3D meshes. Open-weight LLMs have become more influential since 2023. Per Vake et al. (2025), community-driven contributions to open-weight models improve their efficiency and performance via collaborative platforms such as Hugging Face. == Dataset preprocessing == === Tokenization === As machine learning algorithms process numbers rather than text, the text must be converted to numbers. In the first step, a vocabulary is decided upon, then integer indices are arbitrarily but uniquely assigned to each vocabulary entry, and finally, an embedding is associated with the integer index. Algorithms include byte-pair encoding (BPE) and WordPiece. There are also special tokens serving as control characters, such as [MASK] for masked-out token (as used in BERT), and [UNK] ("unknown") for characters not appearing in the vocabulary. Also, some special symbols are used to denote special text formatting. For example, "Ġ" denotes a preceding whitespace in RoBERTa and GPT and "##" denotes continuation of a preceding word in BERT. For example, the BPE tokenizer used by the legacy version of GPT-3 would split tokenizer: texts -> series of numerical "tokens" as Tokenization also compresses the datasets. Because LLMs generally require input to be an array that is not jagged, the shorter texts must be "padded" until they match the length of the longest one. ==== Byte-pair encoding ==== As an example, consider a tokenizer based on byte-pair encoding. In the first step, all unique characters (including blanks and punctuation marks) are treated as an initial set of n-grams (i.e. initial set of uni-grams). Successively the most frequent pair of adjacent characters is merged into a bi-gram and all instances of the pair are replaced by it. All occurrences of adjacent pairs of (previously merged) n-grams that most frequently occur together are then again merged into even lengthier n-gram, until a vocabulary of prescribed size is obtained. After a tokenizer is trained, any text can be tokenized by it, as long as it does not contain characters not appearing in the initial-set of uni-grams. === Dataset cleaning === In the context of training LLMs, datasets are typically cleaned by removing low-quality, duplicated, or toxic data. Cleaned datasets can increase training efficiency and lead to improved downstream performance. A trained LLM can be used to clean datasets for training a further LLM. With the increasing proportion of LLM-generated content on the web, data cleaning in the future may include filtering out such content. LLM-generated content can pose a problem if the content is similar to human text (making filtering difficult) but of lower quality (degrading performance of models trained on it). === Synthetic data === Training of largest language models might need more linguistic data than naturally available, or that the naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. == Training == An LLM is a type of foundation model (large X model) trained on language. LLMs can be trained in different ways. In particular, GPT models are first pretrained to predict the next word on a large amount of data, before being fine-tuned. === Cost === Substantial infrastructure is necessary for training the largest models. The tendency towards larger models is visible in the list of large language models. For example, the training of GPT-2 (i.e. a 1.5-billion-parameter model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameter model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. The qualifier "large" in "large language model" is inherently vague, as there is no definitive threshold for the number of parameters required to qualify as "large". === Fine-tuning === Before being fine-tuned, most LLMs are next-token predictors. The fine-tuning shapes the LLM's behavior via techniques like reinforcement learning from human feedback (RLHF) or constitutional AI. Instruction fine-tuning is a form of supervised learning used to teach LLMs to follow user instructions. In 2022, OpenAI demonstrated InstructGPT, a version of GPT-3 similarly fine-tuned to follow instructions. Reinforcement learning from human feedback (RLHF) involves training a reward model to predict which text humans prefer. Then, the LLM can be fine-tuned through reinforcement learning to better satisfy this reward model. Since humans typically prefer truthful, helpful and harmless answers, RLHF favors such answers. == Architecture == LLMs are generally based on the tra

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  BitFunnel

  BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost. == History == Progress on the implementation of BitFunnel was made public in early 2016, with the expectation that there would be a usable implementation later that year. In September 2016, the source code was made available via GitHub. A paper discussing the BitFunnel algorithm and implementation was released as through the Special Interest Group on Information Retrieval of the Association for Computing Machinery in 2017 and won the Best Paper Award. == Components == BitFunnel consists of three major components: BitFunnel – the text search/retrieval system itself WorkBench – a tool for preparing text for use in BitFunnel NativeJIT – a software component that takes expressions that use C data structures and transforms them into highly optimized assembly code == Algorithm == === Initial problem and solution overview === The BitFunnel paper describes the "matching problem", which occurs when an algorithm must identify documents through the usage of keywords. The goal of the problem is to identify a set of matches given a corpus to search and a query of keyword terms to match against. This problem is commonly solved through inverted indexes, where each searchable item is maintained with a map of keywords. In contrast, BitFunnel represents each searchable item through a signature. A signature is a sequence of bits which describe a Bloom filter of the searchable terms in a given searchable item. The bloom filter is constructed through hashing through several bit positions. === Theoretical implementation of bit-string signatures === The signature of a document (D) can be described as the logical-or of its term signatures: S D → = ⋃ t ∈ D S t → {\displaystyle {\overrightarrow {S_{D}}}=\bigcup _{t\in D}{\overrightarrow {S_{t}}}} Similarly, a query for a document (Q) can be defined as a union: S Q → = ⋃ t ∈ Q S t → {\displaystyle {\overrightarrow {S_{Q}}}=\bigcup _{t\in Q}{\overrightarrow {S_{t}}}} Additionally, a document D is a member of the set M' when the following condition is satisfied: S Q → ∩ S D → = S Q → {\displaystyle {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}} This knowledge is then combined to produce a formula where M' is identified by documents which match the query signature: M ′ = { D ∈ C ∣ S Q → ∩ S D → = S Q → } {\displaystyle M'=\left\{D\in C\mid {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}\right\}} These steps and their proofs are discussed in the 2017 paper. === Pseudocode for bit-string signatures === This algorithm is described in the 2017 paper. M ′ = ∅ foreach D ∈ C do if S D → ∩ S Q → = S Q → then M ′ = M ′ ∪ { D } endif endfor {\displaystyle {\begin{array}{l}M'=\emptyset \\{\texttt {foreach}}\ D\in C\ {\texttt {do}}\\\qquad {\texttt {if}}\ {\overrightarrow {S_{D}}}\cap {\overrightarrow {S_{Q}}}={\overrightarrow {S_{Q}}}\ {\texttt {then}}\\\qquad \qquad M'=M'\cup \{D\}\\\qquad {\texttt {endif}}\\{\texttt {endfor}}\end{array}}}

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  Data integration

  Data integration is the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories). The decision to integrate data tends to arise when the volume, complexity (that is, big data) and need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients. A common use of data integration is in data mining when analyzing and extracting information from existing databases that can be useful for Business information. == History == Issues with combining heterogeneous data sources, often referred to as information silos, under a single query interface have existed for some time. In the early 1980s, computer scientists began designing systems for interoperability of heterogeneous databases. The first data integration system driven by structured metadata was designed in 1991 at the University of Minnesota for the Integrated Public Use Microdata Series (IPUMS). IPUMS used a data warehousing approach, which extracts, transforms, and loads data from heterogeneous sources into a unique view schema so data from different sources become compatible. By making thousands of population databases interoperable, IPUMS demonstrated the feasibility of large-scale data integration. The data warehouse approach offers a tightly coupled architecture because the data are already physically reconciled in a single queryable repository, so it usually takes little time to resolve queries. The data warehouse approach is less feasible for data sets that are frequently updated, requiring the extract, transform, load (ETL) process to be continuously re-executed for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data. This problem frequently emerges when integrating several commercial query services like travel or classified advertisement web applications. A trend began in 2009 favoring the loose coupling of data and providing a unified query-interface to access real time data over a mediated schema (see Figure 2), which allows information to be retrieved directly from original databases. This is consistent with the SOA approach popular in that era. This approach relies on mappings between the mediated schema and the schema of original sources, and translating a query into decomposed queries to match the schema of the original databases. Such mappings can be specified in two ways: as a mapping from entities in the mediated schema to entities in the original sources (the "Global-as-View" (GAV) approach), or as a mapping from entities in the original sources to the mediated schema (the "Local-as-View" (LAV) approach). The latter approach requires more sophisticated inferences to resolve a query on the mediated schema, but makes it easier to add new data sources to a (stable) mediated schema. As of 2010, some of the work in data integration research concerns the semantic integration problem. This problem addresses not the structuring of the architecture of the integration, but how to resolve semantic conflicts between heterogeneous data sources. For example, if two companies merge their databases, certain concepts and definitions in their respective schemas like "earnings" inevitably have different meanings. In one database it may mean profits in dollars (a floating-point number), while in the other it might represent the number of sales (an integer). A common strategy for the resolution of such problems involves the use of ontologies which explicitly define schema terms and thus help to resolve semantic conflicts. This approach represents ontology-based data integration. On the other hand, the problem of combining research results from different bioinformatics repositories requires bench-marking of the similarities, computed from different data sources, on a single criterion such as positive predictive value. This enables the data sources to be directly comparable and can be integrated even when the natures of experiments are distinct. As of 2011, it was determined that current data modeling methods were imparting data isolation into every data architecture in the form of islands of disparate data and information silos. This data isolation is an unintended artifact of the data modeling methodology that results in the development of disparate data models. Disparate data models, when instantiated as databases, form disparate databases. Enhanced data model methodologies have been developed to eliminate the data isolation artifact and to promote the development of integrated data models. One enhanced data modeling method recasts data models by augmenting them with structural metadata in the form of standardized data entities. As a result of recasting multiple data models, the set of recast data models will now share one or more commonality relationships that relate the structural metadata now common to these data models. Commonality relationships are a peer-to-peer type of entity relationships that relate the standardized data entities of multiple data models. Multiple data models that contain the same standard data entity may participate in the same commonality relationship. When integrated data models are instantiated as databases and are properly populated from a common set of master data, then these databases are integrated. Since 2011, data hub approaches have been of greater interest than fully structured (typically relational) Enterprise Data Warehouses. Since 2013, data lake approaches have risen to the level of Data Hubs. (See all three search terms popularity on Google Trends.) These approaches combine unstructured or varied data into one location, but do not necessarily require an (often complex) master relational schema to structure and define all data in the Hub. In recent times, as the number of applications being used have increased many fold and application to application integration have become critical and this has given rise to [Unified APIs] that help application developers integrate their apps with other apps and more recently with [MCP - Model Context Protocol] taking it a step further for AI Agents. Data integration plays a big role in business regarding data collection used for studying the market. Converting the raw data retrieved from consumers into coherent data is something businesses try to do when considering what steps they should take next. Organizations are more frequently using data mining for collecting information and patterns from their databases, and this process helps them develop new business strategies to increase business performance and perform economic analyses more efficiently. Compiling the large amount of data they collect to be stored in their system is a form of data integration adapted for Business intelligence to improve their chances of success. == Example == Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data. A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query. This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a si

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  Why We Post

  Why We Post is a research project funded by the European Research Council and launched in 2012 by Daniel Miller with the objective of examining the global impact of new social media. The study is based on ethnographic data collected through the course of 15 months in China, India, Turkey, Italy, United Kingdom, Trinidad, Chile and Brazil. The results of this project were released on 29 February 2016. This included the first three of eleven Open Access books (available via UCL Press), a five-week e-course (MOOC) on FutureLearn in English, also available in Chinese, Portuguese, Hindi, Tamil, Italian, Turkish, and Spanish on UCLeXtend. In addition a website containing key discoveries, stories and over 100 films is available in the same 8 languages.

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  Office automation

  Office automation refers to the varied computer machinery and software used to digitally create, collect, store, manipulate, and relay office information needed for accomplishing basic tasks. Raw data storage, electronic transfer, and the management of electronic business information comprise the basic activities of an office automation system. Office automation helps in optimizing or automating existing office procedures. The backbone of office automation is a local area network, which allows users to transfer data, mail and voice across the network. All office functions, including dictation, typing, filing, copying, fax, telex, microfilm and records management, telephone and telephone switchboard operations, fall into this category. Office automation was a popular term in the 1970s and 1980s as the desktop computer exploded onto the scene. Advantages of office automation include that it can get many tasks accomplished faster, it eliminates the need for a large staff, less storage is required to store data, and multiple people can update data simultaneously in the event of changes in schedule. == Outline == Businesses can easily purchase and stock their wares with the aid of technology. Many of the manual tasks that used to be done by hand can now be done through hand held devices and UPC and SKU coding. In the retail setting, automation also increases choice. Customers can easily process their payments through automated credit card machines and no longer have to wait in line for an employee to process and manually type in the credit card numbers. Office payrolls have been automated, which means no one has to manually cut checks, and those checks that are cut can be printed through computer programs. Direct deposit can be automatically set up and this further reduces the manual process, and most employees who participate in direct deposit often find their paychecks come earlier than if they'd have to wait for their checks to be written and then cleared by the bank. Other ways automation has reduced employee manpower on tasks is automated voice direction. Through the use of prompts, automated phone menus and directed calls, the need for employees to be dedicated to answer the phones has been reduced, and in some cases, eliminated.

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  Pepper (cryptography)

  In cryptography, a pepper is a secret added to an input such as a password during hashing with a cryptographic hash function. This value differs from a salt in that it is not stored alongside a password hash, but rather the pepper is kept separate using another meachanism, such as a Hardware Security Module. Note that the National Institute of Standards and Technology refers to this value as a secret key rather than a pepper. A pepper is similar in concept to a salt or an encryption key. It is like a salt in that it is a randomized value that is added to a password hash, and it is similar to an encryption key in that it should be kept secret. A pepper performs a comparable role to a salt or an encryption key, but while a salt is not secret (merely unique) and can be stored alongside the hashed output, a pepper is secret and must not be stored with the output. The hash and salt are usually stored in a database, but, if stored, a pepper must be stored separately to prevent it from being obtained by the attacker in case of a database breach. == History == The idea of a site- or service-specific salt (in addition to a per-user salt) has a long history, with Steven M. Bellovin proposing a local parameter in a Bugtraq post in 1995. In 1996 Udi Manber also described the advantages of such a scheme, terming it a secret salt. However, he suggested not storing the value of the secret salt, but instead rediscovering it by trial and error at password verification time. The term pepper has been used, by analogy to salt, but with a variety of meanings. For example, when discussing a challenge-response scheme, pepper has been used for a salt-like quantity, though not used for password storage; it has been used for a data transmission technique where a pepper must be guessed; and even as a part of jokes. The term pepper was proposed for a secret or local parameter stored separately from the password in a discussion of protecting passwords from rainbow table attacks. This usage did not immediately catch on: for example, Fred Wenzel added support to Django password hashing for storage based on a combination of bcrypt and HMAC with separately stored nonces, without using the term. Usage has since become more common. == Types == There are multiple different types of pepper: A shared secret that is common to all users. A randomly-selected number that must be re-discovered on every password input. These mechanisms could be combined with password salting, iterated hashing or even one another. == Shared-secret pepper == Bellovin and Webster suggest prepend a shared secret to the password before hashing, which allows easy use of existing hash functions. For example, consider two users to be added to a database. This table contains two combinations of username and password. The password is not saved, and the 8-byte (64-bit) 44534C70C6883DE2 pepper is saved in a safe place separate from the output values of the hash, in this case SHA256. Unlike the salt, the pepper does not provide protection to users who use the same password, but protects against dictionary attacks, unless the attacker has the pepper value available. Since the same pepper is not shared between different applications, an attacker is unable to reuse the hashes of one compromised database to another. A complete scheme for saving passwords may include both salt and pepper use. For example, it has been suggested to combine the pepper by encrypting salted password hashes, which allows rotation of the pepper. In the case of a shared-secret pepper, a single compromised password (via password reuse or other attack) along with a user's salt can lead to an attack to discover the pepper, rendering it ineffective. If an attacker knows a plaintext password and a user's salt, as well as the algorithm used to hash the password, then discovering the pepper can be a matter of brute forcing the values of the pepper. This is why NIST recommends the secret value be at least 112 bits, so that discovering it by exhaustive search is prohibitively expensive. The pepper must be generated anew for every application it is deployed in, otherwise a breach of one application would result in lowered security of another application. Without knowledge of the pepper, other passwords in the database will be far more difficult to extract from their hashed values, as the attacker would need to guess the password as well as the pepper. A pepper adds security to a database of salts and hashes because unless the attacker is able to obtain the pepper, cracking even a single hash is intractable, no matter how weak the original password. Even with a list of (salt, hash) pairs, an attacker must also guess the secret pepper in order to find the password which produces the hash. The NIST specification for a secret salt suggests using a Password-Based Key Derivation Function (PBKDF) with an approved Pseudorandom Function such as HMAC with SHA-3 as the hash function of the HMAC. The NIST recommendation is also to perform at least 1000 iterations of the PBKDF, and a further minimum 1000 iterations using the secret salt in place of the non-secret salt. == Randomly-selected pepper that must be re-discovered == The aim of this mechanism is to slow down password the password verification step, thus slowing attacks. The aim is similar increasing the iteration count on bcrypt or Argon2, but the mechanism is different. The secret salt or pepper must be rediscovered by the verifier or attacker each time by guessing. In this situation, the password hashing function is calculated using both the password and the pepper. At password storage time, the pepper is chosen randomly from a range between 1 and R, the hash output is calculated using the password and the pepper. The hash output is stored with the username. The pepper is then discarded. At password verification time, the verifier is provided with a username and password to verify. The originally calculated hash is retrieved for the given username, and then the hash of the password and each value between 1 and R is calculated. If any of these hash values match the stored password hash, the password is considered valid. Note, the possible values of the pepper should not be tested in a fixed order known to an attacker, otherwise a timing attack may reveal the pepper. If the password is correct, the correct pepper will be found in R/2 hash evaluations on average. If the password is incorrect, all R values must be tested before the password can be rejected.

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  Data custodian

  In data governance groups, responsibilities for data management are increasingly divided between the business process owners and information technology (IT) departments. Two functional titles commonly used for these roles are data steward and data custodian. Data Stewards are commonly responsible for data content, context, and associated business rules. Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler, ETL developer and data engineer. == Data custodian responsibilities == A data custodian ensures: Access to the data is authorized and controlled Data stewards are identified for each data set Technical processes sustain data integrity Processes exist for data quality issue resolution in partnership with data stewards Technical controls safeguard data Data added to data sets are consistent with the common data model Versions of master data are maintained along with the history of changes Change management practices are applied in maintenance of the database Data content and changes can be audited

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