AI Email Message Generator

AI Email Message Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Mistral Vibe

    Mistral Vibe

    Mistral Vibe or Vibe (Le Chat until May 2026), is a chatbot that uses generative artificial intelligence developed in France by Mistral AI. Mistral Vibe is available in iOS and Android. Its services are operated on a freemium model. == History == In February 2024, Mistral AI released Le Chat. In January 2025, Mistral AI made a content deal with Agence France-Presse (AFP) that lets Le Chat query AFP's entire archive dating back to 1983. On 6 February 2025, a mobile app for Le Chat was released for iOS and Android, and a subscription tier, Pro, was introduced at a cost of $14.99 per month. In July 2025, Mistral AI released Voxtral, an open-source language model that understands and generates audio. Mistral introduced a voice mode for chatting that uses Voxtral, and projects, which allows grouping chats and files. In September 2025, Le Chat introduced the capability to remember previous conversations. In May 2026, Mistral AI announced the rebrand from Le Chat to Mistral Vibe and new features were introduced at the same time.

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  • Internet

    Internet

    The Internet (or internet) is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a network of networks that comprises private, public, academic, business, and government networks of local to global scope, linked by electronic, wireless, and optical networking technologies. The Internet carries a vast range of information services and resources, such as the interlinked hypertext documents and applications of the World Wide Web (WWW), electronic mail, discussion groups, internet telephony, streaming media and file sharing. Most traditional communication media, including telephone, radio, television, paper mail, newspapers, and print publishing, have been transformed by the Internet, giving rise to new media such as email, online music, digital newspapers, news aggregators, and audio and video streaming websites. The Internet has enabled and accelerated new forms of personal interaction through instant messaging, Internet forums, and social networking services. Online shopping has also grown to occupy a significant market across industries, enabling firms to extend brick and mortar presences to serve larger markets. Business-to-business and financial services on the Internet affect supply chains across entire industries. The origins of the Internet date back to research that enabled the time-sharing of computer resources, the development of packet switching, and the design of computer networks for data communication. The set of communication protocols to enable internetworking on the Internet arose from research and development commissioned in the 1970s by the Defense Advanced Research Projects Agency (DARPA) of the United States Department of Defense in collaboration with universities and researchers across the United States, United Kingdom and France. The Internet has no single centralized governance in either technological implementation or policies for access and usage. Each constituent network sets its own policies. The overarching definitions of the two principal name spaces on the Internet, the Internet Protocol address (IP address) space and the Domain Name System (DNS), are directed by a maintainer organization, the Internet Corporation for Assigned Names and Numbers (ICANN). The technical underpinning and standardization of the core protocols is an activity of the non-profit Internet Engineering Task Force (IETF). == Terminology == The word internetted was used as early as 1849, meaning interconnected or interwoven. The word Internet was used in 1945 by the United States War Department in a radio operator's manual, and in 1974 as the shorthand form of Internetwork. Today, the term Internet most commonly refers to the global system of interconnected computer networks, though it may also refer to any group of smaller networks. The word Internet may be capitalized as a proper noun, although this is becoming less common. This reflects the tendency in English to capitalize new terms and move them to lowercase as they become familiar. The word is sometimes still capitalized to distinguish the global internet from smaller networks, though many publications, including the AP Stylebook since 2016, recommend the lowercase form in every case. In 2016, the Oxford English Dictionary found that, based on a study of around 2.5 billion printed and online sources, "Internet" was capitalized in 54% of cases. The terms Internet and World Wide Web are often used interchangeably; it is common to speak of "going on the Internet" when using a web browser to view web pages. However, the World Wide Web, or the Web, is only one of a large number of Internet services. It is the global collection of web pages, documents and other web resources linked by hyperlinks and URLs. == History == === 1960s === In the 1960s, computer scientists began developing systems for time-sharing of computer resources. J. C. R. Licklider proposed the idea of a universal network while working at Bolt Beranek & Newman and, later, leading the Information Processing Techniques Office at the Advanced Research Projects Agency (ARPA) of the United States Department of Defense. Research into packet switching, one of the fundamental Internet technologies, started in the work of Paul Baran at RAND in the early 1960s and, independently, Donald Davies at the United Kingdom's National Physical Laboratory in 1965. After the Symposium on Operating Systems Principles in 1967, packet switching from the proposed NPL network was incorporated into the design of the ARPANET, an experimental resource sharing network proposed by ARPA. ARPANET development began with two network nodes which were interconnected between the University of California, Los Angeles and the Stanford Research Institute on 29 October 1969. The third site was at the University of California, Santa Barbara, followed by the University of Utah. === 1970s === By the end of 1971, 15 sites were connected to the young ARPANET. Thereafter, the ARPANET gradually developed into a decentralized communications network, connecting remote centers and military bases in the United States. Other user networks and research networks, such as the Merit Network and CYCLADES, were developed in the late 1960s and early 1970s. Early international collaborations for the ARPANET were rare. Connections were made in 1973 to Norway (NORSAR and, later, NDRE) and to Peter Kirstein's research group at University College London, which provided a gateway to British academic networks, the first internetwork for resource sharing. ARPA projects, the International Network Working Group and commercial initiatives led to the development of various protocols and standards by which multiple separate networks could become a single network, or a network of networks. In 1974, Vint Cerf at Stanford University and Bob Kahn at DARPA published a proposal for "A Protocol for Packet Network Intercommunication". Cerf and his graduate students used the term internet as a shorthand for internetwork in RFC 675. The Internet Experiment Notes and later RFCs repeated this use. The work of Louis Pouzin and Robert Metcalfe had important influences on the resulting TCP/IP design. National PTTs and commercial providers developed the X.25 standard and deployed it on public data networks. === 1980s === The ARPANET initially served as a backbone for the interconnection of regional academic and military networks in the United States to enable resource sharing. Access to the ARPANET was expanded in 1981 when the National Science Foundation (NSF) funded the Computer Science Network (CSNET). In 1982, the Internet Protocol Suite (TCP/IP) was standardized, which facilitated worldwide proliferation of interconnected networks. TCP/IP network access expanded again in 1986 when the National Science Foundation Network (NSFNet) provided access to supercomputer sites in the United States for researchers, first at speeds of 56 kbit/s and later at 1.5 Mbit/s and 45 Mbit/s. The NSFNet expanded into academic and research organizations in Europe, Australia, New Zealand and Japan in 1988–89. Although other network protocols such as UUCP and PTT public data networks had global reach well before this time, this marked the beginning of the Internet as an intercontinental network. Commercial Internet service providers emerged in 1989 in the United States and Australia. The ARPANET was decommissioned in 1990. === 1990s === The linking of commercial networks and enterprises by the early 1990s, as well as the advent of the World Wide Web, marked the beginning of the transition to the modern Internet. Steady advances in semiconductor technology and optical networking created new economic opportunities for commercial involvement in the expansion of the network in its core and for delivering services to the public. In mid-1989, MCI Mail and Compuserve established connections to the Internet, delivering email and public access products to the half million users of the Internet. Just months later, on 1 January 1990, PSInet launched an alternate Internet backbone for commercial use; one of the networks that added to the core of the commercial Internet of later years. In March 1990, the first high-speed T1 (1.5 Mbit/s) link between the NSFNET and Europe was installed between Cornell University and CERN, allowing much more robust communications than were capable with satellites. Later in 1990, Tim Berners-Lee began writing WorldWideWeb, the first web browser, after two years of lobbying CERN management. By Christmas 1990, Berners-Lee had built all the tools necessary for a working Web: the HyperText Transfer Protocol (HTTP) 0.9, the HyperText Markup Language (HTML), the first Web browser (which was also an HTML editor and could access Usenet newsgroups and FTP files), the first HTTP server software (later known as CERN httpd), the first web server, and the first Web pages that described the project itself. In 1991 the

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  • Interplanetary Internet

    Interplanetary Internet

    The interplanetary Internet is a conceived computer network in space, consisting of a set of network nodes that can communicate with each other. These nodes are the planet's orbiters and landers, and the Earth ground stations. For example, the orbiters collect the scientific data from the Curiosity rover on Mars through near-Mars communication links, transmit the data to Earth through direct links from the Mars orbiters to the Earth ground stations via the NASA Deep Space Network, and finally the data routed through Earth's internal internet. Interplanetary communication is greatly delayed by interplanetary distances, as data transmission can only go as fast as the speed of light, so a new set of protocols and technologies that are tolerant to large delays and errors are required. The interplanetary Internet has been envisioned as a store and forward network of internets that is often disconnected, has a wireless backbone fraught with error-prone links and delays ranging from tens of minutes to even hours, even when there is a connection. As of 2024 agencies and companies working towards bringing the network to fruition include NASA, ESA, SpaceX and Blue Origin. == Challenges and reasons == In the core implementation of Interplanetary Internet, satellites orbiting a planet communicate to other planet's satellites. Simultaneously, these planets revolve around the Sun with long distances, and thus many challenges face the communications. The reasons and the resultant challenges are: The motion and long distances between planets: The interplanetary communication is greatly delayed due to the interplanetary distances and the motion of the planets. The delay is variable and long, ranging from a couple of minutes (Earth-to-Mars), to a couple of hours (Pluto-to-Earth), depending on their relative positions. The interplanetary communication also suspends due to the solar conjunction, when the sun's radiation hinders the direct communication between the planets. As such, the communication characterizes lossy links and intermittent link connectivity. Low embeddable payload: Satellites can only carry a small payload, which poses challenges to the power, mass, size, and cost for communication hardware design. An asymmetric bandwidth would be the result of this limitation. This asymmetry reaches ratios up to 1000:1 as downlink:uplink bandwidth portion. Absence of fixed infrastructure: The graph of participating nodes in a specific planet-to-planet communication keeps changing over time, due to the constant motion. The routes of the planet-to-planet communication are planned and scheduled rather than being opportunistic. The Interplanetary Internet design must address these challenges to operate successfully and achieve good communication with other planets. It also must use the few available resources efficiently in the system. == Development == Space communication technology has steadily evolved from expensive, one-of-a-kind point-to-point architectures, to the re-use of technology on successive missions, to the development of standard protocols agreed upon by space agencies of many countries. This last phase has gone on since 1982 through the efforts of the Consultative Committee for Space Data Systems (CCSDS), a body composed of the major space agencies of the world. It has 11 member agencies, 32 observer agencies, and over 119 industrial associates. The evolution of space data system standards has gone on in parallel with the evolution of the Internet, with conceptual cross-pollination where fruitful, but largely as a separate evolution. Since the late 1990s, familiar Internet protocols and CCSDS space link protocols have integrated and converged in several ways; for example, the successful FTP file transfer to Earth-orbiting STRV 1B on January 2, 1996, which ran FTP over the CCSDS IPv4-like Space Communications Protocol Specifications (SCPS) protocols. Internet Protocol use without CCSDS has taken place on spacecraft, e.g., demonstrations on the UoSAT-12 satellite, and operationally on the Disaster Monitoring Constellation. Having reached the era where networking and IP on board spacecraft have been shown to be feasible and reliable, a forward-looking study of the bigger picture was the next phase. The Interplanetary Internet study at NASA's Jet Propulsion Laboratory (JPL) was started by a team of scientists at JPL led by internet pioneer Vinton Cerf and the late Adrian Hooke. Cerf was appointed as a distinguished visiting scientist at JPL in 1998, while Hooke was one of the founders and directors of CCSDS. While IP-like SCPS protocols are feasible for short hops, such as ground station to orbiter, rover to lander, lander to orbiter, probe to flyby, and so on, delay-tolerant networking is needed to get information from one region of the Solar System to another. It becomes apparent that the concept of a region is a natural architectural factoring of the Interplanetary Internet. A region is an area where the characteristics of communication are the same. Region characteristics include communications, security, the maintenance of resources, perhaps ownership, and other factors. The Interplanetary Internet is a "network of regional internets". What is needed then, is a standard way to achieve end-to-end communication through multiple regions in a disconnected, variable-delay environment using a generalized suite of protocols. Examples of regions might include the terrestrial Internet as a region, a region on the surface of the Moon or Mars, or a ground-to-orbit region. The recognition of this requirement led to the concept of a "bundle" as a high-level way to address the generalized Store-and-Forward problem. Bundles are an area of new protocol development in the upper layers of the OSI model, above the Transport Layer with the goal of addressing the issue of bundling store-and-forward information so that it can reliably traverse radically dissimilar environments constituting a "network of regional internets". Delay-tolerant networking (DTN) was designed to enable standardized communications over long distances and through time delays. At its core is the Bundle Protocol (BP), which is similar to the Internet Protocol, or IP, that serves as the heart of the Internet here on Earth. The big difference between the regular Internet Protocol (IP) and the Bundle Protocol is that IP assumes a seamless end-to-end data path, while BP is built to account for errors and disconnections — glitches that commonly plague deep-space communications. Bundle Service Layering, implemented as the Bundling protocol suite for delay-tolerant networking, will provide general-purpose delay-tolerant protocol services in support of a range of applications: custody transfer, segmentation and reassembly, end-to-end reliability, end-to-end security, and end-to-end routing among them. The Bundle Protocol was first tested in space on the UK-DMC satellite in 2008. An example of one of these end-to-end applications flown on a space mission is the CCSDS File Delivery Protocol (CFDP), used on the Deep Impact comet mission. CFDP is an international standard for automatic, reliable file transfer in both directions. CFDP should not be confused with Coherent File Distribution Protocol, which has the same acronym and is an IETF-documented experimental protocol for rapidly deploying files to multiple targets in a highly networked environment. In addition to reliably copying a file from one entity (such as a spacecraft or ground station) to another entity, CFDP has the capability to reliably transmit arbitrarily small messages defined by the user, in the metadata accompanying the file, and to reliably transmit commands relating to file system management that are to be executed automatically on the remote end-point entity (such as a spacecraft) upon successful reception of a file. == Protocol == The Consultative Committee for Space Data Systems (CCSDS) packet telemetry standard defines the protocol used for the transmission of spacecraft instrument data over the deep-space channel. Under this standard, an image or other data sent from a spacecraft instrument is transmitted using one or more packets. === CCSDS packet definition === A packet is a block of data with length that can vary between successive packets, ranging from 7 to 65,542 bytes, including the packet header. Packetized data is transmitted via frames, which are fixed-length data blocks. The size of a frame, including frame header and control information, can range up to 2048 bytes. Packet sizes are fixed during the development phase. Because packet lengths are variable but frame lengths are fixed, packet boundaries usually do not coincide with frame boundaries. === Telecom processing notes === Data in a frame is typically protected from channel errors by error-correcting codes. Even when the channel errors exceed the correction capability of the error-correcting code, the presence of errors is nearly always detected by the e

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  • Star Awards for Social Media Award

    Star Awards for Social Media Award

    The Star Awards for Social Media Award was an award presented annually from 2014 to 2016 at the Star Awards, where Mediacorp of Singapore recognises entertainers under their employment with awards for artistic and technical merit for outstanding performances of the year. == History == The category was introduced in 2014, at the 20th Star Awards ceremony; Jeanette Aw received the award and it is given in honour of a Mediacorp artiste with the most social media engagement. The results are based on the calculations from three international social media analysis systems; artistes must be active on at least one of the following platforms in order to qualify: Facebook, Twitter and Instagram. Since its inception, the award has been given to two artistes. Carrie Wong is the most recent and final winner in this category. Since the ceremony held in 2016, Aw remains as the only artiste to win in this category twice, surpassing Wong who has one win. The award was discontinued from 2017 onwards as the popularity element of the award is already represented in the Top 10 Most Popular Male Artistes and Top 10 Most Popular Female Artistes awards. == Recipients ==

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  • ARIS Express

    ARIS Express

    ARIS Express is a free-of-charge modeling tool for business process analysis and management. It supports different modeling notations such as BPMN 2, Event-driven Process Chains (EPC), Organizational charts, process landscapes, whiteboards, etc. ARIS Express was initially developed by IDS Scheer, which was bought by Software AG in December 2010. The tool is provided as freeware on the ARIS Community webpage. ARIS Express is notable - having been mentioned in research published by Schumm, Garcia, Krumnow and Greenwood amongst others. == History == ARIS Express was first announced on April 28, 2009 in a press release by IDS Scheer. The first release was on July 28, 2009 in a public beta test on ARIS Community. Only people, who registered before for the beta test were allowed to download and test this beta version. This closed beta test was followed with another public beta test. The official release of ARIS Express 1.0 was on September 9, 2009. In this first stable version, features such as Microsoft Visio import were added, which were not present in the version for the public beta test. On February 26, 2010, ARIS Express 2.0 was released. Major changes compared to version 1.0 include BPMN 2 support, integrated spellchecking and ARISalign integration. On May 25, 2010, version 2.1 of ARIS Express was released. This update improves BPMN 2 support, provides a new online help system for instant feedback, better ARISalign integration and some new symbols in different diagrams. Along with the release, a poster showing the most important modeling concepts supported by ARIS Express was released. In addition, an executable setup is provided for Microsoft Windows-based systems. Beginning of July, an update was released as ARIS Express 2.2, providing bug fixes only. ARIS Express version 2.2 is the current stable release. An official press release published mid of August 2010 said there are more than 50,000 downloads of ARIS Express. On February 2, 2011, version 2.3 of ARIS Express was released. This new version changes the file format of ARIS Express so that models can be shown in an interactive model viewer in ARIS Community. The release announcement contained no details about additional features or changes. == Functionality == === Overview === ARIS Express is a standalone single-user application. It is divided in a home screen and a modeling environment. The home screen is used to create new models or open recently edited ones. The modeling environment is used to edit diagrams. === Supported notations === The following notations are supported by ARIS Express. Users can create diagrams containing an unlimited number of modeling objects. BPMN 2 Collaboration Diagrams Event-driven Process Chains (EPC) Organizational charts Process landscape (value-added chain diagram) Data model in ERM notation IT infrastructure (network diagram) System landscape (component diagram) Whiteboard General diagram === Noteworthy features === Besides common features such as creating new diagrams, saving them as files or adding objects to the modeling canvas, ARIS Express also provides some noteworthy features, which can't be found in most comparable modeling tools. fragments - Often used modeling constructs such as an exclusive decision in a process model can be stored as fragments so that they are available for direct reuse in another model. smart designs - The flow of a process model or hierarchies of other models can be captured in a spreadsheet-like interface. While entering the data in the spreadsheet, the model is generated and laid out in the background while typing. mini toolbar - While moving the mouse pointer over an object in a diagram, a small toolbar is shown allowing quick access to the most important modeling actions. Microsoft Visio import - Diagrams created with Microsoft Visio 2007 or above can be imported to and edited in ARIS Express. A Microsoft Visio export is not provided. ARISalign import - Models created on the online collaboration platform ARISalign can be opened and edited in ARIS Express. === Exports === ARIS Express can export diagrams to different formats such as: PDF JPEG PNG EMF ADF ADF is the file format of ARIS Express. The professional tools of ARIS Platform are able to import diagrams stored in the ADF format. Yet, there are major limitations during import - namely, each object in diagram will be treated as unique object, despite having same type and name, forcing redrawing large sections of diagrams after import. Besides export formats, it is also possible to use the clipboard to copy and paste an ARIS Express diagram into typical office suites such as Microsoft PowerPoint. == Technology == ARIS Express is a Java-based application, which shares some of the features of ARIS Platform products such as ARIS Business Architect and ARIS Business Designer. In contrast to ARIS Platform products, ARIS Express doesn't use a central database for model storage. Instead, each diagram is stored in an ADF file. ARIS Express uses Java Web Start. After download, the application can be started immediately without installation procedure. For Microsoft Windows based systems, an ordinary setup is provided, too. ARIS Express requires Java 1.6.10 or above. On first startup, the user must enter a valid ARIS Community account to register the application. Creating an ARIS Community account is free-of-charge. After installation, no Internet connection is needed to use ARIS Express. ARIS Express uses a mechanism provided by Java Web Start to automatically update the application as soon as a new version becomes available and the user is connected to the Internet during startup. There are reports that this automated update failed while upgrading from version 1.0 to version 2.0. As ARIS Express is based on Java Web Start, it can be installed on any platform supported by Java. The ARIS Community and other Internet sources have reports of successful deployment of ARIS Express on other operating systems than Microsoft Windows. However, ARIS Express is officially supported only on Microsoft Windows. == Miscellaneous == A quick reference sheet is available for ARIS Express. The poster shows all supported diagrams plus the most important modelling concepts for each supported modelling language. ARIS Express contains a hidden game, a so-called Easter Egg. The game can be started by clicking several times on the product logo in the about dialog. Highscores achieved in the game can be submitted to a special page in ARIS Community. A Firefox Personas is available for ARIS Express.

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  • Social search

    Social search

    Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. == Research and implementations == Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles. In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results. In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. In December 2008, Twitter had re-introduced their people search feature. While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. == Social discovery == Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. == Social search engines == A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added. An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aar

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  • Factorization of polynomials over finite fields

    Factorization of polynomials over finite fields

    In mathematics and computer algebra the factorization of a polynomial consists of decomposing it into a product of irreducible factors. This decomposition is theoretically possible and is unique for polynomials with coefficients in any field, but rather strong restrictions on the field of the coefficients are needed to allow the computation of the factorization by means of an algorithm. In practice, algorithms have been designed only for polynomials with coefficients in a finite field, in the field of rationals or in a finitely generated field extension of one of them. All factorization algorithms, including the case of multivariate polynomials over the rational numbers, reduce the problem to this case; see polynomial factorization. It is also used for various applications of finite fields, such as coding theory (cyclic redundancy codes and BCH codes), cryptography (public key cryptography by the means of elliptic curves), and computational number theory. As the reduction of the factorization of multivariate polynomials to that of univariate polynomials does not have any specificity in the case of coefficients in a finite field, only polynomials with one variable are considered in this article. == Background == === Finite field === The theory of finite fields, whose origins can be traced back to the works of Gauss and Galois, has played a part in various branches of mathematics. Due to the applicability of the concept in other topics of mathematics and sciences like computer science there has been a resurgence of interest in finite fields and this is partly due to important applications in coding theory and cryptography. Applications of finite fields introduce some of these developments in cryptography, computer algebra and coding theory. A finite field or Galois field is a field with a finite order (number of elements). The order of a finite field is always a prime or a power of prime. For each prime power q = pr, there exists exactly one finite field with q elements, up to isomorphism. This field is denoted GF(q) or Fq. If p is prime, GF(p) is the prime field of order p; it is the field of residue classes modulo p, and its p elements are denoted 0, 1, ..., p−1. Thus a = b in GF(p) means the same as a ≡ b (mod p). === Irreducible polynomials === Let F be a finite field. As for general fields, a non-constant polynomial f in F[x] is said to be irreducible over F if it is not the product of two polynomials of positive degree. A polynomial of positive degree that is not irreducible over F is called reducible over F. Irreducible polynomials allow us to construct the finite fields of non-prime order. In fact, for a prime power q, let Fq be the finite field with q elements, unique up to isomorphism. A polynomial f of degree n greater than one, which is irreducible over Fq, defines a field extension of degree n which is isomorphic to the field with qn elements: the elements of this extension are the polynomials of degree lower than n; addition, subtraction and multiplication by an element of Fq are those of the polynomials; the product of two elements is the remainder of the division by f of their product as polynomials; the inverse of an element may be computed by the extended GCD algorithm (see Arithmetic of algebraic extensions). It follows that, to compute in a finite field of non prime order, one needs to generate an irreducible polynomial. For this, the common method is to take a polynomial at random and test it for irreducibility. For sake of efficiency of the multiplication in the field, it is usual to search for polynomials of the shape xn + ax + b. Irreducible polynomials over finite fields are also useful for pseudorandom number generators using feedback shift registers and discrete logarithm over F2n. The number of irreducible monic polynomials of degree n over Fq is the number of aperiodic necklaces, given by Moreau's necklace-counting function Mq(n). The closely related necklace function Nq(n) counts monic polynomials of degree n which are primary (a power of an irreducible); or alternatively irreducible polynomials of all degrees d which divide n. === Example === The polynomial P = x4 + 1 is irreducible over Q but not over any finite field. On any field extension of F2, P = (x + 1)4. On every other finite field, at least one of −1, 2 and −2 is a square, because the product of two non-squares is a square and so we have If − 1 = a 2 , {\displaystyle -1=a^{2},} then P = ( x 2 + a ) ( x 2 − a ) . {\displaystyle P=(x^{2}+a)(x^{2}-a).} If 2 = b 2 , {\displaystyle 2=b^{2},} then P = ( x 2 + b x + 1 ) ( x 2 − b x + 1 ) . {\displaystyle P=(x^{2}+bx+1)(x^{2}-bx+1).} If − 2 = c 2 , {\displaystyle -2=c^{2},} then P = ( x 2 + c x − 1 ) ( x 2 − c x − 1 ) . {\displaystyle P=(x^{2}+cx-1)(x^{2}-cx-1).} === Complexity === Polynomial factoring algorithms use basic polynomial operations such as products, divisions, gcd, powers of one polynomial modulo another, etc. A multiplication of two polynomials of degree at most n can be done in O(n2) operations in Fq using "classical" arithmetic, or in O(nlog(n) log(log(n)) ) operations in Fq using "fast" arithmetic. A Euclidean division (division with remainder) can be performed within the same time bounds. The cost of a polynomial greatest common divisor between two polynomials of degree at most n can be taken as O(n2) operations in Fq using classical methods, or as O(nlog2(n) log(log(n)) ) operations in Fq using fast methods. For polynomials h, g of degree at most n, the exponentiation hq mod g can be done with O(log(q)) polynomial products, using exponentiation by squaring method, that is O(n2log(q)) operations in Fq using classical methods, or O(nlog(q)log(n) log(log(n))) operations in Fq using fast methods. In the algorithms that follow, the complexities are expressed in terms of number of arithmetic operations in Fq, using classical algorithms for the arithmetic of polynomials. == Factoring algorithms == Many algorithms for factoring polynomials over finite fields include the following three stages: Square-free factorization Distinct-degree factorization Equal-degree factorization An important exception is Berlekamp's algorithm, which combines stages 2 and 3. === Berlekamp's algorithm === Berlekamp's algorithm is historically important as being the first factorization algorithm which works well in practice. However, it contains a loop on the elements of the ground field, which implies that it is practicable only over small finite fields. For a fixed ground field, its time complexity is polynomial, but, for general ground fields, the complexity is exponential in the size of the ground field. === Square-free factorization === The algorithm determines a square-free factorization for polynomials whose coefficients come from the finite field Fq of order q = pm with p a prime. This algorithm firstly determines the derivative and then computes the gcd of the polynomial and its derivative. If it is not one then the gcd is again divided into the original polynomial, provided that the derivative is not zero (a case that exists for non-constant polynomials defined over finite fields). This algorithm uses the fact that, if the derivative of a polynomial is zero, then it is a polynomial in xp, which is, if the coefficients belong to Fp, the pth power of the polynomial obtained by substituting x by x1/p. If the coefficients do not belong to Fp, the pth root of a polynomial with zero derivative is obtained by the same substitution on x, completed by applying the inverse of the Frobenius automorphism to the coefficients. This algorithm works also over a field of characteristic zero, with the only difference that it never enters in the blocks of instructions where pth roots are computed. However, in this case, Yun's algorithm is much more efficient because it computes the greatest common divisors of polynomials of lower degrees. A consequence is that, when factoring a polynomial over the integers, the algorithm which follows is not used: one first computes the square-free factorization over the integers, and to factor the resulting polynomials, one chooses a p such that they remain square-free modulo p. Algorithm: SFF (Square-Free Factorization) Input: A monic polynomial f in Fq[x] where q = pm Output: Square-free factorization of f R ← 1 # Make w be the product (without multiplicity) of all factors of f that have # multiplicity not divisible by p c ← gcd(f, f′) w ← f/c # Step 1: Identify all factors in w i ← 1 while w ≠ 1 do y ← gcd(w, c) fac ← w / y R ← R · faci w ← y; c ← c / y; i ← i + 1 end while # c is now the product (with multiplicity) of the remaining factors of f # Step 2: Identify all remaining factors using recursion # Note that these are the factors of f that have multiplicity divisible by p if c ≠ 1 then c ← c1/p R ← R·SFF(c)p end if Output(R) The idea is to identify the product of all irreducible factors of f with the same multiplicity. This is done in two steps. The first step uses the formal d

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  • Private message

    Private message

    In computer networking, a private message (PM), or direct message (DM), refers to a private communication, often text-based, sent or received by a user of a private communication channel on any given platform. Unlike public posts, PMs are only viewable by the participants. Long a function present on IRCs and Internet forums, private channels for PMs have also been prevalent features on instant messaging (IM) and on social media networks. It may be either synchronous (e.g. on an IM) or asynchronous (e.g. on an Internet forum). The term private message (PM) originated as a feature on internet forums, while the term direct message (DM) originated as a feature on Twitter. Due to the popularity of the latter service, DM has since been appropriated by other platforms, such as Instagram, and is often genericized in popular usage. == Overview == There are two main types of private messages, and one obscure type: One type includes those found on IRCs and Internet forums, as well as on social media services like Twitter, Facebook, and Instagram, where the focus is public posting, PMs allow users to communicate privately without leaving the platform. The second type are those relayed through instant messaging platforms such as WhatsApp and Snapchat, where users join the networks primarily to exchange PMs. A third type, peer-to-peer messaging, occurs when users create and own the infrastructure used to transmit and store the messages; while features vary depending on application, they give the user full control over the data they transmit. An example of software that enables this kind of messaging is Classified-ads. Besides serving as a tool to connect privately with friends and family, PMs have gained momentum in the workplace. Working professionals use PMs to reach coworkers in other spaces and increase efficiency during meetings. Although useful, using PMs in the workplace may blur the boundary between work and private lives. Some common forms of private messaging today include Facebook messaging (sometimes referred to as "inboxing"), Twitter direct messaging, and Instagram direct messaging. These forms of private messaging provide a private space on a usually public site. For instance, most activity on Twitter is public, but Twitter DMs provide a private space for communication between two users. This differs from mediums like email, texting, and Snapchat, where most or all activity is always private. Modern forms of private messaging may include multimedia messages, such as pictures or videos. == History == Email was first developed to send messages between different computers on ARPANET in 1971. Access to ARPANET was primarily limited to universities and other research institutions. Starting in 1983 or 1984, FidoNet allowed home computer users to send and receive email via bulletin board systems. Information services such as CompuServe, America Online, and Prodigy also helped to popularizes online messaging. The advent of the public World Wide Web in 1993 increased access to email via internet service providers, and later via webmail. Instant messaging systems became popular in the mid 1990s, as Internet access improved and personal computers became more common. The introduction of Skype in 2003 popularized Internet-based voice and video messaging. Direct messaging is now a feature of all major social networking services. == Privacy concerns == In January 2014, Matthew Campbell and Michael Hurley filed a class-action lawsuit against Facebook for breaching the Electronic Communications Privacy Act. They alleged that private messages which contained URLs were being read and used to generate profit, through data mining and user profiling, and that it was misleading for Facebook to refer to the functionality as "private" with the implication that the communication was "free from surveillance". In 2012, some Facebook users misinterpreted a redesign of the Facebook wall as publicly sharing private messages from 2008–2009. These were found to be public wall posts from those years, made at a time when it was not possible to like or comment on a wall post, making the notes look like private messages.

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  • Turret lathe

    Turret lathe

    A turret lathe is a form of metalworking lathe that is used for repetitive production of duplicate parts, which by the nature of their cutting process are usually interchangeable. It evolved from earlier lathes with the addition of the turret, which is an indexable toolholder that allows multiple cutting operations to be performed, each with a different cutting tool, in easy, rapid succession, with no need for the operator to perform set-up tasks in between (such as installing or uninstalling tools) or to control the toolpath. The latter is due to the toolpath's being controlled by the machine, either in jig-like fashion, via the mechanical limits placed on it by the turret's slide and stops, or via digitally-directed servomechanisms for computer numerical control lathes. The name derives from the way early turrets took the general form of a flattened cylindrical block mounted to the lathe's cross-slide, capable of rotating about the vertical axis and with toolholders projecting out to all sides, and thus vaguely resembled a swiveling gun turret. Capstan lathe is the usual name in the UK and Commonwealth, though the two terms are also used in contrast: see below, Capstan versus turret. == History == Turret lathes became indispensable to the production of interchangeable parts and for mass production. The first turret lathe was built by Stephen Fitch in 1845 to manufacture screws for pistol percussion parts. In the mid-nineteenth century, the need for interchangeable parts for Colt revolvers enhanced the role of turret lathes in achieving this goal as part of the "American system" of manufacturing arms. Clock-making and bicycle manufacturing had similar requirements. Christopher Spencer invented the first fully automated turret lathe in 1873, which led to designs using cam action or hydraulic mechanisms. From the late-19th through mid-20th centuries, turret lathes, both manual and automatic (i.e., screw machines and chuckers), were one of the most important classes of machine tools for mass production. They were used extensively in the mass production for the war effort in World War II. The U.S. company Warner & Swasey was one of the premier brands in heavy turret lathes between the 1910s and 1960s; it became the world's largest manufacturer of such lathes by 1928. During World War II, it employed 7,000 people and produced half of the turret lathes manufactured in the United States. == Types == There are many variants of the turret lathe. They can be most generally classified by size (small, medium, or large); method of control (manual, automated mechanically, or automated via computer (numerical control (NC) or computer numerical control (CNC)); and bed orientation (horizontal or vertical). === Archetypical: horizontal, manual === In the late 1830s a "capstan lathe" with a turret was patented in Britain. The first American turret lathe was invented by Stephen Fitch in 1845. The archetypical turret lathe, and the first in order of historical appearance, is the horizontal-bed, manual turret lathe. The term "turret lathe" without further qualification is still understood to refer to this type. The formative decades for this class of machine were the 1840s through 1860s, when the basic idea of mounting an indexable turret on a bench lathe or engine lathe was born, developed, and disseminated from the originating shops to many other factories. Some important tool-builders in this development were Stephen Fitch; Gay, Silver & Co.; Elisha K. Root of Colt; J.D. Alvord of the Sharps Armory; Frederick W. Howe, Richard S. Lawrence, and Henry D. Stone of Robbins & Lawrence; J.R. Brown of Brown & Sharpe; and Francis A. Pratt of Pratt & Whitney. Various designers at these and other firms later made further refinements. === Semi-automatic === Sometimes machines similar to those above, but with power feeds and automatic turret-indexing at the end of the return stroke, are called "semi-automatic turret lathes". This nomenclature distinction is blurry and not consistently observed. The term "turret lathe" encompasses them all. During the 1860s, when semi-automatic turret lathes were developed, they were sometimes called "automatic". What we today would call "automatics", that is, fully automatic machines, had not been developed yet. During that era both manual and semi-automatic turret lathes were sometimes called "screw machines", although we today reserve that term for fully automatic machines. === Automatic === During the 1870s through 1890s, the mechanically automated "automatic" turret lathe was developed and disseminated. These machines can execute many part-cutting cycles without human intervention. Thus the duties of the operator, which were already greatly reduced by the manual turret lathe, were even further reduced, and productivity increased. These machines use cams to automate the sliding and indexing of the turret and the opening and closing of the chuck. Thus, they execute the part-cutting cycle somewhat analogously to the way in which an elaborate cuckoo clock performs an automated theater show. Small- to medium-sized automatic turret lathes are usually called "screw machines" or "automatic screw machines", while larger ones are usually called "automatic chucking lathes", "automatic chuckers", or "chuckers". Such machine tools of the "automatic" variety, which in the pre-computer era meant mechanically automated, had already reached a highly advanced state by World War I. === Computer numerical control === When World War II ended, the digital computer was poised to develop from a colossal laboratory curiosity into a practical technology that could begin to disseminate into business and industry. The advent of computer-based automation in machine tools via numerical control (NC) and then computer numerical control (CNC) displaced to a large extent, but not at all completely, the previously existing manual and mechanically automated machines. Numerically controlled turrets allow automated selection of tools on a turret. CNC lathes may be horizontal or vertical in orientation and mount six separate tools on one or more turrets. Such machine tools can work in two axes per turret, with up to six axes being feasible for complex work. === Vertical === Vertical turret lathes have the workpiece held vertically, which allows the headstock to sit on the floor and the faceplate to become a horizontal rotating table, analogous to a huge potter's wheel. This is useful for the handling of very large, heavy, short workpieces. Vertical lathes in general are also called "vertical boring mills" or often simply "boring mills"; therefore a vertical turret lathe is a vertical boring mill equipped with a turret. == Other variations == === Capstan versus turret === The term "capstan lathe" overlaps in sense with the term "turret lathe" to a large extent. In many times and places, it has been understood to be synonymous with "turret lathe". In other times and places it has been held in technical contradistinction to "turret lathe", with the difference being in whether the turret's slide is fixed to the bed (ram-type turret) or slides on the bed's ways (saddle-type turret). The difference in terminology is mostly a matter of United Kingdom and Commonwealth usage versus United States usage. === Flat === A subtype of horizontal turret lathe is the flat-turret lathe. Its turret is flat (and analogous to a rotary table), allowing the turret to pass beneath the part. Patented by James Hartness of Jones & Lamson, and first disseminated in the 1890s, it was developed to provide more rigidity via requiring less overhang in the tool setup, especially when the part is relatively long. === Hollow-hexagon === Hollow-hexagon turret lathes competed with flat-turret lathes by taking the conventional hexagon turret and making it hollow, allowing the part to pass into it during the cut, analogously to how the part would pass over the flat turret. In both cases, the main idea is to increase rigidity by allowing a relatively long part to be turned without the tool overhang that would be needed with a conventional turret, which is not flat or hollow. === Monitor lathe === The term "monitor lathe" formerly (1860s–1940s) referred to the class of small- to medium-sized manual turret lathes used on relatively small work. The name was inspired by the monitor-class warships, which the monitor lathe's turret resembled. Today, lathes of such appearance, such as the Hardinge DSM-59 and its many clones, are still common, but the name "monitor lathe" is no longer current in the industry. === Toolpost turrets and tailstock turrets === Turrets can be added to non-turret lathes (bench lathes, engine lathes, toolroom lathes, etc.) by mounting them on the toolpost, tailstock, or both. Often these turrets are not as large as a turret lathe's, and they usually do not offer the sliding and stopping that a turret lathe's turret does; but they do offer the ability to index through successive tool

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  • Internet Security Alliance

    Internet Security Alliance

    Internet Security Alliance (ISA) was founded in 2001 as a non-profit collaboration between Carnegie Mellon University's CyLab and Electronic Industries Alliance, a federation of trade associations. The Internet Security Alliance is focused on cyber security, acting as a forum for information sharing and leadership on information security, and lobbying for corporate security interests. == International operations == The Internet Security Alliance operates with a global membership to provide international security for its partners. The organization's membership includes companies located on four continents, and the Executive Committee always includes at least one non-U.S.-based company. The Internet Security Alliance believes that international communication is crucial for long-term greater information security, as it allows for a more realistic approach to addressing the many challenges faced by users of the Internet. == Publications == Published in 2009, The Financial Impact of Cyber Risk is the first known guidance document to attempt to approach the financial impact of cyber risks from the perspective of core business functions. It claims to provide guidance to CFOs and their colleagues responsible for legal issues, business operations and technology, privacy and compliance, risk assessment and insurance, and corporate communications.

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  • Cambridge Semantics

    Cambridge Semantics

    Cambridge Semantics is a privately held company headquartered in Boston, Massachusetts with an office in San Diego, California. The company is an enterprise big data management and exploratory analytics software company. == History == Cambridge Semantics was founded in 2007 by Sean Martin, Lee Feigenbaum, Simon Martin, Rouben Meschian, Ben Szekely and Emmett Eldred who all previously worked at IBM's Advanced Technology Internet Group. In 2012, Cambridge Semantics appointed Chuck Pieper as chief executive. Pieper was previously at Credit Suisse. In January 2016, Cambridge Semantics acquired SPARQL City and its graph database intellectual property. On April 18, 2024, Altair Engineering acquired Cambridge Semantics. On 26 March 2025, Siemens announced the acquisition of Altair. == Products == Anzo Smart Data Lake uses Semantic Web Technologies. It allows IT departments and their business users to access data. AnzoGraph DB Graph database. AnzoGraph DB is a massively parallel processing (MPP) native graph database built for diverse data harmonization and analytics at scale (trillions of triples and more), speed and deep link insights. It is used for embedded analytics that require graph algorithms, graph views, named queries, aggregates, geospatial, built-in data science functions, data warehouse-style BI and reporting functions. It allows users to load and query RDF data using SPARQL or Cypher for OLAP-style analytics. == Marketing == Cambridge Semantics named SIIA Codie award 2018 finalist. Cambridge Semantics named 2018 Gold Stevie Award Winner for 'Big Data Solutions'. Cambridge Semantics named KMWorld’s 2018 ‘100 Companies That Matter in Knowledge Management’. Cambridge Semantics named to Database Trends and Applications' 'Trend-Setting Products in Data and Information Management for 2018'. Cambridge Semantics named to KMWorld Trend-Setting Products of 2017. Cambridge Semantics named to Database Trends and Applications 'DBTA 100: The Companies That Matter Most in Data'. Cambridge Semantics named SIIA Codie award 2017 winner for ‘Best Text Analytics and Semantic Technology Solution’. Cambridge Semantics named 2017 Silver Stevie Award Winner for 'Big Data Solutions'. Cambridge Semantics named KMWorld’s 2017 ‘100 Companies That Matter in Knowledge Management’. Cambridge Semantics named SIIA Codie award 2016 finalist. Cambridge Semantics named KMWorld’s 2016 ‘100 Companies That Matter in Knowledge Management’ and KMWorld Trend-Setting Products of 2015. Cambridge Semantics named 2016 Bio-IT World Best of Show People's Choice Award Contenders and 2015 Bio-IT best of show finalist. Anzo Insider Trading Investigation and Surveillance named 2015 CODiE Award finalist. Cambridge Semantics Selected as Finalist for 2014 MIT Sloan CIO Symposium's Innovation Showcase. Cambridge Semantics named SIIA CODiE Award 2014 finalist. Cambridge Semantics Win 2013 SIIA CODiE Award for best business intelligence and analytics solution. Cambridge Semantics wins KMWorld 2012 Promise Award. Cambridge Semantics wins Best of Show at 2012 Bio-IT World Conference.

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  • White-box cryptography

    White-box cryptography

    In cryptography, the white-box model refers to an extreme attack scenario, in which an adversary has full unrestricted access to a cryptographic implementation, most commonly of a block cipher such as the Advanced Encryption Standard (AES). A variety of security goals may be posed (see the section below), the most fundamental being "unbreakability", requiring that any (bounded) attacker should not be able to extract the secret key hardcoded in the implementation, while at the same time the implementation must be fully functional. In contrast, the black-box model only provides an oracle access to the analyzed cryptographic primitive (in the form of encryption and/or decryption queries). There is also a model in-between, the so-called gray-box model, which corresponds to additional information leakage from the implementation, more commonly referred to as side-channel leakage. White-box cryptography is a practice and study of techniques for designing and attacking white-box implementations. It has many applications, including digital rights management (DRM), pay television, protection of cryptographic keys in the presence of malware, mobile payments and cryptocurrency wallets. Examples of DRM systems employing white-box implementations include CSS and Widevine. White-box cryptography is closely related to the more general notions of obfuscation, in particular, to Black-box obfuscation, proven to be impossible, and to Indistinguishability obfuscation, constructed recently under well-founded assumptions but so far being infeasible to implement in practice. As of January 2023, there are no publicly known unbroken white-box designs of standard symmetric encryption schemes. On the other hand, there exist many unbroken white-box implementations of dedicated block ciphers designed specifically to achieve incompressibility (see § Security goals). == Security goals == Depending on the application, different security goals may be required from a white-box implementation. Specifically, for symmetric-key algorithms the following are distinguished: Unbreakability is the most fundamental goal requiring that a bounded attacker should not be able to recover the secret key embedded in the white-box implementation. Without this requirement, all other security goals are unreachable since a successful attacker can simply use a reference implementation of the encryption scheme together with the extracted key. One-wayness requires that a white-box implementation of an encryption scheme can not be used by a bounded attacker to decrypt ciphertexts. This requirement essentially turns a symmetric encryption scheme into a public-key encryption scheme, where the white-box implementation plays the role of the public key associated to the embedded secret key. This idea was proposed already in the famous work of Diffie and Hellman in 1976 as a potential public-key encryption candidate. Code lifting security is an informal requirement on the context, in which the white-box program is being executed. It demands that an attacker can not extract a functional copy of the program. This goal is particularly relevant in the DRM setting. Code obfuscation techniques are often used to achieve this goal. A commonly used technique is to compose the white-box implementation with so-called external encodings. These are lightweight secret encodings that modify the function computed by the white-box part of an application. It is required that their effect is canceled in other parts of the application in an obscure way, using code obfuscation techniques. Alternatively, the canceling counterparts can be applied on a remote server. Incompressibility requires that an attacker can not significantly compress a given white-box implementation. This can be seen as a way to achieve code lifting security (see above), since exfiltrating a large program from a constrained device (for example, an embedded or a mobile device) can be time-consuming and may be easy to detect by a firewall. Examples of incompressible designs include SPACE cipher, SPNbox, WhiteKey and WhiteBlock. These ciphers use large lookup tables that can be pseudorandomly generated from a secret master key. Although this makes the recovery of the master key hard, the lookup tables themselves play the role of an equivalent secret key. Thus, unbreakability is achieved only partially. Traceability (Traitor tracing) requires that each distributed white-box implementation contains a digital watermark allowing identification of the guilty user in case the white-box program is being leaked and distributed publicly. == History == The white-box model with initial attempts of white-box DES and AES implementations were first proposed by Chow, Eisen, Johnson and van Oorshot in 2003. The designs were based on representing the cipher as a network of lookup tables and obfuscating the tables by composing them with small (4- or 8-bit) random encodings. Such protection satisfied a property that each single obfuscated table individually does not contain any information about the secret key. Therefore, a potential attacker has to combine several tables in their analysis. The first two schemes were broken in 2004 by Billet, Gilbert, and Ech-Chatbi using structural cryptanalysis. The attack was subsequently called "the BGE attack". The numerous consequent design attempts (2005-2022) were quickly broken by practical dedicated attacks. In 2016, Bos, Hubain, Michiels and Teuwen showed that an adaptation of standard side-channel power analysis attacks can be used to efficiently and fully automatically break most existing white-box designs. This result created a new research direction about generic attacks (correlation-based, algebraic, fault injection) and protections against them. == Competitions == Four editions of the WhibOx contest were held in 2017, 2019, 2021 and 2024 respectively. These competitions invited white-box designers both from academia and industry to submit their implementation in the form of (possibly obfuscated) C code. At the same time, everyone could attempt to attack these programs and recover the embedded secret key. Each of these competitions lasted for about 4-5 months. WhibOx 2017 / CHES 2017 Capture the Flag Challenge targeted the standard AES block cipher. Among 94 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for 28 days. WhibOx 2019 / CHES 2019 Capture the Flag Challenge again targeted the AES block cipher. Among 27 submitted implementations, 3 programs stayed unbroken throughout the competition, but were broken after 51 days since the publication. WhibOx 2021 / CHES 2021 Capture the Flag Challenge changed the target to ECDSA, a digital signature scheme based on elliptic curves. Among 97 submitted implementations, all were broken within at most 2 days. WhibOx 2024 / CHES 2024 Capture the Flag Challenge again targeted ECDSA. Among 47 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for almost 5 days.

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  • IMPACT (computer graphics)

    IMPACT (computer graphics)

    IMPACT (sometimes spelled Impact) is a computer graphics architecture for Silicon Graphics computer workstations. IMPACT Graphics was developed in 1995 and was available as a high-end graphics option on workstations released during the mid-1990s. IMPACT graphics gives the workstation real-time 2D and 3D graphics rendering capability similar to that of even high-end PCs made well after IMPACT's introduction. IMPACT graphics systems consist of either one or two Geometry Engines and one or two Raster Engines in various configurations. IMPACT graphics consists of five graphics subsystems: the Command Engine, Geometry Subsystem, Raster Engine, framebuffer and Display Subsystem. IMPACT Graphics can produce resolutions up to 1600 x 1200 pixels with 32-bit color and can also process unencoded NTSC and PAL analog television signals. IMPACT graphics subsystems come in three configurations for SGI Indigo2 IMPACT workstations: Solid IMPACT, High IMPACT, and Maximum IMPACT. The equivalent configurations also exist for the SGI Octane workstation but are referred to as SI, SSI, and MXI (I-series). Later Octane workstations used a similar configuration but with updated ASIC chips and are referred to as SE, SSE, and MXE (E-series). IMPACT uses Rambus RDRAM for texture memory. The IMPACT graphics architecture was superseded by SGI's VPro graphics architecture in 1997.

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  • Data lineage

    Data lineage

    Data lineage refers to the process of tracking how data is generated, transformed, transmitted and used across systems over time. It documents data's origins, transformations and movements, providing detailed visibility into its life cycle. This process simplifies the identification of errors in data analytics workflows, by enabling users to trace issues back to their root causes. Data lineage facilitates the ability to replay specific segments or inputs of the dataflow. This can be used in debugging or regenerating lost outputs. In database systems, this concept is closely related to data provenance, which involves maintaining records of inputs, entities, systems and processes that influence data. Data provenance provides a historical record of data origins and transformations. It supports forensic activities such as data-dependency analysis, error/compromise detection, recovery, auditing and compliance analysis: "Lineage is a simple type of why provenance." Data governance plays a critical role in managing metadata by establishing guidelines, strategies and policies. Enhancing data lineage with data quality measures and master data management adds business value. Although data lineage is typically represented through a graphical user interface (GUI), the methods for gathering and exposing metadata to this interface can vary. Based on the metadata collection approach, data lineage can be categorized into three types: Those involving software packages for structured data, programming languages and Big data systems. Data lineage information includes technical metadata about data transformations. Enriched data lineage may include additional elements such as data quality test results, reference data, data models, business terminology, data stewardship information, program management details and enterprise systems associated with data points and transformations. Data lineage visualization tools often include masking features that allow users to focus on information relevant to specific use cases. To unify representations across disparate systems, metadata normalization or standardization may be required. == Representation of data lineage == Representation broadly depends on the scope of the metadata management and reference point of interest. Data lineage provides sources of the data and intermediate data flow hops from the reference point with backward data lineage, leading to the final destination's data points and its intermediate data flows with forward data lineage. These views can be combined with end-to-end lineage for a reference point that provides a complete audit trail of that data point of interest from sources to their final destinations. As the data points or hops increase, the complexity of such representation becomes incomprehensible. Thus, the best feature of the data lineage view is the ability to simplify the view by temporarily masking unwanted peripheral data points. Tools with the masking feature enable scalability of the view and enhance analysis with the best user experience for both technical and business users. Data lineage also enables companies to trace sources of specific business data to track errors, implement changes in processes and implementing system migrations to save significant amounts of time and resources. Data lineage can improve efficiency in business intelligence BI processes. Data lineage can be represented visually to discover the data flow and movement from its source to destination via various changes and hops on its way in the enterprise environment. This includes how the data is transformed along the way, how the representation and parameters change and how the data splits or converges after each hop. A simple representation of the Data Lineage can be shown with dots and lines, where dots represent data containers for data points, and lines connecting them represent transformations the data undergoes between the data containers. Data lineage can be visualized at various levels based on the granularity of the view. At a very high-level, data lineage is visualized as systems that the data interacts with before it reaches its destination. At its most granular, visualizations at the data point level can provide the details of the data point and its historical behavior, attribute properties and trends and data quality of the data passed through that specific data point in the data lineage. The scope of the data lineage determines the volume of metadata required to represent its data lineage. Usually, data governance and data management of an organization determine the scope of the data lineage based on their regulations, enterprise data management strategy, data impact, reporting attributes and critical data elements of the organization. == Rationale == Distributed systems like Google Map Reduce, Microsoft Dryad, Apache Hadoop (an open-source project) and Google Pregel provide such platforms for businesses and users. However, even with these systems, Big Data analytics can take several hours, days or weeks to run, simply due to the data volumes involved. For example, a ratings prediction algorithm for the Netflix Prize challenge took nearly 20 hours to execute on 50 cores, and a large-scale image processing task to estimate geographic information took 3 days to complete using 400 cores. "The Large Synoptic Survey Telescope is expected to generate terabytes of data every night and eventually store more than 50 petabytes, while in the bioinformatics sector, the 12 largest genome sequencing houses in the world now store petabytes of data apiece. It is very difficult for a data scientist to trace an unknown or an unanticipated result. === Big data debugging === Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Machine learning, among other algorithms, is used to transform and analyze the data. Due to the large size of the data, there could be unknown features in the data. The massive scale and unstructured nature of data, the complexity of these analytics pipelines, and long runtimes pose significant manageability and debugging challenges. Even a single error in these analytics can be extremely difficult to identify and remove. While one may debug them by re-running the entire analytics through a debugger for stepwise debugging, this can be expensive due to the amount of time and resources needed. Auditing and data validation are other major problems due to the growing ease of access to relevant data sources for use in experiments, the sharing of data between scientific communities and use of third-party data in business enterprises. As such, more cost-efficient ways of analyzing data intensive scale-able computing (DISC) are crucial to their continued effective use. === Challenges in Big Data debugging === ==== Massive scale ==== According to an EMC/IDC study, 2.8 ZB of data were created and replicated in 2012. Furthermore, the same study states that the digital universe will double every two years between now and 2020, and that there will be approximately 5.2 TB of data for every person in 2020. Based on current technology, the storage of this much data will mean greater energy usage by data centers. ==== Unstructured data ==== Unstructured data usually refers to information that doesn't reside in a traditional row-column database. Unstructured data files often include text and multimedia content, such as e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages and many other kinds of business documents. While these types of files may have an internal structure, they are still considered "unstructured" because the data they contain doesn't fit neatly into a database. The amount of unstructured data in enterprises is growing many times faster than structured databases are growing. Big data can include both structured and unstructured data, but IDC estimates that 90 percent of Big Data is unstructured data. The fundamental challenge of unstructured data sources is that they are difficult for non-technical business users and data analysts alike to unbox, understand and prepare for analytic use. Beyond issues of structure, the sheer volume of this type of data contributes to such difficulty. Because of this, current data mining techniques often leave out valuable information and make analyzing unstructured data laborious and expensive. In today's competitive business environment, companies have to find and analyze the relevant data they need quickly. The challenge is going through the volumes of data and accessing the level of detail needed, all at a high speed. The challenge only grows as the degree of granularity increases. One possible solution is hardware. Some vendors are using increased memory and parallel processing to crunch large volumes of data quickly. Another method is putting data in-memory but using a grid

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  • Data monetization

    Data monetization

    Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item. There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the digital supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation. Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time. == Types of data monetization == Internal data monetization - An organization's data is used internally, resulting in economic benefit. This is commonly the case in organizations using analytics to uncover insights, resulting in improved profit, cost savings or the avoidance of risk. Internal data monetization is currently the most common form of monetization, requiring far fewer security, intellectual property, and legal precautions when compared to other types. The potential economic gains from this type of data monetization are limited by the organization's internal structure and situation. External data monetization - A person or organization makes data they possess available on a for-fee basis to external parties, or as a broker for same. This type of monetization is less common and requires various methods to distribute the data to potential buyers and consumers. However, the economic gain that results from collecting data, packaging and distributing it, can be quite large. == Steps == Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available. Connect, aggregate, attribute, validate, authenticate, and exchange data - this allows data to be converted directly into actionable or revenue generating insight or services. Set terms and prices and facilitate data trading - methods for data vetting, storage, and access. For example, many global corporations have locked and siloed data storage infrastructures, which hinders efficient access to data and cooperative and real-time exchange. Perform Research and analytics – draw predictive insights from existing data as a basis for using data for to reduce risk, enhance product development or performance, or improve customer experience or business outcomes. Action and leveraging – the last phase of monetizing data includes determining alternative or improved data centric products, ideas, or services. Examples may include real-time actionable triggered notifications or enhanced channels such as web or mobile response mechanisms. == Pricing variables and factors == A fee for use of a platform to connect buyers and sellers use of a platform to configure, organize, and otherwise process data included in a data trade connecting or including a device or sensor into a data supply chain connecting and credentialing a creator of a data source and a data buyer – often through a federated identity connecting a data source to other data sources to be included in a data supply chain use of an internet service or other transmission services for uploading and downloading data – sometimes, for an individual, through a personal cloud use of encrypted keys to achieve secure data transfer use of a search algorithm specifically designed to tag data sources that contain data points of value to the data buyer linking a data creator or generator to a data collection protocol or form server actions – such as a notification – triggered by an update to a data item or data source included in a data supply chain A price or exchange or other trade value assigned by a data creator or generator to a data item or a data source offered by a data buyer to a data creator assigned by a data buyer for a data item or a data source formatted according to criteria set by a data buyer An incremental fee assigned by a data buyer for a data item or a data set scaled to the reputation of the data creator == Benefits == Improved decision-making that leads to real time crowd sourced research, improved profits, decreased costs, reduced risk and improved compliance More impactful decisions (e.g., make real-time decisions) More timely (lower latency) decisions (e.g., a vendor making purchase recommendations while the customer is still on the phone or in the store, a customer connecting with multiple vendors to discover the best price, triggered notifications when thresholds are reached for data values) More granular decisions (e.g., localized pricing decisions at an individual or device or sensor level versus larger aggregates). Targeted Marketing (e.g., Vendors with access to big data can make targeted advertisements to specific customers within a set data pool decreasing costs for the advertiser and reaching most interested customers) == Frameworks == There are a wide variety of industries, firms and business models related to data monetization. The following frameworks have been offered to help understand the types of business models that are used: Roger Ehrenberg of IA Ventures, a venture capital firm that invests in this sector, has defined three basic types of data product firms: Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return – a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms, and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful

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