AI Grammar Rephrase

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2018 Google data breach

The 2018 Google data breach was a major data privacy scandal in which the Google+ API exposed the private data of over five hundred thousand users. Google+ managers first noticed harvesting of personal data in March 2018, during a review following the Facebook–Cambridge Analytica data scandal. The bug, despite having been fixed immediately, exposed the private data of approximately 500,000 Google+ users to the public. Google did not reveal the leak to the network's users. In November 2018, another data breach occurred following an update to the Google+ API. Although Google found no evidence of failure, approximately 52.5 million personal profiles were potentially exposed. In August 2019, Google declared a shutdown of Google+ due to low use and technological challenges. == Overview of Google+ == Google+ was launched in June 2011 as an invite-only social network, but was opened for public access later in the year. It was managed by Vic Gundotra. Similar to Facebook, Google+ also included key features Circles, Hangouts and Sparks. Circles let users personalize their social groups by sorting friends into different categories. Once allowed into a Circle, users could regulate information in their individual spaces. Hangouts included video chatting and instant messaging between users. Sparks allowed Google to track users' past searches to find news and content related to their interests. Google+ was linked to other Google services, such as YouTube, Google Drive and Gmail, giving it access to roughly 2 billion user accounts. However, less than 400 million consumers actively used Google+, with 90% of those users using it for less than five seconds. == The breaches == In March 2018, Google developers found a data breach within the Google+ People API in which external apps acquired access to Profile fields that were not marked as public. According to The Wall Street Journal, Google didn’t disclose the breach when it was first discovered in March to avoid regulatory scrutiny and reputational damage. 500,000 Google+ accounts were included in the breach, which allowed 438 external apps unauthorized access to private users' names, emails, addresses, occupations, genders and ages. This information was available between 2015 and 2018. Google found no evidence of any user's personal information being misused, nor that any third-party app developers were aware of the leak. In November 2018, a software update created another data breach within the Google+ API. The bug impacted 52.5 million users, where, similarly to the March breach, unauthorized apps were able to access Google+ profiles, including users' names, email addresses, occupations and ages. Apps could not access financial information, national identification, numbers, or passwords. Blog posts, messages and phone numbers also remained inaccessible if marked as private. Unlike the previous breach, access was only available for six days before Google+ learned of the breach. Once more, Google+ found no evidence of data being misused by third-party developers. == Responses == In October 2018, the Wall Street Journal published an article outlining the initial breach and Google's decision to not disclose it to users. At the time, there was no federal law that required Google to inform their consumers of data breaches. Google+ originally did not disclose the breach out of fears of being compared to Facebook's recent data leak and subsequent loss of consumer confidence. In response to the Wall Street Journal article, Google announced the shutdown of Google+ in August 2019. After the second data leak, the date was moved to April 2019. In response to the data breach, enterprise consumers were notified of the bug's impact and given instructions on how to save, download and delete their data prior to the Google+ shut down. Google's Privacy and Data Protection Office found no misuse of user data. Prior to the Google+ shutdown, Google set a 10-month period in which users could download and migrate their data. After the 10-month period, user content was deleted. On 4 February 2019, consumers were no longer able to create new Google+ profiles. Google shut down Google+ APIs on 7 March 2019 to ensure that developers did not continue to rely on the APIs prior to the Google+ shutdown. Google is the principal entity of its parent company, Alphabet Inc. After the data breach, Alphabet Inc. share prices fell by 1% to $1,157.06 on 9 October 2018 after an earlier drop of $1,135.40 that morning, the lowest price since 5 July 2018. After the publication of The Wall Street Journal article, share prices dropped as low as 2.1% in two days on 10 October 2018. Share prices steadily increased from this point and met the 8 October 2018 share price on 5 February 2019. Google planned to rebuild Google+ as a corporate enterprise network. Google Play will now assess which apps can ask for permission to access the user's SMS data. Only the default app for telephone distribution is able to make requests. Prior to the data breaches, apps were able to request access to all of a consumer's data simultaneously. Now, each app must request permission for each aspect of a consumer's profile.
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Data steward

A data steward is an oversight or data governance role within an organization, and is responsible for ensuring the quality and fitness for purpose of the organization's data assets, including the metadata for those data assets. A data steward may share some responsibilities with a data custodian, such as the awareness, accessibility, release, appropriate use, security and management of data. A data steward would also participate in the development and implementation of data assets. A data steward may seek to improve the quality and fitness for purpose of other data assets their organization depends upon but is not responsible for. Data stewards have a specialist role that utilizes an organization's data governance processes, policies, guidelines and responsibilities for administering an organizations' entire data in compliance with policy and/or regulatory obligations (e.g., GDPR, HIPAA). The overall objective of a data steward is the data quality of the data assets, datasets, data records and data elements. This includes documenting metainformation for the data, such as definitions, related rules/governance, physical manifestation, and related data models (most of these properties being specific to an attribute/concept relationship), identifying owners/custodian's various responsibilities, relations insight pertaining to attribute quality, aiding with project requirement data facilitation and documentation of capture rules. Data stewards begin the stewarding process with the identification of the data assets and elements which they will steward, with the ultimate result being standards, controls and data entry. The steward works closely with business glossary standards analysts (for standards), with data architect/modelers (for standards), with DQ analysts (for controls) and with operations team members (good-quality data going in per business rules) while entering data. Data stewardship roles are common when organizations attempt to exchange data precisely and consistently between computer systems and to reuse data-related resources. Master data management often makes references to the need for data stewardship for its implementation to succeed. Data stewardship must have precise purpose, fit for purpose or fitness. == Data steward responsibilities == A data steward ensures that each assigned data element: Has clear and unambiguous data element definition Does not conflict with other data elements in the metadata registry (removes duplicates, overlap etc.) Has clear enumerated value definitions if it is of type Code Is still being used (remove unused data elements) Is being used consistently in various computer systems Is being used, fit for purpose = Data Fitness Has adequate documentation on appropriate usage and notes Documents the origin and sources of authority on each metadata element Is protected against unauthorised access or change Responsibilities of data stewards vary between different organisations and institutions. For example, at Delft University of Technology, data stewards are perceived as the first contact point for any questions related to research data. They also have subject-specific background allowing them to easily connect with researchers and to contextualise data management problems to take into account disciplinary practices. == Types of data stewards == Depending on the set of data stewardship responsibilities assigned to an individual, there are 4 types (or dimensions of responsibility) of data stewards typically found within an organization: Data object data steward - responsible for managing reference data and attributes of one business data entity Business data steward - responsible for managing critical data, both reference and transactional, created or used by one business function. The data steward may also serve as a liaison between the organization's data users and technical teams, helping to bridge the gap between business needs and technical requirements. They may also play a role in educating others within the organization about best practices for data management, and advocating for data-driven decision-making. Process data steward - responsible for managing data across one business process System data steward - responsible for managing data for at least one IT system == Benefits of data stewardship == Systematic data stewardship can foster: Faster analysis Consistent use of data management resources Easy mapping of data between computer systems and exchange documents Lower costs associated with migration to (for example) service-oriented architecture (SOA) Mitigation of data risk Better control of dangers associated with privacy, legal, errors, etc. Assignment of each data element to a person sometimes seems like an unimportant process. But multiple groups have found that users have greater trust and usage rates in systems where they can contact a person with questions on each data element. == Examples == Delft University of Technology (TU Delft) offers an example of data stewardship implementation at a research institution. In 2017 the Data Stewardship Project was initiated at TU Delft to address research data management needs in a disciplinary manner across the whole campus. Dedicated data stewards with subject-specific background were appointed at every TU Delft faculty to support researchers with data management questions and to act as a linking point with the other institutional support services. The project is coordinated centrally by TU Delft Library, and it has its own website, blog and a YouTube channel. The [1]EPA metadata registry furnishes an example of data stewardship. Note that each data element therein has a "POC" (point of contact). In 2023, ETH Zurich launched the Data Stewardship Network (DSN) to facilitate collaboration among employees engaged in data management, analysis, and code development across research groups. The DSN serves as a platform for networking and knowledge exchange, aiming to professionalize the role of data stewards who support research data management and reproducible workflows. Established by the team for Research Data Management and Digital Curation at the ETH Library, the DSN collaborates with Scientific IT Services to provide expertise in areas such as storage infrastructure and reproducible workflows. == Data stewardship applications == Information stewardship applications are business solutions used by business users acting in the role of information steward (interpreting and enforcing information governance policy, for example). These developing solutions represent, for the most part, an amalgam of a number of disparate, previously IT-centric tools already on the market, but are organized and presented in such a way that information stewards (a business role) can support the work of information policy enforcement as part of their normal, business-centric, day-to-day work in a range of use cases. The initial push for the formation of this new category of packaged software came from operational use cases — that is, use of business data in and between transactional and operational business applications. This is where most of the master data management efforts are undertaken in organizations. However, there is also now a faster-growing interest in the new data lake arena for more analytical use cases.
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Content inventory

A content inventory is the process and the result of cataloging the entire contents of a website. An allied practice—a content audit—is the process of evaluating that content. A content inventory and a content audit are closely related concepts, and they are often conducted in tandem. == Description == A content inventory typically includes all information assets on a website, such as web pages (HTML), meta elements (e.g., keywords, description, page title), images, audio and video files, and document files (e.g., .pdf, .doc, .ppt). A content inventory is a quantitative analysis of a website. It simply logs what is on a website. The content inventory will answer the question: “What is there?” and can be the start of a website review. A related (and sometimes confused term) is a content audit, a qualitative analysis of information assets on a website. It is the assessment of that content and its place in relationship to surrounding Web pages and information assets. The content audit will answer the question: “Is it any good?” Over the years, techniques for creating and managing a content inventory have been developed and refined in the field of website content management. A spreadsheet application (e.g., Microsoft Excel or LibreOffice Calc) is the preferred tool for keeping a content inventory; the data can be easily configured and manipulated. Typical categories in a content inventory include the following: Link — The URL for the page Format — For example, .HTML, .pdf, .doc, .ppt Meta page title — Page title as it appears in the meta tag Meta keywords — Keywords as they appear in the meta name="keywords" tag element Meta description — Text as it appears in the meta name="description" tag element Content owner — Person responsible for maintaining page content Date page last updated — Date of last page update Audit Comments (or Notes) — Audit findings and notes Other descriptors may need to be captured on the inventory sheet. Content management experts advise capturing information that might be useful for both short- and long-term purposes. Other information could include: the overall topic or area to which the page belongs a short description of the information on the page when the page was created, the date of the last revision, and when the next page review is due pages this page links to pages that link to this page page status – keep, delete, revise, in revision process, planned, being written, being edited, in review, ready for posting, or posted rank of the page on the website – is it a top 50 pages? a bottom 50 page? Initial efforts might be more focused on those pages that visitors use the most and least. Other tabs in the inventory workbook can be created to track related information, such as meta keywords, new Web pages to develop, website tools and resources, or content inventories for sub-areas of the main website. Creating a single, shared location for information related to a website can be helpful for all website content managers, writers, editors, and publishers. Populating the spreadsheet is a painstaking task, but some up-front work can be automated with software, and other tools and resources can assist the audit work. == Value == A content inventory and a content audit are performed to understand what is on a website and why it is there. The inventory sheet, once completed and revised as the site is updated with new content and information assets, can also become a resource for help in maintaining website governance. For an existing website, the information cataloged in a content inventory and content audit will be a resource to help manage all of the information assets on the website. The information gathered in the inventory can also be used to plan a website re-design or site migration to a web content management system. When planning a new website, a content inventory can be a useful project management tool: as a guide to map information architecture and to track new pages, page revision dates, content owners, and so on.</p> <a href="https://bbs.aizhi.co/news/234a899757.html" class="read-more" title="Content inventory">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/34d899957.html" class="card-thumb-link" title="Pinoy baiting"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Automatic_Scorer1.jpg/960px-Automatic_Scorer1.jpg" alt="Pinoy baiting" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/34d899957.html" title="Pinoy baiting">Pinoy baiting</a></h2> <p class="article-excerpt">Pinoy baiting is a phrase that has been used to refer to acts by non-Filipino individuals, usually celebrities or YouTubers, of posting content online purportedly with the intention of getting the attention of Filipinos, by being surprised about the Philippines or its people. Pinoy baiters are defined as giving superficial and allegedly insincere praises and similar reactions that give recognition to the Philippines or its people. Subsequent responses by Filipinos to what have been referred to as acts of Pinoy baiting have been criticized as a form of cultural cringe. This criticism would subsequently give the advice that Filipinos should not constantly require validation from non-Filipinos about themselves or their country. == Pinoy baiting mediums == === Reaction videos === On social media such as YouTube, channels with specific focus on showing their reaction towards and opinions about certain videos or topics are called reaction channels. Reaction videos are very popular and require minimal effort to create, and thus made it easy for alleged Pinoy baiting to thrive within this video-making genre. === Travel vlogs === Vlogging, short for video blogging, grew in popularity in the 2020s. Most of the popular alleged Pinoy-baiting channels tend to be vlog channels, normally following the same script under such titles as "The Philippines changed us/me", "First impression of the Philippines", "Is this really Manila?" and "Filipinos are such Kind/Good People!", and made while travelling to touristy areas such as Boracay or Bonifacio Global City and taste-testing the fast food chain Jollibee, among others. == Criticism of the phrase == Philippines-based Korean vlogger Jessica Lee had been accused by some YouTube viewers of engaging in Pinoy baiting. In a response vlog, Lee acknowledged that there may be individuals engaging in this "business strategy" of gaining views and subscribers from one of the largest communities online. However, she questioned the objectivity of some use of the phrase, citing any vlogging subject as fair game for a negative impression of being a "baiting" tool for the vlogger treating of that subject. She also invoked vloggers' freedom to choose whatever subject they want to talk about in a deep or shallow manner, while enjoining citizens to exercise their free-market right to unfollow vloggers they hate and follow those vloggers that "make them happy". She also gave her critics an explanation why she ended up vlogging about Philippine and Filipino subjects.</p> <a href="https://bbs.aizhi.co/news/34d899957.html" class="read-more" title="Pinoy baiting">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/492f099507.html" class="card-thumb-link" title="Geometric primitive"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/c/c8/Pirate_ordered_dither.png" alt="Geometric primitive" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/492f099507.html" title="Geometric primitive">Geometric primitive</a></h2> <p class="article-excerpt">In vector computer graphics, CAD systems, and geographic information systems, a geometric primitive (or prim) is the simplest (i.e. 'atomic' or irreducible) geometric shape that the system can handle (draw, store). Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segments, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are curves (segments of straight lines, circles and more complicated curves), as well as shapes (boxes, arbitrary polygons, circles). A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles (polygon triangulation). All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves (such as Bézier curves, circles, etc.) may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. == Common primitives == The set of geometric primitives is based on the dimension of the region being represented: Point (0-dimensional), a single location with no height, width, or depth. Line or curve (1-dimensional), having length but no width, although a linear feature may curve through a higher-dimensional space. Planar surface or curved surface (2-dimensional), having length and width. Volumetric region or solid (3-dimensional), having length, width, and depth. In GIS, the terrain surface is often spoken of colloquially as "2 1/2 dimensional," because only the upper surface needs to be represented. Thus, elevation can be conceptualized as a scalar field property or function of two-dimensional space, affording it a number of data modeling efficiencies over true 3-dimensional objects. A shape of any of these dimensions greater than zero consists of an infinite number of distinct points. Because digital systems are finite, only a sample set of the points in a shape can be stored. Thus, vector data structures typically represent geometric primitives using a strategic sample, organized in structures that facilitate the software interpolating the remainder of the shape at the time of analysis or display, using the algorithms of Computational geometry. A Point is a single coordinate in a Cartesian coordinate system. Some data models allow for Multipoint features consisting of several disconnected points. A Polygonal chain or Polyline is an ordered list of points (termed vertices in this context). The software is expected to interpolate the intervening shape of the line between adjacent points in the list as a parametric curve, most commonly a straight line, but other types of curves are frequently available, including circular arcs, cubic splines, and Bézier curves. Some of these curves require additional points to be defined that are not on the line itself, but are used for parametric control. A Polygon is a polyline that closes at its endpoints, representing the boundary of a two-dimensional region. The software is expected to use this boundary to partition 2-dimensional space into an interior and exterior. Some data models allow for a single feature to consist of multiple polylines, which could collectively connect to form a single closed boundary, could represent a set of disjoint regions (e.g., the state of Hawaii), or could represent a region with holes (e.g., a lake with an island). A Parametric shape is a standardized two-dimensional or three-dimensional shape defined by a minimal set of parameters, such as an ellipse defined by two points at its foci, or three points at its center, vertex, and co-vertex. A Polyhedron or Polygon mesh is a set of polygon faces in three-dimensional space that are connected at their edges to completely enclose a volumetric region. In some applications, closure may not be required or may be implied, such as modeling terrain. The software is expected to use this surface to partition 3-dimensional space into an interior and exterior. A triangle mesh is a subtype of polyhedron in which all faces must be triangles, the only polygon that will always be planar, including the Triangulated irregular network (TIN) commonly used in GIS. A parametric mesh represents a three-dimensional surface by a connected set of parametric functions, similar to a spline or Bézier curve in two dimensions. The most common structure is the Non-uniform rational B-spline (NURBS), supported by most CAD and animation software. == Application in GIS == A wide variety of vector data structures and formats have been developed during the history of Geographic information systems, but they share a fundamental basis of storing a core set of geometric primitives to represent the location and extent of geographic phenomena. Locations of points are almost always measured within a standard Earth-based coordinate system, whether the spherical Geographic coordinate system (latitude/longitude), or a planar coordinate system, such as the Universal Transverse Mercator. They also share the need to store a set of attributes of each geographic feature alongside its shape; traditionally, this has been accomplished using the data models, data formats, and even software of relational databases. Early vector formats, such as POLYVRT, the ARC/INFO Coverage, and the Esri shapefile support a basic set of geometric primitives: points, polylines, and polygons, only in two dimensional space and the latter two with only straight line interpolation. TIN data structures for representing terrain surfaces as triangle meshes were also added. Since the mid 1990s, new formats have been developed that extend the range of available primitives, generally standardized by the Open Geospatial Consortium's Simple Features specification. Common geometric primitive extensions include: three-dimensional coordinates for points, lines, and polygons; a fourth "dimension" to represent a measured attribute or time; curved segments in lines and polygons; text annotation as a form of geometry; and polygon meshes for three-dimensional objects. Frequently, a representation of the shape of a real-world phenomenon may have a different (usually lower) dimension than the phenomenon being represented. For example, a city (a two-dimensional region) may be represented as a point, or a road (a three-dimensional volume of material) may be represented as a line. This dimensional generalization correlates with tendencies in spatial cognition. For example, asking the distance between two cities presumes a conceptual model of the cities as points, while giving directions involving travel "up," "down," or "along" a road imply a one-dimensional conceptual model. This is frequently done for purposes of data efficiency, visual simplicity, or cognitive efficiency, and is acceptable if the distinction between the representation and the represented is understood, but can cause confusion if information users assume that the digital shape is a perfect representation of reality (i.e., believing that roads really are lines). == In 3D modelling == In CAD software or 3D modelling, the interface may present the user with the ability to create primitives which may be further modified by edits. For example, in the practice of box modelling the user will start with a cuboid, then use extrusion and other operations to create the model. In this use the primitive is just a convenient starting point, rather than the fundamental unit of modelling. A 3D package may also include a list of extended primitives which are more complex shapes that come with the package. For example, a teapot is listed as a primitive in 3D Studio Max. == In graphics hardware == Various graphics accelerators exist with hardware acceleration for rendering specific primitives such as lines or triangles, frequently with texture mapping and shaders. Modern 3D accelerators typically accept sequences of triangles as triangle strips.</p> <a href="https://bbs.aizhi.co/news/492f099507.html" class="read-more" title="Geometric primitive">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/336e899655.html" class="card-thumb-link" title="Ultra (cryptography)"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/a/ae/Grid_Network.svg/960px-Grid_Network.svg.png" alt="Ultra (cryptography)" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/336e899655.html" title="Ultra (cryptography)">Ultra (cryptography)</a></h2> <p class="article-excerpt">Ultra was the designation adopted by British military intelligence in June 1941 for wartime signals intelligence obtained by breaking high-level encrypted enemy radio and teleprinter communications at the Government Code and Cypher School (GC&CS) at Bletchley Park. Ultra eventually became the standard designation among the western Allies for all such intelligence. The name arose because the intelligence obtained was considered more important than that designated by the highest British security classification then used (Most Secret) and so was regarded as being Ultra Secret. Several other cryptonyms had been used for such intelligence. The code name "Boniface" was used as a cover name for Ultra. In order to ensure that the successful code-breaking did not become apparent to the Germans, British intelligence created a fictional MI6 master spy, Boniface, who controlled a fictional series of agents throughout Germany. Information obtained through code-breaking was often attributed to the human intelligence from the Boniface network. The U.S. used the codename Magic for its decrypts from Japanese sources, including the "Purple" cipher. Much of the German cipher traffic was encrypted on the Enigma machine. Used properly, the German military Enigma would have been virtually unbreakable; in practice, shortcomings in operation allowed it to be broken. The term "Ultra" has often been used almost synonymously with "Enigma decrypts". However, Ultra also encompassed decrypts of the German Lorenz SZ 40/42 machines that were used by the German High Command, and the Hagelin machine. Many observers, at the time and later, regarded Ultra as immensely valuable to the Allies. Winston Churchill was reported to have told King George VI, when presenting to him Stewart Menzies (head of the Secret Intelligence Service and the person who controlled distribution of Ultra decrypts to the government): "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" F. W. Winterbotham quoted the western Supreme Allied Commander, Dwight D. Eisenhower, at war's end describing Ultra as having been "decisive" to Allied victory. Sir Harry Hinsley, Bletchley Park veteran and official historian of British Intelligence in World War II, made a similar assessment of Ultra, saying that while the Allies would have won the war without it, "the war would have been something like two years longer, perhaps three years longer, possibly four years longer than it was." However, Hinsley and others have emphasized the difficulties of counterfactual history in attempting such conclusions, and some historians, such as John Keegan, have said the shortening might have been as little as the three months it took the United States to deploy the atomic bomb. == Sources of intelligence == Most Ultra intelligence was derived from reading radio messages that had been encrypted with cipher machines, complemented by material from radio communications using traffic analysis and direction finding. In the early phases of the war, particularly during the eight-month Phoney War, the Germans could transmit most of their messages using land lines and so had no need to use radio. This meant that those at Bletchley Park had some time to build up experience of collecting and starting to decrypt messages on the various radio networks. German Enigma messages were the main source, with those of the German air force (the Luftwaffe) predominating, as they used radio more and their operators were particularly ill-disciplined. === German === ==== Enigma ==== "Enigma" refers to a family of electro-mechanical rotor cipher machines. These produced a polyalphabetic substitution cipher and were widely thought to be unbreakable in the 1920s, when a variant of the commercial Model D was first used by the Reichswehr. The German Army (Heer), Navy, Air Force, Nazi party, Gestapo and German diplomats used Enigma machines in several variants. Abwehr (German military intelligence) used a four-rotor machine without a plugboard and Naval Enigma used different key management from that of the army or air force, making its traffic far more difficult to cryptanalyse; each variant required different cryptanalytic treatment. The commercial versions were not as secure and Dilly Knox of GC&CS is said to have broken one before the war. German military Enigma was first broken in December 1932 by Marian Rejewski and the Polish Cipher Bureau, using a combination of brilliant mathematics, the services of a spy in the German office responsible for administering encrypted communications, and good luck. The Poles read Enigma to the outbreak of World War II and beyond, in France. At the turn of 1939, the Germans made the systems ten times more complex, which required a tenfold increase in Polish decryption equipment, which they could not meet. On 25 July 1939, the Polish Cipher Bureau handed reconstructed Enigma machines and their techniques for decrypting ciphers to the French and British. Gordon Welchman wrote, Ultra would never have got off the ground if we had not learned from the Poles, in the nick of time, the details both of the German military Enigma machine, and of the operating procedures that were in use. At Bletchley Park, some of the key people responsible for success against Enigma included mathematicians Alan Turing and Hugh Alexander and, at the British Tabulating Machine Company, chief engineer Harold Keen. After the war, interrogation of German cryptographic personnel led to the conclusion that German cryptanalysts understood that cryptanalytic attacks against Enigma were possible but were thought to require impracticable amounts of effort and investment. The Poles' early start at breaking Enigma and the continuity of their success gave the Allies an advantage when World War II began. ==== Lorenz cipher ==== In June 1941, the Germans started to introduce on-line stream cipher teleprinter systems for strategic point-to-point radio links, to which the British gave the code-name Fish. Several systems were used, principally the Lorenz SZ 40/42 (codenamed "Tunny" by the British) and Geheimfernschreiber ("Sturgeon"). These cipher systems were cryptanalysed, particularly Tunny, which the British thoroughly penetrated. It was eventually attacked using Colossus machines, which were the first digital programme-controlled electronic computers. In many respects the Tunny work was more difficult than for the Enigma, since the British codebreakers had no knowledge of the machine producing it and no head-start such as that the Poles had given them against Enigma. Although the volume of intelligence derived from this system was much smaller than that from Enigma, its importance was often far higher because it produced primarily high-level, strategic intelligence that was sent between Wehrmacht high command (Oberkommando der Wehrmacht, OKW). The eventual bulk decryption of Lorenz-enciphered messages contributed significantly, and perhaps decisively, to the defeat of Nazi Germany. Nevertheless, the Tunny story has become much less well known among the public than the Enigma one. At Bletchley Park, some of the key people responsible for success in the Tunny effort included mathematicians W. T. "Bill" Tutte and Max Newman and electrical engineer Tommy Flowers. === Italian === In June 1940, the Italians were using book codes for most of their military messages, except for the Italian Navy, which in early 1941 had started using a version of the Hagelin rotor-based cipher machine C-38. This was broken from June 1941 onwards by the Italian subsection of GC&CS at Bletchley Park. === Japanese === In the Pacific theatre, a Japanese cipher machine, called "Purple" by the Americans, was used for highest-level Japanese diplomatic traffic. It produced a polyalphabetic substitution cipher, but unlike Enigma, was not a rotor machine, being built around electrical stepping switches. It was broken by the US Army Signal Intelligence Service and disseminated as Magic. Detailed reports by the Japanese ambassador to Germany were encrypted on the Purple machine. His reports included reviews of German assessments of the military situation, reviews of strategy and intentions, reports on direct inspections by the ambassador (in one case, of Normandy beach defences), and reports of long interviews with Hitler. The Japanese are said to have obtained an Enigma machine in 1937, although it is debated whether they were given it by the Germans or bought a commercial version, which, apart from the plugboard and internal wiring, was the German Heer/Luftwaffe machine. Having developed a similar machine, the Japanese did not use the Enigma machine for their most secret communications. The chief fleet communications code system used by the Imperial Japanese Navy was called JN-25 by the Americans, and by early 1942 the US Navy had made considerable progress in decrypting Japanese naval messages. The US Army also made progress on the</p> <a href="https://bbs.aizhi.co/news/336e899655.html" class="read-more" title="Ultra (cryptography)">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/480e899511.html" class="card-thumb-link" title="KLJN Secure Key Exchange"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/dc/USA_TV_News_Simulation_1.jpg/960px-USA_TV_News_Simulation_1.jpg" alt="KLJN Secure Key Exchange" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/480e899511.html" title="KLJN Secure Key Exchange">KLJN Secure Key Exchange</a></h2> <p class="article-excerpt">Random-resistor-random-temperature Kirchhoff-law-Johnson-noise key exchange, also known as RRRT-KLJN or simply KLJN, is an approach for distributing cryptographic keys between two parties that claims to offer unconditional security. This claim, which has been contested, is significant, as the only other key exchange approach claiming to offer unconditional security is Quantum key distribution. The KLJN secure key exchange scheme was proposed in 2005 by Laszlo Kish and Granqvist. It has the advantage over quantum key distribution in that it can be performed over a metallic wire with just four resistors, two noise generators, and four voltage measuring devices---equipment that is low-priced and can be readily manufactured. It has the disadvantage that several attacks against KLJN have been identified which must be defended against. "Given that the amount of effort and funding that goes into Quantum Cryptography is substantial (some even mock it as a distraction from the ultimate prize which is quantum computing), it seems to me that the fact that classic thermodynamic resources allow for similar inherent security should give one pause," wrote Henning Dekant, the founder of the Quantum Computing Meetup, in April 2013. The Cybersecurity Curricula 2017, a joint project of the Association for Computing Machinery, the IEEE Computer Society, the Association for Information Systems, and the International Federation for Information Processing Technical Committee on Information Security Education (IFIP WG 11.8) recommends teaching the KLJN Scheme as part of teaching "Advanced concepts" in its knowledge unit on cryptography. == See Also/Further Reading ==</p> <a href="https://bbs.aizhi.co/news/480e899511.html" class="read-more" title="KLJN Secure Key Exchange">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/419d899572.html" class="card-thumb-link" title="CrySyS Lab"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/ResBlock.png/960px-ResBlock.png" alt="CrySyS Lab" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/419d899572.html" title="CrySyS Lab">CrySyS Lab</a></h2> <p class="article-excerpt">CrySyS Lab (Hungarian pronunciation: [ˈkriːsis]) is part of the Department of Telecommunications at the Budapest University of Technology and Economics. The name is derived from "Laboratory of Cryptography and System Security", the full Hungarian name is CrySys Adat- és Rendszerbiztonság Laboratórium. == History == CrySyS Lab. was founded in 2003 by a group of security researchers at the Budapest University of Technology and Economics. Currently, it is located in the Infopark Budapest. The heads of the lab were Dr. István Vajda (2003–2010) and Dr. Levente Buttyán (2010-now). Since its establishment, the lab participated in several research and industry projects, including successful EU FP6 and FP7 projects (SeVeCom, a UbiSecSens and WSAN4CIP). == Research results == CrySyS Lab is recognized in research for its contribution to the area of security in wireless embedded systems. In this area, the members of the lab produced 5 books 4 book chapters 21 journal papers 47 conference papers 3 patents 2 Internet Draft The above publications had an impact factor of 30+ and obtained more than 7500 references. Several of these publications appeared in highly cited journals (e.g., IEEE Transactions on Dependable and Secure Systems, IEEE Transactions on Mobile Computing). == Forensics analysis of malware incidents == The laboratory was involved in the forensic analysis of several high-profile targeted attacks. In October 2011, CrySyS Lab discovered the Duqu malware; pursued the analysis of the Duqu malware and as a result of the investigation, identified a dropper file with an MS 0-day kernel exploit inside; and finally released a new open-source Duqu Detector Toolkit to detect Duqu traces and running Duqu instances. In May 2012, the malware analysis team at CrySyS Lab participated in an international collaboration aiming at the analysis of an as yet unknown malware, which they call sKyWIper. At the same time Kaspersky Lab analyzed the malware Flame and Iran National CERT (MAHER) the malware Flamer. Later, they turned out to be the same. Other analysis published by CrySyS Lab include the password analysis of the Hungarian ISP, Elender, and a thorough Hungarian security survey of servers after the publications of the Kaminsky DNS attack.</p> <a href="https://bbs.aizhi.co/news/419d899572.html" class="read-more" title="CrySyS Lab">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/76f499919.html" class="card-thumb-link" title="Data Science and Predictive Analytics"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/9/96/Facebook_chat_screenshot_%28English%29.png" alt="Data Science and Predictive Analytics" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/76f499919.html" title="Data Science and Predictive Analytics">Data Science and Predictive Analytics</a></h2> <p class="article-excerpt">The first edition of the textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. The second edition of the book was printed in 2023. This textbook covers some of the core mathematical foundations, computational techniques, and artificial intelligence approaches used in data science research and applications. By using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book first edition provide explicit examples of importing, exporting, processing, modeling, visualizing, and interpreting large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets (big data). == Structure == === First edition table of contents === The first edition of the Data Science and Predictive Analytics (DSPA) textbook is divided into the following 23 chapters, each progressively building on the previous content. === Second edition table of contents === The significantly reorganized revised edition of the book (2023) expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build foundational skills to naturally reach biomedical applications of deep learning. Introduction Basic Visualization and Exploratory Data Analytics Linear Algebra, Matrix Computing, and Regression Modeling Linear and Nonlinear Dimensionality Reduction Supervised Classification Black Box Machine Learning Methods Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Rules Learning Unsupervised Clustering Model Performance Assessment, Validation, and Improvement Specialized Machine Learning Topics Variable Importance and Feature Selection Big Longitudinal Data Analysis Function Optimization Deep Learning, Neural Networks == Reception == The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, International Statistical Institute’s ISI Review Journal, and the Journal of the American Library Association. Many scholarly publications reference the DSPA textbook. As of January 17, 2021, the electronic version of the book first edition (ISBN 978-3-319-72347-1) is freely available on SpringerLink and has been downloaded over 6 million times. The textbook is globally available in print (hardcover and softcover) and electronic formats (PDF and EPub) in many college and university libraries and has been used for data science, computational statistics, and analytics classes at various institutions.</p> <a href="https://bbs.aizhi.co/news/76f499919.html" class="read-more" title="Data Science and Predictive Analytics">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/229c899762.html" class="card-thumb-link" title="Copyright"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/2/23/Nicholas_Carlini_AIME_presentation.jpg" alt="Copyright" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/229c899762.html" title="Copyright">Copyright</a></h2> <p class="article-excerpt">A copyright is a type of intellectual property that gives its owner the exclusive legal right to copy, distribute, adapt, display, and perform a creative work, usually for a limited time. The creative work may be in a literary, artistic, educational, or musical form. Copyright is intended to protect the original expression of an idea in the form of a creative work, but not the idea itself. A copyright is subject to limitations based on public interest considerations, such as the fair use doctrine in the United States and fair dealing doctrine in the United Kingdom. Some jurisdictions require "fixing" copyrighted works in a tangible form. It is often shared among multiple authors, each of whom holds a set of rights to use or license the work, and who are commonly referred to as rights holders. These rights normally include reproduction, control over derivative works, distribution, public performance, and moral rights such as attribution. Copyrights can be granted by public law and are in that case considered "territorial rights". This means that copyrights granted by the law of a certain state do not extend beyond the territory of that specific jurisdiction. Copyrights of this type vary by country; many countries, and sometimes a large group of countries, have made agreements with other countries on procedures applicable when works "cross" national borders or national rights are inconsistent. Typically, the public law duration of a copyright expires 50 to 100 years after the creator dies, depending on the jurisdiction. Some countries require certain copyright formalities to establishing copyright, others recognize copyright in any completed work, without a formal registration. When the copyright of a work expires, it enters the public domain. == History == === Background === The concept of copyright developed after the printing press came into use in Europe in the 15th and 16th centuries. It was associated with a common law and rooted in the civil law system. The printing press made it much cheaper to produce works, but as there was initially no copyright law, anyone could buy or rent a press and print any text. Popular new works were immediately re-set and re-published by competitors, so printers needed a constant stream of new material. Fees paid to authors for new works were high and significantly supplemented the incomes of many academics. Printing brought profound social changes. The rise in literacy across Europe led to a dramatic increase in the demand for reading matter. Prices of reprints were low, so publications could be bought by poorer people, creating a mass audience. In German-language markets before the advent of copyright, technical materials, like academic papers and handbooks, were inexpensive and widely available; it has been suggested this contributed to Germany's industrial and economic success. === Conception === The concept of copyright first developed in England. In reaction to the printing of "scandalous books and pamphlets", the English Parliament passed the Licensing of the Press Act 1662, which required all intended publications to be registered with the government-approved Stationers' Company, giving the Stationers the right to regulate what material could be printed. The Statute of Anne, enacted in 1710 in England and Scotland, provided the first legislation to protect copyrights (but not authors' rights). The Copyright Act 1814 extended more rights for authors but did not protect British publications from being reprinted in the US. The Berne International Copyright Convention of 1886 finally provided protection for authors among the countries who signed the agreement, although the US did not join the Berne Convention until 1989. In the US, the Constitution grants Congress the right to establish copyright and patent laws. Shortly after the Constitution was passed, Congress enacted the Copyright Act of 1790, modeling it after the Statute of Anne. While the national law protected authors' published works, authority was granted to the states to protect authors' unpublished works. The most recent major overhaul of copyright in the US, the Copyright Act of 1976, extended federal copyright to works as soon as they are created and "fixed", without requiring publication or registration. State law continues to apply to unpublished works that are not otherwise copyrighted by federal law. This act also changed the calculation of copyright term from a fixed term (then a maximum of fifty-six years) to "life of the author plus 50 years". These changes brought the US closer to conformity with the Berne Convention, and in 1989 the United States further revised its copyright law and joined the Berne Convention officially. Copyright laws allow products of creative human activities, such as literary and artistic production, to be preferentially exploited and thus incentivized. Different cultural attitudes, social organizations, economic models and legal frameworks are seen to account for why copyright emerged in Europe and not, for example, in Asia. In the Middle Ages in Europe, there was generally a lack of any concept of literary property due to the general relations of production, the specific organization of literary production and the role of culture in society. The latter refers to the tendency of oral societies, such as that of Europe in the medieval period, to view knowledge as the product and expression of the collective, rather than to see it as individual property. However, with copyright laws, intellectual production comes to be seen as a product of an individual, with attendant rights. The most significant point is that patent and copyright laws support the expansion of the range of creative human activities that can be commodified. This parallels the ways in which capitalism led to the commodification of many aspects of social life that earlier had no monetary or economic value perse. Copyright has developed into a concept that has a significant effect on nearly every modern industry, including not just literary work, but also forms of creative work such as sound recordings, films, photographs, software, and architecture. === National copyrights === Often seen as the first real copyright law, the 1709 British Statute of Anne gave authors and the publishers to whom they did chose to license their works, the right to publish the author's creations for a fixed period, after which the copyright expired. It was "An Act for the Encouragement of Learning, by Vesting the Copies of Printed Books in the Authors or the Purchasers of such Copies, during the Times therein mentioned." The act also alluded to individual rights of the artist. It began: "Whereas Printers, Booksellers, and other Persons, have of late frequently taken the Liberty of Printing ... Books, and other Writings, without the Consent of the Authors ... to their very great Detriment, and too often to the Ruin of them and their Families:". A right to benefit financially from the work is articulated, and court rulings and legislation have recognized a right to control the work, such as ensuring that the integrity of it is preserved. An irrevocable right to be recognized as the work's creator appears in some countries' copyright laws. The Copyright Clause of the United States, Constitution (1787) authorized copyright legislation: "To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries." That is, by guaranteeing them a period of time in which they alone could profit from their works, they would be enabled and encouraged to invest the time required to create them, and this would be good for society as a whole. A right to profit from the work has been the philosophical underpinning for much legislation extending the duration of copyright, to the life of the creator and beyond, to their heirs. Yet scholars like Lawrence Lessig have argued that copyright terms have been extended beyond the scope imagined by the Framers. Lessig refers to the Copyright Clause as the "Progress Clause" to emphasize the social dimension of intellectual property rights. The original length of copyright in the United States was 14 years, and it had to be explicitly applied for. If the author wished, they could apply for a second 14‑year monopoly grant, but after that the work entered the public domain, so it could be used and built upon by others. === Continental law === In many jurisdictions of the European continent, comparable legal concepts to copyright did exist from the 16th century on but did change under Napoleonic rule into another legal concept: authors' rights or creator's right laws, from French: droits d'auteur and German Urheberrecht. In many modern-day publications the terms copyright and authors' rights are being mixed, or used as translations, but in a juridical sense the legal concepts do essentially differ. Authors' rights are, generally speaking,</p> <a href="https://bbs.aizhi.co/news/229c899762.html" class="read-more" title="Copyright">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/304e899687.html" class="card-thumb-link" title="Eduroam"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Resilience_Diagram.svg/960px-Resilience_Diagram.svg.png" alt="Eduroam" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/304e899687.html" title="Eduroam">Eduroam</a></h2> <p class="article-excerpt">eduroam (a portmanteau of education and roaming) is an international Wi-Fi internet access roaming service for users in research, higher education and further education. It provides researchers, teachers, and students network access when visiting an institution other than their own. Users are authenticated with credentials from their home institution, regardless of the location of the eduroam access point. Authorization to access the Internet and other resources are handled by the visited institution. Users do not have to pay to use eduroam. In some countries, Internet access via eduroam is also available at other locations than the participating institutions, e.g. in libraries, public buildings, railway stations, city centres and airports. It is also available at many primary and secondary education institutions in Brazil and the US. == History == The eduroam initiative started in 2002 when during the preparations for the creation of TERENA's task force TF-Mobility, Klaas Wierenga of SURFnet shared the idea of combining a RADIUS-based infrastructure with IEEE 802.1X technology to provide roaming network access across research and education networks. Initially, the service was joined by institutions in the Netherlands, Germany, Finland, Portugal, Croatia and the United Kingdom. Later, other NRENs in Europe embraced the idea and started joining the infrastructure, which was then called eduroam. Since 2004, the European Union co-funded further research and development work related to the eduroam service through the GN2 and GN3 projects. From September 2007, the European Union also funded through these projects the continued operation and maintenance of the eduroam service at the European level. The first non-European country to join eduroam was Australia, in December 2004. In Canada, eduroam started as an initiative of the University of British Columbia, which was later taken over by CANARIE as a service of its Canadian Access Federation. In the United States, eduroam was initially a pilot project between the National Science Foundation and the University of Tennessee (UTK). In 2012, Internet2 announced the addition of eduroam to its NET+ service offerings. AnyRoam LLC, a private company, was formed by former UTK staff to serve as an Internet2 active corporate member administering the US top-level servers. In 2021, Internet2 assumed direct management of the eduroam service for US-based organizations. == Technology == The eduroam service uses IEEE 802.1X as the authentication method and a hierarchical system of RADIUS servers. The hierarchy typically consists of RADIUS servers at the participating institutions, national RADIUS servers run by the National Roaming Operators, and regional top-level RADIUS servers for individual world regions. In some cases, institutions contact each other directly via DNS lookups () When a user visits a remote institution, the user's device presents their credentials to the local RADIUS server. That RADIUS server discovers that it is not responsible for the realm of the user's home institution and proxies the access request to another RADIUS server, typically the national RADIUS server. If the visited institution is in a different country than the home institution, the request is in turn proxied to the regional top-level RADIUS server, and then to the national RADIUS server of the user's home country. That national server forwards the credentials to the home institution, where they are verified. The RADIUS response travels back over the proxy-hierarchy to the visited institution and the user is granted access. In eduroam, the user credentials are always presented in the form of an EAP method (). The EAP method is responsible for ensuring that the users credentials are secure, and private. The users credentials can then travel via a number of intermediate servers, not under the control of the home institution of the user. This requirement limits the types of EAP methods that can be used. EAP methods which do not provide for security or privacy of user credentials cannot be used in eduroam. The most commonly used EAP methods in eduroam are EAP-TLS, PEAP, and EAP-TTLS. The methods used generally fall into two broad categories: those that use credentials in the form of some public-key mechanism with certificates and those that use so-called tunnelled authentication with "inner" passwords or other credentials. Most institutions use a tunnelled authentication method that requires a server certificate. These server certificates are used to set up a secure tunnel between the mobile device and the authentication server, through which the user credentials (e.g. name and password) are securely transported. A complication arises if the user's home institution does not use a two-letter country-code top-level domain as part of its realm, but a generic top-level domain such as .edu or .org. By inspection of such realms, it is not possible to determine which national RADIUS server the request should be routed to. Such domains will thus, by default, fail to work in international roaming. The workaround for this problem involves the creation of exceptions in the international RADIUS request routing tables; however, this workaround does not scale as the number of exception entries grows. Several solutions have been proposed to eliminate this workaround in the future, the most promising of which is RADIUS over TLS with Dynamic Discovery, which does not rely on static routing tables inside a RADIUS server configuration to route requests to their proper destination. Instead, the participating institution adds one NAPTR DNS resource record to its own domain's DNS zone, which states by which server eduroam authentication for the domain is handled. == Governance == GÉANT has established a lightweight global governance structure. Recognising the large variety in the organisation and funding of research and education (networking) in different countries and regions, rules imposed on the operations of eduroam are limited to technical and administrative requirements that are necessary to ensure the smooth and secure operations of eduroam worldwide. Moreover, the eduroam operators have the leading role in creating and maintaining the rules of the global eduroam governance. The Global eduroam Governance Committee (GeGC) has the central role in the global eduroam governance structure. While its structure has evolved over time, it presently has three representatives from each of five regions — mirroring those used by the Regional Internet registries — serving a two-year term. In addition, GÉANT may appoint one or more experts as non-voting members of the GeGC. == Geographical deployment == eduroam is available at selected locations in countries with a National Roaming Operator that has signed the eduroam Compliance Statement. Those sixty-seven countries are listed below. In addition, there may be pilot deployments in countries that are in the process of joining eduroam. === Middle East === eduroam is deployed in: === Europe === The NRENs that are members of the consortium of the GN3 project have joined the European eduroam confederation by signing the confederation's policy that requires its members to comply with a set of technical and organisational requirements, which are more specific than those in the global eduroam Compliance Statement. As a consequence, eduroam is deployed in the following countries: In addition, three NRENs that are associate members of the consortium of the GN3 project without voting rights joined the European eduroam confederation; they represent Belarus (UIIP), Moldova (RENAM) and Russia (Joint Supercomputer Center of the Russian Academy of Sciences). Finally, five NRENs not involved in the GN3 project joined the European eduroam confederation on a voluntary basis, enabling the deployment of the service in: The European top-level RADIUS servers are operated by SURFnet and Forskningsnettet. === Asia-Pacific === eduroam is deployed in the following countries and economies: The Asia-Pacific top-level RADIUS servers are operated by AARNet and by the University of Hong Kong. === North America === eduroam is deployed in: === Latin America === eduroam is deployed in: === Africa === eduroam is deployed in: The inter-African RADIUS servers are operated by West-African research and education network WACREN, the UbuntuNet Alliance and TENET.</p> <a href="https://bbs.aizhi.co/news/304e899687.html" class="read-more" title="Eduroam">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/292d899699.html" class="card-thumb-link" title="Data Reference Model"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/Face_Swap_Live_Logo.png/960px-Face_Swap_Live_Logo.png" alt="Data Reference Model" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/292d899699.html" title="Data Reference Model">Data Reference Model</a></h2> <p class="article-excerpt">The Data Reference Model (DRM) is one of the five reference models of the Federal Enterprise Architecture. == Overview == The DRM is a framework whose primary purpose is to enable information sharing and reuse across the United States federal government via the standard description and discovery of common data and the promotion of uniform data management practices. The DRM describes artifacts which can be generated from the data architectures of federal government agencies. The DRM provides a flexible and standards-based approach to accomplish its purpose. The scope of the DRM is broad, as it may be applied within a single agency, within a community of interest, or cross-community of interest. == Data Reference Model topics == === DRM structure === The DRM provides a standard means by which data may be described, categorized, and shared. These are reflected within each of the DRM's three standardization areas: Data Description: Provides a means to uniformly describe data, thereby supporting its discovery and sharing. Data Context: Facilitates discovery of data through an approach to the categorization of data according to taxonomies. Additionally, enables the definition of authoritative data assets within a community of interest. Data Sharing: Supports the access and exchange of data where access consists of ad hoc requests (such as a query of a data asset), and exchange consists of fixed, re-occurring transactions between parties. Enabled by capabilities provided by both the Data Context and Data Description standardization areas. === DRM Version 2 === The Data Reference Model version 2 released in November 2005 is a 114-page document with detailed architectural diagrams and an extensive glossary of terms. The DRM also make many references to ISO standards specifically the ISO/IEC 11179 metadata registry standard. === DRM usage === The DRM is not technically a published technical interoperability standard such as web services, it is an excellent starting point for data architects within federal and state agencies. Any federal or state agencies that are involved with exchanging information with other agencies or that are involved in data warehousing efforts should use this document as a guide.</p> <a href="https://bbs.aizhi.co/news/292d899699.html" class="read-more" title="Data Reference Model">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/412e399584.html" class="card-thumb-link" title="StatMuse"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/A_selfie_taken_by_a_man_using_an_%22I_love_Wikipedia%22_filter.jpg/960px-A_selfie_taken_by_a_man_using_an_%22I_love_Wikipedia%22_filter.jpg" alt="StatMuse" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/412e399584.html" title="StatMuse">StatMuse</a></h2> <p class="article-excerpt">StatMuse Inc. is an American artificial intelligence company founded in 2014. It operates an eponymous website that hosts a database of sports statistics covering the four major North American sports leagues, the Women's National Basketball Association (WNBA), NCAA Division I men's basketball, NCAA Division I Football Bowl Subdivision, the Big Five association football leagues in Europe, and various professional golf tours. == History == The company was founded by friends Adam Elmore and Eli Dawson in 2014. In email correspondence to the Springfield News-Leader, Elmore detailed that he and Dawson, fans of the National Basketball Association (NBA), were compelled to create StatMuse after they realized there was no online platform where they could search "Lebron James most points" [sic] and quickly get a result "showing his highest scoring games." As a startup, the company's goal was to utilize a type of artificial intelligence called natural language processing (NLP) for sports. In 2015, the company was part of the second group of startups accepted into the Disney Accelerator program. The company secured support from several investors, including The Walt Disney Company, Techstars, Allen & Company, the NFL Players Association, Greycroft and NBA Commissioner David Stern. As part of their partnership with Disney, StatMuse signed a content deal with ESPN (owned by Disney) to provide stats content on social media and television during the 2015–16 NBA season. Initially, the company only had stats available for the NBA, but eventually expanded to provide stats for the other major North American sports leagues. The company's initial demographic was players of fantasy sports, but it eventually expanded to target general sports fans as well. StatMuse offers responses to user queries in the voices of sports-related public figures. Dawson shared with VentureBeat that StatMuse brings people in and records them saying different words and phrases. These celebrity voices were made accessible through Google's Google Assistant service, Microsoft's Cortana virtual assistant, and Amazon's Echo devices. The company launched its phone app in September 2017. The app allows users to access StatMuse's sports statistics database by submitting queries in their natural language. Upon the launch of the phone app, Fitz Tepper of TechCrunch wrote that: "The technology isn't perfect – some of the pauses between words are a bit awkward, making it clear that some phrases are being stitched together on the fly. But this is the exception, and on the whole, most responses sound pretty good." StatMuse plug-ins for Slack and Facebook Messenger were also made, providing text-based sports stats. In 2019, StatMuse received investment from the Google Assistant Investment program. The service launched a premium option dubbed StatMuse+ in May 2023, offering options that had previously been included for free, such as unlimited searches and full results in data tables. The premium version also included early access to new features and a personalized search history, as well as not having ads. The app received a variety of feedback. In January 2024, the service launched a Premier League version of the website dubbed StatMuse FC. It is planned to introduce more leagues on the website.</p> <a href="https://bbs.aizhi.co/news/412e399584.html" class="read-more" title="StatMuse">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/324b899667.html" class="card-thumb-link" title="Visual cryptography"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Test_scores_of_AI_systems_on_various_capabilities_relative_to_human_performance_-_Our_World_in_Data.png/960px-Test_scores_of_AI_systems_on_various_capabilities_relative_to_human_performance_-_Our_World_in_Data.png" alt="Visual cryptography" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/324b899667.html" title="Visual cryptography">Visual cryptography</a></h2> <p class="article-excerpt">Visual cryptography is a cryptographic technique which allows visual information (pictures, text, etc.) to be encrypted in such a way that the decrypted information appears as a visual image. One of the best-known techniques has been credited to Moni Naor and Adi Shamir, who developed it in 1994. They demonstrated a visual secret sharing scheme, where a binary image was broken up into n shares so that only someone with all n shares could decrypt the image, while any n − 1 shares revealed no information about the original image. Each share was printed on a separate transparency, and decryption was performed by overlaying the shares. When all n shares were overlaid, the original image would appear. There are several generalizations of the basic scheme including k-out-of-n visual cryptography, and using opaque sheets but illuminating them by multiple sets of identical illumination patterns under the recording of only one single-pixel detector, which exposed the image. Using a similar idea, transparencies can be used to implement a one-time pad encryption, where one transparency is a shared random pad, and another transparency acts as the ciphertext. Normally, there is an expansion of space requirement in visual cryptography. But if one of the two shares is structured recursively, the efficiency of visual cryptography can be increased to 100%. Some antecedents of visual cryptography are in patents from the 1960s. Other antecedents are in the work on perception and secure communication. Visual cryptography can be used to protect biometric templates in which decryption does not require any complex computations. == Example == In this example, the binary image has been split into two component images. Each component image has a pair of pixels for every pixel in the original image. These pixel pairs are shaded black or white according to the following rule: if the original image pixel was black, the pixel pairs in the component images must be complementary; randomly shade one ■□, and the other □■. When these complementary pairs are overlapped, they will appear dark gray. On the other hand, if the original image pixel was white, the pixel pairs in the component images must match: both ■□ or both □■. When these matching pairs are overlapped, they will appear light gray. So, when the two component images are superimposed, the original image appears. However, without the other component, a component image reveals no information about the original image; it is indistinguishable from a random pattern of ■□ / □■ pairs. Moreover, if you have one component image, you can use the shading rules above to produce a counterfeit component image that combines with it to produce any image at all. == (2, n) visual cryptography sharing case == Sharing a secret with an arbitrary number of people, n, such that at least 2 of them are required to decode the secret is one form of the visual secret sharing scheme presented by Moni Naor and Adi Shamir in 1994. In this scheme we have a secret image which is encoded into n shares printed on transparencies. The shares appear random and contain no decipherable information about the underlying secret image, however if any 2 of the shares are stacked on top of one another the secret image becomes decipherable by the human eye. Every pixel from the secret image is encoded into multiple subpixels in each share image using a matrix to determine the color of the pixels. In the (2, n) case, a white pixel in the secret image is encoded using a matrix from the following set, where each row gives the subpixel pattern for one of the components: {all permutations of the columns of} : C 0 = [ 1 0 . . . 0 1 0 . . . 0 . . . 1 0 . . . 0 ] . {\displaystyle \mathbf {C_{0}=} {\begin{bmatrix}1&0&...&0\\1&0&...&0\\...\\1&0&...&0\end{bmatrix}}.} While a black pixel in the secret image is encoded using a matrix from the following set: {all permutations of the columns of} : C 1 = [ 1 0 . . . 0 0 1 . . . 0 . . . 0 0 . . . 1 ] . {\displaystyle \mathbf {C_{1}=} {\begin{bmatrix}1&0&...&0\\0&1&...&0\\...\\0&0&...&1\end{bmatrix}}.} For instance in the (2,2) sharing case (the secret is split into 2 shares and both shares are required to decode the secret) we use complementary matrices to share a black pixel and identical matrices to share a white pixel. Stacking the shares we have all the subpixels associated with the black pixel now black while 50% of the subpixels associated with the white pixel remain white. == Cheating the (2, n) visual secret sharing scheme == Horng et al. proposed a method that allows n − 1 colluding parties to cheat an honest party in visual cryptography. They take advantage of knowing the underlying distribution of the pixels in the shares to create new shares that combine with existing shares to form a new secret message of the cheaters choosing. We know that 2 shares are enough to decode the secret image using the human visual system. But examining two shares also gives some information about the 3rd share. For instance, colluding participants may examine their shares to determine when they both have black pixels and use that information to determine that another participant will also have a black pixel in that location. Knowing where black pixels exist in another party's share allows them to create a new share that will combine with the predicted share to form a new secret message. In this way a set of colluding parties that have enough shares to access the secret code can cheat other honest parties. == Visual steganography == 2×2 subpixels can also encode a binary image in each component image. For example, each white pixel of each component image could be represented by two black subpixels, while each black pixel represented by three black subpixels. When overlaid, each white pixel of the secret image is represented by three black subpixels, while each black pixel is represented by all four subpixels black. Each corresponding pixel in the component images is randomly rotated to avoid orientation leaking information about the secret image. == In popular culture == In "Do Not Forsake Me Oh My Darling", a 1967 episode of TV series The Prisoner, the protagonist uses a visual cryptography overlay of multiple transparencies to reveal a secret message – the location of a scientist friend who had gone into hiding.</p> <a href="https://bbs.aizhi.co/news/324b899667.html" class="read-more" title="Visual cryptography">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/news/65e899926.html" class="card-thumb-link" title="Sysomos"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ef/Veo_3_demo_Owl_and_Badger.webm/960px--Veo_3_demo_Owl_and_Badger.webm.jpg" alt="Sysomos" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/news/65e899926.html" title="Sysomos">Sysomos</a></h2> <p class="article-excerpt">Sysomos Inc. is a Toronto-based social media analytics company owned by Outside Insight market leaders Meltwater. The company developed text analytics and machine learning technologies for user generated content, and served 80% of the top agencies and Fortune 500. == History == Sysomos was founded by Nilesh Bansal and Nick Koudas. The company is a spinoff of the University of Toronto research project BlogScope. The BlogScope project, which started in 2005, resulted in creation of the underlying content aggregation and analysis engine commercialized by Sysomos. The company raised venture capital in 2008 and was acquired by Marketwire in 2010. The company's original flagship product, Media Analysis Platform (MAP), mines and analyzes content from social media or user-generated content to create a picture of media coverage. Sysomos launched its flagship offering MAP in Sept 2007, followed by addition of Heartbeat to its product suite in 2009. In addition to the two main products, the company released FourWhere, a free location-based social search service that mashes up Foursquare in March 2010. The company also offers Sysomos Heartbeat which provides social media monitoring and engagement capabilities to communication professionals, brand managers and customer support groups. In 2013, Heartbeat was extended to add publishing components to deliver a complete end-to-end social media marketing platform. On July 6, 2010, it was announced that Marketwire, a press release distribution company, had acquired Sysomos. After the acquisition, Sysomos founders Nick Koudas and Nilesh Bansal, left Sysomos to start Aislelabs. In February 2015, Sysomos split from Marketwired, as an independent company, and appointed Adnan Ahmed as the new CEO. In March 2015, newly independent Sysomos launched a redesign for its Heartbeat product and a new API for its MAP product. In the same year, the company acquired Expion. In September 2016, Peter Heffring was announced as the new CEO. In April 2017, Sysomos showcased a new unified platform offering new insights. In April 2018, media monitoring firm Meltwater announced it had acquired Sysomos. The CEO of Sysomos, Peter Heffring, said the company will continue to operate as an independent unit of Meltwater. Heffring will run the social analytics division of Meltwater. == Reports == Inside Twitter series of reports is the most extensive third-party survey on Twitter's growth and demographics. Another extensive survey regarding the top 5% of most active Twitter users found that over 25% of all tweets are machine created. The report also confirms Twitter's international growth. Inside Facebook Pages report found that only four percent of pages have more than 10,000 fans, 0.76% of pages have more than 100,000 fans, and 0.05% of pages (or 297 in total) have more than a million fans. Inside YouTube reports focus more on video hosting services and YouTube.</p> <a href="https://bbs.aizhi.co/news/65e899926.html" class="read-more" title="Sysomos">Read more →</a> </div> </article> </li> </ul> <nav class="pagination" aria-label="Pagination"> <a href="https://bbs.aizhi.co/aigrammarrephrase/27/" class="page-num">1</a><a href="https://bbs.aizhi.co/aigrammarrephrase/28/" class="page-num">2</a><a href="https://bbs.aizhi.co/aigrammarrephrase/29/" class="page-num">3</a><a href="https://bbs.aizhi.co/aigrammarrephrase/30/" class="page-num">4</a><a href="https://bbs.aizhi.co/aigrammarrephrase/31/" class="page-num">5</a><a href="https://bbs.aizhi.co/aigrammarrephrase/32/" class="page-num">6</a><a href="https://bbs.aizhi.co/aigrammarrephrase/33/" class="page-num">7</a><a href="https://bbs.aizhi.co/aigrammarrephrase/34/" class="page-num">8</a><a href="https://bbs.aizhi.co/aigrammarrephrase/35/" class="page-num">9</a><a href="https://bbs.aizhi.co/aigrammarrephrase/36/" class="page-num">10</a> </nav> </main> <aside class="sidebar"> <section class="sidebar-section"> <h2>All Categories</h2> <ul> <li><a href="https://bbs.aizhi.co/aicodingtools/">AI Coding Tools</a></li><li><a href="https://bbs.aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://bbs.aizhi.co/aivideotools/">AI Video Tools</a></li><li><a href="https://bbs.aizhi.co/aiwritingtools/">AI Writing Tools</a></li><li><a href="https://bbs.aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://bbs.aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://bbs.aizhi.co/ainewsandguides/">AI News and Guides</a></li> </ul> </section> <section class="sidebar-section"> <h2>Trending Guides</h2> <ul> <li><a href="https://bbs.aizhi.co/news/386a499609.html" title="Inferential theory of learning">Inferential theory of learning</a></li><li><a href="https://bbs.aizhi.co/news/306b899685.html" title="Hilscher netx network controller">Hilscher netx network controller</a></li><li><a href="https://bbs.aizhi.co/news/04d899987.html" title="Social media and identity">Social media and identity</a></li><li><a href="https://bbs.aizhi.co/news/449a899542.html" title="Branch number">Branch number</a></li><li><a href="https://bbs.aizhi.co/news/472f499523.html" title="Wadhwani Institute for Artificial Intelligence">Wadhwani Institute for Artificial Intelligence</a></li><li><a href="https://bbs.aizhi.co/news/221d899770.html" title="Cut, copy, and paste">Cut, copy, and paste</a></li><li><a href="https://bbs.aizhi.co/news/245f899746.html" title="Azuqua">Azuqua</a></li><li><a href="https://bbs.aizhi.co/news/91b899900.html" title="Social media reach">Social media reach</a></li><li><a href="https://bbs.aizhi.co/news/128f299869.html" title="Round-trip engineering">Round-trip engineering</a></li><li><a href="https://bbs.aizhi.co/news/457d899534.html" title="Batch cryptography">Batch cryptography</a></li> </ul> </section> </aside> </div> </div> </div> <footer class="site-footer"> <div class="container"> <div class="footer-cols"> <div class="footer-col footer-about"> <a class="brand" href="https://bbs.aizhi.co/" aria-label="Aizhi"> <span class="brand-mark" aria-hidden="true">✦</span> <span class="brand-text">Aizhi</span> </a> <p class="footer-tagline">Hand-picked AI tools, generators and practical how-to guides — independent reviews, updated for 2026.</p> </div> <nav class="footer-col" aria-label="Categories"> <h2 class="footer-h">Categories</h2> <ul> <li><a href="https://bbs.aizhi.co/ainewsandguides/">AI News and Guides</a></li><li><a href="https://bbs.aizhi.co/aicodingtools/">AI Coding Tools</a></li><li><a href="https://bbs.aizhi.co/aiimagegenerators/">AI Image Generators</a></li><li><a href="https://bbs.aizhi.co/aivideotools/">AI Video Tools</a></li><li><a href="https://bbs.aizhi.co/aiforbusiness/">AI for Business</a></li><li><a href="https://bbs.aizhi.co/aichatbotsandassistants/">AI Chatbots and Assistants</a></li><li><a href="https://bbs.aizhi.co/aiwritingtools/">AI Writing Tools</a></li> </ul> </nav> <nav class="footer-col" aria-label="Site"> <h2 class="footer-h">Site</h2> <ul> <li><a href="https://bbs.aizhi.co/">Home</a></li> <li><a href="/sitemap.xml">XML Sitemap</a></li> </ul> </nav> </div> <div class="partner-links" aria-label="Network"> </div> <p class="footer-copy"> © Aizhi. 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