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  Semantic folding

  Semantic folding theory describes a procedure for encoding the semantics of natural language text in a semantically grounded binary representation. This approach provides a framework for modelling how language data is processed by the neocortex. == Theory == Semantic folding theory draws inspiration from Douglas R. Hofstadter's Analogy as the Core of Cognition which suggests that the brain makes sense of the world by identifying and applying analogies. The theory hypothesises that semantic data must therefore be introduced to the neocortex in such a form as to allow the application of a similarity measure and offers, as a solution, the sparse binary vector employing a two-dimensional topographic semantic space as a distributional reference frame. The theory builds on the computational theory of the human cortex known as hierarchical temporal memory (HTM), and positions itself as a complementary theory for the representation of language semantics. A particular strength claimed by this approach is that the resulting binary representation enables complex semantic operations to be performed simply and efficiently at the most basic computational level. == Two-dimensional semantic space == Analogous to the structure of the neocortex, Semantic Folding theory posits the implementation of a semantic space as a two-dimensional grid. This grid is populated by context-vectors in such a way as to place similar context-vectors closer to each other, for instance, by using competitive learning principles. This vector space model is presented in the theory as an equivalence to the well known word space model described in the information retrieval literature. Given a semantic space (implemented as described above) a word-vector can be obtained for any given word Y by employing the following algorithm: For each position X in the semantic map (where X represents cartesian coordinates) if the word Y is contained in the context-vector at position X then add 1 to the corresponding position in the word-vector for Y else add 0 to the corresponding position in the word-vector for Y The result of this process will be a word-vector containing all the contexts in which the word Y appears and will therefore be representative of the semantics of that word in the semantic space. It can be seen that the resulting word-vector is also in a sparse distributed representation (SDR) format [Schütze, 1993] & [Sahlgreen, 2006]. Some properties of word-SDRs that are of particular interest with respect to computational semantics are: high noise resistance: As a result of similar contexts being placed closer together in the underlying map, word-SDRs are highly tolerant of false or shifted "bits". boolean logic: It is possible to manipulate word-SDRs in a meaningful way using boolean (OR, AND, exclusive-OR) and/or arithmetical (SUBtract) functions . sub-sampling: Word-SDRs can be sub-sampled to a high degree without any appreciable loss of semantic information. topological two-dimensional representation: The SDR representation maintains the topological distribution of the underlying map therefore words with similar meanings will have similar word-vectors. This suggests that a variety of measures can be applied to the calculation of semantic similarity, from a simple overlap of vector elements, to a range of distance measures such as: Euclidean distance, Hamming distance, Jaccard distance, cosine similarity, Levenshtein distance, Sørensen-Dice index, etc. == Semantic spaces == Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings). The application of semantic spaces in natural language processing (NLP) aims at overcoming limitations of rule-based or model-based approaches operating on the keyword level. The main drawback with these approaches is their brittleness, and the large manual effort required to create either rule-based NLP systems or training corpora for model learning. Rule-based and machine learning-based models are fixed on the keyword level and break down if the vocabulary differs from that defined in the rules or from the training material used for the statistical models. Research in semantic spaces dates back more than 20 years. In 1996, two papers were published that raised a lot of attention around the general idea of creating semantic spaces: latent semantic analysis from Microsoft and Hyperspace Analogue to Language from the University of California. However, their adoption was limited by the large computational effort required to construct and use those semantic spaces. A breakthrough with regard to the accuracy of modelling associative relations between words (e.g. "spider-web", "lighter-cigarette", as opposed to synonymous relations such as "whale-dolphin", "astronaut-driver") was achieved by explicit semantic analysis (ESA) in 2007. ESA was a novel (non-machine learning) based approach that represented words in the form of vectors with 100,000 dimensions (where each dimension represents an Article in Wikipedia). However practical applications of the approach are limited due to the large number of required dimensions in the vectors. More recently, advances in neural networking techniques in combination with other new approaches (tensors) led to a host of new recent developments: Word2vec from Google and GloVe from Stanford University. Semantic folding represents a novel, biologically inspired approach to semantic spaces where each word is represented as a sparse binary vector with 16,000 dimensions (a semantic fingerprint) in a 2D semantic map (the semantic universe). Sparse binary representation are advantageous in terms of computational efficiency, and allow for the storage of very large numbers of possible patterns. == Visualization == The topological distribution over a two-dimensional grid (outlined above) lends itself to a bitmap type visualization of the semantics of any word or text, where each active semantic feature can be displayed as e.g. a pixel. As can be seen in the images shown here, this representation allows for a direct visual comparison of the semantics of two (or more) linguistic items. Image 1 clearly demonstrates that the two disparate terms "dog" and "car" have, as expected, very obviously different semantics. Image 2 shows that only one of the meaning contexts of "jaguar", that of "Jaguar" the car, overlaps with the meaning of Porsche (indicating partial similarity). Other meaning contexts of "jaguar" e.g. "jaguar" the animal clearly have different non-overlapping contexts. The visualization of semantic similarity using Semantic Folding bears a strong resemblance to the fMRI images produced in a research study conducted by A.G. Huth et al., where it is claimed that words are grouped in the brain by meaning. voxels, little volume segments of the brain, were found to follow a pattern were semantic information is represented along the boundary of the visual cortex with visual and linguistic categories represented on posterior and anterior side respectively.

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  Cryptographic bill of materials

  Cryptographic bill of materials (CBOM—also cryptography bill of materials) is a structured inventory of all cryptographic assets present in a software, firmware, device, or system. It enumerates algorithms (and parameters such as key sizes and modes), cryptographic libraries or modules, digital certificates, keys and related material, and protocols in use, and maps their relationships to the components that implement or invoke them. CBOMs are used to improve security analysis, compliance, and cryptographic agility, and are increasingly referenced in guidance for post‑quantum cryptography (PQC) migration. == Definition and scope == A CBOM inventories cryptographic primitives and materials—such as encryption and signature algorithms (with specific variants and modes), key sizes, cryptographic libraries/modules, digital certificates (e.g., X.509), keys and other related cryptographic material, and security protocols (e.g., TLS, IPsec). It also documents dependencies (for example, an application uses an algorithm provided by a library; a protocol uses several algorithms) and can capture certificate lifecycles, cryptographic module certifications (e.g., FIPS 140‑3), and policy conformance metadata. In common practice, a CBOM may be embedded within an SBOM format (such as CycloneDX) or exported as a separate, linked artifact. === Typical CBOM fields === The exact schema varies by implementation, but common fields are summarized below (see CycloneDX CBOM guide and NIST SP 1800‑38B). == Relation to SBOM == A CBOM is complementary to, but distinct from, a software bill of materials (SBOM). Whereas an SBOM lists software components and their versions, a CBOM focuses specifically on the cryptography present and how it is configured and used. For example, an SBOM might enumerate inclusion of a library such as OpenSSL, while the CBOM would identify which algorithms and parameters that library enables (e.g., RSA‑2048, ECDH P‑256, AES‑GCM) and list relevant keys and certificates. The pairing enables both supply‑chain transparency and cryptographic transparency. == History == The term and practice emerged in the early–mid 2020s alongside software‑supply‑chain transparency and PQC planning. The OWASP CycloneDX standard introduced native CBOM support (v1.6 and later), modeling algorithms, keys, certificates, and protocols as first‑class “cryptographic assets” and providing dependency semantics (uses/implements) between software and cryptography. Open tooling from industry and researchers (e.g., IBM's CBOMkit and related generators/viewers) appeared to automate discovery and representation of cryptographic use in the CycloneDX CBOM schema. == Regulatory and policy context == In the United States, policy has emphasized cryptographic inventories as a prerequisite to PQC migration. The White House's National Security Memorandum 10 (2022) directed a government‑wide transition to quantum‑resistant cryptography; the Office of Management and Budget's M‑23‑02 (November 2022) operationalized this by requiring agencies to submit a prioritized inventory of cryptographic systems (with algorithm and key details) by 4 May 2023 and annually thereafter, and tasked CISA/NSA/NIST to develop automated discovery and inventory strategies. A 2024 Office of the National Cyber Director report reiterated that a “comprehensive cryptographic inventory” is the baseline for PQC planning and must be maintained iteratively with both automated and manual discovery. NIST's NCCoE practice guide (SP 1800‑38B, preliminary draft) provides concrete methods for cryptographic discovery and documentation across enterprises, aligning with CBOM‑style representations. CISA later published a strategy to migrate federal agencies to automated cryptography discovery and inventory tools to support continuous reporting. Separately, NSA, CISA, and NIST issued joint guidance encouraging all organisations to prepare cryptographic inventories and roadmaps for PQC, beyond government environments. == Role in quantum readiness and cryptographic agility == Because large‑scale quantum computing threatens widely used public‑key algorithms (e.g., RSA, ECC), organisations are planning multi‑year transitions to post-quantum cryptography. CBOMs enable that planning by identifying where quantum‑vulnerable algorithms appear, prioritising high‑impact systems, and tracking replacements over time. A machine‑readable CBOM also supports cryptographic agility and incident response: if an algorithm, library, or certificate lifecycle becomes non‑compliant or vulnerable, the CBOM indicates which products and systems are affected and where mitigations must be applied first. == Standards and tooling == CycloneDX (OWASP): Native CBOM modelling (v1.6+) for algorithms, certificates, keys/related material, and protocols, with dependency semantics and examples. The project publishes a CBOM guide and use‑case profiles (e.g., certificate and algorithm inventories). NIST NCCoE SP 1800‑38 series: Practice guides for PQC migration include enterprise cryptographic discovery methods that produce CBOM‑like inventories and integrate multiple discovery tools. Government automation initiatives: Following M‑23‑02, CISA issued a strategy to migrate to automated cryptography discovery and inventory tools to support agency reporting and continuous inventory management. Open‑source and vendor tools: IBM's CBOMkit and related components generate, analyse, and visualise CBOMs; the IBM CBOM specification work was upstreamed into CycloneDX 1.6. === Data model and interchange (example) === CycloneDX provides machine‑readable encodings (JSON/XML) for CBOM content. The example below (subset) shows an application depending on a crypto library that provides the AES‑256‑GCM algorithm, and the application also depends on a leaf X.509 certificate. See the CycloneDX CBOM guide, JSON reference, and the “Implementation details” use‑case for the semantics of `dependsOn` and `provides`. == Relationship to cybersecurity supply chain initiatives == CBOMs complement SBOM‑focused supply‑chain transparency introduced by U.S. Executive Order 14028 and NTIA/NIST SBOM work. SBOMs document software components; CBOMs add detail on embedded cryptography to support risk management, policy compliance (e.g., disallowing deprecated algorithms), and PQC transition planning.

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  Cloud Data Management Interface

  ISO/IEC 17826 Information technology — Cloud Data Management Interface (CDMI) Version 2.0.0 is an international standard that specifies a protocol for self-provisioning, administering and managing access to data stored in cloud storage, object storage, storage area network and network attached storage systems. The CDMI standard is developed and maintained by the Storage Networking Industry Association, who makes a publicly accessible version of the specification available. CDMI defines new resource representations to enable standardized management of any URI-accessible data, and defines RESTful HTTP operations using these representations to discover the capabilities of the storage system, discover stored data, access and update management metadata, specify data storage protocols (such as iSCSI and NFS) through which the stored data is accessed, and provide cross-system and cross-cloud import and export in order to enable data portability. Management functions enabled by CDMI include managing data ownership, identity mapping, access controls, user-specified metadata, and to declaratively specify desired data protection, data retention, constraints on geographic placement, desired quality of service, data versioning and security requirements. CDMI also defines utility services to facilitate data management, such the ability to query data matching specific criteria, and includes extensions to perform bulk updates using CDMI Jobs. == Capabilities == Compliant implementations must provide access to a set of configuration parameters known as capabilities. These are either boolean values that represent whether or not a system supports things such as queues, export via other protocols, path-based storage and so on, or numeric values expressing system limits, such as how much metadata may be placed on an object. As a minimal compliant implementation can be quite small, with few features, clients need to check the cloud storage system for a capability before attempting to use the functionality it represents. Resource allocation assignments limited to the data management interface protocols must possess access bypass capabilities which extend beyond the layered framework. This integral function is vital to the prevention of transport layer session hijacking by unauthorized entities which may circumvent standard interfacing security parameters. == Containers == A CDMI client may access objects, including containers, by either name or object id (OID), assuming the CDMI server supports both methods. When storing objects by name, it is natural to use nested named containers; the resulting structure corresponds exactly to a traditional filesystem directory structure. == Objects == Objects are similar to files in a traditional file system, but are enhanced with an increased amount and capacity for metadata. As with containers, they may be accessed by either name or OID. When accessed by name, clients use URLs that contain the full pathname of objects to create, read, update and delete them. When accessed by OID, the URL specifies an OID string in the cdmi-objectid container; this container presents a flat name space conformant with standard object storage system semantics. Subject to system limits, objects may be of any size or type and have arbitrary user-supplied metadata attached to them. Systems that support query allow arbitrary queries to be run against the metadata. == Domains, Users and Groups == CDMI supports the concept of a domain, similar in concept to a domain in the Windows Active Directory model. Users and groups created in a domain share a common administrative database and are known to each other on a "first name" basis, i.e. without reference to any other domain or system. Domains also function as containers for usage and billing summary data. == Access Control == CDMI exactly follows the ACL and ACE model used for file authorization operations by NFSv4. This makes it also compatible with Microsoft Windows systems. == Metadata == CDMI draws much of its metadata model from the XAM specification. Objects and containers have "storage system metadata", "data system metadata" and arbitrary user specified metadata, in addition to the metadata maintained by an ordinary filesystem (atime etc.). == Queries == CDMI specifies a way for systems to support arbitrary queries against CDMI containers, with a rich set of comparison operators, including support for regular expressions. == Queues == CDMI supports the concept of persistent FIFO (first-in, first-out) queues. These are useful for job scheduling, order processing and other tasks in which lists of things must be processed in order. == Compliance == Both retention intervals and retention holds are supported by CDMI. A retention interval consists of a start time and a retention period. During this time interval, objects are preserved as immutable and may not be deleted. A retention hold is usually placed on an object because of judicial action and has the same effect: objects may not be changed nor deleted until all holds placed on them are removed. == Billing == Summary information suitable for billing clients for on-demand services can be obtained by authorized users from systems that support it. == Serialization == Serialization of objects and containers allows export of all data and metadata on a system and importation of that data into another cloud system. == Foreign protocols == CDMI supports export of containers as NFS or CIFS shares. Clients that mount these shares see the container hierarchy as an ordinary filesystem directory hierarchy, and the objects in the containers as normal files. Metadata outside of ordinary filesystem metadata may or may not be exposed. Provisioning of iSCSI LUNs is also supported. == Client SDKs == CDMI Reference Implementation Droplet libcdmi-java libcdmi-python .NET SDK

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  Data recovery

  In computing, data recovery is a process of retrieving deleted, inaccessible, lost, corrupted, damaged, or overwritten data from secondary storage, removable media or files, when the data stored in them cannot be accessed in a usual way. The data is most often salvaged from storage media such as internal or external hard disk drives (HDDs), solid-state drives (SSDs), USB flash drives, magnetic tapes, CDs, DVDs, RAID subsystems, and other electronic devices. Recovery may be required due to physical damage to the storage devices or logical damage to the file system that prevents it from being mounted by the host operating system (OS). Logical failures occur when the hard drive devices are functional but the user or automated-OS cannot retrieve or access data stored on them. Logical failures can occur due to corruption of the engineering chip, lost partitions, firmware failure, or failures during formatting/re-installation. Data recovery can be a very simple or technical challenge. This is why there are specific software companies specialized in this field that help to get back data on your system. == About == The most common data recovery scenarios involve an operating system failure, malfunction of a storage device, logical failure of storage devices, accidental damage or deletion, etc. (typically, on a single-drive, single-partition, single-OS system), in which case the ultimate goal is simply to copy all important files from the damaged media to another new drive. This can be accomplished using a Live CD, or DVD by booting directly from a ROM or a USB drive instead of the corrupted drive in question. Many Live CDs or DVDs provide a means to mount the system drive and backup drives or removable media, and to move the files from the system drive to the backup media with a file manager or optical disc authoring software. Such cases can often be mitigated by disk partitioning and consistently storing valuable data files (or copies of them) on a different partition from the replaceable OS system files. Another scenario involves a drive-level failure, such as a compromised file system or drive partition, or a hard disk drive failure. In any of these cases, the data is not easily read from the media devices. Depending on the situation, solutions involve repairing the logical file system, partition table, or master boot record, or updating the firmware or drive recovery techniques ranging from software-based recovery of corrupted data, to hardware- and software-based recovery of damaged service areas (also known as the hard disk drive's "firmware"), to hardware replacement on a physically damaged drive which allows for the extraction of data to a new drive. If a drive recovery is necessary, the drive itself has typically failed permanently, and the focus is rather on a one-time recovery, salvaging whatever data can be read. In a third scenario, files have been accidentally "deleted" from a storage medium by the users. Typically, the contents of deleted files are not removed immediately from the physical drive; instead, references to them in the directory structure are removed, and thereafter space the deleted data occupy is made available for later data overwriting. In the mind of end users, deleted files cannot be discoverable through a standard file manager, but the deleted data still technically exists on the physical drive. In the meantime, the original file contents remain, often several disconnected fragments, and may be recoverable if not overwritten by other data files. The term "data recovery" is also used in the context of forensic applications or espionage, where data which have been encrypted, hidden, or deleted, rather than damaged, are recovered. Sometimes data present in the computer gets encrypted or hidden due to reasons like virus attacks which can only be recovered by some computer forensic experts. == Physical damage == A wide variety of failures can cause physical damage to storage media, which may result from human errors and natural disasters. CD-ROMs can have their metallic substrate or dye layer scratched off; hard disks can suffer from a multitude of mechanical failures, such as head crashes, PCB failure, and failed motors; tapes can simply break. Physical damage to a hard drive, even in cases where a head crash has occurred, does not necessarily mean permanent data loss. However, in extreme cases, such as prolonged exposure to moisture and corrosion —like the lost Bitcoin hard drive of James Howells, buried in the Newport landfill for over a decade — recovery is usually impossible. In rare cases, forensic techniques such as magnetic force microscopy (MFM) have been explored to detect residual magnetic traces when data holds exceptional value. Other techniques employed by many professional data recovery companies can typically salvage most, if not all, of the data that had been lost when the failure occurred. Of course, there are exceptions to this, such as cases where severe damage to the hard drive platters may have occurred. However, if the hard drive can be repaired and a full image or clone created, then the logical file structure can be rebuilt in most instances. Most physical damage cannot be repaired by end users. For example, opening a hard disk drive in a normal environment can allow airborne dust to settle on the platter and become caught between the platter and the read/write head. During normal operation, read/write heads float 3 to 6 nanometers above the platter surface, and the average dust particles found in a normal environment are typically around 30,000 nanometers in diameter. When these dust particles get caught between the read/write heads and the platter, they can cause new head crashes that further damage the platter and thus compromise the recovery process. Furthermore, end users generally do not have the hardware or technical expertise required to make these repairs. Consequently, data recovery companies are often employed to salvage important data with the more reputable ones using class 100 dust- and static-free cleanrooms. === Recovery techniques === Recovering data from physically damaged hardware can involve multiple techniques. Some damage can be repaired by replacing parts in the hard disk. This alone may make the disk usable, but there may still be logical damage. A specialized disk-imaging procedure is used to recover every readable bit from the surface. Once this image is acquired and saved on a reliable medium, the image can be safely analyzed for logical damage and will possibly allow much of the original file system to be reconstructed. ==== Hardware repair ==== A common misconception is that a damaged printed circuit board (PCB) may be simply replaced during recovery procedures by an identical PCB from a healthy drive. While this may work in rare circumstances on hard disk drives manufactured before 2003, it will not work on newer drives. Electronics boards of modern drives usually contain drive-specific adaptation data (generally a map of bad sectors and tuning parameters) and other information required to properly access data on the drive. Replacement boards often need this information to effectively recover all of the data. The replacement board may need to be reprogrammed. Some manufacturers (Seagate, for example) store this information on a serial EEPROM chip, which can be removed and transferred to the replacement board. Each hard disk drive has what is called a system area or service area; this portion of the drive, which is not directly accessible to the end user, usually contains drive's firmware and adaptive data that helps the drive operate within normal parameters. One function of the system area is to log defective sectors within the drive; essentially telling the drive where it can and cannot write data. The sector lists are also stored on various chips attached to the PCB, and they are unique to each hard disk drive. If the data on the PCB do not match what is stored on the platter, then the drive will not calibrate properly. In most cases the drive heads will click because they are unable to find the data matching what is stored on the PCB. == Logical damage == The term "logical damage" refers to situations in which the error is not a problem in the hardware and requires software-level solutions. === Corrupt partitions and file systems, media errors === In some cases, data on a hard disk drive can be unreadable due to damage to the partition table or file system, or to (intermittent) media errors. In the majority of these cases, at least a portion of the original data can be recovered by repairing the damaged partition table or file system using specialized data recovery software such as TestDisk; software like ddrescue can image media despite intermittent errors, and image raw data when there is partition table or file system damage. This type of data recovery can be performed by people without expertise in drive hardware as it requires no special physica

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  BeeSafe

  BeeSafe is a personal safety mobile app launched in 2015 as a Slovak startup. It is a location-based security service that notifies family members and friends in case the user of the app gets in danger. The app has received numerous awards. The app has more than 700 downloads and 250 active logins from more than 60 countries worldwide. == History == BeeSafe was founded on March 20, 2015 by Peter Stražovec and Michal Kačerík. The project was a winner of Žilina’s Startup Weekend 2013 and a StartupAwards.SK 2015 finalist. Later on, the app was released in the Android and iOS marketplace. The whole BeeSafe project was in The Spot booster and incubator in Bratislava for three months. BeeSafe entered into an agreement with the city of Piešťany in November 2015 to increase the security of its citizen by connecting the mobile app with the police platform. It is the first city that started using the BeeSafe platform. Further on, the application tries to help people in other Slovak cities. The cities can see the users only if they are in danger. == Awards == BeeSafe app received the Via Bona award, it is a winner of a Slovak startup and has other nominations too.

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  Pinoy baiting

  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.

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  Pamphlet war

  A pamphlet war is a protracted argument or discussion through printed media, especially between the time the printing press became common, and when state intervention like copyright laws made such public discourse more difficult. The purpose was to defend or attack a certain perspective or idea. Pamphlet wars have occurred multiple times throughout history, as both social and political platforms. Pamphlet wars became viable platforms for this protracted discussion with the advent and spread of the printing press. Cheap printing presses, and increased literacy made the late 17th century a key stepping stone for the development of pamphlet wars, a period of prolific use of this type of debate. Over 2200 pamphlets were published between 1600–1715 alone. Pamphlet wars are generally credited for powering many key social changes of the era, including the Reformation and the Revolution Controversy, the English philosophical debate set off by the French Revolution. == History of the pamphlet in England == Throughout Europe in the 16th century, printed tracts were used to argue religious doctrine and foment support for religious causes. In England, Henry VIII used print literature to justify his break from the Catholic Church. During the subsequent reigns of Edward and Mary, print polemics escalated into propaganda warfare, as print media gained enormous potential to sway common opinion. By the 1560s, print was widely used to convey news. In 1562, the first pamphlets appeared, which discussed the English forces sent to aid the Protestant French Huguenots. In 1569, pamphlets reported the revolt of the Northern Earls and the subsequent Rebellion of the same year. In the 1580s, pamphlets began to replace broadsheet ballads as the means to convey information to the general public. Over the next century, the pamphlet became the principal means of garnering support for a cause or an idea, and was particularly influential during the English Civil Wars (1642-1651) and the Glorious Revolution of 1688. Through the ensuing decades, the pamphlet lost some popularity due to the emergence of newspapers and journals, but continued to be an important medium of public debate, as illustrated by the Revolution Controversy a full century later in the 1790s. == Pamphlet printing == Coming from a Latin word, "pamphlet" literally means "small book." In the early days of printing, the format of the book or pamphlet depended on the size of the paper used and the number of times it was folded. If a page was only folded once, it was called a folio. If it was folded twice, it was known as a quarto. An octave was a paper folded three times. A pamphlet was usually 1-12 sheets of paper folded in quarto, or 8-96 pages. It was sold for one or two pennies apiece. The printing of a pamphlet involved many people: the author, the printer, suppliers, print-makers, compositor, correctors, pressmen, binders, and distributors. Once the pamphleteer had written the pamphlet, it was sent to the printing house to be corrected, set into type, and printed. The papers were then given to the printer's warehouse-keeper, who bundled the copies and sent them to the bookseller, who was probably the one financing the printing. He was responsible to bind the pamphlets, usually by sewing them, and then sold them wholesale to individual bookselling vendors. The booksellers then sold them from a stall in the marketplace. == Pamphlet subjects == Pamphlets began as the means of conveyance for religious debates, and therefore religious topics were one of the main subjects they dealt with. The definition of a pamphlet came to mean a short work dealing with social, political, or religious issues. Typical topics included the Civil war, Church of England doctrines, Acts of Parliament, the Popish Plot (see below), the Stuart Era, and Cromwell propaganda. In addition, pamphlets were also used for romantic fiction, autobiography, scurrilous personal abuse, and social criticism. They contained much of the propaganda of the 17th century in the midst of the religious and political turmoil. They were also used for debates between the Puritans and the Anglican. During the Glorious Revolution, pamphlets were political weapons. == Authors == There were many authors of pamphlets. However some of the more popular authors include Daniel Defoe, Thomas Hobbes, Jonathan Swift, John Milton, and Samuel Pepys. Also included in the midst are Thomas Nashe, Joseph Addison, Richard Steele, and Matthew Prior. In 1591–1592, Robert Greene released a series of pamphlets which later inspired many other authors including Thomas Middleton and Thomas Dekker. == Critics == Pamphlets, along with their vast popularity, received criticism. There were many in the time period who believed that pamphlets were full of foolishness. They thought the pamphlets were not good enough literature and that they would turn people from "good" writing. They believed that pamphlets would be the end of the great volumes of literature and that great writing would be forgotten. == News reporting == Pamphlets made a great difference in the way news was reported to the general public. With the publication of pamphlets, it was no longer difficult for people to hear of events taking place far away. The closer the occurrence was to London, the easier and faster people heard of it. For example, the Battle of Edgehill took place on 23 October 1642. The first pamphlet reporting the incident was printed on 25 October 24 hours after some of the orders reported had been given. While not entirely accurate, and hurriedly made, the pamphlet nonetheless was able to tell the general public what had happened in the battle. A more accurate, specific, and readable account was available in a pamphlet printed on 26 October, and the "authorized" version was available only five days after the battle took place. == Marprelate pamphlets == In 1588, a series of pamphlets marked a turning point for the Puritans, dividing them from other Protestants in the country. The authors wrote under the pseudonym of Martin Marprelate and his two sons of the same name. The true identities of the authors were never discovered. The pamphlets aimed to provoke authorities to take action against censorship. The series was among the first to ask questions directly of its readers. == Early pamphlet wars == === Elizabethan pamphlet wars === As a means of forming or swaying public opinion, pamphlets like these had a part in influencing society, even as the content was itself influenced by society. During the 16th century and continuing for a short while in the early 17th century in England there was rise in the use of pamphlet wars to discuss a myriad of issues spanning from the civil war, to religious freedoms and the roles of women in society. The Queen herself participated in these discussions, making sure that she was widely read and understood by her people in order to gain favour and establish herself as the monarch despite being a woman. Examples of her use of this medium appear in To the Troops at Tilbury written in 1588, On Mary's Execution written in 1586, and many more. Another famous writer of this period to take advantage of the pamphlet was Emilia Lanier, famous for her arguments about the role of women. A common idea promoted by many literary works and the general attitude towards women, Lanier's work "Eve's Apology in Defence of Women" refuted the belief that Eve is responsible for the fall of man. A very uncommon and unpopular stance to take, Lanier accomplishes her defence through structuring it as an apology, one of the earliest subversive feminist texts. Similarly, Francis Bacon wrote his Essays to promote his idea of morality and other complicated social issues. For example, his work, "Of Love" examines the various understandings of the concept of love, particularly as it was perceived during the Elizabethan era. === Eikon Series === From 1649 until 1651, some five pamphlets were published in a debate about the execution of King Charles I of England (1600-1649). Prior to his execution, King Charles wrote the first pamphlet in the discussion, Eikon Basilike’’ (from the Greek “eikon” for image and “basileus” for king). The subtitle of this work - Portraiture of His Sacred Majesty in His Solitudes and Sufferings - indicates that Charles sought to portray himself as a martyr to the cause of regal prerogative. In the following months, several response pamphlets were published (collectively known as the "Eikon" series), including: Eikon Alethine, Eikon e Pistes, Eikonoklastes, and Eikon Aklastos,” alternately attacking or defending the king, his regicide, and his self-portrait in “Eikon Basilike.” == Popish Plot and Elizabeth Cellier == In the 1680s, after being acquitted of the "Meal-Tub Plot" for which she was accused, Elizabeth Cellier wrote Malice Defeated, which, along with The Matchless Picaro, sparked a pamphlet war surrounding debate of the ascension of a Catholic king to the thro

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  Social media therapy

  Social media therapy is a form of expressive therapy. It uses the act of creating and sharing user-generated content as a way of connecting with and understanding people. Social media therapy combines different expressive therapy aspects of talk therapy, art therapy, writing therapy, and drama therapy and applies them to the web domain. Within social media therapy, synchronous or asynchronous dialogue occurs through exchanges of audio, text or visual information. The digital content is published online to serve as a form of therapy. == Background == Time spent online via email, websites, instant messaging and social media has increased: since 1999, more than 2,554 million people have become internet users. This alters the way people communicate with each other, and alters the connotation of certain words. The concepts of "identity", "friend", "like" and "connected" have adapted alongside technology. People are influenced by data sharing, social marketing, and technological tools. There are multiple therapeutic services offered through the internet. E-therapy, online counseling, cyber therapy, and social media therapy are similar in that each utilizes the internet in order to provide therapy for patients. == Controversy == There are pros and cons when it comes to the subject of online therapy. Criticism of providing therapy through online methods comes from concerns over the lack of physical contact. There are important features of therapy created through face-to-face therapy such as transference and countertransference that can not be created through online therapy. Patricia R. Recupero and Samara E. Rainey stated in their article "Informed Consent to E-Therapy" of American Journal of Psychotherapy that the lack of face-to-face interaction increased the risk of misdiagnosis and misunderstanding between the E-therapist and patient, thereby increasing the risk of uncertainty for the clinician. There are also concerns over the internet creating a distraction from the therapy itself. Confidentiality and privacy concerns have been raised as well. However, several systematic reviews have found that online psychotherapy can produce clinical outcomes comparable to face-to-face treatment, suggesting that physical distance does not inherently reduce therapeutic effectiveness.

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  Weight initialization

  In deep learning, weight initialization or parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training: weight initialization is the pre-training step of assigning initial values to these parameters. The choice of weight initialization method affects the speed of convergence, the scale of neural activation within the network, the scale of gradient signals during backpropagation, and the quality of the final model. Proper initialization is necessary for avoiding issues such as vanishing and exploding gradients and activation function saturation. Note that even though this article is titled "weight initialization", both weights and biases are used in a neural network as trainable parameters, so this article describes how both of these are initialized. Similarly, trainable parameters in convolutional neural networks (CNNs) are called kernels and biases, and this article also describes these. == Constant initialization == We discuss the main methods of initialization in the context of a multilayer perceptron (MLP). Specific strategies for initializing other network architectures are discussed in later sections. For an MLP, there are only two kinds of trainable parameters, called weights and biases. Each layer l {\displaystyle l} contains a weight matrix W ( l ) ∈ R n l − 1 × n l {\displaystyle W^{(l)}\in \mathbb {R} ^{n_{l-1}\times n_{l}}} and a bias vector b ( l ) ∈ R n l {\displaystyle b^{(l)}\in \mathbb {R} ^{n_{l}}} , where n l {\displaystyle n_{l}} is the number of neurons in that layer. A weight initialization method is an algorithm for setting the initial values for W ( l ) , b ( l ) {\displaystyle W^{(l)},b^{(l)}} for each layer l {\displaystyle l} . The simplest form is zero initialization: W ( l ) = 0 , b ( l ) = 0 {\displaystyle W^{(l)}=0,b^{(l)}=0} Zero initialization is usually used for initializing biases, but it is not used for initializing weights, as it leads to symmetry in the network, causing all neurons to learn the same features. In this page, we assume b = 0 {\displaystyle b=0} unless otherwise stated. Recurrent neural networks typically use activation functions with bounded range, such as sigmoid and tanh, since unbounded activation may cause exploding values. (Le, Jaitly, Hinton, 2015) suggested initializing weights in the recurrent parts of the network to identity and zero bias, similar to the idea of residual connections and LSTM with no forget gate. In most cases, the biases are initialized to zero, though some situations can use a nonzero initialization. For example, in multiplicative units, such as the forget gate of LSTM, the bias can be initialized to 1 to allow good gradient signal through the gate. For neurons with ReLU activation, one can initialize the bias to a small positive value like 0.1, so that the gradient is likely nonzero at initialization, avoiding the dying ReLU problem. == Random initialization == Random initialization means sampling the weights from a normal distribution or a uniform distribution, usually independently. === LeCun initialization === LeCun initialization, popularized in (LeCun et al., 1998), is designed to preserve the variance of neural activations during the forward pass. It samples each entry in W ( l ) {\displaystyle W^{(l)}} independently from a distribution with mean 0 and variance 1 / n l − 1 {\displaystyle 1/n_{l-1}} . For example, if the distribution is a continuous uniform distribution, then the distribution is U ( ± 3 / n l − 1 ) {\displaystyle {\mathcal {U}}(\pm {\sqrt {3/n_{l-1}}})} . === Glorot initialization === Glorot initialization (or Xavier initialization) was proposed by Xavier Glorot and Yoshua Bengio. It was designed as a compromise between two goals: to preserve activation variance during the forward pass and to preserve gradient variance during the backward pass. For uniform initialization, it samples each entry in W ( l ) {\displaystyle W^{(l)}} independently and identically from U ( ± 6 / ( n l + 1 + n l − 1 ) ) {\displaystyle {\mathcal {U}}(\pm {\sqrt {6/(n_{l+1}+n_{l-1})}})} . In the context, n l − 1 {\displaystyle n_{l-1}} is also called the "fan-in", and n l + 1 {\displaystyle n_{l+1}} the "fan-out". When the fan-in and fan-out are equal, then Glorot initialization is the same as LeCun initialization. === He initialization === As Glorot initialization performs poorly for ReLU activation, He initialization (or Kaiming initialization) was proposed by Kaiming He et al. for networks with ReLU activation. It samples each entry in W ( l ) {\displaystyle W^{(l)}} from N ( 0 , 2 / n l − 1 ) {\displaystyle {\mathcal {N}}(0,2/n_{l-1})} . === Orthogonal initialization === (Saxe et al. 2013) proposed orthogonal initialization: initializing weight matrices as uniformly random (according to the Haar measure) semi-orthogonal matrices, multiplied by a factor that depends on the activation function of the layer. It was designed so that if one initializes a deep linear network this way, then its training time until convergence is independent of depth. Sampling a uniformly random semi-orthogonal matrix can be done by initializing X {\displaystyle X} by IID sampling its entries from a standard normal distribution, then calculate ( X X ⊤ ) − 1 / 2 X {\displaystyle \left(XX^{\top }\right)^{-1/2}X} or its transpose, depending on whether X {\displaystyle X} is tall or wide. For CNN kernels with odd widths and heights, orthogonal initialization is done this way: initialize the central point by a semi-orthogonal matrix, and fill the other entries with zero. As an illustration, a kernel K {\displaystyle K} of shape 3 × 3 × c × c ′ {\displaystyle 3\times 3\times c\times c'} is initialized by filling K [ 2 , 2 , : , : ] {\displaystyle K[2,2,:,:]} with the entries of a random semi-orthogonal matrix of shape c × c ′ {\displaystyle c\times c'} , and the other entries with zero. (Balduzzi et al., 2017) used it with stride 1 and zero-padding. This is sometimes called the Orthogonal Delta initialization. Related to this approach, unitary initialization proposes to parameterize the weight matrices to be unitary matrices, with the result that at initialization they are random unitary matrices (and throughout training, they remain unitary). This is found to improve long-sequence modelling in LSTM. Orthogonal initialization has been generalized to layer-sequential unit-variance (LSUV) initialization. It is a data-dependent initialization method, and can be used in convolutional neural networks. It first initializes weights of each convolution or fully connected layer with orthonormal matrices. Then, proceeding from the first to the last layer, it runs a forward pass on a random minibatch, and divides the layer's weights by the standard deviation of its output, so that its output has variance approximately 1. === Fixup initialization === In 2015, the introduction of residual connections allowed very deep neural networks to be trained, much deeper than the ~20 layers of the previous state of the art (such as the VGG-19). Residual connections gave rise to their own weight initialization problems and strategies. These are sometimes called "normalization-free" methods, since using residual connection could stabilize the training of a deep neural network so much that normalizations become unnecessary. Fixup initialization is designed specifically for networks with residual connections and without batch normalization, as follows: Initialize the classification layer and the last layer of each residual branch to 0. Initialize every other layer using a standard method (such as He initialization), and scale only the weight layers inside residual branches by L − 1 2 m − 2 {\displaystyle L^{-{\frac {1}{2m-2}}}} . Add a scalar multiplier (initialized at 1) in every branch and a scalar bias (initialized at 0) before each convolution, linear, and element-wise activation layer. Similarly, T-Fixup initialization is designed for Transformers without layer normalization. === Others === Instead of initializing all weights with random values on the order of O ( 1 / n ) {\displaystyle O(1/{\sqrt {n}})} , sparse initialization initialized only a small subset of the weights with larger random values, and the other weights zero, so that the total variance is still on the order of O ( 1 ) {\displaystyle O(1)} . Random walk initialization was designed for MLP so that during backpropagation, the L2 norm of gradient at each layer performs an unbiased random walk as one moves from the last layer to the first. Looks linear initialization was designed to allow the neural network to behave like a deep linear network at initialization, since W R e L U ( x ) − W R e L U ( − x ) = W x {\displaystyle W\;\mathrm {ReLU} (x)-W\;\mathrm {ReLU} (-x)=Wx} . It initializes a matrix W {\displaystyle W} of shape R n 2 × m {\displaystyle \mathbb {R} ^{{\frac {n}{2}}\times m}} by any method, such as orthogonal initialization, t

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  Radical trust

  Radical trust is the confidence that any structured organization, such as a government, library, business, religion, or museum, has in collaboration and empowerment within online communities. Specifically, it pertains to the use of blogs, wiki and online social networking platforms by organizations to cultivate relationships with an online community that then can provide feedback and direction for the organization's interest. The organization 'trusts' and uses that input in its management. One of the first appearances of the notion of radical trust appears in an info graphic outlining the base principles of web 2.0 in Tim O'Reilly's weblog post "What is Web 2.0". Radical Trust is listed as the guiding example of trusting the validity of consumer generated media. This concept is considered to be an underlying assumption of Library 2.0. The adoption of radical trust by a library would require its management let go of some of its control over the library and building an organization without an end result in mind. The direction a library would take would be based on input provided by people through online communities. These changes in the organization may merely be anecdotal in nature, making this method of organization management dramatically distinct from data-based or evidence based management. In marketing, Collin Douma further describes the notion of radical trust as a key mindset required for marketers and advertisers to enter the social media marketing space. Conventional marketing dictates and maintains control of messages to cause the greatest persuasion in consumer decisions, but Douma argued that in the social media space, brands would need to cede that control in order to build brand loyalty.

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  Serge Belamant

  Serge Belamant (born 1953) is a French-born South African entrepreneur best known for designing the Universal Electronic Payment System (UEPS) and the Chip Offline Pre-authorised Card (COPAC). He founded the cash-payments company Net1 UEPS Technologies in 1989, led it through dual listings on the NASDAQ and the Johannesburg Stock Exchange, and oversaw the contentious welfare-payments contract with the South African Social Security Agency (SASSA) until his retirement in 2017. Since 2018 he has been non-executive chair of London-based buy-now-pay-later fintech Zilch. == Early life and education == Belamant moved from France to South Africa with his family in 1967 and matriculated from Highlands North Boys' High School, Johannesburg. In 1972 he entered the University of the Witwatersrand to study civil engineering but switched to computer science and applied mathematics in his second year. He left the university without a degree and later took short courses in information systems at the University of South Africa (UNISA). == Early career and SASWITCH (1981–1989) == Belamant worked for Control Data Corporation as a systems analyst for a decade before joining SASWITCH Ltd in 1985. Economic sanctions had left the consortium's national ATM network dependent on unsupported Christian Rovsing computers. Belamant led a rebuild on fault-tolerant Stratus hardware and wrote protocol-translation software that allowed fourteen banks to connect without altering their host systems. By 1988 SASWITCH was handling about three million ATM transactions a month, according to the Competition Commission. The switch—now run by BankservAfrica—remains the backbone of South Africa's shared ATM network. == Net1 UEPS Technologies (1989–2017) == === Founding and UEPS === In 1989, Serge Belamant developed the Universal Electronic Payment System (UEPS), enabling secure, real-time transactions even in areas with limited connectivity. In the same year, he founded NET1 UEPS Technologies Inc., serving as its CEO and Director. === COPAC for VISA === In 1995, VISA tasked Belamant with designing the Chip Offline Pre-authorized Card (COPAC), a technology still widely used in chip-enabled credit and debit cards. A year later, he listed his company APLITEC (Applied Technology Holdings Limited) on the Johannesburg Stock Exchange. === Listings and acquisitions === In 1999, Belamant acquired Cash Payment Services (CPS) from First National Bank of South Africa, modernizing its welfare payment system to serve millions in rural areas. In 2005, he led NET1 Technologies to an IPO, listing it as NET1 UEPS Technologies Inc. on the Nasdaq. A secondary listing on the Johannesburg Stock Exchange (JSE) followed in 2008. === SASSA contract === Under Belamant's leadership, NET1 managed welfare payments for the South African Social Security Agency (SASSA), handling payments for over 10 million beneficiaries monthly. Despite criticism over handling the SASSA contract, investigations by the U.S. Department of Justice and the South African Constitutional Court found no wrongdoing. == Zilch (2018–present) == Belamant co-founded London-based "buy-now-pay-later" firm Zilch Technology in 2018 and serves as non-executive chair. Zilch reported £145 million in annual-recurring revenue and 4.5 million customers in January 2025. == Patents == Belamant is listed as inventor on more than a dozen payment-security patents, including: "Funds transfer system" (US RE36,788, 2000) – the basis for UEPS. "Financial transactions with a varying PIN" (WO 2014/037869, 2014).

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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