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

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  My Drama

  My Drama (also may be stylised as MyDrama) is a global streaming service specializing in vertical video series for Duanju. It is owned by the company Holywater Tech. The platform focuses on short-form, emotional storytelling optimized for smartphone viewing, offering content in over 30 languages across 190 countries. == History == My Drama was launched in 2024 by Holywater Tech, founded by Ukrainian entrepreneur Bogdan Nesvit and Anatolii Kasianov. The service gained international traction as part of a growing market for short-form vertical storytelling, influenced by mobile-first entertainment trends. My Drama primarily streams serialized vertical dramas, which are short-form episodes around 1-2 minutes in length designed for mobile consumption. Many series are adaptations of successful stories originally published on Holywater Tech's book platform My Passion. The platform employs AI technology in areas such as content recommendation and story generation, and is one of several Holywater apps focused on interactive entertainment. In 2024, My Drama won a People's Voice award at the 28th Annual Webby Awards. In 2025, My Drama received a Gold Award at the MUSE Creative Awards in the Mobile App: Video Streaming Services category. In 2025, the company received strategic investment from Fox Entertainment, aimed at expanding content creation capabilities and producing over 200 vertical video series. As of 2025, My Drama has produced over 56 titles and reached more than 40 million lifetime users, according to media reports. In January 2026, Holywater Tech raised $22 million in funding to expand its microdrama business in the United States. The investment round was led by Horizon Capital, with participation from U.S.-based investors including Endeavor Catalyst and Wheelhouse. The funding is intended to support the development of Holywater Tech's mobile-first vertical video platform, My Drama, as well as the company's AI-driven content initiatives, such as AI-assisted comics and anime. In February 2026, Holywater bought Jeynix, a studio that uses AI for special effects. This deal helps the company make better-quality shows and translate them into different languages much faster. == Partnerships == In 2024, Holywater Tech entered a partnership with Latin American studio Elefantec Global to distribute vertical dramas in Spanish-language markets. In early 2026, Fox Entertainment entered into a partnership with content creator Dhar Mann to produce a slate of 40 original vertical microdrama series. Under the agreement, the series debut exclusively on the My Drama platform, while global distribution is managed by Fox Entertainment Global. == Reception == My Drama has been highlighted in discussions of the global rise of vertical short drama platforms and has been compared with similar apps such as ReelShort and DramaBox.

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  Chunked transfer encoding

  Chunked transfer encoding is a streaming data transfer mechanism available in Hypertext Transfer Protocol (HTTP) version 1.1, defined in RFC 9112 §7.1. In chunked transfer encoding, the data stream is divided into a series of non-overlapping "chunks". The chunks are sent out and received independently of one another. At any given time, no knowledge of the data stream outside the currently-being-processed chunk is necessary for either the sender or the receiver. Each chunk is preceded by its size in bytes and transmission ends when a zero-length chunk is received. The chunked keyword in the Transfer-Encoding header is used to indicate chunked transfer. Chunked transfer encoding is not supported in HTTP/2, which provides its own mechanisms for data streaming. == Rationale == The introduction of chunked encoding provided various benefits: Chunked transfer encoding allows a server to maintain an HTTP persistent connection for dynamically generated content. In this case, the HTTP Content-Length header cannot be used to delimit the content and the next HTTP request/response, as the content size is not yet known. Chunked encoding has the benefit that it is not necessary to generate the full content before writing the header, as it allows streaming of content as chunks and explicitly signaling the end of the content, making the connection available for the next HTTP request/response. Chunked encoding allows the sender to send additional header fields after the message body. This is important in cases where values of a field cannot be known until the content has been produced, such as when the content of the message must be digitally signed. Without chunked encoding, the sender would have to buffer the content until it was complete in order to calculate a field value and send it before the content. == Applicability == For version 1.1 of the HTTP protocol, the chunked transfer mechanism is considered to be always and anyway acceptable, even if not listed in the Transfer-Encoding (TE) request header field, and when used with other transfer mechanisms, should always be applied last to the transferred data and never more than one time. This transfer encoding method also allows additional entity header fields to be sent after the last chunk if the client specified the "trailers" parameter as an argument of the TE request field. The origin server of the response can also decide to send additional entity trailers even if the client did not specify the "trailers" parameter, but only if the metadata is optional (i.e. the client can use the received entity without them). Whenever the trailers are used, the server should list their names in the Trailer header field; three header field types are specifically prohibited from appearing as a trailer field: Content-Length, Trailer, and Transfer-Encoding. == Format == If a Transfer-Encoding field with a value of "chunked" is specified in an HTTP message (either a request sent by a client or the response from the server), the body of the message consists of one or more chunks and one terminating chunk with an optional trailer before the final ␍␊ sequence (i.e. carriage return followed by line feed). Each chunk starts with the number of octets of the data it embeds expressed as a hexadecimal number in ASCII followed by optional parameters (chunk extension) and a terminating ␍␊ sequence, followed by the chunk data. The chunk is terminated by ␍␊. If chunk extensions are provided, the chunk size is terminated by a semicolon and followed by the parameters, each also delimited by semicolons. Each parameter is encoded as an extension name followed by an optional equal sign and value. These parameters could be used for a running message digest or digital signature, or to indicate an estimated transfer progress, for instance. The terminating chunk is a special chunk of zero length. It may contain a trailer, which consists of a (possibly empty) sequence of entity header fields. Normally, such header fields would be sent in the message's header; however, it may be more efficient to determine them after processing the entire message entity. In that case, it is useful to send those headers in the trailer. Header fields that regulate the use of trailers are Transfer-Encoding with the "trailers" parameter (used in requests) and Trailer (used in responses). == Use with compression == HTTP servers often use compression to optimize transmission, for example with Content-Encoding: gzip or Content-Encoding: deflate. If both compression and chunked encoding are enabled, then the content stream is first compressed, then chunked; so the chunk encoding itself is not compressed, and the data in each chunk is compressed holistically (i.e. based on the whole content). The remote endpoint then decodes the stream by concatenating the chunks and uncompressing the result. == Example == === Encoded data === The following example contains three chunks of size 4, 7, and 11 (hexadecimal "B") octets of data. 4␍␊Wiki␍␊7␍␊pedia i␍␊B␍␊n ␍␊chunks.␍␊0␍␊␍␊ Below is an annotated version of the encoded data. 4␍␊ (chunk size is four octets) Wiki (four octets of data) ␍␊ (end of chunk) 7␍␊ (chunk size is seven octets) pedia i (seven octets of data) ␍␊ (end of chunk) B␍␊ (chunk size is eleven octets) n ␍␊chunks. (eleven octets of data) ␍␊ (end of chunk) 0␍␊ (chunk size is zero octets, no more chunks) ␍␊ (end of final chunk with zero data octets) Note: Each chunk's size excludes the two ␍␊ bytes that terminate the data of each chunk. === Decoded data === Decoding the above example produces the following octets: Wikipedia in ␍␊chunks. The bytes above are typically displayed as Wikipedia in chunks.

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  Atomicity (database systems)

  In database systems, atomicity (; from Ancient Greek: ἄτομος, romanized: átomos, lit. 'undividable') is the property of a database transaction consisting of an indivisible and irreducible series of database operations such that either all occur, or none occur. It is one of the ACID transaction properties: Atomicity, Consistency, Isolation, Durability. A guarantee of atomicity prevents partial database updates from occurring, because they can cause greater problems than rejecting the whole series outright. As a consequence, an atomic transaction cannot be observed to be in progress by another database client: at one moment in time, it has not yet happened, and at the next it has already occurred in whole (or nothing happened if the transaction was cancelled in progress). An example of transaction atomicity could be a digital monetary transfer from bank account A to account B. It consists of two operations, debiting the money from account A and crediting it to account B. Performing both of these operations inside of an atomic transaction ensures that the database remains in a consistent state, if either operation fails there will not be any unaccountable credits or debits affecting either account. The same term is also used in the definition of First normal form in database systems, where it instead refers to the concept that the values for fields may not consist of multiple smaller values to be decomposed, such as a string into which multiple names, numbers, dates, or other types may be packed. == Orthogonality == Atomicity does not behave completely orthogonally with regard to the other ACID properties of transactions. For example, isolation relies on atomicity to roll back the enclosing transaction in the event of an isolation violation such as a deadlock; consistency also relies on atomicity to roll back the enclosing transaction in the event of a consistency violation by an illegal transaction. As a result of this, a failure to detect a violation and roll back the enclosing transaction may cause an isolation or consistency failure. == Implementation == Typically, systems implement Atomicity by providing some mechanism to indicate which transactions have started and which finished; or by keeping a copy of the data before any changes occurred (Read-copy-update). Several filesystems have developed methods for avoiding the need to keep multiple copies of data, using journaling (see journaling file system). Databases usually implement this using some form of logging/journaling to track changes. The system synchronizes the logs (often the metadata) as necessary after changes have successfully taken place. Afterwards, crash recovery ignores incomplete entries. Although implementations vary depending on factors such as concurrency issues, the principle of atomicity – i.e. complete success or complete failure – remain. Ultimately, any application-level implementation relies on operating-system functionality. At the file-system level, POSIX-compliant systems provide system calls such as open(2) and flock(2) that allow applications to atomically open or lock a file. At the process level, POSIX Threads provide adequate synchronization primitives. The hardware level requires atomic operations such as Test-and-set, Fetch-and-add, Compare-and-swap, or Load-Link/Store-Conditional, together with memory barriers. Portable operating systems cannot simply block interrupts to implement synchronization, since hardware that lacks concurrent execution such as hyper-threading or multi-processing is now extremely rare. In distributed and sharded databases, atomicity is complicated by network latency and the potential for partial failures. While traditional distributed systems often employ locking protocols (like 2PC) to ensure cross-shard atomicity, these can introduce performance bottlenecks. Recent research into distributed ledger consensus suggests alternative models, such as "braided synchronization". This technique, utilized in protocols like Cerberus, intertwines the consensus phases of multiple shards to enforce atomic guarantees without a global ordering of all transactions.

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  G.hn

  Gigabit Home Networking (G.hn) is a specification for wired home networking that supports speeds up to 2 Gbit/s and operates over four types of legacy wires: telephone wiring, coaxial cables, power lines and plastic optical fiber. Some benefits of a multi-wire standard are lower equipment development costs and lower deployment costs for service providers (by allowing customer self-install). == History == G.hn was developed under the International Telecommunication Union's Telecommunication Standardization sector (the ITU-T) and promoted by the HomeGrid Forum and several other organizations. ITU-T Recommendation (the ITU's term for standard) G.9960, which received approval on October 9, 2009, specified the physical layers and the architecture of G.hn. The Data Link Layer (Recommendation G.9961) was approved on June 11, 2010. Prominent organizations, including CEPca, HomePNA, and UPA, who were creators of some of these interfaces, rallied behind the latest version of the standard, emphasizing its potential and significance in the home networking domain. Moreover, the ITU-T extended the technology with multiple input, multiple output (MIMO) technology to increase data rates and signaling distance. This new feature was approved in March 2012 under G.9963 Recommendation. The development and promotion of G.hn have been significantly supported by the HomeGrid Forum and several other organizations. The technology was not only designed to address home-networking challenges but also found applications beyond this initial scope, showcasing its versatility and potential in the networking domain. == Technical specifications == === Technical overview === G.hn specifies a single physical layer based on fast Fourier transform (FFT) orthogonal frequency-division multiplexing (OFDM) modulation and low-density parity-check code (LDPC) forward error correction (FEC) code. G.hn includes the capability to notch specific frequency bands to avoid interference with amateur radio bands and other licensed radio services. G.hn includes mechanisms to avoid interference with legacy home networking technologies and also with other wireline systems such as VDSL2 or other types of DSL used to access the home. OFDM systems split the transmitted signal into multiple orthogonal sub-carriers. In G.hn each one of the sub-carriers is modulated using QAM. The maximum QAM constellation supported by G.hn is 4096-QAM (12-bit QAM). The G.hn media access control is based on a time division multiple access (TDMA) architecture, in which a "domain master" schedules Transmission Opportunities (TXOPs) that can be used by one or more devices in the "domain". There are two types of TXOPs: Contention-Free Transmission Opportunities (CFTXOP), which have a fixed duration and are allocated to a specific pair of transmitter and receiver. CFTXOP are used for implementing TDMA Channel Access for specific applications that require quality of service (QoS) guarantees. Shared Transmission Opportunities (STXOP), which are shared among multiple devices in the network. STXOP are divided into Time Slots (TS). There are two types of TS: Contention-Free Time Slots (CFTS), which are used for implementing "implicit" token passing Channel Access. In G.hn, a series of consecutive CFTS is allocated to a number of devices. The allocation is performed by the "domain master" and broadcast to all nodes in the network. There are pre-defined rules that specify which device can transmit after another device has finished using the channel. As all devices know "who is next", there is no need to explicitly send a "token" between devices. The process of "passing the token" is implicit and ensures that there are no collisions during Channel access. Contention-Based Time Slots (CBTS), which are used for implementing CSMA/CARP Channel Access. In general, CSMA systems cannot completely avoid collisions, so CBTS are only useful for applications that do not have strict Quality of Service requirements. ==== Optimization for each medium ==== Although most elements of G.hn are common for all three media supported by the standard (power lines, phone lines and coaxial cable), G.hn includes media-specific optimizations for each media. Some of these media-specific parameters include: OFDM Carrier Spacing: 195.31 kHz in coaxial, 48.82 kHz in phone lines, 24.41 kHz in power lines. FEC Rates: G.hn's FEC can operate with code rates 1/2, 2/3, 5/6, 16/18 and 20/21. Although these rates are not media specific, it is expected that the higher code rates will be used in cleaner media (such as coaxial) while the lower code rates will be used in noisy environments such as power lines. Automatic repeat request (ARQ) mechanisms: G.hn supports operation both with and without ARQ (re-transmission). Although this is not media specific, it is expected that ARQ-less operation is sometimes appropriate for cleaner media (such as coaxial) while ARQ operation is appropriate for noisy environments such as power lines. Power levels and frequency bands: G.hn defines different power masks for each medium. MIMO support: Recommendation G.9963 includes provisions for transmitting G.hn signals over multiple AC wires (phase, neutral, ground), if they are physically available. In July 2016, G.9963 was updated to include MIMO support over twisted pairs. ==== Security ==== G.hn uses the Advanced Encryption Standard (AES) encryption algorithm (with a 128-bit key length) using the CCMP protocol to ensure confidentiality and message integrity. Authentication and key exchange is done following ITU-T Recommendation X.1035. G.hn specifies point-to-point security inside a domain, which means that each pair of transmitter and receiver uses a unique encryption key which is not shared by other devices in the same domain. For example, if node Alice sends data to node Bob, node Eve (in the same domain as Alice and Bob) will not be able to easily eavesdrop their communication. G.hn supports the concept of relays, in which one device can receive a message from one node and deliver it to another node farther away in the same domain. Relaying becomes critical for applications with complex network topologies that need to cover large distances, such as those found in industrial or utility applications. While a relay can read the source and target addresses, it cannot read the message's content due to its body being end-to-end-encrypted. ==== Profiles ==== The G.hn architecture includes the concept of profiles. Profiles are intended to address G.hn nodes with significantly different levels of complexity. In G.hn the higher complexity profiles are proper supersets of lower complexity profiles, so that devices based on different profiles can interoperate with each other. Examples of G.hn devices based on high complexity profiles are Residential Gateways or Set-Top Boxes. Examples of G.hn devices based on low complexity profiles are home automation, home security and smart grid devices. ==== Technical parameters ==== The chart depicts a summary of the crucial technical specifications of the G.hn standard. Many of these technical elements are consistent across different physical media, with variations seen in areas such as Tone Spacing and frequency ranges. This uniformity is essential as it allows silicon manufacturers to produce a singular chip capable of implementing all three media types, leading to cost savings. Presently, G.hn chipsets are compatible with all three media types. This compatibility allows system manufacturers to create devices that can adjust to any wiring type simply by modifying a software configuration in the equipment. === Spectrum === The G.hn spectrum depends on the medium as shown in the diagram below: === Protocol stack === G.hn specifies the physical layer and the data link layer, according to the OSI model. The G.hn Data Link Layer (Recommendation G.9961) is divided into three sub-layers: The Application Protocol Convergence (APC) Layer, which accepts frames (usually in Ethernet format) from the upper layer (Application Entity) and encapsulates them into G.hn APC protocol data units (APDUs). The maximum payload of each APDU is 214 bytes. The logical link control (LLC), which is responsible for encryption, aggregation, segmentation and automatic repeat-request. This sub-layer is also responsible for "relaying" of APDUs between nodes that may not be able to communicate through a direct connection. The medium access control (MAC), which schedules channel access. The G.hn physical layer (Recommendation G.9960) is divided into three sub-layers: The Physical Coding Sub-layer (PCS), responsible for generating PHY headers. The Physical Medium Attachment (PMA), responsible for scrambling and forward error correction coding/decoding. The Physical Medium Dependent (PMD), responsible for bit-loading and OFDM modulation. The interface between the Application Entity and the Data Link Layer is called A-interface. The interface between the Data Link Layer and the ph

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  Artificial intelligence in education

  Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias. == History == Efforts to integrate AI into educational contexts have often followed technological advancement in the history of artificial intelligence. In the 1960s, educators and researchers began developing computer-based instruction systems, such as PLATO, developed by the University of Illinois. In the 1970s and 1980s, intelligent tutoring systems (ITS) were being adapted for classroom instruction. The International Artificial Intelligence in Education Society was founded in 1993. Coinciding with the AI boom of the 2020s, the use of large language models in the global north has been promoted and funded by venture capital and big tech. Companies creating AI services have targeted students and educational institutions as customers. Similarly, pre-AI boom educational companies have expanded their use of AI technologies. These commercial incentives for AIEd use may be related to a potential AI bubble. In the U.S., bipartisan support of AI development in K-12 education has been expressed, but specific implementations and best practices remain contentious. == Theory == AIEd applies theory from education studies, machine learning, and related fields. A 2019 review of the previous decade of studies found that most research prioritized technological design over pedagogical integration. Ouyang and Jiao (2021) propose three paradigms for AI in education, which follow roughly from least to most learner-centered and from requiring least to most technical complexity from the AI systems: AI-directed, learner-as-recipient: AIEd systems present a pre-set curriculum based on statistical patterns that do not adjust to learner's feedback. AI-supported, learner-as-collaborator: Systems that incorporate responsiveness to learner's feedback through, for example, natural language processing, wherein AI can support knowledge construction. AI-empowered, learner-as-leader: This model seeks to position AI as a supplement to human intelligence wherein learners take agency and AI provides consistent and actionable feedback. Some scholars place AI in education within a socio-technical framework. This positions AI alongside other emerging educational technologies, such as computing, the internet, and social media. The framework of Tsao, Heinrichs and Camit (2025) draws on new materialism and posthumanism, specifically Donna Haraway's concept of sympoiesis (making-with). This perspective views learning as an entanglement of human and non-human actors (students, teachers, and AI algorithms), where knowledge is co-composed in contact zones between human context and algorithmic prediction. AI agents have been trained on biased datasets, and thus continue to perpetuate societal biases. Since LLMs were created to produce human-like text, algorithmic bias can be introduced and reproduced. AI's data processing and monitoring reinforce neoliberal approaches to education rather than addressing inequalities. == Applications == Uses of generative AI chatbots in education have included assessment and feedback, machine translations, proof-reading exam question generation and copy editing, or as virtual assistants. Emotional AI in education is the study and development of systems that can detect learners' emotions or provide emotional support in learning. == Usage == === Schools and educators === Following the release of ChatGPT in November 2022, some schools and large school districts blocked access to the site and issued warnings that the use of such tools would be seen as cheating. Governmental and non-governmental organizations such as UNESCO, Article 4 of the European Union's AI Act, and the U.S. Department of Education have published reports advocating for specific AIEd approaches. National higher-education bodies have also published guidance on generative AI, including Ireland's Higher Education Authority, which issued a policy framework for higher education teaching and learning in December 2025. In 2024, UNESCO released updated global guidance for generative AI in education, emphasizing ethical use, teacher training, and data protection to ensure responsible integration of AI tools in learning environments. According to Taso (2025), policy implementation in higher education is interpreted and enacted differently by various organizations. These decentralized policies can lead to inconsistent enforcement and confusion among students regarding what constitutes acceptable use, with the burden of ethical navigation falling on individual teachers and students. AI integration in classrooms has created new forms of invisible labour for educators, who must navigate ambiguous policies, redesign assessments to be AI-resilient, and adjudicate potential academic integrity violations. The use of AI detection tools has also been criticised for creating an adversarial relationship between students and institutions, where students may be falsely accused of misconduct based on probabilistic software. AIEd advocates say that efforts should be made towards increasing global accessibility and training educators to serve underprivileged areas. === Students === Reliance on generative AI has been linked with reduced academic self-esteem and performance, and heightened learned helplessness. Algorithm errors and hallucinations are common flaws in AI agents, making them less trustworthy and reliable. According to a 2025 survey from Inside Higher Ed, 85% of higher education students use generative AI technology in some way, with 25% using AI to complete assignments for them. The most common reason cited for using AI to cheat was pressure to get high grades. 97% of students wanted some form of action from schools on the threat to academic integrity caused by AI, with the most popular options being clearer policies and more education about ethical uses of AI. In September 2025, The Atlantic published an op-ed from a high school senior arguing that the normalization of AI cheating was eroding critical thinking, academic integrity, creativity, and the shared student experience.

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  Code (cryptography)

  In cryptology, a code is a method used to encrypt a message that operates at the level of meaning; that is, words or phrases are converted into something else. A code might transform "change" into "CVGDK" or "cocktail lounge". The U.S. National Security Agency defined a code as "A substitution cryptosystem in which the plaintext elements are primarily words, phrases, or sentences, and the code equivalents (called "code groups") typically consist of letters or digits (or both) in otherwise meaningless combinations of identical length." A codebook is needed to encrypt, and decrypt the phrases or words. By contrast, ciphers encrypt messages at the level of individual letters, or small groups of letters, or even, in modern ciphers, individual bits. Messages can be transformed first by a code, and then by a cipher. Such multiple encryption, or "superencryption" aims to make cryptanalysis more difficult. Another comparison between codes and ciphers is that a code typically represents a letter or groups of letters directly without the use of mathematics. As such the numbers are configured to represent these three values: 1001 = A, 1002 = B, 1003 = C, ... . The resulting message, then would be 1001 1002 1003 to communicate ABC. Ciphers, however, utilize a mathematical formula to represent letters or groups of letters. For example, A = 1, B = 2, C = 3, ... . Thus the message ABC results by multiplying each letter's value by 13. The message ABC, then would be 13 26 39. Codes have a variety of drawbacks, including susceptibility to cryptanalysis and the difficulty of managing the cumbersome codebooks, so ciphers are now the dominant technique in modern cryptography. In contrast, because codes are representational, they are not susceptible to mathematical analysis of the individual codebook elements. In the example, the message 13 26 39 can be cracked by dividing each number by 13 and then ranking them alphabetically. However, the focus of codebook cryptanalysis is the comparative frequency of the individual code elements matching the same frequency of letters within the plaintext messages using frequency analysis. In the above example, the code group, 1001, 1002, 1003, might occur more than once and that frequency might match the number of times that ABC occurs in plain text messages. (In the past, or in non-technical contexts, code and cipher are often used to refer to any form of encryption). == One- and two-part codes == Codes are defined by "codebooks" (physical or notional), which are dictionaries of codegroups listed with their corresponding plaintext. Codes originally had the codegroups assigned in 'plaintext order' for convenience of the code designed, or the encoder. For example, in a code using numeric code groups, a plaintext word starting with "a" would have a low-value group, while one starting with "z" would have a high-value group. The same codebook could be used to "encode" a plaintext message into a coded message or "codetext", and "decode" a codetext back into plaintext message. In order to make life more difficult for codebreakers, codemakers designed codes with no predictable relationship between the codegroups and the ordering of the matching plaintext. In practice, this meant that two codebooks were now required, one to find codegroups for encoding, the other to look up codegroups to find plaintext for decoding. Such "two-part" codes required more effort to develop, and twice as much effort to distribute (and discard safely when replaced), but they were harder to break. The Zimmermann Telegram in January 1917 used the German diplomatic "0075" two-part code system which contained upwards of 10,000 phrases and individual words. == One-time code == A one-time code is a prearranged word, phrase or symbol that is intended to be used only once to convey a simple message, often the signal to execute or abort some plan or confirm that it has succeeded or failed. One-time codes are often designed to be included in what would appear to be an innocent conversation. Done properly they are almost impossible to detect, though a trained analyst monitoring the communications of someone who has already aroused suspicion might be able to recognize a comment like "Aunt Bertha has gone into labor" as having an ominous meaning. Famous example of one time codes include: In the Bible, Jonathan prearranges a code with David, who is going into hiding from Jonathan's father, King Saul. If, during archery practice, Jonathan tells the servant retrieving arrows "the arrows are on this side of you," David may safely return to court; if the command is "the arrows are beyond you," David must flee. "One if by land; two if by sea" in "Paul Revere's Ride" made famous in the poem by Henry Wadsworth Longfellow "Climb Mount Niitaka" - the signal to Japanese planes to begin the attack on Pearl Harbor During World War II the British Broadcasting Corporation's overseas service frequently included "personal messages" as part of its regular broadcast schedule. The seemingly nonsensical stream of messages read out by announcers were actually one time codes intended for Special Operations Executive (SOE) agents operating behind enemy lines. An example might be "The princess wears red shoes" or "Mimi's cat is asleep under the table". Each code message was read out twice. By such means, the French Resistance were instructed to start sabotaging rail and other transport links the night before D-day. "Over all of Spain, the sky is clear" was a signal (broadcast on radio) to start the nationalist military revolt in Spain on July 17, 1936. Sometimes messages are not prearranged and rely on shared knowledge hopefully known only to the recipients. An example is the telegram sent to U.S. President Harry Truman, then at the Potsdam Conference to meet with Soviet premier Joseph Stalin, informing Truman of the first successful test of an atomic bomb. "Operated on this morning. Diagnosis not yet complete but results seem satisfactory and already exceed expectations. Local press release necessary as interest extends great distance. Dr. Groves pleased. He returns tomorrow. I will keep you posted." == Idiot code == An idiot code is a code that is created by the parties using it. This type of communication is akin to the hand signals used by armies in the field. Example: Any sentence where 'day' and 'night' are used means 'attack'. The location mentioned in the following sentence specifies the location to be attacked. Plaintext: Attack X. Codetext: We walked day and night through the streets but couldn't find it! Tomorrow we'll head into X. An early use of the term appears to be by George Perrault, a character in the science fiction book Friday by Robert A. Heinlein: The simplest sort [of code] and thereby impossible to break. The first ad told the person or persons concerned to carry out number seven or expect number seven or it said something about something designated as seven. This one says the same with respect to code item number ten. But the meaning of the numbers cannot be deduced through statistical analysis because the code can be changed long before a useful statistical universe can be reached. It's an idiot code... and an idiot code can never be broken if the user has the good sense not to go too often to the well. Terrorism expert Magnus Ranstorp said that the men who carried out the September 11 attacks on the United States used basic e-mail and what he calls "idiot code" to discuss their plans. == Cryptanalysis of codes == While solving a monoalphabetic substitution cipher is easy, solving even a simple code is difficult. Decrypting a coded message is a little like trying to translate a document written in a foreign language, with the task basically amounting to building up a "dictionary" of the codegroups and the plaintext words they represent. One fingerhold on a simple code is the fact that some words are more common than others, such as "the" or "a" in English. In telegraphic messages, the codegroup for "STOP" (i.e., end of sentence or paragraph) is usually very common. This helps define the structure of the message in terms of sentences, if not their meaning, and this is cryptanalytically useful. Further progress can be made against a code by collecting many codetexts encrypted with the same code and then using information from other sources spies newspapers diplomatic cocktail party chat the location from where a message was sent where it was being sent to (i.e., traffic analysis) the time the message was sent, events occurring before and after the message was sent the normal habits of the people sending the coded messages etc. For example, a particular codegroup found almost exclusively in messages from a particular army and nowhere else might very well indicate the commander of that army. A codegroup that appears in messages preceding an attack on a particular location may very well stand for that location. Cribs can be an immediate giveaway to the definiti

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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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  Data philanthropy

  Data philanthropy refers to the practice of private companies donating corporate data. This data is usually donated to nonprofits or donation-run organizations that have difficulty keeping up with expensive data collection technology. The concept was introduced through the United Nations Global Pulse initiative in 2011 to explore corporate data assets for humanitarian, academic, and societal causes. For example, anonymized mobile data could be used to track disease outbreaks, or data on consumer actions may be shared with researchers to study public health and economic trends. == Definition == A large portion of data collected from the internet consists of user-generated content, such as blogs, social media posts, and information submitted through lead generation and data forms. Additionally, corporations gather and analyze consumer data to gain insight into customer behavior, identify potential markets, and inform investment decisions. United Nations Global Pulse director Robert Kirkpatrick has referred to this type of data as "massive passive data" or "data exhaust." == Challenges == While data philanthropy can enhance development policies, making users' private data available to various organizations raises concerns regarding privacy, ownership, and the equitable use of data. Different techniques, such as differential privacy and alphanumeric strings of information, can allow access to personal data while ensuring user anonymity. However, even if these algorithms work, re-identification may still be possible. Another challenge is convincing corporations to share their data. The data collected by corporations provides them with market competitiveness and insight regarding consumer behavior. Corporations may fear losing their competitive edge if they share the information they have collected with the public. Numerous moral challenges are also encountered. In 2016, Mariarosaria Taddeo, a digital ethics professor at the University of Oxford, proposed an ethical framework to address them. == Sharing strategies == The goal of data philanthropy is to create a global data commons where companies, governments, and individuals can contribute anonymous, aggregated datasets. The United Nations Global Pulse offers four different tactics that companies can use to share their data that preserve consumer anonymity: Share aggregated and derived data sets for analysis under nondisclosure agreements (NDA) Allow researchers to analyze data within the private company's own network under NDAs Real-Time Data Commons: data pooled and aggregated among multiple companies of the same industry to protect competitiveness Public/Private Alerting Network: companies mine data behind their own firewalls and share indicators == Application in various fields == Many corporations take part in data philanthropy, including social networking platforms (e.g., Facebook, Twitter), telecommunications providers (e.g., Verizon, AT&T), and search engines (e.g., Google, Bing). Collecting and sharing anonymized, aggregated user-generated data is made available through data-sharing systems to support research, policy development, and social impact initiatives. By participating in such efforts, these organizations contribute to causes regarded as beneficial to society, allowing institutions to give back meaningfully. With the onset of technological advancements, the sharing of data on a global scale and an in-depth analysis of these data structures could mitigate the effects of global issues such as natural disasters and epidemics. Robert Kirkpatrick, the Director of the United Nations Global Pulse, has argued that this aggregated information is beneficial for the common good and can lead to developments in research and data production in a range of varied fields. === Digital disease detection === Health researchers use digital disease detection by collecting data from various sources—such as social media platforms (e.g., Twitter, Facebook), mobile devices (e.g., cell phones, smartphones), online search queries, mobile apps, and sensor data from wearables and environmental sensors—to monitor and predict the spread of infectious diseases. This approach allows them to track and anticipate outbreaks of epidemics (e.g., COVID-19, Ebola), pandemics, vector-borne diseases (e.g., malaria, dengue fever), and respiratory illnesses (e.g., influenza, SARS), improving response and intervention strategies for the spread of diseases. In 2008, Centers for Disease Control and Prevention collaborated with Google and launched Google Flu Trends, a website that tracked flu-related searches and user locations to track the spread of the flu. Users could visit Google Flu Trends to compare the amount of flu-related search activity versus the reported numbers of flu outbreaks on a graphical map. One drawback of this method of tracking was that Google searches are sometimes performed due to curiosity rather than when an individual is suffering from the flu. According to Ashley Fowlkes, an epidemiologist in the CDC Influenza division, "The Google Flu Trends system tries to account for that type of media bias by modeling search terms over time to see which ones remain stable." Google Flu Trends is no longer publishing current flu estimates on the public website; however, visitors to the site can still view and download previous estimates. Current data can be shared with verified researchers. A study from the Harvard School of Public Health (HSPH), published in the October 12, 2012 issue of Science, discussed how phone data helped curb the spread of malaria in Kenya. The researchers mapped phone calls and texts made by 14,816,521 Kenyan mobile phone subscribers. When individuals left their primary living location, the destination and length of journey were calculated. This data was then compared to a 2009 malaria prevalence map to estimate the disease's commonality in each location. Combining all this information, the researchers could estimate the probability of an individual carrying malaria and map the movement of the disease. This research can be used to track the spread of similar diseases. === Humanitarian aid === Calling patterns of mobile phone users can determine the socioeconomic standings of the populace, which can be used to deduce "its access to housing, education, healthcare, and basic services such as water and electricity." Researchers from Columbia University and Karolinska Institute used daily SIM card location data from both before and after the 2010 Haiti earthquake to estimate the movement of people both in response to the earthquake and during the related 2010 Haiti cholera outbreak. Their research suggests that mobile phone data can provide rapid and accurate estimates of population movements during disasters and outbreaks of infectious disease. Big data can also provide information on looming disasters and can assist relief organizations in rapid-response and locating displaced individuals. By analyzing specific patterns within this 'big data', governments and NGOs can enhance responses to disruptive events such as natural disasters, disease outbreaks, and global economic crises. Leveraging real-time information enables a deeper understanding of individual well-being, allowing for more effective interventions. Corporations utilize digital services, such as human sensor systems, to detect and solve impending problems within communities. This is a strategy used by the private sector to anonymously share customer information for public benefit, while preserving user privacy. === Impoverished areas === Poverty still remains a worldwide issue, with over 2.5 billion people currently impoverished. Statistics indicate the widespread use of mobile phones, even within impoverished communities. Additional data can be collected through Internet access, social media, utility payments and governmental statistics. Data-driven activities can lead to the accumulation of 'big data', which in turn can assist international non-governmental organizations in documenting and evaluating the needs of underprivileged populations. Through data philanthropy, NGOs can distribute information while cooperating with governments and private companies. === Corporate === Data philanthropy incorporates aspects of social philanthropy by allowing corporations to create profound impacts through the act of giving back by dispersing proprietary datasets. The public sector collects and preserves information, considered an essential asset. Companies track and analyze users' online activities to gain insight into their needs related to new products and services. These companies view the welfare of the population as key to business expansion and progression by using their data to highlight global citizens' issues. Experts in the private sector emphasize the importance of integrating diverse data sources—such as retail, mobile, and social media data—to develop essential solutions for global challenges. In Data Philanthropy:

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  Server.com

  Server.com is a domain name that was owned by software as a service (SaaS) company Server Corporation. They offered a suite of services from 1996 until 2007. It was the first SaaS site to offer a variety of services and the first to use the term WebApp to describe its services. It was selected as an Incredibly Useful Site by Yahoo! Internet Life magazine. net magazine listed Server.com among the 100 most influential websites of all time. Server.com launched in 1996 offering the first online personal information manager. In 1997, they rolled out the first threaded message board service; the first web based mailing list manager; one of the first online calendar services; and one of the first online form builders. In 2000, Server.com partnered with NBCi and became server.snap.com until 2001. In 2001, Server.com was serving 100 million monthly pageviews. Media Life declared it one of the 20 biggest ad domains on the Web. In 2002, Server.com developed one of the first web-based RSS aggregators. In 2007, all services were moved to YourWebApps.com. The domain name Server.com was sold in 2009 for $770,000.

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  Data hub

  A data hub is a center of data exchange that is supported by data science, data engineering, and data warehouse technologies to interact with endpoints such as applications and algorithms. == Features == A data hub differs from a data warehouse in that it is generally unintegrated and often at different grains. It differs from an operational data store because a data hub does not need to be limited to operational data. A data hub differs from a data lake by homogenizing data and possibly serving data in multiple desired formats, rather than simply storing it in one place, and by adding other value to the data such as de-duplication, quality, security, and a standardized set of query services. A data lake tends to store data in one place for availability, and allow/require the consumer to process or add value to the data. Data hubs are ideally the "go-to" place for data within an enterprise, so that many point-to-point connections between callers and data suppliers do not need to be made, and so that the data hub organization can negotiate deliverables and schedules with various data enclave teams, rather than being an organizational free-for-all as different teams try to get new services and features from many other teams.

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  Intranet

  An intranet is a computer network for sharing information, easier communication, collaboration tools, operational systems, and other computing services within an organization, usually to the exclusion of access by outsiders. The term is used in contrast to public networks, such as the Internet, but uses the same technology based on the Internet protocol suite. An organization-wide intranet can constitute a focal point of internal communication and collaboration, and provide a single starting point to access internal and external resources. In its simplest form, an intranet is established with the technologies for local area networks (LANs) and wide area networks (WANs). Many modern intranets have search engines, user profiles, blogs, mobile apps with notifications, and events planning within their infrastructure. An intranet is sometimes contrasted to an extranet. While an intranet is generally restricted to employees of the organization, extranets may also be accessed by customers, suppliers, or other approved parties. Extranets extend a private network onto the Internet with special provisions for authentication, authorization and accounting (AAA protocol). == Uses == Intranets are increasingly being used to deliver tools, such as for collaboration (to facilitate working in groups and teleconferencing) or corporate directories, sales and customer relationship management, or project management. Intranets are also used as corporate culture-change platforms. For example, a large number of employees using an intranet forum application to host a discussion about key issues could come up with new ideas related to management, productivity, quality, and other corporate issues. In large intranets, website traffic is often similar to public website traffic and can be better understood by using web metrics software to track overall activity. User surveys also improve intranet website effectiveness. Larger businesses allow users within their intranet to access public internet through firewall servers. They have the ability to screen incoming and outgoing messages, keeping security intact. When part of an intranet is made accessible to customers and others outside the business, it becomes part of an extranet. Businesses can send private messages through the public network using special encryption/decryption and other security safeguards to connect one part of their intranet to another. Intranet user-experience, editorial, and technology teams work together to produce in-house sites. Most commonly, intranets are managed by the communications, HR or CIO departments of large organizations, or some combination of these. Because of the scope and variety of content and the number of system interfaces, the intranets of many organizations are much more complex than their respective public websites. Intranets and the use of intranets are growing rapidly. According to the Intranet Design Annual 2007 from Nielsen Norman Group, the number of pages on participants' intranets averaged 200,000 over the years 2001 to 2003 and has grown to an average of 6 million pages over 2005–2007. == Benefits == Intranets can help users locate and view information faster and use applications relevant to their roles and responsibilities. With a web browser interface, users can access data held in any database the organization wants to make available at any time and — subject to security provisions — from anywhere within company workstations, increasing employees' ability to perform their jobs faster, more accurately, and with confidence that they have the right information. It also helps improve services provided to users. Using hypermedia and Web technology, Web publishing allows for the maintenance of and easy access to cumbersome corporate knowledge, such as employee manuals, benefits documents, company policies, business standards, news feeds, and even training, all of which can be accessed throughout a company using common Internet standards (Acrobat files, Flash files, CGI applications). Because each business unit can update the online copy of a document, the most recent version is usually available to employees using the intranet. Intranets are also used as a platform for developing and deploying applications to support business operations and decisions across the internetworked enterprise. Information is easily accessible to all authorised users, enabling collaboration. Being able to communicate in real-time through integrated third-party tools, such as an instant messenger, promotes the sharing of ideas and removes blockages to communication to help boost a business's productivity. Intranets can serve as powerful tools for communicating (such as through chat, email and/or blogs) within a given organization about vertically strategic initiatives that have a global reach throughout said organization. The type of information that can easily be conveyed is the purpose of the initiative and what it is aiming to achieve, who is driving it, results achieved to date, and whom to speak to for more information. By providing this information on the intranet, staff can keep up-to-date with the strategic focus of their organization. For example, when Nestlé had a number of food processing plants in Scandinavia, their central support system had to deal with a number of queries every day. When Nestlé decided to invest in an intranet, they quickly realized the savings. Gerry McGovern says that the savings from the reduction in query calls was substantially greater than the investment in the intranet. Users can view information and data via a web browser rather than maintaining physical documents such as procedure manuals, internal phone list and requisition forms. This can potentially save the business money on printing, duplicating documents, and the environment, as well as document maintenance overhead. For example, the HRM company PeopleSoft "derived significant cost savings by shifting HR processes to the intranet". McGovern goes on to say the manual cost of enrolling in benefits was found to be US$109.48 per enrollment. "Shifting this process to the intranet reduced the cost per enrollment to $21.79; a saving of 80 percent". Another company that saved money on expense reports was Cisco. "In 1996, Cisco processed 54,000 reports and the amount of dollars processed was USD19 million". Many companies dictate computer specifications which, in turn, may allow Intranet developers to write applications that only have to work on one browser such that there are no cross-browser compatibility issues. Being able to specifically address one's "viewer" is a great advantage. Since intranets are user-specific (requiring database/network authentication prior to access), users know exactly who they are interfacing with and can personalize their intranet based on role (job title, department) or individual ("Congratulations Jane, on your 3rd year with our company!"). Since "involvement in decision making" is one of the main drivers of employee engagement, offering tools (like forums or surveys) that foster peer-to-peer collaboration and employee participation can make employees feel more valued and involved. == Planning and creation == Most organizations devote considerable resources into the planning and implementation of their intranet as it is of strategic importance to the organization's success. Some of the planning would include topics such as determining the purpose and goals of the intranet, identifying persons or departments responsible for implementation and management and devising functional plans, page layouts and designs. The appropriate staff would also ensure that implementation schedules and phase-out of existing systems were organized, while defining and implementing security of the intranet and ensuring it lies within legal boundaries and other constraints. In order to produce a high-value end product, systems planners should determine the level of interactivity (e.g. wikis, on-line forms) desired. Planners may also consider whether the input of new data and updating of existing data is to be centrally controlled or devolve. These decisions sit alongside to the hardware and software considerations (like content management systems), participation issues (like good taste, harassment, confidentiality), and features to be supported. Intranets are often static sites; they are a shared drive, serving up centrally stored documents alongside internal articles or communications (often one-way communication). By leveraging firms which specialise in 'social' intranets, organisations are beginning to think of how their intranets can become a 'communication hub' for their entire team. The actual implementation would include steps such as securing senior management support and funding, conducting a business requirement analysis and identifying users' information needs. From the technical perspective, there would need to be a coordinated installation of the web server and user access netw

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  Hyper-encryption

  Hyper-encryption is a form of encryption invented by Michael O. Rabin which uses a high-bandwidth source of public random bits, together with a secret key that is shared by only the sender and recipient(s) of the message. It uses the assumptions of Ueli Maurer's bounded-storage model as the basis of its secrecy. Although everyone can see the data, decryption by adversaries without the secret key is still not feasible, because of the space limitations of storing enough data to mount an attack against the system. Unlike almost all other cryptosystems except the one-time pad, hyper-encryption can be proved to be information-theoretically secure, provided the storage bound cannot be surpassed. Moreover, if the necessary public information cannot be stored at the time of transmission, the plaintext can be shown to be impossible to recover, regardless of the computational capacity available to an adversary in the future, even if they have access to the secret key at that future time. A highly energy-efficient implementation of a hyper-encryption chip was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be ~205 times more efficient in terms of Energy-Performance-Product.

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  ImHex

  ImHex is a free cross-platform hex editor available on Windows, macOS, and Linux. ImHex is used by programmers and reverse engineers to view and analyze binary data. == History == The initial release of the project in November 2020, saw significant interest on GitHub. == Features == Features include: Hex editor Custom pattern matching and analysis scripting language Visual, node based data pre-processor Disassembler Running and visualizing of YARA rules Bookmarks Binary data diffing Additional Tools MSVC, Itanium, D and Rust name demangler ASCII table Calculator Base converter File utilities IEEE 754 floating point decoder Division by invariant multiplication calculator TCP/IP client and server Support for: Data importing and exporting ASCII string, Unicode string, numeric, hexadecimal and regular expressions search Byte manipulation File hashing Plug-ins

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  Intent-based network

  Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.

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  Sex differences in social media use

  Men and women use social media in different ways and with different frequencies. In general, several researchers have found that women tend to use social network services (SNSs) more than men and primiarly to socialize. == Differences == === Predilection for usage === Many studies have found that women are more likely to use either specific SNSs such as Facebook or MySpace or SNSs in general. In 2015, 73% of online men and 80% of online women used social networking sites. The gap in gender differences has become less apparent in LinkedIn. In 2015 about 26 percent of online men and 25% of online women used the business-and employee-oriented networking site. Researchers who have examined the gender of users of multiple SNSs have found contradictory results. Hargittai's groundbreaking 2007 study examining race, gender, and other differences between undergraduate college student users of SNSs found that women were not only more likely to have used SNSes than men but that they were also more likely to have used many different services, including Facebook, MySpace, and Friendster; these differences persisted in several models and analyses. Although she only surveyed students at one institution – the University of Illinois at Chicago – Hargittai selected that institution intentionally as "an ideal location for studies of how different kinds of people use online sites and services." In contrast, data collected by the Pew Internet & American Life Project found that men were more likely to have multiple SNS profiles. Although the sample sizes of the two surveys are comparable – 1,650 Internet users in the Pew survey compared with 1,060 in Hargittai's survey – the data from the Pew survey are newer and arguably more representative of the entire adult United States population. Pinterest, Facebook, and Instagram attract more females. Picture sharing sites overall are very popular among women. Pinterest alone attracts three times as many female users than male. However, use of Pinterest by men has increased from 5% in 2012. Facebook attracts about 77% of women online. Instagram is also more likely to attract women. Men are more likely to participate in online forums like Reddit, Digg or Slashdot. One in five men claim to be a part of an online forum. === Uses === In general, women seem to use SNSs more to explicitly foster social connections. A study conducted by Pew research centers found that women were more avid users of social media. In November 2010, the gap between men and women was as high as 15%. Female participants in a multi-stage study conducted in 2007 to discover the motivations of Facebook users scored higher on scales for social connection and posting of photographs. Studies have also been conducted on the differences between females and males with regards to blogging. The Pew Research Center found that younger females are more likely to blog than males their own age, even males that are older than them. Similarly, in a study of blogs maintained in MySpace, women were found to be more likely to not only write blogs but also write about family, romantic relationships, friendships, and health in those blogs. A study of Swedish SNS users found that women were more likely to have expressions of friendship, specifically in the areas of (a) publishing photos of their friends, (b) specifically naming their best friends, and (c) writing poems to and about their friends. Women were also more likely to have expressions related to family relationships and romantic relationships. One of the key findings of this research is that those men who do have expressions of romantic relationships in their profile had expressions just as strong as the women. However, the researcher speculated that this may be in part due to a desire to publicly express heterosexual behaviors and mannerisms instead of merely expressing romantic feelings. A large-scale study of gender differences in MySpace found that both men and women tended to have a majority of female Friends, and both men and women tended to have a majority of female "Top" Friends in the site. A later study found women to author disproportionately many (public) comments in MySpace, but an investigation into the role of emotion in public MySpace comments found that women both give and receive stronger positive emotion. It was hypothesised that women are simply more effective at using social networking sites because they are better able to harness positive emotion. A study focused on the influence of gender and personality on individuals' use of online social networking websites such as Facebook, reported that men use social networking sites with the intention of forming new relationships, whereas, women use them more for relationship maintenance. In addition to this, women are more likely to use Facebook or MySpace to compare themselves to others and also to search for information. Men, however, are more likely to look at other people's profiles with in the intention to find friends. Women were less successful at actually finding new friends, but more successful at "maintaining existing relationships, making new relationships, using for academic purposes and following specific agenda". Similarly, men also self-reported this motivation "while women reported using them more for relationship maintenance". === Personality === OCEAN personality traits are known to systematically vary between human males and females. In one study, the same women were more extraverted and agreeable, such as less neurotic while on social media than offline. Other studies associated neuroticism with female use of social media. === Privacy === Privacy has been the primary topic of many studies of SNS users, and many of these studies have found differences between male and female SNS users, although some studies have found results contradictory to those found in other studies. Some researchers have found that women are more protective of their personal information and more likely to have private profiles. Other researchers have found that women are less likely to post some types of information. Acquisti and Gross found that women in their sample were less likely to reveal their sexual orientation, personal address, or cell phone number. This is similar to Pew Internet & American Life research of children users of SNSs that found that boys and girls presented different views of privacy and behaviors, with girls being more concerned about and restrictive of information such as city, town, last name, and cell phone number that could be used to locate them. At least one group of researchers has found that women are less likely to share information that "identifies them directly – last name, cell phone number, and address or home phone number," linking that resistance to women's greater concerns about "cyberstalking", "cyberbullying", and security problems. Despite these concerns about privacy, researchers have found that women are more likely to maintain up-to-date photos of themselves. Further, Kolek and Saunders found in their sample of college student Facebook users that women were more likely to not only post a photograph of themselves in their profile but that they were more likely to have a publicly viewable Facebook account (a contradictory finding compared to many other studies), post photos, and post photo albums. Women were more likely to have: (a) a publicly viewable Facebook account, (b) more photo albums, (c) more photos, (d) a photo of themselves as their profile picture, (e) positive references to alcohol, partying, or drugs, and (f) more positive references to or about the institution or institution-related activities. In general, women were more likely to disclose information about themselves in their Facebook profile, with the primary exception of sharing their telephone number. Similarly, female respondents to Strano's study were more likely to keep their profile photo recent and choose a photo that made them appear attractive, happy, and fun-loving. Citing several examples, Strano opined that there may also be a difference in how men and women Facebook users display and interpret profile photos depicting relationships. Privacy has also been a concern for the SnapChat app, which allows you to send messages either text or photo or video which then disappear. One study has shown that security is not a major concern for the majority of users and that most do not use Snapchat to send sensitive content (although up to 25% may do so experimentally). As part of their research almost no statistically significant gender differences were found. === Cyberbullying === Past research carried out to investigate if there are any gender differences in cyber-bullying has found that boys commit more cyber verbal bullying, cyber forgery and more violence based on hidden identity or presenting themselves as other person. === Mansplaining === A 2021 article found that mansplaining could be seen more prominent online rather than offl

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