AI For Business From University Of Pennsylvania

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  Automaton

  An automaton ( ; pl.: automata or automatons) is a relatively self-operating machine or control mechanism designed to automatically follow a sequence of operations or respond to predetermined instructions. Some automata, such as bellstrikers in mechanical clocks, are designed to give the illusion to the casual observer that they are operating under their own power or will, like a mechanical robot. The term has long been commonly associated with automated puppets that resemble moving humans or animals, built to impress and/or to entertain people. Animatronics are a modern type of automata with electronics, often used for the portrayal of characters or creatures in films and in theme park attractions. == Etymology == The word automaton is the latinization of the Ancient Greek automaton (αὐτόματον), which means "acting of one's own will". It was first used by Homer to describe an automatic door opening, or automatic movement of wheeled tripods. It is more often used to describe non-electronic moving machines, especially those that have been made to resemble human or animal actions, such as the jacks on old public striking clocks, or the cuckoo and any other animated figures on a cuckoo clock. == History == === Ancient === There are many examples of automata in Greek mythology: Hephaestus created automata for his workshop; Talos was an artificial man of bronze; King Alkinous of the Phaiakians employed gold and silver watchdogs. According to Aristotle, Daedalus used quicksilver to make his wooden statue of Aphrodite move. In other Greek legends he used quicksilver to install voice in his moving statues. The automata in the Hellenistic world were intended as tools, toys, religious spectacles, or prototypes for demonstrating basic scientific principles. Numerous water-powered automata were built by Ktesibios, a Greek inventor and the first head of the Great Library of Alexandria; for example, he "used water to sound a whistle and make a model owl move. He had invented the world's first 'cuckoo clock'". This tradition continued in Alexandria with inventors such as the Greek mathematician Hero of Alexandria (sometimes known as Heron), whose writings on hydraulics, pneumatics, and mechanics described siphons, a fire engine, a water organ, the aeolipile, and a programmable cart. Philo of Byzantium was famous for his inventions. Complex mechanical devices are known to have existed in Hellenistic Greece, though the only surviving example is the Antikythera mechanism, the earliest known analog computer. The clockwork is thought to have come originally from Rhodes, where there was apparently a tradition of mechanical engineering; the island was renowned for its automata; to quote Pindar's seventh Olympic Ode: The animated figures stand Adorning every public street And seem to breathe in stone, or move their marble feet. However, the information gleaned from recent scans of the fragments indicate that it may have come from the colonies of Corinth in Sicily and implies a connection with Archimedes. According to Jewish legend, King Solomon used his wisdom to design a throne with mechanical animals which hailed him as king when he ascended it; upon sitting down an eagle would place a crown upon his head, and a dove would bring him a Torah scroll. It is also said that when King Solomon stepped upon the throne, a mechanism was set in motion. As soon as he stepped upon the first step, a golden ox and a golden lion each stretched out one foot to support him and help him rise to the next step. On each side, the animals helped the King up until he was comfortably seated upon the throne. In ancient China, a curious account of automata is found in the Lie Zi text, believed to have originated around 400 BCE and compiled around the fourth century CE. Within it there is a description of a much earlier encounter between King Mu of Zhou (1023–957 BCE) and a mechanical engineer known as Yan Shi, an 'artificer'. The latter proudly presented the king with a very realistic and detailed life-size, human-shaped figure of his mechanical handiwork: The king stared at the figure in astonishment. It walked with rapid strides, moving its head up and down, so that anyone would have taken it for a live human being. The artificer touched its chin, and it began singing, perfectly in tune. He touched its hand, and it began posturing, keeping perfect time...As the performance was drawing to an end, the robot winked its eye and made advances to the ladies in attendance, whereupon the king became incensed and would have had Yen Shih [Yan Shi] executed on the spot had not the latter, in mortal fear, instantly taken the robot to pieces to let him see what it really was. And, indeed, it turned out to be only a construction of leather, wood, glue and lacquer, variously coloured white, black, red and blue. Examining it closely, the king found all the internal organs complete—liver, gall, heart, lungs, spleen, kidneys, stomach and intestines; and over these again, muscles, bones and limbs with their joints, skin, teeth and hair, all of them artificial...The king tried the effect of taking away the heart, and found that the mouth could no longer speak; he took away the liver and the eyes could no longer see; he took away the kidneys and the legs lost their power of locomotion. The king was delighted. Other notable examples of automata include Archytas' dove, mentioned by Aulus Gellius. Similar Chinese accounts of flying automata are written of the 5th century BC Mohist philosopher Mozi and his contemporary Lu Ban, who made artificial wooden birds (ma yuan) that could successfully fly according to the Han Fei Zi and other texts. === Medieval === The manufacturing tradition of automata continued in the Greek world well into the Middle Ages. On his visit to Constantinople in 949 ambassador Liutprand of Cremona described automata in the emperor Theophilos' palace, including "lions, made either of bronze or wood covered with gold, which struck the ground with their tails and roared with open mouth and quivering tongue," "a tree of gilded bronze, its branches filled with birds, likewise made of bronze gilded over, and these emitted cries appropriate to their species" and "the emperor's throne" itself, which "was made in such a cunning manner that at one moment it was down on the ground, while at another it rose higher and was to be seen up in the air." Similar automata in the throne room (singing birds, roaring and moving lions) were described by Luitprand's contemporary the Byzantine emperor Constantine Porphyrogenitus, in his book De Ceremoniis (Perì tês Basileíou Tákseōs). In the mid-8th century, the first wind powered automata were built: "statues that turned with the wind over the domes of the four gates and the palace complex of the Round City of Baghdad". The "public spectacle of wind-powered statues had its private counterpart in the 'Abbasid palaces where automata of various types were predominantly displayed." Also in the 8th century, the Muslim alchemist, Jābir ibn Hayyān (Geber), included recipes for constructing artificial snakes, scorpions, and humans that would be subject to their creator's control in his coded Book of Stones. In 827, Abbasid caliph al-Ma'mun had a silver and golden tree in his palace in Baghdad, which had the features of an automatic machine. There were metal birds that sang automatically on the swinging branches of this tree built by Muslim inventors and engineers. The Abbasid caliph al-Muqtadir also had a silver and golden tree in his palace in Baghdad in 917, with birds on it flapping their wings and singing. In the 9th century, the Banū Mūsā brothers invented a programmable automatic flute player and which they described in their Book of Ingenious Devices. Al-Jazari described complex programmable humanoid automata amongst other machines he designed and constructed in the Book of Knowledge of Ingenious Mechanical Devices in 1206. His automaton was a boat with four automatic musicians that floated on a lake to entertain guests at royal drinking parties. His mechanism had a programmable drum machine with pegs (cams) that bump into little levers that operate the percussion. The drummer could be made to play different rhythms and drum patterns if the pegs were moved around. Al-Jazari constructed a hand washing automaton first employing the flush mechanism now used in modern toilets. It features a female automaton standing by a basin filled with water. When the user pulls the lever, the water drains and the automaton refills the basin. His "peacock fountain" was another more sophisticated hand washing device featuring humanoid automata as servants who offer soap and towels. Mark E. Rosheim describes it as follows: "Pulling a plug on the peacock's tail releases water out of the beak; as the dirty water from the basin fills the hollow base a float rises and actuates a linkage which makes a servant figure appear from behind a door under the peacock and offer soap.

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  Bitmap index

  A bitmap index is a special kind of database index that uses bitmaps. Bitmap indexes have traditionally been considered to work well for low-cardinality columns, which have a modest number of distinct values, either absolutely, or relative to the number of records that contain the data. The extreme case of low cardinality is Boolean data (e.g., does a resident in a city have internet access?), which has two values, True and False. Bitmap indexes use bit arrays (commonly called bitmaps) and answer queries by performing bitwise logical operations on these bitmaps. Bitmap indexes have a significant space and performance advantage over other structures for query of such data. Their drawback is they are less efficient than the traditional B-tree indexes for columns whose data is frequently updated: consequently, they are more often employed in read-only systems that are specialized for fast query - e.g., data warehouses, and generally unsuitable for online transaction processing applications. Some researchers argue that bitmap indexes are also useful for moderate or even high-cardinality data (e.g., unique-valued data) which is accessed in a read-only manner, and queries access multiple bitmap-indexed columns using the AND, OR or XOR operators extensively. Bitmap indexes are also useful in data warehousing applications for joining a large fact table to smaller dimension tables such as those arranged in a star schema. == Example == Continuing the internet access example, a bitmap index may be logically viewed as follows: On the left, Identifier refers to the unique number assigned to each resident, HasInternet is the data to be indexed, the content of the bitmap index is shown as two columns under the heading bitmaps. Each column in the left illustration under the Bitmaps header is a bitmap in the bitmap index. In this case, there are two such bitmaps, one for "has internet" Yes and one for "has internet" No. It is easy to see that each bit in bitmap Y shows whether a particular row refers to a person who has internet access. This is the simplest form of bitmap index. Most columns will have more distinct values. For example, the sales amount is likely to have a much larger number of distinct values. Variations on the bitmap index can effectively index this data as well. We briefly review three such variations. Note: Many of the references cited here are reviewed at (John Wu (2007)). For those who might be interested in experimenting with some of the ideas mentioned here, many of them are implemented in open source software such as FastBit, the Lemur Bitmap Index C++ Library, the Roaring Bitmap Java library and the Apache Hive Data Warehouse system. == Compression == For historical reasons, bitmap compression and inverted list compression were developed as separate lines of research, and only later were recognized as solving essentially the same problem. Software can compress each bitmap in a bitmap index to save space. There has been considerable amount of work on this subject. Though there are exceptions such as Roaring bitmaps, Bitmap compression algorithms typically employ run-length encoding, such as the Byte-aligned Bitmap Code, the Word-Aligned Hybrid code, the Partitioned Word-Aligned Hybrid (PWAH) compression, the Position List Word Aligned Hybrid, the Compressed Adaptive Index (COMPAX), Enhanced Word-Aligned Hybrid (EWAH) and the COmpressed 'N' Composable Integer SEt (CONCISE). These compression methods require very little effort to compress and decompress. More importantly, bitmaps compressed with BBC, WAH, COMPAX, PLWAH, EWAH and CONCISE can directly participate in bitwise operations without decompression. This gives them considerable advantages over generic compression techniques such as LZ77. BBC compression and its derivatives are used in a commercial database management system. BBC is effective in both reducing index sizes and maintaining query performance. BBC encodes the bitmaps in bytes, while WAH encodes in words, better matching current CPUs. "On both synthetic data and real application data, the new word aligned schemes use only 50% more space, but perform logical operations on compressed data 12 times faster than BBC." PLWAH bitmaps were reported to take 50% of the storage space consumed by WAH bitmaps and offer up to 20% faster performance on logical operations. Similar considerations can be done for CONCISE and Enhanced Word-Aligned Hybrid. The performance of schemes such as BBC, WAH, PLWAH, EWAH, COMPAX and CONCISE is dependent on the order of the rows. A simple lexicographical sort can divide the index size by 9 and make indexes several times faster. The larger the table, the more important it is to sort the rows. Reshuffling techniques have also been proposed to achieve the same results of sorting when indexing streaming data. == Encoding == Basic bitmap indexes use one bitmap for each distinct value. It is possible to reduce the number of bitmaps used by using a different encoding method. For example, it is possible to encode C distinct values using log(C) bitmaps with binary encoding. This reduces the number of bitmaps, further saving space, but to answer any query, most of the bitmaps have to be accessed. This makes it potentially not as effective as scanning a vertical projection of the base data, also known as a materialized view or projection index. Finding the optimal encoding method that balances (arbitrary) query performance, index size and index maintenance remains a challenge. Without considering compression, Chan and Ioannidis analyzed a class of multi-component encoding methods and came to the conclusion that two-component encoding sits at the kink of the performance vs. index size curve and therefore represents the best trade-off between index size and query performance. == Binning == For high-cardinality columns, it is useful to bin the values, where each bin covers multiple values and build the bitmaps to represent the values in each bin. This approach reduces the number of bitmaps used regardless of encoding method. However, binned indexes can only answer some queries without examining the base data. For example, if a bin covers the range from 0.1 to 0.2, then when the user asks for all values less than 0.15, all rows that fall in the bin are possible hits and have to be checked to verify whether they are actually less than 0.15. The process of checking the base data is known as the candidate check. In most cases, the time used by the candidate check is significantly longer than the time needed to work with the bitmap index. Therefore, binned indexes exhibit irregular performance. They can be very fast for some queries, but much slower if the query does not exactly match a bin. == History == The concept of bitmap index was first introduced by Professor Israel Spiegler and Rafi Maayan in their research "Storage and Retrieval Considerations of Binary Data Bases", published in 1985. The first commercial database product to implement a bitmap index was Computer Corporation of America's Model 204. Patrick O'Neil published a paper about this implementation in 1987. This implementation is a hybrid between the basic bitmap index (without compression) and the list of Row Identifiers (RID-list). Overall, the index is organized as a B+tree. When the column cardinality is low, each leaf node of the B-tree would contain long list of RIDs. In this case, it requires less space to represent the RID-lists as bitmaps. Since each bitmap represents one distinct value, this is the basic bitmap index. As the column cardinality increases, each bitmap becomes sparse and it may take more disk space to store the bitmaps than to store the same content as RID-lists. In this case, it switches to use the RID-lists, which makes it a B+tree index. == In-memory bitmaps == One of the strongest reasons for using bitmap indexes is that the intermediate results produced from them are also bitmaps and can be efficiently reused in further operations to answer more complex queries. Many programming languages support this as a bit array data structure. For example, Java has the BitSet class and .NET have the BitArray class. Some database systems that do not offer persistent bitmap indexes use bitmaps internally to speed up query processing. For example, PostgreSQL versions 8.1 and later implement a "bitmap index scan" optimization to speed up arbitrarily complex logical operations between available indexes on a single table. For tables with many columns, the total number of distinct indexes to satisfy all possible queries (with equality filtering conditions on either of the fields) grows very fast, being defined by this formula: C n [ n 2 ] ≡ n ! ( n − [ n 2 ] ) ! [ n 2 ] ! {\displaystyle \mathbf {C} _{n}^{\left[{\frac {n}{2}}\right]}\equiv {\frac {n!}{\left(n-\left[{\frac {n}{2}}\right]\right)!\left[{\frac {n}{2}}\right]!}}} . A bitmap index scan combines expressions on different indexes, thus requiring only one index per column t

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  Sentiment analysis

  Sentiment analysis (also known as opinion mining) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. == Types == A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. Precursors to sentimental analysis include the General Inquirer, which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's psychological state based on analysis of their verbal behavior. Subsequently, the method described in a patent by Volcani and Fogel, looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale. Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney, and Pang who applied different methods for detecting the polarity of product reviews and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang and Snyder among others: Pang and Lee expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale). First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 AAAI Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text. Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified. Moreover, it can be proven that specific classifiers such as the Max Entropy and SVMs can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step. This second approach often involves estimating a probability distribution over all categories (e.g. naive Bayes classifiers as implemented by the NLTK). Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles. A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using natural language processing, each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score. This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text. There are various other types of sentiment analysis, such as aspect-based sentiment analysis, grading sentiment analysis (positive, negative, neutral), multilingual sentiment analysis and detection of emotions. === Subjectivity/objectivity identification === This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification. The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su, results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang showed that removing objective sentences from a document before classifying its polarity helped improve performance. Subjective and objective identification, emerging subtasks of sentiment analysis to use syntactic, semantic features, and machine learning knowledge to identify if a sentence or document contains facts or opinions. Awareness of recognizing factual and opinions is not recent, having possibly first presented by Carbonell at Yale University in 1979. The term objective refers to the incident carrying factual information. Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.' The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions, also known as 'private states'. In the example down below, it reflects a private states 'We Americans'. Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu (2010). Furthermore, three types of attitudes were observed by Liu (2010), 1) positive opinions, 2) neutral opinions, and 3) negative opinions. Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.' This analysis is a classification problem. Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al. (2003). A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hand to automated features learning. At the moment, automated learning methods can further separate into supervised and unsupervised machine learning. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers. However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume. Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction. Besides, metaphors take in different forms, which may have been contribu

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  PGP word list

  The PGP Word List ("Pretty Good Privacy word list", also called a biometric word list for reasons explained below) is a list of words for conveying data bytes in a clear unambiguous way via a voice channel. They are analogous in purpose to the NATO phonetic alphabet, except that a longer list of words is used, each word corresponding to one of the 256 distinct numeric byte values. == History and structure == The PGP Word List was designed in 1995 by Patrick Juola, a computational linguist, and Philip Zimmermann, creator of PGP. The words were carefully chosen for their phonetic distinctiveness, using genetic algorithms to select lists of words that had optimum separations in phoneme space. The candidate word lists were randomly drawn from Grady Ward's Moby Pronunciator list as raw material for the search, successively refined by the genetic algorithms. The automated search converged to an optimized solution in about 40 hours on a DEC Alpha, a particularly fast machine in that era. The Zimmermann–Juola list was originally designed to be used in PGPfone, a secure VoIP application, to allow the two parties to verbally compare a short authentication string to detect a man-in-the-middle attack (MiTM). It was called a biometric word list because the authentication depended on the two human users recognizing each other's distinct voices as they read and compared the words over the voice channel, binding the identity of the speaker with the words, which helped protect against the MiTM attack. The list can be used in many other situations where a biometric binding of identity is not needed, so calling it a biometric word list may be imprecise. Later, it was used in PGP to compare and verify PGP public key fingerprints over a voice channel. This is known in PGP applications as the "biometric" representation. When it was applied to PGP, the list of words was further refined, with contributions by Jon Callas. More recently, it has been used in Zfone and the ZRTP protocol, the successor to PGPfone. The list is actually composed of two lists, each containing 256 phonetically distinct words, in which each word represents a different byte value between 0 and 255. Two lists are used because reading aloud long random sequences of human words usually risks three kinds of errors: 1) transposition of two consecutive words, 2) duplicate words, or 3) omitted words. To detect all three kinds of errors, the two lists are used alternately for the even-offset bytes and the odd-offset bytes in the byte sequence. Each byte value is actually represented by two different words, depending on whether that byte appears at an odd or an even offset from the beginning of the byte sequence. The two lists are readily distinguished by the number of syllables; the odd list has words of three syllables, the even list has two. The two lists have a maximum word length of 11 and 9 letters, respectively. Using a two-list scheme was suggested by Zhahai Stewart. == Examples == Each byte in a bytestring is encoded as a single word. A sequence of bytes is rendered in network byte order, from left to right. For example, the leftmost (i.e. byte 0) is considered "even" and is encoded using the PGP Even Word table. The next byte to the right (i.e. byte 1) is considered "odd" and is encoded using the PGP Odd Word table. This process repeats until all bytes are encoded. Thus, "E582" produces "topmost Istanbul", whereas "82E5" produces "miser travesty". A PGP public key fingerprint that displayed in hexadecimal as E582 94F2 E9A2 2748 6E8B 061B 31CC 528F D7FA 3F19 would display in PGP Words (the "biometric" fingerprint) as topmost Istanbul Pluto vagabond treadmill Pacific brackish dictator goldfish Medusa afflict bravado chatter revolver Dupont midsummer stopwatch whimsical cowbell bottomless The order of bytes in a bytestring depends on endianness. == Other word lists for data == There are several other word lists for conveying data in a clear unambiguous way via a voice channel: the NATO phonetic alphabet maps individual letters and digits to individual words the S/KEY system maps 64 bit numbers to 6 short words of 1 to 4 characters each from a publicly accessible 2048-word dictionary. The same dictionary is used in RFC 1760 and RFC 2289. the Diceware system maps five base-6 random digits (almost 13 bits of entropy) to a word from a dictionary of 7,776 distinct words. the Electronic Frontier Foundation has published a set of improved word lists based on the same concept FIPS 181: Automated Password Generator converts random numbers into somewhat pronounceable "words". mnemonic encoding converts 32 bits of data into 3 words from a vocabulary of 1626 words. what3words encodes geographic coordinates in 3 dictionary words. the BIP39 standard permits encoding a cryptographic key of fixed size (128 or 256 bits, usually the unencrypted master key of a Cryptocurrency wallet) into a short sequence of readable words known as the seed phrase, for the purpose of storing the key offline. This is used in cryptocurrencies such as Bitcoin or Monero. Like the PGP word list, the Bytewords standard maps each possible byte to a word. There is only one list, rather than two. The words are uniformly four letters long and can be uniquely identified by their first and last letters

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  Wrike

  Wrike, Inc. is an American project management application service provider based in San Jose, California. Wrike also has offices in India, Dallas, Tallinn, Nicosia, Dublin, Tokyo, Melbourne, and Prague. == History == Wrike was founded in 2006 by Andrew Filev. Currently CEO at Wrike is Thomas Scott. Filev initially self-funded the company before later obtaining investor funding. Wrike released the beta version of its software (also called Wrike) in December 2006. The company then launched a new "Enterprise" platform in December 2013. In June 2015, Wrike announced the opening of an office in Dublin, Ireland and in 2016, Wrike launched a datacenter there to host data in compliance with local privacy regulations. In July 2016, Wrike announced the launch of Wrike for Marketers. That same year, Wrike's headquarters moved from Mountain View to San Jose, California. In January 2021, Citrix Systems announced its intention to acquire Wrike for $2.25 billion. The acquisition closed in March 2021. On January 31, 2022, it was announced that Citrix had been acquired in a $16.5 billion deal by affiliates of Vista Equity Partners and Evergreen Coast Capital. Citrix would merge with TIBCO Software, a Vista portfolio company to form Cloud Software Group (CSG). In September 2022, Wrike separated from Citrix Systems. In July 2023, Vista transferred ownership to Symphony Technology Group. == Investments == Wrike received $1 million in Angel funding in 2012 from TMT Investments. In October, 2013, Wrike secured $10 million in investment funding from Bain Capital. In May 2015, the company secured $15 million in a new round of funding. Investors included Scale Venture Partners, DCM Ventures, and Bain Capital. At that time, Wrike had 8,000 customers, 200 employees, and 30,000 new users each month. On November 29, 2018, Wrike signed a definitive agreement to receive a majority investment by Vista Equity Partners (“Vista”), a firm focused on software, data and technology-enabled businesses. == Software == The Wrike project management software is a Software-as-a-Service (SaaS) product with tools for managing projects, deadlines, schedules, and workflow processes. It includes collaboration features. The application is available in English, French, Spanish, German, Portuguese, Italian, Japanese and Russian. Wrike has triggers for task automation in workflow management. === Features === Wrike features a multi-pane UI and consists of features in two categories: project management, and team collaboration. According to Wrike, project management features are designed to help teams track dates and dependencies associated with projects, manage assignments and resources, and track time. These include an interactive Gantt chart, a workload view, and a sortable table that can be customized to store project data. The software includes a co-editing tool, discussion threads on tasks, and tools for attaching documents, editing them, and tracking their changes. Wrike uses an "inbox" feature and browser notifications to alert users of updates from their colleagues and dashboards for quick overviews of pending tasks. These updates are also available in Wrike's mobile apps on iOS and Android. Wrike has an optional feature set called "Wrike for Marketers" which has several tools for managing marketing workflows. In May 2012, Wrike announced the launch of a freemium version of its software for teams of up to 5 users. That year also saw the integration of a live text coeditor into its workspace to unify collaboration and task management. In late 2013 Wrike released a new feature set called Wrike Enterprise which included advanced analytics and other tools targeted at large business customers. Since then it has released several major updates to Wrike Enterprise, including a customizable spreadsheet called "Dynamic Platform" in late 2014 and custom workflows for teams in 2015. In July 2016, Wrike was updated with a set of add-on features under the name "Wrike for Marketers," which includes integrations with Adobe Photoshop, a tool for submitting requests, and proofing and approval tools for creative assets like videos and images. Wrike is available as native Android and iOS apps. Mobile apps include an interactive Gantt chart that syncs across devices. The apps are available offline, and sync when connection is restored. === Criticism === Critics said new users may have a learning curve with complex features. Wrike has 2,710 customers for an estimated 0.04% market share. Competitors include Google Workspace, Slack (software), and Quip (software).

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  Signals intelligence

  Signals intelligence (SIGINT) is the act and field of intelligence-gathering by interception of signals, whether communications between people (communications intelligence—abbreviated to COMINT) or from electronic signals not directly used in communication (electronic intelligence—abbreviated to ELINT). As classified and sensitive information is usually encrypted, signals intelligence may necessarily involve cryptanalysis (to decipher the messages). Traffic analysis—the study of who is signaling to whom and in what quantity—is also used to integrate information, and it may complement cryptanalysis. == History == === Origins === Electronic interceptions appeared as early as 1900, during the Boer War of 1899–1902. The British Royal Navy had installed wireless sets produced by Marconi on board their ships in the late 1890s, and the British Army used some limited wireless signalling. The Boers captured some wireless sets and used them to make vital transmissions. Since the British were the only people transmitting at the time, the British did not need special interpretation of the signals that they were. The birth of signals intelligence in a modern sense dates from the Russo-Japanese War of 1904–1905. As the Russian fleet prepared for conflict with Japan in 1904, the British ship HMS Diana stationed in the Suez Canal intercepted Russian naval wireless signals being sent out for the mobilization of the fleet, for the first time in history. === Development in World War I === Over the course of the First World War, a new method of signals intelligence reached maturity. Russia's failure to properly protect its communications fatally compromised the Russian Army's advance early in World War I and led to their disastrous defeat by the Germans under Ludendorff and Hindenburg at the Battle of Tannenberg. In 1918, French intercept personnel captured a message written in the new ADFGVX cipher, which was cryptanalyzed by Georges Painvin. This gave the Allies advance warning of the German 1918 Spring Offensive. The British in particular, built up great expertise in the newly emerging field of signals intelligence and codebreaking (synonymous with cryptanalysis). On the declaration of war, Britain cut all German undersea cables. This forced the Germans to communicate exclusively via either (A) a telegraph line that connected through the British network and thus could be tapped; or (B) through radio which the British could then intercept. Rear Admiral Henry Oliver appointed Sir Alfred Ewing to establish an interception and decryption service at the Admiralty; Room 40. An interception service known as 'Y' service, together with the post office and Marconi stations, grew rapidly to the point where the British could intercept almost all official German messages. The German fleet was in the habit each day of wirelessing the exact position of each ship and giving regular position reports when at sea. It was possible to build up a precise picture of the normal operation of the High Seas Fleet, to infer from the routes they chose where defensive minefields had been placed and where it was safe for ships to operate. Whenever a change to the normal pattern was seen, it immediately signalled that some operation was about to take place, and a warning could be given. Detailed information about submarine movements was also available. The use of radio-receiving equipment to pinpoint the location of any single transmitter was also developed during the war. Captain H.J. Round, working for Marconi, began carrying out experiments with direction-finding radio equipment for the army in France in 1915. By May 1915, the Admiralty was able to track German submarines crossing the North Sea. Some of these stations also acted as 'Y' stations to collect German messages, but a new section was created within Room 40 to plot the positions of ships from the directional reports. Room 40 played an important role in several naval engagements during the war, notably in detecting major German sorties into the North Sea. The battle of Dogger Bank was won in no small part due to the intercepts that allowed the Navy to position its ships in the right place. It played a vital role in subsequent naval clashes, including at the Battle of Jutland as the British fleet was sent out to intercept them. The direction-finding capability allowed for the tracking and location of German ships, submarines, and Zeppelins. The system was so successful that by the end of the war, over 80 million words, comprising the totality of German wireless transmission over the course of the war, had been intercepted by the operators of the Y-stations and decrypted. However, its most astonishing success was in decrypting the Zimmermann Telegram, a telegram from the German Foreign Office sent via Washington to its ambassador Heinrich von Eckardt in Mexico. === Postwar consolidation === With the importance of interception and decryption firmly established by the wartime experience, countries established permanent agencies dedicated to this task in the interwar period. In 1919, the British Cabinet's Secret Service Committee, chaired by Lord Curzon, recommended that a peace-time codebreaking agency should be created. The Government Code and Cypher School (GC&CS) was the first peace-time codebreaking agency, with a public function "to advise as to the security of codes and cyphers used by all Government departments and to assist in their provision", but also with a secret directive to "study the methods of cypher communications used by foreign powers". GC&CS officially formed on 1 November 1919, and produced its first decrypt on 19 October. By 1940, GC&CS was working on the diplomatic codes and ciphers of 26 countries, tackling over 150 diplomatic cryptosystems. The US Cipher Bureau was established in 1919 and achieved some success at the Washington Naval Conference in 1921, through cryptanalysis by Herbert Yardley. Secretary of War Henry L. Stimson closed the US Cipher Bureau in 1929 with the words "Gentlemen do not read each other's mail." === World War II === The use of SIGINT had even greater implications during World War II. The combined effort of intercepts and cryptanalysis for the whole of the British forces in World War II came under the code name "Ultra", managed from Government Code and Cypher School at Bletchley Park. Properly used, the German Enigma and Lorenz ciphers should have been virtually unbreakable, but flaws in German cryptographic procedures, and poor discipline among the personnel carrying them out, created vulnerabilities which made Bletchley's attacks feasible. Bletchley's work was essential to defeating the U-boats in the Battle of the Atlantic, and to the British naval victories in the Battle of Cape Matapan and the Battle of North Cape. In 1941, Ultra exerted a powerful effect on the North African desert campaign against German forces under General Erwin Rommel. General Sir Claude Auchinleck wrote that were it not for Ultra, "Rommel would have certainly got through to Cairo". Ultra decrypts featured prominently in the story of Operation SALAM, László Almásy's mission across the desert behind Allied lines in 1942. Prior to the Normandy landings on D-Day in June 1944, the Allies knew the locations of all but two of Germany's fifty-eight Western Front divisions. Winston Churchill was reported to have told King George VI: "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" Supreme Allied Commander, Dwight D. Eisenhower, at the end of the war, described Ultra as having been "decisive" to Allied victory. Official historian of British Intelligence in World War II Sir Harry Hinsley argued that Ultra shortened the war "by not less than two years and probably by four years"; and that, in the absence of Ultra, it is uncertain how the war would have ended. At a lower level, German cryptanalysis, direction finding, and traffic analysis were vital to Rommel's early successes in the Western Desert Campaign until British forces tightened their communications discipline and Australian raiders destroyed his principal SIGINT Company. == Technical definitions == The United States Department of Defense has defined the term "signals intelligence" as: A category of intelligence comprising either individually or in combination all communications intelligence (COMINT), electronic intelligence (ELINT), and foreign instrumentation signals intelligence (FISINT), however transmitted. Intelligence derived from communications, electronic, and foreign instrumentation signals. Being a broad field, SIGINT has many sub-disciplines. The two main ones are communications intelligence (COMINT) and electronic intelligence (ELINT). == Disciplines shared across the branches == === Targeting === A collection system has to know to look for a particular signal. "System", in this context, has several nuances. Targeting is the process of developing collection requirements: "1. A

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  Campus network

  A campus network, campus area network, corporate area network or CAN is a computer network made up of an interconnection of local area networks (LANs) within a limited geographical area. The networking equipments (switches, routers) and transmission media (optical fiber, copper plant, Cat5 cabling etc.) are almost entirely owned by the campus tenant / owner: an enterprise, university, government etc. A campus area network is larger than a local area network but smaller than a metropolitan area network (MAN) or wide area network (WAN). == University campuses == College or university campus area networks often interconnect a variety of buildings, including administrative buildings, academic buildings, laboratories, university libraries, or student centers, residence halls, gymnasiums, and other outlying structures, like conference centers, technology centers, and training institutes. Early examples include the Stanford University Network at Stanford University, Project Athena at MIT, and the Andrew Project at Carnegie Mellon University. == Corporate campuses == Much like a university campus network, a corporate campus network serves to connect buildings. Examples of such are the networks at Googleplex and Microsoft's campus. Campus networks are normally interconnected with high speed Ethernet links operating over optical fiber such as gigabit Ethernet and 10 Gigabit Ethernet. == Area range == The range of CAN is 1 to 5 km (1 to 3 mi). If two buildings have the same domain and they are connected with a network, then it will be considered as CAN only. Though the CAN is mainly used for corporate campuses so the link will be high speed.

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  CryptoParty

  CryptoParty (Crypto-Party) is a grassroots global endeavour to introduce the basics of practical cryptography such as the Tor anonymity network, I2P, Freenet, key signing parties, disk encryption and virtual private networks to the general public. The project primarily consists of a series of free public workshops. == History == As a successor to the Cypherpunks of the 1990s, CryptoParty was conceived in late August 2012 by the Australian journalist Asher Wolf in a Twitter post following the passing of the Cybercrime Legislation Amendment Bill 2011 and the proposal of a two-year data retention law in that country, the Cybercrime Legislation Amendment Bill 2011. The DIY, self-organizing movement immediately went viral, with a dozen autonomous CryptoParties being organized within hours in cities throughout Australia, the US, the UK, and Germany. Many more parties were soon organized or held in Chile, The Netherlands, Hawaii, Asia, etc. Tor usage in Australia itself spiked, and CryptoParty London with 130 attendees—some of whom were veterans of the Occupy London movement—had to be moved from London Hackspace to the Google campus in east London's Tech City. As of mid-October 2012 some 30 CryptoParties have been held globally, some on a continuing basis, and CryptoParties were held on the same day in Reykjavik, Brussels, and Manila. The first draft of the 442-page CryptoParty Handbook (the hard copy of which is available at cost) was pulled together in three days using the book sprint approach, and was released 2012-10-04 under a CC BY-SA license. === Edward Snowden involvement === In May 2014, Wired reported that Edward Snowden, while employed by Dell as an NSA contractor, organized a local CryptoParty at a small hackerspace in Honolulu, Hawaii on December 11, six months before becoming well known for leaking tens of thousands of secret U.S. government documents. During the CryptoParty, Snowden taught 20 Hawaii residents how to encrypt their hard drives and use the Internet anonymously. The event was filmed by Snowden's then-girlfriend, but the video has never been released online. In a follow-up post to the CryptoParty wiki, Snowden pronounced the event a "huge success." == Media response == In 2013, CryptoParty received messages of support from the Electronic Frontier Foundation and (purportedly) AnonyOps, as well as the NSA whistleblower Thomas Drake, WikiLeaks central editor Heather Marsh, and Wired reporter Quinn Norton. Eric Hughes, the author of A Cypherpunk's Manifesto nearly two decades before, delivered the keynote address, Putting the Personal Back in Personal Computers, at the Amsterdam CryptoParty on 2012-09-27. Marcin de Kaminski, founding member of Piratbyrån which in turn founded The Pirate Bay, regarded CryptoParty as the most important civic project in cryptography in 2012, and Cory Doctorow has characterized a CryptoParty as being "like a Tupperware party for learning crypto." Der Spiegel in December 2014 mentioned "crypto parties" in the wake of the Edward Snowden leaks in an article about the NSA.

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  Cognition Network Technology

  Cognition Network Technology (CNT), also known as Definiens Cognition Network Technology, is an object-based image analysis method developed by Nobel laureate Gerd Binnig together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods. To emulate the human mind's cognitive powers, Definiens used patented image segmentation and classification processes, and developed a method to render knowledge in a semantic network. CNT examines pixels not in isolation, but in context. It builds up a picture iteratively, recognizing groups of pixels as objects. It uses the color, shape, texture and size of objects as well as their context and relationships to draw conclusions and inferences, similar to human analysis. == History == In 1994 Professor Gerd Binnig founded Definiens. CNT was first available with the launch of the eCognition software in May 2000. In June 2010, Trimble Navigation Ltd (NASDAQ: TRMB) acquired Definiens business asset in earth sciences markets, including eCognition software, and also licensed Definiens' patented CNT. In 2014, Definiens was acquired by MedImmune, the global biologics research and development arm of AstraZeneca, for an initial consideration of $150 million. == Software == Definiens Tissue Studio Definiens Tissue Studio is a digital pathology image analysis software application based on CNT. The intended use of Definiens Tissue Studio is for biomarker translational research in formalin-fixed, paraffin-embedded tissue samples which have been treated with immunohistochemical staining assays, or hematoxylin and eosin (H&E). The central concept behind Definiens Tissue Studio is a user interface that facilitates machine learning from example digital histopathology images to derive an image analysis solution suitable for the measurement of biomarkers and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments. The derived image analysis solution is then automatically applied to subsequent digital images to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive cancer and other diseases. Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid. eCognition The eCognition suite offers three components that can be used stand-alone or in combination to solve image analysis tasks. eCognition Developer is a development environment for object-based image analysis. It is used in earth sciences to develop rule sets (or applications) for the analysis of remote sensing data. eCognition Architect enables non-technical users to configure, calibrate and execute image analysis workflows created in eCognition Developer. eCognition Server software provides a processing environment for batch execution of image analysis jobs. eCognition software is utilized in numerous remote sensing and geospatial application scenarios and environments, using a variety of data types: Generic: Rapid Mapping, Change Detection, Object Recognition By environment: Diverse Landcover Mapping, Urban Analysis (i.e. impervious surface area analysis for taxation, property assessment for insurance, inventory of green infrastructure), Forestry (i.e. biomass measurement, species identification, firescar measurement), Agriculture (i.e. regional planning, precision farming, crisis response), Marine and Riparian (i.e. ecosystem evaluation, disaster management, harbor monitoring). Other: Defense, security, atmosphere and climate The online eCognition community was launched in July 2009 and had 2813 members as of July 9, 2010. Membership is distributed globally and user conferences are held regularly, the last having taken place in November 2009 in Munich, Germany. The bi-annual GEOBIA (Geographic Object-Based Image Analysis) conference is heavily attended by eCognition users, with the majority of presentations based on eCognition software.

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  Information security

  Information security is the practice of protecting information by mitigating information risks. It is part of information risk management. It typically involves preventing or reducing the probability of unauthorized or inappropriate access to data or the unlawful use, disclosure, disruption, deletion, corruption, modification, inspection, recording, or devaluation of information. It also involves actions intended to reduce the adverse impacts of such incidents. Protected information may take any form, e.g., electronic or physical, tangible (e.g., paperwork), or intangible (e.g., knowledge). Information security's primary focus is the balanced protection of data confidentiality, integrity, and availability (known as the CIA triad, unrelated to the US government organization) while maintaining a focus on efficient policy implementation, all without hampering organization productivity. This is largely achieved through a structured risk management process. To standardize this discipline, academics and professionals collaborate to offer guidance, policies, and industry standards on passwords, antivirus software, firewalls, encryption software, legal liability, security awareness and training, and so forth. This standardization may be further driven by a wide variety of laws and regulations that affect how data is accessed, processed, stored, transferred, and destroyed. While paper-based business operations are still prevalent, requiring their own set of information security practices, enterprise digital initiatives are increasingly being emphasized, with information assurance now typically being dealt with by information technology (IT) security specialists. These specialists apply information security to technology (most often some form of computer system). IT security specialists are almost always found in any major enterprise/establishment due to the nature and value of the data within larger businesses. They are responsible for keeping all of the technology within the company secure from malicious attacks that often attempt to acquire critical private information or gain control of the internal systems. There are many specialist roles in Information Security including securing networks and allied infrastructure, securing applications and databases, security testing, information systems auditing, business continuity planning, electronic record discovery, and digital forensics. == Standards == Information security standards are guidelines generally outlined in published materials that aim to protect a user's or an organization's cyber environment from threats. This environment includes the users themselves, hardware such as devices and networks, software such as applications or services, and any information in storage or transit. These standards comprise security concepts, technologies, and guidelines to deal with an adverse event. They may also include assessment criteria and certification for organizations implementing a minimum level of security. These standards are developed by various international and national bodies to prevent or mitigate cyber-attacks, ensure consistency among developers, and establish a minimum standard in industries susceptible to an attack. The ISO/IEC 27000 family, published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), provides information about the guidelines and requirements for an Information Security Management System (ISMS). The Common Criteria (ISO/IEC 15408) provides guidelines on evaluating and certifying the security of a system. The IEC 62443 establishes security standards for automation and control systems. Similarly, the ISO/SAE 21434, ETSI EN 303 645, and EN 18031 provide standards for road vehicles, the Internet of Things, and radio-based systems respectively. The NIST Cybersecurity Framework (NIST CSF) is a set of guidelines developed by the U.S. National Institute of Standards and Technology to help organizations with risk management. NIST also publishes various Federal Information Processing Standards (FIPS) and Special Publications. The United Kingdom has introduced Cyber Essentials, which is a certification scheme to protect organizations against common security threats. The Australian Cyber Security Centre publishes the Essential Eight mitigation strategies. The Payment Card Industry Data Security Standard (PCI DSS) regulates handling of cardholder data in order to reduce credit card fraud. UL has published standards related to specific industries such as UL 2900-2-3 for security and life safety signaling systems and UL-2900-2-1 for healthcare and wellness systems. == Threats == Information security threats come in many different forms. Some of the most common threats today are software attacks, theft of intellectual property, theft of identity, theft of equipment or information, sabotage, and information extortion. Viruses, worms, phishing attacks, and Trojan horses are a few common examples of software attacks. The theft of intellectual property has also been an extensive issue for many businesses. Identity theft is the attempt to act as someone else usually to obtain that person's personal information or to take advantage of their access to vital information through social engineering. Sabotage usually consists of the destruction of an organization's website in an attempt to cause loss of confidence on the part of its customers. Information extortion consists of theft of a company's property or information as an attempt to receive a payment in exchange for returning the information or property back to its owner, as with ransomware. One of the most functional precautions against these attacks is to conduct periodical user awareness. Governments, military, corporations, financial institutions, hospitals, non-profit organizations, and private businesses amass a great deal of confidential information about their employees, customers, products, research, and financial status. Should confidential information about a business's customers or finances or new product line fall into the hands of a competitor or hacker, a business and its customers could suffer widespread, irreparable financial loss, as well as damage to the company's reputation. From a business perspective, information security must be balanced against cost; the Gordon-Loeb Model provides a mathematical economic approach for addressing this concern. For the individual, information security has a significant effect on privacy, which is viewed very differently in various cultures. == History == Since the early days of communication, diplomats and military commanders understood that it was necessary to provide some mechanism to protect the confidentiality of correspondence and to have some means of detecting tampering. Julius Caesar is credited with the invention of the Caesar cipher c. 50 B.C., which was created in order to prevent his secret messages from being read should a message fall into the wrong hands. However, for the most part protection was achieved through the application of procedural handling controls. Sensitive information was marked up to indicate that it should be protected and transported by trusted persons, guarded and stored in a secure environment or strong box. As postal services expanded, governments created official organizations to intercept, decipher, read, and reseal letters (e.g., the U.K.'s Secret Office, founded in 1653). In the mid-nineteenth century more complex classification systems were developed to allow governments to manage their information according to the degree of sensitivity. For example, the British Government codified this, to some extent, with the publication of the Official Secrets Act in 1889. Section 1 of the law concerned espionage and unlawful disclosures of information, while Section 2 dealt with breaches of official trust. A public interest defense was soon added to defend disclosures in the interest of the state. A similar law was passed in India in 1889, The Indian Official Secrets Act, which was associated with the British colonial era and used to crack down on newspapers that opposed the Raj's policies. A newer version was passed in 1923 that extended to all matters of confidential or secret information for governance. By the time of the First World War, multi-tier classification systems were used to communicate information to and from various fronts, which encouraged greater use of code making and breaking sections in diplomatic and military headquarters. Encoding became more sophisticated between the wars as machines were employed to scramble and unscramble information. The establishment of computer security inaugurated the history of information security. The need for such appeared during World War II. The volume of information shared by the Allied countries during the Second World War necessitated formal alignment of classification systems and procedural controls. An arcane range of markings evol

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  Social Media (Age-Restricted Users) Bill

  The Social Media (Age-Restricted Users) Bill is a member's bill by National Party Member of Parliament Catherine Wedd that seeks to ban children under the age of 16 years from accessing social media by forcing social media companies to implement age verification measures. It is modelled after the Australian government's Online Safety Amendment. In mid October 2025, the New Zealand Parliament confirmed plans to introduce the social media age restriction bill. == Background == In late November 2024, the Albanese government of Australia, with support from the opposition Coalition parties, passed the Online Safety Amendment creating a world-first age verification regime targeting social media platforms operating in the country. The ban targets several social media platforms including Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X (formerly Twitter) and YouTube. These platforms were required to implement age verification systems and to remove under-age users by 10 December 2025, when the law change came into effect. == Draft provisions == The draft Social Media (Age-Restricted Users) Bill defines social media platforms as electronic platforms that enable social media interactions between two or more end-users, facilitates communication between multiple end-users and allows users to post content on the platform. The proposed bill requires social media companies to take action to prevent users under the age of 16 from creating accounts on their platforms. It also creates a framework for courts to impose fines on platforms that fail to take reasonable steps to prevent underaged users from accessing the platform. == Legislative history == === Draft legislation === On 6 May 2025, Wedd announced a private member's bill called the "Social Media (Age-Restricted Users) Bill" that would bar access to social media platforms for people under the age of 16 years. She said that she was motivated as the mother of four children to support families, parents and teachers' efforts to manage their children's online exposure and the passage of the Australian Online Safety Amendment legislation in December 2024. Since National's coalition partner ACT New Zealand had refused to support the bill, the Sixth National Government announce it as a member's bill rather than a government bill. Prime Minister Christopher Luxon has confirmed that National would seek cross-party support for the legislation. ACT MP and the Minister of Internal Affairs Brooke van Velden said that the Government would watch the implementation of the Australian social media age restriction policy. In October 2025, Wedd's bill was drawn from the parliamentary ballot. In addition, Labour Reuben Davidson drafted a similar member's bill that would hold social media providers responsible for restricting "harmful content" and imposed NZ$50,000 fines for non-compliance. In November 2025, Luxon reiterated his support for social media age restriction legislation and said the New Zealand government would introduce a bill in 2026 before the 2026 New Zealand general election. He also confirmed that Education Minister Erica Stanford was leading an investigation into what lessons could be learnt from the Australian legislation. At the request of ACT MP Parmjeet Parmar, Parliament's Education and Workforce Committee held an inquiry into a proposed social media ban in early October 2025. The committee was led by National MP Carl Bates and received 430 submissions from 400 groups and individuals. The committee also heard from 87 in-person submissions. On 10 December 2025, the committee made 12 recommendations including restricting social media access to persons under the age of 16, re-evaluating existing legislation such as the Films, Videos, and Publications Classification Act and the Harmful Digital Communications Act 2015, and regulating online platforms and Internet service providers. The ACT party released a dissenting view disagreeing with the need for a law restricting social media access to under-16 year olds. In mid-May 2026, the Government confirmed that work on the proposed bill to ban under-16 year olds from social media had been paused. The New Zealand Parliament held a debate on the proposed bill on 13 May following a select committee inquiry into the harms caused by social media platforms. While the opposition Labour Party has agreed to support the member's bill, the ACT and Green parties opposed the proposed bill on the grounds that the rules were easy to circumvent, that at-risk groups could become more isolated, and that social media also harmed other age groups. == Responses == === Academia and civil society === In late July 2025, the New Zealand Council for Civil Liberties (NZCCL) expressed concern that the proposed social media age restriction could infringe upon the New Zealand Bill of Rights Act 1990, the Privacy Act 2020 and the United Nations' Convention on the Rights of the Child. The NZCCL also questioned the practicality of age verification software, a social media age limit and whether it would fulfil its stated goal of combating online harm. In August 2025, University of Auckland criminologist and senior lecturer Claire Meehan expressed concern that the social media age restriction legislation would cut children from their friendship and support networks. She also said that children and young people were digital natives who could use VPNs to circumvent the ban. Similar sentiments were echoed by Victoria University of Wellington media and communications lecturer Alex Beattie and "Ocean Today" Instagram social media influencer "Charlie." In October 2025, New Zealand Initiative representative Dr Eric Crampton expressed concern that a social media age restriction would involve the introduction of digital IDs. He argued that a new law was unnecessary and said that parents could limit their children's exposure to social media via Google's Family Link and Apple's equivalent. Similarly, Institute of Economic Affairs public policy fellow Matthew Lesh and the British Free Speech Union expressed concerns that young people could use VPNs to circumvent a social media ban, citing the spike in VPN usage in the United Kingdom following the passage of the Online Safety Act 2023. The advocacy group B416's co-chair Anna Curzon advocated for a social media ban on underage users, stating that social media apps "are made to be addictive" and made it difficult for parents to relate with their children. In late November 2025, B416's co-founder Anna Mowbray expressed support for the Government's social media age restriction bill but expressed disappointment that Luxon had not timed his announcement with the launch of the group's campaign. Generation-Z Aotearoa co-founder Lola Fisher has called on the New Zealand Government to consult with young people on the development of the legislation. === Government agencies and departments === In early October 2025, Privacy Commissioner Michael Webster expressed concern that social media platforms requiring users to prove their age via digital IDs could raise privacy concerns. Webster suggested that age verification systems could relay on various documents including passports. He said that age estimation technologies had high error rates and that age inference technologies relied on data mining. === Political parties === In early May 2025, the National Party government expressed support for a social media age restriction legislation. By contrast, its coalition partner ACT has opposed such legislation. ACT leader David Seymour described the ban as hasty and unworkable since it did not involve parents. Meanwhile, New Zealand First leader Winston Peters expressed support for a social media age restriction but said the bill should be subject to a select committee inquiry. The opposition Labour Party leader Chris Hipkins has expressed interest in a social media age restriction legislation but emphasised the need for consensus. Meanwhile, Green Party co-leader Chlöe Swarbrick said she wanted to learn more about the bill but described it as simplistic. Fellow Greens co-leader Marama Davidson said that the proposed bill would punish children and young people for the harm caused by big tech platforms. === Tech companies === In early October 2025, representatives of TikTok and Meta Platforms cautioned against proposed social media ban on under-16 years olds. During a one-day parliamentary inquiry, Ella Woods-Joyce, TikTok's public policy lead for Australia and New Zealand, and Mia Garlick, Meta's regional director of policy, expressed concern that the social media age restriction could send children and young people to less regulated online spaces. Woods-Joyce highlighted TikTok's policy of closing down accounts belonging to users under the age of 13 years while Garlick highlighted Meta's policy of placing users under the age of 16 in private accounts by default. In early February 2026 Meta's vice president and global head of safety, Antigone Da

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  Experimental SAGE Subsector

  The Experimental Semi-Automatic Ground Environment (SAGE) Sector (ESS, Experimental SAGE Subsector until planned Sectors/Subsectors were renamed NORAD Regions, Divisions, and Sectors) was a prototype Cold War Air Defense Sector for developing the Semi Automatic Ground Environment. The Lincoln Laboratory control center in a new building was at Lexington, Massachusetts. == ESS Computer System == The network's Direction Center was completed in a new 1954 building (Building F, 42°27′37″N 071°16′04″W) with prototype peripherals and a single IBM XD-1 computer, a successor to Lincoln Lab's Whirlwind I computer (WWI). In 1955, Air Force personnel began IBM training at the Kingston, New York, prototype facility, and the "4620th Air Defense Wing (experimental SAGE) was established at Lincoln Laboratory"—its "primary mission was computer programming". ESS had a capacity of 48 tracks and used a pre-SAGE ground environment in a "prototype intercept monitor room [at] MIT's Barta building" with "track situation displays, which geographically showed Air Defense Identification Zone lines and antiaircraft circles [and] each console also had a 5-inch CRT for digital information display. Audible alert signals were used, with a different signal for each symbol on a situation display." == Radar stations == Initial service test models of the Burroughs AN/FST-2 Coordinate Data Transmitting Set were placed with radars at South Truro and West Bath, Maine; followed by Texas Tower#2 (TT2) in the Atlantic Ocean, which provided a "triangular pattern with overlap" radar coverage (TT2 later had a connection from the XD-1 via the GE G/A Data Link Output Subsystem through North Truro Air Force Station.) By August 1955, 13 radar stations were networked by the subsector, e.g.: Chatham Clinton, Massachusetts with gap-filler radar Great Boars Head Halibut Point Killingly, Connecticut (41.865734°N 71.820958°W / 41.865734; -71.820958).with gap-filler radar Rockport Air Force Station Scituate, Massachusetts South Truro West Bath, Maine (43°54′7″N 69°50′43″W) with AN/FPS-31 on Jug Handle Hill: ("Lincoln Laboratories experimental radar station") Required by 21 November 1955 were 44 consoles: 38 for the operations floor, 3 on the computer floor for display maintenance, and 3 near the maintenance console (program checkout). WWI was connected to the Experimental SAGE Subsector to verify crosstelling (collateral communication) with the ESS DC, and WWI was also used for a Ground-to-Air (G/A) experiment using a transmitter of the GE G/A Data Link Output Subsystem on Prospect Hill, Waltham, MA sending data to simulated airborne equipment at Lexington. Transmissions from the WWI SAGE Evaluation (WISE) computer system to XD-1 and back were without error by December 1955 when operational software specifications were frozen. Operating procedures for the ESS external sites were complete in March 1956, and == System Operation Testing == From November 15, 1955, to November 7, 1956, three System Operation Tests were conducted which used voice "Ground-to-Air" communication from the Barta control room to aircraft outfitted with SAGE receivers (F-86 interceptors modified to F-86L models in "Project FOLLOW-ON".) Test teams included employees of Bell Telephone Laboratories, Western Electric-ADES, IBM, the RAND Corporation, and Lincoln Labs' Division 6, Division 3, & Division 2 (Division 6 had been created for ESS support.) The North Truro P-10 AN/FST-2 was moved to Almaden Air Force Station (M-96)c. 1957-8 and on August 7, 1958, control of an airborne BOMARC missile that had malfunctioned transferred from the "Experimental SAGE Sector" to a Westinghouse AN/GPA-35 Ground Environment system and the missile crashed into the Atlantic Ocean. By December 31, 1958, ADC Manual 55-28 described the Model 3 SAGE System. == 1959 Experimental Testing == "To prove out the revised SAGE computer program" for Automatic Targeting and Battery Evaluation and ADDC-AADCP crosstelling, a "SAGE/Missile Master" test was conducted beginning in September 1959 with communications between the ESS XD-1 and Martin AN/FSG-1 Antiaircraft Defense System equipment at Fort Banks planned for the CONAD Joint Control Center at Fort Heath—a "SAGE ATABE Simulation Study" (SASS) was also completed 1959–60 by MITRE Corporation.

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  MobileNet

  MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite. The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet. As of June 2025, the family has five versions, each improving upon the previous one in terms of performance and efficiency. == Features == === V1 === MobileNetV1 was published in April 2017. Its main architectural innovation was incorporation of depthwise separable convolutions. It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size. The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel independently and a pointwise convolution ( 1 × 1 {\displaystyle 1\times 1} convolution) that combines the outputs of the depthwise convolution. This factorization significantly reduces computational cost. The MobileNetV1 has two hyperparameters: a width multiplier α {\displaystyle \alpha } that controls the number of channels in each layer. Smaller values of α {\displaystyle \alpha } lead to smaller and faster models, but at the cost of reduced accuracy, and a resolution multiplier ρ {\displaystyle \rho } , which controls the input resolution of the images. Lower resolutions result in faster processing but potentially lower accuracy. === V2 === MobileNetV2 was published in March 2019. It uses inverted residual layers and linear bottlenecks. Inverted residuals modify the traditional residual block structure. Instead of compressing the input channels before the depthwise convolution, they expand them. This expansion is followed by a 1 × 1 {\displaystyle 1\times 1} depthwise convolution and then a 1 × 1 {\displaystyle 1\times 1} projection layer that reduces the number of channels back down. This inverted structure helps to maintain representational capacity by allowing the depthwise convolution to operate on a higher-dimensional feature space, thus preserving more information flow during the convolutional process. Linear bottlenecks removes the typical ReLU activation function in the projection layers. This was rationalized by arguing that that nonlinear activation loses information in lower-dimensional spaces, which is problematic when the number of channels is already small. === V3 === MobileNetV3 was published in 2019. The publication included MobileNetV3-Small, MobileNetV3-Large, and MobileNetEdgeTPU (optimized for Pixel 4). They were found by a form of neural architecture search (NAS) that takes mobile latency into account, to achieve good trade-off between accuracy and latency. It used piecewise-linear approximations of swish and sigmoid activation functions (which they called "h-swish" and "h-sigmoid"), squeeze-and-excitation modules, and the inverted bottlenecks of MobileNetV2. === V4 === MobileNetV4 was published in September 2024. The publication included a large number of architectures found by NAS. Inspired by Vision Transformers, the V4 series included multi-query attention. It also unified both inverted residual and inverted bottleneck from the V3 series with the "universal inverted bottleneck", which includes these two as special cases. === V5 === MobileNetV5's architecture was published shortly after the release of Gemma 3n in June 2025. While the announcement stated a technical report on MobileNetV5 would be available soon, this has not yet materialised. The network is 10 times larger than the largest V4 variant.

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  Semiotics of social networking

  The semiotics of social networking discusses the images, symbols and signs used in systems that allow users to communicate and share experiences with each other. Examples of social networking systems include Facebook, Twitter and Instagram. == Semiotics == Semiotics is a discipline that studies images, symbols, signs and other similarly related objects in an effort to understand their use and meaning. Semiotic structuralism seeks the meaning of these objects within a social context. Post-structuralist theories take tools from structuralist semiotics in combination with social interaction, creating social semiotics. Social semiotics is “a branch of the field of semiotics which investigates human signifying practices in specific social and cultural circumstances and which tries to explain meaning-making as a social practice.” “Social semiotics also examines semiotic practices, specific to a culture and community, for the making of various kinds of texts and meanings in various situational contexts and contexts of culturally meaningful activity”. Social semiotics is concerned with studying human interactions. == Social networking == Social networking is the communication among people within a virtual social space. This medium of communication allows insight into the significance of social semiotics. “Millions of people now interact through blogs, collaborate through wikis, play multiplayer games, publish podcasts and video, build relationships through social network sites and evaluate all the above forms of communication through feedback and ranking mechanisms”. Social semiotics “unlike speech, writing necessitates some sort of technology in the form of person device interaction”. Social semiotics functions through the triad of communication or Peircean semiotics in the form of sign, object, interpretant (Chart 1) and “Human, Machine, Tag (Information)” (Chart 2). In Peircean semiotics (Chart 1), "A sign…[in the form of representamen] is something which stands to somebody for something in some respect or capacity. It addresses somebody, that is, creates in the mind of that person an equivalent sign, or perhaps a more developed sign. That sign which it creates I call the interpretant of the first sign. The sign stands for an object, not in all respects, but in reference to a sort of idea which I have something called the ground of the representamen". This example of the triangle of Human, Machine, Tag is shown when looking at tagging photographs on Facebook (Chart 3). The Human takes the photo on a camera and puts the digital file (information) on the Machine, the Machine is then navigated to Facebook where the file is downloaded. The Human has the Machine Tag the photo with information (e. g., names, places, data) for other Humans to see. This process then can be continued (see Chart 2). “Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing large amounts of information”.

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  Control-flow diagram

  A control-flow diagram (CFD) is a diagram to describe the control flow of a business process, process or review. Control-flow diagrams were developed in the 1950s, and are widely used in multiple engineering disciplines. They are one of the classic business process modeling methodologies, along with flow charts, drakon-charts, data flow diagrams, functional flow block diagram, Gantt charts, PERT diagrams, and IDEF. == Overview == A control-flow diagram can consist of a subdivision to show sequential steps, with if-then-else conditions, repetition, and/or case conditions. Suitably annotated geometrical figures are used to represent operations, data, or equipment, and arrows are used to indicate the sequential flow from one to another. There are several types of control-flow diagrams, for example: Change-control-flow diagram, used in project management Configuration-decision control-flow diagram, used in configuration management Process-control-flow diagram, used in process management Quality-control-flow diagram, used in quality control. In software and systems development, control-flow diagrams can be used in control-flow analysis, data-flow analysis, algorithm analysis, and simulation. Control and data are most applicable for real time and data-driven systems. These flow analyses transform logic and data requirements text into graphic flows which are easier to analyze than the text. PERT, state transition, and transaction diagrams are examples of control-flow diagrams. == Types of control-flow diagrams == === Process-control-flow diagram === A flow diagram can be developed for the process [control system] for each critical activity. Process control is normally a closed cycle in which a sensor. The application determines if the sensor information is within the predetermined (or calculated) data parameters and constraints. The results of this comparison, which controls the critical component. This [feedback] may control the component electronically or may indicate the need for a manual action. This closed-cycle process has many checks and balances to ensure that it stays safe. It may be fully computer controlled and automated, or it may be a hybrid in which only the sensor is automated and the action requires manual intervention. Further, some process control systems may use prior generations of hardware and software, while others are state of the art. === Performance-seeking control-flow diagram === The figure presents an example of a performance-seeking control-flow diagram of the algorithm. The control law consists of estimation, modeling, and optimization processes. In the Kalman filter estimator, the inputs, outputs, and residuals were recorded. At the compact propulsion-system-modeling stage, all the estimated inlet and engine parameters were recorded. In addition to temperatures, pressures, and control positions, such estimated parameters as stall margins, thrust, and drag components were recorded. In the optimization phase, the operating-condition constraints, optimal solution, and linear-programming health-status condition codes were recorded. Finally, the actual commands that were sent to the engine through the DEEC were recorded.

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