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  • Key–value database

    Key–value database

    A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. Key-value databases differ from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well-defined data types. Exposing the data types to the database program allows it to apply various optimizations. In contrast, key-value systems treat the value as opaque to the database itself, and typically support only simple operations such as storing, retrieving, updating, and deleting a value by its key. This offers considerable flexibility and makes such systems well suited to low-latency, high-throughput workloads dominated by direct key lookups, but less suitable for applications that require complex queries or explicit relationships among records. A lack of standardization, limited transaction support, and relatively simple query interfaces long restricted many key-value systems to specialized uses, but the rapid move to cloud computing after 2010 helped drive renewed interest in them as part of the broader NoSQL movement. Some graph databases, such as ArangoDB, are also key–value databases internally, adding the concept of relationships (pointers) between records as a first-class data type. == Types and examples == Key–value systems span a wide consistency spectrum, from eventually consistent designs to strongly consistent or serializable ones, and some allow the consistency level to be configured as part of the trade-off against latency and availability. Renewed interest in key–value and other NoSQL systems was driven in part by the demands of big data, distributed, and cloud applications. Their scalability and availability made them attractive for cloud data management, although limited transaction support, low-level query interfaces, and the lack of standardization remained obstacles to wider adoption. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks. Some key–value systems add additional structure to their keys. For example, Oracle NoSQL Database organizes records using composite keys with "major" and "minor" components, an arrangement that Oracle compares to a directory-path structure in a file system. More generally, however, key–value stores are defined by their use of unique keys associated with opaque values and by their emphasis on simple key-based operations. Unix included dbm (database manager), a minimal database library written by Ken Thompson for managing associative arrays with a single key and hash-based access. Later implementations and related libraries included sdbm, GNU dbm (gdbm), and Berkeley DB. A more recent example is RocksDB, a persistent key–value storage engine developed at Facebook and designed for large-scale applications. Other examples include in-memory systems such as Memcached and Redis, and persistent systems such as Berkeley DB, Riak, and Voldemort.

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  • Reference Software International

    Reference Software International

    Reference Software International, Inc. (RSI), was an American software developer active from 1985 to 1993 and based in Albuquerque, New Mexico, and San Francisco, California. The company released several productivity and reference software packages, including the Grammatik grammar checker, for MS-DOS. The company was acquired by WordPerfect Corporation in 1993. == History == === Background (1980–1985) === Reference Software International, Inc., was founded by Donald "Don" Emery and Bruce Wampler in 1985 in San Francisco, California. Both Wampler and Emery were college professors when they founded RSI: Wampler at the University of New Mexico as a professor of computer science and Emery a professor of marketing at San Francisco State University. After graduating from the University of Utah in around 1978, Wampler founded his first software company, Aspen Software, in Tijeras, New Mexico, in 1979. Wampler founded Aspen to develop an early spell checker software package, called Proofreader, for the TRS-80, licensing Random House's Webster's Unabridged Dictionary for the package's lexicon. In 1980, he began development on a grammar checker inspired by Writer's Workbench, a pioneering grammar checker for Unix systems. Wampler used Writer's Workbench heavily during the writer of his doctoral dissertation but disliked having to jump between the Apple II on which he composed the dissertation and the mainframe on which Writer's Workbench ran, and so wanted to develop a version of the latter for microcomputers. Wampler's work came to fruition as Grammatik in 1981, eventually ported to several other microcomputer platforms in the early 1980s. In 1983, by which point the company had 12 employees and sold a combined 80,000 units of Grammatik and Proofreader, Wampler sold Aspen to Dictronics, a software company best known for developing the Electronic Thesaurus, an early thesaurus program for microcomputers. Dictronics was in turn purchased by Wang Laboratories; according to Wampler, "Wang bought [Aspen] and sat on it. They did nothing with it". Wampler moved on to teach for the University of New Mexico, but, frustrated by Wang's inaction, got the urge to resurrect his work. In 1985, he was able to license back Grammatik and Proofreader from a small California-based software firm that had grandfathered rights to a forked version of both. In the same year, he met Emery, who, impressed by Wampler's, founded Reference Software International to market his software. RSI's research and development headquarters were based in Albuquerque, while the company's sales and marketing department was based in Walnut Creek, California. === Success (1985–1992) === In August 1985, RSI released their first product: the Random House Reference Set, a new version of Proofreader for the IBM Personal Computer and compatibles, revised to be a terminate-and-stay-resident program that ran atop other word processors such as WordStar or WordPerfect. At the time, Reference Set was the only such program on the market that functioned like this. RSI netted $114,000 from sales of Reference Set by the end of 1985. In June 1986, they released version 2.0 of Grammatik as Grammatik II for the PC. The latter was a breakout hit for RSI, receiving praise in the press (including technology journals such as PC Magazine) and RSI selling 1,000 units a month. In spring 1987, they released Reference Set II, which allowed users to import their own words into the built-in dictionary and added a thesaurus of 300,000 words. In November 1987, they released version 3.0 of Reference Set, which comprised two new field-specific dictionaries for the medical and legal professions. As well as the general Random House dictionary and thesaurus, it included Stedman's Medical Dictionary and Black's Law Dictionary. Emery consulted Paul Brest and Bob Jackson—professors of law at Stanford Law School and San Francisco State respectively—for the curation of the law dictionary; and Burton Grebin—at the time the executive director of Mount Saint Mary's Hospital—for the curation of the medical dictionary. In fall 1988, the company released Grammatik III, a total rewrite that made use of artificial intelligence to more accurately judge the grammar of sentences by breaking them down into a syntactic hierarchy. Grammatik III received universal acclaim, with Gloria Morris of InfoWorld calling it the apparent leader in the grammar checking field and Sandra Anderson of Mac Home Journal calling it "hands down ... the best of the industry" six years after its release. By 1989, the product had competitors in Correct Grammar by Lifetree Software and RightWriter by Rightsoft, Inc. By 1990, RSI achieved annual sales of $9.7 million. In the same year they released Grammatik IV, which was the first to offer direct integration with WordPerfect on both MS-DOS and Windows. In March 1992—by which point RSI had sold 1.5 million copies of Grammatik across all versions—the company released version 5 of the program, another rewrite that updated the lexicon further and added new functions such as word redundancy detection. Around the same time, the company introduced Easy Proof, a pared-down version of Grammatik intended for novice writers, students, and family computers. In 1991, the company was engaged in a trademark dispute with Systems Compatibility Corporation (SCC) of Chicago, Illinois, over the rights to the Software Toolkit title. Both companies had published software bundles bearing the name in the turn of the 1990s; SCC had published theirs first in 1988 and registered the trademark with the USPTO. SCC was granted a restraining order against RSI in January 1991. The following month, RSI agreed to rename their product, preventing a protracted legal battle. === Decline and acquisition (1992–1993) === By early 1992, RSI achieved annual sales of more than $13 million, employed 120 people, and had opened international offices in London, Belgium, and Antwerp to sell foreign versions of Reference Set and Grammatik. The company reached peak employment in the middle of 1992, with 140 employees. However, RSI's launch of six disparate titles in the year proved problematic for the company when they failed to sell as well as they had projected, and the company laid off employees by the dozens. By December 1992, only 71 employees were left, 32 from their San Francisco office. On the last day of 1992, RSI received an acquisition offer from WordPerfect Corporation, makers of the namesake word processor based in Orem, Utah. The deal was inked in January 1993, RSI's stakeholders receiving $19 million. The company's remaining employees were absorbed into WordPerfect in Orem. WordPerfect continued selling Grammatik as a standalone product for several years.

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  • Fatsecret

    Fatsecret

    Fatsecret, commonly styled as fatsecret, is a mobile application, website and API that helps people achieve their weight loss goals and find accurate nutrition information. It also offers a weight loss clinic with coaching and medically supported programs. The platform powers global health apps. == History == Fatsecret was founded in 2006 in Melbourne, Australia by Lenny Moses and Rodney Moses. As of 2019, Lenny serves as the company's CEO. The company is known for its calorie counting and meal tracking app, and by April 2016, the company claimed to have 45 million users of its services. In August 2018, a premium version of its app was released. Since August 2009, the company has operated the Fatsecret Platform API, which allows access to its global food and nutrition database. Fatsecret reportedly had 900,000 downloads of its app in January 2020. In an analysis of several Health & Fitness app subcategories for the United States in January 2021, Fatsecret was reported to have the highest 30 day user retention rate of top Calorie Counter + Meal Planner for Weight Loss apps.

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  • Turret lathe

    Turret lathe

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

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  • Adversarial stylometry

    Adversarial stylometry

    Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in its ability to unmask anonymous authors or to link pseudonyms to an author's other identities, which, for example, creates difficulties for whistleblowers, activists, and hoaxers and fraudsters. The privacy risk is expected to grow as machine learning techniques and text corpora develop. All adversarial stylometry shares the core idea of faithfully paraphrasing the source text so that the meaning is unchanged but the stylistic signals are obscured. Such a faithful paraphrase is an adversarial example for a stylometric classifier. Several broad approaches to this exist, with some overlap: imitation, substituting the author's own style for another's; translation, applying machine translation with the hope that this eliminates characteristic style in the source text; and obfuscation, deliberately modifying a text's style to make it not resemble the author's own. Manually obscuring style is possible, but laborious; in some circumstances, it is preferable or necessary. Automated tooling, either semi- or fully-automatic, could assist an author. How best to perform the task and the design of such tools is an open research question. While some approaches have been shown to be able to defeat particular stylometric analyses, particularly those that do not account for the potential of adversariality, establishing safety in the face of unknown analyses is an issue. Ensuring the faithfulness of the paraphrase is a critical challenge for automated tools. It is uncertain if the practice of adversarial stylometry is detectable in itself. Some studies have found that particular methods produced signals in the output text, but a stylometrist who is uncertain of what methods may have been used may not be able to reliably detect them. == History == Rao & Rohatgi (2000), an early work in adversarial stylometry, identified machine translation as a possibility, but noted that the quality of translators available at the time presented severe challenges. Kacmarcik & Gamon (2006) is another early work. Brennan, Afroz & Greenstadt (2012) performed the first evaluation of adversarial stylometric methods on actual texts. Brennan & Greenstadt (2009) introduced the first corpus of adversarially authored texts specifically for evaluating stylometric methods; other corpora include the International Imitation Hemingway Competition, the Faux Faulkner contest, and the hoax blog A Gay Girl in Damascus. == Motivations == Rao & Rohatgi (2000) suggest that short, unattributed documents (i.e., anonymous posts) are not at risk of stylometric identification, but pseudonymous authors who have not practiced adversarial stylometry in producing corpuses of thousands of words may be vulnerable. Narayanan et al. (2012) attempted large-scale deanonymisation of 100,000 blog authors with mixed results: the identifications were significantly better than chance, but only accurately matched the blog and author a fifth of the time; identification improved with the number of posts written by the author in the corpus. Even if an author is not identified, some of their characteristics may still be deduced stylometrically, or stylometry may narrow the anonymity set of potential authors sufficiently for other information to complete the identification. Detecting author characteristics (e.g., gender or age) is often simpler than identifying an author from a large, possibly open, set of candidates. Modern machine learning techniques offer powerful tools for identification; further development of corpora and computational stylometric techniques are likely to raise further privacy issues. Gröndahl & Asokan (2020a) say that the general validity of the hypothesis underlying stylometry—that authors have invariant, content-independent 'style fingerprints'—is uncertain, but "the deanonymisation attack is a real privacy concern". Those interested in practicing adversarial stylometry and stylistic deception include whistleblowers avoiding retribution; journalists and activists; perpetrators of frauds and hoaxes; authors of fake reviews; literary forgers; criminals disguising their identity from investigators; and, generally, anyone with a desire for anonymity or pseudonymity. Authors, or agents acting on behalf of authors, may also attempt to remove stylistic clues to author characteristics (e.g., race or gender) so that knowledge of those characteristics cannot be used for discrimination (e.g., through algorithmic bias). Another possible use for adversarial stylometry is in disguising automatically generated text as human-authored. == Methods == With imitation, the author attempts to mislead stylometry by matching their style to another author's. An incomplete imitation, where some of the true author's unique characteristics appear alongside the imitated author's, can be a detectable signal for the use of adversarial stylometry. Imitation can be performed automatically with style transfer systems, though this typically requires a large corpus in the target style for the system to learn from. Another approach is translation, which employs machine translation of a source text to eliminate characteristic style, often through multiple translators in sequence to produce a round-trip translation. Such chained translation can lead to texts being significantly altered, even to the point of incomprehensibility; improved translation tools reduce this risk. More simply-structured texts can be easier to machine translate without losing the original meaning. Machine translation blurs into direct stylistic imitation or obfuscation achieved through automated style transfer, which can be viewed as a "translation" with the same language as input and output. With low-quality translation tools, an author can be required to manually correct major translation errors while avoiding the hazard of re-introducing stylistic characteristics. Wang, Juola & Riddell (2022) found that gross errors introduced by Google Translate were rare, but more common with several intermediate translations—however, occasional simple or short sentences and misspellings in the source text appeared verbatim in the output, potentially providing an identifying signal. Chain translation can leave characteristic traces of its application in a document, which may allow reconstruction of the intermediate languages used and the number of translation steps performed. Obfuscation involves deliberately changing the style of a text to reduce its similarity to other texts by some metric; this may be performed at the time of writing by conscious modification, or as part of a revision process with feedback from the metric being targeted as an input to decide when the text has been sufficiently obfuscated. In contrast to translation, complex texts can offer more opportunities for effective obfuscation without altering meaning, and likewise genres with more permissible variation allow more obfuscation. However, longer texts are harder to thoroughly obfuscate. Obfuscation can blend into imitation if the author develops a novel target style, distinct from their original style. With respect to masking author characteristics, obfuscation may aim to achieve a union (adding signals for imitated characteristics) or an intersection (removing signals and normalising) of other authors' styles. Avoiding the author's own idiosyncrasies and producing a "normalised" text is a critical obfuscatory step: an author may have a unique tendency to misspell certain words, use particular variants, or to format a document in a characteristic way. Stylometric signals vary in how simply they can be adversarially masked; an author may easily change their vocabulary by conscious choice, but altering the pattern of grammar or the letter frequency in their text may be harder to achieve, though Juola & Vescovi (2011) report that imitation typically succeeds at masking more characteristics than obfuscation. Automated obfuscation may require large amounts of training data written by the author. Concerning automated implementations of adversarial stylometry, two possible implementations are rule-based systems for paraphrasing; and encoder–decoder architectures, where the text passes through an intermediate format that is (intended to be) style-neutral. Another division in automated methods is whether there is feedback from an identification system or not. With such feedback, finding paraphrases for author masking has been characterised as a heuristic search problem, exploring textual variants until the result is stylistically sufficiently far (in the case of obfuscation) or near (in the case of imitation), which then constitutes an adversarial example for that identification system. == Evaluation == How

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  • Echo Lake (software)

    Echo Lake (software)

    Echo Lake (AKA Family Album Creator) was the most notable multimedia software product produced by Delrina, which debuted in June 1995. It was touted internally as a "cross [of] Quark Xpress and Myst". It featured an immersive 3D environment where a user could go to a virtual desktop in a virtual office and assemble video and audio clips along with images, and then print them out as either a virtual book other users of the program could use, or for print. It was a highly innovative product for its time, and ultimately was hampered by the inability of many users able to input their own multimedia content easily into a computer from that period. Creative Wonders bought the rights to the Echo Lake multimedia product, which was re-shaped as an introductory program on multimedia and re-released as Family Album Creator in 1996.

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  • Fling (social network)

    Fling (social network)

    Fling was a social media app available for IOS and Android. It was founded in 2014 by Marco Nardone and was taken offline in August 2016. == Overview == In 2012, Marco Nardone founded the startup Unii and launched Unii.com, a social network intended for students in the UK. While working on this service, Nardone had the idea for a messaging service where pictures could be sent to strangers in January 2014. The app Fling was then developed and released between March and July 2014. After a month, it already had 375,000 downloads and 180,000 active users on iOS. Users were able to take pictures inside the app and send them to 50 random people all over the world. The recipient could then choose to answer via chat or reply by sending a picture themselves. The app was used by many users as a medium to exchange sexually explicit pictures and for sexting with strangers. This led to the app being removed from the App Store in June 2015. In the 19 days that followed, flings developers rewrote the App almost completely from scratch, working around the clock. The feature to message random strangers was removed, and the app was readmitted into the App Store as a messenger App resembling Snapchat. But the redesigned Application did not have the success of its predecessor. The funding ran out and the parent company Unii went bankrupt. The company was not able to pay their content moderation team anymore, leading to a new surge of pornographic content on the App. Shortly after that, the Social Network was taken offline in August 2016. It has been inactive since. During the 2 years Fling was online, $21 million was raised from investors while generating no revenue at all. Of this $21 million (£16.5m), £5 million came from Nardone's father. == Allegations against CEO == Former employees made multiple allegations against Marco Nardone, the Founder and CEO of Unii and Fling. According to these claims, he behaved erratic and abusive, throwing "things across the office". He hired his girlfriend as the head of human resources to handle issues between him and his staff. Employees who left the company often had "some part of their pay held back". According to the reports, he also spent the money raised from investors irresponsibly, having no clear concept of a budget. Some of that money was used on expensive restaurants in London, a luxurious office for CEO Nardone and advertisements for Fling on Twitter and Facebook. Nardone also spent time partying in Ibiza with two employees, while the developer team in London frantically tried to get Fling back online after it being removed from the App Store. In December 2017 he pleaded guilty to assaulting his girlfriend at a domestic violence court.

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  • NER model

    NER model

    NER is one of several formulas for accessing live subtitles in television broadcasts and events that are produced using speech recognition. The three letters stand for number, edit error and recognition error. It has been promoted as an alternative to Word error rate (Word Error Rate) which is a more objective measure. The overall score is calculated as follows: Firstly, the number of edit and recognition errors is deducted from the total number of words in the live subtitles. This number is then divided by the total number of words in the live subtitles and finally multiplied by one hundred. N E R v a l u e = N − E − R N ∗ 100 {\displaystyle NERvalue={\frac {N-E-R}{N}}100} . The acronyms stand for the following: N (number) = total number of words in the live subtitles E (Edit error) = edit error R (Recognition error) = recognition error This measurement process has been used for public television broadcasts in European countries like Italy and Switzerland. One major drawback with NER is that it requires a human assessor to rate errors as either: 1 Minor edition or recognition errors 2 Normal edition or recognition errors 3 Serious errors which are then weighted in the assessment process. This is both subjective, time consuming and costly. Also, NER fails to account for words left out subtitles which is something that does not take account of the D/deaf audience who want verbatim subtitles. As a result, NER cannot accurately reflect the audience's experience of subtitles. Another problem is the inconsistency of human evaluation of subtitles, particularly with live subtitles, where there are differing opinions of the importance of subtitle errors. By way of contrast, Word error rate is an objective measure of subtitle errors, since it measures the textual discrepancy between the subtitles and the speech.

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  • Data administration

    Data administration

    Data administration or data resource management is an organizational function working in the areas of information systems and computer science that plans, organizes, describes and controls data resources. Data resources are usually stored in databases under a database management system or other software such as electronic spreadsheets. In many smaller organizations, data administration is performed occasionally, or is a small component of the database administrator’s work. In the context of information systems development, data administration ideally begins at system conception, ensuring there is a data dictionary to help maintain consistency, avoid redundancy, and model the database so as to make it logical and usable, by means of data modeling, including database normalization techniques. == Data resource management == According to the Data Management Association (DAMA), data resource management is "the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise". Data Resource management may be thought of as a managerial activity that applies information system and other data management tools to the task of managing an organization’s data resource to meet a company’s business needs, and the information they provide to their shareholders. From the perspective of database design, it refers to the development and maintenance of data models to facilitate data sharing between different systems, particularly in a corporate context. Data Resource Management is also concerned with both data quality and compatibility between data models. Since the beginning of the information age, businesses need all types of data on their business activity. With each data created, when a business transaction is made, need data is created. With these data, new direction is needed that focuses on managing data as a critical resource of the organization to directly support its business activities. The data resource must be managed with the same intensity and formality that other critical resources are managed. Organizations must emphasize the information aspect of information technology, determine the data needed to support the business, and then use appropriate technology to build and maintain a high-quality data resource that provides that support. Data resource quality is a measure of how well the organization's data resource supports the current and the future business information demand of the organization. The data resource cannot support just the current business information demand while sacrificing the future business information demand. It must support both the current and the future business information demand. The ultimate data resource quality is stability across changing business needs and changing technology. A corporate data resource must be developed within single, organization-wide common data architecture. A data architecture is the science and method of designing and constructing a data resource that is business driven, based on real-world objects and events as perceived by the organization, and implemented into appropriate operating environments. It is the overall structure of a data resource that provides a consistent foundation across organizational boundaries to provide easily identifiable, readily available, high-quality data to support the business information demand. The common data architecture is a formal, comprehensive data architecture that provides a common context within which all data at an organization's disposal are understood and integrated. It is subject oriented, meaning that it is built from data subjects that represent business objects and business events in the real world that are of interest to the organization and about which data are captured and maintained.

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  • Automation

    Automation

    Automation describes a wide range of technologies that reduce human intervention in processes, mainly by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices, and computers, usually in combination. Complicated systems, such as modern factories, airplanes, and ships typically use combinations of all of these techniques. The benefits of automation includes labor savings, reducing waste, savings in electricity costs, savings in material costs, and improvements to quality, accuracy, and precision. Automation includes the use of various equipment and control systems such as machinery, processes in factories, boilers, and heat-treating ovens, switching on telephone networks, steering, stabilization of ships, aircraft and other applications and vehicles with reduced human intervention. Examples range from a household thermostat controlling a boiler to a large industrial control system with tens of thousands of input measurements and output control signals. In the simplest type of an automatic control loop, a controller compares a measured value of a process with a desired set value and processes the resulting error signal to change some input to the process, in such a way that the process stays at its set point despite disturbances. This closed-loop control is an application of negative feedback to a system. The mathematical basis of control theory began in the 18th century and advanced rapidly in the 20th. The term automation, inspired by the earlier word automatic (coming from automaton), was not widely used before 1947, when Ford established an automation department. It was during this time that the industry was rapidly adopting feedback controllers, Technological advancements introduced in the 1930s revolutionized various industries significantly. The World Bank's World Development Report of 2019 shows evidence that the new industries and jobs in the technology sector outweigh the economic effects of workers being displaced by automation. Job losses and downward mobility blamed on automation have been cited as one of many factors in the resurgence of nationalist, protectionist and populist politics in the US, UK and France, among other countries since the 2010s. == History == === Early history === It was a preoccupation of the Greeks and Arabs (in the period between about 300 BC and about 1200 AD) to keep an accurate track of time. In Ptolemaic Egypt, about 270 BC, Ctesibius described a float regulator for a water clock, a device not unlike the ball and cock in a modern flush toilet. This was the earliest feedback-controlled mechanism. The appearance of the mechanical clock in the 14th century made the water clock and its feedback control system obsolete. The Persian Banū Mūsā brothers, in their Book of Ingenious Devices (850 AD), described a number of automatic controls. Two-step level controls for fluids, a form of discontinuous variable structure controls, were developed by the Banu Musa brothers. They also described a feedback controller. The design of feedback control systems up through the Industrial Revolution was by trial-and-error, together with a great deal of engineering intuition. It was not until the mid-19th century that the stability of feedback control systems was analyzed using mathematics, the formal language of automatic control theory. The centrifugal governor was invented by Christiaan Huygens in the seventeenth century, and used to adjust the gap between millstones. === Industrial Revolution in Western Europe === The introduction of prime movers, or self-driven machines advanced grain mills, furnaces, boilers, and the steam engine created a new requirement for automatic control systems including temperature regulators (invented in 1624; see Cornelius Drebbel), pressure regulators (1681), float regulators (1700) and speed control devices. Another control mechanism was used to tent the sails of windmills. It was patented by Edmund Lee in 1745. Also in 1745, Jacques de Vaucanson invented the first automated loom. Around 1800, Joseph Marie Jacquard created a punch-card system to program looms. In 1771 Richard Arkwright invented the first fully automated spinning mill driven by water power, known at the time as the water frame. An automatic flour mill was developed by Oliver Evans in 1785, making it the first completely automated industrial process. A centrifugal governor was used by Mr. Bunce of England in 1784 as part of a model steam crane. The centrifugal governor was adopted by James Watt for use on a steam engine in 1788 after Watt's partner Boulton saw one at a flour mill Boulton & Watt were building. The governor could not actually hold a set speed; the engine would assume a new constant speed in response to load changes. The governor was able to handle smaller variations such as those caused by fluctuating heat load to the boiler. Also, there was a tendency for oscillation whenever there was a speed change. As a consequence, engines equipped with this governor were not suitable for operations requiring constant speed, such as cotton spinning. Several improvements to the governor, plus improvements to valve cut-off timing on the steam engine, made the engine suitable for most industrial uses before the end of the 19th century. Advances in the steam engine stayed well ahead of science, both thermodynamics and control theory. The governor received relatively little scientific attention until James Clerk Maxwell published a paper that established the beginning of a theoretical basis for understanding control theory. === 20th century === Relay logic was introduced with factory electrification, which underwent rapid adaptation from 1900 through the 1920s. Central electric power stations were also undergoing rapid growth and the operation of new high-pressure boilers, steam turbines and electrical substations created a great demand for instruments and controls. Central control rooms became common in the 1920s, but as late as the early 1930s, most process controls were on-off. Operators typically monitored charts drawn by recorders that plotted data from instruments. To make corrections, operators manually opened or closed valves or turned switches on or off. Control rooms also used color-coded lights to send signals to workers in the plant to manually make certain changes. The development of the electronic amplifier during the 1920s, which was important for long-distance telephony, required a higher signal-to-noise ratio, which was solved by negative feedback noise cancellation. This and other telephony applications contributed to the control theory. In the 1940s and 1950s, German mathematician Irmgard Flügge-Lotz developed the theory of discontinuous automatic controls, which found military applications during the Second World War to fire control systems and aircraft navigation systems. Controllers, which were able to make calculated changes in response to deviations from a set point rather than on-off control, began being introduced in the 1930s. Controllers allowed manufacturing to continue showing productivity gains to offset the declining influence of factory electrification. Factory productivity was greatly increased by electrification in the 1920s. U.S. manufacturing productivity growth fell from 5.2%/yr 1919–29 to 2.76%/yr 1929–41. Alexander Field notes that spending on non-medical instruments increased significantly from 1929 to 1933 and remained strong thereafter. The First and Second World Wars saw major advancements in the field of mass communication and signal processing. Other key advances in automatic controls include differential equations, stability theory and system theory (1938), frequency domain analysis (1940), ship control (1950), and stochastic analysis (1941). Starting in 1958, various systems based on solid-state digital logic modules for hard-wired programmed logic controllers (the predecessors of programmable logic controllers [PLC]) emerged to replace electro-mechanical relay logic in industrial control systems for process control and automation, including early Telefunken/AEG Logistat, Siemens Simatic, Philips/Mullard/Valvo Norbit, BBC Sigmatronic, ACEC Logacec, Akkord Estacord, Krone Mibakron, Bistat, Datapac, Norlog, SSR, or Procontic systems. In 1959 Texaco's Port Arthur Refinery became the first chemical plant to use digital control. Conversion of factories to digital control began to spread rapidly in the 1970s as the price of computer hardware fell. === Significant applications === The automatic telephone switchboard was introduced in 1892 along with dial telephones. By 1929, 31.9% of the Bell system was automatic. Automatic telephone switching originally used vacuum tube amplifiers and electro-mechanical switches, which consumed a large amount of electricity. Call volume eve

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  • Human–robot collaboration

    Human–robot collaboration

    Human-Robot Collaboration is the study of collaborative processes in human and robot agents work together to achieve shared goals. Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include robots for homes, hospitals, and offices, space exploration and manufacturing. Human-Robot Collaboration (HRC) is an interdisciplinary research area comprising classical robotics, human-computer interaction, artificial intelligence, process design, layout planning, ergonomics, cognitive sciences, and psychology. Industrial applications of human-robot collaboration involve Collaborative Robots, or cobots, that physically interact with humans in a shared workspace to complete tasks such as collaborative manipulation or object handovers. == Collaborative Activity == Collaboration is defined as a special type of coordinated activity, one in which two or more agents work jointly with each other, together performing a task or carrying out the activities needed to satisfy a shared goal. The process typically involves shared plans, shared norms and mutually beneficial interactions. Although collaboration and cooperation are often used interchangeably, collaboration differs from cooperation as it involves a shared goal and joint action where the success of both parties depend on each other. For effective human-robot collaboration, it is imperative that the robot is capable of understanding and interpreting several communication mechanisms similar to the mechanisms involved in human-human interaction. The robot must also communicate its own set of intents and goals to establish and maintain a set of shared beliefs and to coordinate its actions to execute the shared plan. In addition, all team members demonstrate commitment to doing their own part, to the others doing theirs, and to the success of the overall task. == Theories Informing Human-Robot Collaboration == Human-human collaborative activities are studied in depth in order to identify the characteristics that enable humans to successfully work together. These activity models usually aim to understand how people work together in teams, how they form intentions and achieve a joint goal. Theories on collaboration inform human-robot collaboration research to develop efficient and fluent collaborative agents. === Belief Desire Intention Model === The belief-desire-intention (BDI) model is a model of human practical reasoning that was originally developed by Michael Bratman. The approach is used in intelligent agents research to describe and model intelligent agents. The BDI model is characterized by the implementation of an agent's beliefs (the knowledge of the world, state of the world), desires (the objective to accomplish, desired end state) and intentions (the course of actions currently under execution to achieve the desire of the agent) in order to deliberate their decision-making processes. BDI agents are able to deliberate about plans, select plans and execute plans. === Shared Cooperative Activity === Shared Cooperative Activity defines certain prerequisites for an activity to be considered shared and cooperative: mutual responsiveness, commitment to the joint activity and commitment to mutual support. An example case to illustrate these concepts would be a collaborative activity where agents are moving a table out the door, mutual responsiveness ensures that movements of the agents are synchronized; a commitment to the joint activity reassures each team member that the other will not at some point drop his side; and a commitment to mutual support deals with possible breakdowns due to one team member's inability to perform part of the plan. === Joint Intention Theory === Joint Intention Theory proposes that for joint action to emerge, team members must communicate to maintain a set of shared beliefs and to coordinate their actions towards the shared plan. In collaborative work, agents should be able to count on the commitment of other members, therefore each agent should inform the others when they reach the conclusion that a goal is achievable, impossible, or irrelevant. == Approaches to Human-Robot Collaboration == The approaches to human-robot collaboration include human emulation (HE) and human complementary (HC) approaches. Although these approaches have differences, there are research efforts to develop a unified approach stemming from potential convergences such as Collaborative Control. === Human Emulation === The human emulation approach aims to enable computers to act like humans or have human-like abilities in order to collaborate with humans. It focuses on developing formal models of human-human collaboration and applying these models to human-computer collaboration. In this approach, humans are viewed as rational agents who form and execute plans for achieving their goals and infer other people's plans. Agents are required to infer the goals and plans of other agents, and collaborative behavior consists of helping other agents to achieve their goals. === Human Complementary === The human complementary approach seeks to improve human-computer interaction by making the computer a more intelligent partner that complements and collaborates with humans. The premise is that the computer and humans have fundamentally asymmetric abilities. Therefore, researchers invent interaction paradigms that divide responsibility between human users and computer systems by assigning distinct roles that exploit the strengths and overcome the weaknesses of both partners. == Key Aspects == Specialization of Roles: Based on the level of autonomy and intervention, there are several human-robot relationships including master-slave, supervisor–subordinate, partner–partner, teacher–learner and fully autonomous robot. In addition to these roles, homotopy (a weighting function that allows a continuous change between leader and follower behaviors) was introduced as a flexible role distribution. Establishing shared goal(s): Through direct discussion about goals or inference from statements and actions, agents must determine the shared goals they are trying to achieve. Allocation of Responsibility and Coordination: Agents must decide how to achieve their goals, determine what actions will be done by each agent, and how to coordinate the actions of individual agents and integrate their results. Shared context: Agents must be able to track progress toward their goals. They must keep track of what has been achieved and what remains to be done. They must evaluate the effects of actions and determine whether an acceptable solution has been achieved. Communication: Any collaboration requires communication to define goals, negotiate over how to proceed and who will do what, and evaluate progress and results. Adaptation and learning: Collaboration over time require partners to adapt themselves to each other and learn from one's partner both directly or indirectly. Time and space: The time-space taxonomy divides human-robot interaction into four categories based on whether the humans and robots are using computing systems at the same time (synchronous) or different times (asynchronous) and while in the same place (collocated) or in different places (non-collocated). Ergonomics: Human factors and ergonomics are one of the key aspects for a sustainable human-robot collaboration. The robot control system can use biomechanical models and sensors to optimize various ergonomic metrics, such as muscle fatigue.

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  • Multiple satellite imaging

    Multiple satellite imaging

    Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.

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  • ViEWER

    ViEWER

    ViEWER, the Virtual Environment Workbench for Education and Research, is a proprietary, freeware computer program for Microsoft Windows written by researchers at the University of Idaho for the study of visual perception and complex immersive three-dimensional environments. It was created using C++ and OpenGL, and has been used by Dr. Brian Dyre, Dr. Steffen Werner, Dr. Ernesto Bustamante, Dr. Ben Barton, and their undergraduate and graduate researchers in visual perception, signal detection, and child-safety experiments.

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  • Qlone

    Qlone

    Qlone is a 3D scanning app based on photogrammetry for creation of 3D models on mobile devices. The resultant 3D models can be exported for external use. Qlone was featured at the Apple Worldwide Developers Conference in 2021. It was also featured on BBC Click. == Qlone features == === 3D scanning === 3D scanning with Qlone requires the use of an included mat design. The user prints the mat onto a sheet of paper, then places the object to be scanned in the centre of the mat. An augmented reality dome within the Qlone app guides the user through the subsequent scanning process. The iOS version of Qlone allows scanning without the mat. === 3D editing === Qlone's editing features allow users to adjust 3D scanned models using texture mapping, polygon mesh size simplification, digital sculpting, cleaning and smoothing, and artistic effects. === File export === Qlone exports directly to multiple 3D platforms including SketchFab, i.materialise, Lens Studio for Snapchat, Shapeways and CGTrader. Models can also be exported in different 3D formats for use in other 3D tools – OBJ, STL, FBX, USDZ, GLB (Binary gLTF), PLY, and X3D. == Use in Science, Education and Academia == Due to its inexpensive, simple and accessible nature for creating 3D models, Qlone was used in many academically educational and scientific research projects. The European Space Agency used Qlone to scan rocks in a Tele-Robotic rock collection experiment. Neurosurgeons from the University of Southern California and surgeons from Tulane University School of Medicine used Qlone to create 3D models of cadaveric specimens and anatomical models with the aim of increasing access to such components for enhancing anatomy training and allowing realistic surgical simulations for neurosurgeons and practitioners worldwide. Archaeologists from Texas A&M University used Qlone to create 3D replicas of artifacts and models and students from Vancouver iTech Preparatory Middle School used Qlone to create 3D scans of more than 100 artifacts from Fort Vancouver National Historic Site.

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  • Color picker

    Color picker

    A color picker (also color chooser or color tool) is a graphical user interface widget, usually found within graphics software or online, used to select colors and, in some cases, to create color schemes (the color picker might be more sophisticated than the palette included with the program). Operating systems such as Microsoft Windows or macOS have a system color picker, which can be used by third-party programs (e.g., Adobe Photoshop). == History == The concept of color pickers dates back to the early days of computer graphics and digital design. Early versions were rudimentary, often featuring basic color palettes and limited functionality. One of the first drawing programs to include a color picker was SketchPad (also referred to as LisaSketch), designed by Bill Atkinson in 1983 to showcase LisaGraf's capabilities. It used a black and white pattern system, using dithering to create the illusion of color depth. With the increased popularity of personal computers with color graphics, there soon came software similar to SketchPad that supported more than two colors, like Broderbund's Dazzle Draw for the Apple II or Electronic Arts' Deluxe Paint. However, the color pickers present in those programs relied on indexed colors. Color pickers, resembling ones used in modern software with support for direct, 24-bit color, appeared soon after the release of the Macintosh II, with the release of programs like Adobe Photoshop and Corel Painter. As the increase of color depth allowed the choice of significantly more colors, the shape and form of color pickers started to diverge. For example, Adobe Photoshop used a hue-saturation color wheel with a slider for brightness in version 0.63, later on switching to a rectangular design accompanied by a hue slider. Corel Painter pioneered the triangular saturation and brightness picker with a hue ring around it, aiming to better represent the continuity of the hue spectrum and the relationship between saturation and brightness. == Purpose == A color picker is used to select and adjust color values. In graphic design and image editing, users typically choose colors via an interface with a visual representation of a color—organized with quasi-perceptually-relevant hue, saturation and lightness dimensions (HSL) – instead of keying in alphanumeric text values. Because color appearance depends on comparison of neighboring colors (see color vision), many interfaces attempt to clarify the relationships between colors. == Interface == Color tools can vary in their interface. Some may use sliders, buttons, text boxes for color values, or direct manipulation. Often a two-dimensional square is used to create a range of color values (such as lightness and saturation) that can be clicked on or selected in some other manner. Drag and drop, color droppers, and various other forms of interfaces are commonly used as well. Usually, color values are also displayed numerically, so they can be precisely remembered and keyed-in later, such as three values of 0-255 representing red, green, and blue, respectively. === Eyedropper === The eyedropper is a tool present in most color pickers and graphics software that allows a user to read a color at a specific point in an image, or position on a display. This enables the color to be transferred to other applications particularly quickly. Modern implementations of eyedropper tools are also available as browser extensions, allowing users to pick colors directly from web pages, such as in Google Chrome and Microsoft Edge. == Working == A color picker has two main parts, first a color slider and second a color canvas. The color slider has a linear or radial gradient of the seven rainbow colors i.e. Violet, Indigo, Blue, Green, Yellow, Orange and Red. It allows one to choose any of the seven primary colors. The color value chosen from the color slider instantly reflects in the color canvas. The color canvas is a mixture of two linear color gradients. First a linear gradient of the current chosen color and second a linear gradient of the black color. This mixture of color gradients lets one choose a lighter and darker version of the current chosen color from the color slider.

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