Tribute is an American video-sharing website headquartered in Brooklyn. Created in 2014 by Andrew Horn and Rory Petty, the platform lets customers create video montages (called "tributes") for occasions including weddings, birthdays, anniversaries, get well soon, and memorials. Tribute.co allows users to record video messages, request submissions from friends and family, insert photos, add music, and send the resulting video tribute montage to a recipient. == Overview == Tribute's collaborative technology starts with inviting people to contribute via email, SMS or social media. Participants receive a prompt to record a short video via their phone, computer or tablet. The site's video editing software allows users to drag and drop the clips in their desired order without prior video editing experience. == History == When Andrew Horn turned twenty-seven, his girlfriend, Miki Agrawal surprised him with a video montage containing clips of his family and closest friends explaining why they loved him. This resulted in Andrew's idea to create Tribute–a "living eulogy" video-compilation service that he co-founded with software engineer Rory Petty. Founded in 2014, Tribute's activity accelerated in 2020 due to the COVID-19 pandemic, and it had sent over 5 million videos as of December 2021. While social distance restrictions were in effect, the site provided a way for people to connect while in-person celebrations were put on hold. For each video sold, Tribute makes one available to hospitals for free and has partnered with Cleveland Clinic Cancer Center in Ohio, Lurie Children's Hospital in Illinois and CarePoint Health in New Jersey.
Plotting algorithms for the Mandelbrot set
There are many programs and algorithms used to plot the Mandelbrot set and other fractals, some of which are described in fractal-generating software. These programs use a variety of algorithms to determine the color of individual pixels efficiently. == Escape time algorithm == The simplest algorithm for generating a representation of the Mandelbrot set is known as the "escape time" algorithm. A repeating calculation is performed for each x, y point in the plot area and based on the behavior of that calculation, a color is chosen for that pixel. === Unoptimized naïve escape time algorithm === In both the unoptimized and optimized escape time algorithms, the x and y locations of each point are used as starting values in a repeating, or iterating calculation (described in detail below). The result of each iteration is used as the starting values for the next. The values are checked during each iteration to see whether they have reached a critical "escape" condition, or "bailout". If that condition is reached, the calculation is stopped, the pixel is drawn, and the next x, y point is examined. For some starting values, escape occurs quickly, after only a small number of iterations. For starting values very close to but not in the set, it may take hundreds or thousands of iterations to escape. For values within the Mandelbrot set, escape will never occur. The programmer or user must choose how many iterations–or how much "depth"–they wish to examine. The higher the maximal number of iterations, the more detail and subtlety emerge in the final image, but the longer time it will take to calculate the fractal image. Escape conditions can be simple or complex. Because no complex number with a real or imaginary part greater than 2 can be part of the set, a common bailout is to escape when either coefficient exceeds 2. A more computationally complex method that detects escapes sooner, is to compute distance from the origin using the Pythagorean theorem, i.e., to determine the absolute value, or modulus, of the complex number. If this value exceeds 2, or equivalently, when the sum of the squares of the real and imaginary parts exceed 4, the point has reached escape. More computationally intensive rendering variations include the Buddhabrot method, which finds escaping points and plots their iterated coordinates. The color of each point represents how quickly the values reached the escape point. Often black is used to show values that fail to escape before the iteration limit, and gradually brighter colors are used for points that escape. This gives a visual representation of how many cycles were required before reaching the escape condition. To render such an image, the region of the complex plane we are considering is subdivided into a certain number of pixels. To color any such pixel, let c {\displaystyle c} be the midpoint of that pixel. We now iterate the critical point 0 under P c {\displaystyle P_{c}} , checking at each step whether the orbit point has modulus larger than 2. When this is the case, we know that c {\displaystyle c} does not belong to the Mandelbrot set, and we color our pixel according to the number of iterations used to find out. Otherwise, we keep iterating up to a fixed number of steps, after which we decide that our parameter is "probably" in the Mandelbrot set, or at least very close to it, and color the pixel black. In pseudocode, this algorithm would look as follows. The algorithm does not use complex numbers and manually simulates complex-number operations using two real numbers, for those who do not have a complex data type. The program may be simplified if the programming language includes complex-data-type operations. for each pixel (Px, Py) on the screen do x0 := scaled x coordinate of pixel (scaled to lie in the Mandelbrot X scale (-2.00, 0.47)) y0 := scaled y coordinate of pixel (scaled to lie in the Mandelbrot Y scale (-1.12, 1.12)) x := 0.0 y := 0.0 iteration := 0 max_iteration := 1000 while (xx + yy ≤ 22 AND iteration < max_iteration) do xtemp := xx - yy + x0 y := 2xy + y0 x := xtemp iteration := iteration + 1 color := palette[iteration] plot(Px, Py, color) Here, relating the pseudocode to c {\displaystyle c} , z {\displaystyle z} and P c {\displaystyle P_{c}} : z = x + i y {\displaystyle z=x+iy\ } z 2 = x 2 + 2 i x y {\displaystyle z^{2}=x^{2}+2ixy} - y 2 {\displaystyle y^{2}\ } c = x 0 + i y 0 {\displaystyle c=x_{0}+iy_{0}\ } and so, as can be seen in the pseudocode in the computation of x and y: x = R e ( z 2 + c ) = x 2 − y 2 + x 0 {\displaystyle x=\mathop {\mathrm {Re} } (z^{2}+c)=x^{2}-y^{2}+x_{0}} and y = I m ( z 2 + c ) = 2 x y + y 0 . {\displaystyle y=\mathop {\mathrm {Im} } (z^{2}+c)=2xy+y_{0}.\ } To get colorful images of the set, the assignment of a color to each value of the number of executed iterations can be made using one of a variety of functions (linear, exponential, etc.). One practical way, without slowing down calculations, is to use the number of executed iterations as an entry to a palette initialized at startup. If the color table has, for instance, 500 entries, then the color selection is n mod 500, where n is the number of iterations. === Optimized escape time algorithms === The code in the previous section uses an unoptimized inner while loop for clarity. In the unoptimized version, one must perform five multiplications per iteration. To reduce the number of multiplications the following code for the inner while loop may be used instead: x2:= 0 y2:= 0 w:= 0 while (x2 + y2 ≤ 4 and iteration < max_iteration) do x:= x2 - y2 + x0 y:= w - x2 - y2 + y0 x2:= x x y2:= y y w:= (x + y) (x + y) iteration:= iteration + 1 The above code works via some algebraic simplification of the complex multiplication: ( i y + x ) 2 = − y 2 + 2 i y x + x 2 = x 2 − y 2 + 2 i y x {\displaystyle {\begin{aligned}(iy+x)^{2}&=-y^{2}+2iyx+x^{2}\\&=x^{2}-y^{2}+2iyx\end{aligned}}} Using the above identity, the number of multiplications can be reduced to three instead of five. The above inner while loop can be further optimized by expanding w to w = x 2 + 2 x y + y 2 {\displaystyle w=x^{2}+2xy+y^{2}} Substituting w into y = w − x 2 − y 2 + y 0 {\displaystyle y=w-x^{2}-y^{2}+y_{0}} yields y = 2 x y + y 0 {\displaystyle y=2xy+y_{0}} and hence calculating w is no longer needed. The further optimized pseudocode for the above is: x:= 0 y:= 0 x2:= 0 y2:= 0 while (x2 + y2 ≤ 4 and iteration < max_iteration) do x2:= x x y2:= y y y:= 2 x y + y0 x:= x2 - y2 + x0 iteration:= iteration + 1 Note that in the above pseudocode, 2 x y {\displaystyle 2xy} seems to increase the number of multiplications by 1, but since 2 is the multiplier the code can be optimized via ( x + x ) y {\displaystyle (x+x)y} . == Coloring algorithms == In addition to plotting the set, a variety of algorithms have been developed to efficiently color the set in an aesthetically pleasing way show structures of the data (scientific visualisation) === Histogram coloring === A more complex coloring method involves using a histogram which pairs each pixel with said pixel's maximum iteration count before escape/bailout. This method will equally distribute colors to the same overall area, and, importantly, is independent of the maximum number of iterations chosen. This algorithm has four passes. The first pass involves calculating the iteration counts associated with each pixel (but without any pixels being plotted). These are stored in an array IterationCounts[x][y], where x and y are the x and y coordinates of said pixel on the screen respectively. The first step of the second pass is to create an array NumIterationsPerPixel[n], where the array size n is the maximum iteration count. Next, one must iterate over the array of pixel-iteration count pairs IterationCounts[x][y], and retrieve each pixel's saved iteration count, i, via e.g. i = IterationCounts[x][y]. After each pixel's iteration count i is retrieved, it is necessary to index the NumIterationsPerPixel array at i and increment the indexed value (which is initially zero) -- e.g. NumIterationsPerPixel[i] = NumIterationsPerPixel[i] + 1. for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do i:= IterationCounts[x][y] NumIterationsPerPixel[i]++ The third pass iterates through the NumIterationsPerPixel array and adds up all the stored values, saving them in total. The array index represents the number of pixels that reached that iteration count before bailout. total: = 0 for (i = 0; i < max_iterations; i++) do total += NumIterationsPerPixel[i] After this, the fourth pass begins and all the values in the IterationCounts array are indexed, and, for each iteration count i, associated with each pixel, the count is added to a global sum of all the iteration counts from 1 to i in the NumIterationsPerPixel array . This value is then normalized by dividing the sum by the total value computed earlier. hue[][]:= 0.0 for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do iteration:= Iteration
Creator economy
The creator economy, also known as influencer economy, is a platform-driven economy in which creators produce content, products, or services and distribute them directly to their audience through social media platforms and emerging technologies. This economic model is based on the ability of creators to build and maintain communities of users, monetizing their creative activity through multiple channels including advertising, sponsorships, product sales, crowdfunding, and subscription-based services. Creators include various professional categories such as social media influencers, YouTubers, bloggers, artists, online educators, podcasters, and independent professionals, who use platforms as infrastructure to reach their audience without necessarily relying on traditional intermediaries in the cultural and media industry. According to Goldman Sachs Research, the ongoing growth of the creator economy will likely benefit companies that possess a combination of factors, including a large global user base, access to substantial capital, robust AI-powered recommendation engines, versatile monetization tools, comprehensive data analytics, and integrated e-commerce options. Examples of creator economy software platforms include YouTube, TikTok, Instagram, Facebook, Twitch, Spotify, Substack, OnlyFans and Patreon. == History == The term "creator" was coined by YouTube in 2011 to be used instead of "YouTube star", an expression that at the time could only apply to famous individuals on the platform. The term has since become omnipresent and is used to describe anyone creating any form of online content. A number of platforms such as TikTok, Snapchat, YouTube, and Facebook have set up funds with which to pay creators. == Criticism == The large majority of content creators derive no monetary gain for their creations, with most of the benefits accruing to the platforms who can make significant revenues from their uploads. As few as 0.1% of creators are able to earn a living through their channels.
Outline of electronics
The following outline is provided as an overview of and topical guide to electronics: Electronics – branch of physics, engineering and technology dealing with electrical circuits that involve active semiconductor components and associated passive interconnection technologies. == Branches == === Classical electronics === Analog electronics Digital electronics Electronic instrumentation Electronic engineering Microelectronics Optoelectronics Power electronics Printed electronics Semiconductor technology Schematic capture Thermal management Automation Electronics === Advanced topics === Atomtronics Bioelectronics Failure modes of electronics Flexible electronics Low-power electronics Microelectromechanical systems (MEMS) Molecular electronics Nanoelectronics Organic electronics Photonics Piezotronics Quantum electronics Spintronics === History of electronics === History of electronic engineering History of radar History of radio History of television == General concepts == === Data converters === Analog-to-digital converters (ADC) Aliasing Successive approximation ADC Dual-slope ADC Quantization Sensor resolution Sampling Delta-sigma ADC Digital-to-analog converters (DAC) Digital potentiometer Binary weighted resistor converter Charge distribution DAC Pulse width modulator Reconstruction filter The R2R ladder === Digital electronics === Binary decision diagrams Boolean algebra Combinational logic Counters (digital) De Morgan's laws Digital circuit Formal verification Karnaugh maps Logic families Logic gate Logic minimization Logic simulation Logic synthesis Registers Sequential logic State machines Truth tables Transparent latch === Electrical element/discretes === Passive elements: Capacitor Inductor Memristor Resistor Transformer Active elements: Diode Zener diode Light-emitting diode PIN diode Schottky diode Avalanche diode Laser diode Microcontroller Operational amplifier Thyristor DIAC TRIAC IGBT Transistor Bipolar transistor (BJT) Field effect transistor (FET) Darlington transistor Other components Aural devices Battery (electricity) Crystal oscillator Electromechanical devices Sensors Surface acoustic wave (SAW) === Electronics analysis === Electronic packaging Electronic circuit simulation Electronic design automation Electronic noise Mathematical methods in electronics Thermal management of electronic devices and systems === Electronic circuits === Amplifiers Differential amplifiers Feedback amplifiers Power amplifiers Comparators Converters Filters Active filters Passive filters Digital filters Oscillators Phase-locked loops Timers === Electronic equipment === Air conditioner Breathalyzer Central heating Clothes dryer Computer/Notebook Dishwasher Freezer Home robot Home entertainment system Information technologies Cooker Microwave oven Refrigerator Robotic vacuum cleaner Tablet Telephone Water heater Washing machine === Television === Analog television History of television Television show Television broadcaster Timeline of the introduction of television in countries Mechanical television Color television Digital television Digital television transition Smart television Streaming television Internet Protocol television 3D television Terrestrial television ==== Television broadcasting ==== === Electronic instrumentation === Ammeter Capacitance meter Distortionmeter Electric energy meter LCR meter Microwave power meter Multimeter Network analyzer Ohmmeter Oscilloscope Psophometer Q meter Signal analyzer Signal generator Spectrum analyzer Transistor tester Tube tester Wattmeter Vectorscope Video signal generator Voltmeter VU meter === Memory technology === Flash memory Hard drive systems Optical storage Probe Storage Programmable read-only memory Read-only memory Solid-state drive (SSD) Volatile memory === Microcontrollers === Features Analog-to-digital converter Central processing unit (CPU) Clock generator (Quartz timing crystal, resonator or RC circuit) Debugging support Digital-to-analog converters Discrete input and output bits In-circuit programming Non-volatile memory (ROM, EPROM, EEPROM or Flash) Peripherals (Timers, event counters, PWM generators, and watchdog) Serial interface (Input/output such as serial ports (UARTs)) Serial communications (I²C, Serial Peripheral Interface and Controller Area Network) Volatile memory (RAM) 8-bit microcontroller families: AVR - PIC - COP8 - MCS-48 - MCS-51 - Z8 - eZ80 - HC08 - HC11 - H8 - PSoC Some notable suppliers: ARM Atmel Cypress Semiconductor Freescale Intel MIPS Microchip Technology NXP Semiconductors Parallax Propeller PowerPC Rabbit 2000 Renesas RX, V850 Silicon Laboratories STMicroelectronics Texas Instruments Toshiba TLCS === Optoelectronics === Optical fiber Optical properties Optical receivers Optical system design Optical transmitters === Physical laws === Ampère's law Coulomb's law Faraday's law of induction/Faraday-Lenz law Gauss's law Kirchhoff's circuit laws Current law Voltage law Maxwell's equations Gauss's law Faraday's law of induction Ampère's law Ohm's law === Power electronics === Power Devices Gate turn-off thyristor MOS-controlled thyristor (MCT) Power BJT/MOSFET Static induction devices Electric power conversion DC to DC DC to DC converter Voltage stabiliser Linear regulator AC to DC Rectifier Mains power supply unit (PSU) Switched-mode power supply DC to AC Inverter AC to AC Cycloconverter Transformer Variable frequency transformer Voltage converter Voltage regulator Power applications Automotive applications Capacitor charging applications Electronic ballasts Energy harvesting technologies Flexible AC transmission systems (FACTS) High frequency inverters HVDC transmission Motor controller Photovoltaic system Conversion Power factor correction circuits Power supply Renewable energy sources Switching power converters Uninterruptible power supply Wind power === Programmable devices === Application-specific integrated circuit (ASIC) Complex programmable logic device (CPLD) Erasable programmable logic device (EPLD) Simple programmable logic device (SPLD) Macrocell array Programmable array logic (PAL) Programmable logic array (PLA) Programmable logic device (PLD) Field-programmable gate array (FPGA) VHSIC Hardware Description Language (VHDL) Verilog Hardware Description Language Some notable suppliers: Altera - Atmel - Cypress Semiconductor - Lattice Semiconductor - Xilinx === Semiconductors theory === Properties Bipolar junction transistors Capacitance voltage profiling Charge carrier Charge-transfer complex Deep-level transient spectroscopy Depletion region Density of states Diode modelling Direct band gap Electronic band structure Energy level Exciton Field-effect transistors Metal–semiconductor junction MOSFETs N-type semiconductor Organic semiconductors P–n junction P-type semiconductor Photoelectric effect Quantum tunneling Semiconductor chip Semiconductor detector Solar cell Transistor model Thin film Tight-binding model Device Fabrication Semiconductor device fabrication Semiconductor industry Semiconductor consolidation == Applications == Audio electronics Automotive electronics Avionics Control Systems Consumer electronics Data acquisition E-health Electronic book Electronics industry Electronic warfare Embedded systems Home automation Integrated circuits Marine electronics Microwave technology Military electronics Multimedia Nuclear electronics Open hardware Radar and Radionavigation Radio electronics Terahertz technology Video hardware Wired and Wireless Communications
Glossary of operating systems terms
This page is a glossary of Operating systems terminology. == A == access token: In Microsoft Windows operating systems, an access token contains the security credentials for a login session and identifies the user, the user's groups, the user's privileges, and, in some cases, a particular application. == B == binary semaphore: See semaphore. booting: In computing, booting (also known as booting up) is the initial set of operations that a computer performs after electrical power is switched on or when the computer is reset. This can take tens of seconds and typically involves performing a power-on self-test, locating and initializing peripheral devices, and then finding, loading and starting the operating system. == C == cache: In computer science, a cache is a component that transparently stores data so that future requests for that data can be served faster. The data that is stored within a cache might be values that have been computed earlier or duplicates of original values that are stored elsewhere. cloud: Cloud computing operating systems are recent, and were not mentioned in Gagne's 8th Edition (2009). In contrast, by Gagne's 9th (2012), cloud o/s received 3 pages of coverage (41, 42, 716). Doeppner (2011) mentions them (p. 3), but only to prove that operating systems "are not a solved problem" and that even if the day of the dedicated PC is waning, cloud computing has created an entirely new opportunity for o/s development ala sharing, networks, memory, parallelism, etc. Gagne (2012) adds that in addition to numerous traditional o/s's at cloud warehouses, Virtual machine o/s (VMMs), Eucalyptus, Vware, vCloud Director and others are being developed specifically for cloud management with numerous traditional o/s features (security, threads, file and memory management, guis, etc.) (p. 42). Microsoft's investment in cloud aspects of o/s tend to support that argument. concurrency == D == daemon: Operating systems often start daemons at boot time and serve the function of responding to network requests, hardware activity, or other programs by performing some task. Daemons can also configure hardware (like udevd on some Linux systems), run scheduled tasks (like cron), and perform a variety of other tasks. == E == == F == == G == == H == == I == == J == == K == kernel: In computing, the kernel is a computer program that manages input/output requests from software and translates them into data processing instructions for the central processing unit and other electronic components of a computer. The kernel is a fundamental part of a modern computer's operating system. == L == lock: In computer science, a lock or mutex (from mutual exclusion) is a synchronization mechanism for enforcing limits on access to a resource in an environment where there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy. == M == mutual exclusion: Mutual exclusion is to allow only one process at a time to access the same critical section (a part of code which accesses the critical resource). This helps prevent race conditions. mutex: See lock. == N == == O == == P == paging daemon: See daemon. process == Q == == R == == S == semaphore: In computer science, particularly in operating systems, a semaphore is a variable or abstract data type that is used for controlling access, by multiple processes, to a common resource in a parallel programming or a multi user environment. == T == thread: In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by an operating system scheduler. The scheduler itself is a light-weight process. The implementation of threads and processes differs from one operating system to another, but in most cases, a thread is contained inside a process. templating: In an o/s context, templating refers to creating a single virtual machine image as a guest operating system, then saving it as a tool for multiple running virtual machines (Gagne, 2012, p. 716). The technique is used both in virtualization and cloud computing management, and is common in large server warehouses. == U == == V == == W == == Z ==
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
Industry Dive
Industry Dive is a United States-based business-to-business news organization with an estimated 18 million readers in more than 25 industries, such as banking and waste management. Since 2022, it has been owned by Informa plc. Industry Dive aims to serve business executives who read news on their mobile phones. The company had an estimated revenue of more than of more than $110 million in 2023. As of 2020, it has more than 300 employees, including 80 journalists and 12 engineers. Its headquarters is in Washington, D.C. == History == Industry Dive was formed in 2012 by Sean Griffey (president), Eli Dickinson (chief technology officer), and Ryan Willumson (chief revenue officer). It was funded with $900,000 from private investors in 2012 and 2013. The company covered five industries: construction, education, marketing, utility, and waste. In 2016, it began its Dive Awards. Industry Dive's revenues quadrupled from 2015 to 2018, putting it in the top half of the Deloitte Technology Fast 500 and the top 20 percent of the Inc. Top 5000 list. In 2019, Falfurrias Capital Partners acquired a majority stake in the company. ID's content marketing clients included IBM, Siemens, and UPS. In 2020, DCA Live named Industry Dive to its "Red Hot Companies" list, which recognizes the D.C. area's 'fastest-growing' companies. In the same year, Industry Dive acquired CFO. In 2021, Industry Dive acquired PharmaVOICE. In 2022, it was purchased by Informa plc, which bought its majority stake from Falfurrias Capital Partners for about $530 million. == Publications == Industry Dive provides news coverage of a variety of industries including agriculture, banking, construction, education, fashion, healthcare, and manufacturing, each using a different website: == Awards == Industry Dive publications have received several national and regional Awards of Excellence from the American Society of Business Publication Editors, including for a series of 2020 articles about Big Pharma and the race for the coronavirus vaccine. The Washington Post recognized Industry Dive as a top place to work for four consecutive years, from 2016 to 2020.