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  ACLU Mobile Justice

  ACLU Mobile Justice was a video live streaming application developed for smartphones by various state chapters of the American Civil Liberties Union. It was intended to allow instant, secure video recording and transmission of interactions with, and perceived abuses by, law enforcement officers. Since its release by the ACLU of California for California residents, other versions of the app have been released for 16 other states and the District of Columbia by their ACLU chapters. It was discontinued in February 2025.

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  Nanonetwork

  A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometers at most in size) which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in the biomedical field, environmental research, military technology and industrial and consumer goods applications. Nanoscale communication is defined in IEEE P1906.1. == Communication approaches == Classical communication paradigms need to be revised for the nanoscale. The two main alternatives for communication in the nanoscale are based either on electromagnetic communication or on molecular communication. === Electromagnetic === This is defined as the transmission and reception of electromagnetic radiation from components based on novel nanomaterials. Recent advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas. From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for a given input energy, amongst others. For the time being, two main alternatives for electromagnetic communication in the nanoscale have been envisioned. First, it has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nanoradio, i.e., an electromechanically resonating carbon nanotube which is able to decode an amplitude or frequency modulated wave. Second, graphene-based nano-antennas have been analyzed as potential electromagnetic radiators in the terahertz band. === Molecular === Molecular communication is defined as the transmission and reception of information by means of molecules. The different molecular communication techniques can be classified according to the type of molecule propagation in walkaway-based, flow-based or diffusion-based communication. In walkway-based molecular communication, the molecules propagate through pre-defined pathways by using carrier substances, such as molecular motors. This type of molecular communication can also be achieved by using E. coli bacteria as chemotaxis. In flow-based molecular communication, the molecules propagate through diffusion in a fluidic medium whose flow and turbulence are guided and predictable. The hormonal communication through blood streams inside the human body is an example of this type of propagation. The flow-based propagation can also be realized by using carrier entities whose motion can be constrained on the average along specific paths, despite showing a random component. A good example of this case is given by pheromonal long range molecular communications. In diffusion-based molecular communication, the molecules propagate through spontaneous diffusion in a fluidic medium. In this case, the molecules can be subject solely to the laws of diffusion or can also be affected by non-predictable turbulence present in the fluidic medium. Pheromonal communication, when pheromones are released into a fluidic medium, such as air or water, is an example of diffusion-based architecture. Other examples of this kind of transport include calcium signaling among cells, as well as quorum sensing among bacteria. Based on the macroscopic theory of ideal (free) diffusion the impulse response of a unicast molecular communication channel was reported in a paper that identified that the impulse response of the ideal diffusion based molecular communication channel experiences temporal spreading. Such temporal spreading has a deep impact in the performance of the system, for example in creating the intersymbol interference (ISI) at the receiving nanomachine. In order to detect the concentration-encoded molecular signal two detection methods named sampling-based detection (SD) and energy-based detection (ED) have been proposed. While the SD approach is based on the concentration amplitude of only one sample taken at a suitable time instant during the symbol duration, the ED approach is based on the total accumulated number of molecules received during the entire symbol duration. In order to reduce the impact of ISI a controlled pulse-width based molecular communication scheme has been analysed. The work presented in showed that it is possible to realize multilevel amplitude modulation based on ideal diffusion. A comprehensive study of pulse-based binary and sinus-based, concentration-encoded molecular communication system have also been investigated.

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  Influence-for-hire

  Influence-for-hire or collective influence, refers to the economy that has emerged around buying and selling influence on social media platforms. == Overview == Companies that engage in the influence-for-hire industry range from content farms to high-end public relations agencies. Traditionally influence operations have largely been confined to public sector actors like intelligence agencies, in the influence-for-hire industry the groups conduction the operations are private with commerce being their primary consideration. However many of the clients in the influence-for-hire industry are countries or countries acting through proxies. They are often located in countries with less expensive digital labor. == History == In May 2021, Facebook took a Ukrainian influence-for-hire network offline. Facebook attributed the network to organizations and consultants linked to Ukrainian politicians including Andriy Derkach. During the COVID-19 pandemic state sponsored misinformation was spread through influence-for-hire networks. In August 2021, a report published by the Australian Strategic Policy Institute implicated the Chinese government and the ruling Chinese Communist Party in campaigns of online manipulation conducted against Australia and Taiwan using influence-for-hire.

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  Virtual DOM

  A virtual DOM is a lightweight JavaScript representation of the Document Object Model (DOM) used in declarative web frameworks such as React, Vue.js, and Elm. Since generating a virtual DOM is relatively fast, any given framework is free to rerender the virtual DOM as many times as needed relatively cheaply. The framework can then find the differences between the previous virtual DOM and the current one (diffing), and only makes the necessary changes to the actual DOM (reconciliation). While technically slower than using just vanilla JavaScript, the pattern makes it much easier to write websites with a lot of dynamic content, since markup is directly coupled with state. Similar techniques include Ember.js' Glimmer and Angular's incremental DOM. == History == The JavaScript DOM API has historically been inconsistent across browsers, clunky to use, and difficult to scale for large projects. While libraries like jQuery aimed to improve the overall consistency and ergonomics of interacting with HTML, it too was prone to repetitive code that didn't describe the nature of the changes being made well and decoupled logic from markup. The release of AngularJS in 2010 provided a major paradigm shift in the interaction between JavaScript and HTML with the idea of dirty checking. Instead of imperatively declaring and destroying event listeners and modifying individual DOM nodes, changes in variables were tracked and sections of the DOM were invalidated and rerendered when a variable in their scope changed. This digest cycle provided a framework to write more declarative code that coupled logic and markup in a more logical way. While AngularJS aimed to provide a more declarative experience, it still required data to be explicitly bound to and watched by the DOM, and performance concerns were cited over the expensive process of dirty checking hundreds of variables. To alleviate these issues, React was the first major library to adopt a virtual DOM in 2013, which removed both the performance bottlenecks (since diffing and reconciling the DOM was relatively cheap) and the difficulty of binding data (since components were effectively just objects). Other benefits of a virtual DOM included improved security since XSS was effectively impossible and better extensibility since a component's state was entirely encapsulated. Its release also came with the advent of JSX, which further coupled HTML and JavaScript with an XML-like syntax extension. Following React's success, many other web frameworks copied the general idea of an ideal DOM representation in memory, such as Vue.js in 2014, which used a template compiler instead of JSX and had fine-grained reactivity built as part of the framework. In recent times, the virtual DOM has been criticized for being slow due to the additional time required for diffing and reconciling DOM nodes. This has led to the development of frameworks without a virtual DOM, such as Svelte, and frameworks that edit the DOM in-place such as Angular 2. == Implementations == === React === React pioneered the use of a virtual DOM to make components declaratively. Virtual DOM nodes are constructed using the createElement() function, but are often transpiled from JSX to make writing components more ergonomic. In class-based React, virtual DOM nodes are returned from the render() function, while in functional hook-based components, the return value of the function itself serves as the page markup. === Vue.js === Vue.js uses a virtual DOM to handle state changes, but is usually not directly interacted with; instead, a compiler is used to transform HTML templates into virtual DOM nodes as an implementation detail. While Vue supports writing JSX and custom render functions, it's more typical to use the template compiler since a build step isn't required that way. === Svelte === Svelte does not have a virtual DOM, with its creator Rich Harris calling the virtual DOM "pure overhead". Instead of diffing and reconciling DOM nodes at runtime, Svelte uses compile-time reactivity to analyze markup and generate JavaScript code that directly manipulates the DOM, drastically increasing performance.

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  Superquadrics

  In mathematics, the superquadrics or super-quadrics (also superquadratics) are a family of geometric shapes defined by formulas that resemble those of ellipsoids and other quadrics, except that the squaring operations are replaced by arbitrary powers. They can be seen as the three-dimensional relatives of the superellipses. The term may refer to the solid object or to its surface, depending on the context. The equations below specify the surface; the solid is specified by replacing the equality signs by less-than-or-equal signs. The superquadrics include many shapes that resemble cubes, octahedra, cylinders, lozenges and spindles, with rounded or sharp corners. Because of their flexibility and relative simplicity, they are popular geometric modeling tools, especially in computer graphics. It becomes an important geometric primitive widely used in computer vision, robotics, and physical simulation. Some authors, such as Alan Barr, define "superquadrics" as including both the superellipsoids and the supertoroids. In modern computer vision literatures, superquadrics and superellipsoids are used interchangeably, since superellipsoids are the most representative and widely utilized shape among all the superquadrics. Comprehensive coverage of geometrical properties of superquadrics and methods of their recovery from range images and point clouds are covered in several computer vision literatures. == Formulas == === Implicit equation === The surface of the basic superquadric is given by | x | r + | y | s + | z | t = 1 {\displaystyle \left|x\right|^{r}+\left|y\right|^{s}+\left|z\right|^{t}=1} where r, s, and t are positive real numbers that determine the main features of the superquadric. Namely: less than 1: a pointy octahedron modified to have concave faces and sharp edges. exactly 1: a regular octahedron. between 1 and 2: an octahedron modified to have convex faces, blunt edges and blunt corners. exactly 2: a sphere greater than 2: a cube modified to have rounded edges and corners. infinite (in the limit): a cube Each exponent can be varied independently to obtain combined shapes. For example, if r=s=2, and t=4, one obtains a solid of revolution which resembles an ellipsoid with round cross-section but flattened ends. This formula is a special case of the superellipsoid's formula if (and only if) r = s. If any exponent is allowed to be negative, the shape extends to infinity. Such shapes are sometimes called super-hyperboloids. The basic shape above spans from -1 to +1 along each coordinate axis. The general superquadric is the result of scaling this basic shape by different amounts A, B, C along each axis. Its general equation is | x A | r + | y B | s + | z C | t = 1. {\displaystyle \left|{\frac {x}{A}}\right|^{r}+\left|{\frac {y}{B}}\right|^{s}+\left|{\frac {z}{C}}\right|^{t}=1.} === Parametric description === Parametric equations in terms of surface parameters u and v (equivalent to longitude and latitude if m equals 2) are x ( u , v ) = A g ( v , 2 r ) g ( u , 2 r ) y ( u , v ) = B g ( v , 2 s ) f ( u , 2 s ) z ( u , v ) = C f ( v , 2 t ) − π 2 ≤ v ≤ π 2 , − π ≤ u < π , {\displaystyle {\begin{aligned}x(u,v)&{}=Ag\left(v,{\frac {2}{r}}\right)g\left(u,{\frac {2}{r}}\right)\\y(u,v)&{}=Bg\left(v,{\frac {2}{s}}\right)f\left(u,{\frac {2}{s}}\right)\\z(u,v)&{}=Cf\left(v,{\frac {2}{t}}\right)\\&-{\frac {\pi }{2}}\leq v\leq {\frac {\pi }{2}},\quad -\pi \leq u<\pi ,\end{aligned}}} where the auxiliary functions are f ( ω , m ) = sgn ⁡ ( sin ⁡ ω ) | sin ⁡ ω | m g ( ω , m ) = sgn ⁡ ( cos ⁡ ω ) | cos ⁡ ω | m {\displaystyle {\begin{aligned}f(\omega ,m)&{}=\operatorname {sgn}(\sin \omega )\left|\sin \omega \right|^{m}\\g(\omega ,m)&{}=\operatorname {sgn}(\cos \omega )\left|\cos \omega \right|^{m}\end{aligned}}} and the sign function sgn(x) is sgn ⁡ ( x ) = { − 1 , x < 0 0 , x = 0 + 1 , x > 0. {\displaystyle \operatorname {sgn}(x)={\begin{cases}-1,&x<0\\0,&x=0\\+1,&x>0.\end{cases}}} === Spherical product === Barr introduces the spherical product which given two plane curves produces a 3D surface. If f ( μ ) = ( f 1 ( μ ) f 2 ( μ ) ) , g ( ν ) = ( g 1 ( ν ) g 2 ( ν ) ) {\displaystyle f(\mu )={\begin{pmatrix}f_{1}(\mu )\\f_{2}(\mu )\end{pmatrix}},\quad g(\nu )={\begin{pmatrix}g_{1}(\nu )\\g_{2}(\nu )\end{pmatrix}}} are two plane curves then the spherical product is h ( μ , ν ) = f ( μ ) ⊗ g ( ν ) = ( f 1 ( μ ) g 1 ( ν ) f 1 ( μ ) g 2 ( ν ) f 2 ( μ ) ) {\displaystyle h(\mu ,\nu )=f(\mu )\otimes g(\nu )={\begin{pmatrix}f_{1}(\mu )\ g_{1}(\nu )\\f_{1}(\mu )\ g_{2}(\nu )\\f_{2}(\mu )\end{pmatrix}}} This is similar to the typical parametric equation of a sphere: x = x 0 + r sin ⁡ θ cos ⁡ φ y = y 0 + r sin ⁡ θ sin ⁡ φ ( 0 ≤ θ ≤ π , 0 ≤ φ < 2 π ) z = z 0 + r cos ⁡ θ {\displaystyle {\begin{aligned}x&=x_{0}+r\sin \theta \;\cos \varphi \\y&=y_{0}+r\sin \theta \;\sin \varphi \qquad (0\leq \theta \leq \pi ,\;0\leq \varphi <2\pi )\\z&=z_{0}+r\cos \theta \end{aligned}}} which give rise to the name spherical product. Barr uses the spherical product to define quadric surfaces, like ellipsoids, and hyperboloids as well as the torus, superellipsoid, superquadric hyperboloids of one and two sheets, and supertoroids. == Plotting code == The following GNU Octave code generates a mesh approximation of a superquadric:

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  Foreground detection

  Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.). Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene, because an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Foreground detection separates foreground from background based on these changes taking place in the foreground. It is a set of techniques that typically analyze video sequences recorded in real time with a stationary camera. == Description == All detection techniques are based on modelling the background of the image, i.e., setting the background and detecting which changes occur. Defining the background can be difficult when it contains shapes, shadows, and moving objects. In defining the background, it is assumed that stationary objects may vary in color and intensity over time. Scenarios in which these techniques apply tend to be very diverse. There can be highly variable sequences, such as images with different lighting, interiors, exteriors, quality, and noise. In addition to real-time processing, systems need to adapt to these changes. A foreground detection system should be able to: Develop a background model (estimate). Be robust to lighting changes, repetitive movements (leaves, waves, shadows), and long-term changes. == Background subtraction == Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called "background image", or "background model". Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human pose estimation. Background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. Similarly, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes. == Temporal average filter == The temporal average filter is a method that was proposed at the Velastin. This system estimates the background model from the median of all pixels of a number of previous images. The system uses a buffer with the pixel values of the last frames to update the median for each image. To model the background, the system examines all images in a given time period called training time. At this time, we only display images and will find the median, pixel by pixel, of all the plots in the background this time. After the training period for each new frame, each pixel value is compared with the input value of funds previously calculated. If the input pixel is within a threshold, the pixel is considered to match the background model and its value is included in the pixbuf. Otherwise, if the value is outside this threshold pixel is classified as foreground, and not included in the buffer. This method cannot be considered very efficient because they do not present a rigorous statistical basis and requires a buffer that has a high computational cost. == Conventional approaches == A robust background subtraction algorithm should be able to handle lighting changes, repetitive motions from clutter and long-term scene changes. The following analyses make use of the function of V(x,y,t) as a video sequence where t is the time dimension, x and y are the pixel location variables. e.g. V(1,2,3) is the pixel intensity at (1,2) pixel location of the image at t = 3 in the video sequence. === Using frame differencing === A motion detection algorithm begins with the segmentation part where foreground or moving objects are segmented from the background. The simplest way to implement this is to take an image as background and take the frames obtained at the time t, denoted by I(t) to compare with the background image denoted by B. Here using simple arithmetic calculations, we can segment out the objects simply by using image subtraction technique of computer vision meaning for each pixels in I(t), take the pixel value denoted by P[I(t)] and subtract it with the corresponding pixels at the same position on the background image denoted as P[B]. In mathematical equation, it is written as: P [ F ( t ) ] = P [ I ( t ) ] − P [ B ] {\displaystyle P[F(t)]=P[I(t)]-P[B]} The background is assumed to be the frame at time t. This difference image would only show some intensity for the pixel locations which have changed in the two frames. Though we have seemingly removed the background, this approach will only work for cases where all foreground pixels are moving, and all background pixels are static. A threshold "Threshold" is put on this difference image to improve the subtraction (see Image thresholding): | P [ F ( t ) ] − P [ F ( t + 1 ) ] | > T h r e s h o l d {\displaystyle |P[F(t)]-P[F(t+1)]|>\mathrm {Threshold} } This means that the difference image's pixels' intensities are 'thresholded' or filtered on the basis of value of Threshold. The accuracy of this approach is dependent on speed of movement in the scene. Faster movements may require higher thresholds. === Mean filter === For calculating the image containing only the background, a series of preceding images are averaged. For calculating the background image at the instant t: B ( x , y , t ) = 1 N ∑ i = 1 N V ( x , y , t − i ) {\displaystyle B(x,y,t)={1 \over N}\sum _{i=1}^{N}V(x,y,t-i)} where N is the number of preceding images taken for averaging. This averaging refers to averaging corresponding pixels in the given images. N would depend on the video speed (number of images per second in the video) and the amount of movement in the video. After calculating the background B(x,y,t) we can then subtract it from the image V(x,y,t) at time t = t and threshold it. Thus the foreground is: | V ( x , y , t ) − B ( x , y , t ) | > T h {\displaystyle |V(x,y,t)-B(x,y,t)|>\mathrm {Th} } where Th is a threshold value. Similarly, we can also use median instead of mean in the above calculation of B(x,y,t). Usage of global and time-independent thresholds (same Th value for all pixels in the image) may limit the accuracy of the above two approaches. === Running Gaussian average === For this method, Wren et al. propose fitting a Gaussian probabilistic density function (pdf) on the most recent n {\displaystyle n} frames. In order to avoid fitting the pdf from scratch at each new frame time t {\displaystyle t} , a running (or on-line cumulative) average is computed. The pdf of every pixel is characterized by mean μ t {\displaystyle \mu _{t}} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} . The following is a possible initial condition (assuming that initially every pixel is background): μ 0 = I 0 {\displaystyle \mu _{0}=I_{0}} σ 0 2 = ⟨ some default value ⟩ {\displaystyle \sigma _{0}^{2}=\langle {\text{some default value}}\rangle } where I t {\displaystyle I_{t}} is the value of the pixel's intensity at time t {\displaystyle t} . In order to initialize variance, we can, for example, use the variance in x and y from a small window around each pixel. Note that background may change over time (e.g. due to illumination changes or non-static background objects). To accommodate for that change, at every frame t {\displaystyle t} , every pixel's mean and variance must be updated, as follows: μ t = ρ I t + ( 1 − ρ ) μ t − 1 {\displaystyle \mu _{t}=\rho I_{t}+(1-\rho )\mu _{t-1}} σ t 2 = d 2 ρ + ( 1 − ρ ) σ t − 1 2 {\displaystyle \sigma _{t}^{2}=d^{2}\rho +(1-\rho )\sigma _{t-1}^{2}} d = | ( I t − μ t ) | {\displaystyle d=|(I_{t}-\mu _{t})|} Where ρ {\displaystyle \rho } determines the size of the temporal window that is used to fit the pdf (usually ρ = 0.01 {\displaystyle \rho =0.01} ) and d {\displaystyle d} is the Euclidean distance between the mean and the value of the pixel. We can now classify a pixel as background if its current intensity lies within some confidence interval of its distribution's mean: | ( I t − μ t ) | σ t > k ⟶ foreground {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}>k\longrightarrow {\text{foreground}}} | ( I t − μ t ) | σ t ≤ k ⟶ background {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}\leq k\longrightarrow {\text{background}}} where the parameter k {\displaystyle k} is a free threshold (usuall

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  Information Age

  The Information Age is a historical period that began in the mid-20th century. It is characterized by a rapid shift from traditional industries, as established during the Industrial Revolution, to an economy centered on information technology. The onset of the Information Age has been linked to the development of the transistor in 1947. Advances in computer miniaturization, internet communication, and semiconductor technology enabled the rapid expansion of digital systems and global information networks. The Information Age transformed industries such as education, healthcare, finance, entertainment, and communication through digital infrastructure and connected technologies. The rise of smartphones and cloud-based services further accelerated global internet accessibility and digital interaction. == Digital applications and mobile technology == The expansion of Android and iOS ecosystems during the 21st century contributed to the widespread use of utility applications and mobile productivity tools. Applications related to calculations, scheduling, digital organization, and educational support became increasingly common on smartphones and tablets. Mobile utility software demonstrates how modern digital platforms support accessibility and everyday online services. Independent developers have contributed to this technological ecosystem through lightweight applications focused on mobile usability and internet-based functionality. == Influence on modern society == The Information Age has reshaped the way individuals communicate, consume information, and interact with digital services. Social media platforms, artificial intelligence systems, cloud storage, and mobile computing continue to influence modern economies and online communities worldwide. Emerging technologies such as the Internet of things, machine learning, and advanced automation are often associated with the transition toward the Fourth Industrial Revolution. == History == The digital revolution converted technology from analog format to digital format. By doing this, it became possible to make copies that were identical to the original. In digital communications, for example, repeating hardware was able to amplify the digital signal and pass it on with no loss of information in the signal. Of equal importance to the revolution was the ability to easily move the digital information between media and to access or distribute it remotely. One turning point of the revolution was the change from analog to digitally recorded music. During the 1980s, the digital format of optical compact discs gradually replaced analog formats, such as vinyl records and cassette tapes, as the popular medium of choice. === Previous inventions === Humans have manufactured tools for counting and calculating since ancient times, such as the abacus, astrolabe, equatorium, and mechanical timekeeping devices. More complicated devices started appearing in the 1600s, including the slide rule and mechanical calculators. By the early 1800s, the Industrial Revolution had produced mass-market calculators like the arithmometer and the enabling technology of the punch card. Charles Babbage proposed a mechanical general-purpose computer called the Analytical Engine, but it was never successfully built, and was largely forgotten by the 20th century, and unknown to most of the inventors of modern computers. The Second Industrial Revolution, in the last quarter of the 19th century, developed useful electrical circuits and the telegraph. In the 1880s, Herman Hollerith developed electromechanical tabulating and calculating devices using punch cards and unit record equipment, which became widespread in business and government. Meanwhile, various analog computer systems used electrical, mechanical, or hydraulic systems to model problems and calculate answers. These included an 1872 tide-predicting machine, differential analysers, perpetual calendar machines, the Deltar for water management in the Netherlands, network analyzers for electrical systems, and various machines for aiming military guns and bombs. The construction of problem-specific analog computers continued in the late 1940s and beyond, with FERMIAC for neutron transport, Project Cyclone for various military applications, and the Phillips Machine for economic modeling. Building on the complexity of the Z1 and Z2, German inventor Konrad Zuse used electromechanical systems to complete in 1941 the Z3, the world's first working programmable, fully automatic digital computer. Also, during World War II, Allied engineers constructed electromechanical bombes to break the German Enigma machine encoding. The base-10 electromechanical Harvard Mark I was completed in 1944, and was to some degree improved with inspiration from Charles Babbage's designs. === 1947–1969: Origins === In 1947, the first working transistor, the germanium-based point-contact transistor, was invented by John Bardeen and Walter Houser Brattain while working under William Shockley at Bell Labs. This led the way to more advanced digital computers. From the late 1940s, universities, the military, and businesses developed computer systems to digitally replicate and automate previously manually performed mathematical calculations, with the LEO being the first commercially available general-purpose computer. Digital communication became economical for widespread adoption after the invention of the personal computer in the 1970s. Claude Shannon, a Bell Labs mathematician, is generally credited with laying the foundations of digitalization in his pioneering 1948 article, A Mathematical Theory of Communication. In 1948, Bardeen and Brattain patented an insulated-gate transistor (IGFET) with an inversion layer. Their concept forms the basis of CMOS and DRAM technology today. In 1957, at Bell Labs, Frosch and Derick were able to manufacture planar silicon dioxide transistors, later a team at Bell Labs demonstrated a working MOSFET. The first integrated circuit milestone was achieved by Jack Kilby in 1958. Other important technological developments included the invention of the monolithic integrated circuit chip by Robert Noyce at Fairchild Semiconductor in 1959, made possible by the planar process developed by Jean Hoerni. In 1963, complementary MOS (CMOS) was developed by Chih-Tang Sah and Frank Wanlass at Fairchild Semiconductor. The self-aligned gate transistor, which further facilitated mass production, was invented in 1966 by Robert Bower at Hughes Aircraft and independently by Robert Kerwin, Donald Klein, and John Sarace at Bell Labs. In 1962, AT&T deployed the T-carrier for long-haul pulse-code modulation (PCM) digital voice transmission. The T1 format carried 24 pulse-code modulated, time-division multiplexed speech signals, each encoded in 64 kbit/s streams, leaving 8 kbit/s of framing information, which facilitated the synchronization and demultiplexing at the receiver. Over the subsequent decades, the digitisation of voice became the norm for all but the last mile (where analogue continued to be the norm right into the late 1990s). Following the development of MOS integrated circuit chips in the early 1960s, MOS chips reached higher transistor density and lower manufacturing costs than bipolar integrated circuits by 1964. MOS chips further increased in complexity at a rate predicted by Moore's law, leading to large-scale integration (LSI) with hundreds of transistors on a single MOS chip by the late 1960s. The application of MOS LSI chips to computing was the basis for the first microprocessors, as engineers began recognizing that a complete computer processor could be contained on a single MOS LSI chip. In 1968, Fairchild engineer Federico Faggin improved MOS technology with his development of the silicon-gate MOS chip, which he later used to develop the Intel 4004, the first single-chip microprocessor. It was released by Intel in 1971 and laid the foundations for the microcomputer revolution that began in the 1970s. MOS technology also led to the development of semiconductor image sensors suitable for digital cameras. The first such image sensor was the charge-coupled device, developed by Willard S. Boyle and George E. Smith at Bell Labs in 1969, based on MOS capacitor technology. === 1969–1989: Invention of the internet, rise of home computers === The public was first introduced to the concepts that led to the Internet when a message was sent over the ARPANET in 1969. Packet switched networks such as ARPANET, Mark I, CYCLADES, Merit Network, Tymnet, and Telenet, were developed in the late 1960s and early 1970s using a variety of protocols. The ARPANET in particular led to the development of protocols for internetworking, in which multiple separate networks could be joined into a network of networks. The Whole Earth movement of the 1960s advocated the use of new technology. In the 1970s, the home computer was introduced, time-sharing computers, the video game console, the first coin-op vide

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  Far-Play

  Far-Play (stylized fAR-Play, from augmented reality) was a software platform developed at the University of Alberta, for creating location-based, scavenger-hunt style games which use the GPS and web-connectivity features of a player's smartphone. According to the development team, "our long-term objective is to develop a general framework that supports the implementation of AARGs that are fun to play and also educational". It utilizes Layar, an augmented reality smartphone application, QR codes located at particular real-world sites, or a phone's web browser, to facilitate games which require players to be in close physical proximity to predefined "nodes". A node, referred to by the developers as a Virtual Point of Interest (vPOI), is a point in space defined by a set of map coordinates; fAR-Play uses the GPS function of a player's smartphone — or, for indoor games, which are not easily tracked by GPS satellites, specially-created QR codes— to confirm that they are adequately near a given node. Once a player is within a node's proximity, Layar's various augmented reality features can be utilized to display a range of extra content overlaid upon the physical play-space or launch another application for extra functionality. == Development and features == fAR-Play began development in 2008, emerging from a collaborative project undertaken by a group of University of Alberta students from the Computer Science and Humanities Computing departments. fAR-Play is still under development, but a beta version is available for testing by request. fAR-Play's development is managed by a team of interdisciplinary professors and students at the University of Alberta. Currently, the developing team's roster includes Supervising Professors Geoffrey Rockwell and Eleni Stroulia, Developers Lucio Gutierrez and Matthew Delaney, and Website Developers Calen Henry and Garry Wong. === Technology === fAR-Play relies on a number of open- and closed-source web technologies as tools to create, and enhance the users' experience. Layar is the recommended client-side frontend for delivering game content to the player; it is available on Android and iOS, which covers over 91% of smartphones. While Layar is not a requirement to play fAR-Play games, the application does supply additional augmented reality functionality; Layar also includes a built-in QR scanner. Depending on the design of the particular game, the player may instead use a dedicated QR code scanner; the developers recommend BeeTagg, but any such application will do. Layar or a QR code scanner are the maximum software requirements to play a fAR-Play game, making implementation of games on a wide variety of platforms relatively straightforward. fAR-Play games can also be designed for play strictly within a mobile phone's web browser. On the server side, fAR-Play's engine is composed of an Apache server which manages the system's web interface, including the mobile and desktop versions of the fAR-Play website, and a Java-based REST framework for managing the database of nodes. === Features === As a platform for designing AR games, as opposed to an AR game itself, fAR-Play offers little in the way of explicit shapes or patterns for games to take; instead, these elements are left to the game designer or players to develop. However, the nonspecific nature of nodes, the many options they offer for content delivery, and the open design of the platform are such that these elements can be developed extensively. Functionally, fAR-Play is a tool for tracking arbitrary points in space and a given player's proximity to them; what it does beyond that is up to the developers' and players' discretion. However, the fAR-Play website contains a leaderboard which tracks registered user's total scores. Players are assigned levels based on their total score, ranging from Novice — Super Player. Player profiles will display nodes that the player has recently caught, and any achievements the player has gained. Additionally, players can share their adventure progress, achievements, and the capture of vPOIs on Facebook. == How to play == In order to participate in the locative aspects of fAR-Play games, users must have an Android or iOS mobile device and access to wireless internet. Players can participate in fAR-Play anonymously, or create and sign into a fAR-Play account. Those who choose to play anonymously will lose the ability to track their progress across multiple games. When signed in, the player is presented with a list of games that are currently available for play. Each game includes a brief description and the various "adventures" available to the player. Once the game has been started, the player has three different methods for capturing nodes: they may scan a QR in the physical space, discover a node through the Layar camera virtual view, or receive a link in their device's web browser. === QR codes and Layar === QR codes can only be used as a method for capturing nodes and initiating games when there is a physical code present. In order to scan a QR code, players are required to have an application which can capture and recognize QR codes. If the player is utilizing a QR scanning application that has a built in browser, they will be required to log into fAR-Play through the app. Layar is a free to download augmented reality app, containing a built in QR code scanner, which enables its users to participate in fAR-Play games. === Capturing nodes === Layar permits the player to see nodes on their mobile device, guiding the player to their goal. Using this application, the player is able to navigate to their objective with map provided by Google Maps' API or by using their camera — Layar overlays a virtual image onto the real-world scene presented by the camera. The representations on screen expand in size as the player approaches the node destination, simulating relative distance. If the player taps any of the nodes that are presented on the screen, they will be provided additional information about that node, including the node's name and a brief description. Nodes can be captured by tapping the "capture" button. === Playing on browsers === The player can also play fAR-Play games within their mobile device's browser. By visiting https://archive.today/20131123223038/http://farplay.ualberta.ca/far-play/ on a mobile device, players will be presented with a fully realized user interface, permitting full interaction with the games. The player can capture the in game vPOIs through their browser by tapping the "nodes" button. This will bring up a list of all the accessible nodes, complete with a brief description for each location. By clicking on one of the nodes, the player is shown to a screen with a mapped location of the vPOI, an in-depth description of it, and hints. At the top of the page, the player can tap "CAPTURE THIS NODE" and advance in the game. When attempting to capture a node, the developer may or may not associate a challenge with the node. For example, in the game "Zombies ate my Campus", when players are attempting to capture a node, they're presented with a multiple choice question associated with the current node. === Game types === Players complete an adventure when they have captured all of the nodes within it. fAR-Play provides two game modes: in a Virtual Scavenger Hunt, nodes must be captured in a specific order; in a Virtual Treasure Hunt, the order is unimportant. == Existing fAR-Play games == Games currently available through fAR-Play include: Giselle Ever After Thought Hub Comics Arts Capture Challenge Pioneering Edmonton The Intelliphone Challenge A Tour of Atwater Zombies ate my Campus == For developers == fAR-Play's ultimate goal is to provide a simple, effective platform for the creation of locative augmented reality games, but the developer tools are still under active development and not openly available to the public. Access can be granted on a case-by-case basis, however, and a developer's manual is available. Users with development privileges can create new games or edit their existing games, in addition to playing their own or others' games. === Adventures === Games that are developed with fAR-Play are segmented into components called "Adventures". To progress through each game adventure, the player must reach and capture virtual points of interest, referred to in the game as vPOIs. In order to capture a vPOI, the player must travel to a physical location that is set by the developer. It is the developer's choice to include a challenge question to capture the vPOI, though it is not mandatory. A deduction of points can be implemented if the player submits an incorrect answer to a challenge question. === Points and achievements === Each of the nodes will reward the player with a predetermined number of points once they have been captured by the player. These points are added to the player's total points. Each of the adventures that are created require a predetermined number of vPOIs

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  Tertiary review

  In software engineering, a tertiary review is a systematic review of systematic reviews. It is also referred to as a tertiary study in the software engineering literature. However, Umbrella review is the term more commonly used in medicine. Kitchenham et al. suggest that methodologically there is no difference between a systematic review and a tertiary review. However, as the software engineering community has started performing tertiary reviews new concerns unique to tertiary reviews have surfaced. These include the challenge of quality assessment of systematic reviews, search validation and the additional risk of double counting. == Examples of Tertiary reviews in software engineering literature == Test quality Machine Learning Test-driven development

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  Haul video

  A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.

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  Open Rights Group

  The Open Rights Group (ORG) is a UK-based organisation that works to preserve digital rights and freedoms by campaigning on digital rights issues and by fostering a community of grassroots activists. It campaigns on numerous issues including mass surveillance, internet filtering and censorship, and intellectual property rights. == History == The organisation was started by Danny O'Brien, Cory Doctorow, Ian Brown, Rufus Pollock, James Cronin, Stefan Magdalinski, Louise Ferguson and Suw Charman after a panel discussion at Open Tech 2005. O'Brien created a pledge on PledgeBank, placed on 23 July 2005, with a deadline of 25 December 2005: "I will create a standing order of 5 pounds per month to support an organisation that will campaign for digital rights in the UK but only if 1,000 other people will too." The pledge reached 1000 people on 29 November 2005. The Open Rights Group was launched at a "sell-out" meeting in Soho, London. == Work == The group has made submissions to the All Party Internet Group (APIG) inquiry into digital rights management and the Gowers Review of Intellectual Property. The group was honoured in the 2008 Privacy International Big Brother Awards alongside No2ID, Liberty, Genewatch UK and others, as a recognition of their efforts to keep state and corporate mass surveillance at bay. In 2010 the group worked with 38 Degrees to oppose the introduction of the Digital Economy Act, which was passed in April 2010. The group opposes measures in the draft Online Safety Bill introduced in 2021, that it sees as infringing free speech rights and online anonymity. The group campaigns against the Department for Digital, Culture, Media and Sport's plan to switch to an opt-out model for cookies. The group spokesperson stated that "[t]he UK government propose to make online spying the default option" in response to the proposed switch. == Areas of interest == The organisation, though focused on the impact of digital technology on the liberty of UK citizens, operates with an apparently wide range of interests within that category. Its interests include: === Access to knowledge === Copyright Creative Commons Free and open source software The public domain Crown copyright Digital Restrictions Management Software patents === Free speech and censorship === Internet filtering Right to parody s. 127 Communications Act 2003 === Government and democracy === Electronic voting Freedom of information legislation === Privacy, surveillance and censorship === Automatic Vehicle Tracking Communications data retention Identity management Net Neutrality NHS patients' medical database Police DNA Records RFID == Structure == ORG has a paid staff, whose members include: Jim Killock (executive director) Former staff include Suw Charman-Anderson and Becky Hogge, both executive directors, e-voting coordinator Jason Kitcat, campaigner Peter Bradwell, grassroots campaigner Katie Sutton and administrator Katerina Maniadaki. Neil Gaiman was previously the group's patron. As of October 2022, the group had over 43,000 supporters. == ORGCON == ORGCON was the first ever conference dedicated to digital rights in the UK, marketed as "a crash course in digital rights". It was held for the first time in 2010 at City University in London and included keynote talks from Cory Doctorow, politicians and similar pressure groups including Liberty, NO2ID and Big Brother Watch. ORGCON has since been held in 2012, 2013, 2014, 2017, and 2019 where the keynote was given by Edward Snowden.

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  Blue check

  A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official

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  Tom's Planner

  Tom's Planner is a web-based tool and application service provider for project planning, management and collaboration. == History == Tom's Planner is based on Curaçao. In November 2009, it announced its public beta launch on TechCrunch and moved out of beta in August 2010. In 2013 Tom's Planner acquired its competitor Gantto. == Software == Tom's Planner is project management software that enables the creation of project schedules (Gantt charts) using a visual perspective. Tom's Planner uses the Freemium Business Model. Users can register for a free account or choose a paid version. Tom's Planner is available in five languages and is used by thousands of users on a daily basis in more than 100 countries worldwide. Customers range from fortune 500 companies to small mom-and-pop shops. == Reviews == Tom's Planner has been reviewed by PC World, TechCrunch, Lifehacker, and several other periodicals.

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  Content Credentials

  Content Credentials (also known as C2PA signatures) are a digital media metadata specification. They aim to provide provenance information about a piece of media (such as an image or a video) and help prove its authenticity. They are described as the equivalent of nutrition labels for digital media. One of the stated goal of this specification is to fight online disinformation. The specification is written and maintained by the Coalition for Content Provenance and Authenticity (C2PA), a group of many media and tech organizations including Adobe, Amazon, the BBC, Google, Meta, Microsoft, OpenAI and Sony. Another organization, the Content Authenticity Initiative (CAI), is responsible for promoting the standard and accelerate its adoption. The standard relies on cryptographic digital signatures. == Adoption == There are two main stakeholders who can implement Content Credentials: Producers (softwares and hardwares that produce or modify digital media) and publishers (softwares that show digital media to users). === Producers === ==== Adobe ==== Adobe is one of the first companies to implement the specification, announcing support in Photoshop in 2021. Content Credentials can be enabled and the complete history of edits is kept. ==== Google ==== Google announced support for Content Credentials on its Pixel 10 phones in August 2025. The Content Credentials are embedded on each picture taken from the Pixel Camera, and modifications done using Google Photos. Information include picture timestamp and a non-identifiable signature that proves it was taken from a Pixel 10. As for Google Photos, a list of AI and non-AI edits are kept. Google is the first company to introduce support for Content Credentials on either phones or consumer-grade devices, and also the first company to make it available for free to all users. ==== Nikon ==== Nikon announced in 2024 that their Z6 III camera would support embedding Content Credentials in its photos. However, in 2025, a vulnerability was discovered in the software of the camera that allowed to combine unauthentic images with authentic photos and still have the resulting image with a valid digital signature. Nikon revoked the certificates. ==== Media organizations ==== CBC/Radio-Canada and the BBC both have started attaching Content Credentials to media they produce or verify. ==== OpenAI ==== OpenAI embeds Content Credentials on the images and videos it generates that includes that the media was created by AI using their platforms. ==== Sony ==== In June 2025, Sony announced the release of its Camera Verify system for press photographers and news editors using C2PA digital signatures. Initially, the system will be limited to still images, high‑end cameras, and selected news agencies. Registration with Sony Creators' Cloud is also required. === Publishers === ==== LinkedIn ==== In 2024, LinkedIn started showing a "CR" icon on images that contain Content Credentials of AI-generated images. In 2025, they announced a partnership with Adobe to allow photographers to prove ownership of images using Content Credentials. ==== TikTok ==== TikTok announced in 2024 that an "AI-generated" label would be applied to videos containing Content Credentials if they were AI-generated. In 2025, they announced that users could control the amount of AI-generated content they see, using self-reported labels, Content Credentials and an invisible, proprietary AI watermark embedded in videos by their AI editor tool. ==== YouTube ==== In 2024, YouTube started showing to users a label that reads "captured with a camera" on videos that show authentic, unedited videos taken by Content Credentials-compatible cameras.

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  Abjjad

  Abjjad is an Arabic reading application that was launched in June 2012 by Eman Hylooz. Abjjad offers users the ability to download and read thousands of books offline through its iOS and Android applications. In December of 2020, Abjjad had more than 1.5 million registered accounts. == About Abjjad == Abjjad was founded in June 2012 by Eman Hylooz as a reader community dedicated to Arab readers, authors, and book lovers. Abjjad developed into a smart electronic platform to provide Arabic electronic books with ease to Arab readers everywhere after discovering a large gap in the world of Arab publishing, which is the legal electronic publishing, by forming strategic partnership with Arab publishers such as Dar Al-Shorouk, Dar Al Tanweer, Dar Al Adab, and Dar Al Saqi. == History == In May 2012, Oasis500 provided Abjjad with the seed funding to launch the website. In June 2012, Abjjad was launched with a budget of 15 thousand dollars. Within the first three months more than 10 thousand members were registered in Abjjad. Abjjad has participated in different local and international forums to meet several investors and entrepreneurs. In October 2012 Abjjad participated in Global thinkers forum in Amman, Jordan where Eman Hylooz, founder & CEO, presented the concept of Abjjad, its vision and future plans In mid-December 2012 Abjjad participated in Global Entrepreneurship in Dubai where it was presented to investors as a start-up and a new project in the Middle East. In February 2013 Abjjad was one of ten startups MENA apps has nominated from Jordan and Palestine to participate in startup Turkey. In May 2013 Abjjad participated in World Economic Forum in Amman, Jordan and later in June 2013 participated in Arab Net in Dubai. By the end of 2013, Abjjad won the Mohammed Bin Rashid Al Maktoum's Best Arab Start-Up Business Award for 2013. During 29 October 2013 till January 2014 Abjjad has launched their campaign for crowd funding through Eureeca Abjjad managed to raise US$161,000 in 88 days from 43 regional donors, over US$40,000 over its initial target. By the end of 2020. Abjjad had raised a $1 million investment round led by Jordan Entrepreneurship Fund, Ramal Capital Fund, and JordInvest Fund. Because the funds will be used to acquire users and e-books, Abjjad hopes to become the largest Arab electronic library as well as the largest income-generating platform for Arab authors and publishers, while also providing readers with a unique digital reading experience. == Features == The ability to read an unlimited number of books from an electronic library containing thousands of Arabic and translated books. Abjjad ebook library is constantly expanding and cooperating with new publishing houses to add more books. Reading offline without an internet connection. The application allows the user to download books in seconds and read them anywhere. Intuitive feature which include the ability to flip the pages of the book, highlight the reader's favorite quotes, and add notes, in addition to night reading mode and the option to modify the style and size of the front. The ability to interact with other readers and read their book reviews. More than 1.5 million Arabic readers make up the Abjjad reader community, and the user can read and connect with their reviews, book ratings, and favorite quotes. A virtual personal library that enables the user to rate and organize books by placing them on one of the three shelves: I will read it, currently readings, and/or read it. Abjjad's library includes various genres and literary fields, such as: reference books, novels, stories, literature, psychological books, philosophy, biography, politics, history, religion, self-improvement and human development books, as well as international books translated into Arabic. The library includes the most famous works of Arab authors such as: Naguib Mahfouz, Mahmoud Darwish, Radwa Ashour, Tayeb Salih. Aside from Arabic translation of works by well-known worldwide authors including: Elif Shafak, Fyodor Dostoevsky, Mark Manson, and others. == Statistics == In December of 2020, Abjjad had more than 1.5 million registered accounts. == Awards and honors == 2013: Won the Mohammad Bin Rashid Award for Best Arabic Startup 2014: Won the Golden Award for Jawa's "Best Online Community" 2015: Won the Business Women of the Year Award by Bank al Etihad 2016: Won the Said Khoury Award for Entrepreneurs and Innovators 2016: Won the Best Application in the Arabic Region Award by His Highness Sheikh Salem Al-Ali Al-Sabah in Kuwait. 2019: Won the Mohammad Bin Rashid Award for Arabic Language for the best artistic, cultural or intellectual world to serve the Arabic language. == Abjjad in the media == Abjjad has taken a huge interest in the Middle Eastern and western media; the author of Startup Rising: The Entrepreneurial Revolution Remaking the Middle East, Christopher M. Schroeder, has interviewed Eman Hylooz and wrote about her experience with Abjjad in his book. In addition, France24-Monte Carlo Doualiya has interviewed Ms. Hylooz on Retweet program to discuss Abjjad idea and provide the latest statistics of the website. Moreover, Sky News Arabia interviewed Hylooz to relate her experience with Oasis500 and Eureeca in Abjjad's crowdinvestment campaignPage text. furthermore, Al-Aan TV interviewed Ms.Hylooz in ArabNet in Dubai, 2013. Abjjad has been mentioned on Oasis500 website as one of the five startups which the company funded and gained different prizes. Wamda, Mediame and crowdfundinsider have discussed Abjjad's experience in the crowd investment on Eureeca. And the expert in the Arabic literature in English, M. Lynx Qualey, has interviewed Eman Hylooz in March 2013 to talk about Abjjad's story of success, how it differs from other social networks and what are its future plans. Abjjad was also featured in "Hashtag Arabi" website when it launched its premium subscription called "Abjjad Unlimited" in 2017 with the support of the Abdul Hameed Shoman Foundation. In her interview with the Jordan Times, Eman also discussed her background in computer science and software development, which helped her found Abjjad.

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