AI Analytics Healthcare

AI Analytics Healthcare — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Shell Control Box

  Shell Control Box (SCB) is a network security appliance that controls privileged access to remote IT systems, records activities in replayable audit trails, and prevents malicious actions. For example, it records as a system administrator updates a file server or a third-party network operator configures a router. The recorded audit trails can be replayed like a movie to review the events as they occurred. The content of the audit trails is indexed to make searching for events and automatic reporting possible. SCB is a Linux-based device developed by Balabit. It is an application level proxy gateway. In 2017, Balabit changed the name of the product to Privileged Session Management (PSM) and repositioned it as the core module of its Privileged Access Management solution. == Main Features == Balabit’s Privileged Session Management (PSM), Shell Control Box (SCB) is a device that controls, monitors, and audits remote administrative access to servers and network devices. It is a tool to oversee system administrators by controlling the encrypted connections used for administration. PSM (SCB) has full control over the SSH, RDP, Telnet, TN3270, TN5250, Citrix ICA, and VNC connections, providing a framework (with solid boundaries) for the work of the administrators. === Gateway Authentication === PSM (SCB) acts as an authentication gateway, enforcing strong authentication before users access IT assets. PSM can also integrate to user directories (for example, a Microsoft Active Directory) to resolve the group memberships of the users who access the protected servers. Credentials for accessing the server are retrieved transparently from PSM’s credential store or a third-party password management system by PSM impersonating the authenticated user. This automatic password retrieval protects the confidentiality of passwords as users can never access them. === Access Control === PSM controls and audits privileged access over the most wide-spread protocols such as SSH, RDP, or HTTP(s). The detailed access management helps to control who can access what and when on servers. It is also possible to control advanced features of the protocols, like the type of channels permitted. For example, unneeded channels like file transfer or file sharing can be disabled, reducing the security risk on the server. With PSM policies for privileged access can be enforced in one single system. === 4-eyes Authorization === To avoid accidental misconfiguration and other human errors, PSM supports the 4-eyes authorization principle. This is achieved by requiring an authorizer to allow administrators to access the server. The authorizer also has the possibility to monitor – and terminate - the session of the administrator in real-time, as if they were watching the same screen. === Real-time Monitoring and Session Termination === PSM can monitor the network traffic in real time, and execute various actions if a certain pattern (for example, a suspicious command, window title or text) appears on the screen. PSM can also detect specific patterns such as credit card numbers. In case of detecting a suspicious user action, PSM can send an e-mail alert or immediately terminate the connection. For example, PSM can block the connection before a destructive administrator command, such as the „rm” comes into effect. === Session Recording === PSM makes user activities traceable by recording them in tamper-proof and confidential audit trails. It records the selected sessions into encrypted, timestamped, and digitally signed audit trails. Audit trails can be browsed online, or followed real-time to monitor the activities of the users. PSM replays the recorded sessions just like a movie – actions of the users can be seen exactly as they appeared on their monitor. The Balabit Desktop Player enables fast forwarding during replays, searching for events (for example, typed commands or pressing Enter) and texts seen by the user. In the case of any problems (database manipulation, unexpected shutdown, etc.) the circumstances of the event are readily available in the trails, thus the cause of the incident can be identified. In addition to recording audit trails, transferred files can be also recorded and extracted for further analysis.

  Read more →
• 

  Odor source localization

  Odor source localization (OSL) is the problem of locating the origin of an airborne or waterborne chemical plume using one or more mobile sensors, typically robots equipped with chemical sensors. The task sits at the intersection of robotics, fluid dynamics and machine olfaction. Chemical plumes in turbulent flows are intermittent and patchy, and most chemical sensors respond slowly and have limited selectivity, so the instantaneous reading available to a moving sensor is a poor proxy for the underlying time-averaged concentration field. Robotic OSL has been studied since the late 1980s and has applications including the detection of gas leaks, search and rescue after industrial accidents, and environmental monitoring of industrial emissions. == History == Robotic odor search emerged in the late 1980s and 1990s, drawing on earlier work in chemical ecology that had described how moths and other insects locate distant pheromone sources. R. A. Russell at Monash University was among the first to build mobile robots that followed chemical trails on the floor and tracked airborne odor plumes. Distributed and multi-robot odor search were investigated by Hayes, Martinoli and Goodman at the California Institute of Technology and EPFL, who studied cooperative plume-tracing on simulated and physical robot swarms. In 2007 Vergassola, Villermaux and Shraiman introduced infotaxis, an information-theoretic search strategy in which a sensor moves so as to maximize the expected information gain about source location, rather than following a chemical concentration gradient; the paper appeared in Nature and prompted substantial follow-up work in the robotics community. From the mid-2010s, multi-rotor unmanned aerial vehicles carrying lightweight chemical sensors became a common experimental platform for OSL research. == Problem formulation == OSL is generally decomposed into three sub-problems: plume detection (deciding whether a chemical signal is present), plume traversal (moving so as to remain in contact with the plume), and source declaration (deciding when the source has been reached). The mathematical difficulty depends strongly on the assumed dispersion model. In laminar or low-Reynolds number flows a Gaussian advection–diffusion model gives a smooth concentration field with a well-defined gradient. In turbulent flows, which dominate most realistic environments, the plume is filamentary: the sensor receives short, randomly spaced bursts of chemical separated by periods of zero signal, and the time-averaged field is not a useful guide on the time scales at which a robot must act. Source-term estimation, surveyed by Hutchinson and colleagues, additionally aims to recover both the position and the release rate of the source from the observed concentrations, often using probabilistic filters. == Biological inspiration == Many OSL strategies are explicitly modeled on the behavior of male moths flying upwind toward a pheromone source. As reviewed by Cardé and Willis, moths combine an upwind surge whenever they detect a filament of pheromone with a wider crosswind cast when contact is lost, producing a characteristic zig-zag trajectory that has been transposed onto mobile robots by several groups. Other biological models draw on the search behavior of dogs and of marine animals such as blue crabs and lobsters, which integrate chemical and bilateral hydrodynamic cues over much shorter ranges. == Algorithms and strategies == === Reactive strategies === Reactive strategies select the next motion as a direct function of the current sensor reading. Chemotaxis steers along the locally estimated concentration gradient, which is effective in laminar plumes but degrades severely in turbulence. Anemotaxis exploits a measured wind direction by surging upwind when chemical contact is made. The bio-inspired cast-and-surge family combines anemotaxis with a deterministic crosswind cast on contact loss, and is the dominant reactive approach for turbulent environments. === Probabilistic and information-theoretic strategies === Probabilistic methods maintain a posterior distribution over possible source locations and choose actions that improve that distribution. The infotaxis strategy of Vergassola, Villermaux and Shraiman selects the move that maximizes the expected reduction in entropy of the source-location posterior, and is effective in regimes where the spatial gradient is unusable. Bayesian source-term estimation extends this idea by inferring both source position and release rate, typically using particle filters or sequential Monte Carlo. === Map-based strategies === Map-based methods build a spatial model of the time-averaged gas distribution from sensor readings collected along the robot's trajectory and search for local maxima in that model. Lilienthal and colleagues describe a family of kernel-based gas distribution mapping techniques in which point measurements are convolved with a Gaussian kernel to produce a spatially extrapolated estimate. Such methods are most useful when the source can be assumed quasi-stationary and the robot is able to revisit locations. === Multi-robot and swarm strategies === Multiple robots searching cooperatively can shorten search times. Cooperative formations spread the sensors across the crosswind axis, making detection of an intermittent plume more likely. Swarm-based approaches, reviewed by Wang and colleagues, deploy larger numbers of simpler agents and rely on collective behavior rather than centralized planning; reported advantages include improved coverage of the search area and the possibility of locating multiple sources in parallel. == Sensors and platforms == Most OSL systems use metal-oxide semiconductor (MOX) sensors, photoionization detectors or electrochemical cells, which trade off sensitivity, selectivity, response time and power consumption. Ishida and colleagues describe how these sensors interact with airflow around the robot body, an effect that motivates careful aerodynamic design and active sampling. Mobile platforms include wheeled ground robots for indoor and structured outdoor environments, multi-rotor unmanned aerial vehicles for open spaces and elevated sources, and autonomous underwater vehicles for chemical plumes in the marine environment. == Notable systems == Among the early demonstrations, R. A. Russell's series of differential-drive robots at Monash University localized volatile sources in still and ventilated rooms during the 1990s. The Smelling Nano Aerial Vehicle reported by Burgués and colleagues used a Crazyflie nano-quadcopter (approximately 27 grams in mass and 10 cm across) carrying a custom MOX gas sensing board, and built three-dimensional gas distribution maps of indoor releases from sweeping flights of less than three minutes. The GADEN simulator, released by Monroy and colleagues, couples three-dimensional dispersion computed from an OpenFOAM CFD solver with models of MOX and photo-ionization gas sensors, and is widely used to test mobile-robot olfaction algorithms in simulation. == Applications == Reported applications include the localization of natural-gas and methane leaks in urban infrastructure, search for chemical contamination after industrial accidents, search and rescue, and environmental monitoring of industrial emissions. Drug- and explosives-detection robots are an adjacent application area, although these typically rely on close-range sniffing rather than long-range plume tracking. == Open challenges == Open challenges identified in recent reviews include the limited speed, selectivity and stability of available chemical sensors; the scarcity of standardized, large-scale benchmarks comparable to those available in computer vision; reliable handling of multi-source environments, where standard single-source assumptions fail; and the integration of OSL with other autonomous-vehicle subsystems such as obstacle avoidance and navigation in three-dimensional turbulent flow.

  Read more →
• 

  Dyme (company)

  Dyme is a Dutch fintech start-up and subscription management app that allows users to cancel and renegotiate their recurring costs. In 2019, Dyme was the first independent Dutch company to receive a PSD2 licence from the Netherlands' central bank (DNB). == History == Dyme was founded in 2018 by Joran Iedema, David Knap, David Schogt and Wouter Florijn. The four had previously founded Cycleswap, a bicycle rental platform launched in 2015 and sold to the American platform Spinlister in 2016. The company gained notability in the Netherlands in 2020 when it appeared on Dutch television in Dragons Den, where Pieter Schoen made a €750,000 bid in an attempt to acquire 51.01% of the company. Dyme's Joran Iedema rejected the deal. == Recognition == Wired described Dyme as one of the "hottest start-ups in Europe" in 2021. As of 2021, the company reportedly had 350,000 registered users in the Netherlands and Great Britain.

  Read more →
• 

  Microscope image processing

  Microscope image processing is a broad term that covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine, biological research, cancer research, drug testing, metallurgy, etc. A number of manufacturers of microscopes now specifically design in features that allow the microscopes to interface to an image processing system. == Image acquisition == Until the early 1990s, most image acquisition in video microscopy applications was typically done with an analog video camera, often simply closed circuit TV cameras. While this required the use of a frame grabber to digitize the images, video cameras provided images at full video frame rate (25-30 frames per second) allowing live video recording and processing. While the advent of solid state detectors yielded several advantages, the real-time video camera was actually superior in many respects. Today, acquisition is usually done using a CCD camera mounted in the optical path of the microscope. The camera may be full colour or monochrome. Very often, very high resolution cameras are employed to gain as much direct information as possible. Cryogenic cooling is also common, to minimise noise. Often digital cameras used for this application provide pixel intensity data to a resolution of 12-16 bits, much higher than is used in consumer imaging products. Ironically, in recent years, much effort has been put into acquiring data at video rates, or higher (25-30 frames per second or higher). What was once easy with off-the-shelf video cameras now requires special, high speed electronics to handle the vast digital data bandwidth. Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the high image resolution, this approach can generate vast quantities of raw data, which can be a challenge to deal with, even with a modern computer system. While current CCD detectors allow very high image resolution, often this involves a trade-off because, for a given chip size, as the pixel count increases, the pixel size decreases. As the pixels get smaller, their well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a poorer signal-to-noise ratio. For best results, one must select an appropriate sensor for a given application. Because microscope images have an intrinsic limiting resolution, it often makes little sense to use a noisy, high resolution detector for image acquisition. A more modest detector, with larger pixels, can often produce much higher quality images because of reduced noise. This is especially important in low-light applications such as fluorescence microscopy. Moreover, one must also consider the temporal resolution requirements of the application. A lower resolution detector will often have a significantly higher acquisition rate, permitting the observation of faster events. Conversely, if the observed object is motionless, one may wish to acquire images at the highest possible spatial resolution without regard to the time required to acquire a single image. == 2D image techniques == Image processing for microscopy application begins with fundamental techniques intended to most accurately reproduce the information contained in the microscopic sample. This might include adjusting the brightness and contrast of the image, averaging images to reduce image noise and correcting for illumination non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature. Another class of common 2D operations called image convolution are often used to reduce or enhance image details. Such "blurring" and "sharpening" algorithms in most programs work by altering a pixel's value based on a weighted sum of that and the surrounding pixels (a more detailed description of kernel based convolution deserves an entry for itself) or by altering the frequency domain function of the image using Fourier Transform. Most image processing techniques are performed in the Frequency domain. Other basic two dimensional techniques include operations such as image rotation, warping, color balancing etc. At times, advanced techniques are employed with the goal of "undoing" the distortion of the optical path of the microscope, thus eliminating distortions and blurring caused by the instrumentation. This process is called deconvolution, and a variety of algorithms have been developed, some of great mathematical complexity. The end result is an image far sharper and clearer than could be obtained in the optical domain alone. This is typically a 3-dimensional operation, that analyzes a volumetric image (i.e. images taken at a variety of focal planes through the sample) and uses this data to reconstruct a more accurate 3-dimensional image. == 3D image techniques == Another common requirement is to take a series of images at a fixed position, but at different focal depths. Since most microscopic samples are essentially transparent, and the depth of field of the focused sample is exceptionally narrow, it is possible to capture images "through" a three-dimensional object using 2D equipment like confocal microscopes. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D instrument into a 3D instrument, which would not otherwise exist. In recent times this technique has led to a number of scientific discoveries in cell biology. == Analysis == Analysis of images will vary considerably according to application. Typical analysis includes determining where the edges of an object are, counting similar objects, calculating the area, perimeter length and other useful measurements of each object. A common approach is to create an image mask which only includes pixels that match certain criteria, then perform simpler scanning operations on the resulting mask. It is also possible to label objects and track their motion over a series of frames in a video sequence.

  Read more →
• 

  Artisse AI

  Artisse AI is a Hong Kong-based technology company founded by William Wu. The company developed a mobile photography application using generative artificial intelligence to transform selfies into high-quality, personalized images. The app allows users to visualize themselves in various scenarios, outfits, and hairstyles, and they can adjust lighting and ambiance to match their preferences. The app launched in 2023 across multiple markets, including the United States, United Kingdom, Japan, South Korea, Canada, and Australia. By January 2024, users had generated over 5 million images. That same month, the company secured $6.7 million in seed funding to support product development and marketing. == History == Artisse was originally founded in South Korea in 2022 by William Wu. The early concept was connected to a virtual idol initiative developed in collaboration with a K-pop agency, intended to support Wu's blockchain gaming business. The project later evolved into a standalone AI photography application. The current version of the Artisse app was developed following the company's relocation to Hong Kong in 2022. In January 2024, Artisse secured $6.7 million in seed funding, led by The London Fund. The investment was aimed at supporting product development, marketing, and user acquisition. Artisse uses an AI algorithm to create hyperrealistic images from uploaded photos. The app generates personalized images by combining generative AI technology, a global pool of licensed talent, and finished art services. The app works with individual users and businesses, offering professional-grade photos and advertisement images. According to the British newspaper Evening Standard the company has developed the world's first and most advanced AI photographer. It captures 15-30 photos of the user and generates 2D images, placing them in various outfits and locations worldwide. === Catheron Gaming === Artisse AI originated from Catheon Gaming, a blockchain gaming and entertainment company founded in 2021 by William Wu. Catheon Gaming published more than 30 Web3 titles in its first year, developed a blockchain game distribution platform, and offered advisory services to external developers. In 2022, HSBC and KPMG listed Catheon Gaming among the "Top 10 Emerging Giants" in the Asia–Pacific region, selected from a pool of more than 6,000 startups. In June 2023, Catheon Gaming was rebranded as Artisse Interactive, creating two divisions: Artisse Gaming, which continued blockchain and Web3 game development, and Artisse AI, which focused on generative photography technology. == Technology == Artisse uses a proprietary generative AI model combined with open-source imaging frameworks and diffusion models. Users are prompted to upload between 15 and 30 personal images, allowing the AI to train a personalized model in 30 to 40 minutes. After training, the app generates new images based on either textual or visual prompts, with options to adjust elements such as clothing, hairstyles, lighting, and backgrounds. To enhance realism, the app integrates augmented reality features and image refinement tools. The company has introduced features to address representation issues related to body shape and skin tone, although concerns persist about the ethical implications of altering personal traits. == Products == === Artisse mobile app === Available on iOS and Android platforms in 35 languages. Users initially receive 25 free images, after which the app adopts a subscription pricing model ranging from approximately $6 to $30 per month. By early 2024, the app reported around 4,000 paying subscribers out of more than 200,000 downloads. === Business and enterprise services === Artisse provides B2B solutions for creating marketing imagery and partners with agencies like Iconic Management to enable cost-effective virtual photoshoots. Additional features in development include virtual try-on capabilities and augmented reality integration for fashion retail. == Reception == Media coverage has noted the app's photorealistic image outputs with some sources highlighting its ease of use. However, concerns have been raised regarding image authenticity, algorithmic biases, and the potential impact on professional photography and modeling. Artisse has been widely covered by media outlets including TechCrunch, PetaPixel, Forbes Australia, and The Evening Standard. These publications discussed the app's integration of generative AI technology within the consumer photography space, its growing market influence, and its rapid adoption by users worldwide.

  Read more →
• 

  Automaton

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

  Read more →
• 

  Adobe GoLive

  Adobe GoLive was a WYSIWYG HTML editor and web site management application from Adobe Systems. It replaced Adobe PageMill as Adobe's primary HTML editor and was itself discontinued in favor of Dreamweaver. The last version of GoLive that Adobe released was GoLive 9. == History == GoLive originated as the flagship product of a company named GoNet Communication, Inc. then based in Menlo Park, California, and the development company GoNet Communications GmbH in Hamburg, Germany, in 1996. Later GoNet changed its name to GoLive Systems, Inc, and the name of its product to GoLive CyberStudio. Adobe acquired GoLive in 1999 and re-branded the GoLive CyberStudio product to what became Adobe GoLive. Adobe took over the Hamburg office as an Adobe development site to continue to develop the product. At the time of the acquisition, CyberStudio was a Macintosh-only application. In the spring of 1999 Adobe released Adobe GoLive for both Macintosh and Microsoft Windows. The first versions of Dreamweaver and CyberStudio were released in a similar timeframe. However, Dreamweaver eventually became the dominant WYSIWYG HTML editor in market share. After the Adobe acquisition of Macromedia (the company that had owned Dreamweaver), GoLive was progressively re-targeted toward Adobe's traditional design market, and the product became better integrated with Adobe's existing suite of design-oriented software products and less focused on the professional web development market. The Adobe CS2 Premium suite contained GoLive CS2. With the release of Creative Suite 3, Adobe integrated Dreamweaver as a replacement for GoLive and released GoLive 9 as a standalone product. In April 2008, Adobe announced that sales and development of GoLive would cease in favor of Dreamweaver. == General description and distinctive aspects == GoLive incorporated a largely modeless workflow that relied heavily on drag-and-drop. Most user interaction was done via a contextual inspector rather than the modal workflow found in Dreamweaver. Among its features were a separate editor for tables that supported nesting, and a two-dimensional panel for applying CSS styles to elements. GoLive supported drag-and-drop of native Adobe Photoshop and Adobe Illustrator files via what the company called "Smart Objects", which then automatically guided the user through saving those files in web-supported formats. Updates to the original Photoshop or Illustrator assets were automatically tracked by GoLive. It also implemented a tool called "Components" which allowed updates to interface elements throughout a site to be updated globally by changing one single file. As a website management tool, GoLive allowed users to transfer and publish content directly from within the application, and allowed individual files to be excluded from uploading. == Features == One of the new features of GoLive version 5 was Dynamic Link, which was a method of creating dynamic, database-driven web content without the need to know a server-side language and with full WYSIWYG support in the GoLive user interface. GoLive had a powerful set of extensibility API which could be used to add additional functionality to the product. The GoLive SDK provided interfaces which allowed developers to use a combination of XML, JavaScript and C/C++ to create plugins for the product. The extensibility API allowed developers access to custom drawing and event handling using JavaScript, as well as a full JavaScript debugger and command line interpreter. This allowed intermediate-level developers using interpreted JavaScript to create sophisticated user interfaces. == Language and framework structure == Adobe GoLive is coded in the C++ programming language. It uses a custom C++ framework called SCL (Simple Class Library) which was initially built from scratch by the engineers at GoLive Systems Inc. The SCL framework was also used in the short-lived Adobe Atmosphere 3D software. == Release history == As the final version, GoLive 9 was discontinued in April 2008.

  Read more →
• 

  Semi-automation

  Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.

  Read more →
• 

  Hamilton C shell

  Hamilton C shell is a clone of the Unix C shell and utilities for Microsoft Windows created by Nicole Hamilton at Hamilton Laboratories as a completely original work, not based on any prior code. It was first released on OS/2 on December 12, 1988 and on Windows NT in July 1992. The OS/2 version was discontinued in 2003 but the Windows version continues to be actively supported. == Design == Hamilton C shell differs from the Unix C shell in several respects. These include its compiler architecture, its use of threads, and the decision to follow Windows rather than Unix conventions. === Parser === The original C shell uses an ad hoc parser. This has led to complaints about its limitations. It works well enough for the kinds of things users type interactively but not very well for the more complex commands a user might take time to write in a script. It is not possible, for example, to pipe the output of a foreach statement into grep. There was a limit to how complex a command it could handle. By contrast, Hamilton uses a top-down recursive descent parser that allows it to compile statements to an internal form before running them. As a result, statements can be nested or piped arbitrarily. The language has also been extended with built-in and user-defined procedures, local variables, floating point and additional expression, editing and wildcarding operators, including an "indefinite directory" wildcard construct written as "..." that matches zero or more directory levels as required to make the rest of the pattern match. === Threads === Lacking fork or a high performance way to recreate that functionality, Hamilton uses the Windows threads facilities instead. When a new thread is created, it runs within the same process space and it shares all of the process state. If one thread changes the current directory or the contents of memory, it's changed for all the threads. It's much cheaper to create a thread than a process but there's no isolation between them. To recreate the missing isolation of separate processes, the threads cooperate to share resources using locks. === Windows conventions === Hamilton differs from other Unix shells in that it also directly supports Windows conventions for drive letters, filename slashes, escape characters, etc.

  Read more →
• 

  CatDV

  CatDV is a media asset manager program for handling multimedia production workflows developed by Square Box Systems. Quantum Corporation acquired Square Box Systems in 2020. == Versions == The full family of CatDV Products is as follows: CatDV Standalone Products CatDV Professional Edition CatDV Pegasus CatDV Networked Products CatDV Essential - entry level server product CatDV Enterprise Server - for MySQL databases and most common server platforms including Linux, Windows and Mac OS X CatDV Pegasus Server - adds features such as high performance full-text indexing, access control lists, and more CatDV Worker Node - automated workflow and transcoding engine CatDV Web Client - provides access to the CatDV database via a web browser. There is no need to install special software on the desktop, making it easy to deploy to a large number of users. CatDV Professional Edition & Pegasus Clients - designed to support the multi-user capabilities of the CatDV Enterprise and Workgroup Servers from the desktop Using plugins and scripting, which often require additional professional services support to set up, complex integrations with a wide variety of third party systems (including archive, cloud storage, and artificial intelligence) are possible. == Awards == CatDV won two awards in 2010, a blue ribbon from Creative COW Magazine and a "Best of Show Vidy Award" from Videography. In April 2012 Square Box won a Queen's Award for Enterprise for CatDV.

  Read more →
• 

  Automation integrator

  An automation integrator is a systems integrator company or individual who makes different versions of automation hardware and software work together, generally combining several subsystems to work together as one large system. The title may refer to those who only integrate hardware, although these will often work with software integrators. Software created by automation integrators allows devices to communicate with each other, as well as collecting and reporting data. The magazine Control Engineering publishes an annual “Automation Integrator Guide” which lists over 2,000 automation integrators. They also give an annual system integrator of the year award to three automation integration firms. The Control System Integrators Association (CSIA) maintains a buyers' guide of over 1200 member and nonmember systems integrators known as the Industrial Automation Exchange, or CSIA Exchange for short. == Certification == The Control System Integrators Association (CSIA) certifies automation integrators, through an audit based on 79 critical criteria from the best practices manual. Companies must be associate members of the CSIA to be eligible for certification. Integrators can also receive certification through a program launched in 2012 by the Robotics Industries Association. == Industries == Automation Integrators work in a wide variety of industries which use robotics and automation. Some of the most common include:

  Read more →
• 

  Structural similarity index measure

  The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. This distinguishes from other techniques such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR) that instead estimate absolute errors. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. == History == The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. In addition to defining the SSIM quality index, the paper provides a general context for developing and evaluating perceptual quality measures, including connections to human visual neurobiology and perception, and direct validation of the index against human subject ratings. The basic model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM subsequently found strong adoption in the image processing community and in the television and social media industries. The 2004 SSIM paper has been cited over 50,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields. It was recognized with the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2016, indicative of a paper having an unusually high impact for at least 10 years following its publication. Because of its high adoption by the television industry, the authors of the original SSIM paper were each accorded a Primetime Engineering Emmy Award in 2015 by the Television Academy. == Algorithm == The SSIM index is calculated between two windows of pixel values x {\displaystyle x} and y {\displaystyle y} of common size, from corresponding locations in two images to be compared. These SSIM values can be aggregated across the full images by averaging or other variations. === Special-case formula === In one simple special case, further explained in the next section, the SSIM measure between x {\displaystyle x} and y {\displaystyle y} is: SSIM ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) {\displaystyle {\hbox{SSIM}}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}} with: μ x {\displaystyle \mu _{x}} the pixel sample mean of x {\displaystyle x} ; μ y {\displaystyle \mu _{y}} the pixel sample mean of y {\displaystyle y} ; σ x 2 {\displaystyle \sigma _{x}^{2}} the sample variance of x {\displaystyle x} ; σ y 2 {\displaystyle \sigma _{y}^{2}} the sample variance of y {\displaystyle y} ; σ x y {\displaystyle \sigma _{xy}} the sample covariance of x {\displaystyle x} and y {\displaystyle y} ; c 1 = ( k 1 L ) 2 {\displaystyle c_{1}=(k_{1}L)^{2}} , c 2 = ( k 2 L ) 2 {\displaystyle c_{2}=(k_{2}L)^{2}} two variables to stabilize the division with weak denominator; L {\displaystyle L} the dynamic range of the pixel-values (typically this is 2 # b i t s p e r p i x e l − 1 {\displaystyle 2^{\#bits\ per\ pixel}-1} ); k 1 = 0.01 {\displaystyle k_{1}=0.01} and k 2 = 0.03 {\displaystyle k_{2}=0.03} by default. === General formula and components === The SSIM formula is based on three comparison measurements between the samples of x {\displaystyle x} and y {\displaystyle y} : luminance ( l {\displaystyle l} ), contrast ( c {\displaystyle c} ), and structure ( s {\displaystyle s} ). The individual comparison functions are: l ( x , y ) = 2 μ x μ y + c 1 μ x 2 + μ y 2 + c 1 {\displaystyle l(x,y)={\frac {2\mu _{x}\mu _{y}+c_{1}}{\mu _{x}^{2}+\mu _{y}^{2}+c_{1}}}} c ( x , y ) = 2 σ x σ y + c 2 σ x 2 + σ y 2 + c 2 {\displaystyle c(x,y)={\frac {2\sigma _{x}\sigma _{y}+c_{2}}{\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2}}}} s ( x , y ) = σ x y + c 3 σ x σ y + c 3 {\displaystyle s(x,y)={\frac {\sigma _{xy}+c_{3}}{\sigma _{x}\sigma _{y}+c_{3}}}} The SSIM for each block is then a weighted combination of those comparative measures: SSIM ( x , y ) = l ( x , y ) α ⋅ c ( x , y ) β ⋅ s ( x , y ) γ {\displaystyle {\text{SSIM}}(x,y)=l(x,y)^{\alpha }\cdot c(x,y)^{\beta }\cdot s(x,y)^{\gamma }} Choosing the third denominator stabilizing constant as: c 3 = c 2 / 2 {\displaystyle c_{3}=c_{2}/2} leads to a simplification when combining the c and s components with equal exponents ( β = γ {\displaystyle \beta =\gamma } ), as the numerator of c is then twice the denominator of s, leading to a cancellation leaving just a 2. Setting the weights (exponents) α , β , γ {\displaystyle \alpha ,\beta ,\gamma } to 1, the formula can then be reduced to the special case shown above. === Mathematical properties === SSIM satisfies the identity of indiscernibles, and symmetry properties, but not the triangle inequality or non-negativity, and thus is not a distance function. However, under certain conditions, SSIM may be converted to a normalized root MSE measure, which is a distance function. The square of such a function is not convex, but is locally convex and quasiconvex, making SSIM a feasible target for optimization. === Application of the formula === In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation. For an image, it is typically calculated using a sliding Gaussian window of size 11×11 or a block window of size 8×8. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment, the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. === Variants === ==== Multi-scale SSIM ==== A more advanced form of SSIM, called Multiscale SSIM (MS-SSIM) is conducted over multiple scales through a process of multiple stages of sub-sampling, reminiscent of multiscale processing in the early vision system. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. ==== Multi-component SSIM ==== Three-component SSIM (3-SSIM) is a form of SSIM that takes into account the fact that the human eye can see differences more precisely on textured or edge regions than on smooth regions. The resulting metric is calculated as a weighted average of SSIM for three categories of regions: edges, textures, and smooth regions. The proposed weighting is 0.5 for edges, 0.25 for the textured and smooth regions. The authors mention that a 1/0/0 weighting (ignoring anything but edge distortions) leads to results that are closer to subjective ratings. This suggests that edge regions play a dominant role in image quality perception. The authors of 3-SSIM have also extended the model into four-component SSIM (4-SSIM). The edge types are further subdivided into preserved and changed edges by their distortion status. The proposed weighting is 0.25 for all four components. ==== Structural dissimilarity ==== Structural dissimilarity (DSSIM) may be derived from SSIM, though it does not constitute a distance function as the triangle inequality is not necessarily satisfied. DSSIM ( x , y ) = 1 − SSIM ( x , y ) 2 {\displaystyle {\hbox{DSSIM}}(x,y)={\frac {1-{\hbox{SSIM}}(x,y)}{2}}} ==== Video quality metrics and temporal variants ==== It is worth noting that the original vers

  Read more →
• 

  YaDICs

  YaDICs is a program written to perform digital image correlation on 2D and 3D tomographic images. The program was designed to be both modular, by its plugin strategy and efficient, by it multithreading strategy. It incorporates different transformations (Global, Elastic, Local), optimizing strategy (Gauss-Newton, Steepest descent), Global and/or local shape functions (Rigid-body motions, homogeneous dilatations, flexural and Brazilian test models)... == Theoretical background == === Context === In solid mechanics, digital image correlation is a tool that allows to identify the displacement field to register a reference image (called herein fixed image) to images during an experiment (mobile image). For example, it is possible to observe the face of a specimen with a painted speckle on it in order to determine its displacement fields during a tensile test. Before the appearance of such methods, researchers usually used strain gauges to measure the mechanical state of the material but strain gauges only measure the strain on a point and don't allow to understand material with an heterogeneous behavior. One can obtain a full in plane strain tensor by derivation of the displacement fields. Many methods are based upon the optical flow. In fluid mechanics a similar method is used, called Particle Image Velocimetry (PIV); the algorithms are similar to those of DIC but it is impossible to ensure that the optical flow is conserved so a vast majority of the software used the normalized cross correlation metric. In mechanics the displacement or velocity fields are the only concern, registering images is just a side effect. There is another process called image registration using the same algorithms (on monomodal images) but where the goal is to register images and thereby identifying the displacement field is just a side effect. YaDICs uses the general principle of image registration with a particular attention to the displacement fields basis. === Image registration principle === YaDICs can be explained using the classical image registration framework: === Image registration general scheme === The common idea of image registration and digital image correlation is to find the transformation between a fixed image and a moving one for a given metric using an optimization scheme. While there are many methods to achieve such a goal, Yadics focuses on registering images with the same modality. The idea behind the creation of this software is to be able to process data that comes from a μ-tomograph; i.e.: data cube over 10003 voxels. With such a size it is not possible to use naive approach usually used in a two-dimensional context. In order to get sufficient performances OpenMP parallelism is used and data are not globally stored in memory. As an extensive description of the different algorithms is given in. === Sampling === Contrary to image registration, Digital Image Correlation targets the transformation, one wants to extracted the most accurate transformation from the two images and not just match the images. Yadics uses the whole image as a sampling grid: it is thus a total sampling. === Interpolator === It is possible to choose between bilinear interpolation and bicubic interpolation for the grey level evaluation at non integer coordinates. The bi-cubic interpolation is the recommended one. === Metrics === ==== Sum of squared differences (SSD) ==== The SSD is also known as mean squared error. The equation below defines the SSD metric: S S D ( μ , I F , I M ) = 1 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) 2 , {\displaystyle SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {1}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)^{2},} where I F {\displaystyle {\mathcal {I_{F}}}} is the fixed image, I M {\displaystyle {\mathcal {I_{M}}}} the moving one, Ω F {\displaystyle \Omega _{F}} the integration area | Ω F | {\displaystyle \left|\Omega _{F}\right|} the number of pi(vo)xels (cardinal) and T μ {\displaystyle {T}_{\mu }} the transformation parametrized by μ The transformation can be written as: T μ ( x ) = x + { Φ ( x ) } t { μ } . {\displaystyle T_{\mu }(x)=x+\left\{\Phi (x)\right\}^{t}\left\{\mu \right\}.} This metric is the main one used in the YaDICs as it works well with same modality images. One has to find the minimum of this metric ==== Normalized cross-correlation ==== The normalized cross-correlation (NCC) is used when one cannot assure the optical flow conservation; it happens in case of change of lighting or if particles disappear from the scene can occur in particle images velocimetry (PIV). The NCC is defined by: N C C ( μ , I F , I M ) = ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) ( I M ( T μ ( x i ) ) − I M ¯ ) ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) 2 ∑ x i ∈ Ω F ( I M ( T μ ( x i ) ) − I M ¯ ) 2 , {\displaystyle NCC(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)}{\sqrt {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)^{2}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)^{2}}}},} where I F ¯ {\displaystyle {\overline {\mathcal {I_{F}}}}} and I M ¯ {\displaystyle {\overline {\mathcal {I_{M}}}}} are the mean values of the fixed and mobile images. This metric is only used to find local translation in Yadics. This metric with translation transform can be solved using cross-correlation methods, which are non iterative and can be accelerated using Fast Fourier Transform . === Classification of transformations === There are three categories of parametrization: elastic, global and local transformation. The elastic transformations respect the partition of unity, there are no holes created or surfaces counted several times. This is commonly used in Image Registration by the use of B-Spline functions and in solid mechanics with finite element basis. The global transformations are defined on the whole picture using rigid body or affine transformation (which is equivalent to homogeneous strain transformation). More complex transformations can be defined such as mechanically based one. These transformations have been used for stress intensity factor identification by and for rod strain by. The local transformation can be considered as the same global transformation defined on several Zone Of Interest (ZOI) of the fixed image. ==== Global ==== Several global transforms have been implemented: Rigid and homogeneous (Tx,Ty,Rz in 2D; Tx,Ty,Tz,Rx,Ry,Rz,Exx,Eyy,Ezz,Eyz,Exz,Exy in 3D) Brazilian (Only in 2D), Dynamic Flexion, ==== Elastic ==== First-order quadrangular finite elements Q4P1 are used in Yadics. ===== Local ===== Every global transform can be used on a local mesh. === Optimization === The YaDICs optimization process follows a gradient descent scheme. The first step is to compute the gradient of the metric regarding the transform parameters ∂ S S D ( μ , I F , I M ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ∂ I M ( T μ ( x i ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ( ∂ T μ ( x i ) ∂ μ ) t ∂ I M ( T μ ( x i ) ) ∂ x {\displaystyle {\begin{array}{lcl}{\dfrac {\partial SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right){\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i})}{\partial \mu }}\\&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)\left({\dfrac {\partial {T}_{\mu }(x_{i})}{\partial \mu }}\right)^{t}{\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i}))}{\partial x}}\\\end{array}}} ==== Gradient method ==== Once the metric gradient has been computed, one has to find an optimization strategy The gradient method principle is explained below: μ k + 1 = μ k + α k d k {\displaystyle \mu _{k+1}=\mu _{k}+\alpha _{k}d_{k}} The gradient step can be constant or updated at every iteration. d k = − γ k ∂ C ( μ , I F , I M ) ∂ μ {\displaystyle d_{k}=-\gamma _{k}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}} , γ k {\displaystyle \gamma _{k}} allows one to choose between the following methods : γ k {\displaystyle \gamma _{k}} ⟹ {\displaystyle \Longrightarrow } steepest descent, γ k = [ ∂ C ( μ , I F , I M ) ∂ μ ∂ C ( μ , I F , I M ) ∂ μ t ] − 1 {\displaystyle \gamma _{k}=\left[{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}^{t}\right]^{-1}} ⟹ {\displaystyle \Longrightarrow } Gauss-Newto

  Read more →
• 

  ARIS Express

  ARIS Express is a free-of-charge modeling tool for business process analysis and management. It supports different modeling notations such as BPMN 2, Event-driven Process Chains (EPC), Organizational charts, process landscapes, whiteboards, etc. ARIS Express was initially developed by IDS Scheer, which was bought by Software AG in December 2010. The tool is provided as freeware on the ARIS Community webpage. ARIS Express is notable - having been mentioned in research published by Schumm, Garcia, Krumnow and Greenwood amongst others. == History == ARIS Express was first announced on April 28, 2009 in a press release by IDS Scheer. The first release was on July 28, 2009 in a public beta test on ARIS Community. Only people, who registered before for the beta test were allowed to download and test this beta version. This closed beta test was followed with another public beta test. The official release of ARIS Express 1.0 was on September 9, 2009. In this first stable version, features such as Microsoft Visio import were added, which were not present in the version for the public beta test. On February 26, 2010, ARIS Express 2.0 was released. Major changes compared to version 1.0 include BPMN 2 support, integrated spellchecking and ARISalign integration. On May 25, 2010, version 2.1 of ARIS Express was released. This update improves BPMN 2 support, provides a new online help system for instant feedback, better ARISalign integration and some new symbols in different diagrams. Along with the release, a poster showing the most important modeling concepts supported by ARIS Express was released. In addition, an executable setup is provided for Microsoft Windows-based systems. Beginning of July, an update was released as ARIS Express 2.2, providing bug fixes only. ARIS Express version 2.2 is the current stable release. An official press release published mid of August 2010 said there are more than 50,000 downloads of ARIS Express. On February 2, 2011, version 2.3 of ARIS Express was released. This new version changes the file format of ARIS Express so that models can be shown in an interactive model viewer in ARIS Community. The release announcement contained no details about additional features or changes. == Functionality == === Overview === ARIS Express is a standalone single-user application. It is divided in a home screen and a modeling environment. The home screen is used to create new models or open recently edited ones. The modeling environment is used to edit diagrams. === Supported notations === The following notations are supported by ARIS Express. Users can create diagrams containing an unlimited number of modeling objects. BPMN 2 Collaboration Diagrams Event-driven Process Chains (EPC) Organizational charts Process landscape (value-added chain diagram) Data model in ERM notation IT infrastructure (network diagram) System landscape (component diagram) Whiteboard General diagram === Noteworthy features === Besides common features such as creating new diagrams, saving them as files or adding objects to the modeling canvas, ARIS Express also provides some noteworthy features, which can't be found in most comparable modeling tools. fragments - Often used modeling constructs such as an exclusive decision in a process model can be stored as fragments so that they are available for direct reuse in another model. smart designs - The flow of a process model or hierarchies of other models can be captured in a spreadsheet-like interface. While entering the data in the spreadsheet, the model is generated and laid out in the background while typing. mini toolbar - While moving the mouse pointer over an object in a diagram, a small toolbar is shown allowing quick access to the most important modeling actions. Microsoft Visio import - Diagrams created with Microsoft Visio 2007 or above can be imported to and edited in ARIS Express. A Microsoft Visio export is not provided. ARISalign import - Models created on the online collaboration platform ARISalign can be opened and edited in ARIS Express. === Exports === ARIS Express can export diagrams to different formats such as: PDF JPEG PNG EMF ADF ADF is the file format of ARIS Express. The professional tools of ARIS Platform are able to import diagrams stored in the ADF format. Yet, there are major limitations during import - namely, each object in diagram will be treated as unique object, despite having same type and name, forcing redrawing large sections of diagrams after import. Besides export formats, it is also possible to use the clipboard to copy and paste an ARIS Express diagram into typical office suites such as Microsoft PowerPoint. == Technology == ARIS Express is a Java-based application, which shares some of the features of ARIS Platform products such as ARIS Business Architect and ARIS Business Designer. In contrast to ARIS Platform products, ARIS Express doesn't use a central database for model storage. Instead, each diagram is stored in an ADF file. ARIS Express uses Java Web Start. After download, the application can be started immediately without installation procedure. For Microsoft Windows based systems, an ordinary setup is provided, too. ARIS Express requires Java 1.6.10 or above. On first startup, the user must enter a valid ARIS Community account to register the application. Creating an ARIS Community account is free-of-charge. After installation, no Internet connection is needed to use ARIS Express. ARIS Express uses a mechanism provided by Java Web Start to automatically update the application as soon as a new version becomes available and the user is connected to the Internet during startup. There are reports that this automated update failed while upgrading from version 1.0 to version 2.0. As ARIS Express is based on Java Web Start, it can be installed on any platform supported by Java. The ARIS Community and other Internet sources have reports of successful deployment of ARIS Express on other operating systems than Microsoft Windows. However, ARIS Express is officially supported only on Microsoft Windows. == Miscellaneous == A quick reference sheet is available for ARIS Express. The poster shows all supported diagrams plus the most important modelling concepts for each supported modelling language. ARIS Express contains a hidden game, a so-called Easter Egg. The game can be started by clicking several times on the product logo in the about dialog. Highscores achieved in the game can be submitted to a special page in ARIS Community. A Firefox Personas is available for ARIS Express.

  Read more →
• 

  Candid (app)

  Candid was a mobile app for anonymous discussions. It used machine learning to create personalized newsfeeds of opinions and real conversations, and also for moderation and filtering. Users posted under pseudonyms such as "HyperMantis", "SincereGiraffe", "GroundedTurtle" and "ExuberantRaptor", that are unique for each thread. Founder and CEO Bindu Reddy said that she needed "a place to express myself and engage in discussions where ideas can be debated on their own merits instead of being used to attack me as a person", which Candid tried to solve by redirecting off-topic comments to their appropriate groups, removing spam and flagging negative posts. They used natural language processing to identify hate speech, slander and threats, and removed them accordingly with human intervention. Candid software analyzed topics and tried to flag rumors and lies as such. Users could flag problematic posts and a team of ten contractors would review them individually. With time the system analyzed a user's interactions and give them labels, such as socializer, explorer, positive, influencer, hater, gossip, etc. In June 2017, Candid announced that it would be shut down because its parent company, Post Intelligence, was being acquired. The app was forecast to close on June 23, 2017, but didn't actually close until June 25, 2017.

  Read more →