Woken Furies (2005) is a science fiction novel by British writer Richard Morgan. It is the third novel featuring the anti-hero Takeshi Kovacs and is the sequel to Broken Angels. This addition to the series casts light upon Kovacs' early life providing information on his post-envoy activities. Morgan's official website and interviews suggest that Woken Furies could be the last Kovacs novel, although in 2018 (before Netflix cancelled the show) Morgan stated that the Netflix adaptation has "kind of woken it all up again" after all these years, making him possibly reconsider being done with Kovacs. == Plot == Takeshi Kovacs finds himself in a new "sleeve," or human body, back on his home planet of Harlan's World. He is on the run after making numerous attacks against the Knights of the New Revelation, an extremist religious order responsible for the death of his lost love and her daughter. Because she had violated tenets about resleeving, her executioners dropped her and her daughter's cortical stacks in the sea, effectively preventing them from being resleeved (into new bodies). While trying to secure passage after his most recent attack, Kovacs saves a woman named Sylvie from a group of religious zealots. In return, she allows him to take refuge with her mercenary "deCom" crew as they head out to decommission sentient military hardware that has run amok on the island of New Hokkaido (AKA New Hok). Sylvie is the "command head" of her crew, co-ordinating them during missions by using her biologically implanted circuitry and software. During one of these missions, Sylvie collapses, regains consciousness, and Kovacs realizes that her personality seems to have been replaced by that of long-dead revolutionary leader Quellcrist Falconer. Harlan's World is surrounded by automated "orbitals" which target flying objects, such as vehicles, with high-energy beam weapons known as "angelfire"; Falconer is believed to have died without a backup of her cortical stack when her getaway aircraft was destroyed by angelfire 300 years prior. When Sylvie's crew returns from New Hok, they discover a younger version of Kovacs has been illegally duplicated into a different body (AKA "double sleeved") and is hunting them on behalf of the Harlan family that rules the planet. Most of Sylvie's crew is killed and Sylvie/Quellcrist is captured. Kovacs schemes to rescue Sylvie by approaching old criminal associates of his, the Little Blue Bugs. The Little Blue Bugs mount a semi-successful attack on a Harlan fortress and rescue Sylvie/Quellcrist. Hiding from Harlan forces in a floating base, the neo-Quellists are sold out by its owner and recaptured. An assault by Kovacs and a single UN Envoy on the base ends badly when Kovacs is betrayed by the Envoy who was actually embedded with several colleagues. However, Sylvie/Quellcrist has established a connection with the orbitals and calls down angelfire, eliminating their captors. The younger Kovacs is killed in the aftermath. Sylvie explains that angelfire is a destructive recording device. Thus, in destroying Quellcrist and the helicopter carrying her, it copied her. When the technology of the deCom crews advanced far enough, her persona was able to insert itself into Sylvie's implants and co-exist in her body. The novel ends with Kovacs, Virginia Vidaura, and Sylvie/Quellcrist waiting to see if they can use Sylvie/Quellcrist's newfound connection to the orbitals and the expansion of a long-dormant genetic virus to turn the population against the ruling oligarchy.
Enterprise cognitive system
Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome. While general-purpose cognitive systems can be used for different outputs, prescriptive, suggestive, instructive, or simply entertaining, an enterprise cognitive system is focused on action, not insight, to help in assessing what to do in a complex situation. == Key characteristics == ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning from data requires an agile information federation approach to ingest incremental data updates as they occur, and an unsupervised learning approach to ensure that new best practice is leveraged across the organization in a timely manner. Interactive: They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people. In the Enterprise, interactions are controlled via existing workflows and UIs. Therefore, embedding best practices directly into these existing interfaces, in the context of a specific step, is critical to ensure maximum end-user adoption. Iterative and stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. In the Enterprise, business context is often structured by a business process, and therefore sufficiently data-rich to make relevant recommendations without significant iterations from the end-user. A stateful memory of overall interactions across communication channels is critical for understanding of context, as a static profile will not capture intent and outcome potential the way behavior does. Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user's profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). In the Enterprise, Context is fragmented and must be aggregated across data types, sources, and locations. In most business environments, such data is captured in existing enterprise information systems, and the effort is linked to quickly source and unify such information. It is rare to have to directly process sensor, audio or visual data in real-time as direct input into the enterprise cognitive system. Instead, these data types are captured by Enterprise Applications and pre-processed into a binary or text format prior to consumption by the System. == Business applications powered by an ECS == Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring
Internettolken
Internettolken (or InternetPreter) is a web-based machine translating tool. As the first Swedish online translating service, it was started in 2002 and included the English and Swedish languages. Today, there are 14 languages with more than 120 possible combinations. The service is free up to 150 words per day, and as a 2,000-word free testing account. It is available both on its website, and as a gadget on iGoogle. The interface is either English or Swedish. Being a dictionary-based tool, with its own translation software, it can sometimes offer a more accurate translation than Google Translate and others, although the grammar will be incorrect. == Languages currently available ==
Lübke English
The term Lübke English (or, in German, Lübke-Englisch) refers to nonsensical English created by literal word-by-word translation of German phrases, disregarding differences between the languages in syntax and meaning. Lübke English is named after Heinrich Lübke, a president of Germany in the 1960s, whose limited English made him a target of German humorists. In 2006, the German magazine konkret revealed that most of the statements ascribed to Lübke were in fact invented by the editorship of Der Spiegel, mainly by staff writer Ernst Goyke and subsequent letters to the editor. In the 1980s, comedian Otto Waalkes had a routine called "English for Runaways", which is a nonsensical literal translation of Englisch für Fortgeschrittene (actually an idiom for 'English for advanced speakers' in German – note that fortschreiten divides into fort, meaning "away" or "forward", and schreiten, meaning "to walk in steps"). In this mock "course", he translates every sentence back or forth between English and German at least once (usually from German literally into English). Though there are also other, more complex language puns, the title of this routine has gradually replaced the term Lübke English when a German speaker wants to point out naive literal translations.
HOCR
hOCR is an open standard of data representation for formatted text obtained from optical character recognition (OCR). The definition encodes text, style, layout information, recognition confidence metrics and other information using Extensible Markup Language (XML) in the form of Hypertext Markup Language (HTML) or XHTML. == Software == The following OCR software can output the recognition result as hOCR file: OCRopus Tesseract Cuneiform ghostscript HebOCR gcv2hocr gImageReader == Example == The following example is an extract of an hOCR file: The recognized text is stored in normal text nodes of the HTML file. The distribution into separate lines and words is here given by the surrounding span tags. Moreover, the usual HTML entities are used, for example the p tag for a paragraph. Additional information is given in the properties such as: different layout elements such as "ocr_par", "ocr_line", "ocrx_word" geometric information for each element with a bounding box "bbox" language information "lang" some confidence values "x_wconf" == bbox == === General === The Layout of the Bounding Box Object or bbox Object is Grammar. property-name = "bbox" property-value = uint uint uint uint ==== Example ==== bbox 0 0 100 200 The bbox - short for "bounding box" - of an element is a rectangular box around this element, which is defined by the upper-left corner (x0, y0) and the lower-right corner (x1, y1). the values are with reference to the top-left corner of the document image and measured in pixels the order of the values are x0 y0 x1 y1 = "left top right bottom" ===== Usage ===== Use x_bboxes below for character bounding boxes. Do not use bbox unless the bounding box of the layout component is, in fact, rectangular, some non-rectangular layout components may have rectangular bounding boxes if the non-rectangularity is caused by floating elements around which text flows. The bounding box bbox of this line is shown in blue and it is span by the upper-left corner (10, 20) and the lower-right corner (160, 30). All coordinates are measured with reference to the top-left corner of the document image which border is drawn in black. == Searchable PDF files == The hOCR format is most commonly used in order to make searchable PDF files or as an extracted metadata of the PDF file. In order to create searchable PDF files we can use a scanned document image and a .hocr file of the particular image. We can use the following open source tools in order to achieve that. === hocr-tools === Source: hocr-tools is an open source library written in Python. It has a command-line utility attached in the scripts called hocr-pdf that enables us to convert standard hocr files to a searchable PDF file. It is also worth noting that the version for dealing with hocr files in RTL or non-Latin scripts like Arabic, we need to use the GitHub repository at the moment. hocr-pdf We can use the hocr-pdf utility using the following basic syntax. hocr-pdf—savefile final.pdf folder_images_and_hocr The folder_images_and_hocr must contain the respective .jpg and .hocr format files with their file extensions changed. ==== Known issues ==== Some of the known issues of hocr-pdf script in PyPI installation are the following. Not up to date with GitHub repository. hocr-pdf is broken on line 134 due to decodebytes() depreciated after Python 3.1 ==== Known fixes ==== Compile hocr-tools using latest GitHub repository. === hocr2pdf === hocr2pdf is another library that supports the conversion of hocr files. It is written in C++ and is cross-compatible with other libraries. It also has support for UTF-8 languages but that may require some additional debugging and browsing through some google conversation records to achieve that. According to Ubuntu Manpages,ExactImage is a fast C++ image processing library. Unlike many other library frameworks it allows operation in several color spaces and bit depths natively, resulting in low memory and computational requirements. hocr2pdf creates well layouted, searchable PDF files from hOCR (annotated HTML) input obtained from an OCR system. == hOCR to PDF attempts == In addition to the following discussed and stable libraries there have been many contributions to the hOCR format over the years with support from many of the early adopters of this format. You can get access to inlaying text on an Image with hOCR and converting that in a PDF file using Python 2 with this 12-year-old script as of 2021. This script can also be updated and made functional by converting that Python 2 Source code to Python 3 Supported Context. - HOCRConverter by jbrinley (Documentation) === HOCRConverter === The HOCRConverter is a script written in Python 2.x that can used in order to convert a hOCR file with a specified image file in order to convert it to a searchable PDF file. You can see the documentation using the link above. ==== Known issues ==== Has not been tested. Does not natively support Python 3.x
Colour banding
Colour banding is a subtle form of posterisation in digital images, caused by the colour of each pixel being rounded to the nearest of the digital colour levels. While posterisation is often done for artistic effect, colour banding is an undesired artefact. In 24-bit colour modes, 8 bits per channel is usually considered sufficient to render images in Rec. 709 or sRGB. However the eye can see the difference between the colour levels, especially when there is a sharp border between two large areas of adjacent colour levels. This will happen with gradual gradients (like sunsets, dawns or clear blue skies), and also when blurring an image a large amount. Colour banding is more noticeable with fewer bits per pixel (BPP) at 16–256 colours (4–8 BPP), where there are fewer shades with a larger difference between them. The appearance of colour banding is exaggerated by the Mach bands effect. Possible solutions include the introduction of dithering and increasing the number of bits per colour channel. Because the banding comes from limitations in the presentation of the image, blurring the image does not fix this unless the image BPP is higher than the original.
Automatic number-plate recognition
Automatic number-plate recognition (ANPR; see also other names below) is a technology that uses optical character recognition on images to read vehicle registration plates to create vehicle location data. It can use existing closed-circuit television, road-rule enforcement cameras, or cameras specifically designed for the task. ANPR is used by police forces around the world for law enforcement purposes, including checking if a vehicle is registered or licensed. It is also used for electronic toll collection on pay-per-use roads and as a method of cataloguing the movements of traffic, for example by highways agencies. Automatic number-plate recognition can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver. Systems commonly use infrared lighting to allow the camera to take the picture at any time of day or night. ANPR technology must take into account plate variations from place to place. Privacy issues have caused concerns about ANPR, such as government tracking citizens' movements, misidentification, high error rates, and increased government spending. Critics have described it as a form of mass surveillance. == Other names == ANPR is also known by various other terms: Automatic (or automated) license-plate recognition (ALPR) Automatic (or automated) license-plate reader (ALPR) Automatic vehicle identification (AVI) Danish: Automatisk nummerpladegenkendelse, lit. 'Automatic number plate recognition' (ANPG) Car-plate recognition (CPR) License-plate recognition (LPR) French: Lecture automatique de plaques d'immatriculation, lit. 'Automatic reading of registration plates' (LAPI) Mobile license-plate reader (MLPR) Vehicle license-plate recognition (VLPR) Vehicle recognition identification (VRI) == Development == ANPR was invented in 1976 at the Police Scientific Development Branch in Britain. Prototype systems were working by 1979, and contracts were awarded to produce industrial systems, first at EMI Electronics, and then at Computer Recognition Systems (CRS, now part of Jenoptik) in Wokingham, UK. Early trial systems were deployed on the A1 road and at the Dartford Tunnel. The first arrest through detection of a stolen car was made in 1981. However, ANPR did not become widely used until new developments in cheaper and easier to use software were pioneered during the 1990s. The collection of ANPR data for future use (i.e., in solving then-unidentified crimes) was documented in the early 2000s. The first documented case of ANPR being used to help solve a murder occurred in November 2005, in Bradford, UK, where ANPR played a vital role in locating and subsequently convicting the killers of Sharon Beshenivsky. == Components == The software aspect of the system runs on standard home computer hardware and can be linked to other applications or databases. It first uses a series of image manipulation techniques to detect, normalize and enhance the image of the number plate, and then optical character recognition (OCR) to extract the alphanumerics of the license plate. ANPR systems are generally deployed in one of two basic approaches: one allows for the entire process to be performed at the lane location in real-time, and the other transmits all the images from many lanes to a remote computer location and performs the OCR process there at some later point in time. When done at the lane site, the information captured of the plate alphanumeric, date-time, lane identification, and any other information required is completed in approximately 250 milliseconds. This information can easily be transmitted to a remote computer for further processing if necessary, or stored at the lane for later retrieval. In the other arrangement, there are typically large numbers of PCs used in a server farm to handle high workloads, such as those found in the London congestion charge project. Often in such systems, there is a requirement to forward images to the remote server, and this can require larger bandwidth transmission media. === Technology === ANPR uses optical character recognition (OCR) on images taken by cameras. When Dutch vehicle registration plates switched to a different style in 2002, one of the changes made was to the font, introducing small gaps in some letters (such as P and R) to make them more distinct and therefore more legible to such systems. Some license plate arrangements use variations in font sizes and positioning—ANPR systems must be able to cope with such differences to be truly effective. More complicated systems can cope with international variants, though many programs are individually tailored to each country. The cameras used can be existing road-rule enforcement or closed-circuit television cameras, as well as mobile units, which are usually attached to vehicles. Some systems use infrared cameras to take a clearer image of the plates. ==== In mobile systems ==== During the 1990s, significant advances in technology took automatic number-plate recognition (ANPR) systems from limited expensive, hard to set up, fixed based applications to simple "point and shoot" mobile ones. This was made possible by the creation of software that ran on cheaper PC based, non-specialist hardware that also no longer needed to be given the pre-defined angles, direction, size and speed in which the plates would be passing the camera's field of view. Further scaled-down components at lower price points led to a record number of deployments by law enforcement agencies globally. Smaller cameras with the ability to read license plates at higher speeds, along with smaller, more durable processors that fit in the trunks of police vehicles, allowed law enforcement officers to patrol daily with the benefit of license plate reading in real time, when they can interdict immediately. Despite their effectiveness, there are noteworthy challenges related with mobile ANPRs. One of the biggest is that the processor and the cameras must work fast enough to accommodate relative speeds of more than 160 km/h (100 mph), a likely scenario in the case of oncoming traffic. This equipment must also be very efficient since the power source is the vehicle electrical system, and equipment must have minimal space requirements. Relative speed is only one issue that affects the camera's ability to read a license plate. Algorithms must be able to compensate for all the variables that can affect the ANPR's ability to produce an accurate read, such as time of day, weather and angles between the cameras and the license plates. A system's illumination wavelengths can also have a direct impact on the resolution and accuracy of a read in these conditions. Installing ANPR cameras on law enforcement vehicles requires careful consideration of the juxtaposition of the cameras to the license plates they are to read. Using the right number of cameras and positioning them accurately for optimal results can prove challenging, given the various missions and environments at hand. Highway patrol requires forward-looking cameras that span multiple lanes and are able to read license plates at high speeds. City patrol needs shorter range, lower focal length cameras for capturing plates on parked cars. Parking lots with perpendicularly parked cars often require a specialized camera with a very short focal length. Most technically advanced systems are flexible and can be configured with a number of cameras ranging from one to four which can easily be repositioned as needed. States with rear-only license plates have an additional challenge since a forward-looking camera is ineffective with oncoming traffic. In this case one camera may be turned backwards. === Algorithms === There are seven primary algorithms that the software requires for identifying a license plate: Plate localization – responsible for finding and isolating the plate on the picture Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size Normalization – adjusts the brightness and contrast of the image Character segmentation – finds the individual characters on the plates Optical character recognition Syntactical/Geometrical analysis – check characters and positions against country-specific rules The averaging of the recognised value over multiple fields/images to produce a more reliable or confident result, especially given that any single image may contain a reflected light flare, be partially obscured, or possess other obfuscating effects. The complexity of each of these subsections of the program determines the accuracy of the system. During the third phase (normalization), some systems use edge detection techniques to increase the picture difference between the letters and the plate backing. A median filter may also be used to reduce the visual noise on the image. Contemporary ANPR systems use multiple data sources and analytical techniques that go beyond simple number