Ontology learning (ontology extraction, ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical or symbolic techniques are used to extract relation signatures, often based on pattern-based or definition-based hypernym extraction techniques. == Procedure == Ontology learning (OL) is used to (semi-)automatically extract whole ontologies from natural language text. The process is usually split into the following eight tasks, which are not all necessarily applied in every ontology learning system. === Domain terminology extraction === During the domain terminology extraction step, domain-specific terms are extracted, which are used in the following step (concept discovery) to derive concepts. Relevant terms can be determined, e.g., by calculation of the TF/IDF values or by application of the C-value / NC-value method. The resulting list of terms has to be filtered by a domain expert. In the subsequent step, similarly to coreference resolution in information extraction, the OL system determines synonyms, because they share the same meaning and therefore correspond to the same concept. The most common methods therefore are clustering and the application of statistical similarity measures. === Concept discovery === In the concept discovery step, terms are grouped to meaning bearing units, which correspond to an abstraction of the world and therefore to concepts. The grouped terms are these domain-specific terms and their synonyms, which were identified in the domain terminology extraction step. === Concept hierarchy derivation === In the concept hierarchy derivation step, the OL system tries to arrange the extracted concepts in a taxonomic structure. This is mostly achieved with unsupervised hierarchical clustering methods. Because the result of such methods is often noisy, a supervision step, e.g., user evaluation, is added. A further method for the derivation of a concept hierarchy exists in the usage of several patterns that should indicate a sub- or supersumption relationship. Patterns like “X, that is a Y” or “X is a Y” indicate that X is a subclass of Y. Such pattern can be analyzed efficiently, but they often occur too infrequently to extract enough sub- or supersumption relationships. Instead, bootstrapping methods are developed, which learn these patterns automatically and therefore ensure broader coverage. === Learning of non-taxonomic relations === In the learning of non-taxonomic relations step, relationships are extracted that do not express any sub- or supersumption. Such relationships are, e.g., works-for or located-in. There are two common approaches to solve this subtask. The first is based upon the extraction of anonymous associations, which are named appropriately in a second step. The second approach extracts verbs, which indicate a relationship between entities, represented by the surrounding words. The result of both approaches need to be evaluated by an ontologist to ensure accuracy. === Rule discovery === During rule discovery, axioms (formal description of concepts) are generated for the extracted concepts. This can be achieved, e.g., by analyzing the syntactic structure of a natural language definition and the application of transformation rules on the resulting dependency tree. The result of this process is a list of axioms, which, afterwards, is comprehended to a concept description. This output is then evaluated by an ontologist. === Ontology population === At this step, the ontology is augmented with instances of concepts and properties. For the augmentation with instances of concepts, methods based on the matching of lexico-syntactic patterns are used. Instances of properties are added through the application of bootstrapping methods, which collect relation tuples. === Concept hierarchy extension === In this step, the OL system tries to extend the taxonomic structure of an existing ontology with further concepts. This can be performed in a supervised manner with a trained classifier or in an unsupervised manner via the application of similarity measures. === Frame and Event detection === During frame/event detection, the OL system tries to extract complex relationships from text, e.g., who departed from where to what place and when. Approaches range from applying SVM with kernel methods to semantic role labeling (SRL) to deep semantic parsing techniques. == Tools == Dog4Dag (Dresden Ontology Generator for Directed Acyclic Graphs) is an ontology generation plugin for Protégé 4.1 and OBOEdit 2.1. It allows for term generation, sibling generation, definition generation, and relationship induction. Integrated into Protégé 4.1 and OBO-Edit 2.1, DOG4DAG allows ontology extension for all common ontology formats (e.g., OWL and OBO). Limited largely to EBI and Bio Portal lookup service extensions.
Free boundary condition
In image processing, the free boundary condition is the convention used when applying a convolution kernel to a digital image in which pixel locations that lie outside the image boundaries are interpreted as having a value of zero.[1] The question of what value to assign out-of-bounds pixels may arise, for instance, when applying a 3×3 kernel to the corner pixel in an image.
Automatic scorer
An automatic scorer is the computerized scoring system to keep track of scoring in ten-pin bowling. It was introduced en masse in bowling alleys in the 1970s and combined with mechanical pinsetters to detect overturned pins. By eliminating the need for manual score-keeping, these systems have introduced new bowlers into the game who otherwise would not participate because they had to count the score themselves, as many do not understand the mathematical formula involved in bowler scoring. At first, people were skeptical about whether a computer could keep an accurate score. In the twenty-first century, automatic scorers are used in most bowling centers around the world. The three manufacturers of these specialty computers have been Brunswick Bowling, AMF Bowling (later QubicaAMF), and RCA. == History == Automatic equipment is considered a cornerstone of the modern bowling center. The traditional bowling center of the early 20th century was advanced in automation when the pinsetter person ("pin boy"), who set back up by hand the bowled down pins, was replaced by a machine that automatically replaced the pins in their proper play positions. This machine came out in the 1950s. A detection system was developed from the pinsetter mechanism in the 1960s that could tell which pins had been knocked down, and that information could be transferred to a digital computer. Automatic electronic scoring was first conceived by Robert Reynolds, who was described by a newspaper story at the time as "a West Coast electronics calculator expert." He worked with the technical staff of Brunswick Bowling to develop it. The goal was realized in the late 1960s when a specialized computer was designed for the purpose of automatic scorekeeping for bowling. The field test for the automatic scorer took place at Village Lanes bowling center, Chicago in 1967. The scoring machine received approval for official use by the American Bowling Congress in August of that year. They were first used in national official league gaming on October 10, 1967. In November, Brunswick announced that they were accepting orders for the new digital computer, which cost around $3,000 per bowling lane. Bowling centers that installed these new automatic scoring devices in the 1970s charged a ten cents extra per line of scoring for the convenience. == Description == Each Automatic Scorer computer unit kept score for four lanes. It had two bowler identification panels serving two lanes each. The bowler pushed it into his named position when his turn came up so the computer knew who was bowling and score accordingly. After the bowler rolled the bowling ball down the lane and knocked down pins, the pinsetter detected which pins were down and relayed this information back to the computer for scoring. The result was then printed on a scoresheet and projected overhead onto a large screen for all to see. The Automatic Scorer digital computer was mathematically accurate, however the detection system at the pinsetter mechanism sometimes reported the wrong number of pins knocked down. The computer could be corrected manually for any errors in the system; similarly, human errors, such as neglecting to move the bowler identification mechanism, could be corrected for by manual action. The scorer could take into account bowlers' handicaps and could adjust for late-arriving bowlers. The automatic scorer is directly connected to the foul detection unit. As a result, foul line violations are automatically scored. Brunswick had put ten years of research and development into the Automatic Scorer, and by 1972 there were over 500 of these computers installed in bowling centers around the world. AMF Bowling, competitor to Brunswick, entered into the automatic scorer computer field during the 1970s and their systems were installed into their brand of bowling centers. By 1974, RCA was also making these computers for automatic scoring. == Reception and further developments == The purposes of the computerized scoring were to avoid errors by human scorers and to prevent cheating. It had the side benefit of speeding up the progress of the game and introducing new bowlers to the game. Score-keeping for bowling is based on a formula that many new to bowling were not familiar with and thought difficult to learn. These casual bowlers unfamiliar with the formula thought the scores given by the computers were confusing. Some bowlers were not comfortable with automatic scorers when they were introduced in the 1970s, so kept score using the traditional method on paper score sheets. The introduction of this device increased the popularity of the sport. Automatic scorers came to be considered a normal part of modern bowling installations worldwide, with owners and managers saying that bowlers expect such equipment to be present in bowling establishments and that business increased following their introduction. Brunswick introduced a color television style automatic scorer in 1983. Bowling center owners could use these style automatic scorers for advertising, management, videos, and live television. By the 2010s, these types of electronic visual displays could show bowler avatars and social media connections to publicize the bowlers' scores. Some are capable of being extended entertainment systems of games for children and adults. Some scoring systems support variations on traditional bowling, such as different kinds of bingo games where certain pins have to be knocked down at certain times or practice regimes where certain spares have to be accomplished. By this point, QubicaAMF Worldwide, an outgrowth of AMF, was one of the leading providers of bowling scoring equipment.
Autonomous logistics
Autonomous logistics describes systems that provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal human intervention. Autonomous logistics is a new area being researched and currently there are few papers on the topic, with even fewer systems developed or deployed. With web enabled cloud software there are companies focused on developing and deploying such systems which will begin coming online in 2018. == Autonomous logistics vehicles == There are several subclasses of autonomous logistics vehicles: Ground autonomous logistics Based on Unmanned ground vehicle technology, a large autonomous logistics tracked carrier, which can be deployed in a tropical forest for day and night, has been developed. Another example is the TerraMax autonomous truck based on Oshkosh's Medium Tactical Vehicle Replacement (MTVR) military truck platform. Most recently, TerraMax competed in the 2007 Darpa Urban Challenge. The MTVR was designed for the U.S. Marine Corps with a 70% off-road mission profile. TerraMax's unmanned ground vehicle kit does not interfere with the conventional operation of the vehicle. A robust sensor suite allows for 360-degree situational awareness around TerraMax. Elements of the autonomous navigation kit could be used to enhance driver awareness. The complete kit could be used in applications such as snow removal on airport runways. Aerial autonomous logistics Based on unmanned aerial vehicle technology, aerial autonomous logistics (or logistics UAVs) provides transfer of resources and equipment in disaster relief situations, replenishment operations, reconnaissance operations where information is gathered, and general parcel or package delivery. Space autonomous logistics Describes the ability to provide logistics to and from space, be that orbital, lunar or beyond. Current space logistics vehicle examples are the Progress spacecraft, Russian expendable freighter uncrewed resupply spacecraft and the Automated Transfer Vehicle, expendable uncrewed resupply spacecraft developed by the European Space Agency. Above Water autonomous logistics Based on unmanned surface vehicle technology, this class of vehicles provides a range of surface fleet replenishment and equipment transfer capabilities. Subsea autonomous logistics Using autonomous underwater vehicle technology, these vehicles provide re-supply to underwater facilities, reconnaissance of underwater structures, emergency recovery capability, and so on. == Agent-based logistics == Shipping containers handle most of today's intercontinental transport of packaged goods. Managing them in terms of planning and scheduling is a challenging task due to the complexity and dynamics of the involved processes. Hence, recent developments show an increasing trend towards autonomous control with software agents acting on behalf of the logistic objects. Despite the high degree of autonomy it is still necessary to cooperate in order to achieve certain goals. The current trends and recent changes in logistics lead to new, complex and partially conflicting requirements for logistic planning and control systems. Due to the distributed nature of logistics, the usage of agent technology is promising. Due to the mobile nature of logistics, the usage of mobile agent technology is promising as well. Scenarios of usage of mobile agents in logistics has been envisioned.
Bump (application)
Bump was an iOS and Android mobile app that enabled smartphone users to transfer contact information, photos and files between devices. In 2011, it was #8 on Apple's list of all-time most popular free iPhone apps, and by February 2013 it had been downloaded 125 million times. Its developer, Bump Technologies, shut down the service and discontinued the app on January 31, 2014, after being acquired by Google for Google Photos and Android Camera. == Features == Bump sent contact information, photos and files to another device over the internet. Before activating the transfer, each user confirmed what they want to send to the other user. To initiate a transfer, two people physically bumped their phones together. A screen appeared on both users' smartphone displays, allowing them to confirm what they want to send to each other. When two users bumped their phones, software on the phones send a variety of sensor data to an algorithm running on Bump servers, which included the location of the phone, accelerometer readings, IP address, and other sensor readings. The algorithm figured out which two phones felt the same physical bump and then transfers the information between those phones. Bump did not use Near Field Communication. February 2012 release of Bump 3.0 for iOS, the company streamlined the app to focus on its most frequently used features: contact and photo sharing. Bump 3.0 for Android maintained the features eliminated from the iOS version but moved them behind swipeable layers. In May 2012, a Bump update enabled users to transfer photos from their phone to their computer via a web service. To initiate a transfer, the user goes to the Bump website on their computer and bumps the smartphone on the computer keyboard's space bar. By December 2012, various Bump updates for iOS and Android had added the abilities to share video, audio, and any files. Users swipe to access those features. In February 2013, an update to the Bump iOS and Android apps enabled users to transfer photos, videos, contacts and other files from a computer to a smartphone and vice versa via a web service. To perform the transfer, users went to the Bump website on their computer and bump the smartphone on the computer keyboard's space bar. == History == The underlying idea of a synchronous gesture like bumping two devices for content transfer or pairing them was first conceived by Ken Hinkley of Microsoft Research in 2003. This idea was presented at a user interface and technology conference that same year. The paper proposed the use of accelerometers and a bumping gesture of two devices to enable communication, screen sharing and content transfer between them. Similar to this original concept, the idea for Bump app was conceived by David Lieb, a former employee of Texas Instruments, while he was attending the University of Chicago Booth School of Business for his MBA. While going through the orientation and meeting process of business school, he became frustrated by constantly entering contact information into his iPhone and felt that the process could be improved. His fellow Texas Instruments employees Andy Huibers and Jake Mintz, who was a classmate of Lieb's at the University of Chicago's MBA program, joined Lieb to form Bump Technologies. Bump Technologies launched in 2008 and is located in Mountain View, CA. Early funding for the project was provided by startup incubator Y Combinator, Sequoia Capital and other angel investors. It gained attention at the CTIA international wireless conference, due to its accessibility and novelty factor. In October 2009, Bump received $3.4m in Series A funding followed in January 2011 with a $16m series B financing round led by Andreessen Horowitz. Silicon Valley venture capitalist Marc Andreessen sits on the company's board. The Bump app debuted in the Apple iOS App Store in March 2009 and was “one of the apps that helped to define the iPhone” (Harry McCracken, Technologizer). It soon became the billionth download on Apple's App Store. An Android version launched in November 2009. By the time Bump 3.0 for iOS was released in February 2012, the app had been installed 77 million times, with users sharing more than 2 million photos daily. As of February 2013, there had been 125 million Bump app downloads. == Other apps created by Bump Technologies == Bump Technologies worked with PayPal in March 2010 to create a PayPal iPhone application. The application, which allows two users to automatically activate an Internet transfer of money between their accounts, found widespread adoption. A similar version was released for Android in August 2010. The Bump capability in PayPal's apps was removed in March 2012. At that time, Bump Technologies released Bump Pay, an iOS app that lets users transfer money via PayPal by physically bumping two smartphones together. The tool was originally created for the Bump team to use when splitting up restaurant bills. The payment feature was not added to the Bump app because the company “wanted to make it as simple as possible so people understand how this works,” Lieb told ABC News. Bump Pay was the first app from the company's Bump Labs initiative. A goal of Bump Labs is to test new app ideas that may not fit within the main Bump app. ING Direct added a feature to its iPhone app in 2011 that lets users transfer money to each other using Bump's technology. The feature was later added to its Android app, now called Capital One 360. In July 2012, Bump Technologies released Flock, an iPhone photo sharing app. An Android version was released in December 2012. Using geolocation data embedded in photos and a user's Facebook connections, Flock finds pictures the user takes while out with friends and family and puts everyone's photos from that event into a single shared album. Users receive a push notification after the event, asking if they want to share their photos with friends who were there in the moment. The app will also scan previous photos in the iPhone camera roll and uncover photos that have yet to be shared. If location services were enabled at the time a photo was taken, Flock allows users to create an album of photos from the past with the friends who were there with them. == Acquisition by Google == On September 16, 2013, Bump Technologies announced that it had been acquired by Google. On December 31, 2013, they broke the news that both Bump and Flock would be discontinued so that the team could focus on new projects at Google. The apps were removed from the App Store and Google Play on January 31, 2014. The company subsequently deleted all user data and shut down their servers, thus rendering existing installations of the apps inoperable.
Picture Prowler
Picture Prowler was an early piece of photo management software developed around and meant to show off Xing Technology's JPEG image decompression library during the early 1990s. Little known today, it featured thumbnail based picture management, printing, etc. The primary developer was Ray Bunnage from compression / decompression libraries developed by Howard Gordon and Chris Eddy.
Enterprise mobile application
The term enterprise mobile application is used in the context of mobile apps created/brought by individual organizations for their workers to carry out the functions required to run the organization. It is the process of building a mobile application for the requirements of an enterprise. An enterprise mobile application belonging to an organization is expected to be used by only the workers of that organization. The definition of enterprise mobile application does not include the mobile apps that an organization create for its customers or consumers of the products or services generated by the organization. == Example == An organization, whether for-profit or non-profit, may create a mobile app for its members to track inventory levels of supplies they distribute to their target communities or materials used in product manufacturing. Such a mobile app comes under the definition of enterprise mobile application. However, the same organization may also create another mobile app to sell their products to end users or spread awareness of their services to various communities, and that mobile app would not come under definition of enterprise mobile application. == Enterprise mobile solution providers == Enterprise Mobile solution providers create and develop apps for individual organizations that can buy instead of creating the apps themselves. Reasons for Organizations buying the apps include time and cost savings, technical expertise. Today Enterprise Mobility is playing track role for enterprise transformation. Today, enterprises needs productivity is a fast way. Enterprise mobility helps business owners to build their work in a progressive way by assisting enterprise mobility solutions.