The ugly duckling theorem is an argument showing that classification is not really possible without some sort of bias. More particularly, it assumes finitely many properties combinable by logical connectives, and finitely many objects; it asserts that any two different objects share the same number of (extensional) properties. The theorem is named after Hans Christian Andersen's 1843 story "The Ugly Duckling", because it shows that a duckling is just as similar to a swan as two swans are to each other. It was derived by Satosi Watanabe in 1969. == Mathematical formula == Suppose there are n things in the universe, and one wants to put them into classes or categories. One has no preconceived ideas or biases about what sorts of categories are "natural" or "normal" and what are not. So one has to consider all the possible classes that could be, all the possible ways of making a set out of the n objects. There are 2 n {\displaystyle 2^{n}} such ways, the size of the power set of n objects. One can use that to measure the similarity between two objects, and one would see how many sets they have in common. However, one cannot. Any two objects have exactly the same number of classes in common if we can form any possible class, namely 2 n − 1 {\displaystyle 2^{n-1}} (half the total number of classes there are). To see this is so, one may imagine each class is represented by an n-bit string (or binary encoded integer), with a zero for each element not in the class and a one for each element in the class. As one finds, there are 2 n {\displaystyle 2^{n}} such strings. As all possible choices of zeros and ones are there, any two bit-positions will agree exactly half the time. One may pick two elements and reorder the bits so they are the first two, and imagine the numbers sorted lexicographically. The first 2 n / 2 {\displaystyle 2^{n}/2} numbers will have bit #1 set to zero, and the second 2 n / 2 {\displaystyle 2^{n}/2} will have it set to one. Within each of those blocks, the top 2 n / 4 {\displaystyle 2^{n}/4} will have bit #2 set to zero and the other 2 n / 4 {\displaystyle 2^{n}/4} will have it as one, so they agree on two blocks of 2 n / 4 {\displaystyle 2^{n}/4} or on half of all the cases, no matter which two elements one picks. So if we have no preconceived bias about which categories are better, everything is then equally similar (or equally dissimilar). The number of predicates simultaneously satisfied by two non-identical elements is constant over all such pairs. Thus, some kind of inductive bias is needed to make judgements to prefer certain categories over others. === Boolean functions === Let x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} be a set of vectors of k {\displaystyle k} booleans each. The ugly duckling is the vector which is least like the others. Given the booleans, this can be computed using Hamming distance. However, the choice of boolean features to consider could have been somewhat arbitrary. Perhaps there were features derivable from the original features that were important for identifying the ugly duckling. The set of booleans in the vector can be extended with new features computed as boolean functions of the k {\displaystyle k} original features. The only canonical way to do this is to extend it with all possible Boolean functions. The resulting completed vectors have 2 k {\displaystyle 2^{k}} features. The ugly duckling theorem states that there is no ugly duckling because any two completed vectors will either be equal or differ in exactly half of the features. Proof. Let x and y be two vectors. If they are the same, then their completed vectors must also be the same because any Boolean function of x will agree with the same Boolean function of y. If x and y are different, then there exists a coordinate i {\displaystyle i} where the i {\displaystyle i} -th coordinate of x {\displaystyle x} differs from the i {\displaystyle i} -th coordinate of y {\displaystyle y} . Now the completed features contain every Boolean function on k {\displaystyle k} Boolean variables, with each one exactly once. Viewing these Boolean functions as polynomials in k {\displaystyle k} variables over GF(2), segregate the functions into pairs ( f , g ) {\displaystyle (f,g)} where f {\displaystyle f} contains the i {\displaystyle i} -th coordinate as a linear term and g {\displaystyle g} is f {\displaystyle f} without that linear term. Now, for every such pair ( f , g ) {\displaystyle (f,g)} , x {\displaystyle x} and y {\displaystyle y} will agree on exactly one of the two functions. If they agree on one, they must disagree on the other and vice versa. (This proof is believed to be due to Watanabe.) == Discussion == A possible way around the ugly duckling theorem would be to introduce a constraint on how similarity is measured by limiting the properties involved in classification, for instance, between A and B. However Medin et al. (1993) point out that this does not actually resolve the arbitrariness or bias problem since in what respects A is similar to B: "varies with the stimulus context and task, so that there is no unique answer, to the question of how similar is one object to another". For example, "a barberpole and a zebra would be more similar than a horse and a zebra if the feature striped had sufficient weight. Of course, if these feature weights were fixed, then these similarity relations would be constrained". Yet the property "striped" as a weight 'fix' or constraint is arbitrary itself, meaning: "unless one can specify such criteria, then the claim that categorization is based on attribute matching is almost entirely vacuous". Stamos (2003) remarked that some judgments of overall similarity are non-arbitrary in the sense they are useful: "Presumably, people's perceptual and conceptual processes have evolved that information that matters to human needs and goals can be roughly approximated by a similarity heuristic... If you are in the jungle and you see a tiger but you decide not to stereotype (perhaps because you believe that similarity is a false friend), then you will probably be eaten. In other words, in the biological world stereotyping based on veridical judgments of overall similarity statistically results in greater survival and reproductive success." Unless some properties are considered more salient, or 'weighted' more important than others, everything will appear equally similar, hence Watanabe (1986) wrote: "any objects, in so far as they are distinguishable, are equally similar". In a weaker setting that assumes infinitely many properties, Murphy and Medin (1985) give an example of two putative classified things, plums and lawnmowers: "Suppose that one is to list the attributes that plums and lawnmowers have in common in order to judge their similarity. It is easy to see that the list could be infinite: Both weigh less than 10,000 kg (and less than 10,001 kg), both did not exist 10,000,000 years ago (and 10,000,001 years ago), both cannot hear well, both can be dropped, both take up space, and so on. Likewise, the list of differences could be infinite… any two entities can be arbitrarily similar or dissimilar by changing the criterion of what counts as a relevant attribute." According to Woodward, the ugly duckling theorem is related to Schaffer's Conservation Law for Generalization Performance, which states that all algorithms for learning of boolean functions from input/output examples have the same overall generalization performance as random guessing. The latter result is generalized by Woodward to functions on countably infinite domains.
Tango (platform)
Tango (named Project Tango while in testing) was an augmented reality computing platform, developed and authored by the Advanced Technology and Projects (ATAP), a skunkworks division of Google. It used computer vision to enable mobile devices, such as smartphones and tablets, to detect their position relative to the world around them without using GPS or other external signals. This allowed application developers to create user experiences that include indoor navigation, 3D mapping, physical space measurement, environmental recognition, augmented reality, and windows into a virtual world. The first product to emerge from ATAP, Tango was developed by a team led by computer scientist Johnny Lee, a core contributor to Microsoft's Kinect. In an interview in June 2015, Lee said, "We're developing the hardware and software technologies to help everything and everyone understand precisely where they are, anywhere." Google produced two devices to demonstrate the Tango technology: the Peanut phone and the Yellowstone 7-inch tablet. More than 3,000 of these devices had been sold as of June 2015, chiefly to researchers and software developers interested in building applications for the platform. In the summer of 2015, Qualcomm and Intel both announced that they were developing Tango reference devices as models for device manufacturers who use their mobile chipsets. At CES, in January 2016, Google announced a partnership with Lenovo to release a consumer smartphone during the summer of 2016 to feature Tango technology marketed at consumers, noting a less than $500 price-point and a small form factor below 6.5 inches. At the same time, both companies also announced an application incubator to get applications developed to be on the device on launch. On 15 December 2017, Google announced that they would be ending support for Tango on March 1, 2018, in favor of ARCore. == Overview == Tango was different from other contemporary 3D-sensing computer vision products, in that it was designed to run on a standalone mobile phone or tablet and was chiefly concerned with determining the device's position and orientation within the environment. The software worked by integrating three types of functionality: Motion-tracking: using visual features of the environment, in combination with accelerometer and gyroscope data, to closely track the device's movements in space Area learning: storing environment data in a map that can be re-used later, shared with other Tango devices, and enhanced with metadata such as notes, instructions, or points of interest Depth perception: detecting distances, sizes, and surfaces in the environment Together, these generate data about the device in "six degrees of freedom" (3 axes of orientation plus 3 axes of position) and detailed three-dimensional information about the environment. Project Tango was also the first project to graduate from Google X in 2012 Applications on mobile devices use Tango's C and Java APIs to access this data in real time. In addition, an API was also provided for integrating Tango with the Unity game engine; this enabled the conversion or creation of games that allow the user to interact and navigate in the game space by moving and rotating a Tango device in real space. These APIs were documented on the Google developer website. == Applications == Tango enabled apps to track a device's position and orientation within a detailed 3D environment, and to recognize known environments. This allowed the creations of applications such as in-store navigation, visual measurement and mapping utilities, presentation and design tools, and a variety of immersive games. At Augmented World Expo 2015, Johnny Lee demonstrated a construction game that builds a virtual structure in real space, an AR showroom app that allows users to view a full-size virtual automobile and customize its features, a hybrid Nerf gun with mounted Tango screen for dodging and shooting AR monsters superimposed on reality, and a multiplayer VR app that lets multiple players converse in a virtual space where their avatar movements match their real-life movements. Tango apps are distributed through Play. Google has encouraged the development of more apps with hackathons, an app contest, and promotional discounts on the development tablet. == Devices == As a platform for software developers and a model for device manufacturers, Google created two Tango devices. === The Peanut phone === "Peanut" was the first production Tango device, released in the first quarter of 2014. It was a small Android phone with a Qualcomm MSM8974 quad-core processor and additional special hardware including a fisheye motion camera, "RGB-IR" camera for color image and infrared depth detection, and Movidius Vision processing units. A high-performance accelerometer and gyroscope were added after testing several competing models in the MARS lab at the University of Minnesota. Several hundred Peanut devices were distributed to early-access partners including university researchers in computer vision and robotics, as well as application developers and technology startups. Google stopped supporting the Peanut device in September 2015, as by then the Tango software stack had evolved beyond the versions of Android that run on the device. === The Yellowstone tablet === "Yellowstone" was a 7-inch tablet with full Tango functionality, released in June 2014, and sold as the Project Tango Tablet Development Kit. It featured a 2.3 GHz quad-core Nvidia Tegra K1 processor, 128GB flash memory, 1920x1200-pixel touchscreen, 4MP color camera, fisheye-lens (motion-tracking) camera, an IR projector with RGB-IR camera for integrated depth sensing, and 4G LTE connectivity. As of May 27, 2017, the Tango tablet is considered officially unsupported by Google. ==== Testing by NASA ==== In May 2014, two Peanut phones were delivered to the International Space Station to be part of a NASA project to develop autonomous robots that navigate in a variety of environments, including outer space. The soccer-ball-sized, 18-sided polyhedral SPHERES robots were developed at the NASA Ames Research Center, adjacent to the Google campus in Mountain View, California. Andres Martinez, SPHERES manager at NASA, said "We are researching how effective [Tango's] vision-based navigation abilities are for performing localization and navigation of a mobile free flyer on ISS. === Intel RealSense smartphone === Announced at Intel's Developer Forum in August 2015, and offered to public through a Developer Kit since January 2016. It incorporated a RealSense ZR300 camera which had optical features required for Tango, such as the fisheye camera. === Lenovo Phab 2 Pro === Lenovo Phab 2 Pro was the first commercial smartphone with the Tango Technology, the device was announced at the beginning of 2016, launched in August, and available for purchase in the US in November. The Phab 2 Pro had a 6.4 inch screen, a Snapdragon 652 processor, and 64 GB of internal storage, with a rear facing 16 Megapixels camera and 8 MP front camera. === Asus Zenfone AR === Asus Zenfone AR, announced at CES 2017, was the second commercial smartphone with the Tango Technology. It ran Tango AR & Daydream VR on Snapdragon 821, with 6GB or 8GB of RAM and 128 or 256GB of internal memory depending on the configuration.
Umbrella review
In medical research, an umbrella review is a review of systematic reviews or meta-analyses. They may also be called overviews of reviews, reviews of reviews, summaries of systematic reviews, or syntheses of reviews. Umbrella reviews are among the highest levels of evidence currently available in medicine. By summarizing information from multiple overview articles, umbrella reviews make it easier to review the evidence and allow for comparison of results between each of the individual reviews. Umbrella reviews may address a broader question than a typical review, such as discussing multiple different treatment comparisons instead of only one. They are especially useful for developing guidelines and clinical practice, and when comparing competing interventions.
Algorithmic management
Algorithmic management is a term used to describe certain labor management practices in the contemporary digital economy. In scholarly uses, the term was initially coined in 2015 by Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish to describe the managerial role played by algorithms on the Uber and Lyft platforms, but has since been taken up by other scholars to describe more generally the managerial and organisational characteristics of platform economies. However, digital direction of labor was present in manufacturing already since the 1970s and algorithmic management is becoming increasingly widespread across a wide range of industries. The concept of algorithmic management can be broadly defined as the delegation of managerial functions to algorithmic and automated systems. Algorithmic management has been enabled by "recent advances in digital technologies" which allow for the real-time and "large-scale collection of data" which is then used to "improve learning algorithms that carry out learning and control functions traditionally performed by managers". The term does not refer to a specific underlying technology, and encompasses the design choices, organisational policies, and governance that surround the managerial use of algorithms in workplaces. In the contemporary workplace, firms employ an ecology of accounting devices, such as "rankings, lists, classifications, stars and other symbols' in order to effectively manage their operations and create value without the need for traditional forms of hierarchical control." Many of these devices fall under the label of what is called algorithmic management, and were first developed by companies operating in the sharing economy or gig economy, functioning as effective labor and cost cutting measures. The Data&Society explainer of the term, for example, describes algorithmic management as 'a diverse set of technological tools and techniques that structure the conditions of work and remotely manage workforces. Data&Society also provides a list of five typical features of algorithmic management: Prolific data collection and surveillance of workers through technology; Real-time responsiveness to data that informs management decisions; Automated or semi-automated decision-making; Transfer of performance evaluations to rating systems or other metrics; and The use of "nudges" and penalties to indirectly incentivize worker behaviors. Proponents of algorithmic management claim that it "creates new employment opportunities, better and cheaper consumer services, transparency and fairness in parts of the labour market that are characterised by inefficiency, opacity and capricious human bosses." On the other hand, critics of algorithmic management claim that the practice leads to several issues, especially as it impacts the employment status of workers managed by its new array of tools and techniques. == History of the term == "Algorithmic management" was first described by Lee, Kusbit, Metsky, and Dabbish in 2015 in their study of the Uber and Lyft platforms. In their study, Lee et al. termed "software algorithms that assume managerial functions and surrounding institutional devices that support algorithms in practice" algorithmic management. Software algorithms, it was said, are increasingly used to "allocate, optimize, and evaluate work" by platforms in managing their vast workforces. In Lee et al.'s paper on Uber and Lyft this included the use of algorithms to assign work to drivers, as mechanisms to optimise pricing for services, and as systems for evaluating driver performance. In 2016, Alex Rosenblat and Luke Stark sought to extend on this understanding of algorithmic management "to elucidate on the automated implementation of company policies on the behaviours and practices of Uber drivers." Rosenblat and Stark found in their study that algorithmic management practices contributed to a system beset by power asymmetries, where drivers had little control over "critical aspects of their work", whereas Uber had far greater control over the labor of its drivers. Since this time, studies of algorithmic management have extended the use of the term to describe the management practices of various firms, where, for example, algorithms "are taking over scheduling work in fast food restaurants and grocery stores, using various forms of performance metrics ad even mood... to assign the fastest employees to work in peak times." Algorithmic management is seen to be especially prevalent in gig work on platforms, such as on Upwork and Deliveroo, and in the sharing economy, such as in the case of Airbnb. Furthermore, recent research has defined sub-constructs that fall under the umbrella term of algorithmic management, for example, "algorithmic nudging". A Harvard Business Review article published in 2021 explains: "Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior — in other words, learning from their personalized data and altering their choices in some subtle way." While the concept builds on nudging theory popularized by University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein, "due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective." == Relationships with other labor management practices == Algorithmic management has been compared and contrasted with other forms of management, such as Scientific management approaches, as pioneered by Frederick Taylor in the early 1900s. Henri Schildt has called algorithmic management "Scientific management 2.0", where management "is no longer a human practice, but a process embedded in technology." Similarly, Kathleen Griesbach, Adam Reich, Luke Elliott-Negri, and Ruth Milkman suggest that, while "algorithmic control over labor may be relatively new, it replicates many features of older mechanisms of labor control." On the other hand, some commentators have argued that algorithmic management is not simply a new form of Scientific management or digital Taylorism, but represents a distinct approach to labor control in platform economies. David Stark and Ivana Pais, for example, state that, "In contrast to Scientific Management at the turn of the twentieth century, in the algorithmic management of the twenty-first century there are rules but these are not bureaucratic, there are rankings but not ranks, and there is monitoring but it is not disciplinary. Algorithmic management does not automate bureaucratic structures and practices to create some new form of algorithmic bureaucracy. Whereas the devices and practices of Taylorism were part of a system of hierarchical supervision, the devices and practices of algorithmic management take place within a different economy of attention and a new regime of visibility. Triangular rather than vertical, and not as a panopticon, the lines of vision in algorithmic management are not lines of supervision." Similarly, Data&Society's explainer for algorithmic management claims that the practice represents a marked departure from earlier management structures that more strongly rely on human supervisors to direct workers. In analyzing the difference and the similarities to previous management styles, David Stark and Pieter Vanden Broeck expand the applicability of algorithmic management beyond the workplace. They develop a theory of algorithmic management in terms of broader changes in the shape and structure of organization in the 21st century, attentive to the erosion of organization's boundaries whereby heterogeneous actors, assets, and activities, are coopted regardless of their place in organizational space. Stark and Vanden Broeck propose the following means of differentiating algorithmic management from other historical managerial paradigms: == Issues == Algorithmic management can provide an effective and efficient means of workforce control and value creation in the contemporary digital economy. However, commentators have highlighted several issues that algorithmic management poses, especially for the workers it manages. Criticisms of the practice often highlight several key issues pertaining to algorithmic management practices, such as the imperfection and scope of its surveillance and control measures, which also threaten to lock workers out of key decision-making processes; its lack of transparency for users and information asymmetries; its potential for bias and discrimination; its dehumanizing tendencies; and its potential to create conditions which sidestep traditional employer-employee accountability. This last point has been especi
Online public access catalog
The online public access catalog (OPAC), now frequently synonymous with library catalog, is an online database of materials held by a library or group of libraries. Online catalogs have largely replaced the analog card catalogs previously used in libraries. == History == === Early online === Although a handful of experimental systems existed as early as the 1960s, the first large-scale online catalogs were developed at Ohio State University in 1975 and the Dallas Public Library in 1978. These and other early online catalog systems tended to closely reflect the card catalogs that they were intended to replace. Using a dedicated terminal or telnet client, users could search a handful of pre-coordinate indexes and browse the resulting display in much the same way they had previously navigated the card catalog. Throughout the 1980s, the number and sophistication of online catalogs grew. The first commercial systems appeared, and would by the end of the decade largely replace systems built by libraries themselves. Library catalogs began providing improved search mechanisms, including Boolean and keyword searching, as well as ancillary functions, such as the ability to place holds on items that had been checked-out. At the same time, libraries began to develop applications to automate the purchase, cataloging, and circulation of books and other library materials. These applications, collectively known as an integrated library system (ILS) or library management system, included an online catalog as the public interface to the system's inventory. Most library catalogs are closely tied to their underlying ILS system. === Stagnation and dissatisfaction === The 1990s saw a relative stagnation in the development of online catalogs. Although the earlier character-based interfaces were replaced with ones for the Web, both the design and the underlying search technology of most systems did not advance much beyond that developed in the late 1980s. At the same time, organizations outside of libraries began developing more sophisticated information retrieval systems. Web search engines like Google and popular e-commerce websites such as Amazon.com provided simpler to use (yet more powerful) systems that could provide relevancy ranked search results using probabilistic and vector-based queries. Prior to the widespread use of the Internet, the online catalog was often the first information retrieval system library users ever encountered. Now accustomed to web search engines, newer generations of library users have grown increasingly dissatisfied with the complex (and often arcane) search mechanisms of older online catalog systems. This has, in turn, led to vocal criticisms of these systems within the library community itself, and in recent years to the development of newer (often termed 'next-generation') catalogs. === Next-generation catalogs === Newer generations of library catalog systems, typically called discovery systems (or a discovery layer), are distinguished from earlier OPACs by their use of more sophisticated search technologies, including relevancy ranking and faceted search, as well as features aimed at greater user interaction and participation with the system, including tagging and reviews. These new features rely heavily on existing metadata which may be poor or inconsistent, particularly for older records. Newer catalog platforms may be independent of the organization's integrated library system (ILS), instead providing drivers that allow for the synchronization of data between the two systems. While the original online catalog interfaces were almost exclusively built by ILS vendors, libraries have increasingly sought next-generation catalogs built by enterprise search companies and open-source software projects, often led by libraries themselves. == Union catalogs == Although library catalogs typically reflect the holdings of a single library, they can also contain the holdings of a group or consortium of libraries. These systems, known as union catalogs, are usually designed to aid the borrowing of books and other materials among the member institutions via interlibrary loan. Examples of this type of catalogs include COPAC, SUNCAT, NLA Trove, and WorldCat—the last catalogs the collections of libraries worldwide. == Related systems == There are a number of systems that share much in common with library catalogs, but have traditionally been distinguished from them. Libraries utilize these systems to search for items not traditionally covered by a library catalog, although these systems are sometimes integrated into a more comprehensive discovery system. Bibliographic databases—such as Medline, ERIC, PsycINFO, Scopus, Web of Science, and many others—index journal articles and other research data. There are also a number of applications aimed at managing documents, photographs, and other digitized or born-digital items such as Digital Commons and DSpace. Particularly in academic libraries, these systems (often known as digital library systems or institutional repository systems) assist with efforts to preserve documents created by faculty and students. Electronic resource management helps librarians to track selection, acquisition, and licensing of a library's electronic information resources.
Videotex
Videotex (or interactive videotex) was one of the earliest implementations of an end-user information system. From the late 1970s to early 2010s, it was used to deliver information (usually pages of text) to a user in computer-like format, typically to be displayed on a television or a dumb terminal. In a strict definition, videotex is any system that provides interactive content and displays it on a video monitor such as a television, typically using modems to send data in both directions. A close relative is teletext, which sends data in one direction only, typically encoded in a television signal. All such systems are occasionally referred to as viewdata. Unlike the modern Internet, traditional videotex services were highly centralized. Videotex in its broader definition can be used to refer to any such service, including teletext, the Internet, bulletin board systems, online service providers, and even the arrival/departure displays at an airport. This usage is no longer common. With the exception of Minitel in France, videotex elsewhere never managed to attract any more than a very small percentage of the universal mass market once envisaged. By the end of the 1980s its use was essentially limited to a few niche applications. == Initial development and technologies == === United Kingdom === The first attempts at a general-purpose videotex service were created in the United Kingdom in the late 1960s. In about 1970 the BBC had a brainstorming session in which it was decided to start researching ways to send closed captioning information to the audience. As the Teledata research continued the BBC became interested in using the system for delivering any sort of information, not just closed captioning. In 1972, the concept was first made public under the new name Ceefax. Meanwhile, the General Post Office (soon to become British Telecom) had been researching a similar concept since the late 1960s, known as Viewdata. Unlike Ceefax which was a one-way service carried in the existing TV signal, Viewdata was a two-way system using telephones. Since the Post Office owned the telephones, this was considered to be an excellent way to drive more customers to use the phones. Not to be outdone by the BBC, they also announced their service, under the name Prestel. ITV soon joined the fray with a Ceefax-clone known as ORACLE. In 1974, all the services agreed on a standard for displaying the information. The display would be a simple 40×24 grid of text, with some "graphics characters" for constructing simple graphics, revised and finalized in 1976. The standard did not define the delivery system, so both Viewdata-like and Teledata-like services could at least share the TV-side hardware, which was expensive at the time. The standard also introduced a new term that covered all such services, teletext. Ceefax first started operation in 1974 with a limited 30 pages, followed quickly by ORACLE and then Prestel in 1979. By 1981, Prestel International was available in nine countries, and a number of countries, including Sweden, The Netherlands, Finland and West Germany were developing their own national systems closely based on Prestel. General Telephone and Electronics (GTE) acquired an exclusive agency for the system for North America. In the early 1980s, videotex became the base technology for the London Stock Exchange's pricing service called TOPIC. Later versions of TOPIC, notably TOPIC2 and TOPIC3, were developed by Thanos Vassilakis and introduced trading and historic price feeds. === France === Development of a French teletext-like system began in 1973. A very simple 2-way videotex system called Tictac was also demonstrated in the mid-1970s. As in the UK, this led on to work to develop a common display standard for videotex and teletext, called Antiope, which was finalised in 1977. Antiope had similar capabilities to the UK system for displaying alphanumeric text and chunky "mosaic" character-based block graphics. A difference however was that while in the UK standard control codes automatically also occupied one character position on screen, Antiope allowed for "non spacing" control codes. This gave Antiope slightly more flexibility in the use of colours in mosaic block graphics, and in presenting the accents and diacritics of the French language. Meanwhile, spurred on by the 1978 Nora/Minc report, the French government was determined to catch up on a perceived falling behind in its computer and communications facilities. In 1980 it began field trials issuing Antiope-based terminals for free to over 250,000 telephone subscribers in Ille-et-Vilaine region, where the French CCETT research centre was based, for use as telephone directories. The trial was a success, and in 1982 Minitel was rolled out nationwide. === Canada === Since 1970, researchers at the Communications Research Centre (CRC) in Ottawa had been working on a set of "picture description instructions", which encoded graphics commands as a text stream. Graphics were encoded as a series of instructions (graphics primitives) each represented by a single ASCII character. Graphic coordinates were encoded in multiple 6 bit strings of XY coordinate data, flagged to place them in the printable ASCII range so that they could be transmitted with conventional text transmission techniques. ASCII SI/SO characters were used to differentiate the text from graphic portions of a transmitted "page". In 1975, the CRC gave a contract to Norpak to develop an interactive graphics terminal that could decode the instructions and display them on a colour display, which was successfully up and running by 1977. Against the background of the developments in Europe, CRC was able to persuade the Canadian government to develop the system into a fully-fledged service. In August 1978, the Canadian Department of Communications publicly launched it as Telidon, a "second generation" videotex/teletext service, and committed to a four-year development plan to encourage rollout. Compared to the European systems, Telidon offered real graphics, as opposed to block-mosaic character graphics. The downside was that it required much more advanced decoders, typically featuring Zilog Z80 or Motorola 6809 processors. === Japan === Research in Japan was shaped by the demands of the large number of Kanji characters used in Japanese script. With 1970s technology, the ability to generate so many characters on demand in the end-user's terminal was seen as prohibitive. Instead, development focussed on methods to send pages to user terminals pre-rendered, using coding strategies similar to facsimile machines. This led to a videotex system called Captain ("Character and Pattern Telephone Access Information Network"), created by NTT in 1978, which went into full trials from 1979 to 1981. The system also lent itself naturally to photographic images, albeit at only moderate resolution. However, the pages typically took two or three times longer to load, compared to the European systems. NHK developed an experimental teletext system along similar lines, called CIBS ("Character Information Broadcasting Station"). Based on a 388×200 pixel resolution, it was first announced in 1976, and began trials in late 1978. (NHK's ultimate production teletext system launched in 1983). == Standards == Work to establish an international standard for videotex began in 1978 in CCITT. But the national delegations showed little interest in compromise, each hoping that their system would come to define what was perceived to be going to be an enormous new mass-market. In 1980 CCITT therefore issued recommendation S.100 (later T.100), noting the points of similarity but the essential incompatibility of the systems, and declaring all four to be recognised options. Trying to kick-start the market, AT&T Corporation entered the fray, and in May 1981 announced its own Presentation Layer Protocol (PLP). This was closely based on the Canadian Telidon system, but added to it some further graphics primitives and a syntax for defining macros, algorithms to define cleaner pixel spacing for the (arbitrarily sizeable) text, and also dynamically redefinable characters and a mosaic block graphic character set, so that it could reproduce content from the French Antiope. After some further revisions this was adopted in 1983 as ANSI standard X3.110, more commonly called NAPLPS, the North American Presentation Layer Protocol Syntax. It was also adopted in 1988 as the presentation-layer syntax for NABTS, the North American Broadcast Teletext Specification. Meanwhile, the European national Postal Telephone and Telegraph (PTT) agencies were also increasingly interested in videotex, and had convened discussions in European Conference of Postal and Telecommunications Administrations (CEPT) to co-ordinate developments, which had been diverging along national lines. As well as the British and French standards, the Swedes had proposed extending the British Prestel standard with a new se
BioCreative
BioCreAtIvE (A critical assessment of text mining methods in molecular biology) consists in a community-wide effort for evaluating information extraction and text mining developments in the biological domain. It was preceded by the Knowledge Discovery and Data Mining (KDD) Challenge Cup for detection of gene mentions. == Community Challenges == === First edition (2004-2005) === Three main tasks were posed at the first BioCreAtIvE challenge: the entity extraction task, the gene name normalization task, and the functional annotation of gene products task. The data sets produced by this contest serve as a Gold Standard training and test set to evaluate and train Bio-NER tools and annotation extraction tools. === Second edition (2006-2007) === The second BioCreAtIvE challenge (2006-2007) had also 3 tasks: detection of gene mentions, extraction of unique idenfiers for genes and extraction information related to physical protein-protein interactions. It counted with participation of 44 teams from 13 countries. === Third edition (2011-2012) === The third edition of BioCreative included for the first time the InterActive Task (IAT), designed to evaluate the practical usability of text mining tools in real-world biocuration tasks. === Fifth edition (2016) === BioCreative V had 5 different tracks, including an interactive task (IAT) for usability of text mining systems and a track using the BioC format for curating information for BioGRID.