A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as 'accounts', allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system. A user profile can also be considered as the computer representation of a user model. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and the process of obtaining the user profile is called user modeling or profiling. == Origin == The origin of user profiles can be traced to the origin of the passport, an identity document (ID) made mandatory in 1920, after World War I following negotiations at the League of Nations. The passport served as an official government record of an individual. Consequently, Immigration Act of 1924 was established to identify an individual's country of origin. In the 21st century, passports have now become a highly sought-after commodity as it is widely accepted as a source of verifying an individual's identity under the legal system. With the advent of digital revolution and social media websites, user profiles have transitioned to an organised group of data describing the interaction between a user and a system. Social media sites like Instagram allow individuals to create profiles that are representative of their desired personality and image. Filling all fields of profile information may not be necessary to create a meaningful self-presentation, which grants individual more control over of the identity they wish to present by displaying the most meaningful attributes. A personal user profile is a key aspect of an individual's social networking experience, around which his/her public identity is built. == Types of user profiles == A user profile can be of any format if it contains information, settings and/or characteristics specific to an individual. Most popular user profiles include those on photo and video sharing websites such as Facebook and Instagram, accounts on operating systems, such as those on Windows and MacOS and physical documents such as passports and driving licenses. === Social media === Effectively structured user profiles on social media channels such as Instagram and Facebook offer a way for people to form impressions about someone that is predictive or similarly meeting them offline. The condensed format of social media profiles allows for quick filtering of millions of profiles by matching individuals by similar characteristics and interests; information provided upon sign up. A research conducted highlights that only a "thin slice" of information is required to form an impression about an individual online (Stecher and Counts 2008). Online user profiles eliminate the complexity of interaction that is present in 'face-to-face' meetings such as behavioural, facial, and environmental information, resulting in increased predictiveness of user personality. Dating apps and websites solely rely on an individual's user profile and the information provided to form interactions and communication with others on the platform. Despite having control over presented information, lying is minimal in online dating contexts (Hancock, Toma and Ellison, 2007). Apps such as Bumble allow users to 'match' with other individuals based on their characteristics and selected filters that allow users to narrow the spectrum of search to their preference. Information for a user's profile is voluntarily specified by the user and includes information such as height, interests, photographs, gender or education. The requirement of information varies respective to each platform, and there surrounds little consensus to an appropriate amount of information for a condensed user profile. Universally, all social networking platforms display an individual's profile picture and an "about me" page that allows for self-expression. === Influencers === Influencer user profiles are third party endorsers who shape audience attitudes and decisions through social media content such as photos, blogs and tweets. Social Media Influencers (SMI) often hold a significant following on a social media platform which enables them to be recognised as opinion leaders to shape an information influence to their audience. 'Influencer marketing' industry gained prominence in 2018, when the photo sharing app Instagram crossed 1 billion users, subsequently with approximately 60,000 google search queries for 'influencer marketing' the same year. Influencer user profiles hold a unique selling point, or public personality that is unique and charismatic to the needs and wants of their target audience. SMI profiles advertise product information, latest promotions and regularly engage with their followers to maintain their online persona. Messages endorsed by social media influencers are often perceived as reliable and compelling, as a study conducted found 82% of followers were more inclined to follow the suggestions of their favorite influencer. This allows advertisers to leverage online user profiles and their audience rapport to target younger and niche audiences. According to a market survey, influencer marketing through social media profiles yields a return 11 times higher than traditional marketing, as they are more capable of communicating to a niche segment. Most popular influencers include sport starts such as Cristiano Ronaldo and Hollywood personalities such as Dwayne Johnson and Kylie Jenner each with over 200 million followers respectively. === Ecommerce === Online shopping or Ecommerce websites such as Amazon use information from a customer's user profile and interests to generate a list of recommended items to shop. Recommendation algorithms analyse user demographic data, history, and favourite artists to compile suggestions. The store rapidly adapts to changing user needs and preferences, with generation of real time results required within half of a second. New profiles naturally have limited information for algorithms to analyse, and customer data of each interaction provides valuable information which is stored as a database linked with each individual profile. User profiles on ecommerce websites also serve to improve sales of sellers as individuals are recommend products that other "customers who bought this item also bought" to widen the selection of the buyer. A study conducted found that user profiles and recommendation algorithms have significant impact on related product sales and overall spending of an individual. A process known as "collaborative filtering" tries to analyse common products of interest for an individual on the basis of views expressed by other similar behaving profiles. Features such as product ratings, seller ratings and comments allow individual user profiles to contribute to recommendation algorithms, eliminate adverse selection and contribute to shaping an online marketplace adhering to Amazons zero tolerance policy for misleading products. == Digital user profiles == Modern software and applications account for user profiles as a foundation on which a usable application is built. The structure and layout of an application such as its menus, features and controls are often derived from user's selected settings and preferences. The origin of digital user profiles in computer systems was first initiated by Windows NT that held user settings and information in a separate environment variable named %USERPROFILE% and held the framework to a user's profile root. Consequently, operating systems such as MacOS further accelerated prominence of user profiles in Mac OS X 10.0. Iterations since have been made with each operating system release with the aim to maximise user friendliness with the system. Features such as keyboard layouts, time zones, measurement units, synchronisation of different services and privacy preferences are made available during the setup of a user account on the computer === Types of accounts === ==== Administrator ==== Administrator user profiles have complete access to the system and its permissions. It is often the first user profile on a system by design, and is what allows other accounts to be created. However, since the administrator account has no restrictions, they are highly vulnerable to malware and viruses, with potential to impact all other accounts.
Cooperative storage cloud
A cooperative storage cloud is a decentralized model of networked online storage where data is stored on multiple computers (nodes), hosted by the participants cooperating in the cloud. For the cooperative scheme to be viable, the total storage contributed in aggregate must be at least equal to the amount of storage needed by end users. However, some nodes may contribute less storage and some may contribute more. There may be reward models to compensate the nodes contributing more. Unlike a traditional storage cloud, a cooperative does not directly employ dedicated servers for the actual storage of the data, thereby eliminating the need for a significant dedicated hardware investment. Each node in the cooperative runs specialized software which communicates with a centralized control and orchestration server, thereby allowing the node to both consume and contribute storage space to the cloud. The centralized control and orchestration server requires several orders of magnitude less resources (storage, computing power, and bandwidth) to operate, relative to the overall capacity of the cooperative. == Data security == Files hosted in the cloud are fragmented and encrypted before leaving the local machine. They are then distributed randomly using a load balancing and geo-distribution algorithm to other nodes in the cooperative. Users can add an additional layer of security and reduce storage space by compressing and encrypting files before they are copied to the cloud. == Data redundancy == In order to maintain data integrity and high availability across a relatively unreliable set of computers over a wide area network like the Internet, the source node will add some level of redundancy to each data block. This allows the system to recreate the entire block even if some nodes are temporarily unavailable (due to loss of network connectivity, the machine being powered off or a hardware failure). The most storage and bandwidth efficient forms of redundancy use erasure coding techniques like Reed–Solomon. A simple, less CPU intensive but more expensive form of redundancy is duplicate copies. == Flexible contribution == Due to bandwidth or hardware constraints some nodes may not be able to contribute as much space as they consume in the cloud. On the other hand, nodes with large storage space and limited or no bandwidth constraints may contribute more than they consume, thereby the cooperative can stay in balance.
Elastix (image registration)
Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for case-specific applications. It was first developed by Stefan Klein and Marius Staring under the supervision of Josien P.W. Pluim at Image Sciences Institute (ISI). Its first version was command-line based, allowing the final user to employ scripts to automatically process big data-sets and deploy multiple registration pipelines with few lines of code. Nowadays, to further widen its audience, a version called SimpleElastix is also available, developed by Kasper Marstal, which allows the integration of elastix with high level languages, such as Python, Java, and R. == Image registration fundamentals == Image registration is a well-known technique in digital image processing that searches for the geometric transformation that, applied to a moving image, obtains a one-to-one map with a target image. Generally, the images acquired from different sensors (multimodal), time instants (multitemporal), and points of view (multiview) should be correctly aligned to proceed with further processing and feature extraction. Even though there are a plethora of different approaches to image registration, the majority is composed of the same macro building blocks, namely the transformation, the interpolator, the metric, and the optimizer. Registering two or more images can be framed as an optimization problem that requires multiple iterations to converge to the best solution. Starting from an initial transformation computed from the image moments the optimization process searches for the best transformation parameters based on the value of the selected similarity metric. The figure on the right shows the high-level representation of the registration of two images, where the reference remains constant during the entire process, while the moving one will be transformed according to the transformation parameters. In other words, the registration ends when the similarity metric, which is a mathematical function with a certain number of parameters to be optimized, reaches the optimal value which is highly dependent on the specific application. == Main building blocks == Following the structure of the image registration workflow, the elastix toolbox proposes a modular solution that implements for each of the building blocks different algorithms, highly employed in medical image registration, and helps the final users to build their specific pipeline by selecting the most suitable algorithm for each of the main building blocks. Each block is easily configurable both by selecting pre-defined initialization values or by trying multiple sets of parameters and then choosing the most performing one. The registration is performed on images, and the elastix toolbox supports all the data formats supported by ITK, ranging from JPEG and PNG to medical standard formats such as DICOM and NIFTI. It also stores physical pixel spacing, the origin and the relative position to an external world reference system, when provided in the metadata, to facilitate the registration process, especially in medical field applications. === Transformation === The transformation is an essential building block, since it defines the allowable transformations. In image registration, the main distinction can be done between parallel-to-parallel and parallel-to-non parallel (deformable) line mapping transformations. In the elastix toolbox, the final users can select one transformation or compose more transformations either through addition or via composition. Below are reported the different transformation models in order of increasing flexibility, along with the corresponding elastix class names between brackets. Translation (TranslationTransform) allows only translations Rigid (EulerTransform) expands the translation adding rotations and the object is seen as a rigid body Similarity (SimilarityTransform) expands the rigid transformation by introducing isotropic scaling Affine (AffineTransform) expands the rigid transformation allowing both scaling and shear B-splines (BSplineTransform) is a deformable transformation usually preceded by a rigid or affine one Thin-plate splines (SplineKernelTransform) is a deformable transformation belonging to the class of kernel-based transformations that is a composition of and affine and a non-rigid part === Metric === The similarity metric is the mathematical function whose parameters should be optimized to reach the desired registration, and, during the process, it is computed multiple times. Below are reported the available metrics computed employing the reference and the transformed images and the corresponding elastix class names between brackets. Mean squared difference (AdvancedMeanSquares) to be used for mono-modal applications Normalized correlation coefficient (AdvancedNormalizedCorrelation) to be used for images that have an intensity linear relationship Mutual information (AdvancedMattesMutualInformation) to be used for both mono- and multi-modal applications and optimized to reach better performance compared to the normalized version Normalized mutual information (NormalizedMutualInformation) for both mono- and multi-modal applications Kappa statistic (AdvancedKappaStatistic) to be used only for binary images === Sampler === For the computation of the similarity metrics, it is not always necessary to consider all the voxels and, sometimes, it can be useful to use only a fraction of the voxels of the images, i.e. to reduce the execution time for big input images. Below are reported the available criteria for selecting a fraction of the voxels for the similarity metric computation and the corresponding elastix class names between brackets. Full (Full) to employ all the voxels Grid (Grid) to employ a regular grid defined by the user to downsample the image Random (Random) to randomly select a percentage of voxels defined by the users (all voxels have equal probability to be selected) Random coordinate (RandomCoordinate) like the random criterion, but in this case also off-grid positions can be selected to simplify the optimization process === Interpolator === After the application of the transformation, it may occur that the voxels used for the similarity metric computation are at non-voxel positions, so intensity interpolation should be performed to ensure the correctness of the computed values. Below are reported the implemented interpolators and the corresponding elastix class names between brackets. Nearest neighbor (NearestNeighborInterpolator) exploits little resources, but gives low quality results Linear (LinearInterpolator) is sufficient in general applications N-th order B-spline (BSplineInterpolator) can be used to increase the order N, increasing quality and computation time. N=0 and N=1 indicate the nearest neighbor and linear cases respectively. === Optimizer === The optimizer defines the strategy employed for searching the best transformation parameter to reach the correct registration, and it is commonly an iterative strategy. Below are reported some of the implemented optimization strategies. Gradient descent Robbins-Monro, similar to the gradient descent, but employing an approximation of the cost function derivatives A wider range of optimizers is also available, such as Quasi-Newton or evolutionary strategies. === Other features === The elastix software also offers other features that can be employed to speed up the registration procedure and to provide more advanced algorithms to the end-users. Some examples are the introduction of blur and Gaussian pyramid to reduce data complexity, and multi-image and multi-metric framework to deal with more complex applications. == Applications == Elastix has applications mainly in the medical field, where image registration is fundamental to get comprehensive information regarding the analysed anatomical region. It is widely employed in image-guided surgery, tumour monitoring, and treatment assessment. For example, in radiotherapy planning, image registration allows to correctly deliver the treatment and evaluate the obtained results. Thanks to the wide range of implemented algorithms, the use of the elastix software allows physicians and researchers to test different registration pipelines from the simplest to more complex ones, and to save the best one as a configuration file. This file and the fact that the software is completely open-source makes it easy to reproduce the work, that can help supporting the open science paradigm, and allows fast reuse on different patients data. In image-guided surgery, registration time and accuracy are critical points, considering that, during the registration, the patient is on the operating table, and the imag
Indic computing
Indic Computing means "computing in Indic", i.e., Indian Scripts and Languages. It involves developing software in Indic Scripts/languages, Input methods, Localization of computer applications, web development, Database Management, Spell checkers, Speech to Text and Text to Speech applications and OCR in Indian languages. Unicode standard version 15.0 specifies codes for 9 Indic scripts in Chapter 12 titled "South and Central Asia-I, Official Scripts of India". The 9 scripts are Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil and Telugu. A lot of Indic Computing projects are going on. They involve some government sector companies, some volunteer groups and individual people. == Government sector == Indian Union Government made it mandatory for Mobile phone companies whose handsets manufactured, stored, sold and distributed in India to have support for displaying and typing text using fonts for all 22 languages. This move has seen rise in use of Indian languages by millions of users. === TDIL === The Department of Electronics and Information Technology, India initiated the TDIL (Technology Development for Indian Languages) with the objective of developing Information Processing Tools and Techniques to facilitate human-machine interaction without a language barrier; creating and accessing multilingual knowledge resources; and integrating them to develop innovative user products and services. In 2005, it started distributing language software tools developed by Government/Academic/Private companies in the form of CD for non commercial use. Some of the outcomes of TDIL program have been deployed on Indian Language Technology Proliferation & Deployment Centre. This Centre disseminates all the linguistic resources, tools & applications which have been developed under TDIL funding. This programme took to exponential expansion under the leadership of Dr. Swaran Lata who also created international foot-print of the programme. She has now retired. === C-DAC === C-DAC is an India based government software company which is involved in developing language related software. It is best known for developing InScript Keyboard, the standard keyboard for Indian languages. It has also developed lot of Indic language solutions including Word Processors, typing tools, text to speech software, OCR in Indian languages etc. ==== BharateeyaOO.org ==== The work developed out of CDAC, Bangalore (earlier known as NCST, Bangalore) became BharateeyaOO. OpenOffice 2.1 had support for over 10 Indian languages. ==== BOSS ==== BOSS linux was developed by the Centre for Development of Advanced Computing (CDAC) to promote use of open-source software in India. == NGO and Volunteer groups == === Indlinux === Indlinux organisation helped organise the individual volunteers working on different indic language versions of Linux and its applications. === Sarovar === Sarovar.org is India's first portal to host projects under Free/Open source licenses. It is located in Trivandrum, India and hosted at Asianet data center. Sarovar.org is customised, installed and maintained by Linuxense as part of their community services and sponsored by River Valley Technologies. Sarovar.org is built on Debian Etch and GForge and runs off METTLE. === Pinaak === Pinaak is a non-government charitable society devoted to Indic language computing. It works for software localization, developing language software, localizing open source software, enriching online encyclopedias etc. In addition to this Pinaak works for educating people about computing, ethical use of Internet and use of Indian languages on Internet. === Ankur Group === Ankur Group is working toward supporting Bengali language (Bengali) on Linux operating system including localized Bengali GUI, Live CD, English-to-Bengali translator, Bengali OCR and Bengali Dictionary etc. === BhashaIndia === === SMC === SMC is a free software group, working to bridge the language divide in Kerala in the technology front and is today the biggest language computing community in India. == Input methods == === Full size keyboards === With the advent of Unicode inputting Indic text on computer has become very easy. A number of methods exist for this purpose, but the main ones are:- ==== InScript ==== Inscript is the standard keyboard for Indian languages. Developed by C-DAC and standardized by Government of India. Nowadays it comes inbuilt in all major operating systems including Microsoft Windows (2000, XP, Vista, 7), Linux and Macintosh. ==== Phonetic transliteration ==== This is a typing method in which, for instance, the user types text in an Indian language using Roman characters and it is phonetically converted to equivalent text in Indian script in real time. This type of conversion is done by phonetic text editors, word processors and software plugins. Building up on the idea, one can use phonetic IME tools that allow Indic text to be input in any application. Some examples of phonetic transliterators are Xlit, Google Indic Transliteration, BarahaIME, Indic IME, Rupantar, SMC's Indic Keyboard and Microsoft Indic Language Input Tool. SMC's Indic Keyboard has support for as many as 23 languages whereas Google Indic Keyboard only supports 11 Indian languages. They can be broadly classified as: Fixed transliteration scheme based tools – They work using a fixed transliteration scheme to convert text. Some examples are Indic IME, Rupantar and BarahaIME. Intelligent/Learning based transliteration tools – They compare the word with a dictionary and then convert it to the equivalent words in the target language. Some of the popular ones are Google Indic Transliteration, Xlit, Microsoft Indic Language Input Tool and QuillPad. ==== Remington (typewriter) ==== This layout was developed when computers had not been invented or deployed with Indic languages, and typewriters were the only means to type text in Indic scripts. Since typewriters were mechanical and could not include a script processor engine, each character had to be placed on the keyboard separately, which resulted in a very complex and difficult to learn keyboard layout. With the advent of Unicode, the Remington layout was added to various typing tools for sake of backward compatibility, so that old typists did not have to learn a new keyboard layout. Nowadays this layout is only used by old typists who are used to this layout due to several years of usage. One tool to include Remington layout is Indic IME. A font that is based on the Remington keyboard layout is Kruti Dev. Another online tool that very closely supports the old Remington keyboard layout using Kruti Dev is the Remington Typing tool. === Braille === IBus Sharada Braille, which supports seven Indian languages was developed by SMC. === Mobile phones with Numeric keyboards === Mobile/Hand/cell phone basic models have 12 keys like the plain old telephone keypad. Each key is mapped to 3 or 4 English letters to facilitate data entry in English. For inputting Indian languages with this kind of keypad, there are two ways to do so. First is the Multi-tap Method and second uses visual help from the screen like Panini Keypad. The primary usage is SMS. 140 characters size used for English/Roman languages can be used to accommodate only about 70 language characters when Unicode Proprietary compression is used some times to increase the size of single message for Complex script languages like Hindi. A research study of the available methods and recommendations of proposed standard was released by Broadband Wireless Consortium of India (BWCI). ==== Transliteration/Phonetic methods ==== English is used to type in Indian languages. QuillPad IndiSMS ==== Native methods ==== In native methods, the letters of the language are displayed on the screen corresponding to the numeral keys based on the probabilities of those letters for that language. Additional letters can be accessed by using a special key. When a word is partially typed, options are presented from which the user can make a selection. === Smart phones with Qwerty keyboards === Most smart phones have about 35 keys catering primarily to the English language. Numerals and some symbols are accessed with a special key called Alt. Indic input methods are yet to evolve for these types of phones, as support of Unicode for rendering is not widely available. === For Smart Phones with Soft/Virtual keyboards === Inscript is being adopted for smart phone usage. For Android phones which can render Indic languages, Swalekh Multilingual Keypad Multiling Keyboard app are available. Gboard offers support for several Indian languages. == Localization == Localization means translating software, operating systems, websites etc. various applications in Indian language. Various volunteers groups are working in this direction. === Mandrake Tamil Version === A notable example is the Tamil version of Mandrake linux(defunct since 2011). Tamil speakers in Toronto (Canada) released Mandrake,
Automatic taxonomy construction
Automatic taxonomy construction (ATC) is the use of software programs to generate taxonomical classifications from a body of texts called a corpus. ATC is a branch of natural language processing, which in turn is a branch of artificial intelligence. A taxonomy (or taxonomical classification) is a scheme of classification, especially, a hierarchical classification, in which things are organized into groups or types. Among other things, a taxonomy can be used to organize and index knowledge (stored as documents, articles, videos, etc.), such as in the form of a library classification system, or a search engine taxonomy, so that users can more easily find the information they are searching for. Many taxonomies are hierarchies (and thus, have an intrinsic tree structure), but not all are. Manually developing and maintaining a taxonomy is a labor-intensive task requiring significant time and resources, including familiarity of or expertise in the taxonomy's domain (scope, subject, or field), which drives the costs and limits the scope of such projects. Also, domain modelers have their own points of view which inevitably, even if unintentionally, work their way into the taxonomy. ATC uses artificial intelligence techniques to quickly automatically generate a taxonomy for a domain in order to avoid these problems and remove limitations. == Approaches == There are several approaches to ATC. One approach is to use rules to detect patterns in the corpus and use those patterns to infer relations such as hyponymy. Other approaches use machine learning techniques such as Bayesian inferencing and Artificial Neural Networks. === Keyword extraction === One approach to building a taxonomy is to automatically gather the keywords from a domain using keyword extraction, then analyze the relationships between them (see Hyponymy, below), and then arrange them as a taxonomy based on those relationships. === Hyponymy and "is-a" relations === In ATC programs, one of the most important tasks is the discovery of hypernym and hyponym relations among words. One way to do that from a body of text is to search for certain phrases like "is a" and "such as". In linguistics, is-a relations are called hyponymy. Words that describe categories are called hypernyms and words that are examples of categories are hyponyms. For example, dog is a hypernym and Fido is one of its hyponyms. A word can be both a hyponym and a hypernym. So, dog is a hyponym of mammal and also a hypernym of Fido. Taxonomies are often represented as is-a hierarchies where each level is more specific than (in mathematical language "a subset of") the level above it. For example, a basic biology taxonomy would have concepts such as mammal, which is a subset of animal, and dogs and cats, which are subsets of mammal. This kind of taxonomy is called an is-a model because the specific objects are considered instances of a concept. For example, Fido is-a instance of the concept dog and Fluffy is-a cat. == Applications == ATC can be used to build taxonomies for search engines, to improve search results. ATC systems are a key component of ontology learning (also known as automatic ontology construction), and have been used to automatically generate large ontologies for domains such as insurance and finance. They have also been used to enhance existing large networks such as Wordnet to make them more complete and consistent. == ATC software == == Other names == Other names for automatic taxonomy construction include: Automated outline building Automated outline construction Automated outline creation Automated outline extraction Automated outline generation Automated outline induction Automated outline learning Automated outlining Automated taxonomy building Automated taxonomy construction Automated taxonomy creation Automated taxonomy extraction Automated taxonomy generation Automated taxonomy induction Automated taxonomy learning Automatic outline building Automatic outline construction Automatic outline creation Automatic outline extraction Automatic outline generation Automatic outline induction Automatic outline learning Automatic taxonomy building Automatic taxonomy creation Automatic taxonomy extraction Automatic taxonomy generation Automatic taxonomy induction Automatic taxonomy learning Outline automation Outline building Outline construction Outline creation Outline extraction Outline generation Outline induction Outline learning Semantic taxonomy building Semantic taxonomy construction Semantic taxonomy creation Semantic taxonomy extraction Semantic taxonomy generation Semantic taxonomy induction Semantic taxonomy learning Taxonomy automation Taxonomy building Taxonomy construction Taxonomy creation Taxonomy extraction Taxonomy generation Taxonomy induction Taxonomy learning
Zardoz (computer security)
In computer security, the Security-Digest list, better known as the Zardoz list, was a semi-private full disclosure mailing list run by Neil Gorsuch from 1989 through 1991. It identified weaknesses in systems and gave directions on where to find them. It was a perennial target for computer hackers, who sought archives of the list for information on undisclosed software vulnerabilities. == Membership restrictions == Access to Zardoz was approved on a case-by-case basis by Gorsuch, principally by reference to the user account used to send subscription requests; requests were approved for root users, valid UUCP owners, or system administrators listed at the NIC. The openness of the list to users other than Unix system administrators was a regular topic of conversation, with participants expressing concern that vulnerabilities and exploitation details disclosed on the list were liable to spread to hackers. The circulation of Zardoz postings was an open secret among computer hackers, and mocked in a Phrack parody of an IRC channel populated by security experts. == Notable participants == Keith Bostic discussed BSD Sendmail vulnerabilities Chip Salzenberg discussed Peter Honeyman's posting of a UUCP worm, and shell script security Gene Spafford discussed VMS and Ultrix bugs, and relayed law enforcement enquiries about the Morris Worm Tom Christiansen discussed SUID shell scripts Chris Torek discussed devising exploits from general descriptions of vulnerabilities Henry Spencer discussed Unix security Brendan Kehoe discussed systems security Alec Muffett announced Crack, the Unix password cracker The majority of Zardoz participants were Unix systems administrators and C software developers. Neil Gorsuch and Gene Spafford were the most prolific contributors to the list.
Calais (Reuters product)
Calais is a service created by Thomson Reuters that automatically extracts semantic information from web pages in a format that can be used on the semantic web. Calais was launched in January 2008, and is free to use. The technology is now available via the website of Refinitiv, a provider of financial market data and infrastructure founded in 2018, that is a subsidiary of London Stock Exchange Group. The Calais Web service reads unstructured text and returns Resource Description Framework formatted results identifying entities, facts and events within the text. The service appears to be based on technology acquired when Reuters purchased ClearForest in 2007. The technology has also been used to automatically tag blog articles, and organize museum collections. Calais uses natural language processing technologies delivered via a web service interface.