AI Content Improver Free Online

AI Content Improver Free Online — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Online service provider

  An online service provider (OSP) can, for example, be an Internet service provider, an email provider, a news provider (press), an entertainment provider (music, movies), a search engine, an e-commerce site, an online banking site, a health site, an official government site, social media, a wiki, or a Usenet newsgroup. In its original more limited definition, it referred only to a commercial computer communication service in which paid members could dial via a computer modem the service's private computer network and access various services and information resources such as bulletin board systems, downloadable files and programs, news articles, chat rooms, and electronic mail services. The term "online service" was also used in references to these dial-up services. The traditional dial-up online service differed from the modern Internet service provider in that they provided a large degree of content that was only accessible by those who subscribed to the online service, while ISP mostly serves to provide access to the Internet and generally provides little if any exclusive content of its own. In the U.S., the Online Copyright Infringement Liability Limitation Act (OCILLA) portion of the U.S. Digital Millennium Copyright Act has expanded the legal definition of online service in two different ways for different portions of the law. It states in section 512(k)(1): (A) As used in subsection (a), the term "service provider" means an entity offering the transmission, routing, or providing of connections for digital online communications, between or among points specified by a user, of material of the user's choosing, without modification to the content of the material as sent or received. (B) As used in this section, other than subsection (a), the term "service provider" means a provider of online services or network access, or the operator of facilities therefore, and includes an entity described in subparagraph (A). These broad definitions make it possible for numerous web businesses to benefit from the OCILLA. == History == The first commercial online services went live in 1969. CompuServe (owned in the 1980s and 1990s by H&R Block) and The Source (for a time owned by The Reader's Digest) are considered the first major online services created to serve the market of personal computer users. Utilizing text-based interfaces and menus, these services allowed anyone with a modem and communications software to use email, chat, news, financial and stock information, bulletin boards, special interest groups (SIGs), forums and general information. Subscribers could exchange email only with other subscribers of the same service. (For a time a service called DASnet carried mail among several online services, and CompuServe, MCI Mail, and other services experimented with X.400 protocols to exchange email until the Internet rendered these outmoded.) Other text-based online services followed such as Delphi, GEnie and MCI Mail. The 1980s also saw the rise of independent Computer Bulletin Boards, or BBSes. (Online services are not BBSes. An online service may contain an electronic bulletin board, but the term "BBS" is reserved for independent dialup, microcomputer-based services that are usually single-user systems.) The commercial services used pre-existing packet-switched (X.25) data communications networks, or the services' own networks (as with CompuServe). In either case, users dialed into local access points and were connected to remote computer centers where information and services were located. As with telephone service, subscribers paid by the minute, with separate day-time and evening/weekend rates. As the use of computers that supported color and graphics, such the Atari 8-bit computers, Commodore 64, TI-99/4A, Apple II, and early IBM PC compatibles, increased, online services gradually developed framed or partially graphical information displays. Early services such as CompuServe added increasingly sophisticated graphics-based front end software to present their information, though they continued to offer text-based access for those who needed or preferred it. In 1985 Viewtron, which began as a Videotex service requiring a dedicated terminal, introduced software allowing home computer owners access. Beginning in the mid-1980s graphics based online services such as PlayNET, Prodigy, and Quantum Link (aka Q-Link) were developed. Quantum Link, which was based on Commodore-only Playnet software, later developed AppleLink Personal Edition, PC-Link (based on Tandy's DeskMate), and Promenade (for IBM), all of which (including Q-Link) were later combined as America Online. These online services presaged the web browser that would change global online life 10 years later. Before Quantum Link, Apple computer had developed its own service, called AppleLink, which was mostly a support network targeted at Apple dealers and developers. Later, Apple offered the short-lived eWorld, targeted at Mac consumers and based on the Mac version of the America Online software. Beginning in 1992, the Internet, which had previously been limited to government, academic, and corporate research settings, was opened to commercial entities. The first online service to offer Internet access was DELPHI, which had developed TCP/IP access much earlier, in connection with an environmental group that rated Internet access. The explosion of popularity of the World Wide Web in 1994 accelerated the development of the Internet as an information and communication resource for consumers and businesses. The sudden availability of low- to no-cost email and appearance of free independent web sites broke the business model that had supported the rise of the early online service industry. CompuServe, BIX, AOL, DELPHI, and Prodigy gradually added access to Internet e-mail, Usenet newsgroups, ftp, and to web sites. At the same time, they moved from usage-based billing to monthly subscriptions. Similarly, companies that paid to have AOL host their information or early online stores began to develop their own web sites, putting further stress on the economics of the online industry. Only the largest services like AOL (which later acquired CompuServe, just as CompuServe acquired The Source) were able to make the transition to the Internet-centric world. A new class of online service provider arose to provide access to the Internet, the internet service provider or ISP. Internet-only service providers like UUNET, The Pipeline, Panix, Netcom, the World, EarthLink, and MindSpring provided no content of their own, concentrating their efforts on making it easy for nontechnical users to install the various software required to "get online" before consumer operating systems came internet-enabled out of the box. In contrast to the online services' multitiered per-minute or per-hour rates, many ISPs offered flat-fee, unlimited access plans. Independent companies sprang up to offer access and packages to compete with the big networks (eg, the-wire.com, 1994 in Toronto and bway.net 1995 in New York). These providers first offered access through telephone and modem, just as did the early online services providers. By the early 2000s, these independent ISPs had largely been supplanted by high speed and broadband access through cable and phone companies, as well as wireless access. The importance of the online services industry was vital in "paving the road" for the information superhighway. When Mosaic and Netscape were released in 1994, they had a ready audience of more than 10 million people who were able to download their first web browser through an online service. Though ISPs quickly began offering software packages with setup to their customers, this brief period gave many users their first online experience. Two online services in particular, Prodigy and AOL, are often confused with the Internet, or the origins of the Internet. Prodigy's Chief Technical Officer said in 1999: "Eleven years ago, the Internet was just an intangible dream that Prodigy brought to life. Now it is a force to be reckoned with." Despite that statement, neither service provided the back bone for the Internet, nor did either start the Internet. == Online service interfaces == The first online service used a simple text-based interface in which content was largely text only and users made choices via a command prompt. This allowed just about any computer with a modem and terminal communications program the ability to access these text-based online services. CompuServe would later offer, with the advent of the Apple Macintosh and Microsoft Windows-based PCs, a GUI interface program for their service. This provided a very rudimentary GUI interface. CompuServe continued to offer text-only access for those needing it. Online services like Prodigy and AOL developed their online service around a GUI and thus unlike CompuServe's early GUI-based software, these online services provided a more robust GUI interface. Early GUI-base

  Read more →
• 

  AIVA

  AIVA (Artificial Intelligence Virtual Artist) is an electronic composer recognized by the SACEM. == Description == Created in February 2016, AIVA specializes in classical and symphonic music composition. It became the world's first virtual composer to be recognized by a music society (SACEM). By reading a large collection of existing works of classical music (written by human composers such as Bach, Beethoven, Mozart) AIVA is capable of detecting regularities in music and on this base composing on its own. The algorithm AIVA is based on deep learning and reinforcement learning architectures. Since January 2019, the company offers a commercial product, Music Engine, capable of generating short (up to 3 minutes) compositions in various styles (rock, pop, jazz, fantasy, shanty, tango, 20th century cinematic, modern cinematic, and Chinese). AIVA was presented at TED by Pierre Barreau. == Discography == AIVA is a published composer; its first studio album "Genesis" was released in November 2016. Second album "Among the Stars" in 2018. 2016 CD album « Genesis » Hv-Com – LEPM 048427. Track listing "Genesis": 2018 CD album « Among the Stars » Hv-Com – LEPM 048708 Avignon Symphonic Orchestra [ORAP] also performed Aiva's compositions [2] in April 2017.

  Read more →
• 

  Terminology extraction

  Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc. The development of terminology extraction is also essential to the language industry. One of the first steps to model a knowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domain concepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature. Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging, phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminological noun phrases. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent. Terminological entries are then filtered from the candidate list using statistical and machine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of a domain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point for semantic similarity, knowledge management, human translation and machine translation, etc. == Bilingual terminology extraction == The methods for terminology extraction can be applied to parallel corpora. Combined with e.g. co-occurrence statistics, candidates for term translations can be obtained. Bilingual terminology can be extracted also from comparable corpora (corpora containing texts within the same text type, domain but not translations of documents between each other).

  Read more →
• 

  Archetype (information science)

  In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==

  Read more →
• 

  Semantic decomposition (natural language processing)

  A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts. The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to artificial intelligence or machine learning. Semantic decomposition is common in natural language processing applications. The basic idea of a semantic decomposition is taken from the learning skills of adult humans, where words are explained using other words. It is based on Meaning-text theory. Meaning-text theory is used as a theoretical linguistic framework to describe the meaning of concepts with other concepts. == Background == Given that an AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An artificial notion of meaning needs to be created for a strong AI to emerge. Creating an artificial representation of meaning requires the analysis of what meaning is. Many terms are associated with meaning, including semantics, pragmatics, knowledge and understanding or word sense. Each term describes a particular aspect of meaning, and contributes to a multitude of theories explaining what meaning is. These theories need to be analyzed further to develop an artificial notion of meaning best fit for our current state of knowledge. == Graph representations == Representing meaning as a graph is one of the two ways that both an AI cognition and a linguistic researcher think about meaning (connectionist view). Logicians utilize a formal representation of meaning to build upon the idea of symbolic representation, whereas description logics describe languages and the meaning of symbols. This contention between 'neat' and 'scruffy' techniques has been discussed since the 1970s. Research has so far identified semantic measures and with that word-sense disambiguation (WSD) - the differentiation of meaning of words - as the main problem of language understanding. As an AI-complete environment, WSD is a core problem of natural language understanding. AI approaches that use knowledge-given reasoning creates a notion of meaning combining the state of the art knowledge of natural meaning with the symbolic and connectionist formalization of meaning for AI. The abstract approach is shown in Figure. First, a connectionist knowledge representation is created as a semantic network consisting of concepts and their relations to serve as the basis for the representation of meaning. This graph is built out of different knowledge sources like WordNet, Wiktionary, and BabelNET. The graph is created by lexical decomposition that recursively breaks each concept semantically down into a set of semantic primes. The primes are taken from the theory of Natural Semantic Metalanguage, which has been analyzed for usefulness in formal languages. Upon this graph marker passing is used to create the dynamic part of meaning representing thoughts. The marker passing algorithm, where symbolic information is passed along relations form one concept to another, uses node and edge interpretation to guide its markers. The node and edge interpretation model is the symbolic influence of certain concepts. Future work uses the created representation of meaning to build heuristics and evaluate them through capability matching and agent planning, chatbots or other applications of natural language understanding.

  Read more →
• 

  Artificial intelligence in Brazilian industry

  In 2022, 16.9% (1,620) of the 9,586 Brazilian industrial companies with 100 or more employees used artificial intelligence in their operations Among the companies that used AI, the areas of administration (73.8%), product project development (65.9%), processes, services and marketing (65.1%) were those that used it the most, followed by the areas of production (56.4%) and logistics (48.4%). == Current scenario == === Adoption in Brazilian industrial sectors === In senior management, the majority (56%) of executives have a long-term vision for its use. The study also shows that IT, Innovation, and Marketing are the areas where AI use is most widespread, and that 43% of companies are developing or adapting the algorithms they use. The majority of large institutions that reported some type of AI use purchased these solutions from other companies (76%). Some factors for the adoption of artificial intelligence in companies include the establishment of an autonomous strategy by the company (87.0%), and the influence of suppliers and/or customers (63.0%) and the main difficulties in using technologies were high costs (80.8%), lack of qualified personnel in the company (54.6%) and excessive economic risks (49.5%). Three variables are considered the most relevant to explain the option to use AI: the implementation of a digital security policy, the size of companies with 250 or more employees and the characteristics of the company related to information and communication. When analyzing AI use by company size in Brazil, large companies have the highest proportion of AI use, mainly due to their investment capacity and technology experimentation. However, when comparing Brazil and Europe, indicators show an acceleration in AI use among large European companies, while in Brazil the situation remains stable. In 2023, 30% of large companies in the European bloc used some type of AI, a figure that rose to 41% in 2024, while in Brazil these proportions were 41% in 2023 and 38% in 2024. === Workforce === The challenge of upskilling begins with employees who are capable of understanding recent technological changes. Similarly, companies must create the environment and conditions for workforce development conducive to innovation, and universities must be prepared to provide knowledge aligned with the transition process, which in turn must be supported by public policies. The concern with training a specialized workforce in AI can be seen in the low number of graduates and PhDs in computer science and computer engineering in Brazil, compared to the number shown in other countries. As recorded in the document Recommendations for the Advancement of Artificial Intelligence in Brazil, 2019 data from the Coordination for the Improvement of Higher Education Personnel (CAPES) indicate that "the number of PhDs graduated annually in computing remained below 400 in 2016, and is not expected to have increased during the Covid-19 pandemic" (ABC, 2023). In the United States, by contrast, the number of PhDs graduated in these two areas has remained around 1,800 for the past 11 years, and during this period, the number of PhDs specializing in AI jumped from 10% to 19%. Based on data from the CNPq Lattes Platform (October 2019), it is possible to observe that the number of professionals in the AI field in Brazil is 4,429 specialists. This is still a small number compared to the 415,166 IT jobs in the country's business sector alone. === R&D, scientific production and integration with industry === China and the United States lead in the number of publications. These two countries are followed by the G7 members: India, Austria, South Korea, and Spain. Brazil appears in the next group, alongside the Netherlands, Russia, Indonesia, and Ireland. Regarding the promotion of research and technologies related to AI, public entities such as the Coordination for the Improvement of Higher Education Personnel (Capes) and the National Council for Scientific and Technological Development (CNPq) stood out as the main funders. Currently, different countries and territories have been promoting the development of Artificial Intelligence (AI). In the Brazilian case, one of the main initiatives is the creation of Engineering Research Centers/Applied Research Centers (CPE/CPA) in AI by the São Paulo Research Foundation (FAPESP), in collaboration with the Ministry of Science, Technology and Innovation (MCTI), the Ministry of Communications (MC) and the Brazilian Internet Steering Committee (CGI.br). In terms of the number of patents filed and the volume of investments, the leading nations in AI are the United States, China, France, Germany, the United Kingdom, Russia, India, Switzerland, Japan, South Korea, the Netherlands, Sweden, Finland, Ireland, Singapore, Canada, Israel, and Italy. Brazil appears among the top twenty countries in some rankings, mainly due to its good number of publications (approximately 10% of the number of articles published by the United States). The US is home to approximately 60% of the world's top AI researchers, followed by China (11%), Europe (10%), and Canada (6%). To change this scenario, in August 2024, the Brazilian government announced an investment of R$23 billion until 2028 in artificial intelligence, seeking to “transform the country into a global reference in innovation”. == Future challenges == The Organization for Economic Cooperation and Development (2020) report highlighted three factors that hinder the digital transformation journey and application of AI in Brazil: insufficient infrastructure, high costs due to the tax system, and financial limitations, such as limited access to financing. The costs of adopting technology, its incompatibility with the business, and the lack of training also represent obstacles that Brazilian industry must overcome. There are also inherent obstacles for companies. A McKinsey review emphasizes that once a company chooses one or more sectors to focus on, it must select specific applications. Buyers aren't interested in artificial intelligence simply because it's a breakthrough technology; they want AI to generate a good return on investment, whether by solving specific problems, saving money, or increasing sales. If an AI vendor tried to offer a horizontal solution, the value proposition might not be as compelling. Part of the solution to Brazil's technological backwardness involves building an ecosystem fueled by private institutions, universities, and governments.

  Read more →
• 

  Emotion Markup Language

  An Emotion Markup Language (EML or EmotionML) has first been defined by the W3C Emotion Incubator Group (EmoXG) as a general-purpose emotion annotation and representation language, which should be usable in a large variety of technological contexts where emotions need to be represented. Emotion-oriented computing (or "affective computing") is gaining importance as interactive technological systems become more sophisticated. Representing the emotional states of a user or the emotional states to be simulated by a user interface requires a suitable representation format; in this case a markup language is used. EmotionML version 1.0 was published by the group in May 2014. == Example == Here is an example of an EmotionML document describing emotions expressed in a video recording of the interaction between a teacher, Alice, and a student, Bob. == History == In 2006, a first W3C Incubator Group, the Emotion Incubator Group (EmoXG), was set up "to investigate a language to represent the emotional states of users and the emotional states simulated by user interfaces" with the final Report published on 10 July 2007. In 2007, the Emotion Markup Language Incubator Group (EmotionML XG) was set up as a follow-up to the Emotion Incubator Group, "to propose a specification draft for an Emotion Markup Language, to document it in a way accessible to non-experts, and to illustrate its use in conjunction with a number of existing markups." The final report of the Emotion Markup Language Incubator Group, Elements of an EmotionML 1.0, was published on 20 November 2008. The work then was continued in 2009 in the frame of the W3C's Multimodal Interaction Activity, with the First Public Working Draft of "Emotion Markup Language (EmotionML) 1.0" being published on 29 October 2009. The Last Call Working Draft of "Emotion Markup Language 1.0", was published on 7 April 2011. The Last Call Working Draft addressed all open issues that arose from feedback of the community on the First Call Working Draft as well as results of a workshop held in Paris in October 2010. Along with the Last Call Working Draft, a list of vocabularies for EmotionML has been published to aid developers using common vocabularies for annotating or representing emotions. Annual draft updates were published until the 1.0 version was finished in 2014. == Reasons for defining an emotion markup language == A standard for an emotion markup language would be useful for the following purposes: To enhance computer-mediated human-human or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states. To enhance systems' processing efficiency. Emotion and intelligence are strongly interconnected. The modeling of human emotions in computer processing can help to build more efficient systems, e.g. using emotional models for time-critical decision enforcement. To allow the analysis of non-verbal behavior, emotion, mental states that can be provided using web services to enable data collection, analysis, and reporting. Concrete examples of existing technology that could apply EmotionML include: Opinion mining / sentiment analysis in Web 2.0, to automatically track customer's attitude regarding a product across blogs; Affective monitoring, such as ambient assisted living applications, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction; Wellness technologies that provide assistance according to a person's emotional state with the goal to improve the person's well-being; Character design and control for games and virtual worlds; Building web services to capture, analysis, and report data of non-verbal behavior, emotion and mental states of an individual or group across the internet using standard web technologies such as HTML5 and JSON. Social robots, such as guide robots engaging with visitors; Expressive speech synthesis, generating synthetic speech with different emotions, such as happy or sad, friendly or apologetic; expressive synthetic speech would for example make more information available to blind and partially sighted people, and enrich their experience of the content; Emotion recognition (e.g., for spotting angry customers in speech dialog systems, to improve computer games or e-Learning applications); Support for people with disabilities, such as educational programs for people with autism. EmotionML can be used to make the emotional intent of content explicit. This would enable people with learning disabilities (such as Asperger syndrome) to realise the emotional context of the content; EmotionML can be used for media transcripts and captions. Where emotions are marked up to help deaf or hearing impaired people who cannot hear the soundtrack, more information is made available to enrich their experience of the content. The Emotion Incubator Group has listed 39 individual use cases for an Emotion markup language. A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases, and the standard can be used to ease a market of providers for sub-modules of emotion processing systems, e.g. a web service for the recognition of emotion from text, speech or multi-modal input. == The challenge of defining a generally usable emotion markup language == Any attempt to standardize the description of emotions using a finite set of fixed descriptors is doomed to failure, as there is no consensus on the number of relevant emotions, on the names that should be given to them or how else best to describe them. For example, the difference between ":)" and "(:" is small, but using a standardized markup it would make one invalid. Even more basically, the list of emotion-related states that should be distinguished varies depending on the application domain and the aspect of emotions to be focused. Basically, the vocabulary needed depends on the context of use. On the other hand, the basic structure of concepts is less controversial: it is generally agreed that emotions involve triggers, appraisals, feelings, expressive behavior including physiological changes, and action tendencies; emotions in their entirety can be described in terms of categories or a small number of dimensions; emotions have an intensity, and so on. For details, see the Scientific Descriptions of Emotions in the Final Report of the Emotion Incubator Group. Given this lack of agreement on descriptors in the field, the only practical way of defining an emotion markup language is the definition of possible structural elements and to allow users to "plug in" vocabularies that they consider appropriate for their work. An additional challenge lies in the aim to provide a markup language that is generally usable. The requirements that arise from different use cases are rather different. Whereas manual annotation tends to require all the fine-grained distinctions considered in the scientific literature, automatic recognition systems can usually distinguish only a very small number of different states and affective avatars need yet another level of detail for expressing emotions in an appropriate way. For the reasons outlined here, it is clear that there is an inevitable tension between flexibility and interoperability, which need to be weighed in the formulation of an EmotionML. The guiding principle in the following specification has been to provide a choice only where it is needed, and to propose reasonable default options for every choice. == Applications and web services benefiting from an emotion markup language == There are a range of existing projects and applications to which an emotion markup language will enable the building of webservices to measure capture data of individuals non-verbal behavior, mental states, and emotions and allowing results to be reported and rendered in a standardized format using standard web technologies such as JSON and HTML5. One such project is measuring affect data across the Internet using EyesWeb.

  Read more →
• 

  Tuple

  In mathematics, a tuple is a finite sequence (or ordered list) of numbers. More generally, it is a sequence of mathematical objects, called the elements of the tuple. An n-tuple is a tuple of n elements, where n is a non-negative integer. There is only one 0-tuple, called the empty tuple. A 1-tuple and a 2-tuple are commonly called a singleton and an ordered pair, respectively. The term "infinite tuple" is occasionally used for "infinite sequences". Tuples are usually written by listing the elements within parentheses "( )" and separated by commas; for example, (2, 7, 4, 1, 7) denotes a 5-tuple. Other types of brackets are sometimes used, although they may have a different meaning. An n-tuple can be formally defined as the image of a function that has the set of the first n natural numbers as its domain (1, 2, ..., n). Tuples may be also defined from ordered pairs by a recurrence starting from an ordered pair; indeed, an n-tuple can be identified with the ordered pair of its (n − 1) first elements and its nth element, for example, ( ( ( 1 , 2 ) , 3 ) , 4 ) = ( 1 , 2 , 3 , 4 ) {\displaystyle \left(\left(\left(1,2\right),3\right),4\right)=\left(1,2,3,4\right)} . In computer science, tuples come in many forms. Most typed functional programming languages implement tuples directly as product types, tightly associated with algebraic data types, pattern matching, and destructuring assignment. Many programming languages offer an alternative to tuples, known as record types, featuring unordered elements accessed by label. A few programming languages combine ordered tuple product types and unordered record types into a single construct, as in C structs and Haskell records. Relational databases may formally identify their rows (records) as tuples. Tuples also occur in relational algebra; when programming the semantic web with the Resource Description Framework (RDF); in linguistics; and in philosophy. == Etymology == The term originated as an abstraction of the sequence: single, couple/double, triple, quadruple, quintuple, sextuple, septuple, octuple, ..., n‑tuple, ..., where the prefixes are taken from the Latin names of the numerals. The unique 0-tuple is called the null tuple or empty tuple. A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer. For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as an 8‑tuple, and a sedenion can be represented as a 16‑tuple. Although these uses treat ‑tuple as the suffix, the original suffix was ‑ple as in "triple" (three-fold) or "decuple" (ten‑fold). This originates from medieval Latin plus (meaning "more") related to Greek ‑πλοῦς, which replaced the classical and late antique ‑plex (meaning "folded"), as in "duplex". == Properties == The general rule for the identity of two n-tuples is ( a 1 , a 2 , … , a n ) = ( b 1 , b 2 , … , b n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})=(b_{1},b_{2},\ldots ,b_{n})} if and only if a 1 = b 1 , a 2 = b 2 , … , a n = b n {\displaystyle a_{1}=b_{1},{\text{ }}a_{2}=b_{2},{\text{ }}\ldots ,{\text{ }}a_{n}=b_{n}} . Thus a tuple has properties that distinguish it from a set: A tuple may contain multiple instances of the same element, so tuple ( 1 , 2 , 2 , 3 ) ≠ ( 1 , 2 , 3 ) {\displaystyle (1,2,2,3)\neq (1,2,3)} ; but set { 1 , 2 , 2 , 3 } = { 1 , 2 , 3 } {\displaystyle \{1,2,2,3\}=\{1,2,3\}} . Tuple elements are ordered: tuple ( 1 , 2 , 3 ) ≠ ( 3 , 2 , 1 ) {\displaystyle (1,2,3)\neq (3,2,1)} , but set { 1 , 2 , 3 } = { 3 , 2 , 1 } {\displaystyle \{1,2,3\}=\{3,2,1\}} . A tuple has a finite number of elements, while a set or a multiset may have an infinite number of elements. == Definitions == There are several definitions of tuples that give them the properties described in the previous section. === Tuples as functions === The 0 {\displaystyle 0} -tuple may be identified as the empty function. For n ≥ 1 , {\displaystyle n\geq 1,} the n {\displaystyle n} -tuple ( a 1 , … , a n ) {\displaystyle \left(a_{1},\ldots ,a_{n}\right)} may be identified with the surjective function F : { 1 , … , n } → { a 1 , … , a n } {\displaystyle F~:~\left\{1,\ldots ,n\right\}~\to ~\left\{a_{1},\ldots ,a_{n}\right\}} with domain domain ⁡ F = { 1 , … , n } = { i ∈ N : 1 ≤ i ≤ n } {\displaystyle \operatorname {domain} F=\left\{1,\ldots ,n\right\}=\left\{i\in \mathbb {N} :1\leq i\leq n\right\}} and with codomain codomain ⁡ F = { a 1 , … , a n } , {\displaystyle \operatorname {codomain} F=\left\{a_{1},\ldots ,a_{n}\right\},} that is defined at i ∈ domain ⁡ F = { 1 , … , n } {\displaystyle i\in \operatorname {domain} F=\left\{1,\ldots ,n\right\}} by F ( i ) := a i . {\displaystyle F(i):=a_{i}.} That is, F {\displaystyle F} is the function defined by 1 ↦ a 1 ⋮ n ↦ a n {\displaystyle {\begin{alignedat}{3}1\;&\mapsto &&\;a_{1}\\\;&\;\;\vdots &&\;\\n\;&\mapsto &&\;a_{n}\\\end{alignedat}}} in which case the equality ( a 1 , a 2 , … , a n ) = ( F ( 1 ) , F ( 2 ) , … , F ( n ) ) {\displaystyle \left(a_{1},a_{2},\dots ,a_{n}\right)=\left(F(1),F(2),\dots ,F(n)\right)} necessarily holds. Tuples as sets of ordered pairs Functions are commonly identified with their graphs, which is a certain set of ordered pairs. Indeed, many authors use graphs as the definition of a function. Using this definition of "function", the above function F {\displaystyle F} can be defined as: F := { ( 1 , a 1 ) , … , ( n , a n ) } . {\displaystyle F~:=~\left\{\left(1,a_{1}\right),\ldots ,\left(n,a_{n}\right)\right\}.} === Tuples as nested ordered pairs === Another way of modeling tuples in set theory is as nested ordered pairs. This approach assumes that the notion of ordered pair has already been defined. The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } . An n-tuple, with n > 0, can be defined as an ordered pair of its first entry and an (n − 1)-tuple (which contains the remaining entries when n > 1): ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , a 3 , … , a n ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},a_{3},\ldots ,a_{n}))} This definition can be applied recursively to the (n − 1)-tuple: ( a 1 , a 2 , a 3 , … , a n ) = ( a 1 , ( a 2 , ( a 3 , ( … , ( a n , ∅ ) … ) ) ) ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=(a_{1},(a_{2},(a_{3},(\ldots ,(a_{n},\emptyset )\ldots ))))} Thus, for example: ( 1 , 2 , 3 ) = ( 1 , ( 2 , ( 3 , ∅ ) ) ) ( 1 , 2 , 3 , 4 ) = ( 1 , ( 2 , ( 3 , ( 4 , ∅ ) ) ) ) {\displaystyle {\begin{aligned}(1,2,3)&=(1,(2,(3,\emptyset )))\\(1,2,3,4)&=(1,(2,(3,(4,\emptyset ))))\\\end{aligned}}} A variant of this definition starts "peeling off" elements from the other end: The 0-tuple is the empty set ∅ {\displaystyle \emptyset } . For n > 0: ( a 1 , a 2 , a 3 , … , a n ) = ( ( a 1 , a 2 , a 3 , … , a n − 1 ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((a_{1},a_{2},a_{3},\ldots ,a_{n-1}),a_{n})} This definition can be applied recursively: ( a 1 , a 2 , a 3 , … , a n ) = ( ( … ( ( ( ∅ , a 1 ) , a 2 ) , a 3 ) , … ) , a n ) {\displaystyle (a_{1},a_{2},a_{3},\ldots ,a_{n})=((\ldots (((\emptyset ,a_{1}),a_{2}),a_{3}),\ldots ),a_{n})} Thus, for example: ( 1 , 2 , 3 ) = ( ( ( ∅ , 1 ) , 2 ) , 3 ) ( 1 , 2 , 3 , 4 ) = ( ( ( ( ∅ , 1 ) , 2 ) , 3 ) , 4 ) {\displaystyle {\begin{aligned}(1,2,3)&=(((\emptyset ,1),2),3)\\(1,2,3,4)&=((((\emptyset ,1),2),3),4)\\\end{aligned}}} === Tuples as nested sets === Using Kuratowski's representation for an ordered pair, the second definition above can be reformulated in terms of pure set theory: The 0-tuple (i.e. the empty tuple) is represented by the empty set ∅ {\displaystyle \emptyset } ; Let x {\displaystyle x} be an n-tuple ( a 1 , a 2 , … , a n ) {\displaystyle (a_{1},a_{2},\ldots ,a_{n})} , and let x → b ≡ ( a 1 , a 2 , … , a n , b ) {\displaystyle x\rightarrow b\equiv (a_{1},a_{2},\ldots ,a_{n},b)} . Then, x → b ≡ { { x } , { x , b } } {\displaystyle x\rightarrow b\equiv \{\{x\},\{x,b\}\}} . (The right arrow, → {\displaystyle \rightarrow } , could be read as "adjoined with".) In this formulation: ( ) = ∅ ( 1 ) = ( ) → 1 = { { ( ) } , { ( ) , 1 } } = { { ∅ } , { ∅ , 1 } } ( 1 , 2 ) = ( 1 ) → 2 = { { ( 1 ) } , { ( 1 ) , 2 } } = { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } ( 1 , 2 , 3 ) = ( 1 , 2 ) → 3 = { { ( 1 , 2 ) } , { ( 1 , 2 ) , 3 } } = { { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } } , { { { { { ∅ } , { ∅ , 1 } } } , { { { ∅ } , { ∅ , 1 } } , 2 } } , 3 } } {\displaystyle {\begin{array}{lclcl}()&&&=&\emptyset \\&&&&\\(1)&=&()\rightarrow 1&=&\{\{()\},\{(),1\}\}\\&&&=&\{\{\emptyset \},\{\emptyset ,1\}\}\\&&&&\\(1,2)&=&(1)\rightarrow 2&=&\{\{(1)\},\{(1),2\}\}\\&&&=&\{\{\{\{\emptyset \},\{\emptyset ,1\}\}\},\\&&&&\{\{\{\emptyset \},\{\emptyset ,1\}\},2\}\}\\&&&&\\(1,2,3)&=&(1,2)\rightarrow 3&=&\{\{(1,2)\},\{(1,2),3\}\}\\&&&=&\{\{\{\{\{\{\empty

  Read more →
• 

  Exercism

  Exercism is an online, open-source, free coding platform that offers code practice and mentorship on 77 different programming languages. == History == Software developer Katrina Owen created Exercism while she was teaching programming at Jumpstart Labs. The platform was developed as an internal tool to solve the problem of her own students not receiving feedback on the coding problems they were practicing. Katrina put the site publicly online and found that people were sharing it with their friends, practicing together and giving each other feedback. Within 12 months, the site had organically grown to see over 6,000 users had submitted code or feedback, and hundreds of volunteers contribute to the languages or tooling on the platform. In 2016, Jeremy Walker joined as co-founder and CEO. In July 2018, the site was relaunched with a new design and centered around a formal mentoring mode, at which point Katrina stepped back from day-to-day involvement. == Product == In the past, the website differed from other coding platforms by requiring students to download exercises through a command line client, solve the code on their own computers then submit the solution for feedback, at which point they can also view other's solutions to the same problem. Since its second relaunch in 2021, solutions can be edited and submitted through a web editor, though the command line client remains available. Exercism has tracks for 74 programming languages. Among the notable languages taught: ABAP, C, C#, C++, CoffeeScript, Delphi, Elm, Erlang, F#, Gleam, Go, Java, JavaScript, Julia, Kotlin, Objective-C, PHP, Python, Raku, Red, Ruby, Rust, Scala, Swift, and V (Vlang). In 2023, the site launched a "12 in 23" challenge for users to learn the basics of 12 different languages - one per month in 2023. == Open source == The Exercism codebase is open source. In April 2016, it consisted of 50 repositories including website code, API code, command-line code and, most of all, over 40 stand-alone repositories for different language tracks. As of February 2024 Exercism has 14,344 contributors, maintains 366 repositories, and 19,603 mentors.

  Read more →
• 

  Divide-and-conquer algorithm

  In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem. The divide-and-conquer technique is the basis of efficient algorithms for many problems, such as sorting (e.g., quicksort, merge sort), multiplying large numbers (e.g., the Karatsuba algorithm), finding the closest pair of points, syntactic analysis (e.g., top-down parsers), SAT solving, and computing the discrete Fourier transform (FFT). Designing efficient divide-and-conquer algorithms can be difficult. As in mathematical induction, it is often necessary to generalize the problem to make it amenable to a recursive solution. The correctness of a divide-and-conquer algorithm is usually proved by mathematical induction, and its computational cost is often determined by solving recurrence relations. == Divide and conquer == The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Problems of sufficient simplicity are solved directly. For example, to sort a given list of n natural numbers, split it into two lists of about n/2 numbers each, sort each of them in turn, and interleave both results appropriately to obtain the sorted version of the given list (see the picture). This approach is known as the merge sort algorithm. The name "divide and conquer" is sometimes applied to algorithms that reduce each problem to only one sub-problem, such as the binary search algorithm for finding a record in a sorted list (or its analogue in numerical computing, the bisection algorithm for root finding). These algorithms can be implemented more efficiently than general divide-and-conquer algorithms; in particular, if they use tail recursion, they can be converted into simple loops. Under this broad definition, however, every algorithm that uses recursion or loops could be regarded as a "divide-and-conquer algorithm". Therefore, some authors consider that the name "divide and conquer" should be used only when each problem may generate two or more subproblems. The name decrease and conquer has been proposed instead for the single-subproblem class. An important application of divide and conquer is in optimization, where if the search space is reduced ("pruned") by a constant factor at each step, the overall algorithm has the same asymptotic complexity as the pruning step, with the constant depending on the pruning factor (by summing the geometric series); this is known as prune and search. == Early historical examples == Early examples of these algorithms are primarily decrease and conquer – the original problem is successively broken down into single subproblems, and indeed can be solved iteratively. Binary search, a decrease-and-conquer algorithm where the subproblems are of roughly half the original size, has a long history. While a clear description of the algorithm on computers appeared in 1946 in an article by John Mauchly, the idea of using a sorted list of items to facilitate searching dates back at least as far as Babylonia in 200 BC. Another ancient decrease-and-conquer algorithm is the Euclidean algorithm to compute the greatest common divisor of two numbers by reducing the numbers to smaller and smaller equivalent subproblems, which dates to several centuries BC. An early example of a divide-and-conquer algorithm with multiple subproblems is Gauss's 1805 description of what is now called the Cooley–Tukey fast Fourier transform (FFT) algorithm, although he did not analyze its operation count quantitatively, and FFTs did not become widespread until they were rediscovered over a century later. An early two-subproblem D&C algorithm that was specifically developed for computers and properly analyzed is the merge sort algorithm, invented by John von Neumann in 1945. Another notable example is the algorithm invented by Anatolii A. Karatsuba in 1960 that could multiply two n-digit numbers in O ( n log 2 ⁡ 3 ) {\displaystyle O(n^{\log _{2}3})} operations (in Big O notation). This algorithm disproved Andrey Kolmogorov's 1956 conjecture that Ω ( n 2 ) {\displaystyle \Omega (n^{2})} operations would be required for that task. As another example of a divide-and-conquer algorithm that did not originally involve computers, Donald Knuth gives the method a post office typically uses to route mail: letters are sorted into separate bags for different geographical areas, each of these bags is itself sorted into batches for smaller sub-regions, and so on until they are delivered. This is related to a radix sort, described for punch-card sorting machines as early as 1929. == Advantages == === Solving difficult problems === Divide and conquer is a powerful tool for solving conceptually difficult problems: all it requires is a way of breaking the problem into sub-problems, of solving the trivial cases, and of combining sub-problems to the original problem. Similarly, decrease and conquer only requires reducing the problem to a single smaller problem, such as the classic Tower of Hanoi puzzle, which reduces moving a tower of height n {\displaystyle n} to move a tower of height n − 1 {\displaystyle n-1} . === Algorithm efficiency === The divide-and-conquer paradigm often helps in the discovery of efficient algorithms. It was the key, for example, to Karatsuba's fast multiplication method, the quicksort and mergesort algorithms, the Strassen algorithm for matrix multiplication, and fast Fourier transforms. In all these examples, the D&C approach led to an improvement in the asymptotic cost of the solution. For example, if (a) the base cases have constant-bounded size, the work of splitting the problem and combining the partial solutions is proportional to the problem's size n {\displaystyle n} , and (b) there is a bounded number p {\displaystyle p} of sub-problems of size ~ n p {\displaystyle {\frac {n}{p}}} at each stage, then the cost of the divide-and-conquer algorithm will be O ( n log p ⁡ n ) {\displaystyle O(n\log _{p}n)} . For other types of divide-and-conquer approaches, running times can also be generalized. For example, when a) the work of splitting the problem and combining the partial solutions take c n {\displaystyle cn} time, where n {\displaystyle n} is the input size and c {\displaystyle c} is some constant; b) when n < 2 {\displaystyle n<2} , the algorithm takes time upper-bounded by c {\displaystyle c} , and c) there are q {\displaystyle q} subproblems where each subproblem has size ~ n 2 {\displaystyle {\frac {n}{2}}} . Then, the running times are as follows: if the number of subproblems q > 2 {\displaystyle q>2} , then the divide-and-conquer algorithm's running time is bounded by O ( n log 2 ⁡ q ) {\displaystyle O(n^{\log _{2}q})} . if the number of subproblems is exactly one, then the divide-and-conquer algorithm's running time is bounded by O ( n ) {\displaystyle O(n)} . If, instead, the work of splitting the problem and combining the partial solutions take c n 2 {\displaystyle cn^{2}} time, and there are 2 subproblems where each has size n 2 {\displaystyle {\frac {n}{2}}} , then the running time of the divide-and-conquer algorithm is bounded by O ( n 2 ) {\displaystyle O(n^{2})} . === Parallelism === Divide-and-conquer algorithms are naturally adapted for execution in multi-processor machines, especially shared-memory systems where the communication of data between processors does not need to be planned in advance because distinct sub-problems can be executed on different processors. === Memory access === Divide-and-conquer algorithms naturally tend to make efficient use of memory caches. The reason is that once a sub-problem is small enough, it and all its sub-problems can, in principle, be solved within the cache, without accessing the slower main memory. An algorithm designed to exploit the cache in this way is called cache-oblivious, because it does not contain the cache size as an explicit parameter. Moreover, D&C algorithms can be designed for important algorithms (e.g., sorting, FFTs, and matrix multiplication) to be optimal cache-oblivious algorithms–they use the cache in a probably optimal way, in an asymptotic sense, regardless of the cache size. In contrast, the traditional approach to exploiting the cache is blocking, as in loop nest optimization, where the problem is explicitly divided into chunks of the appropriate size—this can also use the cache optimally, but only when the algorithm is tuned for the specific cache sizes of a particular machine. The same advantage exists with regards to other hierarchical storage systems, such as NUMA or virtual memory, as well as for multip

  Read more →
• 

  Causal AI

  Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.

  Read more →
• 

  Applied Information Science in Economics

  The Applied Information Science in Economics (Russian: Прикладная информатика в Экономике) or Applied Computer Science in Economics is a professional qualification generally awarded in Russian Federation. The degree inherited from the U.S.S.R. education system also known as Specialist degree. The degree is awarded after five years of full-time study and includes several internships, course-works, thesis writing and defense. The degree has similarities with German Magister Artium or Diplom degree. However, due to the Bologna Process number of such degrees are declining. Degree focuses on applying mathematical methods in economics involving maximum information technology. It is very close to applied mathematics, but includes also major part of computer science. == List of specialty codes in the education system == 080801 - Applied computer science in economics 351400 - Applied computer science == Fields of activity == Organization and management; Project design; Experimental research; Marketing; Consulting; Operational and Maintenance. == Major == Information Science and Programming. High Level Methods of Information Science and Programming. Information Technologies in Economics. Computer Systems, Networks and Telecommunications Services. Operational Environments, Systems and Shells. Architecture and Design of Information Systems for Companies. Data Bases. Information security. Information Management. Imitative Simulation.

  Read more →
• 

  Toad (software)

  Toad is a database management toolset from Quest Software for managing relational and non-relational databases using SQL aimed at database developers, database administrators, and data analysts. The Toad toolset runs against Oracle, SQL Server, IBM DB2 (LUW & z/OS), SAP and MySQL. A Toad product for data preparation supports many data platforms. == History == A practicing Oracle DBA, Jim McDaniel, designed Toad for his own use in the mid-1990s. He called it Tool for Oracle Application Developers, shortened to "TOAD". McDaniel initially distributed the tool as shareware and later online as freeware. Quest Software acquired TOAD in October 1998. Quest Software itself was acquired by Dell in 2012 to form Dell Software. In June 2016, Dell announced the sale of their software division, including the Quest business, to Francisco Partners and Elliott Management Corporation. On October 31, 2016, the sale was finalized. On November 1, 2016, the sale of Dell Software to Francisco Partners and Elliott Management was completed, and the company re-launched as Quest Software. == Features == Connection Manager - Allow users to connect natively to the vendor’s database whether on-premise or DBaaS. Browser - Allow users to browse all the different database/schema objects and their properties effective management. Editor - A way to create and maintain scripts and database code with debugging and integration with source control. Unit Testing (Oracle) - Ensures code is functionally tested before it is released into production. Static code review (Oracle) - Ensures code meets required quality level using a rules-based system. SQL Optimization - Provides developers with a way to tune and optimize SQL statements and database code without relying on a DBA. Advanced optimization enables DBAs to tune SQL effectively in production. Scalability testing and database workload replay - Ensures that database code and SQL will scale properly before it gets released into production. == Books == Toad Pocket Reference for Oracle plsql 1st Edition by Jim McDaniel and Patrick McGrath, O'Reilly, 2002 (ISBN 0596003374, ISBN 978-0-596-00337-1) Toad Pocket Reference for Oracle 2nd Edition by Jeff Smith, Bert Scalzo, and Patrick McGrath, O'Reilly, 2005 (ISBN 0596009712, ISBN 978-0-596-00971-7) TOAD Handbook by Bert Scalzo and Dan Hotka, Sams, 2003 (ISBN 0672324865, ISBN 978-0-672-32486-4) TOAD Handbook 2nd Edition by Bert Scalzo and Dan Hotka, Addison-Wesley Professional, 2009 (ISBN 0321649109, ISBN 978-0-321-64910-2). TOAD Handbook 2nd Edition by Bert Scalzo and Dan Hotka, Addison-Wesley Professional, 2009 (ISBN 0321649109, ISBN 978-0-321-64910-2).

  Read more →
• 

  Tagsistant

  Tagsistant is a semantic file system for the Linux kernel, written in C and based on FUSE. Unlike traditional file systems that use hierarchies of directories to locate objects, Tagsistant introduces the concept of tags. == Design and differences with hierarchical file systems == In computing, a file system is a type of data store which could be used to store, retrieve and update files. Each file can be uniquely located by its path. The user must know the path in advance to access a file and the path does not necessarily include any information about the content of the file. Tagsistant uses a complementary approach based on tags. The user can create a set of tags and apply those tags to files, directories and other objects (devices, pipes, ...). The user can then search all the objects that match a subset of tags, called a query. This kind of approach is well suited for managing user contents like pictures, audio recordings, movies and text documents but is incompatible with system files (like libraries, commands and configurations) where the univocity of the path is a security requirement to prevent the access to a wrong content. == The tags/ directory == A Tagsistant file system features four main directories: archive/ relations/ stats/ tags/ Tags are created as sub directories of the tags/ directory and can be used in queries complying to this syntax: tags/subquery/[+/subquery/[+/subquery/]]/@/ where a subquery is an unlimited list of tags, concatenated as directories: tag1/tag2/tag3/.../tagN/ The portion of a path delimited by tags/ and @/ is the actual query. The +/ operator joins the results of different sub-queries in one single list. The @/ operator ends the query. To be returned as a result of the following query: tags/t1/t2/+/t1/t4/@/ an object must be tagged as both t1/ and t2/ or as both t1/ and t4/. Any object tagged as t2/ or t4/, but not as t1/ will not be retrieved. The query syntax deliberately violates the POSIX file system semantics by allowing a path token to be a descendant of itself, like in tags/t1/t2/+/t1/t4/@ where t1/ appears twice. As a consequence a recursive scan of a Tagsistant file system will exit with an error or endlessly loop, as done by Unix find: This drawback is balanced by the possibility to list the tags inside a query in any order. The query tags/t1/t2/@/ is completely equivalent to tags/t2/t1/@/ and tags/t1/+/t2/t3/@/ is equivalent to tags/t2/t3/+/t1/@/. The @/ element has the precise purpose of restoring the POSIX semantics: the path tags/t1/@/directory/ refers to a traditional directory and a recursive scan of this path will properly perform. == The reasoner and the relations/ directory == Tagsistant features a simple reasoner which expands the results of a query by including objects tagged with related tags. A relation between two tags can be established inside the relations/ directory following a three level pattern: relations/tag1/rel/tag2/ The rel element can be includes or is_equivalent. To include the rock tag in the music tag, the Unix command mkdir can be used: mkdir -p relations/music/includes/rock The reasoner can recursively resolve relations, allowing the creation of complex structures: mkdir -p relations/music/includes/rock mkdir -p relations/rock/includes/hard_rock mkdir -p relations/rock/includes/grunge mkdir -p relations/rock/includes/heavy_metal mkdir -p relations/heavy_metal/includes/speed_metal The web of relations created inside the relations/ directory constitutes a basic form of ontology. == Autotagging plugins == Tagsistant features an autotagging plugin stack which gets called when a file or a symlink is written. Each plugin is called if its declared MIME type matches The list of working plugins released with Tagsistant 0.6 is limited to: text/html: tags the file with each word in