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  Label noise

  Label noise refers to errors or inaccuracies in the class labels of data instances. This is a widespread issue in machine learning datasets, arising from human annotator mistakes, unclear labeling instructions, automated labeling methods, or adversarial attacks in supervised learning. Label noise can be roughly divided into random noise, where labels are flipped independently of input features, and systematic noise, where mislabeling is dependent on certain patterns or biases in the data. Label noise can be damaging to model performance, especially for complex models that may overfit to noisy labels rather than generalizable patterns. Many approaches have been proposed to deal with the effects of label noise, including robust loss functions, noise-tolerant algorithms, data cleaning methods, and semi-supervised learning approaches. To reduce the impact of wrong labels during training, techniques like label smoothing, sample reweighting and using trusted validation sets are used. The role of noise-robust training paradigms and curriculum learning strategies to improve resilience against mislabeled data is also explored in recent research.

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  Magiran

  Magiran (Persian: مگیران)—Iran's publications database—is a digital library that was founded in 2000 and includes digitized versions of scientific journals, which currently provides the possibility of searching among the full text of 1,500 journals. Registration is required for full access to the database, but access to some items such as newspapers is also possible without registration. A list of Iranian researchers is also maintained there.

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  Imieliński–Lipski algebra

  In database theory, Imieliński–Lipski algebra is an extension of relational algebra onto tables with different types of null values. It is used to operate on relations with incomplete information. Imieliński–Lipski algebras are defined to satisfy precise conditions for semantically meaningful extension of the usual relational operators, such as projection, selection, union, and join, from operators on relations to operators on relations with various kinds of "null values". These conditions require that the system be safe in the sense that no incorrect conclusion is derivable by using a specified subset F of the relational operators; and that it be complete in the sense that all valid conclusions expressible by relational expressions using operators in F are in fact derivable in this system. For example, it is well known that the three-valued logic approach to deal with null values, supported treatment of nulls values by SQL is not complete, see Ullman book. To show this, let T be: Take SQL query Q SQL query Q will return empty set (no results) under 3-valued semantics currently adopted by all variants of SQL. This is the case because in SQL, NULL is never equal to any constant – in this case, neither to “Spring” nor “Fall” nor “Winter” (if there is Winter semester in this school). NULL='Spring' will evaluate to MAYBE and so will NULL='Fall'. The disjunction MAYBE OR MAYBE evaluates to MAYBE (not TRUE). Thus Igor will not be part of the answer (and of course neither will Rohit). But Igor should be returned as the answer. Indeed, regardless what semester Igor took the Networks class (no matter what was the unknown value of NULL), the selection condition will be true. This “Igor” will be missed by SQL and the SQL answer would be incomplete according to completeness requirements specified in Tomasz Imieliński, Witold Lipski, 'Incomplete Information in Relational Databases'. It is also argued there that 3-valued logic (TRUE, FALSE, MAYBE) can never provide guarantee of complete answer for tables with incomplete information. Three algebras which satisfy conditions of safety and completeness are defined as Imielinski–Lipski algebras: the Codd-Tables algebra, the V-tables algebra and the Conditional tables (C-tables) algebra. == Codd-tables algebra == Codd-tables algebra is based on the usual Codd's single NULL values. The table T above is an example of Codd-table. Codd-table algebra supports projection and positive selections only. It is also demonstrated in [IL84 that it is not possible to correctly extend more relational operators over Codd-Tables. For example, such basic operation as join is not extendable over Codd-tables. It is not possible to define selections with Boolean conditions involving negation and preserve completeness. For example, queries like the above query Q cannot be supported. In order to be able to extend more relational operators, more expressive form of null value representation is needed in tables which are called V-table. == V-tables algebra == V-tables algebra is based on many different ("marked") null values or variables allowed to appear in a table. V-tables allow to show that a value may be unknown but the same for different tuples. For example, in the table below Gaurav and Igor order the same (but unknown) beer in two unknown bars (which may, or may not be different – but remain unknown). Gaurav and Jane frequent the same unknown bar (Y1). Thus, instead one NULL value, we use indexed variables, or Skolem constants . V-tables algebra is shown to correctly support projection, positive selection (with no negation occurring in the selection condition), union, and renaming of attributes, which allows for processing arbitrary conjunctive queries. A very desirable property enjoyed by the V-table algebra is that all relational operators on tables are performed in exactly the same way as in the case of the usual relations. === Conditional tables (c-tables) algebra === Example of conditional table (c-table) is shown below. It has additional column “con” which is a Boolean condition involving variables, null values – same as in V-tables. over the following table c-table Conditional tables algebra, mainly of theoretical interest, supports projection, selection, union, join, and renaming. Under closed-world assumption, it can also handle the operator of difference, thus it can support all relational operators. == History == Imieliński–Lipski algebras were introduced by Tomasz Imieliński and Witold Lipski Jr. in Incomplete Information in Relational Databases.

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  Spatiotemporal reservoir resampling

  Spatiotemporal reservoir resampling, commonly known as ReSTIR (from "Reservoir-based SpatioTemporal Importance Resampling"), is a collection of computer graphics techniques for reusing samples during rendering. It was developed primarily to allow more realistic lighting in real-time rendering, because relatively few rays can be traced per pixel while maintaining an acceptable frame rate. It can also be used to speed up off-line path tracing. The first ReSTIR paper, published in 2020, provided algorithms for direct lighting, allowing scenes containing thousands of lights to be rendered in real time on a high-end GPU. Researchers later proposed versions for rendering indirect lighting (and more recently, motion blur and depth of field) and built up a framework of mathematical concepts and notation conventions that help analyze such algorithms. A major focus of this work is removing or reducing the bias that could be introduced when samples from other pixels or frames are reused—or selectively allowing some bias in order to speed up rendering and reduce variance (visible as "noise" in the image). Versions for path tracing apply transformations called shift mappings to samples, typically reusing parts of paths closer to the light and modifying the portion closer to the camera. ReSTIR-related papers and talks have been presented every year at the SIGGRAPH conference since 2020. One of the first games to incorporate ReSTIR into its rendering was Cyberpunk 2077. == Overview and motivation == According to Chris Wyman, one of the co-authors of the original paper, although developers commonly thought that bias was acceptable for real-time rendering, end users (e.g. gamers) are well-aware of the artifacts caused by bias and many have a negative opinion of common sample-reuse techniques such as temporal anti-aliasing (TAA), which may cause "ghosting" when the camera moves, and denoising, which causes blurring and other artifacts. ReSTIR techniques can reduce or avoid these types of bias by reusing samples of the set of possible paths taken by light to reach the camera, instead of reusing rendered pixel color values (which are typically the average of multiple samples, discarding information such as the direction of the light). While other techniques reuse samples in a generic post-processing step, ReSTIR passes can test for shadowing, and reused samples are converted into pixel color values by rendering code that takes the characteristics of different materials into account (e.g. by implementing BRDFs). However the output of ReSTIR is noisy, and a denoising pass is typically still used. Stochastic ray tracing techniques such as path tracing need to average multiple samples (produced by tracing individual rays) in order to render a visually acceptable image. When using a simple unbiased renderer based on Monte Carlo integration, halving the deviation of the result (apparent as "noise" in the image) requires multiplying the number of samples by four, meaning that a rapidly increasingly number of samples is needed to improve quality, Standard ways to mitigate this problem include importance sampling (which requires finding improved sampling distributions for specific situations), and quasi-Monte Carlo integration (which usually still requires tracing a large number of rays). ReSTIR offers a solution that multiplies the effective number of samples while tracing a fixed number of additional rays per frame. Temporal reuse multiplies the effective sample count by the number of frames rendered. Spatial reuse multiplies the effective count by the number of neighboring pixels examined. These two types of reuse can be combined, allowing spatial reuse to be applied recursively, which appears to offer an exponentially increasing effective sample count, however this is quickly limited by the size of the neighborhood used for spatial reuse. Spatial reuse is also potentially less effective near shadow and object edges, especially for objects with fine geometric detail, and temporal reuse is limited by movement of the camera and scene elements. == Variations == Many variations of ReSTIR have been proposed that generalize or improve the original technique (which builds on an earlier method called RIS), specialize it for particular types of illumination or other visual effects, or allow incorporation into rendering algorithms other than standard path tracing. Some published versions are listed below. == Algorithms == === Basic algorithm === ReSTIR uses a combination of resampled importance sampling (RIS) and weighted reservoir sampling (WRS) which the authors call streaming RIS. RIS processes samples from an initial probability distribution (e.g. a probability distribution for which a cheap sampling method exists) and generates samples in a new probability distribution (e.g. a sampling distribution that is optimal for rendering but is impractical to draw samples from directly). WRS allows this to be done while storing only a small number of samples in memory, which is especially helpful on a GPU. Information about the samples is stored in a data structure called a reservoir. WRS also allows samples from multiple reservoirs to be combined ("merged") into a single reservoir; this is crucial for sample reuse. Each pixel has a reservoir, typically containing only a single sample when ReSTIR is used for real-time rendering (some implementations use a larger number, e.g. four samples). The reservoir is typically initialized to a sample drawn using a simple method and is then updated by RIS steps and by reservoir merging, so that the pixel value produced by shading using the sample(s) currently in the reservoir, times the weight for the sample, is always an unbiased estimate of the correct pixel value. If appropriate resampling steps are used, the variance of this estimate (or some function of it, typically the luminance of the RGB color value) decreases with each step. A possible sequence of steps performed for each frame, suitable for computing unbiased direct illumination (DI) is: Perform reservoir resampling by drawing multiple light samples and using streaming RIS to choose one, using probabilities based on a target function, e.g. the luminance of the sample's contribution to the pixel. A weight is also computed for the sample. Typically, a single visibility check is performed here, after choosing a sample, setting the weight to 0 if the light is shadowed. Resampling (combined with the visibility check) ensures that the expected value of the weight times the sample brightness is the correct (unbiased) value for the pixel. (temporal reuse) For each pixel, merge the sample(s) from the previous frame into the current reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in the previous frame's reservoirs may have a different target probability distribution if the objects, lights, or camera have moved. (spatial reuse) For each pixel, choose one or more neighboring pixels and merge their samples into the current pixel's reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in each pixel's reservoir have a different target probability distribution. Because computing unbiased MIS weights requires tracing additional rays (along with other work such as evaluating BRDFs), real-time rendering often uses only a single neighboring pixel. Use the sample in each pixel's reservoir, along with its weight, to determine the color of the pixel for the current frame. Alternatively, multiple samples examined during the preceding steps may be averaged and used to shade the pixel instead (decoupled shading and sampling). For direct lighting, the initial samples used in step 1 are typically drawn by importance sampling from the set of lights in a scene. The algorithm above (from the original ReSTIR paper) draws many lower-quality light samples (e.g. 32) using a fast method, without considering visibility, and chooses one using streaming RIS. Visibility is then tested for the final chosen sample. Considering visibility for each sample drawn would require tracing 32 rays, which would make it much more expensive. The intent is to reduce the number of rays traced, relying on the sample reuse in steps 2 and 3 to make up for the loss of quality caused by rejecting many of the rays due to shadowing. A large part of the initial efforts to optimize ReSTIR (to make it run in real-time on available hardware) went into reducing the cost of randomly sampling the lights. Glossy surfaces may require a larger number of samples, and combining light sampling with BRDF sampling (using MIS) may increase quality. Step 2 (temporal reuse) is sometimes skipped for off-line rendering, and the output of multiple repetitions of initial sampling and spatial reuse is averaged instead; this helps avoids artifacts due to correlations. Step 3 (spatial reuse) may be repeated multiple times in a single frame.

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  Vulnerabilities Equities Process

  The Vulnerabilities Equities Process (VEP) is a process used by the U.S. federal government to determine on a case-by-case basis how it should treat zero-day computer security vulnerabilities: whether to disclose them to the public to help improve general computer security, or to keep them secret for offensive use against the government's adversaries. The VEP was first developed during the period 2008–2009, but only became public in 2016, when the government released a redacted version of the VEP in response to a FOIA request by the Electronic Frontier Foundation. Following public pressure for greater transparency in the wake of the Shadow Brokers affair, the U.S. government made a more public disclosure of the VEP process in November 2017. == Participants == According to the VEP plan published in 2017, the Equities Review Board (ERB) is the primary forum for interagency deliberation and determinations concerning the VEP. The ERB meets monthly, but may also be convened sooner if an immediate need arises. The ERB consists of representatives from the following agencies: Office of Management and Budget Office of the Director of National Intelligence (including the Intelligence Community-Security Coordination Center) United States Department of the Treasury United States Department of State United States Department of Justice (including the Federal Bureau of Investigation and the National Cyber Investigative Joint Task Force) Department of Homeland Security (including the National Cybersecurity and Communications Integration Center and the United States Secret Service) United States Department of Energy United States Department of Defense (to include the National Security Agency, including Information Assurance and Signals Intelligence elements), United States Cyber Command, and DoD Cyber Crime Center) United States Department of Commerce Central Intelligence Agency The National Security Agency serves as the executive secretariat for the VEP. == Process == According to the November 2017 version of the VEP, the process is as follows: === Submission and notification === When an agency finds a vulnerability, it will notify the VEP secretariat as soon as is possible. The notification will include a description of the vulnerability and the vulnerable products or systems, together with the agency's recommendation to either disseminate or restrict the vulnerability information. The secretariat will then notify all participants of the submission within one business day, requesting them to respond if they have an relevant interest. === Equity and discussions === An agency expressing an interest must indicate whether it concurs with the original recommendation to disseminate or restrict within five business days. If it does not, it will hold discussions with the submitting agency and the VEP secretariat within seven business days to attempt to reach consensus. If no consensus is reached, the participants will suggest options for the Equities Review Board. === Determination to disseminate or restrict === Decisions whether to disclose or restrict a vulnerability should be made quickly, in full consultation with all concerned agencies, and in the overall best interest of the competing interests of the missions of the U.S. government. As far as possible, determinations should be based on rational, objective methodologies, taking into account factors such as prevalence, reliance, and severity. If the review board members cannot reach consensus, they will vote on a preliminary determination. If an agency with an equity disputes that decision, they may, by providing notice to the VEP secretariat, elect to contest the preliminary determination. If no agency contests a preliminary determination, it will be treated as a final decision. === Handling and follow-on actions === If vulnerability information is released, this will be done as quickly as possible, preferably within seven business days. Disclosure of vulnerabilities will be conducted according to guidelines agreed on by all members. The submitting agency is presumed to be most knowledgeable about the vulnerability and, as such, will be responsible for disseminating vulnerability information to the vendor. The submitting agency may elect to delegate dissemination responsibility to another agency on its behalf. The releasing agency will promptly provide a copy of the disclosed information to the VEP secretariat for record keeping. Additionally, the releasing agency is expected to follow up so the ERB can determine whether the vendor's action meets government requirements. If the vendor chooses not to address a vulnerability, or is not acting with urgency consistent with the risk of the vulnerability, the releasing agency will notify the secretariat, and the government may take other mitigation steps. == Criticism == The VEP process has been criticized for a number of deficiencies, including restriction by non-disclosure agreements, lack of risk ratings, special treatment for the NSA, and less than whole-hearted commitment to disclosure as the default option. == UK equivalent == British intelligence agencies—GCHQ in particular—follow a similar approach, also known as the Equities Process, to determine whether to disclose or retain security vulnerabilities. The Investigatory Powers Act 2016 was amended in 2022 to bring oversight of the operation of the process within the remit of the Investigatory Powers Commissioner. Details of the process were made public in 2018.

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  IDMS

  The Integrated Database Management System (IDMS) is a network model (CODASYL) database management system for mainframes. It was first developed at BFGoodrich and later marketed by Cullinane Database Systems (renamed Cullinet in 1983). Since 1989 the product has been owned by Computer Associates (now CA Technologies), who renamed it Advantage CA-IDMS and later simply to CA IDMS. In 2018 Broadcom acquired CA Technologies, renaming it back to IDMS. == History == The roots of IDMS go back to the pioneering database management system called Integrated Data Store (IDS), developed at General Electric by a team led by Charles Bachman and first released in 1964. In the early 1960s IDS was taken from its original form, by the computer group of the BFGoodrich Chemical Division, and re-written in a language called Intermediate System Language (ISL). ISL was designed as a portable system programming language able to produce code for a variety of target machines. Since ISL was actually written in ISL, it was able to be ported to other machine architectures with relative ease, and then to produce code that would execute on them. The Chemical Division computer group had given some thought to selling copies of IDMS to other companies, but was told by management that they were not in the software products business. Eventually, a deal was struck with John Cullinane to buy the rights and market the product. Because Cullinane was required to remit royalties back to B.F. Goodrich, all add-on products were listed and billed as separate products – even if they were mandatory for the core IDMS product to work. This sometimes confused customers. The original platforms were the GE 235 computer and GE DATANET-30 message switching computer: later the product was ported to IBM mainframes and to DEC and ICL hardware. The IBM-ported version runs on IBM mainframe systems (System/360, System/370, System/390, zSeries, System z9). In the mid-1980s, it was claimed that some 2,500 IDMS licenses had been sold. Users included the Strategic Air Command, Ford of Canada, Ford of Europe, Jaguar Cars, Clarks Shoes UK, Axa/PPP, MAPFRE, Royal Insurance, Tesco, Manulife, Hudson's Bay Company, Cleveland Clinic, Bank of Canada, General Electric, Aetna and BT in the UK. A version for use on the Digital Equipment Corporation PDP-11 series of computers was sold to DEC and was marketed as DBMS-11. In 1976 the source code was licensed to ICL, who ported the software to run on their 2900 series mainframes, and subsequently also on the older 1900 range. ICL continued development of the software independently of Cullinane, selling the original ported product under the name ICL 2900 IDMS and an enhanced version as IDMSX. In this form it was used by many large UK users, an example being the Pay-As-You-Earn system operated by Inland Revenue. Many of these IDMSX systems for UK Government were still running in 2013. In the early to mid-1980s, relational database management systems started to become more popular, encouraged by increasing hardware power and the move to minicomputers and client–server architecture. Relational databases offered improved development productivity over CODASYL systems, and the traditional objections based on poor performance were slowly diminishing. Cullinet attempted to continue competing against IBM's DB2 and other relational databases by developing a relational front-end and a range of productivity tools. These included Automatic System Facility (ASF), which made use of a pre-existing IDMS feature called LRF (Logical Record Facility). ASF was a fill-in-the-blanks database generator that would also develop a mini-application to maintain the tables. It is difficult to judge whether such features may have been successful in extending the selling life of the product, but they made little impact in the long term. Those users who stayed with IDMS were primarily interested in its high performance, not in its relational capabilities. It was widely recognized (helped by a high-profile campaign by E. F. Codd, the father of the relational model) that there was a significant difference between a relational database and a network database with a relational veneer. In 1989 Computer Associates continued after Cullinet acquisition with the development and released Release 12.0 with full SQL in 1992–93. CA Technologies continued to market and support the CA IDMS and enhanced IDMS in subsequent releases by TCP/IP support, two phase commit support, XML publishing, zIIP specialty processor support, Web-enabled access in combination with CA IDMS Server, SQL Option and GUI database administration via CA IDMS Visual DBA tool. CA-IDMS systems are today still running businesses worldwide. Many customers have opted to web-enable their applications via the CA-IDMS SQL Option which is part of CA Technologies' Dual Database Strategy. == Integrated Data Dictionary == One of the sophisticated features of IDMS was its built-in Integrated data dictionary (IDD). The IDD was primarily developed to maintain database definitions. It was itself an IDMS database. DBAs (database administrators) and other users interfaced with the IDD using a language called Data Dictionary Definition Language (DDDL). IDD was also used to store definitions and code for other products in the IDMS family such as ADS/Online and IDMS-DC. IDD's power was that it was extensible and could be used to create definitions of just about anything. Some companies used it to develop in-house documentation. == Overview == === Logical Data Model === The data model offered to users is the CODASYL network model. The main structuring concepts in this model are records and sets. Records essentially follow the COBOL pattern, consisting of fields of different types: this allows complex internal structure such as repeating items and repeating groups. The most distinctive structuring concept in the Codasyl model is the set. Not to be confused with a mathematical set, a Codasyl set represents a one-to-many relationship between records: one owner, many members. The fact that a record can be a member in many different sets is the key factor that distinguishes the network model from the earlier hierarchical model. As with records, each set belongs to a named set type (different set types model different logical relationships). Sets are in fact ordered, and the sequence of records in a set can be used to convey information. A record can participate as an owner and member of any number of sets. Records have identity, the identity being represented by a value known as a database key. In IDMS, as in most other Codasyl implementations, the database key is directly related to the physical address of the record on disk. Database keys are also used as pointers to implement sets in the form of linked lists and trees. This close correspondence between the logical model and the physical implementation (which is not a strictly necessary part of the Codasyl model, but was a characteristic of all successful implementations) is responsible for the efficiency of database retrieval, but also makes operations such as database loading and restructuring rather expensive. Records can be accessed directly by database key, by following set relationships, or by direct access using key values. Initially the only direct access was through hashing, a mechanism known in the Codasyl model as CALC access. In IDMS, CALC access is implemented through an internal set, linking all records that share the same hash value to an owner record that occupies the first few bytes of every disk page. In subsequent years, some versions of IDMS added the ability to access records using BTree-like indexes. === Storage === IDMS organizes its databases as a series of files. These files are mapped and pre-formatted into so-called areas. The areas are subdivided into pages which correspond to physical blocks on the disk. The database records are stored within these blocks. The DBA allocates a fixed number of pages in a file for each area. The DBA then defines which records are to be stored in each area, and details of how they are to be stored. IDMS intersperses special space-allocation pages throughout the database. These pages are used to keep track of the free space available in each page in the database. To reduce I/O requirements, the free space is only tracked for all pages when the free space for the area falls below 30%. Four methods are available for storing records in an IDMS database: Direct, Sequential, CALC, and VIA. The Fujitsu/ICL IDMSX version extends this with two more methods, Page Direct, and Random. In direct mode the target database key is specified by the user and is stored as close as possible to that DB key, with the actual DB key on which the record is stored being returned to the application program. Sequential placement (not to be confused with indexed sequential), simply places each new record at the end of the area. This option is rarely used. CALC uses a hashing algo

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  Outline of databases

  The following is provided as an overview of and topical guide to databases: Database – organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). == What type of things are databases? == Databases can be described as all of the following: Information – sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals. Data – values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Computer data – information in a form suitable for use with a computer. Data is often distinguished from programs. A program is a sequence of instructions that detail a task for the computer to perform. In this sense, data is everything in software that is not program code. == Types of databases == Active database – Database with event driven features Animation database – Database for storing and reusing animation fragments or motion capture data Back-end database – Organized collection of data in computingPages displaying short descriptions of redirect targets Bibliographic database – database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. Centralized database – database located and maintained in one location, unlike a distributed database. Cloud database – Database running on a cloud computing platform Collection database – collection catalog of a museum or archive implemented using a computerized database, in which the institution's objects or material are catalogued. Collective Optimization Database – open repository to enable sharing of benchmarks, data sets and optimization cases from the community, provide web services and Plug-in (computing)|plugins to analyze optimization data and predict program transformations or better hardware designs for multi-objective optimizations based on statistical and machine learning techniques provided there is enough information collected in the repository from multiple users. Configuration management database – Database used to store info on hardware and software assets Cooperative database – holds information on customers and their transactions. Current database – conventional database that stores data that is valid now. Directory – repository or database of information which is optimized for reading, under the assumption that data updates are very rare compared to data reads. Commonly, a directory supports search and browsing in addition to simple lookups. Distributed database – database in which storage devices are not all attached to a common CPU. Document-oriented database – computer program designed for storing, retrieving, and managing document-oriented, or Semi-structured model|semi structured data, information. EDA database – database specialized for the purpose of electronic design automation. Endgame tablebase – computerized database that contains precalculated exhaustive analysis of a chess endgame position. Food composition database (FCDB) – provides detailed information on the nutritional composition of foods. Full-text database – database that contains the complete text of books, dissertations, journals, magazines, newspapers or other kinds of textual documents. Also called a "complete-text database". Government database – collects personal information for various reasons (mass surveillance, Schengen Information System in the European Union, social security, statistics, etc.). Graph database – uses graph structures with nodes, edges, and properties to represent and store data. Knowledge base – special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organised, shared, searched and utilised. Mobile database – can be connected to by a mobile computing device over a mobile network. Navigational database – database in which objects (or records) in it are found primarily by following references from other objects. Non-native speech database – speech database of non-native pronunciations of English. Online database – database accessible from a network, including from the Internet. Operational database – accessed by an Operational System to carry out regular operations of an organization. Parallel database – improves performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Probabilistic database – uncertain database in which the possible worlds have associated probabilities. Real-time database – processing system designed to handle workloads whose state is constantly changing (Buchmann). Relational database – collection of data items organized as a set of formally described tables from which data can be accessed easily. Spatial database – database that is optimized to store and query data that is related to objects in space, including points, lines and polygons. Temporal database – database with built-in time aspects, for example a temporal data model and a temporal version of Structured Query Language (SQL). Time series database – a time series is an associative array of numbers indexed by a datetime or a datetime range. These time series are often called profiles or curves, depending upon the market. A time series of stock prices might be called a price curve, or a time series of energy consumption might be called a load profile. Despite the disparate naming, the operations performed on them are sufficiently common as to demand special database treatment. Triplestore – purpose-built database for the storage and retrieval of triples, a triple being a data entity composed of subject-predicate-object, like "Bob is 35" or "Bob knows Fred". Very large database (VLDB) – contains an extremely high number of tuples (database rows), or occupies an extremely large physical filesystem storage space. Vulnerability database – platform aimed at collecting, maintaining, and disseminating information about discovered vulnerabilities targeting real computer systems. XLDB – Stands for "eXtremely Large Data Base". XML database – data stored in XML format, where it can be queried, exported and serialized into the desired format. == History of databases == History of databases – History of database management systems –: == Database use == Database usage requirements – Database theory – encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems. Database machine – or is a computer or special hardware that stores and retrieves data from a database. Also called a "back end processor" Database server – computer program that provides database services to other computer programs or computers, as defined by the client-server model. Database application – computer program whose primary purpose is entering and retrieving information from a computer-managed database. Database management system (DBMS) – software package with computer programs that control the creation, maintenance, and use of a database. Database connection – facility in computer science that allows client software to communicate with database server software, whether on the same machine or not. Datasource – name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The Database Source Name (DSN) does not have to be the same as the filename for the database. For example, a database file named "friends.mdb" could be set up with a DSN of "school". Then DSN "school" would then be used to refer to the database when performing a query. Data Source Name (DSN) – are data structures used to describe a connection to a data source. Sometimes known as a database source name though data sources are not limited to databases. Database administrator (DBA) – is a person responsible for the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases. Lock – Comparison of database tools – (provides tables for comparing general and technical information for a number of available database administrator tools.) Database-centric architecture – software architectures in which databases play a crucial role. Also called "data-centric architecture". Intelligent database – was put forward as a system that manages information (rather than data) in a way that appears natural to users and which goes beyond simple record keeping. Two-phase locking (2PL) – is a

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  Deductive language

  A deductive language is a computer programming language in which the program is a collection of predicates ('facts') and rules that connect them. Such a language is used to create knowledge based systems or expert systems which can deduce answers to problem sets by applying the rules to the facts they have been given. An example of a deductive language is Prolog, or its database-query cousin, Datalog. == History == As the name implies, deductive languages are rooted in the principles of deductive reasoning; making inferences based upon current knowledge. The first recommendation to use a clausal form of logic for representing computer programs was made by Cordell Green (1969) at Stanford Research Institute (now SRI International). This idea can also be linked back to the battle between procedural and declarative information representation in early artificial intelligence systems. Deductive languages and their use in logic programming can also be dated to the same year when Foster and Elcock introduced Absys, the first deductive/logical programming language. Shortly after, the first Prolog system was introduced in 1972 by Colmerauer through collaboration with Robert Kowalski. == Components == The components of a deductive language are a system of formal logic and a knowledge base upon which the logic is applied. === Formal Logic === Formal logic is the study of inference in regards to formal content. The distinguishing feature between formal and informal logic is that in the former case, the logical rule applied to the content is not specific to a situation. The laws hold regardless of a change in context. Although first-order logic is described in the example below to demonstrate the uses of a deductive language, no formal system is mandated and the use of a specific system is defined within the language rules or grammar. As input, a predicate takes any object(s) in the domain of interest and outputs either one of two Boolean values: true or false. For example, consider the sentences "Barack Obama is the 44th president" and "If it rains today, I will bring an umbrella". The first is a statement with an associated truth value. The second is a conditional statement relying on the value of some other statement. Either of these sentences can be broken down into predicates which can be compared and form the knowledge base of a deductive language. Moreover, variables such as 'Barack Obama' or 'president' can be quantified over. For example, take 'Barack Obama' as variable 'x'. In the sentence "There exists an 'x' such that if 'x' is the president, then 'x' is the commander in chief." This is an example of the existential quantifier in first order logic. Take 'president' to be the variable 'y'. In the sentence "For every 'y', 'y' is the leader of their nation." This is an example of the universal quantifier. === Knowledge Base === A collection of 'facts' or predicates and variables form the knowledge base of a deductive language. Depending on the language, the order of declaration of these predicates within the knowledge base may or may not influence the result of applying logical rules. Upon application of certain 'rules' or inferences, new predicates may be added to a knowledge base. As new facts are established or added, they form the basis for new inferences. As the core of early expert systems, artificial intelligence systems which can make decisions like an expert human, knowledge bases provided more information than databases. They contained structured data, with classes, subclasses, and instances. == Prolog == Prolog is an example of a deductive, declarative language that applies first- order logic to a knowledge base. To run a program in Prolog, a query is posed and based upon the inference engine and the specific facts in the knowledge base, a result is returned. The result can be anything appropriate from a new relation or predicate, to a literal such as a Boolean (true/false), depending on the engine and type system.

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  Celia (virtual assistant)

  Celia is an artificially intelligent virtual assistant developed by Huawei for their latest HarmonyOS and Android-based EMUI smartphones that lack Google Services and a Google Assistant. The assistant can perform day-to-day tasks, which include making a phone call, setting a reminder and checking the weather. It was unveiled on 7 April 2020 and got publicly released on 27 April 2020 via an OTA update solely to selected devices that can update their software to EMUI 10.1. Huawei had initially referred to the new assistant in late 2019 by having announced that there would be an English version of their already 2018 Chinese speaker assistant—Xiaoyi—to be released into the European markets. Due to the on-going China–United States trade war, the company's newly released smartphones were left without any Google services, including the loss of Google Assistant. This subsequently led to the development and release of Celia. AI technology is integrated into the software of Celia, which allows it to translate text using a phones camera and to identify everyday objects — similar to that of Google Lens. == Features == Celia has many features that are similar to that of its rivals: the Google Assistant and Siri. It can be triggered by the words, 'Hey Celia' or be summoned by pressing and holding down on the power button. The default search engine for Celia is Bing, but this can be changed in settings. Celia can make calls, check the agenda, send a message, show the weather, set alarms and control home appliances. The assistant also has the ability to integrate itself with the stock apps of the EMUI software and toggle with the device's settings, such as by turning on the flashlight and playing multimedia content, but with the users command. With the AI that is installed in Celia, it can identify food, everyday objects and translate text using the phones camera. In China, Chinese Xiaoyi packs with an LLM model called PanGu-Σ 3.0 AI on HarmonyOS 4.0 major upgrade improvements from Celia, making the assistant smarter and more advanced compared to when it was launched in 2020 on EMUI handsets in China and internationally, surpassing Apple and Google by the being the first in the AI industry, with a dedicated AI system framework of APIs on the latest operating system that evolves to a complete large dedicated AI software stack called Harmony Intelligence of Pangu Embedded variant model and MindSpore AI framework with Neural Network Runtime on OpenHarmony-based HarmonyOS NEXT base system to replace the dual framework system with a single frame HarmonyOS 5.0 version by Q4 2024, first introduced on June 21, 2024, in Developer Beta 1 preview release at HDC 2024. == Availability by country and language == Currently, Celia is available only in German, English, French and Spanish, and has been released in Germany, the UK, France, Spain, Chile, Mexico and Colombia. Huawei has said, that there will be more regions and languages to come. == Compatible devices == Celia only became available with the EMUI 10.1 update that was released in April, which means that a limited number of devices are compatible with it. More devices will be added to the list throughout the coming months as Celia's availability increases. The current list is shown below: === Huawei P series === Huawei P50 (Pro) Huawei P40 (Lite, Pro & Pro+) Huawei P30 (Pro) === Huawei Mate series === Huawei Mate 40 Huawei Mate 30 (Lite, Pro & RS Porche Design) Huawei MatePad Pro Huawei Mate 20 (Pro, 20X 4G, 20X 5G and RS Porche Design) Huawei Mate X & Xs === Huawei Nova series === Huawei Nova 6 (Nova 6 5G & Nova 6 SE) Huawei Nova 5 (Nova 5 Pro, Nova 5i Pro & Nova 5Z) Huawei Nova Y60 === Huawei Enjoy series === Huawei Enjoy 10S == Issues == Technology news website Engadget has noted that when saying, 'Hey Celia', out aloud in the presence of an iPhone, Siri will respond along with Celia; this is apparently because 'Celia' sounds similar to 'Siri'.

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  Reflection (computer graphics)

  Reflection in computer graphics is used to render reflective objects like mirrors and shiny surfaces. Accurate reflections are commonly computed using ray tracing whereas approximate reflections can usually be computed faster by using simpler methods such as environment mapping. Reflections on shiny surfaces like wood or tile can add to the photorealistic effects of a 3D rendering. == Approaches to reflection rendering == For rendering environment reflections there exist many techniques that differ in precision, computational and implementation complexity. Combination of these techniques are also possible. Image order rendering algorithms based on tracing rays of light, such as ray tracing or path tracing, typically compute accurate reflections on general surfaces, including multiple reflections and self reflections. However these algorithms are generally still too computationally expensive for real time rendering (even though specialized HW exists, such as Nvidia RTX) and require a different rendering approach from typically used rasterization. Reflections on planar surfaces, such as planar mirrors or water surfaces, can be computed simply and accurately in real time with two pass rendering — one for the viewer, one for the view in the mirror, usually with the help of stencil buffer. Some older video games used a trick to achieve this effect with one pass rendering by putting the whole mirrored scene behind a transparent plane representing the mirror. Reflections on non-planar (curved) surfaces are more challenging for real time rendering. Main approaches that are used include: Environment mapping (e.g. cube mapping): a technique that has been widely used e.g. in video games, offering reflection approximation that's mostly sufficient to the eye, but lacking self-reflections and requiring pre-rendering of the environment map. The precision can be increased by using a spatial array of environment maps instead of just one. It is also possible to generate cube map reflections in real time, at the cost of memory and computational requirements. Screen space reflections (SSR): a more expensive technique that traces rays come from pixel data.This requires the data of surface normal and either depth buffer (local space) or position buffer (world space).The disadvantage is that objects not captured in the rendered frame cannot appear in the reflections, which results in unresolved and or false intersections causing artefacts such as reflection vanishment and virtual image. SSR was originally introduced as Real Time Local Reflections in CryENGINE 3. == Types of reflection == Polished - A polished reflection is an undisturbed reflection, like a mirror or chrome surface. Blurry - A blurry reflection means that tiny random bumps, or microfacets, on the surface of the material causes the reflection to be blurry. Metallic - A reflection is metallic if the highlights and reflections retain the color of the reflective object. Glossy - This term can be misused: sometimes, it is a setting which is the opposite of blurry (e.g. when "glossiness" has a low value, the reflection is blurry). Sometimes the term is used as a synonym for "blurred reflection". Glossy used in this context means that the reflection is actually blurred. === Polished or mirror reflection === Mirrors are usually almost 100% reflective. === Metallic reflection === Normal (nonmetallic) objects reflect light and colors in the original color of the object being reflected. Metallic objects reflect lights and colors altered by the color of the metallic object itself. === Blurry reflection === Many materials are imperfect reflectors, where the reflections are blurred to various degrees due to surface roughness that scatters the rays of the reflections. === Glossy reflection === Fully glossy reflection, shows highlights from light sources, but does not show a clear reflection from objects. == Examples of reflections == === Wet floor reflections === The wet floor effect is a graphic effects technique popular in conjunction with Web 2.0 style pages, particularly in logos. The effect can be done manually or created with an auxiliary tool which can be installed to create the effect automatically. Unlike a standard computer reflection (and the Java water effect popular in first-generation web graphics), the wet floor effect involves a gradient and often a slant in the reflection, so that the mirrored image appears to be hovering over or resting on a wet floor.

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  Wavelet noise

  Wavelet noise is an alternative to Perlin noise which reduces the problems of aliasing and detail loss that are encountered when Perlin noise is summed into a fractal. == Algorithm detail == The basic algorithm for 2-dimensional wavelet noise is as follows: Create an image, R {\displaystyle R} , filled with uniform white noise. Downsample R {\displaystyle R} to half-size to create R ↓ {\displaystyle R^{\downarrow }} , then upsample it back up to full size to create R ↓↑ {\displaystyle R^{\downarrow \uparrow }} . Subtract R ↓↑ {\displaystyle R^{\downarrow \uparrow }} from R {\displaystyle R} to create the end result, N {\displaystyle N} . This results in an image that contains all the information that cannot be represented at half-scale. From here, N {\displaystyle N} can be used similarly to Perlin noise to create fractal patterns.

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  IT8

  IT8 is a set of American National Standards Institute (ANSI) standards for color communications and control specifications. Formerly governed by the IT8 Committee, IT8 activities were merged with those of the Committee for Graphics Arts Technologies Standards (CGATS Archived November 9, 2018, at the Wayback Machine) in 1994. == Standards list == The following is a list of the IT8 standards, according to the NPES Standards Blue Book Archived July 19, 2011, at the Wayback Machine: === IT8.6 - 2002 - Graphic technology - Prepress digital data exchange - Diecutting data (DDES3) === This standard establishes a data exchange format to enable transfer of numerical control information between diecutting systems and electronic prepress systems. The information will typically consist of numerical control information used in the manufacture of dies. 37 pp. === IT8.7/1 - 1993 (R2003) - Graphic technology - Color transmission target for input scanner calibration === This standard defines an input test target that will allow any color input scanner to be calibrated with any film dye set used to create the target. It is intended to address the color transparency products that are generally used for input to the preparatory process for printing and publishing. This standard defines the layout and colorimetric values of a target that can be manufactured on any positive color transparency film and that is intended for use in the calibration of a photographic film/scanner combination. 32 pp. === IT8.7/2 - 1993 (R2003) Graphic technology - Color reflection target for input scanner calibration === This standard defines an input test target that will allow any color input scanner to be calibrated with any film dye set used to create the target. It is intended to address the color photographic paper products that are generally used for input to the preparatory process for printing and publishing. It defines the layout and colorimetric values of the target that can be manufactured on any color photographic paper and is intended for use in the calibration of a photographic paper/scanner combination. 29 pp. === IT8.7/3 - 1993 (R2003) Graphic technology - Input data for characterization of 4-color process printing === The purpose of this standard is to specify an input data file, a measurement procedure and an output data format to characterize any four-color printing process. The output data (characterization) file should be transferred with any four-color (cyan, magenta, yellow and black) halftone image files to enable a color transformation to be undertaken when required. 29 pp. == Targets == Calibrating all devices involved in the process chain (original, scanner/digital camera, monitor/printer) is required for an authentic color reproduction, because their actual color spaces differ device-specifically from the reference color spaces. An IT8 calibration is done with what are called IT8 targets, which are defined by the IT8 standards. Example Special targets, implementing the IT8.7/1 (transparent target) or IT8.7/2 (reflective target) standards, are needed for calibrating scanners. These targets consists of 24 grey fields and 264 color fields in 22 columns: Column 01 to 12: HCL color model, which differ in Hue, Chroma, and Lightness Column 13 to 16: CMYK-Colors Cyan, Magenta, Yellow, and Key (black) in different steps of brightness Column 17 to 19: RGB-Colors Red, Green, and Blue in different steps of brightness Column 20 to 22: undefined, producers' choice After scanning such a target, an ICC profile gets calculated on the basis of reference values. This profile is used for all subsequent scans and assures color fidelity.

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  Visual Turing Test

  The Visual Turing Test is “an operator-assisted device that produces a stochastic sequence of binary questions from a given test image”. The query engine produces a sequence of questions that have unpredictable answers given the history of questions. The test is only about vision and does not require any natural language processing. The job of the human operator is to provide the correct answer to the question or reject it as ambiguous. The query generator produces questions such that they follow a “natural story line”, similar to what humans do when they look at a picture. == History == Research in computer vision dates back to the 1960s when Seymour Papert first attempted to solve the problem. This unsuccessful attempt was referred to as the Summer Vision Project. The reason why it was not successful was because computer vision is more complicated than what people think. The complexity is in alignment with the human visual system. Roughly 50% of the human brain is devoted in processing vision, which indicates that it is a difficult problem. Later there were attempts to solve the problems with models inspired by the human brain. Perceptrons by Frank Rosenblatt, which is a form of the neural networks, was one of the first such approaches. These simple neural networks could not live up to their expectations and had certain limitations due to which they were not considered in future research. Later with the availability of the hardware and some processing power the research shifted to image processing which involves pixel-level operations, like finding edges, de-noising images or applying filters to name a few. There was some great progress in this field but the problem of vision which was to make the machines understand the images was still not being addressed. During this time the neural networks also resurfaced as it was shown that the limitations of the perceptrons can be overcome by Multi-layer perceptrons. Also in the early 1990s convolutional neural networks were born which showed great results on digit recognition but did not scale up well on harder problems. The late 1990s and early 2000s saw the birth of modern computer vision. One of the reasons this happened was due to the availability of key, feature extraction and representation algorithms. Features along with the already present machine learning algorithms were used to detect, localise and segment objects in Images. While all these advancements were being made, the community felt the need to have standardised datasets and evaluation metrics so the performances can be compared. This led to the emergence of challenges like the Pascal VOC challenge and the ImageNet challenge. The availability of standard evaluation metrics and the open challenges gave directions to the research. Better algorithms were introduced for specific tasks like object detection and classification. Visual Turing Test aims to give a new direction to the computer vision research which would lead to the introduction of systems that will be one step closer to understanding images the way humans do. == Current evaluation practices == A large number of datasets have been annotated and generalised to benchmark performances of difference classes of algorithms to assess different vision tasks (e.g., object detection/recognition) on some image domain (e.g., scene images). One of the most famous datasets in computer vision is ImageNet which is used to assess the problem of object level Image classification. ImageNet is one of the largest annotated datasets available and has over one million images. The other important vision task is object detection and localisation which refers to detecting the object instance in the image and providing the bounding box coordinates around the object instance or segmenting the object. The most popular dataset for this task is the Pascal dataset. Similarly there are other datasets for specific tasks like the H3D dataset for human pose detection, Core dataset to evaluate the quality of detected object attributes such as colour, orientation, and activity. Having these standard datasets has helped the vision community to come up with well performing algorithms for all these tasks. The next logical step is to create a larger task encompassing of these smaller subtasks. Having such a task would lead to building systems that would understand images, as understanding images would inherently involve detecting objects, localising them and segmenting them. == Details == The Visual Turing Test (VTT) unlike the Turing test has a query engine system which interrogates a computer vision system in the presence of a human co-ordinator. It is a system that generates a random sequence of binary questions specific to the test image, such that the answer to any question k is unpredictable given the true answers to the previous k − 1 questions (also known as history of questions). The test happens in the presence of a human operator who serves two main purposes: removing the ambiguous questions and providing the correct answers to the unambiguous questions. Given an Image infinite possible binary questions can be asked and a lot of them are bound to be ambiguous. These questions if generated by the query engine are removed by the human moderator and instead the query engine generates another question such that the answer to it is unpredictable given the history of the questions. The aim of the Visual Turing Test is to evaluate the Image understanding of a computer system, and an important part of image understanding is the story line of the image. When humans look at an image, they do not think that there is a car at ‘x’ pixels from the left and ‘y’ pixels from the top, but instead they look at it as a story, for e.g. they might think that there is a car parked on the road, a person is exiting the car and heading towards a building. The most important elements of the story line are the objects and so to extract any story line from an image the first and the most important task is to instantiate the objects in it, and that is what the query engine does. === Query engine === The query engine is the core of the Visual Turing Test and it comprises two main parts : Vocabulary and Questions ==== Vocabulary ==== Vocabulary is a set of words that represent the elements of the images. This vocabulary when used with appropriate grammar leads to a set of questions. The grammar is defined in the next section in a way that it leads to a space of binary questions. The vocabulary V {\displaystyle {\mathcal {V}}} consist of three components: Types of Objects T {\displaystyle {\mathcal {T}}} Type-dependent attributes of objects A ( t ) {\displaystyle {\mathcal {A}}(t)} Type-dependent relationships between two objects R ( t , t ′ ) {\displaystyle {\mathcal {R}}(t,t')} For Images of urban street scenes the types of objects include people, vehicle and buildings. Attributes refer to the properties of these objects, for e.g. female, child, wearing a hat or carrying something, for people and moving, parked, stopped, one tire visible or two tires visible for vehicles. Relationships between each pair of object classes can be either “ordered” or “unordered”. The unordered relationships may include talking, walking together and the ordered relationships include taller, closer to the camera, occluding, being occluded etc. Additionally all of this vocabulary is used in context of rectangular image regions w \in W which allow for the localisation of objects in the image. An extremely large number of such regions are possible and this complicates the problem, so for this test, regions at specific scales are only used which include 1/16 the size of image, 1/4 the size of image, 1/2 the size of image or larger. ==== Questions ==== The question space is composed of four types of questions: Existence questions: The aim of the existence questions is to find new objects in the image that have not been uniquely identified previously. They are of the form : Qexist = 'Is there an instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' Uniqueness questions: A uniqueness question tries to uniquely identify an object to instantiate it. Quniq = 'Is there a unique instance of an object of type t with attributes A partially visible in region w that was not previously instantiated?' The uniqueness questions along with the existence questions form the instantiation questions. As mentioned earlier instantiating objects leads to other interesting questions and eventually a story line. Uniqueness questions follow the existence questions and a positive answer to it leads to instantiation of an object. Attribute questions: An attribute question tries to find more about the object once it has been instantiated. Such questions can query about a single attribute, conjunction of two attributes or disjunction of two attributes. Qatt(ot) = {'Does object ot have attribute a?' , 'Does object

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  Datasource

  A datasource or DataSource is a name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The data source name (DSN) need not be the same as the filename for the database. For example, a database file named friends.mdb could be set up with a DSN of school. Then DSN school would be used to refer to the database when performing a query. == Sun's version of DataSource [1] == A factory for connections to the physical data source that this DataSource object represents. An alternative to the DriverManager facility, a DataSource object is the preferred means of getting a connection. An object that implements the DataSource interface will typically be registered with a naming service based on the Java Naming and Directory Interface (JNDI) API. The DataSource interface is implemented by a driver vendor. There are three types of implementations: Basic implementation — produces a standard Connection object Connection pooling implementation — produces a Connection object that will automatically participate in connection pooling. This implementation works with a middle-tier connection pooling manager. Distributed transaction implementation — produces a Connection object that may be used for distributed transactions and almost always participates in connection pooling. This implementation works with a middle-tier transaction manager and almost always with a connection pooling manager. A DataSource object has properties that can be modified when necessary. For example, if the data source is moved to a different server, the property for the server can be changed. The benefit is that because the data source's properties can be changed, any code accessing that data source does not need to be changed. A driver that is accessed via a DataSource object does not register itself with the DriverManager. Rather, a DataSource object is retrieved through a lookup operation and then used to create a Connection object. With a basic implementation, the connection obtained through a DataSource object is identical to a connection obtained through the DriverManager facility. == Sun's DataSource Overview [2] == A DataSource object is the representation of a data source in the Java programming language. In basic terms, a data source is a facility for storing data. It can be as sophisticated as a complex database for a large corporation or as simple as a file with rows and columns. A data source can reside on a remote server, or it can be on a local desktop machine. Applications access a data source using a connection, and a DataSource object can be thought of as a factory for connections to the particular data source that the DataSource instance represents. The DataSource interface provides two methods for establishing a connection with a data source. Using a DataSource object is the preferred alternative to using the DriverManager for establishing a connection to a data source. They are similar to the extent that the DriverManager class and DataSource interface both have methods for creating a connection, methods for getting and setting a timeout limit for making a connection, and methods for getting and setting a stream for logging. Their differences are more significant than their similarities, however. Unlike the DriverManager, a DataSource object has properties that identify and describe the data source it represents. Also, a DataSource object works with a Java Naming and Directory Interface (JNDI) naming service and can be created, deployed, and managed separately from the applications that use it. A driver vendor will provide a class that is a basic implementation of the DataSource interface as part of its Java Database Connectivity (JDBC) 2.0 or 3.0 driver product. What a system administrator does to register a DataSource object with a JNDI naming service and what an application does to get a connection to a data source using a DataSource object registered with a JNDI naming service are described later in this chapter. Being registered with a JNDI naming service gives a DataSource object two major advantages over the DriverManager. First, an application does not need to hardcode driver information, as it does with the DriverManager. A programmer can choose a logical name for the data source and register the logical name with a JNDI naming service. The application uses the logical name, and the JNDI naming service will supply the DataSource object associated with the logical name. The DataSource object can then be used to create a connection to the data source it represents. The second major advantage is that the DataSource facility allows developers to implement a DataSource class to take advantage of features like connection pooling and distributed transactions. Connection pooling can increase performance dramatically by reusing connections rather than creating a new physical connection each time a connection is requested. The ability to use distributed transactions enables an application to do the heavy duty database work of large enterprises. Although an application may use either the DriverManager or a DataSource object to get a connection, using a DataSource object offers significant advantages and is the recommended way to establish a connection. Since 1.4 Since Java EE 6 a JNDI-bound DataSource can alternatively be configured in a declarative way directly from within the application. This alternative is particularly useful for self-sufficient applications or for transparently using an embedded database. == Yahoo's version of DataSource [3] == A DataSource is an abstract representation of a live set of data that presents a common predictable API for other objects to interact with. The nature of your data, its quantity, its complexity, and the logic for returning query results all play a role in determining your type of DataSource. For small amounts of simple textual data, a JavaScript array is a good choice. If your data has a small footprint but requires a simple computational or transformational filter before being displayed, a JavaScript function may be the right approach. For very large datasets—for example, a robust relational database—or to access a third-party webservice you'll certainly need to leverage the power of a Script Node or XHR DataSource.

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  List of security assessment tools

  This is a list of available software and hardware tools that are designed for or are particularly suited to various kinds of security assessment and security testing. == Operating systems and tool suites == Several operating systems and tool suites provide bundles of tools useful for various types of security assessment. === Operating system distributions === Kali Linux (formerly BackTrack), a penetration-test-focused Linux distribution based on Debian Pentoo, a penetration-test-focused Linux distribution based on Gentoo ParrotOS, a Linux distro focused on penetration testing, forensics, and online anonymity. == Tools ==

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