AI Email Tools

AI Email Tools — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  EXAPT

  EXAPT (a portmanteau of "Extended Subset of APT") is a production-oriented programming language that allows users to generate NC programs with control information for machining tools and facilitates decision-making for production-related issues that may arise during various machining processes. EXAPT was first developed to address industrial requirements. Through the years, the company created additional software for the manufacturing industry. Today, EXAPT offers a suite of SAAS products and services for the manufacturing industry. The trade name, EXAPT, is most commonly associated with the CAD/CAM-System, production data, and tool management software of the German company EXAPT Systemtechnik GmbH based in Aachen, DE. == General == EXAPT is a modularly built programming system for all NC machining operations as Drilling Turning Milling Turn-Milling Nibbling Flame-, laser-, plasma- and water jet cutting Wire eroding Operations with industrial robots Due to the modular structure, the main product groups, EXAPTcam and EXAPTpdo, are gradually expandable and permit individual software for the manufacturing industry used individually and also in a compound with an existing IT environment. == Functionality == EXAPTcam meets the requirements for NC planning, especially for the cutting operations such as turning, drilling, and milling up to 5-axis simultaneous machining. Thereby new process technologies, tool, and machine concepts are constantly involved. In the NC programming data from different sources such as 3D CAD models, drawings or tables can flow in. The possibilities of NC programming reaches from language-oriented to feature-oriented NC programming. The integrated EXAPT knowledge database and intelligent and scalable automatisms support the user. The EXAPT NC planning also covers the generation of production information as clamping and tool plans, presetting data or time calculations. The realistic simulation possibilities of NC planning and NC control data provide with production reliability. EXAPTpdo (EXAPT ProductionsDataOrganization) provides a neutrally applicable technology platform for the information compound of the NC planning - to the shop floor. This applies to all NC production data that are necessary for the set-up of NC machines, for the provision, presetting, and stocking of manufacturing resources and provided by EXAPTpdo in a central database. Besides classical functions of the tool management system (TMS) as the management of cutting tools, measuring, testing and clamping devices the technology data management and tool lifecycle management (TLM) is also included. System-supported "where-used lists" helps to handle the manufacturing resource cycle by secured requirement determination and requirement fulfillment. Unnecessary transports and unplanned dispositive adjustments are dropped, stocks are reduced, set-up times reduced and the throughput is increased. EXAPTpdo synchronizes involved systems within the value chain. Stock systems, MES systems or ERP systems (e.g. from the purchasing or production areas) do not work in isolation from each other but they interact with each other. EXAPTpdo provides the base to Smart Factory, for more flexibility in production and faster communication. == History == With the foundation of the EXAPT-Verein in 1967 as spin-off of the universities Aachen, Berlin and Stuttgart the further development "EXAPT (EXtended Subset of APT)" of the programming language "APT (Automatically Programmed Tool)" was focused and so the first milestone for the EXAPT history was set. In the same year the system EXAPT 1 for drilling and simple milling tasks became available. 1969 The industrial application of EXAPT 2 for the programming of NC machines with 2-axis linear and path control begins. In the following year, the development of the EXAPT modular system starts. 1972 BASIC-EXAPT is provided for the universal, homogeneous programming of all NC tasks. The support is made by the EXAPT applications consultancy. 1973 EXAPT 1.1 is provided for the programming of straight-cut and continuous-path controlled drilling and milling machines and machining centers. At the Hanover Fair (IHA 73) the interactive access to a mainframe via a time-sharing terminal for the part program entry and correction is presented and starts the replacement of the punch card. 1974 The possibilities for the use of process computers for the NC data transfer are leveled out. EXAPT offers the possibility of the result simulation when using plotters with display of tool paths and tools in assignment to the workpiece. In April 1975, the EXAPT NC Systemtechnik GmbH was founded with the aim, of enabling entry into the NC technique for small and medium-sized companies by a complete product and service program. In the following year, the system portfolio is extended with further system modules and service programs and the provision of postprocessors. 1978 The development activities on the EXAPT module system started in 1970 are completed. Using modern software techniques, the different system parts BASIC-EXAPT, EXAPT 1, EXAPT 1.1, and EXAPT 2 are composed of a total system. System support and applications consultancy become a new working focus. From the beginning to the middle of the 1980s Beside new portable software modules for CAD/CAM applications (e. g. CAPEX, NESTEX, CADEX, CADCPL), the first version of the EXAPT DNC system and extensions of the EXAPT NC programming system for the machining of sculptured surfaces are presented. 1988 EXAPT expands the software product range by systems for tool data management (BMO) and production data management (FDO). EXAPT trains more than 1,300 course participants including company-specific courses. 1992 The first version of the completely new product generation EXAPTplus is presented and the agency in Dresden is opened. 1993 The company name "EXAPT NC Systemtechnik GmbH" is changed to "EXAPT Systemtechnik GmbH." EXAPTplus is presented on PC under Windows NT at the EMO '93. The decentralization of the use of EXAPT systems expands the range of applications. In the following year, EXAPT-DNC is executable under Windows on a customary PC. Special hardware is not needed and so it can be used in compound with the database-supported EXAPT production data management system (FDO). 1995 EXAPTplus is also ready for complex application cases such as machining of tubes at extrusion tools. EXAPT-CADI provides the transfer of 2D CAD data to EXAPTplus. With the new office Gießen the marketing is strengthened. In the following year the EXAPT NC editor is developed for the direct processing of NC control data with tool path display and visualization of the tools. In the course of the market entry of more comfortable 3D CAD systems for the solid modelling of components a detailed evaluation of current systems is made in 1997. It is decided to use SolidWorks as a reference system for the solid-oriented NC planning with EXAPT. 1998 The first solution for the transfer of geometry data between SolidWorks and EXAPTplus is generated. The EXAPT organization systems are (beside SQL) also executable under Oracle now. The use of client server solutions supports the data flow in the production. 1999 AFR functions are provided in connection with EXAPTsolid to support a workpiece modelling for NC. The millennium capability is ensured for all EXAPT systems. AFR is a ground-breaking for the integration of third-party products. 2002 EXAPT-BMG is developed for the generation and visualization of tools with additional functions for the assembly from components. The acquisition of tools with their geometric and technological presentation offers extensive support of the NC planning with EXAPT systems. 2003 EXAPTpdo is available to optimize the process chains in production planning and production execution optimally regarding the increasing requirements of changing production conditions. 2004 Diverse system extensions are made in EXAPTplus, EXAPTsolid, EXAPT NC editor, EXAPTpdo for the complete machining on turning/milling centres with result reliability because of more extensive simulation based on realNC (Tecnomatix), for the use of new complex tool systems and the compound use between ERP systems as SAP and intelligent CNC systems. In the following year, EXAPTpdo is extended for the cross-order set-up optimization and provision of manufacturing re-sources especially for single and small series production with connection to purchase and physical portfolio management. 2006 The EXAPT systems are available for extended use as an information platform for production, the time management, and similar requirements. EXAPTsolid is extended for the feature-oriented milling operation and machine simulation. The NC programming of complex machine tools, e.g. three-turret-turning/milling centers is supported by EXAPT systems, as well as the use of multi-functional tools. 2007 A module for 3-5-axis simultaneous milling machining is presented.

  Read more →
• 

  JustWatch

  JustWatch is a website that provides information on the availability of films and TV shows on various streaming platforms such as Netflix, HBO Max, Disney+, Hulu, Peacock, Fandango at Home, Apple TV, and Amazon Prime Video, among others. It is also available as a mobile application and smart TV application. JustWatch provides a search engine that allows users to discover which digital platforms host a particular movie or TV series. As of November 2023, JustWatch is available to users in 139 countries. == Features == JustWatch functions as a search engine by aggregating information about the online availability of films and TV series from video-on-demand streaming services. It aggregates information from more than 100 video content libraries, as well providing information about video resolution quality, pricing, and purchase or rental options. The website includes various filters for searching, including genre, price, release date, rating, and popularity. Users are also able to create lists of shows and movies and to share these lists with other users. == History == JustWatch GmbH is an international database company that is privately held and headquartered in Berlin, Germany. The company specializes in the online availability of movies and TV series. In addition to its user-facing website, the company also has an advertising-focused arm, JustWatch Media, that works with corporate clients, using data about what people watch that it gleans from user behavior to help entertainment companies tailor their marketing strategies. Its clients include Universal Pictures, Paramount Pictures, and Sony Pictures, among others. Development of the website began in 2014, and it was launched in the U.S. and Germany in February 2015. In 2018, the company received funding to improve databases within the European Union. In December 2019, the company acquired a rival streaming aggregation service, GoWatchIt, from Plexus Entertainment. JustWatch also used the acquisition to open its first New York office. In 2019, JustWatch had over 30 million users across 38 countries. By 2020, the company's streaming aggregation service was available in over 45 countries. By November 2023, it was available in 139 countries, and had over 40 million monthly users. === Founding === JustWatch was co-founded in 2013 by David Croyé, Cristoph Hoyer, Kevin Hiller, Dominik Raute, Ingke Weimert, and Michael Wilken. In a company blog post from February 2017, Croyé described the group of co-founders as all having previously "worked in leading roles at successful international tech-startups in Berlin." Croyé, who currently holds the title of CEO at JustWatch GmbH, had previously worked as the chief marketing officer at kaufDA, a European location-based mobile coupon and promotion service, and the background of other co-founders included time at the adtech company Trademob and the streaming site MyVideo. Startup capital for the website initially came from the founders themselves. Croyé in particular was able to reinvest funds he had obtained from the sale of kaufDA to Axel Springer, a European media company, in March 2011. Since 2015, the company has had at least one additional round of seed funding, with investors including venture capital groups CG Partners and STS Ventures.

  Read more →
• 

  Multi-model database

  In the field of database design, a multi-model database is a database management system designed to support multiple data models against a single, integrated backend. In contrast, most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. Document, graph, relational, and key–value models are examples of data models that may be supported by a multi-model database. == Background == The relational data model became popular after its publication by Edgar F. Codd in 1970. Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety of data models, with document, graph, and key–value models being popular. A multi-model database is a database that can store, index and query data in more than one model. For some time, databases have primarily supported only one model, such as: relational database, document-oriented database, graph database or triplestore. A database that combines many of these is multi-model. This should not be confused with multimodal database systems such as Pixeltable or ApertureDB, which focus on unified management of different media types (images, video, audio, text) rather than different data models. For some time, it was all but forgotten (or considered irrelevant) that there were any other database models besides relational. The relational model and notion of third normal form were the default standard for all data storage. However, prior to the dominance of relational data modeling, from about 1980 to 2005, the hierarchical database model was commonly used. Since 2000 or 2010, many NoSQL models that are non-relational, including documents, triples, key–value stores and graphs are popular. Arguably, geospatial data, temporal data, and text data are also separate models, though indexed, queryable text data is generally termed a "search engine" rather than a database. The first time the word "multi-model" has been associated to the databases was on May 30, 2012 in Cologne, Germany, during the Luca Garulli's key note "NoSQL Adoption – What’s the Next Step?". Luca Garulli envisioned the evolution of the 1st generation NoSQL products into new products with more features able to be used by multiple use cases. The idea of multi-model databases can be traced back to Object–Relational Data Management Systems (ORDBMS) in the early 1990s and in a more broader scope even to federated and integrated DBMSs in the early 1980s. An ORDBMS system manages different types of data such as relational, object, text and spatial by plugging domain specific data types, functions and index implementations into the DBMS kernels. A multi-model database is most directly a response to the "polyglot persistence" approach of knitting together multiple database products, each handing a different model, to achieve a multi-model capability as described by Martin Fowler. This strategy has two major disadvantages: it leads to a significant increase in operational complexity, and there is no support for maintaining data consistency across the separate data stores, so multi-model databases have begun to fill in this gap. Multi-model databases are intended to offer the data modeling advantages of polyglot persistence, without its disadvantages. Operational complexity, in particular, is reduced through the use of a single data store. == Benchmarking multi-model databases == As more and more platforms are proposed to deal with multi-model data, there are a few works on benchmarking multi-model databases. For instance, Pluciennik, Oliveira, and UniBench reviewed existing multi-model databases and made an evaluation effort towards comparing multi-model databases and other SQL and NoSQL databases respectively. They pointed out that the advantages of multi-model databases over single-model databases are as follows : == Architecture == The main difference between the available multi-model databases is related to their architectures. Multi-model databases can support different models either within the engine or via different layers on top of the engine. Some products may provide an engine which supports documents and graphs while others provide layers on top of a key-key store. With a layered architecture, each data model is provided via its own component. == User-defined data models == In addition to offering multiple data models in a single data store, some databases allow developers to easily define custom data models. This capability is enabled by ACID transactions with high performance and scalability. In order for a custom data model to support concurrent updates, the database must be able to synchronize updates across multiple keys. ACID transactions, if they are sufficiently performant, allow such synchronization. JSON documents, graphs, and relational tables can all be implemented in a manner that inherits the horizontal scalability and fault-tolerance of the underlying data store. == Theoretical Foundation for Multi-Model Databases == The traditional theory of relations is not enough to accurately describe multi-model database systems. Recent research is focused on developing a new theoretical foundation for these systems. Category theory can provide a unified, rigorous language for modeling, integrating, and transforming different data models. By representing multi-model data as sets and their relationships as functions or relations within the Set category, we can create a formal framework to describe, manipulate, and understand various data models and how they interact.

  Read more →
• 

  IT baseline protection

  The IT baseline protection (German: IT-Grundschutz) approach from the German Federal Office for Information Security (BSI) is a methodology to identify and implement computer security measures in an organization. The aim is the achievement of an adequate and appropriate level of security for IT systems. To reach this goal the BSI recommends "well-proven technical, organizational, personnel, and infrastructural safeguards". Organizations and federal agencies show their systematic approach to secure their IT systems (e.g. Information Security Management System) by obtaining an ISO/IEC 27001 Certificate on the basis of IT-Grundschutz. == Overview baseline security == The term baseline security signifies standard security measures for typical IT systems. It is used in various contexts with somewhat different meanings. For example: Microsoft Baseline Security Analyzer: Software tool focused on Microsoft operating system and services security Cisco security baseline: Vendor recommendation focused on network and network device security controls Nortel baseline security: Set of requirements and best practices with a focus on network operators ISO/IEC 13335-3 defines a baseline approach to risk management. This standard has been replaced by ISO/IEC 27005, but the baseline approach was not taken over yet into the 2700x series. There are numerous internal baseline security policies for organizations, The German BSI has a comprehensive baseline security standard, that is compliant with the ISO/IEC 27000-series == BSI IT baseline protection == The foundation of an IT baseline protection concept is initially not a detailed risk analysis. It proceeds from overall hazards. Consequently, sophisticated classification according to damage extent and probability of occurrence is ignored. Three protection needs categories are established. With their help, the protection needs of the object under investigation can be determined. Based on these, appropriate personnel, technical, organizational and infrastructural security measures are selected from the IT Baseline Protection Catalogs. The Federal Office for Security in Information Technology's IT Baseline Protection Catalogs offer a "cookbook recipe" for a normal level of protection. Besides probability of occurrence and potential damage extents, implementation costs are also considered. By using the Baseline Protection Catalogs, costly security analyses requiring expert knowledge are dispensed with, since overall hazards are worked with in the beginning. It is possible for the relative layman to identify measures to be taken and to implement them in cooperation with professionals. The BSI grants a baseline protection certificate as confirmation for the successful implementation of baseline protection. In stages 1 and 2, this is based on self declaration. In stage 3, an independent, BSI-licensed auditor completes an audit. Certification process internationalization has been possible since 2006. ISO/IEC 27001 certification can occur simultaneously with IT baseline protection certification. (The ISO/IEC 27001 standard is the successor of BS 7799-2). This process is based on the new BSI security standards. This process carries a development price which has prevailed for some time. Corporations having themselves certified under the BS 7799-2 standard are obliged to carry out a risk assessment. To make it more comfortable, most deviate from the protection needs analysis pursuant to the IT Baseline Protection Catalogs. The advantage is not only conformity with the strict BSI, but also attainment of BS 7799-2 certification. Beyond this, the BSI offers a few help aids like the policy template and the GSTOOL. One data protection component is available, which was produced in cooperation with the German Federal Commissioner for Data Protection and Freedom of Information and the state data protection authorities and integrated into the IT Baseline Protection Catalog. This component is not considered, however, in the certification process. == Baseline protection process == The following steps are taken pursuant to the baseline protection process during structure analysis and protection needs analysis: The IT network is defined. IT structure analysis is carried out. Protection needs determination is carried out. A baseline security check is carried out. IT baseline protection measures are implemented. Creation occurs in the following steps: IT structure analysis (survey) Assessment of protection needs Selection of actions Running comparison of nominal and actual. === IT structure analysis === An IT network includes the totality of infrastructural, organizational, personnel, and technical components serving the fulfillment of a task in a particular information processing application area. An IT network can thereby encompass the entire IT character of an institution or individual division, which is partitioned by organizational structures as, for example, a departmental network, or as shared IT applications, for example, a personnel information system. It is necessary to analyze and document the information technological structure in question to generate an IT security concept and especially to apply the IT Baseline Protection Catalogs. Due to today's usually heavily networked IT systems, a network topology plan offers a starting point for the analysis. The following aspects must be taken into consideration: The available infrastructure, The organizational and personnel framework for the IT network, Networked and non-networked IT systems employed in the IT network. The communications connections between IT systems and externally, IT applications run within the IT network. === Protection needs determination === The purpose of the protection needs determination is to investigate what protection is sufficient and appropriate for the information and information technology in use. In this connection, the damage to each application and the processed information, which could result from a breach of confidentiality, integrity or availability, is considered. Important in this context is a realistic assessment of the possible follow-on damages. A division into the three protection needs categories "low to medium", "high" and "very high" has proved itself of value. "Public", "internal" and "secret" are often used for confidentiality. === Modelling === Heavily networked IT systems typically characterize information technology in government and business these days. As a rule, therefore, it is advantageous to consider the entire IT system and not just individual systems within the scope of an IT security analysis and concept. To be able to manage this task, it makes sense to logically partition the entire IT system into parts and to separately consider each part or even an IT network. Detailed documentation about its structure is prerequisite for the use of the IT Baseline Protection Catalogs on an IT network. This can be achieved, for example, via the IT structure analysis described above. The IT Baseline Protection Catalog’s' components must ultimately be mapped onto the components of the IT network in question in a modelling step. === Baseline security check === The baseline security check is an organisational instrument offering a quick overview of the prevailing IT security level. With the help of interviews, the status quo of an existing IT network (as modelled by IT baseline protection) relative to the number of security measures implemented from the IT Baseline Protection Catalogs are investigated. The result is a catalog in which the implementation status "dispensable", "yes", "partly", or "no" is entered for each relevant measure. By identifying not yet, or only partially, implemented measures, improvement options for the security of the information technology in question are highlighted. The baseline security check gives information about measures, which are still missing (nominal vs. actual comparison). From this follows what remains to be done to achieve baseline protection through security. Not all measures suggested by this baseline check need to be implemented. Peculiarities are to be taken into account! It could be that several more or less unimportant applications are running on a server, which have lesser protection needs. In their totality, however, these applications are to be provided with a higher level of protection. This is called the (cumulation effect). The applications running on a server determine its need for protection. Several IT applications can run on an IT system. When this occurs, the application with the greatest need for protection determines the IT system’s protection category. Conversely, it is conceivable that an IT application with great protection needs does not automatically transfer this to the IT system. This may happen because the IT system is configured redundantly, or because only an inconsequential part is running on it. This is called the (distribution effect). This is the case, fo

  Read more →
• 

  BERT (language model)

  Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state of the art for large language models. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. With this training, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It improved on ELMo and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT was originally implemented in the English language at two model sizes, BERTBASE (110 million parameters) and BERTLARGE (340 million parameters). Both were trained on the Toronto BookCorpus (800M words) and English Wikipedia (2,500M words). The weights were released on GitHub. On March 11, 2020, 24 smaller models were released, the smallest being BERTTINY with just 4 million parameters. == Architecture == BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: Tokenizer: This module converts a piece of English text into a sequence of integers ("tokens"). Embedding: This module converts the sequence of tokens into an array of real-valued vectors representing the tokens. It represents the conversion of discrete token types into a lower-dimensional Euclidean space. Encoder: a stack of Transformer blocks with self-attention, but without causal masking. Task head: This module converts the final representation vectors into one-shot encoded tokens again by producing a predicted probability distribution over the token types. It can be viewed as a simple decoder, decoding the latent representation into token types, or as an "un-embedding layer". The task head is necessary for pre-training, but it is often unnecessary for so-called "downstream tasks," such as question answering or sentiment classification. Instead, one removes the task head and replaces it with a newly initialized module suited for the task, and finetune the new module. The latent vector representation of the model is directly fed into this new module, allowing for sample-efficient transfer learning. === Embedding === This section describes the embedding used by BERTBASE. The other one, BERTLARGE, is similar, just larger. The tokenizer of BERT is WordPiece, which is a sub-word strategy like byte-pair encoding. Its vocabulary size is 30,000, and any token not appearing in its vocabulary is replaced by [UNK] ("unknown"). The first layer is the embedding layer, which contains three components: token type embeddings, position embeddings, and segment type embeddings. Token type: The token type is a standard embedding layer, translating a one-hot vector into a dense vector based on its token type. Position: The position embeddings are based on a token's position in the sequence. BERT uses absolute position embeddings, where each position in a sequence is mapped to a real-valued vector. Each dimension of the vector consists of a sinusoidal function that takes the position in the sequence as input. Segment type: Using a vocabulary of just 0 or 1, this embedding layer produces a dense vector based on whether the token belongs to the first or second text segment in that input. In other words, type-1 tokens are all tokens that appear after the [SEP] special token. All prior tokens are type-0. The three embedding vectors are added together representing the initial token representation as a function of these three pieces of information. After embedding, the vector representation is normalized using a LayerNorm operation, outputting a 768-dimensional vector for each input token. After this, the representation vectors are passed forward through 12 Transformer encoder blocks, and are decoded back to 30,000-dimensional vocabulary space using a basic affine transformation layer. === Architectural family === The encoder stack of BERT has 2 free parameters: L {\displaystyle L} , the number of layers, and H {\displaystyle H} , the hidden size. There are always H / 64 {\displaystyle H/64} self-attention heads, and the feed-forward/filter size is always 4 H {\displaystyle 4H} . By varying these two numbers, one obtains an entire family of BERT models. For BERT: the feed-forward size and filter size are synonymous. Both of them denote the number of dimensions in the middle layer of the feed-forward network. the hidden size and embedding size are synonymous. Both of them denote the number of real numbers used to represent a token. The notation for encoder stack is written as L/H. For example, BERTBASE is written as 12L/768H, BERTLARGE as 24L/1024H, and BERTTINY as 2L/128H. == Training == === Pre-training === BERT was pre-trained simultaneously on two tasks: Masked language modeling (MLM): In this task, BERT ingests a sequence of words, where one word may be randomly changed ("masked"), and BERT tries to predict the original words that had been changed. For example, in the sentence "The cat sat on the [MASK]," BERT would need to predict "mat." This helps BERT learn bidirectional context, meaning it understands the relationships between words not just from left to right or right to left but from both directions at the same time. Next sentence prediction (NSP): In this task, BERT is trained to predict whether one sentence logically follows another. For example, given two sentences, "The cat sat on the mat" and "It was a sunny day", BERT has to decide if the second sentence is a valid continuation of the first one. This helps BERT understand relationships between sentences, which is important for tasks like question answering or document classification. ==== Masked language modeling ==== In masked language modeling, 15% of tokens would be randomly selected for masked-prediction task, and the training objective was to predict the masked token given its context. In more detail, the selected token is: replaced with a [MASK] token with probability 80%, replaced with a random word token with probability 10%, not replaced with probability 10%. The reason not all selected tokens are masked is to avoid the dataset shift problem. The dataset shift problem arises when the distribution of inputs seen during training differs significantly from the distribution encountered during inference. A trained BERT model might be applied to word representation (like Word2Vec), where it would be run over sentences not containing any [MASK] tokens. It is later found that more diverse training objectives are generally better. As an illustrative example, consider the sentence "my dog is cute". It would first be divided into tokens like "my1 dog2 is3 cute4". Then a random token in the sentence would be picked. Let it be the 4th one "cute4". Next, there would be three possibilities: with probability 80%, the chosen token is masked, resulting in "my1 dog2 is3 [MASK]4"; with probability 10%, the chosen token is replaced by a uniformly sampled random token, such as "happy", resulting in "my1 dog2 is3 happy4"; with probability 10%, nothing is done, resulting in "my1 dog2 is3 cute4". After processing the input text, the model's 4th output vector is passed to its decoder layer, which outputs a probability distribution over its 30,000-dimensional vocabulary space. ==== Next sentence prediction ==== Given two sentences, the model predicts if they appear sequentially in the training corpus, outputting either [IsNext] or [NotNext]. During training, the algorithm sometimes samples two sentences from a single continuous span in the training corpus, while at other times, it samples two sentences from two discontinuous spans. The first sentence starts with a special token, [CLS] (for "classify"). The two sentences are separated by another special token, [SEP] (for "separate"). After processing the two sentences, the final vector for the [CLS] token is passed to a linear layer for binary classification into [IsNext] and [NotNext]. For example: Given "[CLS] my dog is cute [SEP] he likes playing [SEP]", the model should predict [IsNext]. Given "[CLS] my dog is cute [SEP] how do magnets work [SEP]", the model should predict [NotNext]. === Fine-tuning === BERT is meant as a general pretrained model for various applications in natural language processing. That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification, and sequence-to-sequence-based language generation tasks such as question answering and conversational response generation. The original BERT paper published results demonstrating that a small amount of fine

  Read more →
• 

  Sports Card Investor

  Sports Card Investor is an American sports collectibles media platform and mobile application founded by Geoff Wilson. The platform provides market data, analysis, and editorial content focused on sports trading cards and related collectibles. It operates a website, mobile app, and digital media channels covering developments in the sports card industry. The company posted its first YouTube video in July 2019, shortly before a period of rapid growth in sports card collecting in the early 2020s, which was marked by increased trading volumes and mainstream media attention. == History == Sports Card Investor was founded by Geoff Wilson, an entrepreneur and collector who began publishing sports card–related content online before launching the platform's dedicated app and subscription tools. In February 2020, the company launched Market Movers, the first website and app to chart sports card prices and track card collections. The platform expanded its media presence through partnerships and distribution agreements. In 2023, Yahoo Sports announced a new collectibles coverage initiative that included additional content from Sports Card Investor. In February 2024, the Sports Card Investor studio relocated to CardsHQ in Atlanta, Georgia, and visitors to the facility can watch Sports Card Investor videos being filmed. == Platform and content == The Sports Card Investor app provides users with pricing data, portfolio-tracking tools, and market-trend analysis for trading cards. The company also produces video and editorial content discussing market developments, grading trends, and major card releases. Coverage in industry publications has referenced Sports Card Investor in discussions about shifts in sports card licensing rights and hobby market reactions. == Industry context == The growth of Sports Card Investor coincided with a broader resurgence in trading card markets, including record sales and expanded retail presence. Mainstream outlets have cited the company and its founder in reporting on collectibles investing trends, grading practices, and market volatility. The Sports Card Investor app has attracted over 37,000 reviews on the Apple App Store, reflecting its strong user engagement within the sports card community.

  Read more →
• 

  Dark data

  Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics. Organizations retain dark data for a multitude of reasons, and it is estimated that most companies are only analyzing 1% of their data. Often it is stored for regulatory compliance and record keeping. Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information. Because storage is inexpensive, storing data is easy. However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit. In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn. He uses it to describe research data, especially from the long tail of science (the many, small research projects), which are not or no longer available for research because they disappear in a drawer without adequate data management. Without this, the data become dark, and further reasons for this are e.g. missing metadata annotation, missing data management plans and data curators. == Analysis == The term "dark data" very often refers to data that is not amenable to computer processing. For example, a company might have a great deal of data that exists only as scanned page-images. Even the bare text in such documents is not available without something like Optical character recognition, which can vary greatly in accuracy. Even with OCR, the significance of each part of the data is unavailable. An obvious examples is whether a capitalized word is a name or not, and if so, whether it represents a person, place, organization, or even a work of art. Bibliographic and other references, data within tables (that may be labeled quite adequately for humans, but not for processing), and countless assertions represented with the full complexity and ambiguity of human language. A lot of unused data is very valuable, and would be used if it could be; but is blocked because it is in formats that are difficult to process, categorise, identify, and analyse. Often the reason that business does not use their dark data is because of the amount of resources it would take and the difficulty of having that data analysed. In other words, the data is "dark" not because it is not used, but because it cannot (feasibly or affordably) be used, given its poor representation. There are many data representations that can make data much more accessible for automation. However, a great deal of information lacks any such identification of information items or relationships; and much more loses it during "downhill" conversion such as saving to page-oriented representations, printing, scanning, or faxing. The journey back "uphill" can be costly. According to Computer Weekly, 60% of organisations believe that their own business intelligence reporting capability is "inadequate" and 65% say that they have "somewhat disorganised content management approaches". == Relevance == Useful data may become dark data after it becomes irrelevant, as it is not processed fast enough. This is called "perishable insights" in "live flowing data". For example, if the geolocation of a customer is known to a business, the business can make offer based on the location, however if this data is not processed immediately, it may be irrelevant in the future. According to IBM, about 60 percent of data loses its value immediately. == Storage == According to the New York Times, 90% of energy used by data centres is wasted. If data was not stored, energy costs could be saved. Furthermore, there are costs associated with the underutilisation of information and thus missed opportunities. According to Datamation, "the storage environments of EMEA organizations consist of 54 percent dark data, 32 percent redundant, obsolete and trivial data and 14 percent business-critical data. By 2020, this can add up to $891 billion in storage and management costs that can otherwise be avoided." The continuous storage of dark data can put an organisation at risk, especially if this data is sensitive. In the case of a breach, this can result in serious repercussions. These can be financial, legal and can seriously hurt an organisation's reputation. For example, a breach of private records of customers could result in the stealing of sensitive information, which could result in identity theft. Another example could be the breach of the company's own sensitive information, for example relating to research and development. These risks can be mitigated by assessing and auditing whether this data is useful to the organisation, employing strong encryption and security and finally, if it is determined to be discarded, then it should be discarded in a way that it becomes unretrievable. == Future == It is generally considered that as more advanced computing systems for analysis of data are built, the higher the value of dark data will be. It has been noted that "data and analytics will be the foundation of the modern industrial revolution". Of course, this includes data that is currently considered "dark data" since there are not enough resources to process it. All this data that is being collected can be used in the future to bring maximum productivity and an ability for organisations to meet consumers' demand. Technology advancements are helping to leverage this dark data affordably. Furthermore, many organisations do not realise the value of dark data right now, for example in healthcare and education organisations deal with large amounts of data that could create a significant "potential to service students and patients in the manner in which the consumer and financial services pursue their target population".

  Read more →
• 

  Intel Management Engine

  The Intel Management Engine (ME), also known as the Intel Manageability Engine, is an autonomous subsystem that has been incorporated in virtually all of Intel's processor chipsets since 2008. It is located in the Platform Controller Hub of modern Intel motherboards. The Intel Management Engine always runs as long as the motherboard is receiving power, even when the computer is turned off. This issue can be mitigated with the deployment of a hardware device which is able to disconnect all connections to mains power as well as all internal forms of energy storage. The Electronic Frontier Foundation and some security researchers have voiced concern that the Management Engine is a backdoor. Intel's main competitor, AMD, has incorporated the equivalent AMD Secure Technology (formally called Platform Security Processor) in virtually all of its post-2013 CPUs. == Difference from Intel AMT == The Management Engine is often confused with Intel AMT (Intel Active Management Technology). AMT runs on the ME, but is only available on processors with vPro. AMT gives device owners remote administration of their computer, such as powering it on or off, and reinstalling the operating system. However, the ME itself has been built into all Intel chipsets since 2008, not only those with AMT. While AMT can be unprovisioned by the owner, there is no official, documented way to disable the ME. == Design == The subsystem primarily consists of proprietary firmware running on a separate microprocessor that performs tasks during boot-up, while the computer is running, and while it is asleep. As long as the chipset or SoC is supplied with power (via battery or power supply), it continues to run even when the system is turned off. Intel claims the ME is required to provide full performance. Its exact workings are largely undocumented and its code is obfuscated using confidential Huffman tables stored directly in hardware, so the firmware does not contain the information necessary to decode its contents. === Hardware === Starting with ME 11 (introduced in Skylake CPUs), it is based on the Intel Quark x86-based 32-bit CPU and runs the MINIX 3 operating system. The ME firmware is stored in a partition of the SPI BIOS Flash, using the Embedded Flash File System (EFFS). Previous versions were based on an ARC core, with the Management Engine running the ThreadX RTOS. Versions 1.x to 5.x of the ME used the ARCTangent-A4 (32-bit only instructions) whereas versions 6.x to 8.x used the newer ARCompact (mixed 32- and 16-bit instruction set architecture). Starting with ME 7.1, the ARC processor could also execute signed Java applets. The ME has its own MAC and IP address for the out-of-band management interface, with direct access to the Ethernet controller; one portion of the Ethernet traffic is diverted to the ME even before reaching the host's operating system, for what support exists in various Ethernet controllers, exported and made configurable via Management Component Transport Protocol (MCTP). The ME also communicates with the host via PCI interface. Under Linux, communication between the host and the ME is done via /dev/mei or /dev/mei0. Until the release of Nehalem processors, the ME was usually embedded into the motherboard's northbridge, following the Memory Controller Hub (MCH) layout. With the newer Intel architectures (Intel 5 Series onwards), the ME is integrated into the Platform Controller Hub (PCH). === Firmware === By Intel's current terminology as of 2017, ME is one of several firmware sets for the Converged Security and Manageability Engine (CSME). Prior to AMT version 11, CSME was called Intel Management Engine BIOS Extension (Intel MEBx). Management Engine (ME) – mainstream chipsets Server Platform Services (SPS) – server chipsets and SoCs Trusted Execution Engine (TXE) – tablet/embedded/low power It was also found that the ME firmware version 11 runs MINIX 3. Management of the ME modules for provisioning inside the UEFI is done via a tool called Intel Flash Image Tool (FITC). ==== Modules ==== Active Management Technology (AMT) Intel Boot Guard (IBG) and Secure Boot Quiet System Technology (QST), formerly known as Advanced Fan Speed Control (AFSC), which provides support for acoustically optimized fan speed control, and monitoring of temperature, voltage, current and fan speed sensors that are provided in the chipset, CPU and other devices present on the motherboard. Communication with the QST firmware subsystem is documented and available through the official software development kit (SDK). Protected Audio Video Path, enforces HDCP Intel Anti-Theft Technology (AT), discontinued in 2015 Serial over LAN (SOL) Intel Platform Trust Technology (PTT), a firmware-based Trusted Platform Module (TPM) Near Field Communication, a middleware for NFC readers and vendors to access NFC cards and provide secure element access, found in later MEI versions. == The intricacies of working with Intel ME == It should also be noted that the ME region requires special cleaning and subsequent initialisation, for example, after replacing the platform hub on the motherboard. Usually, this requires an SPI programmer. There are known successful cases of this operation being performed. == Security vulnerabilities == Several weaknesses have been found in the ME. On May 1, 2017, Intel confirmed a Remote Elevation of Privilege bug (SA-00075) in its Management Technology. Every Intel platform with provisioned Intel Standard Manageability, Active Management Technology, or Small Business Technology, from Nehalem in 2008 to Kaby Lake in 2017 has a remotely exploitable security hole in the ME. Several ways to disable the ME without authorization that could allow ME's functions to be sabotaged have been found. Additional major security flaws in the ME affecting a very large number of computers incorporating ME, Trusted Execution Engine (TXE), and Server Platform Services (SPS) firmware, from Skylake in 2015 to Coffee Lake in 2017, were confirmed by Intel on November 20, 2017 (SA-00086). Unlike SA-00075, this bug is even present if AMT is absent, not provisioned or if the ME was "disabled" by any of the known unofficial methods. In July 2018, another set of vulnerabilities was disclosed (SA-00112). In September 2018, yet another vulnerability was published (SA-00125). === Ring −3 rootkit === A ring −3 rootkit was demonstrated by Invisible Things Lab for the Q35 chipset; it does not work for the later Q45 chipset as Intel implemented additional protections. The exploit worked by remapping the normally protected memory region (top 16 MB of RAM) reserved for the ME. The ME rootkit could be installed regardless of whether the AMT is present or enabled on the system, as the chipset always contains the ARC ME coprocessor. (The "−3" designation was chosen because the ME coprocessor works even when the system is in the S3 state. Thus, it was considered a layer below the System Management Mode rootkits.) For the vulnerable Q35 chipset, a keystroke logger ME-based rootkit was demonstrated by Patrick Stewin. === Zero-touch provisioning === Another security evaluation by Vassilios Ververis showed serious weaknesses in the GM45 chipset implementation. In particular, it criticized AMT for transmitting unencrypted passwords in the SMB provisioning mode when the IDE redirection and Serial over LAN features are used. It also found that the "zero touch" provisioning mode (ZTC) is still enabled even when the AMT appears to be disabled in BIOS. For about 60 euros, Ververis purchased from GoDaddy a certificate that is accepted by the ME firmware and allows remote "zero touch" provisioning of (possibly unsuspecting) machines, which broadcast their HELLO packets to would-be configuration servers. === SA-00075 (a.k.a. Silent Bob is Silent) === In May 2017, Intel confirmed that many computers with AMT have had an unpatched critical privilege escalation vulnerability (CVE-2017-5689). The vulnerability was nicknamed "Silent Bob is Silent" by the researchers who had reported it to Intel. It affects numerous laptops, desktops and servers sold by Dell, Fujitsu, Hewlett-Packard (later Hewlett Packard Enterprise and HP Inc.), Intel, Lenovo, and possibly others. Those researchers claimed that the bug affects systems made in 2010 or later. Other reports claimed the bug also affects systems made as long ago as 2008. The vulnerability was described as giving remote attackers: "full control of affected machines, including the ability to read and modify everything. It can be used to install persistent malware (possibly in firmware), and read and modify any data." === PLATINUM === In June 2017, the PLATINUM cybercrime group became notable for exploiting the serial over LAN (SOL) capabilities of AMT to perform data exfiltration of stolen documents. SOL is disabled by default and must be enabled to exploit this vulnerability. === SA-00086 === Some months after the previous bugs, and subsequent warnings from the EFF, securi

  Read more →
• 

  Cloud-based integration

  Cloud-based integration is a form of systems integration business delivered as a cloud computing service that addresses data, process, service-oriented architecture (SOA) and application integration. == Description == Integration platform as a service (iPaaS) is a suite of cloud services enabling customers to develop, execute and govern integration flows between disparate applications. Under the cloud-based iPaaS integration model, customers drive the development and deployment of integrations without installing or managing any hardware or middleware. The iPaaS model allows businesses to achieve integration without big investment into skills or licensed middleware software. iPaaS used to be regarded primarily as an integration tool for cloud-based software applications, used mainly by small to mid-sized business. Over time, a hybrid type of iPaaS—hybrid-IT iPaaS—that connects cloud to on-premises, is becoming increasingly popular. Additionally, large enterprises are exploring new ways of integrating iPaaS into their existing IT infrastructures. Cloud integration was created to break down the data silos, improve connectivity and optimize the business process. Cloud integration has increased in popularity as the usage of Software as a Service solutions has grown. Prior to the emergence of cloud computing in the early 2000s, integration could be categorized as either internal or business to business (B2B). Internal integration requirements were serviced through an on-premises middleware platform and typically utilized a service bus to manage exchange of data between systems. B2B integration was serviced through EDI gateways or value-added network (VAN). The advent of SaaS applications created a new kind of demand which was met through cloud-based integration. Since their emergence, many such services have also developed the capability to integrate legacy or on-premises applications, as well as function as EDI gateways. The following essential features were proposed by one marketing company: Deployed on a multi-tenant, elastic cloud infrastructure Subscription model pricing (operating expense, not capital expenditure) No software development (required connectors should already be available) Users do not perform deployment or manage the platform itself Presence of integration management and monitoring features The emergence of this sector led to new cloud-based business process management tools that do not need to build integration layers - since those are now a separate service. Drivers of growth include the need to integrate mobile app capabilities with proliferating API publishing resources and the growth in demand for the Internet of things functionalities as more 'things' connect to the Internet.

  Read more →
• 

  Lost Art-Database

  The Lost Art-Datenbank is an online database published by the German Lost Art Foundation (Deutsches Zentrum Kulturgutverluste. It contains information on cultural objects looted from Jewish collectors or transferred due to Nazi persecution during the Nazi era. Until 2015, it was managed by the Koordinierungsstelle für Kulturgutverluste (Magdeburg Coordination Office). == Creation == Following the Washington Conference of 1998, and the commitments to provide more transparency regarding looted art, Germany launched the Lost Art Database in 2000 order to help Holocaust victims and their families track down artworks that had been looted from them or lost due to Nazi persecution. == Functionality == The Lost Art Database lists art and books and other cultural objects that were lost, seized, stolen or forceably sold during the Nazi era. The database is divided into search requests from victims' families, heirs or institutions and "found" reports from cultural institutions on items with unresolved provenance gaps from the Nazi periods. The section on reports of finds lists objects that are known to have been unlawfully seized or relocated as a result of the war. In addition, reports are published here on cultural objects for which an uncertain or incomplete provenance may indicate a possible unlawful seizure or war-related relocation. The publication of reports in the Lost Art Internet Database is carried out on behalf of and with the consent of the reporting persons and institutions. The responsibility for the content of the reports lies with these legal or natural persons. There have been controversies over which items should be included in the database. Lost Art is based on the Washington Principles adopted in 1998, which Germany has committed itself to implementing (Joint Declaration, 1999). The Lost Art Database is considered a key resource in the search for looted art and the victims of persecution. Every item in the Lost Art Database has an identifier, known as a Lost Art ID. Proveana is the linked research database. == Other lost art databases == Other countries have launched databases to help identify Nazi looted art. Each database has its own area of focus. The German Lost Art Database allows families or heirs to submit information. Other countries have databases that focus on looted artworks that have not been found or artworks that were repatriated to the national authorities after the defeat of the Nazis but were never returned to their original owners. Other databases have been created for stolen antiquities, looted art from colonial era, art stolen from Syria, Iraq, Ukraine, or from museums or collectors.

  Read more →
• 

  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.

  Read more →
• 

  Document-oriented database

  A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown alongside the adoption of NoSQL itself. XML databases are a subclass of document-oriented databases optimized for XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document-oriented databases are conceptually an extension of the key–value store, another type of NoSQL database. In key-value stores, data is treated as opaque by the database, whereas document-oriented systems exploit the internal structure of documents to extract metadata and optimize storage and queries. Although in practice the distinction can be minimal due to modern tooling, document stores are designed to provide a richer programming experience with modern programming techniques. Document databases differ significantly from traditional relational databases (RDBs). Relational databases store data in predefined tables, often requiring an object to be split across multiple tables. In contrast, document databases store all information for a given object in a single document, with each document potentially having a unique structure. This design eliminates the need for object-relational mapping when loading data into the database. == Documents == The central concept of a document-oriented database is the notion of a document. Although implementations vary in their specific definitions, document-oriented databases generally treat documents as self-contained units that encapsulate and encode data in a standardized format. Common encoding formats include XML, YAML, JSON, as well as binary representations such as BSON. Documents in a document store are equivalent to the programming concept of an object. They are not required to adhere to a fixed schema, and documents within the same collection may contain different fields or structures. Fields may be optional, and documents of the same logical type may differ in composition. For example, the following illustrates a document encoded in JSON: A second document might be encoded in XML as: The two example documents share some structural elements but also contain unique fields. The structure, text, and other data within each document are collectively referred to as the document's content and can be accessed or modified using retrieval or editing operations. Unlike relational databases, in which each record contains the same fields and unused fields are left empty, document-oriented databases do not require uniform fields across documents. This design allows new information to be added to some documents without affecting the structure of others. Document databases often support the storage of additional metadata alongside the document content. Such metadata may relate to organizational features, security, indexing, or other implementation-specific features. === CRUD operations === The core operations supported by a document-oriented database for manipulating documents are similar to those in other databases. Although terminology is not perfectly standardized, these operations are generally recognized as Create, Read, Update, and Delete (CRUD). Creation (C): Adds a new document to the database. Retrieval (R): Retrieves documents or fields based on queries. Update (U): Modifies the contents of existing documents. Deletion (D): Removes documents from the database. === Keys === Documents in a document-oriented database are addressed via a unique identifier. This identifier, often a string, URI, or path, can be used to retrieve the document from the database. Most document stores maintain an index on the key to optimize retrieval, and in some implementations the key is required when creating or inserting a new document. === Retrieval === In addition to key-based access, document-oriented databases typically provide an API or query language that enables retrieval based on document content or associated metadata. For example, a query may return all documents with a specific field matching a given value. The available query features, indexing options, and performance characteristics vary across implementations. Document stores differ from key-value stores in that they exploit the internal structure and metadata of stored documents. In many key-value stores, values are treated as opaque or "black-box" data, meaning the database system does not interpret their internal structure. By contrast, document-oriented databases can classify and interpret document content. This enables queries that distinguish between types of data––for example, retrieving all phone numbers containing "555" without also matching a postal code such as "55555." === Editing === Document databases typically provide mechanisms for updating or editing the content or metadata of a document. Updates may involve replacing the entire document or modifying individual elements or fields within the document. === Organization === Document database implementations support a variety of methods for organizing documents, including: Collections: Groups of documents. Depending on the implementation, a document may be required to belong to a single collection or may be allowed in multiple collections. Tags and non-visible metadata: Additional data stored outside the main document content. Directory hierarchies: Documents organized in a tree-like structure, often based on path or URI. These organizational structures may differ between logical and physical representations (e.g. on disk or in memory). == Relationship to other databases == === Relationship to key-value stores === A document-oriented database can be viewed as a specialized form of key-value store, which is itself a category of NoSQL database. In a basic key-value store, the stored value is typically treated as opaque by the database system. By contrast, a document-oriented database provides APIs or a query and update language that allows queries and modifications based on the internal structure of the document. For users who do not require advanced query, retrieval, or update capabilities, the distinction between document-oriented databases and key-value stores may be minimal. === Relationship to search engines === Some search engine and information retrieval systems, such as Apache Solr and Elasticsearch, provide document storage and support core document operations. As a result, they may meet certain functional definitions of a document-oriented database, although their primary design goals differ. === Relationship to relational databases === In a relational database, data is organized into predefined types represented as tables. Each table contains rows (records) with a fixed set of columns (fields), so all records in a table share the same structure. Administrators typically define indexes on selected fields to improve query performance. A central principle of relational database design is database normalization, in which data that might otherwise be repeated is stored in separate tables and linked using keys. When records in different tables are related, a foreign key is used to associate them. For example, an address book application may store a contact's name, image, phone numbers, mailing addresses, and email addresses. In a normalized relational design, separate tables might be created for contacts, phone numbers, and email addresses. The phone number table would include a foreign key referencing the associated contact. To reconstruct a complete contact record, the database retrieves related information from each table using the foreign keys and combines it into a single record. In contrast, a document-oriented database stores all data related to an object within a single document, and stored in the database as a single entry. In the address book example,the contact's name, image, and contact information may be stored together in one document. The document is retrieved using a unique key, and all related information is returned together, without needing to look up multiple tables. A key difference between the document-oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in a database, and documents can change in type and form over time. For example, a new field such as COUNTRY_FLAG can be added to new documents as they are inserted without affecting existing documents. To aid retrieval, document-oriented systems generally allow the administrator to provide hints to the database for locating certain types of information. These hints work in a similar fashion to indexes in relational databases. Many systems also allow additional metadata outside the content of the document itself

  Read more →
• 

  Graphics software

  In computer graphics, graphics software refers to a program or collection of programs that enable a person to manipulate images or models visually on a computer. Computer graphics can be classified into two distinct categories: raster graphics and vector graphics, with further 2D and 3D variants. Many graphics programs focus exclusively on either vector or raster graphics, but there are a few that operate on both. It is simple to convert from vector graphics to raster graphics, but going the other way is harder. Some software attempts to do this. In addition to static graphics, there are animation and video editing software. Different types of software are often designed to edit different types of graphics such as video, photos, and vector-based drawings. The exact sources of graphics may vary for different tasks, but most can read and write files. Most graphics programs have the ability to import and export one or more graphics file formats, including those formats written for a particular computer graphics program. Such programs include, but are not limited to: GIMP, Adobe Photoshop, CorelDRAW, Microsoft Publisher, Picasa, etc. The use of a swatch is a palette of active colours that are selected and rearranged by the preference of the user. A swatch may be used in a program or be part of the universal palette on an operating system. It is used to change the colour of a text or image and in video editing. Vector graphics animation can be described as a series of mathematical transformations that are applied in sequence to one or more shapes in a scene. Raster graphics animation works in a similar fashion to film-based animation, where a series of still images produces the illusion of continuous movement. == History == SuperPaint was one of the earliest graphics software applications, first conceptualized in 1972 and achieving its first stable image in 1973 Fauve Matisse (later Macromedia xRes) was a pioneering program of the early 1990s, notably introducing layers in customer software. Currently Adobe Photoshop is one of the most used and best-known graphics programs in the Americas, having created more custom hardware solutions in the early 1990s, but was initially subject to various litigation. GIMP is a popular open-source alternative to Adobe Photoshop.

  Read more →
• 

  Per-pixel lighting

  In computer graphics, per-pixel lighting refers to any technique for lighting an image or scene that calculates illumination for each pixel on a rendered image. This is in contrast to other popular methods of lighting such as vertex lighting, which calculates illumination at each vertex of a 3D model and then interpolates the resulting values over the model's faces to calculate the final per-pixel color values. Per-pixel lighting is commonly used with techniques, such as blending, alpha blending, alpha to coverage, anti-aliasing, texture filtering, clipping, hidden-surface determination, Z-buffering, stencil buffering, shading, mipmapping, normal mapping, bump mapping, displacement mapping, parallax mapping, shadow mapping, specular mapping, shadow volumes, high-dynamic-range rendering, ambient occlusion (screen space ambient occlusion, screen space directional occlusion, ray-traced ambient occlusion), ray tracing, global illumination, and tessellation. Each of these techniques provides some additional data about the surface being lit or the scene and light sources that contributes to the final look and feel of the surface. Most modern video game engines implement lighting using per-pixel techniques instead of vertex lighting to achieve increased detail and realism. The id Tech 4 engine, used to develop such games as Brink and Doom 3, was one of the first game engines to implement a completely per-pixel shading engine. All versions of the CryENGINE, Frostbite Engine, and Unreal Engine, among others, also implement per-pixel shading techniques. Deferred shading is a recent development in per-pixel lighting notable for its use in the Frostbite Engine and Battlefield 3. Deferred shading techniques are capable of rendering potentially large numbers of small lights inexpensively (other per-pixel lighting approaches require full-screen calculations for each light in a scene, regardless of size). == History == While only recently have personal computers and video hardware become powerful enough to perform full per-pixel shading in real-time applications such as games, many of the core concepts used in per-pixel lighting models have existed for decades. Frank Crow published a paper describing the theory of shadow volumes in 1977. This technique uses the stencil buffer to specify areas of the screen that correspond to surfaces that lie in a "shadow volume", or a shape representing a volume of space eclipsed from a light source by some object. These shadowed areas are typically shaded after the scene is rendered to buffers by storing shadowed areas with the stencil buffer. Jim Blinn first introduced the idea of normal mapping in a 1978 SIGGRAPH paper. Blinn pointed out that the earlier idea of unlit texture mapping proposed by Edwin Catmull was unrealistic for simulating rough surfaces. Instead of mapping a texture onto an object to simulate roughness, Blinn proposed a method of calculating the degree of lighting a point on a surface should receive based on an established "perturbation" of the normals across the surface. == Hardware rendering == Real-time applications, such as video games, usually implement per-pixel lighting through the use of pixel shaders, allowing the GPU hardware to process the effect. The scene to be rendered is first rasterized onto a number of buffers storing different types of data to be used in rendering the scene, such as depth, normal direction, and diffuse color. Then, the data is passed into a shader and used to compute the final appearance of the scene, pixel-by-pixel. Deferred shading is a per-pixel shading technique that has recently become feasible for games. With deferred shading, a "g-buffer" is used to store all terms needed to shade a final scene on the pixel level. The format of this data varies from application to application depending on the desired effect, and can include normal data, positional data, specular data, diffuse data, emissive maps and albedo, among others. Using multiple render targets, all of this data can be rendered to the g-buffer with a single pass, and a shader can calculate the final color of each pixel based on the data from the g-buffer in a final "deferred pass". Because deferred shading assumes only one visible fragment per pixel sample, transparent objects are generally handled in a separate forward pass. == Software rendering == Per-pixel lighting is also performed in software on many high-end commercial rendering applications which typically do not render at interactive framerates. This is called offline rendering or software rendering. NVidia's mental ray rendering software, which is integrated with such suites as Autodesk's Softimage is a well-known example.

  Read more →
• 

  Imo.im

  imo.im is a proprietary audio/video calling and instant messaging software service. It allows sending music, video, PDFs and other files, along with various free stickers. It supports encrypted group video and voice calls with up to 20 participants. According to its developer, the service possesses over 200 million users and over 50 million messages per day are sent through it. == History == The product was created as a web-based application in 2005 for accessing multiple chat platforms, including Facebook Messenger, Google Talk, Yahoo! Messenger, and Skype chat. It was developed by Pagebites, which is a subsidiary of Singularity IM, Inc. and required a subscriber's phone number to verify the users' account. In March 2014, support for all third-party messaging networks ended. In January 2018, the app reached 500 million installs. imo.im has implemented end-to-end encryption for its chats and calls, ensuring that the conversations remain private between the sender and receiver.

  Read more →