AI Assistant Job Interview

AI Assistant Job Interview — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Schema-agnostic databases

Schema-agnostic databases or vocabulary-independent databases aim at supporting users to be abstracted from the representation of the data, supporting the automatic semantic matching between queries and databases. Schema-agnosticism is the property of a database of mapping a query issued with the user terminology and structure, automatically mapping it to the dataset vocabulary. The increase in the size and in the semantic heterogeneity of database schemas bring new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. == Description == The evolution of data environments towards the consumption of data from multiple data sources and the growth in the schema size, complexity, dynamicity and decentralisation (SCoDD) of schemas increases the complexity of contemporary data management. The SCoDD trend emerges as a central data management concern in Big Data scenarios, where users and applications have a demand for more complete data, produced by independent data sources, under different semantic assumptions and contexts of use, which is the typical scenario for Semantic Web Data applications. The evolution of databases in the direction of heterogeneous data environments strongly impacts the usability, semiotics and semantic assumptions behind existing data accessibility methods such as structured queries, keyword-based search and visual query systems. With schema-less databases containing potentially millions of dynamically changing attributes, it becomes unfeasible for some users to become aware of the 'schema' or vocabulary in order to query the database. At this scale, the effort in understanding the schema in order to build a structured query can become prohibitive. == Schema-agnostic queries == Schema-agnostic queries can be defined as query approaches over structured databases which allow users satisfying complex information needs without the understanding of the representation (schema) of the database. Similarly, Tran et al. defines it as "search approaches, which do not require users to know the schema underlying the data". Approaches such as keyword-based search over databases allow users to query databases without employing structured queries. However, as discussed by Tran et al.: "From these points, users however have to do further navigation and exploration to address complex information needs. Unlike keyword search used on the Web, which focuses on simple needs, the keyword search elaborated here is used to obtain more complex results. Instead of a single set of resources, the goal is to compute complex sets of resources and their relations." The development of approaches to support natural language interfaces (NLI) over databases have aimed towards the goal of schema-agnostic queries. Complementarily, some approaches based on keyword search have targeted keyword-based queries which express more complex information needs. Other approaches have explored the construction of structured queries over databases where schema constraints can be relaxed. All these approaches (natural language, keyword-based search and structured queries) have targeted different degrees of sophistication in addressing the problem of supporting a flexible semantic matching between queries and data, which vary from the completely absence of the semantic concern to more principled semantic models. While the demand for schema-agnosticism has been an implicit requirement across semantic search and natural language query systems over structured data, it is not sufficiently individuated as a concept and as a necessary requirement for contemporary database management systems. Recent works have started to define and model the semantic aspects involved on schema-agnostic queries. === Schema-agnostic structured queries === Consist of schema-agnostic queries following the syntax of a structured standard (for example SQL, SPARQL). The syntax and semantics of operators are maintained, while different terminologies are used. ==== Example 1 ==== SELECT ?y { BillClinton hasDaughter ?x . ?x marriedTo ?y . } which maps to the following SPARQL query in the dataset vocabulary: ==== Example 2 ==== which maps to the following SPARQL query in the dataset vocabulary: === Schema-agnostic keyword queries === Consist of schema-agnostic queries using keyword queries. In this case the syntax and semantics of operators are different from the structured query syntax. ==== Example ==== "Bill Clinton daughter married to" "Books by William Goldman with more than 300 pages" == Semantic complexity == As of 2016 the concept of schema-agnostic queries has been developed primarily in academia. Most of schema-agnostic query systems have been investigated in the context of Natural Language Interfaces over databases or over the Semantic Web. These works explore the application of semantic parsing techniques over large, heterogeneous and schema-less databases. More recently, the individuation of the concept of schema-agnostic query systems and databases have appeared more explicitly within the literature. Freitas et al. provide a probabilistic model on the semantic complexity of mapping schema-agnostic queries.
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Data validation and reconciliation

Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes. The use of PDR allows for extracting accurate and reliable information about the state of industry processes from raw measurement data and produces a single consistent set of data representing the most likely process operation. == Models, data and measurement errors == Industrial processes, for example chemical or thermodynamic processes in chemical plants, refineries, oil or gas production sites, or power plants, are often represented by two fundamental means: Models that express the general structure of the processes, Data that reflects the state of the processes at a given point in time. Models can have different levels of detail, for example one can incorporate simple mass or compound conservation balances, or more advanced thermodynamic models including energy conservation laws. Mathematically the model can be expressed by a nonlinear system of equations F ( y ) = 0 {\displaystyle F(y)=0\,} in the variables y = ( y 1 , … , y n ) {\displaystyle y=(y_{1},\ldots ,y_{n})} , which incorporates all the above-mentioned system constraints (for example the mass or heat balances around a unit). A variable could be the temperature or the pressure at a certain place in the plant. === Error types === Data originates typically from measurements taken at different places throughout the industrial site, for example temperature, pressure, volumetric flow rate measurements etc. To understand the basic principles of PDR, it is important to first recognize that plant measurements are never 100% correct, i.e. raw measurement y {\displaystyle y\,} is not a solution of the nonlinear system F ( y ) = 0 {\displaystyle F(y)=0\,\!} . When using measurements without correction to generate plant balances, it is common to have incoherencies. Measurement errors can be categorized into two basic types: random errors due to intrinsic sensor accuracy and systematic errors (or gross errors) due to sensor calibration or faulty data transmission. Random errors means that the measurement y {\displaystyle y\,\!} is a random variable with mean y ∗ {\displaystyle y^{}\,\!} , where y ∗ {\displaystyle y^{}\,\!} is the true value that is typically not known. A systematic error on the other hand is characterized by a measurement y {\displaystyle y\,\!} which is a random variable with mean y ¯ {\displaystyle {\bar {y}}\,\!} , which is not equal to the true value y ∗ {\displaystyle y^{}\,} . For ease in deriving and implementing an optimal estimation solution, and based on arguments that errors are the sum of many factors (so that the Central limit theorem has some effect), data reconciliation assumes these errors are normally distributed. Other sources of errors when calculating plant balances include process faults such as leaks, unmodeled heat losses, incorrect physical properties or other physical parameters used in equations, and incorrect structure such as unmodeled bypass lines. Other errors include unmodeled plant dynamics such as holdup changes, and other instabilities in plant operations that violate steady state (algebraic) models. Additional dynamic errors arise when measurements and samples are not taken at the same time, especially lab analyses. The normal practice of using time averages for the data input partly reduces the dynamic problems. However, that does not completely resolve timing inconsistencies for infrequently-sampled data like lab analyses. This use of average values, like a moving average, acts as a low-pass filter, so high frequency noise is mostly eliminated. The result is that, in practice, data reconciliation is mainly making adjustments to correct systematic errors like biases. === Necessity of removing measurement errors === ISA-95 is the international standard for the integration of enterprise and control systems It asserts that: Data reconciliation is a serious issue for enterprise-control integration. The data have to be valid to be useful for the enterprise system. The data must often be determined from physical measurements that have associated error factors. This must usually be converted into exact values for the enterprise system. This conversion may require manual, or intelligent reconciliation of the converted values [...]. Systems must be set up to ensure that accurate data are sent to production and from production. Inadvertent operator or clerical errors may result in too much production, too little production, the wrong production, incorrect inventory, or missing inventory. == History == PDR has become more and more important due to industrial processes that are becoming more and more complex. PDR started in the early 1960s with applications aiming at closing material balances in production processes where raw measurements were available for all variables. At the same time the problem of gross error identification and elimination has been presented. In the late 1960s and 1970s unmeasured variables were taken into account in the data reconciliation process., PDR also became more mature by considering general nonlinear equation systems coming from thermodynamic models., , Quasi steady state dynamics for filtering and simultaneous parameter estimation over time were introduced in 1977 by Stanley and Mah. Dynamic PDR was formulated as a nonlinear optimization problem by Liebman et al. in 1992. == Data reconciliation == Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors. From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation. Given n {\displaystyle n} measurements y i {\displaystyle y_{i}} , data reconciliation can mathematically be expressed as an optimization problem of the following form: min x , y ∗ ∑ i = 1 n ( y i ∗ − y i σ i ) 2 subject to F ( x , y ∗ ) = 0 y min ≤ y ∗ ≤ y max x min ≤ x ≤ x max , {\displaystyle {\begin{aligned}\min _{x,y^{}}&\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\\{\text{subject to }}&F(x,y^{})=0\\&y_{\min }\leq y^{}\leq y_{\max }\\&x_{\min }\leq x\leq x_{\max },\end{aligned}}\,\!} where y i ∗ {\displaystyle y_{i}^{}\,\!} is the reconciled value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), y i {\displaystyle y_{i}\,\!} is the measured value of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), x j {\displaystyle x_{j}\,\!} is the j {\displaystyle j} -th unmeasured variable ( j = 1 , … , m {\displaystyle j=1,\ldots ,m\,\!} ), and σ i {\displaystyle \sigma _{i}\,\!} is the standard deviation of the i {\displaystyle i} -th measurement ( i = 1 , … , n {\displaystyle i=1,\ldots ,n\,\!} ), F ( x , y ∗ ) = 0 {\displaystyle F(x,y^{})=0\,\!} are the p {\displaystyle p\,\!} process equality constraints and x min , x max , y min , y max {\displaystyle x_{\min },x_{\max },y_{\min },y_{\max }\,\!} are the bounds on the measured and unmeasured variables. The term ( y i ∗ − y i σ i ) 2 {\displaystyle \left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}\,\!} is called the penalty of measurement i. The objective function is the sum of the penalties, which will be denoted in the following by f ( y ∗ ) = ∑ i = 1 n ( y i ∗ − y i σ i ) 2 {\displaystyle f(y^{})=\sum _{i=1}^{n}\left({\frac {y_{i}^{}-y_{i}}{\sigma _{i}}}\right)^{2}} . In other words, one wants to minimize the overall correction (measured in the least squares term) that is needed in order to satisfy the system constraints. Additionally, each least squares term is weighted by the standard deviation of the corresponding measurement. The standard deviation is related to the accuracy of the measurement. For example, at a 95% confidence level, the standard deviation is about half the accuracy. === Redundancy === Data reconciliation relies strongly on the concept of redundancy to correct the measurements as little as possible in order to satisfy the process constraints. Here, redundancy is defined differently from redundancy in information theory. Instead, redundancy arises from combining sensor data with the model (algebraic constraints), sometimes more specifically called "spatial redundancy", "analytical redundancy", or "topological redundancy". Redundancy can be due to sensor redundancy, where sensors are duplicated in order to have more than one measurement of the same quantity. Redundancy also arises when a single variable can be estimated in several independent ways from separate sets of measurements at a given time or time averaging period, using the algebraic constraints. Redundancy is linked to the concept
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Sumazi

Sumazi is a social media and social intelligence platform for enterprises, brands, and celebrities. Its technology performs social data analysis across social networking services including Facebook, Twitter and LinkedIn, to identify key people in his/her network who are experts, influencers or are located in a specific area for marketing, advertising or sales campaigns. The technology company was founded in 2011 by former Sun Microsystems employee Sumaya Kazi. The company was headquartered in San Francisco, California. The company was out of business by 2017. == Reception == Sumazi was one of 25 startups selected out of more than 1,200 to compete at TechCrunch Disrupt Startup Battlefield, where it won the Omidyar Network award for the startup "Most Likely to Change the World." Sumazi, which was based out of San Francisco, California, had been profiled in The New York Times as well as USA Today, which commented the advantages of the startup's location in the Silicon Valley. American Express OPEN Forum also featured Sumazi as a "Startup of the Week". Sumazi has additionally been mentioned in articles by Mashable, The Wall Street Journal, Current Editorials, Harvard Business Review, Smashing Magazine, and TechCrunch.
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Virtual collective consciousness

Virtual collective consciousness (VCC) is a term rebooted and promoted by two behavioral scientists, Yousri Marzouki and Olivier Oullier in their 2012 Huffington Post article titled: "Revolutionizing Revolutions: Virtual Collective Consciousness and the Arab Spring", after its first appearance in 1999-2000. VCC is now defined as an internal knowledge catalyzed by social media platforms and shared by a plurality of individuals driven by the spontaneity, the homogeneity, and the synchronicity of their online actions. VCC occurs when a large group of persons, brought together by a social media platform think and act with one mind and share collective emotions. Thus, they are able to coordinate their efforts efficiently, and could rapidly spread their word to a worldwide audience. When interviewed about the concept of VCC that appeared in the book - Hyperconnectivity and the Future of Internet Communication - he edited, Professor of Pervasive Computing, Adrian David Cheok mentioned the following: "The idea of a global (collective) virtual consciousness is a bottom-up process and a rather emergent property resulting from a momentum of complex interactions taking place in social networks. This kind of collective behaviour (or intelligence) results from a collision between a physical world and a virtual world and can have a real impact in our life by driving collective action." == Etymology == In 1999-2000, Richard Glen Boire provided a cursory mention and the only occurrence of the term "Virtual collective consciousness" in his text as follows: The trend of technology is to overcome the limitations of the human body. And, the Web has been characterized as a virtual collective consciousness and unconsciousness The recent definition of VCC evolved from the first empirical study that provided a cyberpsychological insight into the contribution of Facebook to the 2011 Tunisian revolution. In this study, the concept was originally called "collective cyberconsciousness". The latter is an extension of the idea of "collective consciousness" coupled with "citizen media" usage. The authors of this study also made a parallel between this original definition of VCC and other comparable concepts such as Durkheim's collective representation, Žižek's "collective mind" or Boguta's "new collective consciousness" that he used to describe the computational history of the Internet shutdown during the Egyptian revolution. Since VCC is the byproduct of the network's successful actions, then these actions must be timely, acute, rapid, domain-specific, and purpose-oriented to successfully achieve their goal. Before reaching a momentum of complexity, each collective behavior starts by a spark that triggers a chain of events leading to a crystallized stance of a tremendous amount of interactions. Thus, VCC is an emergent global pattern from these individual actions. In 2012, the term virtual collective consciousness resurfaced and was brought to light after extending its applications to the Egyptian case and the whole social networking major impact on the success of the so-called Arab Spring. Moreover, the acronym VCC was suggested to identify the theoretical framework covering on-line behaviors leading to a virtual collective consciousness. Hence, online social networks have provided a new and faster way of establishing or modifying "collective consciousness" that was paramount to the 2011 uprisings in the Arab world. == Theoretical underpinnings of VCC == Various theoretical references in fields ranging from sociology to computer science were mentioned in order to account for the key features that render the framework for a virtual collective consciousness. The following list is not exhaustive, but the references it contains are often highlighted: Émile Durkheim's collective representations are at the heart of VCC since collectivity taken decisions according to Durkheim's assumptions will approve or disapprove individuals' actions and help them eventually reach their final goal. Marshall McLuhan's global village: The shrinking of our big world to a small place called cyberspace is made possible by technological extensions of human consciousness. Carl Jung's collective unconscious: When a society witnesses significant changes, the anchoring of archetypal images (e.g., political leaders) seems to be deeply rooted in individuals' collective unconscious that is likely to bias their political choices. Individual memories of public events were also supposed to convey a "collective awareness" that can be subconsciously altered by the instantaneous spread of information through social networking around the world. Daniel Wegner's transactive memory (TM): social-networking platforms such as Facebook during the Tunisian revolution or Twitter during the Egyptian revolution served as placeholders of a VCC where information can be harnessed and steered to the highly specific revolutionary purpose. Although research on TM was originally limited to couples, small groups, and organizations, recent studies strongly suggest that an effective TM can operate on a very large scale too. James Surowiecki's wisdom of crowds Collective influence algorithm: The CI (Collective influence) algorithm is effective in finding influential nodes in a variety of networks, including social networks, communication networks, and biological networks. It has been used to identify influencers on social-media platforms, to identify key nodes in transportation networks, and to identify potential drug-targets in biological networks. == Some illustrations of VCC == Besides the studied effect of social networking on the Tunisian and Egyptian revolutions, the former via Facebook and the latter via Twitter other applications were studied under the prism of VCC framework: The Whitacre's virtual choir: A compelling example of the degree of autonomy and self-identity members of a spontaneously created network through a VCC is Eric Whitacre's unique musical project that involved a collection of singers performing remotely to create a virtual Choir. The effect of all the voices illustrated a genuine virtual collective empathy merging the artist's mind with all the singers through his silent conducting gestures. The Harlem Shake dance: The Bitcoin protocol: It was questioned whether or not the Bitcoin protocol can morph into virtual collective consciousness. The Byzantine generals problem was used as an analogy to understand the behavioral complexity of the community of Bitcoin's users. Artificial Social Networking Intelligence (ASNI): refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize users' interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping our digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.
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Digital video effect

Digital video effects (DVEs) are visual effects that provide comprehensive live video image manipulation, in the same form as optical printer effects in film. DVEs differ from standard video switcher effects (often referred to as analog effects) such as wipes or dissolves, in that they deal primarily with resizing, distortion or movement of the image. Modern video switchers often contain internal DVE functionality. Modern DVE devices are incorporated in high-end broadcast video switchers. Early examples of DVE devices found in the broadcast post-production industry include the Ampex Digital Optics (ADO), Quantel DPE-5000, Vital Squeezoom, NEC E-Flex and the Abekas A5x series of DVEs. By 1988, Grass Valley Group caught up with the competition with their Kaleidoscope, which integrated ADO-type effects with their widely used line of broadcast switching gear. DVEs are used by the broadcast television industry in live television production environments like television studios and outside broadcasts. They are commonly used in video post-production.
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Cleo Communications

Cleo Communications LLC, simply referred to as Cleo, is a privately held software company founded in 1976. The company is best known for its ecosystem integration platform, Cleo Integration Cloud with RADAR. == History == Cleo originally began as a division of Phone 1 Inc., a voice data gathering systems manufacturer, and built data concentrators and terminal emulators — multi-bus computers, modems, and terminals to interface with IBM mainframes via bisynchronous communications. The company then began developing mainframe middleware in the 1980s, and with the rise of the PC, moved into B2B data communications and secure file transfer software. Cleo Communications was acquired in 2012 by Global Equity Partners along with other investment companies. Since being acquired in 2012, the company’s offerings have evolved into Cleo Integration Cloud, a platform for enterprise business integration. == Business == Based in Rockford, Illinois (USA), with offices in Chicago, Pennsylvania, London, and Bangalore, Cleo has about 400 employees and more than 4,100 direct customers. The company's flagship offering, Cleo Integration Cloud, provides both on-premise and cloud-based integration technologies and comprises solutions for B2B/EDI, application integration, data movement and data transformation. Previous products now incorporated into the Cleo Integration Cloud platform include Cleo Harmony, Cleo Clarify, and Cleo Jetsonic. Cleo solutions span a variety of industries, including manufacturing, logistics and supply chain, retail, third-party logistics, warehouse management and transportation management, healthcare, financial services and government. The U.S. Department of Veterans Affairs adopted Cleo's fax technology, Cleo Streem, in 2013 when in need of FIPS 140-2-compliant technology to protect information, and the City of Atlanta has used Cleo Streem for network and desktop faxing since 2006. Cleo also serves U.S. transportation logistics company MercuryGate International and SaaS-based food logistics organization ArrowStream. It powers the architecture for several major supply chain companies, such as Blue Yonder and SAP. Cleo integrates the pharmaceutical supply chain for such companies as Octapharma. Key partners include FourKites and ClientsFirst, among many others. In May 2023, Cleo announced it entered a global partnership with consulting and multinational information technology services company, Cognizant (NASDAQ: CTSH). Together, the companies announced CCIB, powered by Cleo, which is a B2B iPaaS solution that provides B2B managed services with built-in, scalable infrastructure on the cloud. The solution comprises elements from Cleo’s flagship offering, Cleo Integration Cloud. == Expansion == In June 2014, Cleo opened an office in Chicago for members of its support and Ashok and teams. In 2014, the company hired Jorge Rodriguez as Senior Vice President of Product Development and John Thielens as Vice President of Technology. Cleo hired Dave Brunswick as Vice President of Solutions for North America in 2015, and Cleo hired Ken Lyons to lead global sales in 2016. Lyons now serves as the company's Chief Revenue Officer. More recent additions to the company's leadership team include Vipin Mittal, Vice President, Customer Experience, and Tushar Patel, CMO. Cleo opened its product development facility in Bengaluru, India, in 2015 and expanded its hybrid cloud integration teams into a new office there in 2017. The company also opened a London office in 2016 and expanded its network of channel partners in EMEA. In 2016, Cleo acquired EXTOL International, a Pottsville, Pa.-based business and EDI integration and data transformation company for an undisclosed amount. In 2017, the company moved its headquarters from Loves Park, Illinois, to Rockford. In 2021 the company received a significant growth investment from H.I.G. Capital. In July 2022, Cleo opened a new, 5,000-square-foot office located in Chicago's Loop. In November 2022, Cleo launched an accelerator for Microsoft Dynamics 365 SCM-to-X12 and a connector for Microsoft Dynamics 365 Business Central. These pre-built solutions allow businesses and users to quickly build integration flows that integrate their digital ecosystems. In March 2023, Cleo released CIC PAVE (Procurement Automation and Vendor Enablement). PAVE provides customers with enhanced supply chain visibility via a supplier portal that allows the customer to keep vendor interaction in a single location, even if they cannot use EDI or have API-ready applications. In December 2023, Cleo acquired ECS International, an integration technology company based in the Netherlands. == Certification == Cleo regularly submits its products to Drummond Group's interoperability software testing for AS2, AS3 and ebMS 2.0. In January 2020, Cleo announced that its new application connector for Acumatica ERP has been recognized as an Acumatica-Certified Application (ACA). The company also holds SOC 2, Type 2 certification. == Awards == Cleo was a Xerox partner of the year award for five years, from 2009 to 2014. The Cleo Streem solution integrates with Xerox multi-function products, providing customers with solutions for network fax and interactive messaging needs. Cleo was named to Food Logistics’ FL100+ Top Software and Technology Providers Lists in 2016, 2017, 2019 and 2020. Cleo CEO, Mahesh Rajasekharan was named an Ernst & Young Entrepreneur Of The Year 2022 Midwest Award winner. Rajasekharan is serving as a judge for the 2023 Ernst & Young Entrepreneur Of the Year Awards. As of April 2022, Cleo has been named a Leader in EDI on the G2 Grid, a peer-to-peer review site, for 20 straight quarters. In Spring 2023, Cleo won 23 G2 awards—including EDI Leader Enterprise, MFT Leader Enterprise, On-Premise Data Integration Best Support Enterprise, and iPaaS High Performer Asia.
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Cut, copy, and paste

Cut, copy, and paste are essential commands of modern human–computer interaction and user interface design. They offer an interprocess communication technique for transferring data through a computer's user interface. The cut command removes the selected data from its original position, and the copy command creates a duplicate; in both cases the selected data is kept in temporary storage called the clipboard. Clipboard data is later inserted wherever a paste command is issued. The data remains available to any application supporting the feature, thus allowing easy data transfer between applications. The command names are a (skeuomorphic) interface metaphor based on the physical procedure used in manuscript print editing to create a page layout, like with paper. The commands were pioneered into computing by Xerox PARC in 1974, popularized by Apple Computer in the 1983 Lisa workstation and the 1984 Macintosh computer, and in a few home computer applications such as the 1984 word processor Cut & Paste. This interaction technique has close associations with related techniques in graphical user interfaces (GUIs) that use pointing devices such as a computer mouse (by drag and drop, for example). Typically, clipboard support is provided by an operating system as part of its GUI and widget toolkit. The capability to replicate information with ease, changing it between contexts and applications, involves privacy concerns because of the risks of disclosure when handling sensitive information. Terms like cloning, copy forward, carry forward, or re-use refer to the dissemination of such information through documents, and may be subject to regulation by administrative bodies. == History == === Origins === The term "cut and paste" comes from the traditional practice in manuscript editing, whereby people cut paragraphs from a page with scissors and paste them onto another page. This practice remained standard into the 1980s. Stationery stores sold "editing scissors" with blades long enough to cut an 8½"-wide page. The advent of photocopiers made the practice easier and more flexible. The act of copying or transferring text from one part of a computer-based document ("buffer") to a different location within the same or different computer-based document was a part of the earliest on-line computer editors. As soon as computer data entry moved from punch-cards to online files (in the mid/late 1960s) there were "commands" for accomplishing this operation. This mechanism was often used to transfer frequently-used commands or text snippets from additional buffers into the document, as was the case with the QED text editor. === Early methods === The earliest editors (designed for teleprinter terminals) provided keyboard commands to delineate a contiguous region of text, then delete or move it. Since moving a region of text requires first removing it from its initial location and then inserting it into its new location, various schemes had to be invented to allow for this multi-step process to be specified by the user. Often this was done with a "move" command, but some text editors required that the text be first put into some temporary location for later retrieval/placement. In 1983, the Apple Lisa became the first text editing system to call that temporary location "the clipboard". Earlier control schemes such as NLS used a verb—object command structure, where the command name was provided first and the object to be copied or moved was second. The inversion from verb—object to object—verb on which copy and paste are based, where the user selects the object to be operated before initiating the operation, was an innovation crucial for the success of the desktop metaphor as it allowed copy and move operations based on direct manipulation. === Popularization === Inspired by early line and character editors, such as Pentti Kanerva's TV-Edit, that broke a move or copy operation into two steps—between which the user could invoke a preparatory action such as navigation—Lawrence G. "Larry" Tesler proposed the names "cut" and "copy" for the first step and "paste" for the second step. Beginning in 1974, he and colleagues at Xerox PARC implemented several text editors that used cut/copy-and-paste commands to move and copy text. Apple Computer popularized this paradigm with its Lisa (1983) and Macintosh (1984) operating systems and applications. The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY keyboard. These are the standard shortcuts: Control-Z (or ⌘ Command+Z) to undo Control-X (or ⌘ Command+X) to cut Control-C (or ⌘ Command+C) to copy Control-V (or ⌘ Command+V) to paste The IBM Common User Access (CUA) standard also uses combinations of the Insert, Del, Shift and Control keys. Early versions of Windows used the IBM standard. Microsoft later also adopted the Apple key combinations with the introduction of Windows, using the control key as modifier key. Similar patterns of key combinations, later borrowed by others, are widely available in most GUI applications. The original cut, copy, and paste workflow, as implemented at PARC, utilizes a unique workflow: With two windows on the same screen, the user could use the mouse to pick a point at which to make an insertion in one window (or a segment of text to replace). Then, by holding shift and selecting the copy source elsewhere on the same screen, the copy would be made as soon as the shift was released. Similarly, holding shift and control would copy and cut (delete) the source. This workflow requires many fewer keystrokes/mouse clicks than the current multi-step workflows, and did not require an explicit copy buffer. It was dropped, one presumes, because the original Apple and IBM GUIs were not high enough density to permit multiple windows, as were the PARC machines, and so multiple simultaneous windows were rarely used. == Cut and paste == Computer-based editing can involve very frequent use of cut-and-paste operations. Most software-suppliers provide several methods for performing such tasks, and this can involve (for example) key combinations, pulldown menus, pop-up menus, or toolbar buttons. The user selects or "highlights" the text or file for moving by some method, typically by dragging over the text or file name with the pointing-device or holding down the Shift key while using the arrow keys to move the text cursor. The user performs a "cut" operation via key combination Ctrl+x (⌘+x for Macintosh users), menu, or other means. Visibly, "cut" text immediately disappears from its location. "Cut" files typically change color to indicate that they will be moved. Conceptually, the text has now moved to a location often called the clipboard. The clipboard typically remains invisible. On most systems only one clipboard location exists, hence another cut or copy operation overwrites the previously stored information. Many UNIX text-editors provide multiple clipboard entries, as do some Macintosh programs such as Clipboard Master, and Windows clipboard-manager programs such as the one in Microsoft Office. The user selects a location for insertion by some method, typically by clicking at the desired insertion point. A paste operation takes place which visibly inserts the clipboard text at the insertion point. (The paste operation does not typically destroy the clipboard text: it remains available in the clipboard and the user can insert additional copies at other points). Whereas cut-and-paste often takes place with a mouse-equivalent in Windows-like GUI environments, it may also occur entirely from the keyboard, especially in UNIX text editors, such as Pico or vi. Cutting and pasting without a mouse can involve a selection (for which Ctrl+x is pressed in most graphical systems) or the entire current line, but it may also involve text after the cursor until the end of the line and other more sophisticated operations. The clipboard usually stays invisible, because the operations of cutting and pasting, while actually independent, usually take place in quick succession, and the user (usually) needs no assistance in understanding the operation or maintaining mental context. Some application programs provide a means of viewing, or sometimes even editing, the data on the clipboard. == Copy and paste == The term "copy-and-paste" refers to the popular, simple method of reproducing text or other data from a source to a destination. It differs from cut and paste in that the original source text or data does not get deleted or removed. The popularity of this method stems from its simplicity and the ease with which users can move data between various applications visually – without resorting to permanent storage. Use in healthcare do
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Air Force Network

Air Force Network (AFNet) is an Indian Air Force (IAF) owned, operated and managed digital information grid. The AFNet replaces the Indian Air Force's (IAF) old communication network set-up using the tropo-scatter technology of the 1950s making it a true net-centric combat force. The IAF project is part of the overall mission to network all three services; The Indian Army, The Indian Navy and The Indian Air Force. The former Defence Minister AK Antony inaugurated the IAF's the AFNET on 14 September 2010 dedicating it to the people of India, for their direct or indirect participation in the communication revolution. == Background == Armed Forces in India has been using troposcatters as primary means of military communications since the 1950s, thereby occupying huge and expensive 2G and 3G spectrums which otherwise could have been used for expanding and de-clogging the civilian wireless communication network. The rapid expansion of civilian mobile telephony leading to need for larger bandwidth for wireless communication and commercial need to operate the 3G network necessitated the Government of India to have the Indian Armed Forces vacate the spectrum occupied by them. Thus the government of India through Department of Telecommunication (DoT) started a project called "Network for Spectrum" to set up a fiber optics network for the exclusive use of Indian Armed Forces in exchange for spectrum being released by the Defence Forces. The aim of 'Network for Spectrum' being twofold - to facilitate the growth of national tele-density on the one hand, and ensuring modernization of defence communications with the state-of-the-art communication infrastructure, and to support net-centric military operations. The Department of Telecom and the Ministry of Defence signed the memorandum of understanding for vacating the spectrum and setting up dedicated network for the use of defence forces. In this MoU, DoT agreed to laying of 40,000 route kilometres of optical fibre cable connecting 219 Army stations, 33 Navy stations and 162 points for the Air Force. It further agreed to setting up an exclusive defence band and Defence Interest Zone along 100 km of the international border, where spectrum will be reserved only for use by the Armed Forces. The total cost of implementing "Network for Spectrum" project is estimated to be ₹ 10,000 crores. AFNet is Indian Air Force component of Digital Information Grid under "Network for Spectrum" project and the AFNet has been extended and connected to the Digital Information Grid Project under implementation for the Indian Navy and the Indian Army on 2015. == Project Origin == The Air Force Network (AFNet) had been developed by the Indian Air Force at a cost of ₹1,077 crore (US$235.53 million) in collaboration with HCL Technologies and Bharat Sanchar Nigam Limited. It will replace the Air Force's more than half-a-century-old telecom network. This project is part of the defence ministry's initiative to digitize the communication systems of the three armed forces under "Network for Spectrum" initiative to improve coordination among themselves and other Military and Strategic Institution. IAF was the first to complete this gigabyte digital information grid implemented under the AFNet project. AFNet will be connected and extended to a Unified Digital Grid encompassing all the legs of Indian Armed Forces. The then defence minister, A. K. Antony, inaugurated the AFNet, IAF's gigabyte digital information grid. The grid is aimed at improving the network-centric warfare capability of the Air Force. The event also saw the presence of other personalities including the then Minister of Communication & IT, A. Raja; the Marshal of the Air Force, Arjan Singh; the Chief of the Air Staff, the Chief of the Army Staff and other officials from the three services and members of the Industry. The event also featured a practice interception of a simulated aerial target by a MiG-29 which took off from an airbase in the Punjab sector using the AFNet capabilities. Further capabilities in line with network centric warfare were also demonstrated. This included sharing information, videos and pictures by operational assets and platforms like UAVs and AWACS to decision-makers who are several hundred kilometres apart. == Technology, Design & Structure == AFNet incorporates the latest traffic transportation technology in form of Internet Protocol (IP) packets over the network using Multiprotocol Label Switching (MPLS). A large Voice over Internet Protocol (VoIP) layer with stringent quality of service enforcement will facilitate robust, high quality voice, video and conferencing solutions. AFNet will prove to be an effective force multiplier for intelligence analysis, mission planning and control, post-mission feedback and related activities like maintenance, logistics and administration. A comprehensive design with multi-layer security precautions for “Defence in Depth” have been planned by incorporating encryption technologies, Intrusion Prevention Systems to ensure the resistance of the IT system against information manipulation and eavesdropping. The network is secured with a host of advanced state-of-the-art encryption technologies. It is designed for high reliability with redundancy built into the network design itself. The AFNet is also capable of transmitting video from unmanned surveillance aircraft (UAV), pictures from airborne warning and control systems (AWACS) to decision makers on the ground and providing intelligence inputs from remote areas. The AFNet is also expected to facilitate accelerated economic growth by providing radio frequency spectrum for telecommunication purposes. AFNET will be the largest Multi-protocol Label Switching (MPLS) network in the defence segment. == Demonstration == At the AFNet launch, the IAF showcased a practice interception of simulated enemy targets by a pair of Mig-29 fighter aircraft airborne from an advanced airbase in the Punjab sector using the gigabyte digital information grid. During the AFNet-assisted operations, the Indian fighter jets neutralised intruding targets in the western sector, which was played out live on the giant screens at the Air Force auditorium offering a glimpse of the harnessed potential of the system. The final orders for engaging the enemy targets were issued live by Antony, whose queries about how the operation went were responded to by the pilot as "excellent". Various other functionalities contributing towards Network Centric Warfare were also showcased. These consisted of facilitating video from Unmanned Aerial Vehicle (UAV), pictures from an AWACS aircraft to the decision-makers on ground sitting hundreds of kilometres away, providing intelligence inputs from far-flung areas at central locations seamlessly. This was possible mainly because of the robust networking platform provided by AFNet. == Integrated Air Command and Control System == Integrated Air Command and Control System (IACCS) is an automated command and control system for air defence operated by the Indian Air Force. IACCS operations rides the AFNET backbone integrating all ground-based and airborne sensors, air defense weapon systems and command and control (C2) nodes. Subsequent integration with other services networks and civil radars will provide an integrated Air Situation Picture to operators to carry out AD role. The project was envisaged in 1995 following the Purulia arms drop case and was a part of IAF’s first Air Power Doctrinal manual issued in the 2000s, later revised in 2022. The first node in the western sectors had been operationalised by September 2010. The first five nodes located in the western and south western sectors were commissioned in 2011. The Air Force was preparing to seek clearance for five further nodes which would cover the rest of the nation including the island territories. Through the IACCS, IAF will connect all of its space, air and ground assets quickly, for total awareness of a region. This will offer connectivity for all the ground platforms and airborne platforms (including AEW&C), as a part of the network centricity of IAF. The IACCS also facilitates real-time transport of images, data and voice, amongst satellites, aircraft and ground stations. By 2018, five IACCS nodes had been established including Barnala (Punjab), Wadsar (Gujarat), Aya Nagar (Delhi), Jodhpur (Rajasthan) and Ambala (Haryana). Following this, under Phase-II, 4 additional nodes and 10 sub-nodes are to be set up. The major nodes will be established in the Eastern, Central, Southern and Andaman and Nicobar sectors. The second phase will cost ₹8,000 crore (equivalent to ₹110 billion or US$1.1 billion in 2023). IACCS successfully integrated all operating radars, including its own, the Army's, and civilian ones, in 2023. This enabled the autonomous firing response capability to take down incoming missiles, aircraft, and UAVs. The Akashteer system of the Indian Army is being integrated with the IACCS
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Sketch Engine

Sketch Engine is a corpus manager and text analysis software developed by Lexical Computing since 2003. Its purpose is to enable people studying language behaviour (lexicographers, researchers in corpus linguistics, translators or language learners) to search large text collections according to complex and linguistically motivated queries. Sketch Engine gained its name after one of the key features, word sketches: one-page, automatic, corpus-derived summaries of a word's grammatical and collocational behaviour. Currently, it supports and provides corpora in over 100 languages. == History of development == Sketch Engine is a product of Lexical Computing, a company founded in 2003 by the lexicographer and research scientist Adam Kilgarriff. He started a collaboration with Pavel Rychlý, a computer scientist working at the Natural Language Processing Centre, Masaryk University, and the developer of Manatee and Bonito (two major parts of the software suite). Kilgarriff also introduced the concept of word sketches. Since then, Sketch Engine has been commercial software, however, all the core features of Manatee and Bonito that were developed by 2003 (and extended since then) are freely available under the GPL license within the NoSketch Engine suite. == Features == A list of tools available in Sketch Engine: Word sketches – a one-page automatic derived summary of a word's grammatical and collocational behaviour Word sketch difference – compares and contrasts two words by analysing their collocations Distributional thesaurus – automated thesaurus for finding words with similar meaning or appearing in the same/similar context Concordance search – finds occurrences of a word form, lemma, phrase, tag or complex structure Collocation search – word co-occurrence analysis displaying the most frequent words (for a search word) which can be regarded as collocation candidates Word lists – generates frequency lists which can be filtered with complex criteria n-grams – generates frequency lists of multi-word expressions Terminology / Keyword extraction (both monolingual and bilingual) – automatic extraction of key words and multi-word terms from texts (based on frequency count and linguistic criteria) Diachronic analysis (Trends) – detecting words which undergo changes in the frequency of use in time (show trending words) Corpus building and management – create corpora from the Web or uploaded texts including part-of-speech tagging and lemmatization which can be used as data mining software Parallel corpus (bilingual) facilities – looking up translation examples (EUR-Lex corpus, Europarl corpus, OPUS corpus, etc.) or building a parallel corpus from own aligned texts Text type analysis – statistics of metadata in the corpus === Keywords and terminology extraction === Sketch Engine can perform automatic term extraction by identifying words typical of a particular corpus, document, or text. Single words and multi-word units can be extracted from monolingual or bilingual texts. The terminology extraction feature provides a list of relevant terms based on comparison with a large corpus of general language. This functionality is also available as a separate service called OneClick Terms with a dedicated interface. === SKELL === A free web service based on Sketch Engine and aimed at language learners and teachers is SKELL (formerly SkELL). It exploits Sketch Engine's proprietary GDEX (Good Dictionary Examples) scoring function to provide authentic example sentences for specific target words. Results are drawn from a special corpus of high-quality texts covering everyday, standard, formal, and professional language and displayed as a concordance. SKELL also includes simplified versions of Sketch Engine's word sketch and thesaurus functions. It has been suggested that SKELL can be used, for instance, to help students understand the meaning and/or usage of a word or phrase; to help teachers wanting to use example sentences in a class; to discover and explore collocates; to create gap-fill exercises; to teach various kinds of homonyms and polysemous words. SKELL was first presented in 2014, when only English was supported. Later, support was added for Russian, Czech, German, Italian and Estonian. == List of text corpora == Sketch Engine provides access to more than 800 text corpora. There are monolingual as well as multilingual corpora of different sizes (from one thousand words up to 85 billion words) and various sources (e.g. web, books, subtitles, legal documents). The list of corpora includes British National Corpus, Brown Corpus, Cambridge Academic English Corpus and Cambridge Learner Corpus, CHILDES corpora of child language, OpenSubtitles (a set of 60 parallel corpora), 24 multilingual corpora of EUR-Lex documents, the TenTen Corpus Family (multi-billion web corpora), and Trends corpora (monitor corpora with daily updates). == Architecture == Sketch Engine consists of three main components: an underlying database management system called Manatee, a web interface search front-end called Bonito, and a web interface for corpus building and management called Corpus Architect. === Manatee === Manatee is a database management system specifically devised for effective indexing of large text corpora. It is based on the idea of inverted indexing (keeping an index of all positions of a given word in the text). It has been used to index text corpora comprising tens of billions of words. Searching corpora indexed by Manatee is performed by formulating queries in the Corpus Query Language (CQL). Manatee is written in C++ and offers an API for a number of other programming languages including Python, Java, Perl and Ruby. Recently, it was rewritten into Go for faster processing of corpus queries. === Bonito === Bonito is a web interface for Manatee providing access to corpus search. In the client–server model, Manatee is the server and Bonito plays the client part. It is written in Python. === Corpus Architect === Corpus Architect is a web interface providing corpus building and management features. It is also written in Python. == Applications == Sketch Engine has been used by major British and other publishing houses for producing dictionaries such as Macmillan English Dictionary, Dictionnaires Le Robert, Oxford University Press or Shogakukan. Four of United Kingdom's five biggest dictionary publishers use Sketch Engine.
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Data refuge

Data Refuge is a public and collaborative project designed to address concerns about federal climate and environmental data that is in danger of being lost. In particular, the initiative addresses five main concerns: What are the best ways to safeguard data? How do federal agencies play a crucial role in collecting, managing, and distributing data? How do government priorities impact data's accessibility? Which projects and research fields depend on federal data? Which data sets are of value to research and local communities, and why? Data Refuge began as a grassroots organization in opposition to government data on climate change and the environment not being archived systemically. Data Refuge's main goal is to collect and allocate data in multiple safe locations to create a sustainable way of archiving old and new data. Data Refuge was initiated in 2016 to protect federal climate and environmental data that is vulnerable under an administration that denies climate change. The system aims to make public research-quality copies of federal climate and environmental data. Data Refuge is supported by the National Geographic Foundation, private donors, Libraries+ Network, Preserving Electronic Governance Initiative (PEGI), the Union of Concerned Scientists (USC), and the Penn Program in Environmental Humanities (PPEH). == Types of data == Data Refuge collects public federal data on the climate and environment in the form of satellite imagery, PDFs, and stories. The data are stored in multiple trusted locations as they are less vulnerable if in only one location, and to ensure accessibility for researchers. Through the Data Rescue events, Data Refuge has accumulated 4 terabytes of data, 30,000 URLs, and 800 participants. === Storytelling === Data Refuge collects stories on vulnerable federal climate and environmental data through: surveys, oral history, photo essays, maps, video shorts, and animations. The stories are archived in a public bank that showcase how federal environmental data support health and safety in communities. Data Stories are collected at Data Rescue events, which are partnered with universities, city and town halls, and advocacy groups. Data stories are collected and used to emphasize the importance of Data Refuge, in how the data on climate change and the environment are being used by people in the United States and across the world for meaningful practices.
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SCinet

SCinet is the high-performance network built annually by volunteers in support of SC (formerly Supercomputing, the International Conference for High Performance Computing, Networking, Storage and Analysis). SCinet is the primary network for the yearly conference and is used by attendees and exhibitors to demonstrate and test high-performance computing and networking applications. == International Community == SCinet is also a hub for the international networking community. It provides a platform to share the latest research, technologies, and demonstrations for networks, network technology providers, and even software developers who are in charge of supporting HPC communities at their own institutions or organizations. == Volunteers == Nearly 200 volunteers from educational institutions, high performance computing sites, equipment vendors, research and education networks, government agencies and telecommunications carriers collaborate via technology and in-person to design, build and operate SCinet. While many of these credentialed individuals have volunteered at SCinet for years, first timers join the team each year. They include international students and participants in the National Science Foundation-funded Women in IT Networking at SC (WINS) program. The 2017 SCinet team included women and men from high performance computing institutions in the U.S. and throughout the world. == History == Originated in 1991 as an initiative within the SC conference to provide networking to attendees, SCinet has grown to become the "World's Fastest Network" during the duration of the conference. For 29 years, SCinet has provided SC attendees and the high performance computing (HPC) community with the innovative network platform necessary to internationally interconnect, transport, and display HPC research during SC. Historically, SCinet has been used as a platform to test networking technology and applications which have found their way into common use. == Research and development == In the past years, SCinet deployed conference wide networking technologies such as ATM, FDDI, HiPPi before they were deployed commercially.
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Critical data studies

Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of critical theory, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. Interest in this unique field of critical data studies began in 2011 with scholars danah boyd and Kate Crawford posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and Phoebe Sengers focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. == Motivation == In their article in which they coin the term 'critical data studies,' Dalton and Thatcher also provide several justifications as to why data studies is a discipline worthy of a critical approach. First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. Big data is important to critical data studies because it is the type of data used within this field. Big data does not necessarily refer to a large data set, it can have a data set with millions of rows, but also a data set that just has a wide variety and expansive scope of data with a smaller type of dataset. As well as having whole populations in the data set and not just sample sizes. Furthermore, big data as a technological tool and the information that it yields are not neutral, according to Dalton and Thatcher, making it worthy of critical analysis in order to identify and address its biases. Building off this idea, another justification for a critical approach is that the relationship between big data and society is an important one, and therefore worthy of study. Ribes et. al. argue there is a need for an interdisciplinary understanding of data as a historical artifact as a motivating aspect of critical data studies.The overarching consensus in the Computer-Supported Cooperative Work (CSCW) field, is that people should speak for the data, and not let the data speak for itself. The sources of big data and it’s relationship to varied metadata can be a complicated one, which leads to data disorder and a need for an ethical analysis. Additionally, Iliadis and Russo (2016) have called for studying data assemblages. This is to say, data has innate technological, political, social, and economic histories that should be taken into consideration. Kitchin argues data is almost never raw, and it is almost always cooked, meaning that it is always spoken for by the data scientists utilizing it. Thus, Big Data should be open to a variety of perspectives, especially those of cultural and philosophical nature. Further, data contains hidden histories, ideologies, and philosophies. Big data technology can cause significant changes in society's structure and in the everyday lives of people, and, being a product of society, big data technology is worthy of sociological investigation. Moreover, data sets are almost never completely without any influence. Rather, data are shaped by the vision or goals of those gathering the data, and during the data collection process, certain things are quantified, stored, sorted and even discarded by the research team. A critical approach is thus necessary in order to understand and reveal the intent behind the information being presented.One of these critical approaches has been through feminist data studies. This method applies feminist principles to critical studies and data collecting and analysis. The goal of this is to address the power imbalance in data science and society. According to Catherine D’Ignazio and Lauren F. Klein, a power analysis can be performed by examining power, challenging power, evaluating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Feminist data studies is part of the movement towards making data to benefit everyone and not to increase existing inequalities. Moreover, data alone cannot speak for themselves; in order to possess any concrete meaning, data must be accompanied by theoretical insight or alternative quantitative or qualitative research measures. Based on different social topics such as anti-racist data studies, critical data studies give a focus on those social issues concerning data. Specifically in anti-racist data studies they use a classification approach to get representation for those within that community. Desmond Upton Patton and others used their own classification system in the communities of Chicago to help target and reduce violence with young teens on twitter. They had students in those communities help them to decipher the terminology and emojis of these teens to target the language used in tweets that followed with violence outside of the computer screens. This is just one real world example of critical data studies and its application. Dalton and Thatcher argue that if one were to only think of data in terms of its exploitative power, there is no possibility of using data for revolutionary, liberatory purposes. Finally, Dalton and Thatcher propose that a critical approach in studying data allows for 'big data' to be combined with older, 'small data,' and thus create more thorough research, opening up more opportunities, questions and topics to be explored. == Issues and concerns for critical data scholars == Data plays a pivotal role in the emerging knowledge economy, driving productivity, competitiveness, efficiency, sustainability, and capital accumulation. The ethical, political, and economic dimensions of data dynamically evolve across space and time, influenced by changing regimes, technologies, and priorities. Technically, the focus lies on handling, storing, and analyzing vast data sets, utilizing machine learning-based data mining and analytics. This technological advancement raises concerns about data quality, encompassing validity, reliability, authenticity, usability, and lineage. The use of data in modern society brings about new ways of understanding and measuring the world, but also brings with it certain concerns or issues. Data scholars attempt to bring some of these issues to light in their quest to be critical of data. Technical and organizational issues could include the scope of the data set, meaning there is too little or too much data to work with, leading to inaccurate results. It becomes crucial for critical data scholars to carefully consider the adequacy of data volume for their analyses. The quality of the data itself is another facet of concern. The data itself could be of poor quality, such as an incomplete or messy data set with missing or inaccurate data values. This would lead researchers to have to make edits and assumptions about the data itself. Addressing these issues often requires scholars to make edits and assumptions about the data to ensure its reliability and relevance. Data scientists could have improper access to the actual data set, limiting their abilities to analyze it. Linnet Taylor explains how gaps in data can arise when people of varying levels of power have certain rights to their data sources. These people in power can control what data is collected, how it is displayed and how it is analyzed. The capabilities of the research team also play a crucial role in the quality of data analytics. The research team may have inadequate skills or organizational capabilities which leads to the actual analytics performed on the dataset to be biased. This can also lead to ecological fallacies, meaning an assumption is made about an individual based on data or results from a larger group of people. These technical and organizational challenges highlight the complexity of working with data and
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IMPACT (computer graphics)

IMPACT (sometimes spelled Impact) is a computer graphics architecture for Silicon Graphics computer workstations. IMPACT Graphics was developed in 1995 and was available as a high-end graphics option on workstations released during the mid-1990s. IMPACT graphics gives the workstation real-time 2D and 3D graphics rendering capability similar to that of even high-end PCs made well after IMPACT's introduction. IMPACT graphics systems consist of either one or two Geometry Engines and one or two Raster Engines in various configurations. IMPACT graphics consists of five graphics subsystems: the Command Engine, Geometry Subsystem, Raster Engine, framebuffer and Display Subsystem. IMPACT Graphics can produce resolutions up to 1600 x 1200 pixels with 32-bit color and can also process unencoded NTSC and PAL analog television signals. IMPACT graphics subsystems come in three configurations for SGI Indigo2 IMPACT workstations: Solid IMPACT, High IMPACT, and Maximum IMPACT. The equivalent configurations also exist for the SGI Octane workstation but are referred to as SI, SSI, and MXI (I-series). Later Octane workstations used a similar configuration but with updated ASIC chips and are referred to as SE, SSE, and MXE (E-series). IMPACT uses Rambus RDRAM for texture memory. The IMPACT graphics architecture was superseded by SGI's VPro graphics architecture in 1997.
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Social bot

A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a
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Format-transforming encryption

In cryptography, format-transforming encryption (FTE) refers to encryption where the format of the input plaintext and output ciphertext are configurable. Descriptions of formats can vary, but are typically compact set descriptors, such as a regular expression. Format-transforming encryption is closely related to, and a generalization of, format-preserving encryption. == Applications of FTE == === Restricted fields or formats === Similar to format-preserving encryption, FTE can be used to control the format of ciphertexts. The canonical example is a credit card number, such as 1234567812345670 (16 bytes long, digits only). However, FTE does not enforce that the input format must be the same as the output format. === Censorship circumvention === FTE is used by the Tor Project to circumvent deep packet inspection by pretending to be some other protocols. The implementation is fteproxy; it was written by the authors who came up with the FTE concept.
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