AI Coding Humanizer

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List of publications in data science

This is a list of publications in data science, generally organized by order of use in a data analysis workflow. See the list of publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary. General article inclusion criteria are: Papers from notable practitioners or notable professors, either with a Wikipedia page or reference to their notability Common knowledge all data professionals should know, with references validating this claim Highly cited applied statistics and machine learning publications Discussion-facilitating papers on the field of data science as a whole (for example, the Attention Is All You Need paper is arguably a landmark paper that can be added here, but it is specific to generative artificial intelligence, not for all practitioners of data) Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic Breakthrough – A publication that changed scientific knowledge significantly Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of data science. When possible, a reference is used to validate the inclusion of the publication in this list. == History == Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) Author: Leo Breiman Publication data: Online version: https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Description: Describes two cultures of statistics, one using a parsimonious and generative stochastic model, while the other is an algorithmic model with no known mechanism for how the data is generated. Breiman argues that while statistics has traditionally favored using the stochastic model, there is value in expanding the methods that statisticians can use to study phenomenon. Importance: Influence on the philosophies of statisticians right before the increased use of machine learning and deep learning methods. In a 20-year retrospective on this article, "Breiman's words are perhaps more relevant than ever". Notable statisticians at the time wrote opinion pieces about the publication. Although overall critical of the publication, David Cox writes that the publication "contains enough truth and exposes enough weaknesses to be thought-provoking." Bradley Efron commented that this publication is a "stimulating paper". Emanuel Parzen also comments about this publication that "Breiman alerts us to systematic blunders (leading to wrong conclusions) that have been committed applying current statistical practice of data modeling". Data Scientist: The Sexiest Job of the 21st Century Author: Thomas H. Davenport and DJ Patil Publication data: Online version: hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century Description: Describes the new role at companies that is coined "Data scientist", what they do, how an organization might recruit one to their organization, and how to work with one effectively. Importance: This publication has been an influence on the data community as mentioned near the time it was published in 2012 by institutions like IEEE Spectrum, but also mentioned nearly a decade later asking the same question the title poses. In a retrospective response to their own publication 10 years earlier, authors Davenport and Patil have reflected that the role of a data scientist has "become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown". 50 Years of Data Science Author: David Donoho Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734 Description: Retrospective discussion paper on the history and origins of data science, with a number of commentary from notable statisticians. Importance: This has been described as "the first in the field to present such a comprehensive and in-depth survey and overview", and helps to define the field that has many definitions. The Composable Data Management System Manifesto Author: Pedro Pedreira, Orri Erling, Konstantinos Karanasos, Scott Schneider, Wes McKinney, Satya R Valluri, Mohamed Zait, Jacques Nadeau Publication data: Online version: https://www.vldb.org/pvldb/vol16/p2679-pedreira.pdf Description: The vision paper advocating for a paradigm shift in how data management systems are designed using standard, composable, interoperable tools rather than siloed software tools. Importance: A paradigm shifting view on how future data science software tools should be designed for more efficient workflows, the principles of which "will be especially crucial for addressing fragmentation, improving interoperability, and promoting user-centricity as data ecosystems grow increasingly complex". == Data collection and organization == Tidy Data Author: Hadley Wickham Publication data: Online version: https://www.jstatsoft.org/article/view/v059i10/ https://vita.had.co.nz/papers/tidy-data.pdf Description: Describes a framework for data cleaning that is summarized in the quote, "each variable is a column, each observation is a row, and each type of observational unit is a table". This allows a standard data structure for which data analysis tools can be consistently built around. Importance: Cited over 1,500 times, this effort for tidy data has been described by David Donoho as having "more impact on today's practice of data analysis than many highly regarded theoretical statistics articles". In the context of data visualization, this publication is said to support "efficient exploration and prototyping because variables can be assigned different roles in the plot without modifying anything about the original dataset". Data Organization in Spreadsheets Author: Karl W. Broman and Kara H. Woo Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989 Description: This article offers practical recommendations for organizing data in spreadsheets, like Microsoft Excel and Google Sheets, to reduce errors and lower the barrier for later analyses due to limitations in spreadsheets or quirks in the software. Importance: Influences teaching both data and non-data practitioners to create more analysis-friendly spreadsheets, and has been described to outline "spreadsheet best practices". == Data visualizations == Quantitative Graphics in Statistics: A Brief History Author: James R. Beniger and Dorothy L. Robyn Publication data: Online version: https://www.jstor.org/stable/2683467 Description: Outlines history and evolution of quantitative graphics in statistics, going through spatial organization (17th and 18th centuries), discrete comparison (18th and 19th centuries), continuous distribution (19th century), and multivariate distribution and correlation (late 19th and 20th centuries). Importance: Helps put into perspective for learning data practitioners the recency of graphics that are used. A later publication "Graphical Methods in Statistics" by Stephen Fienberg in 1979 writes that his publication "owes much to the work of Beniger and Robyn". == Practice == Data Science for Business Author: Foster Provost and Tom Fawcett Publication data: Online version: N/A Description: Broadly outlines principles of data science and data-analytic thinking for businesses. Importance: Cited over 3,000 times, it is "highly recommended for students" but also it is also recommended due to its "relevance to senior management leaders who want to build and lead a team of data scientists and implement data science in solving complex business problems". == Tooling == Hidden Technical Debt in Machine Learning Systems Author: D. Sculley, Gary Holy, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison Publication data: Online version: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Description: This paper argues that it is "dangerous to think of [complex machine learning] quick wins as coming for free" and overviews risk factors to account for when implementing a machine learning system. Importance: All authors worked for Google, article is cited over 2,000 times, and helped practitioners thinking about quickly implementing a machine learning tool without understanding the long-term maintenance of the tool. A few useful things to know about machine learning Author: Pedro Domingos Publication data: Online version: https://dl.acm.org/doi/10.1145/2347736.2347755 https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Description: The purpose of this paper is to distill inaccessible "folk knowledge" to effectively implement machine learning projects because "machin
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Social trading

Social trading is a form of investing that allows investors to observe the trading behavior of their peers and expert traders. The primary objective is to follow their investment strategies using copy trading or mirror trading. Social trading requires little or no knowledge about financial markets. == History == One of the first social trading platforms was Collective2] which began offering a social trading functionality to retail traders as early as 2003 (preceding ZuluTrade by four years). In 2010, social trading started to achieve a greater degree of mainstream appeal with eToro, followed by Wikifolio in 2012. Europe-based NAGA, listed on Frankfurt Stock Exchange since 2017, claims more than EUR 27 billion was traded on its platform in the second half of 2019. Some of the other contemporary social trading platforms and tech providers are Trading Motion, Brokeree Solutions, iSystems, and FX Junction, among others. === Research === MIT Computer Scientist and researcher Yaniv Altshuler described social trading networks as complex adaptive systems, and in his 2014 research on eToro's OpenBook, wrote that "Having the inherent ability to share ideas and information between each others, OpenBook's users are given a new source of information they can use in order to enhance their trading performance. As the users are not playing against each other but rather – against the market, this situation becomes a non zero-sum game, hence incentivizing the users to share as much information as possible." His paper concludes that "social trading provides much better opportunities for profiting compared with individual trading," but that users make "excellent but sometimes not optimal decisions in selecting experts when they can see others' choices." A 2015 World Economic Forum report described social trading networks as disruptors, which "have emerged to provide low-cost, sophisticated alternatives to traditional wealth managers. These solutions cater to a broader customer base and empower customers to have more control of their wealth management," and "pose a tangible threat to the traditional practices of the wealth management industry". Economist Nouriel Roubini's thinktank predicted in 2016 that "newer forms of investment, such as socially responsible investments and social trading will bring some of the largest industry growth in the coming years." A 2017 St. John's University study found that 'leader' traders, or those with followers, are more susceptible to the disposition effect than investors that are not being followed by any other traders, with the authors suggesting the observation may be explained by "leaders feeling responsible towards their followers and an urge to not let them down, by fear of losing followers when admitting a bad investment decision and signaling confidence in their initial investment choice, or by an attempt of newly appointed leaders to manage their self-image." Social trading may potentially also change how much risk investors take. A recent experimental study argues that merely providing information on the success of others may lead to a significant increase in risk taking. This increase in risk taking may even be larger when subjects are provided with the option to directly copy others. == Characteristics == Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing and copying their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Social trading platforms or networks can be considered a subcategory of social networking services. Social trading allows traders to trade online with the help of others and some have claimed shortens the learning curve from novice to experienced trader. Traders can interact with others, watch others take trades, then duplicate their trades and learn what prompted the top performer to take a trade in the first place. By copying trades, traders can learn which strategies work and which do not work. Social trading is used to do speculation; in the moral context speculative practices are considered negatively and to be avoided by each individual. who conversely should maintain a long-term horizon avoiding any types of short term speculation. Social Media has permeated the trading world such that two main types of trading has evolved: Traditional Trades Single (or non-social) trade: Trader A places a normal trade by himself or herself; This can by manual or automated Social Trading There are two main types of social trading: Copy trade: Trader A places exactly the same trade as trader B's one single trade; (iii) Mirror trade: Trader A automatically executes trader B's every single trade, i.e., trader A follows exactly trader B's trading activities. Other variations offered on some platforms allow users to copy another trader's portfolio (copy portfolio), and follow a trader's dividends (copy dividends), where whenever a followed trader withdraws money from his or her account, a proportional amount of money will be withdrawn from the balance of their follower, in real time. === Key features === Information flow: Unencumbered access to information is important in financial markets and that makes the free exchange of information of interest to small scale as well as individual investors. Cooperative trading: Social trading offers traders the opportunity to work together in trading teams which can trade the markets collaboratively, whether by pooling funds, dividing research or through sharing information. Monetization: As with social networks in the broader sense, monetization strategies are not always clear. As with social networks in general, it is possible, however, that the long-term worth of such websites may come from the variety and depth of data about their users which their active communities are likely to generate. Transparency: Social trading platforms reveal traders' performance stats, open and past positions, and market sentiment, giving members complete information to assess the credibility of the contributors they follow on the platform.
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Intent-based network

Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.
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Data Transformation Services

Data Transformation Services (DTS) is a Microsoft database tool with a set of objects and utilities to allow the automation of extract, transform and load operations to or from a database. The objects are DTS packages and their components, and the utilities are called DTS tools. DTS was included with earlier versions of Microsoft SQL Server, and was almost always used with SQL Server databases, although it could be used independently with other databases. DTS allows data to be transformed and loaded from heterogeneous sources using OLE DB, ODBC, or text-only files, into any supported database. DTS can also allow automation of data import or transformation on a scheduled basis, and can perform additional functions such as FTPing files and executing external programs. In addition, DTS provides an alternative method of version control and backup for packages when used in conjunction with a version control system, such as Microsoft Visual SourceSafe. DTS has been superseded by SQL Server Integration Services in later releases of Microsoft SQL Server though there was some backwards compatibility and ability to run DTS packages in the new SSIS for a time. == History == In SQL Server versions 6.5 and earlier, database administrators (DBAs) used SQL Server Transfer Manager and Bulk Copy Program, included with SQL Server, to transfer data. These tools had significant shortcomings, and many DBAs used third-party tools such as Pervasive Data Integrator to transfer data more flexibly and easily. With the release of SQL Server 7 in 1998, "Data Transformation Services" was packaged with it to replace all these tools. The concept, design, and implementation of the Data Transformation Services was led by Stewart P. MacLeod (SQL Server Development Group Program Manager), Vij Rajarajan (SQL Server Lead Developer), and Ted Hart (SQL Server Lead Developer). The goal was to make it easier to import, export, and transform heterogeneous data and simplify the creation of data warehouses from operational data sources. SQL Server 2000 expanded DTS functionality in several ways. It introduced new types of tasks, including the ability to FTP files, move databases or database components, and add messages into Microsoft Message Queue. DTS packages can be saved as a Visual Basic file in SQL Server 2000, and this can be expanded to save into any COM-compliant language. Microsoft also integrated packages into Windows 2000 security and made DTS tools more user-friendly; tasks can accept input and output parameters. DTS comes with all editions of SQL Server 7 and 2000, but was superseded by SQL Server Integration Services in the Microsoft SQL Server 2005 release in 2005. == DTS packages == The DTS package is the fundamental logical component of DTS; every DTS object is a child component of the package. Packages are used whenever one modifies data using DTS. All the metadata about the data transformation is contained within the package. Packages can be saved directly in a SQL Server, or can be saved in the Microsoft Repository or in COM files. SQL Server 2000 also allows a programmer to save packages in a Visual Basic or other language file (when stored to a VB file, the package is actually scripted—that is, a VB script is executed to dynamically create the package objects and its component objects). A package can contain any number of connection objects, but does not have to contain any. These allow the package to read data from any OLE DB-compliant data source, and can be expanded to handle other sorts of data. The functionality of a package is organized into tasks and steps. A DTS Task is a discrete set of functionalities executed as a single step in a DTS package. Each task defines a work item to be performed as part of the data movement and data transformation process or as a job to be executed. Data Transformation Services supplies a number of tasks that are part of the DTS object model and that can be accessed graphically through the DTS Designer or accessed programmatically. These tasks, which can be configured individually, cover a wide variety of data copying, data transformation and notification situations. For example, the following types of tasks represent some actions that you can perform by using DTS: executing a single SQL statement, sending an email, and transferring a file with FTP. A step within a DTS package describes the order in which tasks are run and the precedence constraints that describe what to do in the case damage or of failure. These steps can be executed sequentially or in parallel. Packages can also contain global variables which can be used throughout the package. SQL Server 2000 allows input and output parameters for tasks, greatly expanding the usefulness of global variables. DTS packages can be edited, password protected, scheduled for execution, and retrieved by version. == DTS tools == DTS tools packaged with SQL Server include the DTS wizards, DTS Designer, and DTS Programming Interfaces. === DTS wizards === The DTS wizards can be used to perform simple or common DTS tasks. These include the Import/Export Wizard and the Copy of Database Wizard. They provide the simplest method of copying data between OLE DB data sources. There is a great deal of functionality that is not available by merely using a wizard. However, a package created with a wizard can be saved and later altered with one of the other DTS tools. A Create Publishing Wizard is also available to schedule packages to run at certain times. This only works if SQL Server Agent is running; otherwise the package will be scheduled, but will not be executed. === DTS Designer === The DTS Designer is a graphical tool used to build complex DTS Packages with workflows and event-driven logic. DTS Designer can also be used to edit and customize DTS Packages created with the DTS wizard. Each connection and task in DTS Designer is shown with a specific icon. These icons are joined with precedence constraints, which specify the order and requirements for tasks to be run. One task may run, for instance, only if another task succeeds (or fails). Other tasks may run concurrently. The DTS Designer has been criticized for having unusual quirks and limitations, such as the inability to visually copy and paste multiple tasks at one time. Many of these shortcomings have been overcome in SQL Server Integration Services, DTS's successor. === DTS Query Designer === A graphical tool used to build queries in DTS. === DTS Run Utility === DTS Packages can be run from the command line using the DTSRUN Utility. The utility is invoked using the following syntax: dtsrun /S server_name[\instance_name] { {/[~]U user_name [/[~]P password]} | /E } ] { {/[~]N package_name } | {/[~]G package_guid_string} | {/[~]V package_version_guid_string} } [/[~]M package_password] [/[~]F filename] [/[~]R repository_database_name] [/A global_variable_name:typeid=value] [/L log_file_name] [/W NT_event_log_completion_status] [/Z] [/!X] [/!D] [/!Y] [/!C] ] When passing in parameters which are mapped to Global Variables, you are required to include the typeid. This is rather difficult to find on the Microsoft site. Below are the TypeIds used in passing in these values.
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Scikit-learn

scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. == Overview == The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub. At that time, the project had over 1,400 contributors and the documentation received 42 million visits in 2018. According to a 2022 Kaggle survey of nearly 24,000 respondents from 173 countries, scikit-learn was identified as the most widely used machine learning framework. == Features == Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting == Examples == Fitting a random forest classifier: == Implementation == scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible. scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more. == History == scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010. The project released its first stable version, 1.0.0, on September 24, 2021. The release was the result of over 2,100 merged pull requests, approximately 800 of which were dedicated to improving documentation. Development continues to focus on bug fixes, efficiency and feature expansion. The latest version, 1.8, was released on December 10, 2025. This update introduced native Array API support, enabling the library to perform GPU computations by directly using PyTorch and CuPy arrays. This version also included bug fixes, improvements and new features, such as efficiency improvements to the fit time of linear models. == Applications == Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library: === Finance and Insurance === AXA uses scikit-learn to speed up the compensation process for car accidents and to detect insurance fraud. Zopa, a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing. BNP Paribas Cardif uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks. J.P. Morgan reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making. === Retail and E-Commerce === Booking.com uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents. HowAboutWe uses it to predict user engagement and preferences on a dating platform. Lovely leverages the library to understand user behaviour and detect fraudulent activity on its platform. Data Publica uses it for customer segmentation based on the success of past partnerships. Otto Group integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations. === Media, Marketing, and Social Platforms === Spotify applies scikit-learn in its recommendation systems. Betaworks uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data. PeerIndex used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics. Bestofmedia Group employs it for spam detection and ad click prediction. Machinalis utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization. Change.org applies scikit-learn for targeted email outreach based on user behaviour. === Technology === AWeber uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns. Solido applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning. Evernote, Dataiku, and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem. === Academia === Télécom ParisTech integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum. == Awards == 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize: Awarded in recognition of scikit-learn's impact as a major free software breakthrough in machine learning and its role in the digital transformation of science and industry. 2022 Open Science Award for Open Source Research Software: Awarded by the French Ministry of Higher Education and Research as part of the second National Plan for Open Science. The project was recognized in the "Community" category for its technical quality, its large international contributor network, and the quality of its documentation.
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Transmission security

Transmission security (TRANSEC) is the component of communications security (COMSEC) that results from the application of measures designed to protect transmissions from interception and exploitation by means other than cryptanalysis. Goals of transmission security include: Low probability of interception (LPI) Low probability of detection (LPD) Antijam — resistance to jamming (EPM or ECCM) This involves securing communication links from being compromised by techniques like jamming, eavesdropping, and signal interception. TRANSEC includes the use of frequency hopping, spread spectrum and the physical protection of communication links to obscure the patterns of transmission. It is particularly vital in military and government communication systems, where the security of transmitted data is critical to prevent adversaries from gathering intelligence or disrupting operations. TRANSEC is often implemented alongside COMSEC (Communications Security) to form a comprehensive approach to communication security. Methods used to achieve transmission security include frequency hopping and spread spectrum where the required pseudorandom sequence generation is controlled by a cryptographic algorithm and key. Such keys are known as transmission security keys (TSK). Modern U.S. and NATO TRANSEC-equipped radios include SINCGARS and HAVE QUICK.
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Social media stock bubble

The social media bubble is a hypothesis stating that there was a speculative boom and bust phenomenon in the field of social media in the 2010s, particularly in the United States. The Wall Street Journal defined a bubble as stocks "priced above a level that can be justified by economic fundamentals," but this bubble includes social media. Social networking services (SNS) have seen huge growth since 2006, but some investors believed around 2014-2015, that the "bubble" was similar to the dot-com bubble of the late 1990s and early 2000s. In 2015, Mark Cuban, owner of the Dallas Mavericks NBA team and star of the TV show, Shark Tank, sounded an alarm on his personal blog over the social media bubble, calling it worse than the tech bubble in 2000 due to the lack of liquidity in social media stocks. A year prior, however, Cuban told CNBC that he did not believe social media stocks were on the verge of a bubble. In a letter to investors in 2014, David Einhorn, who runs the hedge-fund Greenlight Capital, wrote that "we are witnessing our second tech bubble in 15 years." He went on to write, "What is uncertain is how much further the bubble can expand, and what might pop it." Einhorn cited several factors supporting the existence an over-exuberance including "rejection of conventional valuation methods" and "huge first day IPO pops for companies that have done little more than use the right buzzwords and attract the right venture capital." Since those claims, services like Facebook, Twitter, Instagram, and Snapchat have grown to become multi-billion-dollar corporations generating enormous revenues, though some continue to lose money. == History of social networking services == Social networking services have grown and evolved with time since the launch of SixDegrees.com in 1997. Cutting edge at its time, SixDegrees.com allowed users to create a profile, invite friends, and connect within its platform. At its peak, SixDegrees.com had more than 3.5 million users. Between 1997 and 2001 more social sites aimed at allowing users to connect with others for personal, professional, or dating reasons. Friendster and MySpace were next to enter the social SNS arena, followed by Facebook in 2004. Even though MySpace had a following of more than 300 million users, it could not compete with Facebook, which now has overtaken the social networking world. However, as development of SNS started to emerge, a market saturation began to take effect. Some classrooms have begun to incorporate technology in daily learning as well as social channels specific to student's course work. Traditional social media sites are used, as are educational oriented sites such as ShowMe and Educreations Interactive Whiteboard. == Controversies == While SNS continue to play an influential role in helping people form real-world connections via the Internet, renewed concerns over the social media bubble have surfaced due to recent controversies. These threats include growing concerns about breaches in data, the rise of bot accounts, and the sharing of fake news on SNS platforms. There are also concerns that big data figures associated with these SNS are inflated or fake, as well as worries about the role the platforms played in national elections (see Russian interference in the 2016 United States elections). These issues have resulted in a lack of trust among the sites' users.
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Voice inversion

Voice inversion scrambling is an analog method of obscuring the content of a transmission. It is sometimes used in public service radio, automobile racing, cordless telephones and the Family Radio Service. Without a descrambler, the transmission makes the speaker "sound like Donald Duck". Despite the term, the technique operates on the passband of the information and so can be applied to any information being transmitted. == Forms and details == There are various forms of voice inversion which offer differing levels of security. Overall, voice inversion scrambling offers little true security as software and even hobbyist kits are available from kit makers for scrambling and descrambling. The cadence of the speech is not changed. It is often easy to guess what is happening in the conversation by listening for other audio cues like questions, short responses and other language cadences. In the simplest form of voice inversion, the frequency p {\displaystyle p} of each component is replaced with s − p {\displaystyle s-p} , where s {\displaystyle s} is the frequency of a carrier wave. This can be done by amplitude modulating the speech signal with the carrier, then applying a low-pass filter to select the lower sideband. This will make the low tones of the voice sound like high ones and vice versa. This process also occurs naturally if a radio receiver is tuned to a single sideband transmission but set to decode the wrong sideband. There are more advanced forms of voice inversion which are more complex and require more effort to descramble. One method is to use a random code to choose the carrier frequency and then change this code in real time. This is called Rolling Code voice inversion and one can often hear the "ticks" in the transmission which signal the changing of the inversion point. Another method is split band voice inversion. This is where the band is split and then each band is inverted separately. A rolling code can also be added to this method for variable split band inversion (VSB). Common carrier frequencies are: 2.632 kHz, 2.718 kHz, 2.868 kHz, 3.023 kHz, 3.107 kHz, 3.196 kHz, 3.333 kHz, 3.339 kHz, 3.496 kHz, 3.729 kHz and 4.096 kHz. Voice inversion offers no security at all and software is available to restore the original voice, which is why it is no longer used to protect conversations today. However, voice inversion is still found in low-end Chinese walkie talkies.
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Human-centered AI

Human-centered AI is the initiative at the intersection of the fields of artificial intelligence (AI) and human-computer interaction (HCI) to develop AI systems in a way that prioritizes human values, needs, and general flourishing. Emphasis is placed on the recognition that artificial intelligence systems are rapidly changing, and will continue to influence, many aspects of the human experience, in areas ranging from scientific inquiry, governance and policy, labor and the economy, and creative expression, with an aim set to adapt current developments and guide future developments on a trajectory which is most beneficial to the human population at large, with the goal of augmenting human intelligence and capacities across these areas, as opposed to replacing them. Particular attention is paid to mitigating negative effects of AI automation on the livelihoods of the labor force, the use of AI in healthcare fields, and imbuing AI systems with societal values. Human-centered AI is linked to related endeavors in AI alignment and AI safety, but while these fields primarily focus on mitigating risks posed by AI that is unaligned to human values and/or uncontrollable AI self-development, human-centered AI places significant focus in exploring how AI systems can augment human capacities and serve as collaborators. == Conceptual history == The importance of the alignment of artificial intelligence development towards human values in some sense predates artificial intelligence itself, as before the modern conception of artificial intelligence as coined at the 1956 Dartmouth Workshop, the conception of robots as constructed, autonomous agents entered the cultural consciousness as early as the 1920s, with Karel Capek's Rossum's Universal Robots. The imagined issues relating to robots' aims and values requiring intentional alignment and direction with those of humans followed soon after, most widely known from science fiction author Isaac Asimov’s Three Laws of Robotics, dating to his 1942 short story “Runaround”. Two of the three eponymous laws are directly concerned with robots’ interaction with and positioned deference towards humans, and have in recent times been reexamined in the face of modern AI. In 1985, after artificial intelligence research had taken off and its effects were more acutely conceptualized, Asimov added a Rule Zero, treating robots' relationship with humanity as a whole, distinct from individual humans. While modern artificial intelligence is largely distinct from robotics, the conceptualization of both robots and AI systems as autonomous agents positions this as a foundation for conceptions of human-centered AI. Aside from robots, artificially intelligent autonomous agents in interaction with humans have been conceived of for at least 75 years. In 1950, Alan Turing published his famous "Imitation Game", often also called the Turing Test, a thought experiment that uses human-machine interaction as an assessor for the intelligence of a system. In recent times, artificial intelligence researchers such as Stanford's Erik Brynjolfsson have conceived of rapid AI development leading to a so-called "Turing Trap". == Augmentation and automation == A major stated aim of human-centered AI is to promote the development of AI in ways that augment human capabilities, rather than replacing them. To this end, organizations and initiatives that take a human-centered approach to AI development focus on frameworks that encourage collaboration between humans and artificial intelligence systems to build towards even greater progress, rather than attempting to automate tasks currently handled by humans. Such avenues include everything from data visualization for big data, allowing human engineers to better understand extremely large datasets, allowing for the design of better machine learning models to handle them, to AI-powered sensors to monitor vitals, allowing for better responsiveness from healthcare providers. Many human-centered AI initiatives often position it as a better alternative to the apparent mainstream in AI development, which is primarily concerned with automation. Driven by the pressures of the market economy, AI development that does replace tasks currently performed by humans with automated processes is incentivized, as it allows for greater profit margins; this often comes at the detriment of the human whose performance is replaced, thus leading to an environment wherein human workers are outcompeted by AI systems across various service-sector and technology-based industries. At the same time, automation and augmentation are not always incompatible; a major aim of human-centered AI is towards the automation of rote tasks that would otherwise hinder a human’s productivity or creativity, freeing them to direct their energy and intelligence towards higher-level tasks, thus achieving augmentation through automation. Empirical research in pharmaceutical sales has shown that a human-centered implementation - where work procedures, training, and incentives are designed around individuals' cognitive needs - improves augmentation performance, while implementation without such adaptation can worsen outcomes relative to a legacy system. == Research == Much of the work done on human-centered AI comes from research institutes, within universities, companies, and as freestanding organizations. The Stanford Institute for Human-Centered AI (abbreviated to HAI) is one such group, engaging academics, industry professionals, and policymakers centered in Stanford University to conduct research and inform policy in various areas in human-centered AI, including on aspects of the intelligence itself, augmentation, and on measuring the impacts of AI systems on sociopolitcal and cultural institutions. Similar groups exist at other universities, including the Chicago Human + AI (CHAI) Lab at the University of Chicago, the HCAI@GU group at the University of Gothenburg, and the Human-Centered AI (HAI) Lab at the University of Oxford. Outside of the academy, companies such as IBM have research initiatives dedicated to advancements in human-centered AI. At Kenyon College, the Integrated Program for Humane Studies (IPHS) launched a human-centered AI program in 2016 integrating artificial intelligence research with humanities and social science inquiry. This approach treats computation and humanistic scholarship as a single unified field of research rather than as separate disciplines requiring collaboration. The program's researchers have published in both AI venues (such as the International Conference on Machine Learning and Frontiers of Computer Science) and humanities journals (such as PMLA and Poetics Today), and the lab was selected in December 2025 by Schmidt Sciences for its Humanities and AI Virtual Institute to apply AI methods to cultural heritage preservation.
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VK (service)

VK (short for its original name VKontakte; Russian: ВКонтакте, lit. 'InContact') is a Russian online social media and social networking service based in Saint Petersburg. VK is available in multiple languages but it is predominantly used by Russian speakers. VK users can message each other publicly or privately, edit messages, create groups, public pages, and events; share and tag images, audio, and video; and play browser-based games. As of August 2018, VK had at least 500 million accounts. As of November 2022, it was the sixth most popular website in Russia. The network was also popular in Ukraine until it was banned by the Verkhovna Rada in 2017. According to Semrush, in 2024, VK was the 30th most visited website in the world; as YouTube is subject to blocking in Russia, VK Video overtook Google's top position in monthly web traffic for the first time in December 2024, as part of the major substitution to domestic business. == History == VKontakte was conceived in 2006 when Pavel Durov, creator of the popular student forum spbgu.ru, met his former classmate Vyacheslav Mirilashvili in St. Petersburg after graduating from the Faculty of Philology at St Petersburg State University. Vyacheslav showed Durov the increasingly popular Facebook, after which the friends decided to create a new Russian social network. Lev Leviev, an Israeli classmate of Vyacheslav Mirilashivili, became the third co-founder. Vyacheslav Mirilashvili borrowed the money from his billionaire father and became the largest shareholder. Lev Leviev took over operational management, and Durov became CEO. Pavel Durov convinced his older brother Nikolai, a multiple winner of international math and programming competitions, to develop the site. Durov launched VKontakte for beta testing in September 2006. The following month, the domain name Vkontakte.ru was registered. The new project was incorporated on 19 January 2007 as a Russian private limited company. In February 2007 the site reached a user base of over 100,000 and was recognized as the second largest company in Russia's nascent social network market. In the same month, the site was subjected to a severe DDoS attack, which briefly put it offline. The user base reached 1 million in July 2007, and 10 million in April 2008. In December 2008 VK overtook rival Odnoklassniki as Russia's most popular social networking service. == Website == Similar to many social networks, the platform's fundamental features revolve around private messaging, sharing photos, posting status updates, and exchanging links with friends. VK also provides tools for administering online communities and managing celebrity pages. The site allows its users to upload, search and stream media content, such as videos and music. VK features an advanced search engine, that allows complex queries for finding friends, as well as a real-time news search. VK updated its features and design in April 2016. === Features === Messaging. VK Private Messages can be exchanged between groups of 2 to 500 people. An email address can also be specified as the recipient. Each message may contain up to 10 attachments: Photos, Videos, Audio Files, Maps (an embedded map with a manually placed marker), and Documents. News. VK users can post on their profile walls, each post may contain up to 10 attachments – media files, maps, and documents (see above). User mentions and hashtags are supported. In the case of multiple photo attachments, the previews are automatically scaled and arranged in a magazine-style layout. The news feed can be switched between all news (default) and most interesting modes. The site features a news-recommendation engine, global real-time search, and individual search for posts and comments on specific users' walls. Communities. VK features three types of communities. Groups are better suited for decentralized communities (discussion boards, wiki-style articles, editable by all members, etc.). Public pages is a news feed-orientated broadcasting tool for celebrities and businesses. The two types are largely interchangeable, the main difference being in the default settings. The third type of community is called Events, which are used for appropriately organizing concerts and events in an appropriate way. Like buttons. VK like buttons for posts, comments, media, and external sites operate differently from Facebook. Liked content doesn't get automatically pushed to the user's wall, but is saved in the private Favorites section instead. The user has to press a second 'share with friends' button to share an item on their wall or send it via private message to a friend. Privacy. Users can control the availability of their content within the network and on the Internet. Blanket and granular privacy settings are available for pages and individual content. Synchronization with other social networks. Any news published on the VK wall will appear on Facebook or Twitter. Certain news may not be published by clicking on the logo next to the "Send" button. Editing a post in VK does not change the post in Facebook or Twitter and vice versa. However, removing the news in VK will remove it from other social networks. SMS service. Russian users can receive and reply to a private message or leave a comment for community news using SMS. Music. Users have access to the audio files uploaded by other users. In addition, users can upload the audio files themselves, create playlists and share audios with others by attaching to messages and wall posts. The uploaded audio files cannot violate copyright laws. === Popularity === As of May 2017, according to Alexa Internet ranking, VK is one of the most visited websites in some Eurasian countries. It is: 4th most visited in Russia; 3rd most visited in Belarus; 6th most visited in Kazakhstan; 8th most visited in Kyrgyzstan and Moldova; 12th most visited in Latvia. It was the fourth most viewed site in Ukraine until, in May 2017, the Ukrainian government banned the use of VK in Ukraine. According to a study for May 2018 conducted by Factum Group Ukraine VK remained the fourth most viewed site in Ukraine, but Facebook was twice as much visited. For 2019, VK appeared as the most visited social network in Ukraine according to Alexa. According to the Internet Association of Ukraine the share of Ukrainian Internet users who visit VK daily had fallen from 54% to 10% from September 2016 to September 2019. They also claimed in November 2019 that Facebook was the most popular social network. VK was expected to gain most of the users lost by Facebook and Instagram after they were blocked in Russia in 2022, according to a Calltouch poll. == Ownership == Initially, founder and CEO Pavel Durov owned 20% of shares (although he had majority voting power through proxy votes), and a trio of Russian-Israeli investors Yitzchak Mirilashvili, his father Mikhael Mirilashvili, and Lev Leviev owned 60%, 10%, and 10% respectively. In 2007, Digital Sky Technologies, an investment company managed by Yuri Milner, acquired a total of 24.99% of the shares from shareholders, investing $16.3 million. In preparation for the IPO in September 2010, DST separated international and Russian assets: the former formed the DST Global fund, while the latter, including VKontakte and rival social network Odnoklassniki, were merged into Mail.ru Group. Mail.ru Group used part of the money to acquire 7.5% of the social network for $112.5 million at a valuation of the entire project of 1.5 billion dollars. After exercising a 7.5% option in July 2011 for $111.7 million, Mail.ru Group accumulated a 39.99% stake in VKontakte. The head of Mail.ru Group, Dmitry Grishin, voiced the company's intention to gain 100% control over VKontakte. MRG was discussing with shareholders to buy out shares from the valuation of the entire company in $2-3 billion. In the summer of 2011, Mirilashvili and Leviev were ready to accept in payment owned by Mail.ru Group shares of Facebook, Groupon, and Zynga, but the deal failed due to Durov's unwillingness to sell a stake on MRG terms. Later, the co-founders considered VKontakte's IPO as an alternative. In March 2012, Durov "accidentally" became plugged into the negotiations where Mirilashvili and Leviev discussed selling their stakes directly to Mail.ru Group's main investor, Alisher Usmanov. On the same day, Durov deleted the pages of the first co-investors, stopped contacting them, and soon announced that VKontakte would postpone its IPO indefinitely. On 29 May 2012, Mail.ru Group announced its decision to yield control of the company to Durov by offering him the voting rights on its shares. Combined with Durov's personal 12% stake, this gave him 52% of the votes. In April 2013, the Mirilashvili family sold its 40% share in VK to United Capital Partners for $1.12 billion, while Lev Leviev sold his 8% share in the same deal, giving United Capital Partners 48% ownership. In January 2014, VK's founder Pavel Durov sold his 12% stake in the company to I
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Time-lock puzzle

A time-lock puzzle, or time-released cryptography, encrypts a message that cannot be decrypted until a specified amount of time has passed. The concept was first described by Timothy C. May, and a solution first introduced by Ron Rivest, Adi Shamir, and David A. Wagner in 1996. Time-lock puzzle are useful in cases where confidentiality of information is determined by time, such as a diarist who does not want their views released until 50 years after their death, an auction where bids are sealed until the bidding period is closed, electronic voting, and contract signing. They can additionally be used in creating further cryptographic primitives, such as verifiable delay functions and zero knowledge proofs. Time-released cryptography can be achieved through several different mechanisms. Use mathematical problems requiring sequential calculations to solve, and cannot be solved with parallelization. Thus, adding more computers to a problem will not help solve the problem faster. Use of a trusted agent, or multiple agents who each hold a part of the message and cryptographic keys, who release the message after a specified time period has passed. Distribute public encryption keys to users, and place private cryptographic keys with a trusted agent in an offline location, to be released at a later date.
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Death of Molly Russell

In November 2017, Molly Russell, a fourteen-year-old British schoolgirl from Harrow, London, was found dead in her bedroom by her parents. In an inquest, the coroner stated that she had died from an act of self-harm following depression and the results of social media consumption, including material on Instagram and Pinterest. She also had a Twitter account in which she documented her growing depression. == Life == Russell had been a pupil at Hatch End High School. At the inquest, the school's head teacher expressed shock that she was able to access distressing online content. Her parents stated that she had never shown any previous signs of struggle and was doing very well in school. It was revealed at the inquest that in the six months prior to her death, 2,100 of 16,300 pieces of content she had interacted with on Instagram were on topics such as self-harm, depression, and suicide. It was also noted that throughout her experience on social media, there were never any warning signs about the information she viewed on these platforms. == Subsequent events == Dr. Navin Venugopal, the child psychiatrist assigned to the case investigating her death, called the material she viewed "disturbing and distressing" and said he was unable to sleep well for weeks after viewing it. The coroner Andrew Walker concluded that Molly's death was "an act of self harm suffering from depression and the negative effects of online content". He issued a prevention of future deaths report regarding her death, in which he made a number of recommendations for operators of online platforms, including: separating platforms for adults and children age verification changes in policy on filtering of age-specific content adding features for parental supervision and control data retention of material viewed by children He suggested that this could be accomplished by either legislation or self-regulation. The lawyer representing her family at the inquest stated that the findings "captured all of the elements of why this material is so harmful." The case has been cited as a motivator for the passage of the Online Safety Act. A charity, the Molly Rose Foundation, was set up in her memory, with the goal of suicide prevention for young people. Meta and Pinterest are believed to have made substantial donations to the charity.
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PagedAttention

PagedAttention is an attention algorithm for efficient serving of large language models (LLMs). It was introduced in 2023 by Woosuk Kwon and colleagues in the paper Efficient Memory Management for Large Language Model Serving with PagedAttention, alongside the vLLM serving engine. The method stores the key–value cache used during autoregressive decoding in fixed-size blocks that can be mapped to non-contiguous physical memory, borrowing ideas from virtual memory, paging, and operating system design. == Background == In transformer inference, the key–value cache grows with sequence length and the number of concurrent requests. Kwon et al. argued that earlier serving systems typically reserved contiguous cache regions in advance, which caused reserved space, internal fragmentation, and external fragmentation. In their experiments, the paper reported that the effective memory utilization of previous systems could fall as low as 20.4%. == Description == PagedAttention partitions the cache of each sequence into fixed-size KV blocks. A request's cache is represented as a sequence of logical blocks, while a block table maps those logical blocks to physical GPU-memory blocks. As a result, neighboring logical blocks do not need to be contiguous in physical memory, and new blocks can be allocated on demand as generation proceeds. The design also makes it easier to share cache state across related decoding paths. In vLLM, physical blocks can be reference-counted and shared among requests or branches, with block-granularity copy-on-write used when a shared block must be modified. The original paper applied this design to parallel sampling, beam search, and prompts with shared prefixes. == Mathematical formulation == For a query token i {\displaystyle i} in causal self-attention, the standard attention output can be written as a i j = exp ⁡ ( q i ⊤ k j / d ) ∑ t = 1 i exp ⁡ ( q i ⊤ k t / d ) , o i = ∑ j = 1 i a i j v j {\displaystyle a_{ij}={\frac {\exp(\mathbf {q} _{i}^{\top }\mathbf {k} _{j}/{\sqrt {d}})}{\sum _{t=1}^{i}\exp(\mathbf {q} _{i}^{\top }\mathbf {k} _{t}/{\sqrt {d}})}},\;\mathbf {o} _{i}=\sum _{j=1}^{i}a_{ij}\mathbf {v} _{j}} where q i {\displaystyle \mathbf {q} _{i}} , k j {\displaystyle \mathbf {k} _{j}} , and v j {\displaystyle \mathbf {v} _{j}} are the query, key, and value vectors, and d {\displaystyle d} is the attention dimension. If the cache is partitioned into blocks of size B {\displaystyle B} , the key and value blocks may be written as K j = ( k ( j − 1 ) B + 1 , … , k j B ) , V j = ( v ( j − 1 ) B + 1 , … , v j B ) {\displaystyle \mathbf {K} _{j}=(\mathbf {k} _{(j-1)B+1},\ldots ,\mathbf {k} _{jB}),\;\mathbf {V} _{j}=(\mathbf {v} _{(j-1)B+1},\ldots ,\mathbf {v} _{jB})} PagedAttention then performs the computation blockwise: A i j = exp ⁡ ( q i ⊤ K j / d ) ∑ t = 1 ⌈ i / B ⌉ exp ⁡ ( q i ⊤ K t / d ) , o i = ∑ j = 1 ⌈ i / B ⌉ V j A i j ⊤ {\displaystyle \mathbf {A} _{ij}={\frac {\exp(\mathbf {q} _{i}^{\top }\mathbf {K} _{j}/{\sqrt {d}})}{\sum _{t=1}^{\lceil i/B\rceil }\exp(\mathbf {q} _{i}^{\top }\mathbf {K} _{t}/{\sqrt {d}})}},\;\mathbf {o} _{i}=\sum _{j=1}^{\lceil i/B\rceil }\mathbf {V} _{j}\mathbf {A} _{ij}^{\top }} where A i j {\displaystyle \mathbf {A} _{ij}} is the vector of attention scores for the j {\displaystyle j} -th KV block. In the formulation given by Kwon et al., this preserves the causal attention calculation while allowing the key and value blocks to reside in non-contiguous physical memory. == Performance and use == The vLLM paper reported that, on its evaluated workloads, the use of PagedAttention and the associated memory-management design improved serving throughput by 2–4× over the compared baselines, including FasterTransformer and Orca, while preserving model outputs. In experiments on OPT-13B with the Alpaca trace, the paper also reported memory savings of 6.1–9.8% for parallel sampling and 37.6–55.2% for beam search through KV-block sharing. A 2024 survey of LLM serving systems described PagedAttention as having become an industry norm in LLM serving frameworks, citing support in TGI, vLLM, and TensorRT-LLM. == Limitations and alternatives == Subsequent work has described trade-offs in the approach. The 2025 vAttention paper argued that PagedAttention requires attention kernels to be rewritten to support paging and increases software complexity, portability issues, redundancy, and execution overhead, proposing instead a memory manager that keeps the cache contiguous in virtual memory while relying on demand paging for physical allocation. === vAttention === Unlike PagedAttention, vAttention does not introduce a different attention rule; it retains the standard attention computation Attention ⁡ ( q i , K , V ) = softmax ⁡ ( q i K ⊤ s c a l e ) V . {\displaystyle \operatorname {Attention} (q_{i},K,V)=\operatorname {softmax} \left({\frac {q_{i}K^{\top }}{\mathrm {scale} }}\right)V.} In the notation of Prabhu et al., the key and value tensors for a request seen so far are K , V ∈ R L ′ × ( H × D ) {\displaystyle K,V\in \mathbb {R} ^{L'\times (H\times D)}} , where L ′ {\displaystyle L'} is the context length seen so far, H {\displaystyle H} is the number of KV heads on a worker, and D {\displaystyle D} is the dimension of each KV head. In systems prior to PagedAttention, the K cache (or V cache) at each layer of a worker is typically allocated as a 4D tensor of shape [ B , L , H , D ] , {\displaystyle [B,L,H,D],} where B {\displaystyle B} is batch size and L {\displaystyle L} is the maximum context length supported by the model. vAttention preserves this contiguous virtual-memory view while deferring physical-memory allocation to runtime. A serving framework maintains separate K and V tensors for each layer, so vAttention reserves 2 N {\displaystyle 2N} virtual-memory buffers on a worker, where N {\displaystyle N} is the number of layers managed by that worker. The maximum size of one virtual-memory buffer is B S = B × S , {\displaystyle BS=B\times S,} where S {\displaystyle S} is the maximum size of a single request's per-layer K cache (or V cache) on a worker. The paper defines S = L × H × D × P , {\displaystyle S=L\times H\times D\times P,} where P {\displaystyle P} is the number of bytes needed to store one element. In this formulation, vAttention keeps the KV cache contiguous in virtual memory and relies on demand paging for physical allocation, rather than modifying the attention kernel to operate over non-contiguous KV-cache blocks.
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G.9972

G.9972 (also known as G.cx) is a Recommendation developed by ITU-T that specifies a coexistence mechanism for networking transceivers capable of operating over electrical power line wiring. It allows G.hn devices to coexist with other devices implementing G.9972 and operating on the same power line wiring. G.9972 received consent during the meeting of ITU-T Study Group 15, on October 9, 2009, and final approval on June 11, 2010. G.9972 specifies two mechanisms for coexistence between G.hn home networks and broadband over power lines (BPL) Internet access networks: Frequency-division multiplexing (FDM), in which the available spectrum is divided into two parts: frequencies below 10 or 14 MHz (specific value can be selected by the access network) are reserved for the access network, while frequencies above them are reserved for the in-home network. Time-division multiplexing (TDM), in which the available channel time is split equally between both networks. 50% of time slots are allocated for the access network, and 50% are allocated to the in-home network.
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Data monetization

Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item. There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the digital supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation. Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time. == Types of data monetization == Internal data monetization - An organization's data is used internally, resulting in economic benefit. This is commonly the case in organizations using analytics to uncover insights, resulting in improved profit, cost savings or the avoidance of risk. Internal data monetization is currently the most common form of monetization, requiring far fewer security, intellectual property, and legal precautions when compared to other types. The potential economic gains from this type of data monetization are limited by the organization's internal structure and situation. External data monetization - A person or organization makes data they possess available on a for-fee basis to external parties, or as a broker for same. This type of monetization is less common and requires various methods to distribute the data to potential buyers and consumers. However, the economic gain that results from collecting data, packaging and distributing it, can be quite large. == Steps == Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available. Connect, aggregate, attribute, validate, authenticate, and exchange data - this allows data to be converted directly into actionable or revenue generating insight or services. Set terms and prices and facilitate data trading - methods for data vetting, storage, and access. For example, many global corporations have locked and siloed data storage infrastructures, which hinders efficient access to data and cooperative and real-time exchange. Perform Research and analytics – draw predictive insights from existing data as a basis for using data for to reduce risk, enhance product development or performance, or improve customer experience or business outcomes. Action and leveraging – the last phase of monetizing data includes determining alternative or improved data centric products, ideas, or services. Examples may include real-time actionable triggered notifications or enhanced channels such as web or mobile response mechanisms. == Pricing variables and factors == A fee for use of a platform to connect buyers and sellers use of a platform to configure, organize, and otherwise process data included in a data trade connecting or including a device or sensor into a data supply chain connecting and credentialing a creator of a data source and a data buyer – often through a federated identity connecting a data source to other data sources to be included in a data supply chain use of an internet service or other transmission services for uploading and downloading data – sometimes, for an individual, through a personal cloud use of encrypted keys to achieve secure data transfer use of a search algorithm specifically designed to tag data sources that contain data points of value to the data buyer linking a data creator or generator to a data collection protocol or form server actions – such as a notification – triggered by an update to a data item or data source included in a data supply chain A price or exchange or other trade value assigned by a data creator or generator to a data item or a data source offered by a data buyer to a data creator assigned by a data buyer for a data item or a data source formatted according to criteria set by a data buyer An incremental fee assigned by a data buyer for a data item or a data set scaled to the reputation of the data creator == Benefits == Improved decision-making that leads to real time crowd sourced research, improved profits, decreased costs, reduced risk and improved compliance More impactful decisions (e.g., make real-time decisions) More timely (lower latency) decisions (e.g., a vendor making purchase recommendations while the customer is still on the phone or in the store, a customer connecting with multiple vendors to discover the best price, triggered notifications when thresholds are reached for data values) More granular decisions (e.g., localized pricing decisions at an individual or device or sensor level versus larger aggregates). Targeted Marketing (e.g., Vendors with access to big data can make targeted advertisements to specific customers within a set data pool decreasing costs for the advertiser and reaching most interested customers) == Frameworks == There are a wide variety of industries, firms and business models related to data monetization. The following frameworks have been offered to help understand the types of business models that are used: Roger Ehrenberg of IA Ventures, a venture capital firm that invests in this sector, has defined three basic types of data product firms: Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return – a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms, and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful
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