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  DeepRoute.ai

  DeepRoute.ai (Chinese: 元戎启行) is a Chinese autonomous driving company founded in 2019 and headquartered in Shenzhen, China. The company develops full-stack self-driving solutions including perception, decision-making, and control systems. == History == DeepRoute.ai was founded in February 2019 in Shenzhen, China, by Zhou Guang (周光), who serves as the company's CEO. In September 2019, the company collaborated with Dongfeng for a live-streamed autonomous driving demonstration. In October 2019, during the 7th Military World Games, DeepRoute.ai conducted Robotaxi demonstration operations. In November 2019, it obtained an intelligent connected vehicle road test permit for public roads in Shenzhen. In October 2020, DeepRoute.ai signed an "Autonomous Driving Leadership Project" with Dongfeng to build one of China's largest autonomous fleets. In August 2020, DeepRoute.ai announced its partnership with Cao Cao Mobility, a Geely-backed ride-hailing company, to test Robotaxis in Hangzhou for daily operations, planning to provide Robotaxis during the 2022 Asian Games. In September 2021, DeepRoute.ai secured US$300 million in a Series B funding round led by Alibaba. In December 2021, the company unveiled its DeepRoute-Driver 2.0, an L4-level autonomous driving solution comprising five solid-state lidar sensors, eight cameras, a proprietary computing system and an optional millimeter-wave radar. with a production cost of under US$10,000. In June 2022, it partnered with Deppon Express to provide autonomous light truck freight transfer services. In March 2023, the company launched its high-precision map-free intelligent driving solution, DeepRoute-Driver 3.0. In November 2024, Great Wall Motor announced a $100 million Series C funding round for Deeproute. With this, Deeproute has completed five rounds of financing, raising a cumulative total of over $500 million. Its shareholders include Fosun RZ Capital, Yunqi Partners, Alibaba, Vision Plus Capital, and Dongfeng, among others. In the same month, Deeproute.ai emphasised that they were in "deep cooperation" with Nvidia and spoke on being part of the first batch of companies in China to get a hold of Nvidia's newer Thor chip for cars which will be used in a new system released next year. This new system will help manage more complex driving scenarios through visual cues. == Products == === VLA Model === VLA Model is a Vision–language–action model designed for autonomous driving systems. It integrates visual perception, semantic understanding, and action decision-making into a unified framework, aiming to enhance the safety and adaptability of advanced driver-assistance systems (ADAS) in complex road environments. The model was officially launched on August 26, 2025, as the core of DeepRoute.ai's DeepRoute IO 2.0 platform. The VLA model is characterized by its "visual-language-action" architecture, which incorporates a chain-of-thought (CoT) reasoning capability inspired by large language models. This design is intended to address the "black box" limitations of traditional end-to-end autonomous driving systems by enabling the model to analyze information, infer causality, and make decisions in a more transparent and interpretable manner. === Appliance === The company has partnered with several automakers including Dongfeng Motor Corporation and Geely to develop and test autonomous vehicles.

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  Social employee

  A social employee is a worker operating within a social business model. Following an organization's social computing guidelines, social employees use social media tools both for internal workflow and collaboration purposes and for external engagement with customers, prospects and stakeholders through a combination of social media marketing, content marketing, social marketing, and social selling. Social employee programs are considered to be as much about culture and engagement as they are about business processes and best practices. In addition to increased leads and sales, social employee best practices are said to improve business outcomes important to social media marketing, such as increased connections and web traffic, improved brand identification and "chatter", and better customer advocacy. == Overview == The term "social employee" was first introduced to describe those exhibiting the emerging characteristics of workers operating under a social business model. The term is often used interchangeably with similar designations like "employee advocate" or "social employee advocate". Crucial to the perceived value of the social employee is the concept of the digital footprint. While organizations are able to generate large bases of followers through social media, research shows that brand marketing and engagement efforts through these networks are not as effective as those of individual employees. In fact, some research indicates that employee experts are more trusted than any other member of an organization. Because of this, social employee programs are designed to train, empower, and support employee engagement efforts in the hopes of authentically engaging larger communities, increasing the frequency of shares, reviews, and other forms of "earned media" and expanding the brand's presence on the web. == The personal or employee brand == A foundational concept of the social employee is the idea of the personal or employee brand. This concept first gained popular attention in a 1997 FastCompany article by business leader Tom Peters titled "The Brand Called You". In the article, Peters argued that the premium placed on branding impacted workers' lives to such an extent that creating and cultivating a distinct personal brand had become a professional necessity. According to Peters, doing so built trust, loyalty, visibility, influence, and employability. With increased adoption of social media tools by both businesses and consumers in the early 21st century, many business leaders became increasingly concerned with social engagement, both internally among employees and externally with customers and other stakeholders. While many in the business community acknowledged the potential social tools had for improved collaboration, productivity, and brand messaging, the concern that employees would misrepresent their brand, disclose proprietary information, or otherwise damage their company's reputation or ability to conduct business persisted. As a result, many began to advocate for employee branding as a solution to this problem. This helped give new meaning to the concept of brand ambassadorship, positioning everyday employees in public, and potentially high-profile, engagement roles. == Characteristics == === Engaged === Because social employee advocacy is dependent on the perceived authenticity of the employee, engagement is highly valued in social organizations. Further, data show the measurable impact of employee engagement on organizational productivity and profitability: Happy employees were found to be 12 percent more productive. In one study, engaged employees were found to be 38 percent more likely to produce at above-average rates. In another, organizations with engaged employees had a 19 percent higher than average shareholder return, while organizations with disengaged employees experienced shareholder return that was 44 percent below average. Engaged companies were found to outperform disengaged companies by up to 202 percent. Companies with strong focus on culture were found to have an average 13.9 percent turnover rate, while those with a low focus experience were found to have a 48.4 percent turnover rate. === Flexible job environment and work–life balance === The number of professionals working mobile or remote has risen considerably since 2010. While estimates vary, one study found that number of organizations with mobile or remote employees is expected to rise from 24 percent in 2012 to 89 percent by 2020. Other research has estimated that by 2020, 105.4 million professionals will work remotely in America, about 72.3 percent of the total workforce. This change has been linked to a rise in social technologies, including biometrics, wearables, near-field communications, and augmented reality. Social employees have also put a greater emphasis on work–life balance, with many believing that advances in technology can directly support efforts in this area. Purported benefits of this shift include a more flexible workforce, reduced business costs, and greater organizational leverage in attracting and retaining top talent. === Buys into the brand's story === In 2009, thought leader Simon Sinek presented a speech called "How Great Leaders Inspire Action" at a TEDxPugetSound event. Sinek's central argument in this speech was, "People don't buy what you do. They buy why you do it." This concept—that the story behind a business or product offering is a more compelling sales tool than the product itself—is frequently cited in social media marketing as a way to build authentic connections with stakeholders. However, others have argued that for employees to share a brand's story authentically, they must be engaged in that story themselves, and as a result, many companies have made storytelling part of their culture programs. === Collaborative === An implicit tenet in social business is that social technologies aren't a barrier to productivity, but rather a path to increased connectivity. The shift in enterprise software systems like IBM Connections to incorporate social communication models, such as mentions, wikis, and newsfeeds, reflects the changing communication dynamics within business. With an increase in diversity and sophistication in collaborative software platforms, social organizations have sought to find new creative ways to utilize these tools and secure employee buy-in around them. Crowdsourcing has also become popular in social businesses. Examples include AT&T's program The Innovation Pipeline (TIP), begun in 2009, which has generated over 28,000 ideas that have led to over 75 projects with funding exceeding $44 million. IBM has also put considerable resources into such processes, producing its social computing guidelines through employee crowdsourcing, as well as its Connections platform through the Technology Adoption Program (TAP), a more formalized crowdsourcing initiative. Another popular form of internal collaboration is the hack day, or hackathon. Organizations such as Netflix, Facebook, and IBM use hack days to pull employees out of their day-to-day work environments and encourage them to collaborate in nontraditional ways in an attempt to drive disruptive innovation. Social employees are often encouraged to seek external collaboration opportunities with customers and prospects. For example, Procter & Gamble introduced the Live Well Collaborative to connect with external stakeholders and develop products and services for the 50+ demographic. === Social listener === A social listener is someone who engages in social listening, or social media monitoring, for professional means. Social employees can use social media monitoring for a variety of reasons, including professional development, industry news and trends, and gauging market sentiment. Some have argued that social listening is one of the most important components of social business, as it enables organizations to collect rich market data, make more informed strategic decisions, and respond to customer needs more authentically. === Customer-centric === Advocates of customer-centricity in social business argue that social media has changed the dynamic from one-way brand messaging to shared interactions between brand and customer. Brand and customer engagement is seen as a means of creating more lasting connections with customers and prospects and empowering them to become brand promoters. Customer-centric interactions are seen to have distinct value to brands, as research shows that prospects are far more likely to trust brand-related messaging from a friend or family member than they are from a brand. As a means of building social employees, some social advocates have also called for a broader definition of customer to include the employees themselves. In the book The Pursuit of Social Business Excellence, authors Vala Afshar and Brad Martin made the following argument: A social business operates with the guiding principle that each employee's responsi

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  Campus network

  A campus network, campus area network, corporate area network or CAN is a computer network made up of an interconnection of local area networks (LANs) within a limited geographical area. The networking equipments (switches, routers) and transmission media (optical fiber, copper plant, Cat5 cabling etc.) are almost entirely owned by the campus tenant / owner: an enterprise, university, government etc. A campus area network is larger than a local area network but smaller than a metropolitan area network (MAN) or wide area network (WAN). == University campuses == College or university campus area networks often interconnect a variety of buildings, including administrative buildings, academic buildings, laboratories, university libraries, or student centers, residence halls, gymnasiums, and other outlying structures, like conference centers, technology centers, and training institutes. Early examples include the Stanford University Network at Stanford University, Project Athena at MIT, and the Andrew Project at Carnegie Mellon University. == Corporate campuses == Much like a university campus network, a corporate campus network serves to connect buildings. Examples of such are the networks at Googleplex and Microsoft's campus. Campus networks are normally interconnected with high speed Ethernet links operating over optical fiber such as gigabit Ethernet and 10 Gigabit Ethernet. == Area range == The range of CAN is 1 to 5 km (1 to 3 mi). If two buildings have the same domain and they are connected with a network, then it will be considered as CAN only. Though the CAN is mainly used for corporate campuses so the link will be high speed.

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  Multistage interconnection networks

  Multistage interconnection networks (MINs) are a class of high-speed computer networks usually composed of processing elements (PEs) on one end of the network and memory elements (MEs) on the other end, connected by switching elements (SEs). The switching elements themselves are usually connected to each other in stages, hence the name. MINs are typically used in high-performance or parallel computing as a low-latency interconnection (as opposed to traditional packet switching networks), though they could be implemented on top of a packet switching network. Though the network is typically used for routing purposes, it could also be used as a co-processor to the actual processors for such uses as sorting; cyclic shifting, as in a perfect shuffle network; and bitonic sorting. == Background == Interconnection network are used to connect nodes, where nodes can be a single processor or group of processors, to other nodes. Interconnection networks can be categorized on the basis of their topology. Topology is the pattern in which one node is connected to other nodes. There are two main types of topology: static and dynamic. Static interconnect networks are hard-wired and cannot change their configurations. A regular static interconnect is mainly used in small networks made up of loosely couple nodes. The regular structure signifies that the nodes are arranged in specific shape and the shape is maintained throughout the networks. Some examples of static regular interconnections are: Completely connected network In a mesh network, multiple nodes are connected with each other. Each node in the network is connected to every other node in the network. This arrangement allows proper communication of the data between the nodes. But, there are a lot of communication overheads due to the increased number of node connections. Shared busThis network topology involves connection of the nodes with each other over a bus. Every node communicates with every other node using the bus. The bus utility ensures that no data is sent to the wrong node. But, the bus traffic is an important parameter which can affect the system. RingThis is one of the simplest ways of connecting nodes with each other. The nodes are connected with each other to form a ring. For a node to communicate with some other node, it has to send the messages to its neighbor. Therefore, the data message passes through a series of other nodes before reaching the destination. This involves increased latency in the system. TreeThis topology involves connection of the nodes to form a tree. The nodes are connected to form clusters and the clusters are in-turn connected to form the tree. This methodology causes increased complexity in the network. Hypercube This topology consists of connections of the nodes to form cubes. The nodes are also connected to the nodes on the other cubes. ButterflyThis is one of the most complex connections of the nodes. As the figure suggests, there are nodes which are connected and arranged in terms of their ranks. They are arranged in the form of a matrix. In dynamic interconnect networks, the nodes are interconnected via an array of simple switching elements. This interconnection can then be changed by use of routing algorithms, such that the path from one node to other nodes can be varied. Dynamic interconnections can be classified as: Single stage Interconnect Network Multistage interconnect Network Crossbar switch connections == Crossbar Switch Connections == In crossbar switch, there is a dedicated path from one processor to other processors. Thus, if there are n inputs and m outputs, we will need nm switches to realize a crossbar. As the number of outputs increases, the number of switches increases by factor of n. For large network this will be a problem. An alternative to this scheme is staged switching. == Single Stage Interconnect Network == In a single stage interconnect network, the input nodes are connected to output via a single stage of switches. The figure shows 88 single stage switch using shuffle exchange. As one can see, from a single shuffle, not all input can reach all output. Multiple shuffles are required for all inputs to be connected to all the outputs. == Multistage Interconnect Network == A multistage interconnect network is formed by cascading multiple single stage switches. The switches can then use their own routing algorithm, or be controlled by a centralized router, to form a completely interconnected network. Multistage Interconnect Network can be classified into three types: Non-blocking: A non-blocking network can connect any idle input to any idle output, regardless of the connections already established across the network. Crossbar is an example of this type of network. Rearrangeable non-blocking: This type of network can establish all possible connections between inputs and outputs by rearranging its existing connections. Blocking: This type of network cannot realize all possible connections between inputs and outputs. This is because a connection between one free input to another free output is blocked by an existing connection in the network. The number of switching elements required to realize a non-blocking network in highest, followed by rearrangeable non-blocking. Blocking network uses least switching elements. == Examples == Multiple types of multistage interconnection networks exist. === Omega network === An Omega network consists of multiple stages of 22 switching elements. Each input has a dedicated connection to an output. An NN omega network has log2(N) stages and N/2 switching elements in each stage for a perfect shuffle between stages. Thus the network has complexity of 0(N log(N)). Each switching element can employ its own switching algorithm. Consider an 88 omega network. There are 8! = 40320 1-to-1 mappings from input to output. There are 12 switching element for a total permutation of 2^12 = 4096. Thus, it is a blocking network. === Clos network === A Clos network uses 3 stages to switch from N inputs to N outputs. In the first stage, there are r= N/n crossbar switches and each switch is of size nm. In the second stage there are m switches of size rr and finally the last stage is a mirror of the first stage with r switches of size mn. A clos network will be completely non-blocking if m >= 2n-1. The number of connections, though more than omega network is much less than that of a crossbar network. === Beneš network === A Beneš network is a rearrangeably non-blocking network derived from the clos network by initializing n = m = 2. There are (2log2(N) - 1) stages, with each stage containing N/2 22 crossbar switches. An 88 Beneš network has 5 stages of switching elements, and each stage has 4 switching elements. The center three stages has two 44 benes network. The 44 Beneš network, can connect any input to any output recursively.

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  XLeratorDB

  XLeratorDB is a suite of database function libraries that enable Microsoft SQL Server to perform a wide range of additional (non-native) business intelligence and ad hoc analytics. The libraries, which are embedded and run centrally on the database, include more than 450 individual functions similar to those found in Microsoft Excel spreadsheets. The individual functions are grouped and sold as six separate libraries based on usage: finance, statistics, math, engineering, unit conversions and strings. WestClinTech, the company that developed XLeratorDB, claims it is "the first commercial function package add-in for Microsoft SQL Server." == Company history == WestClinTech (LLC), founded by software industry veterans Charles Flock and Joe Stampf in 2008, is located in Irvington, New York, United States. Flock was a co-founder of The Frustum Group, developer of the OPICS enterprise banking and trading platform, which was acquired by London-based Misys, PLC in 1996. Stampf joined Frustum in 1994 and with Flock remained active with the company after acquisition, helping to develop successive generations of OPICS now employed by over 150 leading financial institutions worldwide. Following a full year of research, development and testing, WestClinTech introduced and recorded its first commercial sale of XLeratorDB in April 2009. In September 2009, XLeratorDB became available to all Federal agencies through NASA's Strategic Enterprise-Wide Procurement (SEWP-IV) program, a government-wide acquisition contract. == Technology == XLeratorDB uses Microsoft SQL CLR(Common Language Runtime) technology. SQL CLR allows managed code to be hosted by, and run in, the Microsoft SQL Server environment. SQL CLR relies on the creation, deployment and registration of .NET Framework assemblies that are physically stored in managed code dynamic-link libraries (DLL). The assemblies may contain .NET namespaces, classes, functions, and properties. Because managed code compiles to native code prior to execution, functions using SQL CLR can achieve significant performance increases versus the equivalent functions written in T-SQL in some scenarios. XLeratorDB requires Microsoft SQL Server 2005 or SQL Server 2005 Express editions, or later (compatibility mode 90 or higher). The product installs with PERMISSION_SET=SAFE. SAFE mode, the most restrictive permission set, is accessible by all users. Code executed by an assembly with SAFE permissions cannot access external system resources such as files, the network, the internet, environment variables, or the registry. == Functions == In computer science, a function is a portion of code within a larger program which performs a specific task and is relatively independent of the remaining code. As used in database and spreadsheet applications these functions generally represent mathematical formulas widely used across a variety of fields. While this code may be user-generated, it is also embedded as a pre-written sub-routine in applications. These functions are typically identified by common nomenclature which corresponds to their underlying operations: e.g. IRR identifies the function which calculates Internal Rate of Return on a series of periodic cash flows. === Function uses === As subroutines, functions can be integrated and used in a variety of ways, and as part of larger, more complicated applications. Within large enterprise applications they may, for example, play an important role in defining business rules or risk management parameters, while remaining virtually invisible to end users. Within database management systems and spreadsheets, however, these kinds of functions also represent discrete sets of tools; they can be accessed directly and utilized on a stand-alone basis, or in more complex, user-defined configurations. In this context, functions can be used for business intelligence and ad hoc analysis of data in fields such as finance, statistics, engineering, math, etc. === Function types === XLeratorDB uses three kinds of functions to perform analytic operations: scalar, aggregate, and a hybrid form which WestClinTech calls Range Queries. Scalar functions take a single value, perform an operation and return a single value. An example of this type of function is LOG, which returns the logarithm of a number to a specified base. Aggregate functions operate on a series of values but return a single, summarizing value. An example of this type of function is AVG, which returns the average of values in a specified group. In XLeratorDB there are some functions which have characteristics of aggregate functions (operating on multiple series of values) but cannot be processed in SQL CLR using single column inputs, such as AVG does. For example, irregular internal rate of return (XIRR), a financial function, operates on a collection of cash flow values from one column, but must also apply variable period lengths from another column and an initial iterative assumption from a third, in order to return a single, summarizing value. WestClinTech documentation notes that Range Queries specify the data to be included in the result set of the function independently of the WHERE clause associated with the T-SQL statement, by incorporating a SELECT statement into the function as a string argument; the function then traps that SELECT statement, executes it internally and processes the result. Some XLeratorDB functions that employ Range Queries are: NPV, XNPV, IRR, XIRR, MIRR, MULTINOMIAL, and SERIESSUM. Within the application these functions are identified by a "_q" naming convention: e.g. NPV_q, IRR_q, etc. == Analytic functions == === SQL Server functions === Microsoft SQL Server is the #3 selling database management system (DBMS), behind Oracle and IBM. (While versions of SQL Server have been on the market since 1987, XLeratorDB is compatible with only the 2005 edition and later.) Like all major DBMS, SQL Server performs a variety of data mining operations by returning or arraying data in different views (also known as drill-down). In addition, SQL Server uses Transact-SQL (T-SQL) to execute four major classes of pre-defined functions in native mode. Functions operating on the DBMS offer several advantages over client layer applications like Excel: they utilize the most up-to-date data available; they can process far larger quantities of data; and, the data is not subject to exporting and transcription errors. SQL Server 2008 includes a total of 58 functions that perform relatively basic aggregation (12), math (23) and string manipulation (23) operations useful for analytics; it includes no native functions that perform more complex operations directly related to finance, statistics or engineering. === Excel functions === Microsoft Excel, a component of Microsoft Office suite, is one of the most widely used spreadsheet applications on the market today. In addition to its inherent utility as a stand-alone desktop application, Excel overlaps and complements the functionality of DBMS in several ways: storing and arraying data in rows and columns; performing certain basic tasks such as pivot table and aggregating values; and facilitating sharing, importing and exporting of database data. Excel's chief limitation relative to a true database is capacity; Excel 2003 is limited to some 65k rows and 256 columns; Excel 2007 extends this capacity to roughly 1million rows and 16k columns. By comparison, SQL Server is able to manage over 500k terabytes of memory. Excel offers, however, an extensive library of specialized pre-written functions which are useful for performing ad hoc analysis on database data. Excel 2007 includes over 300 of these pre-defined functions, although customized functions can also be created by users, or imported from third party developers as add-ons. Excel functions are grouped by type: === Excel business intelligence functions === Operating on the client computing layer Excel plays an important role as a business intelligence tool because it: performs a wide array of complex analytic functions not native to most DBMS software offers far greater ad hoc reporting and analytic flexibility than most enterprise software provides a medium for sharing and collaborating because of its ubiquity throughout the enterprise Microsoft reinforces this positioning with Business Intelligence documentation that positions Excel in a clearly pivotal role. === XLeratorDB vs. Excel functions === While operating within the database environment, XLeratorDB functions utilize the same naming conventions and input formats, and in most cases, return the same calculation results as Excel functions. XLeratorDB, coupled with SQL Server's native capabilities, compares to Excel's function sets as follows:

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  Data steward

  A data steward is an oversight or data governance role within an organization, and is responsible for ensuring the quality and fitness for purpose of the organization's data assets, including the metadata for those data assets. A data steward may share some responsibilities with a data custodian, such as the awareness, accessibility, release, appropriate use, security and management of data. A data steward would also participate in the development and implementation of data assets. A data steward may seek to improve the quality and fitness for purpose of other data assets their organization depends upon but is not responsible for. Data stewards have a specialist role that utilizes an organization's data governance processes, policies, guidelines and responsibilities for administering an organizations' entire data in compliance with policy and/or regulatory obligations (e.g., GDPR, HIPAA). The overall objective of a data steward is the data quality of the data assets, datasets, data records and data elements. This includes documenting metainformation for the data, such as definitions, related rules/governance, physical manifestation, and related data models (most of these properties being specific to an attribute/concept relationship), identifying owners/custodian's various responsibilities, relations insight pertaining to attribute quality, aiding with project requirement data facilitation and documentation of capture rules. Data stewards begin the stewarding process with the identification of the data assets and elements which they will steward, with the ultimate result being standards, controls and data entry. The steward works closely with business glossary standards analysts (for standards), with data architect/modelers (for standards), with DQ analysts (for controls) and with operations team members (good-quality data going in per business rules) while entering data. Data stewardship roles are common when organizations attempt to exchange data precisely and consistently between computer systems and to reuse data-related resources. Master data management often makes references to the need for data stewardship for its implementation to succeed. Data stewardship must have precise purpose, fit for purpose or fitness. == Data steward responsibilities == A data steward ensures that each assigned data element: Has clear and unambiguous data element definition Does not conflict with other data elements in the metadata registry (removes duplicates, overlap etc.) Has clear enumerated value definitions if it is of type Code Is still being used (remove unused data elements) Is being used consistently in various computer systems Is being used, fit for purpose = Data Fitness Has adequate documentation on appropriate usage and notes Documents the origin and sources of authority on each metadata element Is protected against unauthorised access or change Responsibilities of data stewards vary between different organisations and institutions. For example, at Delft University of Technology, data stewards are perceived as the first contact point for any questions related to research data. They also have subject-specific background allowing them to easily connect with researchers and to contextualise data management problems to take into account disciplinary practices. == Types of data stewards == Depending on the set of data stewardship responsibilities assigned to an individual, there are 4 types (or dimensions of responsibility) of data stewards typically found within an organization: Data object data steward - responsible for managing reference data and attributes of one business data entity Business data steward - responsible for managing critical data, both reference and transactional, created or used by one business function. The data steward may also serve as a liaison between the organization's data users and technical teams, helping to bridge the gap between business needs and technical requirements. They may also play a role in educating others within the organization about best practices for data management, and advocating for data-driven decision-making. Process data steward - responsible for managing data across one business process System data steward - responsible for managing data for at least one IT system == Benefits of data stewardship == Systematic data stewardship can foster: Faster analysis Consistent use of data management resources Easy mapping of data between computer systems and exchange documents Lower costs associated with migration to (for example) service-oriented architecture (SOA) Mitigation of data risk Better control of dangers associated with privacy, legal, errors, etc. Assignment of each data element to a person sometimes seems like an unimportant process. But multiple groups have found that users have greater trust and usage rates in systems where they can contact a person with questions on each data element. == Examples == Delft University of Technology (TU Delft) offers an example of data stewardship implementation at a research institution. In 2017 the Data Stewardship Project was initiated at TU Delft to address research data management needs in a disciplinary manner across the whole campus. Dedicated data stewards with subject-specific background were appointed at every TU Delft faculty to support researchers with data management questions and to act as a linking point with the other institutional support services. The project is coordinated centrally by TU Delft Library, and it has its own website, blog and a YouTube channel. The [1]EPA metadata registry furnishes an example of data stewardship. Note that each data element therein has a "POC" (point of contact). In 2023, ETH Zurich launched the Data Stewardship Network (DSN) to facilitate collaboration among employees engaged in data management, analysis, and code development across research groups. The DSN serves as a platform for networking and knowledge exchange, aiming to professionalize the role of data stewards who support research data management and reproducible workflows. Established by the team for Research Data Management and Digital Curation at the ETH Library, the DSN collaborates with Scientific IT Services to provide expertise in areas such as storage infrastructure and reproducible workflows. == Data stewardship applications == Information stewardship applications are business solutions used by business users acting in the role of information steward (interpreting and enforcing information governance policy, for example). These developing solutions represent, for the most part, an amalgam of a number of disparate, previously IT-centric tools already on the market, but are organized and presented in such a way that information stewards (a business role) can support the work of information policy enforcement as part of their normal, business-centric, day-to-day work in a range of use cases. The initial push for the formation of this new category of packaged software came from operational use cases — that is, use of business data in and between transactional and operational business applications. This is where most of the master data management efforts are undertaken in organizations. However, there is also now a faster-growing interest in the new data lake arena for more analytical use cases.

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  Social Media (Age-Restricted Users) Bill

  The Social Media (Age-Restricted Users) Bill is a member's bill by National Party Member of Parliament Catherine Wedd that seeks to ban children under the age of 16 years from accessing social media by forcing social media companies to implement age verification measures. It is modelled after the Australian government's Online Safety Amendment. In mid October 2025, the New Zealand Parliament confirmed plans to introduce the social media age restriction bill. == Background == In late November 2024, the Albanese government of Australia, with support from the opposition Coalition parties, passed the Online Safety Amendment creating a world-first age verification regime targeting social media platforms operating in the country. The ban targets several social media platforms including Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X (formerly Twitter) and YouTube. These platforms were required to implement age verification systems and to remove under-age users by 10 December 2025, when the law change came into effect. == Draft provisions == The draft Social Media (Age-Restricted Users) Bill defines social media platforms as electronic platforms that enable social media interactions between two or more end-users, facilitates communication between multiple end-users and allows users to post content on the platform. The proposed bill requires social media companies to take action to prevent users under the age of 16 from creating accounts on their platforms. It also creates a framework for courts to impose fines on platforms that fail to take reasonable steps to prevent underaged users from accessing the platform. == Legislative history == === Draft legislation === On 6 May 2025, Wedd announced a private member's bill called the "Social Media (Age-Restricted Users) Bill" that would bar access to social media platforms for people under the age of 16 years. She said that she was motivated as the mother of four children to support families, parents and teachers' efforts to manage their children's online exposure and the passage of the Australian Online Safety Amendment legislation in December 2024. Since National's coalition partner ACT New Zealand had refused to support the bill, the Sixth National Government announce it as a member's bill rather than a government bill. Prime Minister Christopher Luxon has confirmed that National would seek cross-party support for the legislation. ACT MP and the Minister of Internal Affairs Brooke van Velden said that the Government would watch the implementation of the Australian social media age restriction policy. In October 2025, Wedd's bill was drawn from the parliamentary ballot. In addition, Labour Reuben Davidson drafted a similar member's bill that would hold social media providers responsible for restricting "harmful content" and imposed NZ$50,000 fines for non-compliance. In November 2025, Luxon reiterated his support for social media age restriction legislation and said the New Zealand government would introduce a bill in 2026 before the 2026 New Zealand general election. He also confirmed that Education Minister Erica Stanford was leading an investigation into what lessons could be learnt from the Australian legislation. At the request of ACT MP Parmjeet Parmar, Parliament's Education and Workforce Committee held an inquiry into a proposed social media ban in early October 2025. The committee was led by National MP Carl Bates and received 430 submissions from 400 groups and individuals. The committee also heard from 87 in-person submissions. On 10 December 2025, the committee made 12 recommendations including restricting social media access to persons under the age of 16, re-evaluating existing legislation such as the Films, Videos, and Publications Classification Act and the Harmful Digital Communications Act 2015, and regulating online platforms and Internet service providers. The ACT party released a dissenting view disagreeing with the need for a law restricting social media access to under-16 year olds. In mid-May 2026, the Government confirmed that work on the proposed bill to ban under-16 year olds from social media had been paused. The New Zealand Parliament held a debate on the proposed bill on 13 May following a select committee inquiry into the harms caused by social media platforms. While the opposition Labour Party has agreed to support the member's bill, the ACT and Green parties opposed the proposed bill on the grounds that the rules were easy to circumvent, that at-risk groups could become more isolated, and that social media also harmed other age groups. == Responses == === Academia and civil society === In late July 2025, the New Zealand Council for Civil Liberties (NZCCL) expressed concern that the proposed social media age restriction could infringe upon the New Zealand Bill of Rights Act 1990, the Privacy Act 2020 and the United Nations' Convention on the Rights of the Child. The NZCCL also questioned the practicality of age verification software, a social media age limit and whether it would fulfil its stated goal of combating online harm. In August 2025, University of Auckland criminologist and senior lecturer Claire Meehan expressed concern that the social media age restriction legislation would cut children from their friendship and support networks. She also said that children and young people were digital natives who could use VPNs to circumvent the ban. Similar sentiments were echoed by Victoria University of Wellington media and communications lecturer Alex Beattie and "Ocean Today" Instagram social media influencer "Charlie." In October 2025, New Zealand Initiative representative Dr Eric Crampton expressed concern that a social media age restriction would involve the introduction of digital IDs. He argued that a new law was unnecessary and said that parents could limit their children's exposure to social media via Google's Family Link and Apple's equivalent. Similarly, Institute of Economic Affairs public policy fellow Matthew Lesh and the British Free Speech Union expressed concerns that young people could use VPNs to circumvent a social media ban, citing the spike in VPN usage in the United Kingdom following the passage of the Online Safety Act 2023. The advocacy group B416's co-chair Anna Curzon advocated for a social media ban on underage users, stating that social media apps "are made to be addictive" and made it difficult for parents to relate with their children. In late November 2025, B416's co-founder Anna Mowbray expressed support for the Government's social media age restriction bill but expressed disappointment that Luxon had not timed his announcement with the launch of the group's campaign. Generation-Z Aotearoa co-founder Lola Fisher has called on the New Zealand Government to consult with young people on the development of the legislation. === Government agencies and departments === In early October 2025, Privacy Commissioner Michael Webster expressed concern that social media platforms requiring users to prove their age via digital IDs could raise privacy concerns. Webster suggested that age verification systems could relay on various documents including passports. He said that age estimation technologies had high error rates and that age inference technologies relied on data mining. === Political parties === In early May 2025, the National Party government expressed support for a social media age restriction legislation. By contrast, its coalition partner ACT has opposed such legislation. ACT leader David Seymour described the ban as hasty and unworkable since it did not involve parents. Meanwhile, New Zealand First leader Winston Peters expressed support for a social media age restriction but said the bill should be subject to a select committee inquiry. The opposition Labour Party leader Chris Hipkins has expressed interest in a social media age restriction legislation but emphasised the need for consensus. Meanwhile, Green Party co-leader Chlöe Swarbrick said she wanted to learn more about the bill but described it as simplistic. Fellow Greens co-leader Marama Davidson said that the proposed bill would punish children and young people for the harm caused by big tech platforms. === Tech companies === In early October 2025, representatives of TikTok and Meta Platforms cautioned against proposed social media ban on under-16 years olds. During a one-day parliamentary inquiry, Ella Woods-Joyce, TikTok's public policy lead for Australia and New Zealand, and Mia Garlick, Meta's regional director of policy, expressed concern that the social media age restriction could send children and young people to less regulated online spaces. Woods-Joyce highlighted TikTok's policy of closing down accounts belonging to users under the age of 13 years while Garlick highlighted Meta's policy of placing users under the age of 16 in private accounts by default. In early February 2026 Meta's vice president and global head of safety, Antigone Da

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  Undeniable signature

  An undeniable signature is a digital signature scheme which allows the signer to be selective to whom they allow to verify signatures. The scheme adds explicit signature repudiation, preventing a signer later refusing to verify a signature by omission; a situation that would devalue the signature in the eyes of the verifier. It was invented by David Chaum and Hans van Antwerpen in 1989. == Overview == In this scheme, a signer possessing a private key can publish a signature of a message. However, the signature reveals nothing to a recipient/verifier of the message and signature without taking part in either of two interactive protocols: Confirmation protocol, which confirms that a candidate is a valid signature of the message issued by the signer, identified by the public key. Disavowal protocol, which confirms that a candidate is not a valid signature of the message issued by the signer. The motivation for the scheme is to allow the signer to choose to whom signatures are verified. However, that the signer might claim the signature is invalid at any later point, by refusing to take part in verification, would devalue signatures to verifiers. The disavowal protocol distinguishes these cases removing the signer's plausible deniability. It is important that the confirmation and disavowal exchanges are not transferable. They achieve this by having the property of zero-knowledge; both parties can create transcripts of both confirmation and disavowal that are indistinguishable, to a third-party, of correct exchanges. The designated verifier signature scheme improves upon deniable signatures by allowing, for each signature, the interactive portion of the scheme to be offloaded onto another party, a designated verifier, reducing the burden on the signer. == Zero-knowledge protocol == The following protocol was suggested by David Chaum. A group, G, is chosen in which the discrete logarithm problem is intractable, and all operation in the scheme take place in this group. Commonly, this will be the finite cyclic group of order p contained in Z/nZ, with p being a large prime number; this group is equipped with the group operation of integer multiplication modulo n. An arbitrary primitive element (or generator), g, of G is chosen; computed powers of g then combine obeying fixed axioms. Alice generates a key pair, randomly chooses a private key, x, and then derives and publishes the public key, y = gx. === Message signing === Alice signs the message, m, by computing and publishing the signature, z = mx. === Confirmation (i.e., avowal) protocol === Bob wishes to verify the signature, z, of m by Alice under the key, y. Bob picks two random numbers: a and b, and uses them to blind the message, sending to Alice: c = magb. Alice picks a random number, q, uses it to blind, c, and then signing this using her private key, x, sending to Bob: s1 = cgq ands2 = s1x. Note that s1x = (cgq)x = (magb)xgqx = (mx)a(gx)b+q = zayb+q. Bob reveals a and b. Alice verifies that a and b are the correct blind values, then, if so, reveals q. Revealing these blinds makes the exchange zero knowledge. Bob verifies s1 = cgq, proving q has not been chosen dishonestly, and s2 = zayb+q, proving z is valid signature issued by Alice's key. Note that zayb+q = (mx)a(gx)b+q. Alice can cheat at step 2 by attempting to randomly guess s2. === Disavowal protocol === Alice wishes to convince Bob that z is not a valid signature of m under the key, gx; i.e., z ≠ mx. Alice and Bob have agreed an integer, k, which sets the computational burden on Alice and the likelihood that she should succeed by chance. Bob picks random values, s ∈ {0, 1, ..., k} and a, and sends: v1 = msga and v2 = zsya, where exponentiating by a is used to blind the sent values. Note that v2 = zsya = (mx)s(gx)a = v1x. Alice, using her private key, computes v1x and then the quotient, v1xv2−1 = (msga)x(zsgxa)−1 = msxz−s = (mxz−1)s. Thus, v1xv2−1 = 1, unless z ≠ mx. Alice then tests v1xv2−1 for equality against the values: (mxz−1)i for i ∈ {0, 1, …, k}; which are calculated by repeated multiplication of mxz−1 (rather than exponentiating for each i). If the test succeeds, Alice conjectures the relevant i to be s; otherwise, she conjectures random value. Where z = mx, (mxz−1)i = v1xv2−1 = 1 for all i, s is unrecoverable. Alice commits to i: she picks a random r and sends hash(r, i) to Bob. Bob reveals a. Alice confirms that a is the correct blind (i.e., v1 and v2 can be generated using it), then, if so, reveals r. Revealing these blinds makes the exchange zero knowledge. Bob checks hash(r, i) = hash(r, s), proving Alice knows s, hence z ≠ mx. If Alice attempts to cheat at step 3 by guessing s at random, the probability of succeeding is 1/(k + 1). So, if k = 1023 and the protocol is conducted ten times, her chances are 1 to 2100.

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  Perplexity AI

  Perplexity AI, Inc., or simply Perplexity, is an American privately held software company offering a web search engine that processes user queries and synthesizes responses. Perplexity products use large language models and incorporate real-time web search capabilities, providing responses based on current Internet content, citing sources used. Its real-time search engine is called Sonar and is based on Meta's Llama model. A free public version is available, while a paid Pro subscription offers access to more advanced language models and additional features. Perplexity AI, Inc., was founded in August 2022 by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski. As of September 2025, the company was valued at US$20 billion. Perplexity AI has attracted legal scrutiny over allegations of copyright infringement, unauthorized content use, and trademark issues from several major media organizations, including the BBC, Dow Jones, and The New York Times. According to separate analyses by Wired and later Cloudflare, Perplexity uses undisclosed web crawlers with spoofed user-agent strings to scrape the content of websites which prohibit, or explicitly block, web scraping. == History == In August 2022, Perplexity AI, Inc., was founded by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski, engineers with backgrounds in back-end systems, artificial intelligence (AI) and machine learning. It launched its main search engine on December 7, 2022, and has since released a Google Chrome extension and apps for iOS and Android. In February 2023, Perplexity reported two million unique visitors. By April 2024, Perplexity had raised $165 million in funding, valuing the company at over $1 billion. As of June 2025, Perplexity closed a $500 million round of funding that elevated its valuation to $14 billion. Investors in Perplexity AI have included Jeff Bezos, Tobias Lütke, Nat Friedman, Nvidia, and Databricks. Perplexity has also received funding from 1789 Capital, a venture capital firm notable for its association with Donald Trump Jr. During Bloomberg’s Tech Summit 2025, Srinivas shared that the company processed 780 million queries in May 2025, experiencing more than 20% month-over-month growth, processing around 30 million queries daily. In July 2024, Perplexity announced the launch of a new publishers' program to share advertising revenue with partners. On January 18, 2025, the day before the impending U.S. ban on the social media app TikTok, Perplexity submitted a proposal for a merger with TikTok US. On August 12, 2025, Perplexity made a bid to buy Chrome from Google for $34.5 billion. Perplexity stated that the sale could remedy anti-trust litigation against Google, in which a judge was considering compelling the sale of Chrome. In December 2025, Cristiano Ronaldo took an undisclosed stake in Perplexity AI and entered a global brand partnership with the company. === Business Strategy and Finance (2026) === As of early 2026, Perplexity AI reached a valuation of $21.21 billion following its Series E-6 funding round. The company's Annual Recurring Revenue (ARR) grew from $80 million in late 2024 to an estimated $200 million by February 2026. In January 2026, the company entered into a three-year, $750 million commitment with Microsoft Azure to secure the GPU capacity required for its advanced "Deep Research" and "Model Council" features. In February 2026, Perplexity transitioned to a subscription-first model by discontinuing its AI-integrated advertising strategy. Leadership stated the move was intended to preserve user trust in the "answer engine," prioritizing objective results over ad revenue. The company also introduced the "Model Council" feature on February 5, 2026, which allows users to compare outputs from multiple large language models, such as GPT-5.2 and Claude 4.6, simultaneously. To expand its user base, Perplexity began offering a free year of Pro access to students, U.S. Military Veterans, and government employees. == Products and services == === Search engine web portal === Perplexity’s primary offering is an online information retrieval system (search engine) that uses large language models to generate responses to user queries by searching and summarizing web-based content. Perplexity offers a feature known as Perplexity Pages that generates structured summaries and report-like content from user queries by aggregating cited sources. Perplexity is available without charge or registration to Web users, a freemium model. === Perplexity Pro === Perplexity Pro is a subscription tier, a more capable paid "enterprise" service, including stronger security and data protection and additional tools, including the ability to search uploaded documents alongside web content and access to a programmatic application programming interface (API). It allows the user to select between backend models such as GPT-5.4, Claude 4.6 and Gemini 3.1 Pro. The company has also developed its own models, Sonar (based on Llama 3.3) and R1 1776 (based on DeepSeek R1). === Internal Knowledge Search === Internal Knowledge Search enables Pro and Enterprise Pro users to simultaneously search across web content and internal documents. Users can upload and search through Excel, Word, PDF, and other common file formats. Enterprise Pro users can upload and index up to 500 files. === Search API === Perplexity's Search API provides AI developers with programmatic access to the company's search infrastructure. The September 2025 release includes a software development kit, an open-source evaluation framework called search_evals, and documentation detailing the API's design and optimization. === Shopping hub === Perplexity's Shopping Hub is an online shopping platform that provides AI-generated product recommendations, and enables users to purchase products directly through Perplexity's interface. It was launched in November 2024 with backing by Amazon and Nvidia. === Finance === In October 2024, Perplexity AI introduced new finance-related features, including looking up stock prices and company earnings data. The tool provides real-time stock quotes and price tracking, industry peer comparisons and basic financial analysis tools. The platform sources its financial data from Financial Modeling Prep. === Assistant === In January 2025, Perplexity launched the Perplexity Assistant, an AI-powered tool designed to enhance the functionality of its search engine. It can perform tasks across multiple apps, such as hailing a ride or searching for a song, and can maintain context across actions. The assistant is also multi-modal, meaning it can use a phone's camera to provide answers about the user's surroundings or on-screen content. Perplexity has acknowledged that the assistant is still in development and may not always function as expected. For instance, certain features, such as summarizing unread emails or upcoming calendar events, require users to enable a workaround based on notifications. === Comet === In July 2025, Perplexity launched Comet, an AI browser based on Chromium. Initially, access to the browser was limited to users subscribed to the most expensive subscription tier. The browser was later released for free download in October 2025. A key feature is integration of the Perplexity search engine, which can perform a variety of tasks such as generating article summaries, describing an image, conducting research about a topic and composing emails. === Truth Social chatbot === Perplexity has been contracted to produce a chatbot for Donald Trump's social media platform Truth Social. == Leadership == Aravind Srinivas is the CEO and co-founder of Perplexity AI. He previously held research positions at OpenAI, Google DeepMind, and other AI research institutions focusing on machine learning and artificial intelligence. In a March 2026 All-In episode, Srinivas said the incoming AI-related layoffs were "glorious future" to "look forward", as it freed people from jobs they didn't like and gave them opportunities to pursue entrepreneurship. == Controversies == === Copyright and trademark infringement allegations === In June 2024, Forbes publicly criticized Perplexity for using their content. According to Forbes, Perplexity published a story largely copied from a proprietary Forbes article without mentioning or prominently citing Forbes. In response, Srinivas said that the feature had some "rough edges" and accepted feedback but maintained that Perplexity only "aggregates" rather than plagiarizes information. In October 2024, The New York Times sent a cease-and-desist notice to Perplexity to stop accessing and using NYT content, claiming that Perplexity is violating its copyright by scraping data from its website. In June 2024, Dow Jones and New York Post filed a lawsuit against Perplexity, alleging copyright infringement. The lawsuit also alleged that Perplexity harmed their brand by attributing hallucinated quotes, for example on F-16 jets for Ukraine, to artic

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  Sysomos

  Sysomos Inc. is a Toronto-based social media analytics company owned by Outside Insight market leaders Meltwater. The company developed text analytics and machine learning technologies for user generated content, and served 80% of the top agencies and Fortune 500. == History == Sysomos was founded by Nilesh Bansal and Nick Koudas. The company is a spinoff of the University of Toronto research project BlogScope. The BlogScope project, which started in 2005, resulted in creation of the underlying content aggregation and analysis engine commercialized by Sysomos. The company raised venture capital in 2008 and was acquired by Marketwire in 2010. The company's original flagship product, Media Analysis Platform (MAP), mines and analyzes content from social media or user-generated content to create a picture of media coverage. Sysomos launched its flagship offering MAP in Sept 2007, followed by addition of Heartbeat to its product suite in 2009. In addition to the two main products, the company released FourWhere, a free location-based social search service that mashes up Foursquare in March 2010. The company also offers Sysomos Heartbeat which provides social media monitoring and engagement capabilities to communication professionals, brand managers and customer support groups. In 2013, Heartbeat was extended to add publishing components to deliver a complete end-to-end social media marketing platform. On July 6, 2010, it was announced that Marketwire, a press release distribution company, had acquired Sysomos. After the acquisition, Sysomos founders Nick Koudas and Nilesh Bansal, left Sysomos to start Aislelabs. In February 2015, Sysomos split from Marketwired, as an independent company, and appointed Adnan Ahmed as the new CEO. In March 2015, newly independent Sysomos launched a redesign for its Heartbeat product and a new API for its MAP product. In the same year, the company acquired Expion. In September 2016, Peter Heffring was announced as the new CEO. In April 2017, Sysomos showcased a new unified platform offering new insights. In April 2018, media monitoring firm Meltwater announced it had acquired Sysomos. The CEO of Sysomos, Peter Heffring, said the company will continue to operate as an independent unit of Meltwater. Heffring will run the social analytics division of Meltwater. == Reports == Inside Twitter series of reports is the most extensive third-party survey on Twitter's growth and demographics. Another extensive survey regarding the top 5% of most active Twitter users found that over 25% of all tweets are machine created. The report also confirms Twitter's international growth. Inside Facebook Pages report found that only four percent of pages have more than 10,000 fans, 0.76% of pages have more than 100,000 fans, and 0.05% of pages (or 297 in total) have more than a million fans. Inside YouTube reports focus more on video hosting services and YouTube.

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  MIME Object Security Services

  MIME Object Security Services (MOSS) is a protocol that uses the multipart/signed and multipart/encrypted framework to apply digital signature and encryption services to MIME objects. == Details == The services are offered through the use of end-to-end cryptography between an originator and a recipient at the application layer. Asymmetric (public key) cryptography is used in support of the digital signature service and encryption key management. Symmetric (secret key) cryptography is used in support of the encryption service. The procedures are intended to be compatible with a wide range of public key management approaches, including both ad hoc and certificate-based schemes. Mechanisms are provided to support many public key management approaches. == Spreading == MOSS was never widely deployed and is now abandoned, largely due to the popularity of PGP.

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  Social search

  Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. == Research and implementations == Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles. In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results. In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. In December 2008, Twitter had re-introduced their people search feature. While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. == Social discovery == Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. == Social search engines == A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added. An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aar

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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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  Group key

  In cryptography, a group key is a cryptographic key that is shared between a group of users. Typically, group keys are distributed by sending them to individual users, either physically, or encrypted individually for each user using either that user's pre-distributed private key. A common use of group keys is to allow a group of users to decrypt a broadcast message that is intended for that entire group of users, and no one else. For example, in the Second World War, group keys (known as "iodoforms", a term invented by a classically educated non-chemist, and nothing to do with the chemical of the same name) were sent to groups of agents by the Special Operations Executive. These group keys allowed all the agents in a particular group to receive a single coded message. In present-day applications, group keys are commonly used in conditional access systems, where the key is the common key used to decrypt the broadcast signal, and the group in question is the group of all paying subscribers. In this case, the group key is typically distributed to the subscribers' receivers using a combination of a physically distributed secure cryptoprocessor in the form of a smartcard and encrypted over-the-air messages.

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  Social television

  Social television is the union of television and social media. Millions of people now share their TV experience with other viewers on social media such as Twitter and Facebook using smartphones and tablets. TV networks and rights holders are increasingly sharing video clips on social platforms to monetise engagement and drive tune-in. The social TV market covers the technologies that support communication and social interaction around TV as well as companies that study television-related social behavior and measure social media activities tied to specific TV broadcasts – many of which have attracted significant investment from established media and technology companies. The market is also seeing numerous tie-ups between broadcasters and social networking players such as Twitter and Facebook. The market is expected to be worth $256bn by 2017. Social TV was named one of the 10 most important emerging technologies by the MIT Technology Review on Social TV in 2010. And in 2011, David Rowan, the editor of Wired magazine, named Social TV at number three of six in his peek into 2011 and what tech trends to expect to get traction. Ynon Kreiz, CEO of the Endemol Group told the audience at the Digital Life Design (DLD) conference in January 2011: "Everyone says that social television will be big. I think it's not going to be big—it's going to be huge". Much of the investment in the earlier years of social TV went into standalone social TV apps. The industry believed these apps would provide an appealing and complimentary consumer experience which could then be monetized with ads. These apps featured TV listings, check-ins, stickers and synchronised second-screen content but struggled to attract users away from Twitter and Facebook. Most of these companies have since gone out of business or been acquired amid a wave of consolidation and the market has instead focused on the activities of the social media channels themselves – such as Twitter Amplify, Facebook Suggested Videos and Snapchat Discover – and the technologies that support them. == Twitter == Twitter and Facebook are both helping users connect around media, which can provoke strong debate and engagement. Both social platforms want to be the 'digital watercooler' and host conversation around TV because the engagement and data about what media people consume can then be used to generate advertising revenue. As an open platform, conversation on Twitter is closely aligned with real-time events. In May 2013, it launched Twitter Amplify – an advertising product for media and consumer brands. With Amplify, Twitter runs video highlights from major live broadcasts, with advertisers' names and messages playing before the clip. By February 2014, all four major U.S. TV networks had signed up to the Amplify program, bringing a variety of premium TV content onto the social platform in the form of in-tweet real-time video clips. In June 2014, Twitter acquired its Twitter Amplify partner in the U.S. SnappyTV, a company that was helping broadcasters and rights holders to share video content both organically across social and via Twitter's Amplify program. Twitter continues to rely on Grabyo, which has also struck numerous deals with some of the largest broadcasters and rights holders in Europe and North America to share video content across Facebook and Twitter. == Facebook == Facebook made significant changes to its platform in 2014 including updates to its algorithm to enhance how it serves video in users' feeds. It also launched video autoplay to get users to watch the videos in their feeds. It rapidly surpassed Twitter and by the end of 2014 it was enjoying three billion video views a day on its platform and had announced a partnership with the NFL, one of Twitter's most active Twitter Amplify partners. In April 2015, at its F8 Developer Conference, it revealed it was working with Grabyo among other technology partners to bring video onto its platform. Then in July it announced it would be launching Facebook Suggested Videos, bringing related videos and ads to anyone that clicks on a video – a move that not only competed with Twitter's commercial video offering but also put it in direct competition with YouTube. == TV Time == TV Time is a television dedicated social network that allows users to keep track of the television series they watch, as well as films. It also allows them to express their reaction to the media they have seen with episode specific voting for favorite characters and emotional reaction to episodes, as well as commenting in episode restrictive pages. This way users are able to avoid spoilers while also finding a precise audience and community for each of their interactions, as opposed to bigger, non-television dedicated social medias such as Facebook and Twitter where the likelihood of unintentionally reading spoilers is much higher. TV Time offers an analytics service called "TVLytics" where the votes and reactions collected from users can be studied for research and television production purposes. == Advertising == According to Businessinsider.com, there are variety of applications for social TV, including support for TV ad sales, optimizing TV ad buys, making ad buys more efficient, as a complement to audience measurement, and eventually, audience forecasting and real-time optimization. Social TV data can ease access to focus groups and may create a positive feedback loop for generating ultra-sticky TV programming and multi-screen ad campaigns. == In numbers == Viewers share their TV experience on social media in real-time as events unfold: between 88-100m Facebook users login to the platform during the primetime hours of 8pm – 11pm in the US. The volume of social media engagement in TV is also rising – according to Nielsen SocialGuide, there was a 38% increase in tweets about TV in 2013 to 263m. For the 2014 Super Bowl, Twitter reported that a record 24.9 million tweets about the game were sent during the telecast, peaking at 381,605 tweets per minute. Facebook reported that 50 million people discussed the Super Bowl, generating 185 million interactions. The 2014 Oscars generated 5m tweets, viewed by an audience of 37m unique Twitter users and delivering 3.3bn impressions globally as conversation and key moments were shared virally across the platform. In 2014 the All England Lawn Tennis Club (AELTC), hosts of Wimbledon, used Grabyo to share video content across social. The videos were viewed 3.5 million times across Facebook and Twitter. In partnered with Grabyo again in 2015 and the videos generated over 48 million views across Facebook and Twitter. == Television shows with social integration == Here are some examples of how TV executives are integrating social elements with TV shows: C-SPAN streamed tweets from US Senators and Representatives during the quorum call The Voice had the judges of the program tweet during the show and the posts scrolls on the bottom of the screen. The use of Twitter also led to an increase in viewers. "Glee" Entertainment Weekly created a second screen viewing platform for the Glee season 3 premiere. == Related publications == Erika Jonietz. "Making TV Social, Virtually" MIT Technology Review. (January 11, 2010) AmigoTV (Alcatel-Lucent; Coppens et al.) – 2004 www.ist-ipmedianet.org/Alcatel_EuroiTV2004_AmigoTV_short_paper_S4-2.pdf Nextream (MIT Media Lab, Martin et al.) – 2010 Social Interactive Television: Immersive Shared Experiences and Perspectives (P. Cesar, D. Geerts, and K. Chorianopoulos (eds.)) – 2009 Social TV and the Emergence of Interactive TV – Multimedia Research Group – November 2010 Interactive Social TV on Service Oriented Environments: Challenges and Enablers (May 2011) == Systems == Boxee – acquired by Samsung GetGlue – acquired by i.TV Grabyo KIT digital Miso TV Tank Top TV WiO Xbox Live

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