AI Chatbot Zoho

AI Chatbot Zoho — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  BigDog

  BigDog is a dynamically stable quadruped military robot platform that was created in 2005 by Boston Dynamics with the Harvard University Concord Field Station. It was funded by the U.S. Defense Advanced Research Projects Agency (DARPA), but the project was shelved after the BigDog's gas engine was deemed too loud for combat. == History == BigDog was funded by the Defense Advanced Research Projects Agency (DARPA) in the hopes that it would be able to serve as a mechanic pack mule to accompany soldiers in terrain too rough for conventional vehicles. Instead of wheels or treads, BigDog uses four legs for movement, allowing it to move across surfaces that would be difficult for wheels. The legs contain a variety of sensors, including joint position and ground contact. BigDog also features a laser gyroscope and a stereo vision system. BigDog is 3 feet (0.91 m) long, stands 2.5 feet (0.76 m) tall, and weighs 240 pounds (110 kg), making it about the size of a small mule. It is capable of traversing difficult terrain, running at four miles per hour (6.4 km/h), carrying 340 pounds (150 kg), and climbing a 35 degree incline. Locomotion is controlled by an onboard computer that receives input from the robot's various sensors. Navigation and balance are also managed by the control system. BigDog's walking pattern is controlled through four legs, each equipped with four low-friction hydraulic cylinder actuators that power the joints. BigDog's locomotion behaviors can vary greatly. It can stand up, sit down, walk with a crawling gait that lifts one leg at a time, walk with a trotting gait lifting diagonal legs, or trot with a running gait. The travel speed of BigDog varies from a 0.62 mph (1 km/h) crawl to a 3.3 mph (5.3 km/h) trot. The BigDog project was headed by Dr. Martin Buehler, who received the Joseph Engelberger Award from the Robotics Industries Association in 2012 for the work. Dr. Buehler while previously a professor at McGill University, headed the robotics lab there, developing four-legged walking and running robots. Built onto the actuators are sensors for joint position and force, and movement is ultimately controlled through an onboard computer which manages the sensors. Approximately 50 sensors are located on BigDog. These measure the attitude and acceleration of the body, motion, and force of joint actuators as well as engine speed, temperature and hydraulic pressure inside the robot's internal engine. Low-level control, such as position and force of the joints, and high-level control such as velocity and altitude during locomotion, are both controlled through the onboard computer. BigDog was featured in episodes of Web Junk 20 and Hungry Beast, and in articles in New Scientist, Popular Science, Popular Mechanics, and The Wall Street Journal. In September 2011 Boston Dynamics released video footage of a new generation of BigDog known as AlphaDog. The footage shows AlphaDog's ability to walk on rough terrain and recover its balance when kicked from the side. The refined equivalent has been designed by Boston Dynamics to exceed the BigDog in terms of capabilities and use to dismounted soldiers. In February 2012, with further DARPA support, the militarized Legged Squad Support System (LS3) variant of BigDog demonstrated its capabilities during a hike over a rough terrain. Starting in the summer of 2012, DARPA planned to complete the overall development of the system and refine its key capabilities in 18 months, ensuring its worth to dismounted warfighters before it is rolled out to squads operating in-theatre. BigDog must be able to demonstrate its ability to complete a 20-mile (32 km) trail in 24 hours, without refuelling, while carrying a 325-pound (150 kg) load. A refinement of its vision sensors will also be conducted. At the end of February 2013, Boston Dynamics released video footage of a modified BigDog with an arm. The arm could pick up objects and throw them. The robot is relying on its legs and torso to help power the motions of the arm. It is believed that it can lift weights around 55 pounds (25 kg). This work was funded by the United States Army Research Laboratory and paved the way for integrating manipulators with quadrupeds as found on Spot, the spiritual successor of BigDog. === Discontinuation === At the end of December 2013, the BigDog project was discontinued. Despite hopes that it would one day work like a pack mule for US soldiers in the field, the gasoline-powered engine was deemed too noisy for use in combat, and it could be heard from hundreds of meters away. A similar project for an all-electric robot named Spot in 2016 was much quieter, but could only carry 45 pounds (20 kg). Both projects are no longer in progress, but the Spot was only released in 2020. == Hardware == BigDog is powered by a small two-stroke, one-cylinder, 15-brake-horsepower (11 kW) engine operating at 9,000 RPM. The engine drives a hydraulic pump, which in turn drives the hydraulic leg actuators. Each leg has four actuators (two for the hip joint, and two each for the knee and ankle joints), for a total of 16. Each actuator unit consists of a hydraulic cylinder, servo valve, position sensor, and force sensor. Onboard computing power is a ruggedized PC/104 board stack with two computers, one running a Pentium M processor running QNX (used for sensor data processing) and another running a Core Duo processor (used for visual data processing). == Gallery ==

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  Data profiling

  Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category Assess data quality, including whether the data conforms to particular standards or patterns Assess the risk involved in integrating data in new applications, including the challenges of joins Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies Assess whether known metadata accurately describes the actual values in the source database Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality. == Introduction == Data profiling refers to the analysis of information for use in a data warehouse in order to clarify the structure, content, relationships, and derivation rules of the data. Profiling helps to not only understand anomalies and assess data quality, but also to discover, register, and assess enterprise metadata. The result of the analysis is used to determine the suitability of the candidate source systems, usually giving the basis for an early go/no-go decision, and also to identify problems for later solution design. == How data profiling is conducted == Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition. The metadata can then be used to discover problems such as illegal values, misspellings, missing values, varying value representation, and duplicates. Different analyses are performed for different structural levels. E.g. single columns could be profiled individually to get an understanding of frequency distribution of different values, type, and use of each column. Embedded value dependencies can be exposed in a cross-columns analysis. Finally, overlapping value sets possibly representing foreign key relationships between entities can be explored in an inter-table analysis. Normally, purpose-built tools are used for data profiling to ease the process. The computational complexity increases when going from single column, to single table, to cross-table structural profiling. Therefore, performance is an evaluation criterion for profiling tools. == When is data profiling conducted? == According to Kimball, data profiling is performed several times and with varying intensity throughout the data warehouse developing process. A light profiling assessment should be undertaken immediately after candidate source systems have been identified and DW/BI business requirements have been satisfied. The purpose of this initial analysis is to clarify at an early stage if the correct data is available at the appropriate detail level and that anomalies can be handled subsequently. If this is not the case the project may be terminated. Additionally, more in-depth profiling is done prior to the dimensional modeling process in order assess what is required to convert data into a dimensional model. Detailed profiling extends into the ETL system design process in order to determine the appropriate data to extract and which filters to apply to the data set. Additionally, data profiling may be conducted in the data warehouse development process after data has been loaded into staging, the data marts, etc. Conducting data at these stages helps ensure that data cleaning and transformations have been done correctly and in compliance of requirements. == Benefits and examples == Data profiling can improve data quality, shorten the implementation cycle of major projects, and improve users' understanding of data. Discovering business knowledge embedded in data itself is one of the significant benefits derived from data profiling. It can improve data accuracy in corporate databases.

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  Data definition specification

  In computing, a data definition specification (DDS) is a guideline to ensure comprehensive and consistent data definition. It represents the attributes required to quantify data definition. A comprehensive data definition specification encompasses enterprise data, the hierarchy of data management, prescribed guidance enforcement and criteria to determine compliance. == Overview == A data definition specification may be developed for any organization or specialized field, improving the quality of its products through consistency and transparency. It eliminates redundancy (since all contributing areas are referencing the same specification) and provides standardization and degrees of compliance, making it easier and more efficient to create, modify, verify, analyze and share information across the enterprise. To understand how a data definition specification works in an enterprise, we must look at the elements of a DDS. Writing data definitions, defining business terms (or rules) in the context of a particular environment, provides structure for an organization's data architecture. In developing these definitions, the words used must be traceable to clearly defined data. A data definition specification may be used in the following activities: Business intelligence Business process modeling Business rules management Data analysis and modeling Information architecture Metadata modeling Data mastering Report generation == Criteria == A data definition specification requires data definitions to be: Atomic – singular, describing only one concept. Commonly used and ambiguous terms should be defined. While a term refers to one concept, several words may be used in a term: File – A concept identifiable with one word File extension – A concept identifiable with more than one word Traceable – Mapped to a specific data element. In business, a term may be traced to an entity (for example, a customer) or an attribute (such as a customer's name). A term may be a value in a data set (such as gender), or designate the data set itself. Traceability indicates relationships in the data hierarchy. Consistent - Used in a standard syntax; if used in a specific context, the context is noted Accurate - Precise, correct and unambiguous, stating what the term is and is not Clear - Readily understood by the reader Complete - With the term, its description and contextual references Concise - To avoid circular references == Applications == === Enterprise data === A data definition specification was produced by the Open Mobile Alliance to document charging data. The document, the centralized catalog of data elements defined for interfaces, specifies the mapping of these data elements to protocol fields in the interfaces. Created for the exchange of financial data, Market Data Definition Language (MDDL) is an XML specification designed to enable the interchange of information necessary to account, to analyze, and to trade financial instruments of the world's markets. It defines an XML-based interchange format and common data dictionary on the fields needed to describe: (1) financial instruments, (2) corporate events affecting value and tradability, and (3) market-related, economic and industrial indicators. The principal function of MDDL is to allow entities to exchange market data by standardizing formats and definitions. MDDL provides a common format for market data so that it can be efficiently passed from one processing system to another and provides a common understanding of market data content by standardizing terminology and by normalizing the relationships of various data elements to one another ... From the user perspective, the goal of MDDL is to enable users to integrate data from multiple sources by standardizing both the input feeds used for data warehousing (i.e., define what's being provided by vendors) and the output methods by which client applications request the data (i.e., ensure compatibility on how to get data in and out of applications)." === Clinical submissions === The Clinical Data Interchange Standards Consortium, a global, multidisciplinary, non-profit organization, has established standards to support the acquisition, exchange, submission and archiving of clinical research data and metadata. CDISC standards are vendor-neutral, platform-independent and freely available from the CDISC website. The Case Report Tabulation Data Definition Specification (define.xml) draft version 2.0, the oldest data definition specification, is part of the evolution from the 1999 FDA electronic submission (eSub) guidance and electronic Common Technical Document (eCTD) documents specifying that a document describing the content and structure of included data be included in a submission. Define.xml was developed to automate the review process by generating a machine-readable data-definition document. Define.xml has standardized submissions to the Food and Drug Administration, reducing review times from over two years to several months. === Archival data === A data definition specification is the foundation of metadata for scientific data archiving. The Metadata Encoding and Transmission Standard (METS) uses one principle of a DDS: consistent use of key terms to catalog digital objects for global use. The METS schema is a flexible mechanism for encoding descriptive, administrative and structural metadata for a digital library object and expressing complex links between metadata, and can provide a useful standard for the exchange of digital-library objects between repositories. A similar effort is underway to preserve complex data associated with video-game archiving. Preserving Virtual Worlds attempted to address archival-format deficiencies, citing the lack of suitable documentation for interactive fiction and games at the bit level: specifically, the absence of "representation information" needed to map raw bits into higher-level data constructs. Preserving Virtual Worlds 2 is a research project expanding on initial efforts in this field.

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  Social media measurement

  Social media measurement, also called social media controlling, is the management practice of evaluating successful social media communications of brands, companies, or other organizations. Key performance indicators may be measured by extracting information from social media channels, such as blogs, wikis, micro-blogs such as Twitter, social networking sites, or video/photo sharing websites, forums from time to time. It is also used by companies to gauge current trends in the industry. The process first gathers data from different websites and then performs analysis based on different metrics like time spent on the page, click through rate, content share, comments, text analytics to identify positive or negative emotions about the brand. Some other social media metrics include share of voice, owned mentions, and earned mentions. The social media measurement process starts with defining a goal that needs to be achieved and defining the expected outcome of the process. The expected outcome varies per the goal and is usually measured by a variety of metrics. This is followed by defining possible social strategies to be used to achieve the goal. Then the next step is designing strategies to be used and setting up configuration tools that ease the process of collecting the data. In the next step, strategies and tools are deployed in real-time. This step involves conducting Quality Assurance tests of the methods deployed to collect the data. And in the final step, data collected from the system is analyzed and if the need arises, it is refined on the run time to enhance the methodologies used. The last step ensures that the result obtained is more aligned with the goal defined in the first step. == Data Acquisition == Acquiring data from social media is in demand of an exploring the user participation and population with the purpose of retrieving and collecting so many kinds of data(ex: comments, downloads etc.). There are several prevalent techniques to acquire data such as Network traffic analysis, Ad-hoc application and Crawling Network Traffic Analysis - Network traffic analysis is the process of capturing network traffic and observing it closely to determine what is happening in the network. It is primarily done to improve the performance, security and other general management of the network. However concerned about the potential tort of privacy on the Internet, network traffic analysis is always restricted by the government. Furthermore, high-speed links are not adaptable to traffic analysis because of the possible overload problem according to the packet sniffing mechanism Ad-hoc Application - Ad-hoc application is a kind of application that provides services and games to social network users by developing the APIs offered by social network companies (Facebook Developer Platform). The infrastructure of Ad-hoc application allows the user to interact with the interface layer instead of the application servers. The API provides a path for application to access information after the user login. Moreover, the size of the data set collected vary with the popularity of the social media platform i.e. social media platforms having high number of users will have more data than platforms having less user base. Scraping is a process in which the APIs collect online data from social media. The data collected from Scraping is in raw format. However, having access to these types of data is a bit difficult because of its commercial value. Crawling - Crawling is a process in which a web crawler creates indexes of all the words in a web-page, stores them, then follows all the hyperlinks and indexes on that page and again stores them. It is the most popular technique for data acquisition and is also well known for its easy operation based on prevalent Object-Orientated Programming Language (Java or Python etc.). And most important, social network companies (YouTube, Flicker, Facebook, Instagram, etc.) are friendly to crawling techniques by providing public APIs == Applications == === For branding === Monitoring social media allows researchers to find insights into a brand's overall visibility on social media, to measure the impact of campaigns, to identify opportunities for engagement, to assess competitor activity and share of voice, and to detect impending crises. It can also provide valuable information about emerging trends and what consumers and clients think about specific topics, brands or products. This is the work of a cross-section of groups that include market researchers, PR staff, marketing teams, social-engagement, and community staff, agencies and sales teams. Several different providers have developed tools to facilitate the monitoring of a variety of social media channels - from blogging to internet video to internet forums. This allows companies to track what consumers say about their brands and actions. Companies can then react to these conversations and interact with consumers through social media platforms. === In government === Apart from commercial applications, social media monitoring has become a pervasive technique applied by public organizations and governments. Monitoring is a tradition within the public sector, and social-media monitoring provides a real-time approach to detecting and responding to social developments. Governments have come to realize the need for strategies to cope with surprises from the rapid expansion of public issues. Sobkowicz introduced a framework with three blocks of social-media opinion tracking, simulating and forecasting. It includes: real-time detection of emotions, topics and opinions information-flow modelling and agent-based simulation modeling of opinion networks Bekkers introduced the application of social media monitoring in the Netherlands. Public organizations in the Netherlands (such as the Tax Agency and the Education Ministry) have started to use social media monitoring to obtain better insights into the sentiments of target groups. On the one hand, the public sector will be enabled to provide timely and efficient answers to the public by using social media monitoring techniques, but on the other hand, they also have to deal with concerns about ethical issues such as transparency and privacy. == Quantifying social media == Social media management software (SMMS) is an application program or software that facilitates an organization's ability to successfully engage in social media across different communication channels. SMMS is used to monitor inbound and outbound conversations, support customer interaction, audit or document social marketing initiatives and evaluate the usefulness of a social media presence. It can be difficult to measure all social media conversations. Due to privacy settings and other issues, not all social media conversations can be found and reported by monitoring tools. However, whilst social media monitoring cannot give absolute figures, it can be extremely useful for identifying trends and for benchmarking, in addition to the uses mentioned above. These findings can, in turn, influence and shape future business decisions. In order to access social media data (posts, Tweets, and meta-data) and to analyze and monitor social media, many companies use software technologies built for business. These range from in-platform analytics dashboards to dedicated third-party platforms, which offer more advanced capabilities including cross-platform audience intelligence, sentiment analysis, and trend detection at scale. == Location-based == Most social media networks allow users to add a location to their posts (reference all of our feeds). The location can be classified as either 'at-the-location' or 'about-the-location'. "'At-the-location' services can be defined as services where location-based content is created at the geographic location. 'About-the-location' services can be defined as services which are referring to a particular location but the content is not necessarily created in this particular physical place." The added information available from geotagged (link to Geotagging article) posts means that they can be displayed on a map. This means that a location can be used as the start of a social media search rather than a keyword or hashtag. This has major implications for disaster relief, event monitoring, safety and security professionals since a large portion of their job is related to tracking and monitoring specific locations. == Technologies used == Various monitoring platforms use different technologies for social media monitoring and measurement. These technology providers may connect to the API provided by social platforms that are created for 3rd party developers to develop their own applications and services that access data. Facebook's Graph API is one such API that social media monitoring solution products would connect to pull data from. Some social media monitoring and analytics companies use calls to data providers each time an end-user d

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  Tandem (app)

  Tandem is a mobile language exchange and language learning app. == History == Tandem was founded in Hannover, Germany in 2014 by Arnd Aschentrup, Tobias Dickmeis, and Matthias Kleimann. Prior to founding Tandem, the trio had launched Vive, a members-only mobile video chat platform. Tandem has been criticised for not accepting members into the community immediately, as opposed to competitors including HelloTalk, Speaky or Cafehub. In some countries, there is a waiting list and applicants can wait up to seven days for their application to be processed by human moderators. In 2015, Tandem completed its first funding round (seed funding) of €600,000. Participating investors included business angels such as Atlantic Labs (Christophe Maire), Hannover Beteiligungsfonds, Marcus Englert (Chairman of the Supervisory Board of Rocket Internet SE ), Catagonia, Ludwig zu Salm, Florian Langenscheidt, Heiko Hubertz, Martin Sinner, and Zehden Enterprises. In 2016, the company received a further €2 million from new investors Rubylight and Faber Ventures, as well as from existing investors Hannover Beteiligungsfonds, Atlantic Labs, and Zehden Enterprises. Since 2018, the premium membership Tandem Pro has been available, which offers members unlimited access to all language learning features of the app as well as the removal of advertising for a monthly fee.

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  Frame (networking)

  A frame is a digital data transmission unit in computer networking and telecommunications. In packet switched systems, a frame is a simple container for a single network packet. In other telecommunications systems, a frame is a repeating structure supporting time-division multiplexing. A frame typically includes frame synchronization features consisting of a sequence of bits or symbols that indicate to the receiver the beginning and end of the payload data within the stream of symbols or bits it receives. If a receiver is connected to the system during frame transmission, it ignores the data until it detects a new frame synchronization sequence. == Packet switching == In the OSI model of computer networking, a frame is the protocol data unit at the data link layer. Frames are the result of the final layer of encapsulation before the data is transmitted over the physical layer. A frame is "the unit of transmission in a link layer protocol, and consists of a link layer header followed by a packet." Each frame is separated from the next by an interframe gap. A frame is a series of bits generally composed of frame synchronization bits, the packet payload, and a frame check sequence. Examples are Ethernet frames, Wi-Fi frames, 4G frames, Point-to-Point Protocol (PPP) frames, Fibre Channel frames, and V.42 modem frames. Often, frames of several different sizes are nested inside each other. For example, when using Point-to-Point Protocol (PPP) over asynchronous serial communication, the eight bits of each individual byte are framed by start and stop bits, the payload data bytes in a network packet are framed by the header and footer, and several packets can be framed with frame boundary octets. == Time-division multiplex == In telecommunications, specifically in time-division multiplex (TDM) and time-division multiple access (TDMA) variants, a frame is a cyclically repeated data block that consists of a fixed number of time slots, one for each logical TDM channel or TDMA transmitter. In this context, a frame is typically an entity at the physical layer. TDM application examples are SONET/SDH and the ISDN circuit-switched B-channel, while TDMA examples are Circuit Switched Data used in early cellular voice services. The frame is also an entity for time-division duplex, where the mobile terminal may transmit during some time slots and receive during others.

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  Influencer

  An influencer is an individual who has the capacity to shape the attitudes, behavior, or decisions of others through authority, knowledge, position, or the nature of the relationship with the audience. The term is used in various fields such as media, business, politics, religion, and communication, referring to influencers such as social media influencers, podcasters, public speakers, religious influencers, writers, and newsletter writers etc who have dedicated followings in various areas. One writer defines influencers as "a range of third parties who exercise influence over the organization and its potential customers." Another writer defines an influencer as a "third party who significantly shapes the customer's purchasing decision but may never be accountable for it." According to another writer, influencers are "well-connected, create an impact, have active minds, and are trendsetters". Just because a person has many followers does not necessarily mean they have much influence over those people. In contemporary usage, the term frequently refers to a social media influencer, (also known as an online influencer or simply influencer) a person who builds a grassroots online presence through engaging content such as photos, videos, and updates. This is done by using direct audience interaction to establish authenticity, expertise, and appeal, and by standing apart from traditional celebrities by growing their platform through social media rather than pre-existing fame. The modern referent of the term is commonly a paid role in which a business entity pays for the social media influence-for-hire activity to promote its products and services, known as influencer marketing. A 1% increase in spending on influencer marketing can lead to a 0.5% increase in audience engagement. As such, an influencer effectively acts as a modern salesperson or a marketer. Types of influencers include fashion influencer, travel influencer, and virtual influencer, and they involve content creators and streamers. Some influencers are associated primarily with specific social media apps such as TikTok, Instagram, or Pinterest; many influencers are also considered internet celebrities. As of 2023, Instagram is the social media platform businesses spend the most advertising money towards marketing with influencers. However, influencers can have an impact on any social media network. == History == === Origins === The word influencer in its general sense of a person or thing that exerts influence, is attested in historical sources at least since the 17th century. The Oxford English Dictionary (OED) gives 1664 as the earliest example of usage and cites a sentence from Henry More's A Modest Enquiry into the Mystery of Iniquity: "The head and influencer of the whole Church". The origins of online influencing can be traced back to the emergence of digital blogs and platforms in the early 2000s. Nevertheless, recent studies demonstrate that Instagram, an application with more than one billion users, harbors the majority of the influencer demographic. These individuals are sometimes referred to as "Instagrammers" or "Instafamous". A crucial aspect of influencing is their association with sponsors. The 2015 debut of Vamp, a company that links influencers with sponsorships, transformed the landscape of influencing. There is much debate about whether social media influencers can be considered celebrities, as their path to fame is often less traditional and arguably easier. Melody Nouri addressed the differences between the two types in her article "The Power of Influence: Traditional Celebrities vs Social Media Influencer". Nouri asserts that social media platforms have a greater negative impact on young, impressionable audiences in comparison with traditional media such as magazines, billboards, advertisements, and tabloids featuring celebrities. Online, it is thought to be simpler to manipulate an image and lifestyle in such a way that viewers are more susceptible to believing it. One theory considers the former American First Lady Eleanor Roosevelt (1884–1962) to be the "original media influencer." While she achieved celebrity in her role as First Lady, she built a global personal brand as a wise, informative, trustworthy American woman. Her voice was her own, unrestricted by political advisors and powerful men, and with it, Roosevelt exerted unprecedented social and cultural influence in radio, print, public speaking, film, and television until she died. In one notable example, it may have been Roosevelt's television support of John F. Kennedy which nudged his "hairline victory" during the 1960 Presidential campaign. In another example, David Ogilvy paid Roosevelt more than a quarter of a million dollars in today's currency to make a TV commercial for Good Luck margarine (1959), in which Roosevelt also managed to mention world hunger. As a content creator, she wrote My Day, a popular daily newspaper column that ran nationwide for twenty-six years. Like a social media post, My Day covered all aspects of her life, and in it Roosevelt often recommended movies, books, and products that she admired. Roosevelt also had a hand in designing all three of her public affairs television shows. Unlike contemporary influencers, she was less motivated by a pay-to-play situation than by a desire to educate and inspire; but she did use her influence to benefit the entertainment industry careers of her children, and she welcomed the revenue that her influence bought, most of which was donated to charity. === 2000s === The early 2000s showed corporate endeavors to leverage the internet for influence, with some companies participating in forums for promotions or providing bloggers with complimentary products in return for favorable reviews. A few of these practices were viewed as unethical for taking advantage of the labor of young individuals without providing remuneration. In 2004, The Blogstar Network was established by Ted Murphy of MindComet. Bloggers were encouraged to join an email list and receive remunerated offers from corporations in exchange for creating specific posts. For instance, bloggers were compensated for writing reviews of fast-food meals on their blogs. Blogstar is widely regarded as the first influencer marketing network. Murphy succeeded Blogstar with PayPerPost, which was introduced in 2006. This platform compensated significant posters on prominent forums and social media platforms for every post made about a corporate product. Payment rates were determined by the influencer's status. Though very popular, PayPerPost, received a great deal of criticism as these influencers were not required to disclose their involvement with PayPerPost as traditional journalism would have. With the success of PayPerPost, the public became aware that there was a drive for corporate interests to influence what some people were posting to these sites. The platform also incentivized other firms to establish comparable programs. Despite concerns, marketing networks with influencers continued to grow throughout the 2000s and into the 2010s. The influencer marketing industry was worth as much as $8 billion in 2019, according to estimates from Business Insider Intelligence, which are based on Mediakix data. Evan Asano, the Former CEO and founder of the agency Mediakix, previously spoke with Business Insider and said he believed influencer marketing on Instagram would continue to grow despite likes being hidden. === 2010s === By the 2010s, the term "influencer" described digital content creators with a large following, distinctive brand persona, and a patterned relationship with commercial sponsors. By this period, influencer marketing had become a widely researched field globally, with systematic reviews drawing on hundreds of studies that documented the growing role of authenticity, audience engagement, and parasocial relationships in shaping how consumers responded to influencer content across different markets. During this period, influencer culture also developed through distinct channels outside Western markets. In South Korea, the global spread of Korean pop culture, also called K-Pop, through platforms such as YouTube, Facebook, and Twitter gave rise to what scholars have called 'Hallyu 2.0' or the 'New Korean Wave', where fans throughout Southeast Asia, North America, Latin America, and Europe shared, subtitled, and redistributed Korean music and film content on a large scale. This helped Korean entertainers to build substantial followings internationally. Consumers often mistakenly view celebrities as reliable, leading to trust and confidence in the products being promoted. A 2001 study from Rutgers University discovered that individuals were using "internet forums as influential sources of consumer information." The study proposes that consumers preferred internet forums and social media when making purchasing decisions over conventional advertising and print sources. An in

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  List of cryptography journals

  List of cryptography journals includes notable peer-reviewed academic journals that focus on cryptography, cryptanalysis, information security, and related areas in computer science and mathematics. == Notable journals == Cryptologia Designs, Codes and Cryptography IEEE Transactions on Information Theory International Journal of Information Security Journal of Cryptology Journal of Computer Security ACM Transactions on Privacy and Security Information Processing Letters Information and Computation

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  Frame grabber

  A frame grabber is an electronic device that captures (i.e., "grabs") individual, digital still frames from an analog video signal or a digital video stream. It is usually employed as a component of a computer vision system, in which video frames are captured in digital form and then displayed, stored, transmitted, analyzed, or combinations of these. Historically, frame grabber expansion cards were the predominant way to interface cameras to PCs. Other interface methods have emerged since then, with frame grabbers (and in some cases, cameras with built-in frame grabbers) connecting to computers via interfaces such as USB, Ethernet and IEEE 1394 ("FireWire"). Early frame grabbers typically had only enough memory to store a single digitized video frame, whereas many modern frame grabbers can store multiple frames. Modern frame grabbers often are able to perform functions beyond capturing a single video input. For example, some devices capture audio in addition to video, and some devices provide, and concurrently capture frames from multiple video inputs. Other operations may be performed as well, such as deinterlacing, text or graphics overlay, image transformations (e.g., resizing, rotation, mirroring), and conversion to JPEG or other compressed image formats. To satisfy the technological demands of applications such as radar acquisition, manufacturing and remote guidance, some frame grabbers can capture images at high frame rates, high resolutions, or both. == Circuitry == Analog frame grabbers, which accept and process analog video signals, include these circuits: Input signal conditioner that buffers the analog video input signal to protect downstream circuitry Video decoder that converts SD analog video (e.g., NTSC, SECAM, PAL) or HD analog video (e.g., AHD, HD-TVI, HD-CVI) to a digital format Digital frame grabbers, which accept and process digital video streams, include these circuits: Digital video decoder that interfaces to and converts a specific type of digital video source, such as Camera Link, CoaXPress, DVI, GigE Vision, LVDS, or SDI Circuitry common to both analog and digital frame grabbers: Memory for storing the acquired image (i.e., a frame buffer) A bus interface through which a processor can control the acquisition and access the data General purpose I/O for triggering image acquisition or controlling external equipment == Applications == === Healthcare === Frame grabbers are used in medicine for many applications, including telenursing and remote guidance. In situations where an expert at another location needs to be consulted, frame grabbers capture the image or video from the appropriate medical equipment, so it can be sent digitally to the distant expert. === Manufacturing === "Pick and place" machines are often used to mount electronic components on circuit boards during the circuit board assembly process. Such machines use one or more cameras to monitor the robotics that places the components. Each camera is paired with a frame grabber that digitizes the analog video, thus converting the video to a form that can be processed by the machine software. === Network security === Frame grabbers may be used in security applications. For example, when a potential breach of security is detected, a frame grabber captures an image or a sequence of images, and then the images are transmitted across a digital network where they are recorded and viewed by security personnel. === Personal use === In recent years with the rise of personal video recorders like camcorders, mobile phones, etc. video and photo applications have gained ascending prominence. Frame grabbing is becoming very popular on these devices. === Astronomy & astrophotography === Amateur astronomers and astrophotographers use frame grabbers when using analog "low light" cameras for live image display and internet video broadcasting of celestial objects. Frame grabbers are essential to connect the analog cameras used in this application to the computers that store or process the images.

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  Public Services Network

  The Public Services Network (PSN) is a UK government's high-performance network, which helps public sector organisations work together, reduce duplication and share resources. It unified the provision of network infrastructure across the United Kingdom public sector into an interconnected "network of networks" to increase efficiency and reduce overall public expenditure. It is now a legacy network and public sector organisations are being migrated to using services on the public internet. == Origins == The Public Services Network (PSN) was launched officially as part of the Transformational Government Strategy commencing in 2005, under the original name of the Public Sector Network. Prior to this, some parts of local government had already successfully implemented the concept. The Hampshire Public Services Network (HPSN) was the first PSN, launched in 1999, followed closely by Kent County Councils partnerships with the KPSN. The HPSN, encompassing all of the borough, district and unitary councils, with the County Council, as well as the Fire Services, the Isle of Wight Council and 540 schools. National PSN technical and architecture compliance criteria were established from 2007, by GDS working with local government leaders from Socitm (the Society of Information Technology Management) on the National CIO Council and the Local CIO Council. The PSN's aim was to bring public services organisations with a common interest onto a single, coherent and standards-based ‘network of networks’. This would create influence, economies of scale and a commonality of standards for secure and easy inter-connection between public service organisations. The original concept of a network of networks strategy was based upon the work already undertaken in local government and recognition of Communities of Interest (COI) within the Criminal Justice Sector during work by the Office for Criminal Justice Reform (OCJR) between 2005 and 2007 to enable data sharing across business units. In this context a COI was defined as groups of Government departments and external partners who in combination provided services within a specific area of operation and used the same data, with a similar risk profile, shared risk appetite and common governance framework. Historically each group member had implemented their own networks and standards of operation in isolation with little or no consideration as to how services and data may be shared and resulting in increased costs of operation. The Network of Networks strategy proposed within OCJR recommended the creation of specific networks based upon these Communities of Interest which were joined together through data interchange gateways supporting common standards. Under this approach networks would be arranged by data type and business functions such as Criminal Justice, Health and Social Care, Defence and Intelligence or Public Finance rather than solely on established departmental boundaries. Within a COI, trust relationships and data interchange are readily supported, enabling data sharing without a need to cross network boundaries and providing benefits of scale without the challenges and compromises intrinsic to homogeneous cross sector networks. Data is made available without a need to transport it between organisations and control is retained by the data originator. In early 2007 a group of UK Government department CTOs in conjunction with the Office for Government Commerce Buying Solutions (OGC BS) established the vision for a single commonly provided, procured and managed public sector voice and data network infrastructure to replace the multitude of separately procured and managed networks serving various segments of the UK public sector; Education, Health, Central Government, Local Government etc. In 2008 an Industry Working Group was established to document the objectives and requirements more clearly. Their report set out the architectural and commercial principles as well as anticipated security, service management, governance and transition arrangements. == Architecture == The PSN comprises a core network, the Government Conveyancing Network or GCN provided by GCN Service Providers or GCNSPs. The GCN interconnects multiple operator networks, termed Direct Network Service Providers or DNSPs. Subscriber organisations contract to a connection from a local participating DNSP, connect via that to GCN and hence onwards to other interconnected networks and services. The GCN network is entirely based on IPv4 and MPLS and the GCNSPs are not currently mandated to provide IPv6, though they should have a roadmap to implementing it if and when required. == Commercial framework == In 2010 Virgin Media Business, BT, Cable & Wireless and Global Crossing signed Deeds of Undertaking (DoU) and subsequently achieved accreditation for providing GCN and IP VPN services. In March 2012, BT, Cable & Wireless, Capita Business Services, Eircom, Fujitsu, Kcom, Level 3, Logicalis, MDNX, Thales, Updata and Virgin Media Business were successful bidders for the initial two-year PSN Connectivity framework. In June 2012, 29 companies were confirmed as suppliers of ICT services to the UK public sector under the Government's PSN Services framework contract. Apart from most of the previous suppliers, additional companies also included 2e2, Airwave Solutions, Azzurri Communications, Cassidian, CSC Computer Sciences, Computacenter, Daisy Communications, Easynet Global Services, EE, Freedom Communications, Icom Holdings, NextiraOne, PageOne Communications, Phoenix IT Group, Siemens Communications, Specialist Computer Centres, Telefónica, telent Technology Services, Uniworld Communications and Vodafone. == Governance == The PSN is managed within the Cabinet Office where it is part of the Government Digital Service. == Early implementations == There were already notable initiatives in progress in county council areas, demonstrating public sector network integration in both the Hampshire HPSN2 network and in Kent's community network. Project Pathway was established as a pilot linking these two county-wide networks, with Virgin Media Business and Global Crossing the subscriber and GCN network elements. Staffordshire County Council was the first council in England to establish a PSN that included the county's NHS Health partners. Other county councils have since followed the leads of these councils. == Transition == Centrally procured public sector networks are expected to migrate across to the PSN framework as they reach the end of their contract terms, either through an interim framework or directly. The Government Secure Intranet (GSi) contracts expired in September 2011, running on to 12 February 2012 and were replaced by the transitional Government Secure Intranet Convergence Framework (GCF). The Managed Telephony Service (MTS) contract expired on 31 December 2011 and was replaced by the Managed Telephony Convergence Framework (MTCF). == Future plan == In a blog post published on 20 January 2017, Government Digital Service announced that the Technology Leaders Network (TLN) had agreed that government was starting a journey away from the PSN. This was because using the Internet was considered suitable for the vast majority of the work that the public sector does. The blog post confirmed that the 'move was not going to happen immediately' and stated that 'there's quite a bit of work to do across the public sector to prepare for the changes'. It also stated that it was too early for a full timeline to be provided, although all PSN-connected organisations would be updated as the process evolved. The blog post confirmed that organisations that need to access services that are only available on the PSN would still need to connect to it for the time being and continue to meet its assurance requirements. In a blog post published on 16 March 2017, Government Digital Service (GDS) set out its plans for PSN assurance. The blog post confirmed that the PSN compliance process wasn't 'going anywhere, certainly for a while yet'. It explained that the TLN agreed that – as one of the only recognised, externally accredited, cross-government common assurance standards – it 'needs to live on far beyond the end of the physical PSN network'. Government Digital Service, along with the National Cyber Security Centre (NCSC) and the Cyber and Government Security Directorate, are now looking at ways to expand and reframe PSN compliance in a new context that, while retaining the assurance principles that are the basis of the existing process, will aim to improve the process. A GDS blog post titled 'The road to closing down the PSN' published on 8 September 2020 describes how the public sector will migrate away from the PSN. The Cabinet Office has set up a programme called Future Networks for Government (FN4G) to help organisations move away from the PSN.

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  Microsoft Security Development Lifecycle

  The Microsoft Security Development Lifecycle (SDL) is the approach Microsoft uses to integrate security into DevOps processes (sometimes called a DevSecOps approach). You can use this SDL guidance and documentation to adapt this approach and practices to your organization. == Overview == The practices outlined in the SDL approach are applicable to all types of software development and across all platforms, ranging from traditional waterfall methodologies to modern DevOps approaches. They can generally be applied to the following: Software – whether you are developing software code for firmware, AI applications, operating systems, drivers, IoT Devices, mobile device apps, web services, plug-ins or applets, hardware microcode, low-code/no-code apps, or other software formats. Note that most practices in the SDL are applicable to secure computer hardware development as well. Platforms – whether the software is running on a ‘serverless’ platform approach, on an on-premises server, a mobile device, a cloud hosted VM, a user endpoint, as part of a Software as a Service (SaaS) application, a cloud edge device, an IoT device, or anywhere else. == Practices == The SDL recommends 10 security practices to incorporate into your development workflows. Applying the 10 security practices of SDL is an ongoing process of improvement so a key recommendation is to begin from some point and keep enhancing as you proceed. This continuous process involves changes to culture, strategy, processes, and technical controls as you embed security skills and practices into DevOps workflows. The 10 SDL practices are: Establish security standards, metrics, and governance Require use of proven security features, languages, and frameworks Perform security design review and threat modeling Define and use cryptography standards Secure the software supply chain Secure the engineering environment Perform security testing Ensure operational platform security Implement security monitoring and response Provide security training == Versions ==

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  SIPRNet

  The Secret Internet Protocol Router Network (SIPRNet) is "a system of interconnected computer networks used by the U.S. Department of Defense and the U.S. Department of State to transmit classified information (up to and including information classified SECRET) by packet switching over the 'completely secure' environment". It also provides services such as hypertext document access and electronic mail. SIPRNet is a component of the Defense Information Systems Network. Other components handle communications with other security needs, such as the NIPRNet, which is used for nonsecure communications, and the Joint Worldwide Intelligence Communications System (JWICS), which is used for Top Secret communications. == Access == According to the U.S. Department of State Web Development Handbook, domain structure and naming conventions are the same as for the open internet, except for the addition of a second-level domain, like, e.g., "sgov" between state and gov: openforum.state.sgov.gov. Files originating from SIPRNet are marked by a header tag "SIPDIS" (SIPrnet DIStribution). A corresponding second-level domain smil.mil exists for DoD users. Access is also available to a "...small pool of trusted allies, including Australia, Canada, the United Kingdom and New Zealand...". This group (including the US) is known as the Five Eyes. SIPRNet was one of the networks accessed by Chelsea Manning, convicted of leaking the video used in WikiLeaks' "Collateral Murder" release as well as the source of the US diplomatic cables published by WikiLeaks in November 2010. == Alternate names == SIPRNet and NIPRNet are referred to colloquially as SIPPERnet and NIPPERnet (or simply sipper and nipper), respectively.

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  Conversica

  Conversica is a US-based cloud software technology company, headquartered in San Mateo, California, that provides two-way AI-driven conversational software and a suite of Intelligent Virtual Assistants for businesses to engage customers via email, chat, and SMS. == History == 2007: The company was founded by Ben Brigham in Bellingham, Washington, originally as AutoFerret.com. The company's initial product was a Customer Relationship Management (CRM) targeted at automotive dealerships. This soon expanded to lead generation, and then lead validation and qualification. The AI Conversica uses currently was made to follow up on and filter out low-quality leads. The focus of the company shifted toward this automated lead engagement technology. 2010: The company started commercially selling AVA, the first Automated Virtual Assistant for sales, and the company name was changed to AVA.ai. Early customers for AVA were automotive dealerships. As the company moved away from generating leads themselves, and providing the CRM themselves, it became necessary to integrate with existing CRM and Marketing Automation platforms, such as DealerSocket, VinSolutions and Salesforce. 2013: The company raised $16m Series A funding, led by Kennet Partners, and named Mark Bradley as CEO. It also moved its headquarters from Bellingham, Washington to Foster City, California. 2014: The company changed its name from AVA.ai to Conversica. 2015: Alex Terry joined Conversica as its CEO. The business expanded to include customers in additional verticals, including technology, education, and financial services. 2016: The company raised $34m Series B funding, led by Providence Strategic Growth. 2017: Conversica expanded its intelligent automation platform and IVAs to support additional communication channels (e-mail and SMS text messaging) and communication languages. Conversica also opened a new technology center in Seattle, Washington to expand its AI and machine learning capabilities. 2018: The company raised $31m Series C funding, led by Providence Strategic Growth. Conversica also acquired Intelligens.ai, providing a regional presence in Latin America with an office in Las Condes, Santiago, Chile. The company launched an AI-powered Admissions Assistant for Higher Education industry. 2019: Conversica was selected by Fast Company magazine as one of the Top 10 Most Innovative AI Companies in the World, and was named Marketo's Technology Partner of the Year. The company officially expanded into the EMEA region with the opening of a London office. As of August 2019, Conversica has over 50 different integrations with third parties. In October Conversica won three awards at the fourth annual Global Annual Achievement Awards for Artificial Intelligence. Also that month, Alex Terry stepped down from his role as CEO and was replaced by Jim Kaskade. 2020: As part of Conversica's response to COVID-19, they optimized the business to become profitable in both 2Q20 and 3Q20, before reinvesting in 4Q20. The company transitioned both international operations in EMEA and LATAM to an indirect model with partners (LeadFabric and Nectia Cloud Solutions respectively), and moved a portion of its US-based employees to near-shore centers in Mexico and Brazil, effectively downsizing the company from 250 to 200. Conversica's reseller partner, Nectia, is a major Latin American affiliate and Chile's number one Salesforce partner, and, as part of the partnership, Nectia devoted capital to a brand new company segment, Predict-IA, dedicated to web-based artificial intelligent solutions. Predict-IA was able to immediately service all LATAM opportunities and clients with Conversica's AI Assistants with end-to-end services (marketing, sales, professional services, customer success, and technical support). Conversica's reseller partner, Leadfabric, has offices in Belgium, Amsterdam, Paris, UK, Taiwan, and Romania. == Technology == Conversica's Revenue Digital Assistants™ are AI assistants who engage with leads, prospects, customers, employees, and other persons of interest (Contacts) in a two-way human-like manner, via email, SMS text, and website chat, in English, French, German, Spanish, Portuguese, and Japanese. The RDAs are built on an Intelligent Automation platform that leverages natural language understanding, natural language processing, natural language generation, deep learning and machine learning. The Assistants are generally deployed alongside sales and marketing, customer success, account management, and higher education admissions teams, as part of an augmented workforce. The Intelligent Automation platform integrates with over 50 external systems, including CRM, Marketing Automation, and other systems of record. A partial list of integration partners includes: Salesforce, Marketo, Oracle, HubSpot, DealerSocket, Reynolds & Reynolds, CDK Global, VinSolutions and many more.

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  Tumblr

  Tumblr ( TUM-blər) is a microblogging and social media platform founded by David Karp in 2007 and operated by American company Tumblr, Inc., a subsidiary of Automattic. The service allows users to post multimedia and other content to a short-form blog. It has attracted significant attention and controversy for hosting a wide range of progressive user-generated content. == History == === Beginnings (2006–2012) === Development of Tumblr began in 2006 during a two-week gap between contracts at David Karp's software consulting company, Davidville. Karp had been interested in tumblelogs (short-form blogs, hence the name Tumblr) for some time and was waiting for one of the established blogging platforms to introduce their own tumblelogging platform. As none had done so after a year of waiting, Karp and developer Marco Arment began working on their own platform. Tumblr was launched in February 2007, and within two weeks had gained 75,000 users. Arment left the company in September 2010 to work on Instapaper. In June 2012, Tumblr featured its first major brand advertising campaign in collaboration with Adidas, who launched an official soccer Tumblr blog and bought ad placements on the user dashboard. This launch came only two months after Tumblr announced it would be moving towards paid advertising on its site. === Ownership by Yahoo! (2013–2018) === On May 20, 2013, it was announced that Yahoo and Tumblr had reached an agreement for Yahoo! Inc. to acquire Tumblr for $1.1 billion in cash. Many of Tumblr's users were unhappy with the news, causing some to start a petition, achieving nearly 170,000 signatures. David Karp remained CEO and the deal was finalized on June 20, 2013. Advertising sales goals were not met and in 2016 Yahoo wrote down $712 million of Tumblr's value. Verizon Communications acquired Yahoo in June 2017, and placed Yahoo and Tumblr under its Oath subsidiary. Karp announced in November 2017 that he would be leaving Tumblr by the end of the year. Jeff D'Onofrio, Tumblr's president and COO, took over leading the company. The site, along with the rest of the Oath division (renamed Verizon Media Group in 2019), continued to struggle under Verizon. In March 2019, Similarweb estimated Tumblr had lost 30% of its user traffic since December 2018, when the site had introduced a stricter content policy with heavier restrictions on adult content (which had been a notable draw to the service). In May 2019, it was reported that Verizon was considering selling the site due to its continued struggles since the purchase (as it had done with another Yahoo property, Flickr, via its sale to SmugMug). Following this news, Pornhub's vice president publicly expressed interest in purchasing Tumblr, with a promise to reinstate the previous adult content policies. === Automattic (2019–present) === On August 12, 2019, Verizon Media announced that it would sell Tumblr to Automattic, the operator of blog service WordPress.com and corporate backer of the open source blog software of the same name. The sale was for an undisclosed amount, but Axios reported that the sale price was less than $3 million, less than 0.3% of Yahoo's original purchase price. Automattic CEO Matt Mullenweg stated that the site will operate as a complementary service to WordPress.com, and that there were no plans to reverse the content policy decisions made during Verizon ownership. In November 2022, Mullenweg stated that Tumblr will add support for the decentralized social networking protocol ActivityPub. In November 2023, most of Tumblr's product development and marketing teams were transferred to other groups within Automattic. Mullenweg stated that focus would shift to core functionality and streamlining existing features. In February 2024, Automattic announced that it would begin selling user data from Tumblr and WordPress.com to Midjourney and OpenAI. Tumblr users are opted-in by default, with an option to opt out. In August 2024, Automattic announced that it would migrate Tumblr's backend to an architecture derived from WordPress, in order to ease development and code sharing between the platforms. The company stated that this migration would not impact the service's user experience and content, and that users "won't even notice a difference from the outside". In January 2025, Mullenweg stated that the migration, once completed, would also "unlock" ActivityPub access for Tumblr, including native support for the company's official ActivityPub plugin for WordPress. In April 2025, Automattic announced layoffs for 16% of its workforce, reducing a large portion of Tumblr staff. On March 16, 2026, Tumblr implemented a change to how notes were assigned to reblogs, making it more similar to sites like Twitter and Bluesky. The change was rolled back the next day after heavy user backlash. == Features == === Blog management === Dashboard: The dashboard is the primary tool for the typical Tumblr user. It is a live feed of recent posts from blogs that they follow. Through the dashboard, users are able to comment, reblog, and like posts from other blogs that appear on their dashboard. The dashboard allows the user to upload text posts, images, videos, quotes, or links to their blog with a click of a button displayed at the top of the dashboard. Users are also able to connect their blogs to their Twitter and Facebook accounts, so that whenever they make a post, it will also be sent as a tweet and a status update. As of June 2022, users can also turn off reblogs on specific posts through the dashboard. Queue: Users are able to set up a schedule to delay posts that they make. They can spread their posts over several hours or even days. Tags: Users can help their audience find posts about certain topics by adding tags. If someone were to upload a picture to their blog and wanted their viewers to find pictures, they would add the tag #picture, and their viewers could use that word to search for posts with the tag #picture. HTML editing: Tumblr allows users to edit their blog's theme using HTML to control the appearance of their blog. Custom themes are able to be shared and used by other users, or sold. Custom domains: Tumblr allows users to use custom domains for their blogs. Users must purchase a domain from Tumblr Domains, an in-house registrar that provides domains that can only be used with Tumblr unless removed from the user's blog and transferred to another registrar. Blogs previously were able to be linked with any domain/subdomain from any registrar, however following the introduction of the Tumblr Domains service, now requires you to purchase a domain directly from Tumblr to be used with a blog. Users who kept their blogs connected to a domain after the introduction got to keep their custom domain, as long as they do not disconnect it from Tumblr or let the domain expire. === Tags === The tagging system on the website operates on a hybrid tagging system, involving both self-tagging (user write their own tags on their posts) and an auto-manual function (the website will recommend popular tags and ones that the user has used before.) Only the first 20 tags added to any post will be indexed by the site. The tags are prefaced by a hashtag and separated by commas, and spaces and special characters are allowed, but only up to 140 characters total per tag. There are two main types used by Tumblr users: descriptive tagging, and opinion or commentary tagging. Descriptive tags are usually introduced by the original poster, and describe what is in the post (e.g. #art, #sky). These are important for the original poster to use, so their post will be indexed and searchable by others wishing to view that subject of content. Tags used as a form of communication are unique to Tumblr, and are typically more personal, expressing opinions, reactions, meta-commentary, background information, and more. Instead of adding onto the reblogged post (with their comments becoming an addition to each subsequent reblog from them) a user may add their comments in the tags, not changing the content or appearance of the original post in any way. Not all users choose to use tags this way, but those who do use tags for commentary may prefer it over adding a comment on the actual post. === Mobile === With Tumblr's 2009 acquisition of Tumblerette, an iOS application created by Jeff Rock and Garrett Ross, the service launched its official iPhone app. The site became available to BlackBerry smartphones on April 17, 2010, via a Mobelux application in BlackBerry World. In June 2012, Tumblr released a new version of its iOS app, Tumblr 3.0, allowing support for Spotify integration, hi-res images and offline access. An app for Android is also available. A Windows Phone app was released on April 23, 2013. An app for Google Glass was released on May 16, 2013. === Inbox and messaging === Tumblr blogs have the option to allow users to submit questions, either as themselves or anonymously, to the blog for a response. Tumblr

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  Cleo Communications

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

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