Generalized filtering is a generic Bayesian filtering scheme for nonlinear state-space models. It is based on a variational principle of least action, formulated in generalized coordinates of motion. Note that "generalized coordinates of motion" are related to—but distinct from—generalized coordinates as used in (multibody) dynamical systems analysis. Generalized filtering furnishes posterior densities over hidden states (and parameters) generating observed data using a generalized gradient descent on variational free energy, under the Laplace assumption. Unlike classical (e.g. Kalman-Bucy or particle) filtering, generalized filtering eschews Markovian assumptions about random fluctuations. Furthermore, it operates online, assimilating data to approximate the posterior density over unknown quantities, without the need for a backward pass. Special cases include variational filtering, dynamic expectation maximization and generalized predictive coding. == Definition == Definition: Generalized filtering rests on the tuple ( Ω , U , X , S , p , q ) {\displaystyle (\Omega ,U,X,S,p,q)} : A sample space Ω {\displaystyle \Omega } from which random fluctuations ω ∈ Ω {\displaystyle \omega \in \Omega } are drawn Control states U ∈ R {\displaystyle U\in \mathbb {R} } – that act as external causes, input or forcing terms Hidden states X : X × U × Ω → R {\displaystyle X:X\times U\times \Omega \to \mathbb {R} } – that cause sensory states and depend on control states Sensor states S : X × U × Ω → R {\displaystyle S:X\times U\times \Omega \to \mathbb {R} } – a probabilistic mapping from hidden and control states Generative density p ( s ~ , x ~ , u ~ ∣ m ) {\displaystyle p({\tilde {s}},{\tilde {x}},{\tilde {u}}\mid m)} – over sensory, hidden and control states under a generative model m {\displaystyle m} Variational density q ( x ~ , u ~ ∣ μ ~ ) {\displaystyle q({\tilde {x}},{\tilde {u}}\mid {\tilde {\mu }})} – over hidden and control states with mean μ ~ ∈ R {\displaystyle {\tilde {\mu }}\in \mathbb {R} } Here ~ denotes a variable in generalized coordinates of motion: u ~ = [ u , u ′ , u ″ , … ] T {\displaystyle {\tilde {u}}=[u,u',u'',\ldots ]^{T}} === Generalized filtering === The objective is to approximate the posterior density over hidden and control states, given sensor states and a generative model – and estimate the (path integral of) model evidence p ( s ~ ( t ) | m ) {\displaystyle p({\tilde {s}}(t)\vert m)} to compare different models. This generally involves an intractable marginalization over hidden states, so model evidence (or marginal likelihood) is replaced with a variational free energy bound. Given the following definitions: μ ~ ( t ) = a r g m i n μ ~ { F ( s ~ ( t ) , μ ~ ) } {\displaystyle {\tilde {\mu }}(t)={\underset {\tilde {\mu }}{\operatorname {arg\,min} }}\{F({\tilde {s}}(t),{\tilde {\mu }})\}} G ( s ~ , x ~ , u ~ ) = − ln p ( s ~ , x ~ , u ~ | m ) {\displaystyle G({\tilde {s}},{\tilde {x}},{\tilde {u}})=-\ln p({\tilde {s}},{\tilde {x}},{\tilde {u}}\vert m)} Denote the Shannon entropy of the density q {\displaystyle q} by H [ q ] = E q [ − log ( q ) ] {\displaystyle H[q]=E_{q}[-\log(q)]} . We can then write the variational free energy in two ways: F ( s ~ , μ ~ ) = E q [ G ( s ~ , x ~ , u ~ ) ] − H [ q ( x ~ , u ~ | μ ~ ) ] = − ln p ( s ~ | m ) + D K L [ q ( x ~ , u ~ | μ ~ ) | | p ( x ~ , u ~ | s ~ , m ) ] {\displaystyle F({\tilde {s}},{\tilde {\mu }})=E_{q}[G({\tilde {s}},{\tilde {x}},{\tilde {u}})]-H[q({\tilde {x}},{\tilde {u}}\vert {\tilde {\mu }})]=-\ln p({\tilde {s}}\vert m)+D_{KL}[q({\tilde {x}},{\tilde {u}}\vert {\tilde {\mu }})\vert \vert p({\tilde {x}},{\tilde {u}}\vert {\tilde {s}},m)]} The second equality shows that minimizing variational free energy (i) minimizes the Kullback-Leibler divergence between the variational and true posterior density and (ii) renders the variational free energy (a bound approximation to) the negative log evidence (because the divergence can never be less than zero). Under the Laplace assumption q ( x ~ , u ~ ∣ μ ~ ) = N ( μ ~ , C ) {\displaystyle q({\tilde {x}},{\tilde {u}}\mid {\tilde {\mu }})={\mathcal {N}}({\tilde {\mu }},C)} the variational density is Gaussian and the precision that minimizes free energy is C − 1 = Π = ∂ μ ~ μ ~ G ( μ ~ ) {\displaystyle C^{-1}=\Pi =\partial _{{\tilde {\mu }}{\tilde {\mu }}}G({\tilde {\mu }})} . This means that free-energy can be expressed in terms of the variational mean (omitting constants): F = G ( μ ~ ) + 1 2 ln | ∂ μ ~ μ ~ G ( μ ~ ) | {\displaystyle F=G({\tilde {\mu }})+\textstyle {1 \over 2}\ln \vert \partial _{{\tilde {\mu }}{\tilde {\mu }}}G({\tilde {\mu }})\vert } The variational means that minimize the (path integral) of free energy can now be recovered by solving the generalized filter: μ ~ ˙ = D μ ~ − ∂ μ ~ F ( s ~ , μ ~ ) {\displaystyle {\dot {\tilde {\mu }}}=D{\tilde {\mu }}-\partial _{\tilde {\mu }}F({\tilde {s}},{\tilde {\mu }})} where D {\displaystyle D} is a block matrix derivative operator of identify matrices such that D u ~ = [ u ′ , u ″ , … ] T {\displaystyle D{\tilde {u}}=[u',u'',\ldots ]^{T}} === Variational basis === Generalized filtering is based on the following lemma: The self-consistent solution to μ ~ ˙ = D μ ~ − ∂ μ ~ F ( s , μ ~ ) {\displaystyle {\dot {\tilde {\mu }}}=D{\tilde {\mu }}-\partial _{\tilde {\mu }}F(s,{\tilde {\mu }})} satisfies the variational principle of stationary action, where action is the path integral of variational free energy S = ∫ d t F ( s ~ ( t ) , μ ~ ( t ) ) {\displaystyle S=\int dt\,F({\tilde {s}}(t),{\tilde {\mu }}(t))} Proof: self-consistency requires the motion of the mean to be the mean of the motion and (by the fundamental lemma of variational calculus) μ ~ ˙ = D μ ~ ⇔ ∂ μ ~ F ( s ~ , μ ~ ) = 0 ⇔ δ μ ~ S = 0 {\displaystyle {\dot {\tilde {\mu }}}=D{\tilde {\mu }}\Leftrightarrow \partial _{\tilde {\mu }}F({\tilde {s}},{\tilde {\mu }})=0\Leftrightarrow \delta _{\tilde {\mu }}S=0} Put simply, small perturbations to the path of the mean do not change variational free energy and it has the least action of all possible (local) paths. Remarks: Heuristically, generalized filtering performs a gradient descent on variational free energy in a moving frame of reference: μ ~ ˙ − D μ ~ = − ∂ μ ~ F ( s , μ ~ ) {\displaystyle {\dot {\tilde {\mu }}}-D{\tilde {\mu }}=-\partial _{\tilde {\mu }}F(s,{\tilde {\mu }})} , where the frame itself minimizes variational free energy. For a related example in statistical physics, see Kerr and Graham who use ensemble dynamics in generalized coordinates to provide a generalized phase-space version of Langevin and associated Fokker-Planck equations. In practice, generalized filtering uses local linearization over intervals Δ t {\displaystyle \Delta t} to recover discrete updates Δ μ ~ = ( exp ( Δ t ⋅ J ) − I ) J − 1 μ ~ ˙ J = ∂ μ ~ μ ~ ˙ = D − ∂ μ ~ μ ~ F ( s ~ , μ ~ ) {\displaystyle {\begin{aligned}\Delta {\tilde {\mu }}&=(\exp(\Delta t\cdot J)-I)J^{-1}{\dot {\tilde {\mu }}}\\J&=\partial _{\tilde {\mu }}{\dot {\tilde {\mu }}}=D-\partial _{{\tilde {\mu }}{\tilde {\mu }}}F({\tilde {s}},{\tilde {\mu }})\end{aligned}}} This updates the means of hidden variables at each interval (usually the interval between observations). == Generative (state-space) models in generalized coordinates == Usually, the generative density or model is specified in terms of a nonlinear input-state-output model with continuous nonlinear functions: s = g ( x , u ) + ω s x ˙ = f ( x , u ) + ω x {\displaystyle {\begin{aligned}s&=g(x,u)+\omega _{s}\\{\dot {x}}&=f(x,u)+\omega _{x}\end{aligned}}} The corresponding generalized model (under local linearity assumptions) obtains the from the chain rule s ~ = g ~ ( x ~ , u ~ ) + ω ~ s s = g ( x , u ) + ω s s ′ = ∂ x g ⋅ x ′ + ∂ u g ⋅ u ′ + ω s ′ s ″ = ∂ x g ⋅ x ″ + ∂ u g ⋅ u ″ + ω s ″ ⋮ x ~ ˙ = f ~ ( x ~ , u ~ ) + ω ~ x x ˙ = f ( x , u ) + ω x x ˙ ′ = ∂ x f ⋅ x ′ + ∂ u f ⋅ u ′ + ω x ′ x ˙ ″ = ∂ x f ⋅ x ″ + ∂ u f ⋅ u ″ + ω x ″ ⋮ {\displaystyle {\begin{aligned}{\tilde {s}}&={\tilde {g}}({\tilde {x}},{\tilde {u}})+{\tilde {\omega }}_{s}\\\\s&=g(x,u)+\omega _{s}\\s'&=\partial _{x}g\cdot x'+\partial _{u}g\cdot u'+\omega '_{s}\\s''&=\partial _{x}g\cdot x''+\partial _{u}g\cdot u''+\omega ''_{s}\\&\vdots \\\end{aligned}}\qquad {\begin{aligned}{\dot {\tilde {x}}}&={\tilde {f}}({\tilde {x}},{\tilde {u}})+{\tilde {\omega }}_{x}\\\\{\dot {x}}&=f(x,u)+\omega _{x}\\{\dot {x}}'&=\partial _{x}f\cdot x'+\partial _{u}f\cdot u'+\omega '_{x}\\{\dot {x}}''&=\partial _{x}f\cdot x''+\partial _{u}f\cdot u''+\omega ''_{x}\\&\vdots \end{aligned}}} Gaussian assumptions about the random fluctuations ω {\displaystyle \omega } then prescribe the likelihood and empirical priors on the motion of hidden states p ( s ~ , x ~ , u ~ | m ) = p ( s ~ | x ~ , u ~ , m ) p ( D x ~ | x , u ~ , m ) p ( x | m ) p ( u ~ | m ) p ( s ~ | x ~ , u ~ , m ) = N ( g ~ ( x ~ , u ~ ) , Σ ~ ( x ~ , u ~ ) s ) p ( D x ~ | x , u ~ , m ) = N ( f ~ ( x ~ , u ~ ) , Σ ~ ( x ~ , u ~ ) x ) {\displayst
International Conference on Language Resources and Evaluation
The International Conference on Language Resources and Evaluation is an international conference organised by the ELRA Language Resources Association every other year (on even years) with the support of institutions and organisations involved in Natural language processing. The series of LREC conferences was launched in Granada in 1998. == History of conferences == The survey of the LREC conferences over the period 1998-2013 was presented during the 2014 conference in Reykjavik as a closing session. It appears that the number of papers and signatures is increasing over time. The average number of authors per paper is higher as well. The percentage of new authors is between 68% and 78%. The distribution between male (65%) and female (35%) authors is stable over time. The most frequent technical term is "annotation", then comes "part-of-speech". == The LRE Map == The LRE Map was introduced at LREC 2010 and is now a regular feature of the LREC submission process for both the conference papers and the workshop papers. At the submission stage, the authors are asked to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors are then gathered in a global matrix called the LRE Map. This feature has been extended to several other conferences.
SPKAC
SPKAC (Signed Public Key and Challenge, also known as Netscape SPKI) is a format for sending a certificate signing request (CSR): it encodes a public key, that can be manipulated using OpenSSL. It is created using the little documented HTML keygen element inside a number of Netscape compatible browsers. == Standardisation == There exists an ongoing effort to standardise SPKAC through an Internet Draft in the Internet Engineering Task Force (IETF). The purpose of this work has been to formally define what has existed prior as a de facto standard, and to address security deficiencies, particular with respect to historic insecure use of MD5 that has since been declared unsafe for use with digital signatures. == Implementations == HTML5 originally specified the
VK (service)
VK (short for its original name VKontakte; Russian: ВКонтакте, lit. 'InContact') is a Russian online social media and social networking service based in Saint Petersburg. VK is available in multiple languages but it is predominantly used by Russian speakers. VK users can message each other publicly or privately, edit messages, create groups, public pages, and events; share and tag images, audio, and video; and play browser-based games. As of August 2018, VK had at least 500 million accounts. As of November 2022, it was the sixth most popular website in Russia. The network was also popular in Ukraine until it was banned by the Verkhovna Rada in 2017. According to Semrush, in 2024, VK was the 30th most visited website in the world; as YouTube is subject to blocking in Russia, VK Video overtook Google's top position in monthly web traffic for the first time in December 2024, as part of the major substitution to domestic business. == History == VKontakte was conceived in 2006 when Pavel Durov, creator of the popular student forum spbgu.ru, met his former classmate Vyacheslav Mirilashvili in St. Petersburg after graduating from the Faculty of Philology at St Petersburg State University. Vyacheslav showed Durov the increasingly popular Facebook, after which the friends decided to create a new Russian social network. Lev Leviev, an Israeli classmate of Vyacheslav Mirilashivili, became the third co-founder. Vyacheslav Mirilashvili borrowed the money from his billionaire father and became the largest shareholder. Lev Leviev took over operational management, and Durov became CEO. Pavel Durov convinced his older brother Nikolai, a multiple winner of international math and programming competitions, to develop the site. Durov launched VKontakte for beta testing in September 2006. The following month, the domain name Vkontakte.ru was registered. The new project was incorporated on 19 January 2007 as a Russian private limited company. In February 2007 the site reached a user base of over 100,000 and was recognized as the second largest company in Russia's nascent social network market. In the same month, the site was subjected to a severe DDoS attack, which briefly put it offline. The user base reached 1 million in July 2007, and 10 million in April 2008. In December 2008 VK overtook rival Odnoklassniki as Russia's most popular social networking service. == Website == Similar to many social networks, the platform's fundamental features revolve around private messaging, sharing photos, posting status updates, and exchanging links with friends. VK also provides tools for administering online communities and managing celebrity pages. The site allows its users to upload, search and stream media content, such as videos and music. VK features an advanced search engine, that allows complex queries for finding friends, as well as a real-time news search. VK updated its features and design in April 2016. === Features === Messaging. VK Private Messages can be exchanged between groups of 2 to 500 people. An email address can also be specified as the recipient. Each message may contain up to 10 attachments: Photos, Videos, Audio Files, Maps (an embedded map with a manually placed marker), and Documents. News. VK users can post on their profile walls, each post may contain up to 10 attachments – media files, maps, and documents (see above). User mentions and hashtags are supported. In the case of multiple photo attachments, the previews are automatically scaled and arranged in a magazine-style layout. The news feed can be switched between all news (default) and most interesting modes. The site features a news-recommendation engine, global real-time search, and individual search for posts and comments on specific users' walls. Communities. VK features three types of communities. Groups are better suited for decentralized communities (discussion boards, wiki-style articles, editable by all members, etc.). Public pages is a news feed-orientated broadcasting tool for celebrities and businesses. The two types are largely interchangeable, the main difference being in the default settings. The third type of community is called Events, which are used for appropriately organizing concerts and events in an appropriate way. Like buttons. VK like buttons for posts, comments, media, and external sites operate differently from Facebook. Liked content doesn't get automatically pushed to the user's wall, but is saved in the private Favorites section instead. The user has to press a second 'share with friends' button to share an item on their wall or send it via private message to a friend. Privacy. Users can control the availability of their content within the network and on the Internet. Blanket and granular privacy settings are available for pages and individual content. Synchronization with other social networks. Any news published on the VK wall will appear on Facebook or Twitter. Certain news may not be published by clicking on the logo next to the "Send" button. Editing a post in VK does not change the post in Facebook or Twitter and vice versa. However, removing the news in VK will remove it from other social networks. SMS service. Russian users can receive and reply to a private message or leave a comment for community news using SMS. Music. Users have access to the audio files uploaded by other users. In addition, users can upload the audio files themselves, create playlists and share audios with others by attaching to messages and wall posts. The uploaded audio files cannot violate copyright laws. === Popularity === As of May 2017, according to Alexa Internet ranking, VK is one of the most visited websites in some Eurasian countries. It is: 4th most visited in Russia; 3rd most visited in Belarus; 6th most visited in Kazakhstan; 8th most visited in Kyrgyzstan and Moldova; 12th most visited in Latvia. It was the fourth most viewed site in Ukraine until, in May 2017, the Ukrainian government banned the use of VK in Ukraine. According to a study for May 2018 conducted by Factum Group Ukraine VK remained the fourth most viewed site in Ukraine, but Facebook was twice as much visited. For 2019, VK appeared as the most visited social network in Ukraine according to Alexa. According to the Internet Association of Ukraine the share of Ukrainian Internet users who visit VK daily had fallen from 54% to 10% from September 2016 to September 2019. They also claimed in November 2019 that Facebook was the most popular social network. VK was expected to gain most of the users lost by Facebook and Instagram after they were blocked in Russia in 2022, according to a Calltouch poll. == Ownership == Initially, founder and CEO Pavel Durov owned 20% of shares (although he had majority voting power through proxy votes), and a trio of Russian-Israeli investors Yitzchak Mirilashvili, his father Mikhael Mirilashvili, and Lev Leviev owned 60%, 10%, and 10% respectively. In 2007, Digital Sky Technologies, an investment company managed by Yuri Milner, acquired a total of 24.99% of the shares from shareholders, investing $16.3 million. In preparation for the IPO in September 2010, DST separated international and Russian assets: the former formed the DST Global fund, while the latter, including VKontakte and rival social network Odnoklassniki, were merged into Mail.ru Group. Mail.ru Group used part of the money to acquire 7.5% of the social network for $112.5 million at a valuation of the entire project of 1.5 billion dollars. After exercising a 7.5% option in July 2011 for $111.7 million, Mail.ru Group accumulated a 39.99% stake in VKontakte. The head of Mail.ru Group, Dmitry Grishin, voiced the company's intention to gain 100% control over VKontakte. MRG was discussing with shareholders to buy out shares from the valuation of the entire company in $2-3 billion. In the summer of 2011, Mirilashvili and Leviev were ready to accept in payment owned by Mail.ru Group shares of Facebook, Groupon, and Zynga, but the deal failed due to Durov's unwillingness to sell a stake on MRG terms. Later, the co-founders considered VKontakte's IPO as an alternative. In March 2012, Durov "accidentally" became plugged into the negotiations where Mirilashvili and Leviev discussed selling their stakes directly to Mail.ru Group's main investor, Alisher Usmanov. On the same day, Durov deleted the pages of the first co-investors, stopped contacting them, and soon announced that VKontakte would postpone its IPO indefinitely. On 29 May 2012, Mail.ru Group announced its decision to yield control of the company to Durov by offering him the voting rights on its shares. Combined with Durov's personal 12% stake, this gave him 52% of the votes. In April 2013, the Mirilashvili family sold its 40% share in VK to United Capital Partners for $1.12 billion, while Lev Leviev sold his 8% share in the same deal, giving United Capital Partners 48% ownership. In January 2014, VK's founder Pavel Durov sold his 12% stake in the company to I
Cryptographic High Value Product
Cryptographic High Value Product (CHVP) is a designation used within the information security community to identify assets that have high value, and which may be used to encrypt / decrypt secure communications, but which do not retain or store any classified information. When disconnected from the secure communication network, the CHVP equipment may be handled with a lower level of controls than required for COMSEC equipment.
24SevenOffice
24SevenOffice is a Norwegian software company headquartered in Oslo, Norway, with offices in Stockholm, Sweden and London, United Kingdom. Founded in 1997, the company specializes in web-based (SaaS) ERP and CRM systems. == Company history == 24SevenOffice was founded in 1997 in Porsgrunn, Norway, as IKT Interactive AS and marketed as kontorplassen.no. The name "24SevenOffice" was introduced for the company's London branch when the company entered the British market in 2003. The company changed its name to 24SevenOffice in February 2005. Originally based in Skien, the company later moved to Oslo Innovation Center, then to Tjuvholmen in the waterfront Fjord City of Oslo, and now the headquarters are located in Inkognitogaten 33, Solli plass, Oslo. The idea for the company's product was developed in 1996, and 24SevenOffice was an early innovator in the Scandinavian market in web-based enterprise resource planning solutions (ERP). A British office was established at Surrey Business Park in May 2003, with the company launching its web-based (SaaS) utility computing system to the UK SME market in 2004. An office in Chennai, India, was established in 2005, and 24SevenOffice entered the Swedish market when they acquired the leading competitor and ERP-provider Start & Run in a cash deal. In August 2005, the company had an initial public offering that raised NOK 15 million, and the company entered The Norwegian Over the Counter Market list as of 5 October 2005 (the ticker was 24SO), reaching a market value of NOK 175 million, with 5000 customers in Norway. In 2006, the company signed a deal to sponsor rally driver Petter Solberg, at the time the largest private sponsorship in Norwegian sport. Instead of receiving NOK 5 million in cash, Solberg received a 2.9 per cent ownership in the company. The company entered the German-speaking market in April 2006 when an office in Frankfurt am Main was opened. In late August/early September, they established an office with ten sales agents plus a general manager in Stockholm for the Swedish market. 24SevenOffice initiated strategic cooperation with Active 24 in early 2006 to develop a common platform. During the summer, Active 24 was bought by 24SevenOffice's ERP/CRM competitor Mamut (company), and 24SevenOffice terminated the contract with Active 24 in October demanding NOK 200 million in compensation for lost revenue. After a breakdown of settlement negotiations in the Forliksråd in January 2007, 24SevenOffice filed a case against Active 24 for breach of agreement in the Oslo District Court in March. 24SevenOffice lost on all counts in the District Court in December 2007. In January 2008, 24SevenOffice appealed the case to the Borgarting Court of Appeal, reducing the cause of action from NOK 250 to 30 million. 24SevenOffice lost on all counts in the Court of Appeal in December 2008, and was ordered to cover the costs incurred by Active 24 in connection with the dispute totaling NOK 6.91 million. 24SevenOffice appealed the case to the Supreme Court of Norway, but the Supreme Court Appeals Committee in March 2008 unanimously rejected the appeal from 24SevenOffice over the Borgarting Appeal Court's unanimous judgment of December 2008. On a counterclaim from Active 24 and Mamut against 24SevenOffice, the Oslo District Court in May 2010 found, that 24SevenOffice should pay Active 24 NOK 12 million in compensation for wrongfully having terminated the agreement, and a further NOK 360.000 of the opponent's legal costs. 24SevenOffice disagreed with the court ruling, and appealed once again. The Borgarting Court of Appeal in November 2011, ruled to reduce the amount of damages to NOK 4.4 million plus NOK 900.000 in penal interest. With several scrip issues, 24SevenOffice raised 25 million NOK (about $4 million at the time) between October 2005 and July 2006. They entered into a strategic partnership with Bluegarden, who for 30 years had delivered digital services for payroll, human resource planning, recruitment and training, in March 2006, and they made a large-scale agreement in April 2006, with US telecommunications software company Webex, a competitor to Norwegian Tandberg videoconferencing equipment manufacturer. In September 2006, 24SevenOffice signed an agreement with Fokus Bank to provide their customers with extended functionality in Internet banking. 24SevenOffice had by 2007 reportedly 9000 customers, joined the OpenAjax Alliance, and entered into a strategic partnership with Dun & Bradstreet in May 2007, but despite getting listed on Oslo Axess on 22 June (ticker: TFSO), reaching a market capitalization of NOK 120 million, the company was still losing money. The company ended 2007 with a revenue of NOK 21.7 million. In 2008, 24SevenOffice bought 50% of the stocks in telecommunication company Oyatel, partnered with Nets Group to facilitate invoicing for businesses, and telecommunications company Telipol chose 24SevenOffice's second-generation Internet platform for its 8,000 users. They announced an increase in revenues in Q2 to 11.1 million, up from 4.7 million in the same period the year before. 24SevenOffice had a turnover of NOK 37 million in the first half of 2009, a doubling compared to the same period the previous year, and presented its first positive EBITDA in Q2. The Norwegian Association of Auditors signed an agreement with 24SevenOffice in 2011, whereby they only recommend 24SevenOffice as a system for their members to use. On 27 June 2013, the shareholders of 24SevenOffice took off from the stock exchange and privatized the company. In recent years, the company has invested heavily in finance and accounting – and got leading auditing companies such as PwC and KPMG on the customer list. == Products == 24SevenOffice is a web-based (SaaS) ERP system. It includes modules for CRM, accounting, invoicing, e-mail, file/document management and project management. == Awards == 24SevenOffice won the Seal of Excellence in Multimedia Award at the 2004 CeBIT, became Norwegian Gazelle Company of the year 2004, chosen by Dagens Næringsliv and Dun & Bradstreet, won Product of the Year in the Norwegian finance magazine Kapital, and the IKT Grenland Innovation Award in 2008.
Virtual influencer
A virtual influencer, sometimes described as a virtual persona or virtual model, is a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of online human "influencers". Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations. Common derivatives of virtual influencers include VTubers, which broadly refer to online entertainers and YouTubers who represent themselves using virtual avatars instead of their physical selves. == History == Virtual influencers are fundamentally synonymous with virtual idols, which originate from Japan's anime and Japanese idol culture that dates back to the 1980s. The first virtual idol created was Lynn Minmay, a fictional singer and main character of the anime television series Super Dimension Fortress Macross (1982) and the animated film adaptation Macross: Do You Remember Love? (1984). Minmay's success led to the production of more Japanese virtual idols, such as EVE from the Japanese cyberpunk anime Megazone 23 (1985), and Sharon Apple in Macross Plus (1994). Virtual idols were not always well received – in 1995, Japanese talent agency Horipro created Kyoko Date, which was inspired by the Macross franchise and dating sim games such as Tokimeki Memorial (1994). Date failed to gain commercial success despite drawing headlines for her debut as a CGI idol, largely due to technical limitations leading to issues such as unnatural movements, an issue also known as the uncanny valley. Since their inception, many virtual idols created have achieved continual success, with notable names including the Vocaloid singer Hatsune Miku, and the VTuber Kizuna AI. Technological advancements have also enabled production teams to use artificial intelligence and advanced techniques to customize the personalities and behavior of virtual idols. Due to modern-day advancements in technology, many virtual idols have held real-life tours and events. Notable ones include Hatsune Miku's titular tour Miku Expo and Hololive's concerts with many of their idols from their English, Japanese and Indonesian branches. Some notable events including virtual singers and influencers have included: Hatsune Miku opening for Lady Gaga in 2014 and Hoshimachi Suisei's concerts at the famous Budokan venue in Japan and her addition to the Forbes Japan list of '30 Under 30' individuals who are changing the world in their respective fields. == Benefits and criticism == From a branding perspective, virtual influencers are perceived to be much less likely to be mired in scandals. In China, celebrities caught in bad publicity such as singer Wang Leehom and entertainer Kris Wu have heightened the appeal of virtual influencers, since their existence relies entirely on computer-generated imagery and they are therefore unlikely to cause any damage to a brand's image by association. Some studies have also suggested that Generation Z consumers have a unique appetite for virtual idols and influencers, since they grew up in the age of the internet. Studies also show that human-like appearance of virtual influencers show higher message credibility than anime-like virtual influencers. Scholars and commentators have also questioned the ethics and cultural impact of virtual influencers, arguing that computer-generated personas can entrench unrealistic beauty standards while diffusing accountability for labor, identity, and consent. Business and marketing analysts have also warned that disclosure and governance remain inconsistent, recommending clearer guardrails and transparency when brands deploy synthetic spokespeople. In 2025, reporting highlighted concerns that AI-driven "virtual humans" could displace human creators and sales workers, intensifying debates over the future of creative labor and authenticity online. == Notable examples == === Virtual bands === Eternity - A South Korean virtual idol group formed by Pulse9. Gorillaz - A virtual band formed in 1998. K/DA - A virtual K-pop girl group created as part of the League of Legends video game franchise. MAVE: - A South Korean virtual girl group formed in 2023 by Metaverse Entertainment. Pentakill - A virtual heavy metal band created as part of the League of Legends video game franchise. Plave (band) - A South Korean virtual boy band formed by VLast. Squid Sisters and Off the Hook - Two virtual pop idol duos as part of the Splatoon series. Studio Killers - A Finnish-Danish-British virtual band formed in 2011. === Vocaloids === Hatsune Miku (modeled after Saki Fujita) Kagamine Rin/Len (modeled after Asami Shimoda) Megurine Luka (modeled after Yū Asakawa) Meiko (modeled after Meiko Haigō) Kaito (modeled after Naoto Fūga) === VTubers === Kano Kizuna AI Neuro-sama VShojo Ironmouse Projekt Melody Nijisanji Hololive Akai Haato Gawr Gura Hoshimachi Suisei Natsuiro Matsuri === Other examples === Ami Yamato Crazy Frog FN Meka IA Kuki AI Kyoko Date Kyra Miquela Naevis Shudu Gram