AI Chatbot Addiction Reddit

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

  • Semantic decomposition (natural language processing)

    Semantic decomposition (natural language processing)

    A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts. The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to artificial intelligence or machine learning. Semantic decomposition is common in natural language processing applications. The basic idea of a semantic decomposition is taken from the learning skills of adult humans, where words are explained using other words. It is based on Meaning-text theory. Meaning-text theory is used as a theoretical linguistic framework to describe the meaning of concepts with other concepts. == Background == Given that an AI does not inherently have language, it is unable to think about the meanings behind the words of a language. An artificial notion of meaning needs to be created for a strong AI to emerge. Creating an artificial representation of meaning requires the analysis of what meaning is. Many terms are associated with meaning, including semantics, pragmatics, knowledge and understanding or word sense. Each term describes a particular aspect of meaning, and contributes to a multitude of theories explaining what meaning is. These theories need to be analyzed further to develop an artificial notion of meaning best fit for our current state of knowledge. == Graph representations == Representing meaning as a graph is one of the two ways that both an AI cognition and a linguistic researcher think about meaning (connectionist view). Logicians utilize a formal representation of meaning to build upon the idea of symbolic representation, whereas description logics describe languages and the meaning of symbols. This contention between 'neat' and 'scruffy' techniques has been discussed since the 1970s. Research has so far identified semantic measures and with that word-sense disambiguation (WSD) - the differentiation of meaning of words - as the main problem of language understanding. As an AI-complete environment, WSD is a core problem of natural language understanding. AI approaches that use knowledge-given reasoning creates a notion of meaning combining the state of the art knowledge of natural meaning with the symbolic and connectionist formalization of meaning for AI. The abstract approach is shown in Figure. First, a connectionist knowledge representation is created as a semantic network consisting of concepts and their relations to serve as the basis for the representation of meaning. This graph is built out of different knowledge sources like WordNet, Wiktionary, and BabelNET. The graph is created by lexical decomposition that recursively breaks each concept semantically down into a set of semantic primes. The primes are taken from the theory of Natural Semantic Metalanguage, which has been analyzed for usefulness in formal languages. Upon this graph marker passing is used to create the dynamic part of meaning representing thoughts. The marker passing algorithm, where symbolic information is passed along relations form one concept to another, uses node and edge interpretation to guide its markers. The node and edge interpretation model is the symbolic influence of certain concepts. Future work uses the created representation of meaning to build heuristics and evaluate them through capability matching and agent planning, chatbots or other applications of natural language understanding.

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  • Social media use in African politics

    Social media use in African politics

    Since the Egyptian Revolution in 2011 and the Tunisian Revolution, social media, especially Facebook, Twitter, and YouTube, began to gain traction as a political tool in Africa. Various political actors have used social media to pursue a wide range of political objectives. State actors can use social media to encourage political discourse, campaign, or implement censorship and surveillance. Non-state actors, such as civil society organizations and opposition movements, can use social media to address political concerns and to organize widespread uprisings, such as the 2014 Burkinabé uprising. Meanwhile, extremist organizations can use social media to further their propaganda and recruitment. However, social media has been criticized for its limited accessibility and for facilitating the spread of misinformation, causing some skepticism about its effectiveness. Due to low entry barriers and user-generated content, social media provides a platform where people from different social classes can engage and interact with one another. Under traditional media, the public had limited opportunities to voice their political opinions. Social media enables people to both create and consume content. The public has become increasingly comfortable and confident in expressing political opinions online, often away from government scrutiny. Scholars argue that social media use has democratizing effects in African countries. == State actors == === Promoting political discourse === Through social media, the government and its citizens can discuss policy ideas, policy implementation, and political actions. Regardless of geographical location and distance, people are able to voice their opinions to the government. Social media includes citizens who were previously not able to express their discontent or share their ideas to the government. As state actors keep the public informed, social media can increase civic engagement. With more civic engagement, policies can be discussed without politicization. Before the commonplace use of social media, African countries faced weak feedback mechanisms that effectively excluded the average African citizen from policy discourse. In South Africa, the government uses social media to connect with constituencies. The South African president runs an official Twitter, Facebook, YouTube, and Flickr accounts to engage with the public. === Campaigning === Political parties also use social media for political campaigns during election periods. In South Africa, the ANC (African National Congress) and DA (Democratic Alliance) use social media for political purposes. These parties specifically use Facebook as a tool for campaigning and engaging with the public to improve their relationship with citizens. Nigerian President Goodluck Jonathan employed social media to campaign for the presidential election in 2011, which he won. When President Goodluck Jonathan announced his bid for the presidency on social media in 2010, it reached about 217,000 people. As his campaign progressed, President Goodluck Jonathan was able to increase his followers to half a million by early 2011. === Censorship & Surveillance === While state actors can use social media to encourage their party or discourse, social media can be used to censor and surveil citizens. For example, the ANC and DA use Facebook to monitor South Africans. The government is able to track down people who have spoken against the government and translate this information into physical action to stop any possibility of a revolution. Social media platforms can be shut down to manipulate the flow of information. In Chad, citizens cannot access information through online platforms. This censorship blocked "Facebook, Twitter, WhatsApp and Viber". In the Democratic Republic of Congo, the government shut down the internet before contested elections. In Zimbabwe, the government shut down the internet to hide civilian protests against fuel price increases. == Non-state actors == === Civil society organizations (CSOs) === Civil society organizations have also used social media networks in an effort to recruit supporters and communicate with the public. CSOs can use social media to mobilize people to support their cause, such as the Ghanaian Committee for Joint Action (CJA). In 2005 and 2006, the CJA gathered support to protest against the 50% fuel price increase. CSOs can play the role of a counterforce against state actors and state propaganda during times of crises, such as protests and military clashes. In some cases, CSOs release their own videos and photos on social media which challenges traditional forms of media. CSOs have also served to monitor elections to reduce corruption and violence during election day. For instance, the Zambian Bantu Watch started the #bantuwatch social media campaign to monitor the 2011 presidential election. Zambians used Facebook and Twitter to report polling station results to mitigate election fraud and election violence. In South Africa, CSOs created 'amandla.mobi' to campaign for public policies by creating petitions. Through 'amandla.mobi', CSOs are able to circulate petitions on social media to collect signatures. South African CSOs reported how social media helped their organizations to gain support and share ideas. However, CSOs struggle to attract media attention and often have to pay for media coverage. === Opposition forces against the government === Social media is also used by the public or opposition forces against the government. Through horizontal social media, organizing can lead to street protests and revolutions, some of which are successful. For instance, during the Egyptian revolution of 2011, "The Day of the Revolution Against Torture, Poverty, Corruption, and Unemployment" and "We Are All Khaled Said" gathered support against President Hosni Mubarak. In particular, "We Are All Khaled Said" had Egyptian citizens gather around the death of Khaled Said who was brutally tortured and killed by the Egyptian government because Said wanted to uncover government corruption. As unrest erupted into public demonstrations, President Hosni Mubarak was forced to resign. Witnessing the success of social media during the Egyptian revolution, the Tunisian Revolution, or the Jasmine Revolution, mobilized through Facebook and Twitter. Likewise, in South Africa, Malawi, and Mozambique, these countries have used social media as "new protest drums." Due to social media's low entry barrier, opposition forces against the government can facilitate political discourse that can lead to accountability. Whistleblowers and opposition forces are able to expose corruption through social media, where they face less repression while reaching a larger audience. For example, the youth of Zimbabwe and South Africa use Facebook to discuss politics without judgment. Specifically, in Zimbabwe, political youth used Facebook to avoid state surveillance. Social media is used as a supplemental tool for activism. In 2015, South African student activists started the hashtag #RhodesMustFall to push the issue of colonialism and racism at the forefront of the public. === Extremist organizations === Social media is easily accessible and created by user-based content. Therefore, marginalized groups are able to use social media to spread extremist ideas. For instance, Boko Haram created the Media Office of West Africa Province and perpetuated propaganda through Twitter and YouTube. Boko Haram's online propaganda campaign targets and persuades young dissuaded Nigerians to join their cause. It is important to note that social media has also been used against Boko Haram. In April 2014, Boko Haram kidnapped 276 schoolgirls and an international campaign fought for their return through #BringBackOurGirls. Another extremist group, Al-Shabaab, has created an online presence through Twitter and YouTube. Through these social media networks, Al-Shabaab recruits new members to their extremist group through their propaganda which emphasizes the group's successes. Albeit their efforts, Al-Shabaab has not been very successful in coordinating their members but they are successful in financing their group. Furthermore, the Islamic State of Iraq and the Levant (ISIL) use social media to target and recruit individuals to their cause. ISIL's social media usage is more diverse compared to Boko Haram and Al-Shabaab; ISIL uses "Facebook, Twitter, YouTube, WhatsApp, Telegram, JustPaste.it, Kik and Ask.fm." Since ISIL's Twitter accounts kept getting shut down, ISIL uses Telegram and WhatsApp chat rooms to privately conduct meetings. Due to the spread of extremist ideology, Zhuravskaya et al. acknowledge social media's potential to be misused. == Challenges == Although social media can be used as a political tool, it faces challenges in Africa. Due to low literacy rates in Africa, social media networks exclude many of the population members. In addition, lack of access to electricity and the internet can fur

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  • G.9972

    G.9972

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

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  • Air Force Network

    Air Force Network

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

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  • Exploration–exploitation dilemma

    Exploration–exploitation dilemma

    The exploration–exploitation dilemma, also known as the explore–exploit tradeoff, is a fundamental concept in decision-making that arises in many domains. It is depicted as the balancing act between two opposing strategies. Exploitation involves choosing the best option based on current knowledge of the system (which may be incomplete or misleading), while exploration involves trying out new options that may lead to better outcomes in the future at the expense of an exploitation opportunity. Finding the optimal balance between these two strategies is a crucial challenge in many decision-making problems whose goal is to maximize long-term benefits. == Application in machine learning == In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or delayed. The agent must decide whether to exploit the current best-known policy or explore new policies to improve its performance. === Multi-armed bandit methods === The multi-armed bandit (MAB) problem was a classic example of the tradeoff, and many methods were developed for it, such as epsilon-greedy, Thompson sampling, and the upper confidence bound (UCB). See the page on MAB for details. In more complex RL situations than the MAB problem, the agent can treat each choice as a MAB, where the payoff is the expected future reward. For example, if the agent performs an epsilon-greedy method, then the agent will often "pull the best lever" by picking the action that had the best predicted expected reward (exploit). However, it would pick a random action with probability epsilon (explore). Monte Carlo tree search, for example, uses a variant of the UCB method. === Exploration problems === There are some problems that make exploration difficult. Sparse reward. If rewards occur only once a long while, then the agent might not persist in exploring. Furthermore, if the space of actions is large, then the sparse reward would mean the agent would not be guided by the reward to find a good direction for deeper exploration. A standard example is Montezuma's Revenge. Deceptive reward. If some early actions give immediate small reward, but other actions give later large reward, then the agent might be lured away from exploring the other actions. Noisy TV problem. If certain observations are irreducibly noisy (such as a television showing random images), then the agent might be trapped exploring those observations (watching the television). === Exploration reward === This section based on. The exploration reward (also called exploration bonus) methods convert the exploration-exploitation dilemma into a balance of exploitations. That is, instead of trying to get the agent to balance exploration and exploitation, exploration is simply treated as another form of exploitation, and the agent simply attempts to maximize the sum of rewards from exploration and exploitation. The exploration reward can be treated as a form of intrinsic reward. We write these as r t i , r t e {\displaystyle r_{t}^{i},r_{t}^{e}} , meaning the intrinsic and extrinsic rewards at time step t {\displaystyle t} . However, exploration reward is different from exploitation in two regards: The reward of exploitation is not freely chosen, but given by the environment, but the reward of exploration may be picked freely. Indeed, there are many different ways to design r t i {\displaystyle r_{t}^{i}} described below. The reward of exploitation is usually stationary (i.e. the same action in the same state gives the same reward), but the reward of exploration is non-stationary (i.e. the same action in the same state should give less and less reward). Count-based exploration uses N n ( s ) {\displaystyle N_{n}(s)} , the number of visits to a state s {\displaystyle s} during the time-steps 1 : n {\displaystyle 1:n} , to calculate the exploration reward. This is only possible in small and discrete state space. Density-based exploration extends count-based exploration by using a density model ρ n ( s ) {\displaystyle \rho _{n}(s)} . The idea is that, if a state has been visited, then nearby states are also partly-visited. In maximum entropy exploration, the entropy of the agent's policy π {\displaystyle \pi } is included as a term in the intrinsic reward. That is, r t i = − ∑ a π ( a | s t ) ln ⁡ π ( a | s t ) + ⋯ {\displaystyle r_{t}^{i}=-\sum _{a}\pi (a|s_{t})\ln \pi (a|s_{t})+\cdots } . === Prediction-based === This section based on. The forward dynamics model is a function for predicting the next state based on the current state and the current action: f : ( s t , a t ) ↦ s t + 1 {\displaystyle f:(s_{t},a_{t})\mapsto s_{t+1}} . The forward dynamics model is trained as the agent plays. The model becomes better at predicting state transition for state-action pairs that had been done many times. A forward dynamics model can define an exploration reward by r t i = ‖ f ( s t , a t ) − s t + 1 ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-s_{t+1}\|_{2}^{2}} . That is, the reward is the squared-error of the prediction compared to reality. This rewards the agent to perform state-action pairs that had not been done many times. This is however susceptible to the noisy TV problem. Dynamics model can be run in latent space. That is, r t i = ‖ f ( s t , a t ) − ϕ ( s t + 1 ) ‖ 2 2 {\displaystyle r_{t}^{i}=\|f(s_{t},a_{t})-\phi (s_{t+1})\|_{2}^{2}} for some featurizer ϕ {\displaystyle \phi } . The featurizer can be the identity function (i.e. ϕ ( x ) = x {\displaystyle \phi (x)=x} ), randomly generated, the encoder-half of a variational autoencoder, etc. A good featurizer improves forward dynamics exploration. The Intrinsic Curiosity Module (ICM) method trains simultaneously a forward dynamics model and a featurizer. The featurizer is trained by an inverse dynamics model, which is a function for predicting the current action based on the features of the current and the next state: g : ( ϕ ( s t ) , ϕ ( s t + 1 ) ) ↦ a t {\displaystyle g:(\phi (s_{t}),\phi (s_{t+1}))\mapsto a_{t}} . By optimizing the inverse dynamics, both the inverse dynamics model and the featurizer are improved. Then, the improved featurizer improves the forward dynamics model, which improves the exploration of the agent. Random Network Distillation (RND) method attempts to solve this problem by teacher–student distillation. Instead of a forward dynamics model, it has two models f , f ′ {\displaystyle f,f'} . The f ′ {\displaystyle f'} teacher model is fixed, and the f {\displaystyle f} student model is trained to minimize ‖ f ( s ) − f ′ ( s ) ‖ 2 2 {\displaystyle \|f(s)-f'(s)\|_{2}^{2}} on states s {\displaystyle s} . As a state is visited more and more, the student network becomes better at predicting the teacher. Meanwhile, the prediction error is also an exploration reward for the agent, and so the agent learns to perform actions that result in higher prediction error. Thus, we have a student network attempting to minimize the prediction error, while the agent attempting to maximize it, resulting in exploration. The states are normalized by subtracting a running average and dividing a running variance, which is necessary since the teacher model is frozen. The rewards are normalized by dividing with a running variance. Exploration by disagreement trains an ensemble of forward dynamics models, each on a random subset of all ( s t , a t , s t + 1 ) {\displaystyle (s_{t},a_{t},s_{t+1})} tuples. The exploration reward is the variance of the models' predictions. === Noise === For neural network–based agents, the NoisyNet method changes some of its neural network modules by noisy versions. That is, some network parameters are random variables from a probability distribution. The parameters of the distribution are themselves learnable. For example, in a linear layer y = W x + b {\displaystyle y=Wx+b} , both W , b {\displaystyle W,b} are sampled from Gaussian distributions N ( μ W , Σ W ) , N ( μ b , Σ b ) {\displaystyle {\mathcal {N}}(\mu _{W},\Sigma _{W}),{\mathcal {N}}(\mu _{b},\Sigma _{b})} at every step, and the parameters μ W , Σ W , μ b , Σ b {\displaystyle \mu _{W},\Sigma _{W},\mu _{b},\Sigma _{b}} are learned via the reparameterization trick.

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  • Star Awards for Social Media Award

    Star Awards for Social Media Award

    The Star Awards for Social Media Award was an award presented annually from 2014 to 2016 at the Star Awards, where Mediacorp of Singapore recognises entertainers under their employment with awards for artistic and technical merit for outstanding performances of the year. == History == The category was introduced in 2014, at the 20th Star Awards ceremony; Jeanette Aw received the award and it is given in honour of a Mediacorp artiste with the most social media engagement. The results are based on the calculations from three international social media analysis systems; artistes must be active on at least one of the following platforms in order to qualify: Facebook, Twitter and Instagram. Since its inception, the award has been given to two artistes. Carrie Wong is the most recent and final winner in this category. Since the ceremony held in 2016, Aw remains as the only artiste to win in this category twice, surpassing Wong who has one win. The award was discontinued from 2017 onwards as the popularity element of the award is already represented in the Top 10 Most Popular Male Artistes and Top 10 Most Popular Female Artistes awards. == Recipients ==

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  • Influencer

    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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  • Key (cryptography)

    Key (cryptography)

    A key in cryptography is a piece of information, usually a string of numbers or letters that are stored in a file, which, when processed through a cryptographic algorithm, can encode or decode cryptographic data. Based on the used method, the key can be different sizes and varieties, but in all cases, the strength of the encryption relies on the security of the key being maintained. A key's security strength is dependent on its algorithm, the size of the key, the generation of the key, and the process of key exchange. == Scope == The key is what is used to encrypt data from plaintext to ciphertext. There are different methods for utilizing keys and encryption. === Symmetric cryptography === Symmetric cryptography refers to the practice of the same key being used for both encryption and decryption. === Asymmetric cryptography === Asymmetric cryptography has separate keys for encrypting and decrypting. These keys are known as the public and private keys, respectively. == Purpose == Since the key protects the confidentiality and integrity of the system, it is important to be kept secret from unauthorized parties. With public key cryptography, only the private key must be kept secret, but with symmetric cryptography, it is important to maintain the confidentiality of the key. Kerckhoff's principle states that the entire security of the cryptographic system relies on the secrecy of the key. == Key sizes == Key size is the number of bits in the key defined by the algorithm. This size defines the upper bound of the cryptographic algorithm's security. The larger the key size, the longer it will take before the key is compromised by a brute force attack. Since perfect secrecy is not feasible for key algorithms, researches are now more focused on computational security. In the past, keys were required to be a minimum of 40 bits in length, however, as technology advanced, these keys were being broken quicker and quicker. As a response, restrictions on symmetric keys were enhanced to be greater in size. Currently, 2048 bit RSA is commonly used, which is sufficient for current systems. However, current RSA key sizes would all be cracked quickly with a powerful quantum computer. "The keys used in public key cryptography have some mathematical structure. For example, public keys used in the RSA system are the product of two prime numbers. Thus public key systems require longer key lengths than symmetric systems for an equivalent level of security. 3072 bits is the suggested key length for systems based on factoring and integer discrete logarithms which aim to have security equivalent to a 128 bit symmetric cipher." == Key generation == To prevent a key from being guessed, keys need to be generated randomly and contain sufficient entropy. The problem of how to safely generate random keys is difficult and has been addressed in many ways by various cryptographic systems. A key can directly be generated by using the output of a Random Bit Generator (RBG), a system that generates a sequence of unpredictable and unbiased bits. A RBG can be used to directly produce either a symmetric key or the random output for an asymmetric key pair generation. Alternatively, a key can also be indirectly created during a key-agreement transaction, from another key or from a password. Some operating systems include tools for "collecting" entropy from the timing of unpredictable operations such as disk drive head movements. For the production of small amounts of keying material, ordinary dice provide a good source of high-quality randomness. == Establishment scheme == The security of a key is dependent on how a key is exchanged between parties. Establishing a secured communication channel is necessary so that outsiders cannot obtain the key. A key establishment scheme (or key exchange) is used to transfer an encryption key among entities. Key agreement and key transport are the two types of a key exchange scheme that are used to be remotely exchanged between entities . In a key agreement scheme, a secret key, which is used between the sender and the receiver to encrypt and decrypt information, is set up to be sent indirectly. All parties exchange information (the shared secret) that permits each party to derive the secret key material. In a key transport scheme, encrypted keying material that is chosen by the sender is transported to the receiver. Either symmetric key or asymmetric key techniques can be used in both schemes. The Diffie–Hellman key exchange and Rivest-Shamir-Adleman (RSA) are the most two widely used key exchange algorithms. In 1976, Whitfield Diffie and Martin Hellman constructed the Diffie–Hellman algorithm, which was the first public key algorithm. The Diffie–Hellman key exchange protocol allows key exchange over an insecure channel by electronically generating a shared key between two parties. On the other hand, RSA is a form of the asymmetric key system which consists of three steps: key generation, encryption, and decryption. Key confirmation delivers an assurance between the key confirmation recipient and provider that the shared keying materials are correct and established. The National Institute of Standards and Technology recommends key confirmation to be integrated into a key establishment scheme to validate its implementations. == Management == Key management concerns the generation, establishment, storage, usage and replacement of cryptographic keys. A key management system (KMS) typically includes three steps of establishing, storing and using keys. The base of security for the generation, storage, distribution, use and destruction of keys depends on successful key management protocols. == Key vs password == A password is a memorized series of characters including letters, digits, and other special symbols that are used to verify identity. It is often produced by a human user or a password management software to protect personal and sensitive information or generate cryptographic keys. Passwords are often created to be memorized by users and may contain non-random information such as dictionary words. On the other hand, a key can help strengthen password protection by implementing a cryptographic algorithm which is difficult to guess or replace the password altogether. A key is generated based on random or pseudo-random data and can often be unreadable to humans. A password is less safe than a cryptographic key due to its low entropy, randomness, and human-readable properties. However, the password may be the only secret data that is accessible to the cryptographic algorithm for information security in some applications such as securing information in storage devices. Thus, a deterministic algorithm called a key derivation function (KDF) uses a password to generate the secure cryptographic keying material to compensate for the password's weakness. Various methods such as adding a salt or key stretching may be used in the generation.

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  • Autonomic networking

    Autonomic networking

    Autonomic networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of today. == Increasing size and complexity == The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future. What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnected. Pervasive Computing not only adds features, but also burdens existing networking infrastructure with more and more tasks that sooner or later will not be manageable by human intervention alone. Another important aspect is the price of manually controlling huge numbers of vitally important devices of current network infrastructures. == Autonomic nervous system == The autonomic nervous system (ANS) is the part of complex biological nervous systems that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. == Components of autonomic networking == As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions including respiration, blood pressure and circulation, and emotive response. The autonomic nervous system is the interconnecting fabric that supports feedback loops between internal states and various sources by which internal and external conditions are monitored. === Autognostics === Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and external states and conditions – for example, this might be viewed as the eyes, visual cortex and perceptual organs of the system. Autognostics, or literally "self-knowledge", provides the autonomic system with a basis for response and validation. A rich autognostic capability may include many different "perceptual senses". For example, the human body gathers information via the usual five senses, the so-called sixth sense of proprioception (sense of body position and orientation), and through emotive states that represent the gross wellness of the body. As conditions and states change, they are detected by the sensory monitors and provide the basis for adaptation of related systems. Implicit in such a system are imbedded models of both internal and external environments such that relative value can be assigned to any perceived state - perceived physical threat (e.g. a snake) can result in rapid shallow breathing related to fight-flight response, a phylogenetically effective model of interaction with recognizable threats. In the case of autonomic networking, the state of the network may be defined by inputs from: individual network elements such as switches and network interfaces including specification and configuration historical records and current state traffic flows end-hosts application performance data logical diagrams and design specifications Most of these sources represent relatively raw and unprocessed views that have limited relevance. Post-processing and various forms of analysis must be applied to generate meaningful measurements and assessments against which current state can be derived. The autognostic system interoperates with: configuration management - to control network elements and interfaces policy management - to define performance objectives and constraints autodefense - to identify attacks and accommodate the impact of defensive responses === Configuration management === Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various circumstances. In the biological metaphor, these are the hands and, to some degree, the memory of the autonomic system. On a network, remediation and provisioning are applied via configuration setting of specific devices. Implementation affecting access and selective performance with respect to role and relationship are also applied. Almost all the "actions" that are currently taken by human engineers fall under this area. With only a few exceptions, interfaces are set by hand, or by extension of the hand, through automated scripts. Implicit in the configuration process is the maintenance of a dynamic population of devices under management, a historical record of changes and the directives which invoked change. Typical to many accounting functions, configuration management should be capable of operating on devices and then rolling back changes to recover previous configurations. Where change may lead to unrecoverable states, the sub-system should be able to qualify the consequences of changes prior to issuing them. As directives for change must originate from other sub-systems, the shared language for such directives must be abstracted from the details of the devices involved. The configuration management sub-system must be able to translate unambiguously between directives and hard actions or to be able to signal the need for further detail on a directive. An inferential capacity may be appropriate to support sufficient flexibility (i.e. configuration never takes place because there is no unique one-to-one mapping between directive and configuration settings). Where standards are not sufficient, a learning capacity may also be required to acquire new knowledge of devices and their configuration. Configuration management interoperates with all of the other sub-systems including: autognostics - receives direction for and validation of changes policy management - implements policy models through mapping to underlying resources security - applies access and authorization constraints for particular policy targets autodefense - receives direction for changes === Policy management === Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy-based management is required for: constraining different kinds of behavior including security, privacy, resource access, and collaboration configuration management describing business processes and defining performance defining role and relationship, and establishing trust and reputation It provides the models of environment and behavior that represent effective interaction according to specific goals. In the human nervous system metaphor, these models are implicit in the evolutionary "design" of biological entities and specific to the goals of survival and procreation. Definition of what constitutes a policy is necessary to consider what is involved in managing it. A relatively flexible and abstract framework of values, relationships, roles, interactions, resources, and other components of the network environment is required. This sub-system extends far beyond the physical network to the applications in use and the processes and end-users that employ the network to achieve specific goals. It must express the relative values of various resources, outcomes, and processes and include a basis for assessing states and conditions. Unless embodied in some system outside the autonomic network or implicit to the specific policy implementation, the framework must also accommodate the definition of process, objectives and goals. Business process definitions and descriptions are then an integral part of the policy implementation. Further, as policy management represents the ultimate basis for the operation of the autonomic system, it must be able to report on its operation with respect to the details of its implementation. The policy management sub-system interoperates (at least) indirectly with all other sub-systems but primarily interacts with: autognostics - providing the definition of performance and accepting reports on conditions configuration management - providing constraints on device configuration security - providing definitions of roles, access and permissions === Autodefense === Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it is optimally capable of balancing performance objectives with typically over-riding threat management actions. In the

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  • Cryptographic nonce

    Cryptographic nonce

    In cryptography, a nonce is an arbitrary number that can be used just once in a cryptographic communication. It is often a random or pseudo-random number issued in an authentication protocol to ensure that each communication session is unique, and therefore that old communications cannot be reused in replay attacks. Nonces can also be useful as initialization vectors and in cryptographic hash functions. == Definition == A nonce is an arbitrary number used only once in a cryptographic communication, in the spirit of a nonce word. They are often random or pseudo-random numbers. Many nonces also include a timestamp to ensure exact timeliness, though this requires clock synchronisation between organisations. The addition of a client nonce ("cnonce") helps to improve the security in some ways as implemented in digest access authentication. To ensure that a nonce is used only once, it should be time-variant (including a suitably fine-grained timestamp in its value), or generated with enough random bits to ensure an insignificantly low chance of repeating a previously generated value. Some authors define pseudo-randomness (or unpredictability) as a requirement for a nonce. Nonce is a word dating back to Middle English for something only used once or temporarily (often with the construction "for the nonce"). It descends from the construction "then anes" ("the one [purpose]"). A false etymology claiming it to stand for "number used once" or similar is incorrect. == Usage == === Authentication === Authentication protocols may use nonces to ensure that old communications cannot be reused in replay attacks. For instance, nonces are used in HTTP digest access authentication to calculate an MD5 digest of the password. The nonces are different each time the 401 authentication challenge response code is presented, thus making replay attacks virtually impossible. The scenario of ordering products over the Internet can provide an example of the usefulness of nonces in replay attacks. An attacker could take the encrypted information and—without needing to decrypt—could continue to send a particular order to the supplier, thereby ordering products over and over again under the same name and purchase information. The nonce is used to give 'originality' to a given message so that if the company receives any other orders from the same person with the same nonce, it will discard those as invalid orders. A nonce may be used to ensure security for a stream cipher. Where the same key is used for more than one message and then a different nonce is used to ensure that the keystream is different for different messages encrypted with that key; often the message number is used. Secret nonce values are used by the Lamport signature scheme as a signer-side secret which can be selectively revealed for comparison to public hashes for signature creation and verification. === Hashing === Nonces are used in proof-of-work systems to vary the input to a cryptographic hash function so as to obtain a hash for a certain input that fulfils certain arbitrary conditions. In doing so, it becomes far more difficult to create a "desirable" hash than to verify it, shifting the burden of work onto one side of a transaction or system. For example, proof of work, using hash functions, was considered as a means to combat email spam by forcing email senders to find a hash value for the email (which included a timestamp to prevent pre-computation of useful hashes for later use) that had an arbitrary number of leading zeroes, by hashing the same input with a large number of values until a "desirable" hash was obtained. Similarly, the Bitcoin blockchain hashing algorithm can be tuned to an arbitrary difficulty by changing the required minimum/maximum value of the hash so that the number of bitcoins awarded for new blocks does not increase linearly with increased network computation power as new users join. This is likewise achieved by forcing Bitcoin miners to add nonce values to the value being hashed to change the hash algorithm output. As cryptographic hash algorithms cannot easily be predicted based on their inputs, this makes the act of blockchain hashing and the possibility of being awarded bitcoins something of a lottery, where the first "miner" to find a nonce that delivers a desirable hash is awarded bitcoins.

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  • Cryptographic Service Provider

    Cryptographic Service Provider

    A cryptographic service provider (CSP) is a package that "provides a concrete implementation of certain cryptographic services." A CSP offers operations and protocols to support a variety of use cases. The cryptographic application programming interface (API) provided by the CSP provides common solutions for different platforms, for example hardware and cloud services. == Microsoft Windows == In Microsoft Windows, a Cryptographic Service Provider is a software library that implements the Microsoft CryptoAPI (CAPI). CSPs implement encoding and decoding functions, which computer application programs may use, for example, to implement strong user authentication or for secure email. CSPs are independent modules that can be used by different applications. A user program calls CryptoAPI functions and these are redirected to CSPs functions. Since CSPs are responsible for implementing cryptographic algorithms and standards, applications do not need to be concerned about security details. Furthermore, each application can define which CSP it is going to use on its calls to CryptoAPI. In fact, all cryptographic activity is implemented in CSPs. CryptoAPI only works as a bridge between the application and the CSP. CSPs are implemented basically as a special type of DLL with special restrictions on loading and use. Every CSP must be digitally signed by Microsoft and the signature is verified when Windows loads the CSP. In addition, after being loaded, Windows periodically re-scans the CSP to detect tampering, either by malicious software such as computer viruses or by the user him/herself trying to circumvent restrictions (for example on cryptographic key length) that might be built into the CSP's code. To obtain a signature, non-Microsoft CSP developers must supply paperwork to Microsoft promising to obey various legal restrictions and giving valid contact information. As of circa 2000, Microsoft did not charge any fees to supply these signatures. For development and testing purposes, a CSP developer can configure Windows to recognize the developer's own signatures instead of Microsoft's, but this is a somewhat complex and obscure operation unsuitable for nontechnical end users. The CAPI/CSP architecture had its origins in the era of restrictive US government controls on the export of cryptography. Microsoft's default or "base" CSP then included with Windows was limited to 512-bit RSA public-key cryptography and 40-bit symmetric cryptography, the maximum key lengths permitted in exportable mass market software at the time. CSPs implementing stronger cryptography were available only to U.S. residents, unless the CSPs themselves had received U.S. government export approval. The system of requiring CSPs to be signed only on presentation of completed paperwork was intended to prevent the easy spread of unauthorized CSPs implemented by anonymous or foreign developers. As such, it was presented as a concession made by Microsoft to the government, in order to get export approval for the CAPI itself. After the Bernstein v. United States court decision establishing computer source code as protected free speech and the transfer of cryptographic regulatory authority from the U.S. State Department to the more pro-export Commerce Department, the restrictions on key lengths were dropped, and the CSPs shipped with Windows now include full-strength cryptography. The main use of third-party CSPs is to interface with external cryptography hardware such as hardware security modules (HSM) or smart cards. === Smart Card CSP === These cryptographic functions can be realized by a smart card, thus the Smart Card CSP is the Microsoft way of a PKCS#11. Microsoft Windows is identifying the correct Smart Card CSP, which have to be used, analyzing the answer to reset (ATR) of the smart card, which is registered in the Windows Registry. Installing a new CSP, all ATRs of the supported smart cards are enlisted in the registry. === Use of CSP in MS Office password protection === Cryptographic service providers can be used for encryption of Word, Excel, and PowerPoint documents starting from Microsoft Office XP. A standard encryption algorithm with a 40-bit key is used by default, but enabling a CSP enhances key length and thus makes decryption process more continuous. This only applies to passwords that are required to open document because this password type is the only one that encrypts a password-protected document.

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  • Social news website

    Social news website

    A social news website is a website that features user-posted stories. Such stories are ranked based on popularity, as voted on by other users of the site or by website administrators. Users typically comment online on the news posts and these comments may also be ranked in popularity. Since their emergence with the birth of Web 2.0, social news sites have been used to link many types of information, including news, humor, support, and discussion. All such websites allow the users to submit content and each site differs in how the content is moderated. On the Slashdot and Fark websites, administrators decide which articles are selected for the front page. On Reddit and Digg, the articles that get the most votes from the community of users will make it to the front page. Many social news websites also feature an online comment system, where users discuss the issues raised in an article. Some of these sites have also applied their voting system to the comments, so that the most popular comments are displayed first. Some social news websites also have a social networking service, in that users can set up a user profile and follow other users' online activity on the website. Like many other Web 2.0 tools, social news websites use the collective intelligence of all of the users to operate. Social news websites also "impl[y] the technical, economic, legal, and human enhancement of a universally distributed intelligence that will unleash a positive dynamic of recognition and skills mobilization". Social news websites help participants to share a collective vision and awareness of how their actions are integrated with those of other individuals. Social news websites provide a new and innovative way to participate in a community that is constantly being flooded with new information. These social news websites "include opportunities for peer-to-peer learning, a changed attitude toward intellectual property, the diversification of cultural expression, the development of skills valued in the modern workplace, and a more empowered conception of citizenship". These websites can help to shape and reshape democratic opinions and perspectives. Social news sites may mitigate the gatekeeping of mainstream news sources and allow the public to decide what counts as "news", which may facilitate a more participatory culture. Social news sites may also support democratic participation by allowing users from across geographic and national boundaries to access the same information, respond to fellow users' views and beliefs, and create a virtual sphere for users to contribute within. == Websites == === Active === ==== Fark ==== Fark, which started in 1997, features news on any topic. On Fark, users can submit articles to the administrators of the site. Each day, these administrators pick out 50 articles to display on the front page. ==== Slashdot ==== Slashdot, started in 1997, was one of the first social news websites. It focuses mainly on science and technology-related news. Users can submit stories and the editors pick out the best stories each day for the front page. Users can then post comments on the stories. The influx of web traffic that resulted from Slashdot linking to external websites led to the effect being called the Slashdot effect ==== Digg ==== Digg, started in December 2004, introduced the voting system. This system allows users to "digg" or "bury" articles. "Digging" is the equivalent of voting positively, so that popular articles are displayed first. "Burying" does not lower an article's score. However, if an article is buried enough times, it will be automatically deleted from the site. Digg offers a social networking service, as members can follow other members and build personal profiles with information about their interests. ==== Reddit ==== Reddit, started in June 2005, is a social news website where users can submit articles and comments and vote on these submissions. The submissions are organized into categories called "subreddits". Unlike Digg, with Reddit, users can directly affect an article's score. An "upvote" will increase the score and a "downvote" will decrease it. Articles with the highest scores are displayed on the front page. There is also a page for "controversial" articles, that have an almost equal number of upvotes and downvotes. Free speech debates have arisen due to the shutting down of obscene or potentially illegal "subreddits" (including /r/jailbait, a collection of sexually suggestive underage pictures.) Reddit introduced a system of user-created communities called "subreddits", which are essentially categories for a specific type of news. Comments on the featured posts are shown in a hierarchical fashion also based on votes. Users have the ability to earn "karma" for their participation and time on the website. ==== Hacker News ==== Hacker News, started in February 2007, is a social news site focusing on computer science and entrepreneurship, created by Paul Graham and run by his startup incubator, Y Combinator. === Defunct === ==== Newsvine ==== Newsvine, started in March 2006, was a social news website mostly focused on politics, both international and domestic. The Newsvine home page allowed users to customize "seeds" and story feeds. Users received articles via "The Wire" from sources including The Associated Press or The Huffington Post, and from "The Vine" a stream of content from other Newsvine users. The "Top of the Vine" displayed the most voted and commented on articles of the day, week, month, or year. Additionally, Newsvine allowed members to create their own "Customizable Column", which could highlight a user's content posted, recent comments, and information about the specific Newsvine member. ==== feedalizr ==== feedalizr was a cross-platform, desktop social media aggregator built using Adobe Integrated Runtime that consolidates the updates from social media and social networking websites. Users can then use this application to update those sites from their desktop and view a consolidated stream of information. ==== Voat ==== Voat, launched in April 2014 and discontinued in December of 2020, was also a social news website and is very similar to Reddit visually and functionally. The site's userbase included a large number of alt right users, many of whom migrated to Voat after being banned on Reddit. ==== Prismatic ==== Prismatic combined machine learning, user experience design, and interaction design to create a new way to discover, consume, and share media. Prismatic software used social network aggregation and machine learning algorithms to filter the content that aligns with the interests of a specific user. Prismatic integrated with Facebook, Twitter, and Pocket to gather information about user's interests and suggest the most relevant stories to read. ==== Artifact ==== Artifact was an iOS and Android app that used machine learning to personalize news recommendations to readers, and also had social features such as liking articles, commenting, and reputation scores for users.

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  • Outline of databases

    Outline of databases

    The following is provided as an overview of and topical guide to databases: Database – organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). == What type of things are databases? == Databases can be described as all of the following: Information – sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals. Data – values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Computer data – information in a form suitable for use with a computer. Data is often distinguished from programs. A program is a sequence of instructions that detail a task for the computer to perform. In this sense, data is everything in software that is not program code. == Types of databases == Active database – Database with event driven features Animation database – Database for storing and reusing animation fragments or motion capture data Back-end database – Organized collection of data in computingPages displaying short descriptions of redirect targets Bibliographic database – database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. Centralized database – database located and maintained in one location, unlike a distributed database. Cloud database – Database running on a cloud computing platform Collection database – collection catalog of a museum or archive implemented using a computerized database, in which the institution's objects or material are catalogued. Collective Optimization Database – open repository to enable sharing of benchmarks, data sets and optimization cases from the community, provide web services and Plug-in (computing)|plugins to analyze optimization data and predict program transformations or better hardware designs for multi-objective optimizations based on statistical and machine learning techniques provided there is enough information collected in the repository from multiple users. Configuration management database – Database used to store info on hardware and software assets Cooperative database – holds information on customers and their transactions. Current database – conventional database that stores data that is valid now. Directory – repository or database of information which is optimized for reading, under the assumption that data updates are very rare compared to data reads. Commonly, a directory supports search and browsing in addition to simple lookups. Distributed database – database in which storage devices are not all attached to a common CPU. Document-oriented database – computer program designed for storing, retrieving, and managing document-oriented, or Semi-structured model|semi structured data, information. EDA database – database specialized for the purpose of electronic design automation. Endgame tablebase – computerized database that contains precalculated exhaustive analysis of a chess endgame position. Food composition database (FCDB) – provides detailed information on the nutritional composition of foods. Full-text database – database that contains the complete text of books, dissertations, journals, magazines, newspapers or other kinds of textual documents. Also called a "complete-text database". Government database – collects personal information for various reasons (mass surveillance, Schengen Information System in the European Union, social security, statistics, etc.). Graph database – uses graph structures with nodes, edges, and properties to represent and store data. Knowledge base – special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organised, shared, searched and utilised. Mobile database – can be connected to by a mobile computing device over a mobile network. Navigational database – database in which objects (or records) in it are found primarily by following references from other objects. Non-native speech database – speech database of non-native pronunciations of English. Online database – database accessible from a network, including from the Internet. Operational database – accessed by an Operational System to carry out regular operations of an organization. Parallel database – improves performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Probabilistic database – uncertain database in which the possible worlds have associated probabilities. Real-time database – processing system designed to handle workloads whose state is constantly changing (Buchmann). Relational database – collection of data items organized as a set of formally described tables from which data can be accessed easily. Spatial database – database that is optimized to store and query data that is related to objects in space, including points, lines and polygons. Temporal database – database with built-in time aspects, for example a temporal data model and a temporal version of Structured Query Language (SQL). Time series database – a time series is an associative array of numbers indexed by a datetime or a datetime range. These time series are often called profiles or curves, depending upon the market. A time series of stock prices might be called a price curve, or a time series of energy consumption might be called a load profile. Despite the disparate naming, the operations performed on them are sufficiently common as to demand special database treatment. Triplestore – purpose-built database for the storage and retrieval of triples, a triple being a data entity composed of subject-predicate-object, like "Bob is 35" or "Bob knows Fred". Very large database (VLDB) – contains an extremely high number of tuples (database rows), or occupies an extremely large physical filesystem storage space. Vulnerability database – platform aimed at collecting, maintaining, and disseminating information about discovered vulnerabilities targeting real computer systems. XLDB – Stands for "eXtremely Large Data Base". XML database – data stored in XML format, where it can be queried, exported and serialized into the desired format. == History of databases == History of databases – History of database management systems –: == Database use == Database usage requirements – Database theory – encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems. Database machine – or is a computer or special hardware that stores and retrieves data from a database. Also called a "back end processor" Database server – computer program that provides database services to other computer programs or computers, as defined by the client-server model. Database application – computer program whose primary purpose is entering and retrieving information from a computer-managed database. Database management system (DBMS) – software package with computer programs that control the creation, maintenance, and use of a database. Database connection – facility in computer science that allows client software to communicate with database server software, whether on the same machine or not. Datasource – name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The Database Source Name (DSN) does not have to be the same as the filename for the database. For example, a database file named "friends.mdb" could be set up with a DSN of "school". Then DSN "school" would then be used to refer to the database when performing a query. Data Source Name (DSN) – are data structures used to describe a connection to a data source. Sometimes known as a database source name though data sources are not limited to databases. Database administrator (DBA) – is a person responsible for the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases. Lock – Comparison of database tools – (provides tables for comparing general and technical information for a number of available database administrator tools.) Database-centric architecture – software architectures in which databases play a crucial role. Also called "data-centric architecture". Intelligent database – was put forward as a system that manages information (rather than data) in a way that appears natural to users and which goes beyond simple record keeping. Two-phase locking (2PL) – is a

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  • Philco computers

    Philco computers

    Philco was one of the pioneers of transistorized computers, also known as second-generation computers. After the company developed the surface-barrier transistor, which was much faster than previous point-contact types, it was awarded contracts for military and government computers. Commercialized derivatives of some of these designs became successful business and scientific computers. The TRANSAC (Transistor Automatic Computer) Model S-1000 was released as a scientific computer. The TRANSAC S-2000 mainframe computer system was first produced in 1958, and a family of compatible machines, with increasing performance, was released over the next several years. However, the mainframe computer market was dominated by IBM. Other companies could not deploy resources for development, customer support and marketing on the scale that IBM could afford, making competition in this segment difficult after the introduction of the IBM 360 family. Philco went bankrupt and was purchased in 1961 by Ford Motor Company, but the computer division carried on until the Philco division of Ford exited the computer business in 1963. The Ford company maintained one Philco mainframe in use until 1981. == The surface-barrier transistor == The surface-barrier transistor developed by Philco in 1953 had a much higher frequency response than the original point-contact transistors. The transistor was made of a thin crystal of germanium, which was electrolytically etched with pits on either side forming a very thin base region, on the order of 5 micrometers. Philco's process for etching was United States patent number 2,885,571. Philco surface-barrier transistors were used in TX-0, and in early models of what would become the DEC PDP product line. Although relatively fast, the small size of the devices limited their power to circuits operating at a few tens of milliwatts. == Military and government == Between 1955 and 1957, Philco built transistor computers for use in aircraft, models C-1000, C-1100, and C-1102, intended for airborne real-time applications. By 1957, the C-1102 had been used by a civilian sector customer. The BASICPAC AN/TYK 6V (first delivery in 1961), COMPAC AN/TYK 4V (not completed), and LOGICPAC systems were built for the US Army as transportable computer systems for use with their Fieldata concept of integrated information management. BASICPAC was a transistorized computer with up to 28,672 words of 38-bit core memory (including sign and parity), available in several configurations from a minimum system, to a truck-borne mobile version, to a fully expanded system. Basic clock periods was 1 microsecond (which gives a clock rate of 1 MHz), with 12 microsecond memory access and a fixed-point multiplication taking 242 microseconds. Input/output was by paper tape reader and punch, or through a teletypewriter. With additional hardware, magnetic tape storage was also available, with up to seven I/O devices. The instruction set had 31 basic operation codes and nine opcodes for I/O === CXPQ === Philco was contracted by the US Navy to build the CXPQ computer. One model was completed and installed at the David Taylor Model Basin. This design was later adapted to become the commercial TRANSAC S-2000. Only one CXPQ was built. The CXPQ is a 48-bit transistorized computer. === SOLO === In 1955, the National Security Agency through the US Navy contracted with Philco to produce a computer suitable for use as a workstation, with an architecture based on the vacuum-tube computer system called Atlas II already in use at the NSA, and similar to the commercial UNIVAC 1103. At the time, Philco was the largest producer of surface barrier transistors, which were the only type available with the speed and quantities required for a computer. The SOLO prototype was delivered in 1958, but required extensive debugging at NSA. Difficulties were encountered with core memory and power supplies. SOLO used paper tape and teleprinter machines for input and output. SOLO cost about $1 million US, and contained 8,000 transistors. While the system was extensively used for training, testing, research and development, no additional units were ordered. SOLO was removed from active service in 1963. The design of the SOLO became commercialized as Philco's TRANSAC Model S-1000. == Commercial == === S-1000 === The TRANSAC S-1000 was a scientific computer with a 36-bit word length and 4096 words of core memory. It was packaged in a container about the size of a large office desk, and used only 1.2 kilowatts, much less than vacuum-tube-based computers of similar capacity. In a 1961 survey, about 15 S-1000 computer installations had been identified. It weighed about 1,650 pounds (750 kg). === S-2000 === The TRANSAC S-2000 was a large mainframe system intended for both business and scientific work. It had a 48-bit word length and supported calculations in fixed point, floating point and binary-coded decimal formats. The original S-2000 "TRANSAC" (Transistor Automatic Computer) released in 1958 was later designated Model 210; it was used internally at Philco. Similar to the Control Data Corporation Model 1604, it was a 48-bit fully transistorized computer. Three succeeding models were released in the series, all compatible with the software of the original model. The Model 211 was introduced in 1960, using micro-alloy diffused field-effect transistors, requiring significant redesign of circuits compared to the original. The TRANSAC S-2000/Philco 210/211 weighed about 2,000 pounds (910 kg). By 1964, eighteen Model 210, eighteen Model 211 and seven Model 212 systems had been sold. After Philco was purchased by Ford Motor Company, the Model 212 was introduced in 1962 and released in 1963. It had 65,535 words of 48-bit memory. Initially made with 6-microsecond core memory, it had better performance than the IBM 7094 transistor computer. It was later upgraded in 1964 to 2-microsecond core memory, which gave the machine floating-point performance greater than the IBM 7030 Stretch computer. A Model 213 was announced in 1964 but never built. By that time competition from IBM had made the Philco computer operations no longer profitable for Ford, and the division was closed down. The Model 212 could carry out a floating-point multiplication in 22 microseconds. Each word contained two 24-bit instructions with 16 bits of address information and eight bits for the opcode. There were 225 different valid opcodes in the Model 212; invalid opcodes were detected and halted the machine. The CPU had an accumulator register of 48 bits, three general-purpose registers of 24 bits, and 32 index registers of 15 bits. Main memory size ranged from 4K words to 64K words. Only the first model had a magnetic drum memory; later editions used tape drives. The Model 212 weighed about 6,500 pounds (3.3 short tons; 2.9 t). Software for the S-2000 initially consisted of TAC (Translator-Assembler-Compiler), and ALTAC, a FORTRAN II-like language with some differences from the IBM 704 FORTRAN implementation. A COBOL compiler was also available, targeted at business applications. The Philco 2400 was the input/output system for the S-2000. Operations such as reading cards or printing were carried out through magnetic tapes, thereby offloading the S-2000 from relatively slow input/output processing. The 2400 had a 24-bit word length and could be supplied with 4K to 32K characters (1K to 8K words) of core memory, rated at 3-microsecond cycle time. The instruction set was aimed at character I/O use. The idea of base registers, implemented in Philco computers, influenced the design of IBM/360. The last Philco TRANSAC S-2000 Model 212 was taken out of service in December 1981, after 19 years of service at Ford.

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  • Undeniable signature

    Undeniable signature

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

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