Best AI Image Generators

Best AI Image Generators — hands-on reviews, top picks, pricing, pros and cons and a practical how-to guide on Aizhi.

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  Canva

  Canva Pty Ltd. is an Australian multinational proprietary software company launched in 2013 based in Sydney, Australia. The platform provides a graphic design platform to create visual content for presentations, websites, and other digital products. Its uses include templates for presentations, posters, and social media content, as well as photo and video editing functionality. The platform uses a drag-and-drop interface designed for users without professional design training or experience. Canva operates on a freemium model and has added features such as print services and video editing tools over time. == History == === 2013–2020 === Canva was founded in Perth, Australia, by Melanie Perkins, Cliff Obrecht and Cameron Adams on 1 January 2013. One of the company's early investors was Susan Wu, an American entrepreneur. In its first year, Canva had more than 750,000 users. In 2017, the company reached profitability and had 294,000 paying customers. In January 2018, Perkins announced that the company had raised A$40 million from Sequoia Capital, Blackbird Ventures, and Felicis Ventures, and the company was valued at A$1 billion. It raised A$70 million in May 2019, followed by A$85 million in October 2019 and the launch of Canva for Enterprise. In December 2019, Canva announced Canva for Education, a free product for schools and other educational institutions intended to facilitate collaboration between students and teachers. === 2021–2025 === In June 2020, Canva announced a partnership with FedEx Office and with Office Depot the following month. As of June 2020, Canva's valuation had risen to A$6 billion, rising to A$40 billion by September 2021. In September 2021, Canva raised US$200 million, with its value peaking that year at US$40 billion. By September 2022, the valuation of the company had leveled at US$26 billion. While Canva's value declined from its 2021 peak by mid-2022, it remained one of Australia's most prominent technology companies, alongside Atlassian. In March 2022, Canva had over 75 million monthly active users. In 2023, the pair were named in the Australian Financial Review's AFR Rich List as among the 10 most wealthy people in Australia. On 7 December 2022, Canva launched Magic Write, which is the platform's AI-powered copywriting assistant. On 22 March 2023, Canva announced its new Assistant tool, which makes recommendations on graphics and styles that match the user's existing design. On 11 January 2024, Canva launched its own GPT in OpenAI's GPT Store. The company has announced it intends to compete with Google and Microsoft in the office software category with website and whiteboard products. In May 2024, the company announced the launch of Canva Enterprise, a plan designed for large organisations, alongside new tools including Work Kits, Courses and AI capabilities. In 2024, it announced a co-funded solar energy project to enhance its sustainability efforts. On 10 April 2025, Canva released Visual Suite 2. The new interface combines Canva's design and productivity tools. New features include a spreadsheets application (Canva Sheets), a generative AI coding assistant (Canva Code), a chatbot, and an updated photo editor that can modify or remove background objects. In August 2025, Canva launched a stock sale to employees, valuing the company at US$42 billion. == Acquisitions == In 2018, the company acquired presentations startup Zeetings for an undisclosed amount, as part of its expansion into the presentations space. In May 2019, the company announced the acquisitions of Pixabay and Pexels, two free stock photography sites based in Germany, which enabled Canva users to access their photos for designs. In February 2021, Canva acquired Austrian startup Kaleido.ai and the Czech-based Smartmockups. In 2022, Canva acquired Flourish, a London-based data visualization startup. In March 2024, Canva acquired UK-based Serif, the developers of the Affinity suite of graphic design software, for approximately $380 million. In August 2024, Canva acquired the AI image generation platform and startup, Leonardo AI, for an undisclosed amount. In June 2025, it was announced that Canva had acquired Australian AI marketing startup MagicBrief for an undisclosed amount. In February 2026, Canva acquired two startups: Cavalry, which specializes in animation software, and MangoAI, which focuses on improving advertising performance. In April 2026, Canva acquired Simtheory, an AI Workflow Tool, and Ortto, a marketing automation tool. == Philanthropy == Canva's co-founders, Melanie Perkins and Cliff Obrecht, have publicly stated their intention to donate a significant portion of their personal wealth to charity. In 2021, Canva started a partnership with GiveDirectly, a nonprofit organization operating in low income areas that makes unconditional cash transfers to families living in extreme poverty. Since then, the company has donated $50 million to support GiveDirectly's work across Malawi. In 2025, Canva announced an additional $100 million commitment to expand its GiveDirectly partnership. == Controversies == === Data breach === In May 2019, Canva experienced a data breach in which the data of roughly 139 million users was exposed. The exposed data included real names of users, usernames, email addresses, geographical information, and password hashes for some users. In January 2020, approximately 4 million user passwords were decrypted and shared online. Canva responded by resetting the passwords of every user who had not changed their password since the initial breach. === Russian operations === In May 2022 Canva was criticized for continuing to provide free access to its services in Russia, even after suspending payment processing in the country. Activists from the Ukrainian diaspora in Australia and others said this could be viewed as indirectly supporting Russia’s war effort. They noted the company was the only one of several major Australian firms to receive the lowest “digging in” rating on a tracker run by the Yale School of Management for failing to pull out of Russia. Canva responded that it had suspended financial transactions in Russia from March 2022 and maintained the free version to allow the continued creation and sharing of “pro-peace and anti-war” content for its 1.4 million Russian users.

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  Cryptochannel

  In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  Sentiment analysis

  Sentiment analysis (also known as opinion mining) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. == Types == A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise. Precursors to sentimental analysis include the General Inquirer, which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's psychological state based on analysis of their verbal behavior. Subsequently, the method described in a patent by Volcani and Fogel, looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales. A current system based on their work, called EffectCheck, presents synonyms that can be used to increase or decrease the level of evoked emotion in each scale. Many other subsequent efforts were less sophisticated, using a mere polar view of sentiment, from positive to negative, such as work by Turney, and Pang who applied different methods for detecting the polarity of product reviews and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang and Snyder among others: Pang and Lee expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale). First steps to bringing together various approaches—learning, lexical, knowledge-based, etc.—were taken in the 2004 AAAI Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text. Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified. Moreover, it can be proven that specific classifiers such as the Max Entropy and SVMs can benefit from the introduction of a neutral class and improve the overall accuracy of the classification. There are in principle two ways for operating with a neutral class. Either, the algorithm proceeds by first identifying the neutral language, filtering it out and then assessing the rest in terms of positive and negative sentiments, or it builds a three-way classification in one step. This second approach often involves estimating a probability distribution over all categories (e.g. naive Bayes classifiers as implemented by the NLTK). Whether and how to use a neutral class depends on the nature of the data: if the data is clearly clustered into neutral, negative and positive language, it makes sense to filter the neutral language out and focus on the polarity between positive and negative sentiments. If, in contrast, the data are mostly neutral with small deviations towards positive and negative affect, this strategy would make it harder to clearly distinguish between the two poles. A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral, or positive sentiment are given an associated number on a −10 to +10 scale (most negative up to most positive) or simply from 0 to a positive upper limit such as +4. This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using natural language processing, each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score. This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text. There are various other types of sentiment analysis, such as aspect-based sentiment analysis, grading sentiment analysis (positive, negative, neutral), multilingual sentiment analysis and detection of emotions. === Subjectivity/objectivity identification === This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification. The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Moreover, as mentioned by Su, results are largely dependent on the definition of subjectivity used when annotating texts. However, Pang showed that removing objective sentences from a document before classifying its polarity helped improve performance. Subjective and objective identification, emerging subtasks of sentiment analysis to use syntactic, semantic features, and machine learning knowledge to identify if a sentence or document contains facts or opinions. Awareness of recognizing factual and opinions is not recent, having possibly first presented by Carbonell at Yale University in 1979. The term objective refers to the incident carrying factual information. Example of an objective sentence: 'To be elected president of the United States, a candidate must be at least thirty-five years of age.' The term subjective describes the incident contains non-factual information in various forms, such as personal opinions, judgment, and predictions, also known as 'private states'. In the example down below, it reflects a private states 'We Americans'. Moreover, the target entity commented by the opinions can take several forms from tangible product to intangible topic matters stated in Liu (2010). Furthermore, three types of attitudes were observed by Liu (2010), 1) positive opinions, 2) neutral opinions, and 3) negative opinions. Example of a subjective sentence: 'We Americans need to elect a president who is mature and who is able to make wise decisions.' This analysis is a classification problem. Each class's collections of words or phrase indicators are defined for to locate desirable patterns on unannotated text. For subjective expression, a different word list has been created. Lists of subjective indicators in words or phrases have been developed by multiple researchers in the linguist and natural language processing field states in Riloff et al. (2003). A dictionary of extraction rules has to be created for measuring given expressions. Over the years, in subjective detection, the features extraction progression from curating features by hand to automated features learning. At the moment, automated learning methods can further separate into supervised and unsupervised machine learning. Patterns extraction with machine learning process annotated and unannotated text have been explored extensively by academic researchers. However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume. Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction. Besides, metaphors take in different forms, which may have been contribu

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  Trace zero cryptography

  First proposed by Gerhard Frey in 1998, trace zero cryptography refers to the use of trace zero varieties (TZV) for cryptographic purpose. Trace zero varieties are subgroups of the divisor class group on a low genus hyperelliptic curve defined over a finite field. These groups can be used to establish asymmetric cryptography using the discrete logarithm problem as cryptographic primitive. Trace zero varieties feature a better scalar multiplication performance than elliptic curves. This allows fast arithmetic in these groups, which can speed up the calculations with a factor 3 compared with elliptic curves and hence speed up the cryptosystem. Another advantage is that for groups of cryptographically relevant size, the order of the group can simply be calculated using the characteristic polynomial of the Frobenius endomorphism. This is not the case, for example, in elliptic curve cryptography when the group of points of an elliptic curve over a prime field is used for cryptographic purpose. However, to represent an element of the trace zero variety more bits are needed compared with elements of elliptic or hyperelliptic curves. Another disadvantage is the fact that it is possible to reduce the security of the TZV of 1/6th of the bit length using cover attack. == Mathematical background == A hyperelliptic curve C of genus g over a prime field F q {\displaystyle \mathbb {F} _{q}} where q = pn (p prime) of odd characteristic is defined as C : y 2 + h ( x ) y = f ( x ) , {\displaystyle C:~y^{2}+h(x)y=f(x),} where f monic, deg(f) = 2g + 1 and deg(h) ≤ g. The curve has at least one F q {\displaystyle \mathbb {F} _{q}} -rational Weierstraßpoint. The Jacobian variety J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} of C is for all finite extension F q n {\displaystyle \mathbb {F} _{q^{n}}} isomorphic to the ideal class group Cl ⁡ ( C / F q n ) {\displaystyle \operatorname {Cl} (C/\mathbb {F} _{q^{n}})} . With the Mumford's representation it is possible to represent the elements of J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} with a pair of polynomials [u, v], where u, v ∈ F q n [ x ] {\displaystyle \mathbb {F} _{q^{n}}[x]} . The Frobenius endomorphism σ is used on an element [u, v] of J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} to raise the power of each coefficient of that element to q: σ([u, v]) = [uq(x), vq(x)]. The characteristic polynomial of this endomorphism has the following form: χ ( T ) = T 2 g + a 1 T 2 g − 1 + ⋯ + a g T g + ⋯ + a 1 q g − 1 T + q g , {\displaystyle \chi (T)=T^{2g}+a_{1}T^{2g-1}+\cdots +a_{g}T^{g}+\cdots +a_{1}q^{g-1}T+q^{g},} where ai in Z {\displaystyle \mathbb {Z} } With the Hasse–Weil theorem it is possible to receive the group order of any extension field F q n {\displaystyle \mathbb {F} _{q^{n}}} by using the complex roots τi of χ(T): | J C ( F q n ) | = ∏ i = 1 2 g ( 1 − τ i n ) {\displaystyle |J_{C}(\mathbb {F} _{q^{n}})|=\prod _{i=1}^{2g}(1-\tau _{i}^{n})} Let D be an element of the J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} of C, then it is possible to define an endomorphism of J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} , the so-called trace of D: Tr ⁡ ( D ) = ∑ i = 0 n − 1 σ i ( D ) = D + σ ( D ) + ⋯ + σ n − 1 ( D ) {\displaystyle \operatorname {Tr} (D)=\sum _{i=0}^{n-1}\sigma ^{i}(D)=D+\sigma (D)+\cdots +\sigma ^{n-1}(D)} Based on this endomorphism one can reduce the Jacobian variety to a subgroup G with the property, that every element is of trace zero: G = { D ∈ J C ( F q n ) | Tr ( D ) = 0 } , ( 0 neutral element in J C ( F q n ) {\displaystyle G=\{D\in J_{C}(\mathbb {F} _{q^{n}})~|~{\text{Tr}}(D)={\textbf {0}}\},~~~({\textbf {0}}{\text{ neutral element in }}J_{C}(\mathbb {F} _{q^{n}})} G is the kernel of the trace endomorphism and thus G is a group, the so-called trace zero (sub)variety (TZV) of J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} . The intersection of G and J C ( F q ) {\displaystyle J_{C}(\mathbb {F} _{q})} is produced by the n-torsion elements of J C ( F q ) {\displaystyle J_{C}(\mathbb {F} _{q})} . If the greatest common divisor gcd ( n , | J C ( F q ) | ) = 1 {\displaystyle \gcd(n,|J_{C}(\mathbb {F} _{q})|)=1} the intersection is empty and one can compute the group order of G: | G | = | J C ( F q n ) | | J C ( F q ) | = ∏ i = 1 2 g ( 1 − τ i n ) ∏ i = 1 2 g ( 1 − τ i ) {\displaystyle |G|={\dfrac {|J_{C}(\mathbb {F} _{q^{n}})|}{|J_{C}(\mathbb {F} _{q})|}}={\dfrac {\prod _{i=1}^{2g}(1-\tau _{i}^{n})}{\prod _{i=1}^{2g}(1-\tau _{i})}}} The actual group used in cryptographic applications is a subgroup G0 of G of a large prime order l. This group may be G itself. There exist three different cases of cryptographical relevance for TZV: g = 1, n = 3 g = 1, n = 5 g = 2, n = 3 == Arithmetic == The arithmetic used in the TZV group G0 based on the arithmetic for the whole group J C ( F q n ) {\displaystyle J_{C}(\mathbb {F} _{q^{n}})} , But it is possible to use the Frobenius endomorphism σ to speed up the scalar multiplication. This can be archived if G0 is generated by D of order l then σ(D) = sD, for some integers s. For the given cases of TZV s can be computed as follows, where ai come from the characteristic polynomial of the Frobenius endomorphism : For g = 1, n = 3: s = q − 1 1 − a 1 mod ℓ {\displaystyle s={\dfrac {q-1}{1-a_{1}}}{\bmod {\ell }}} For g = 1, n = 5: s = q 2 − q − a 1 2 q + a 1 q + 1 q − 2 a 1 q + a 1 3 − a 1 2 + a 1 − 1 mod ℓ {\displaystyle s={\dfrac {q^{2}-q-a_{1}^{2}q+a_{1}q+1}{q-2a_{1}q+a_{1}^{3}-a_{1}^{2}+a_{1}-1}}{\bmod {\ell }}} For g = 2, n = 3: s = − q 2 − a 2 + a 1 a 1 q − a 2 + 1 mod ℓ {\displaystyle s=-{\dfrac {q^{2}-a_{2}+a_{1}}{a_{1}q-a_{2}+1}}{\bmod {\ell }}} Knowing this, it is possible to replace any scalar multiplication mD (|m| ≤ l/2) with: m 0 D + m 1 σ ( D ) + ⋯ + m n − 1 σ n − 1 ( D ) , where m i = O ( ℓ 1 / ( n − 1 ) ) = O ( q g ) {\displaystyle m_{0}D+m_{1}\sigma (D)+\cdots +m_{n-1}\sigma ^{n-1}(D),~~~~{\text{where }}m_{i}=O(\ell ^{1/(n-1)})=O(q^{g})} With this trick the multiple scalar product can be reduced to about 1/(n − 1)th of doublings necessary for calculating mD, if the implied constants are small enough. == Security == The security of cryptographic systems based on trace zero subvarieties according to the results of the papers comparable to the security of hyper-elliptic curves of low genus g' over F p ′ {\displaystyle \mathbb {F} _{p'}} , where p' ~ (n − 1)(g/g' ) for |G| ~128 bits. For the cases where n = 3, g = 2 and n = 5, g = 1 it is possible to reduce the security for at most 6 bits, where |G| ~ 2256, because one can not be sure that G is contained in a Jacobian of a curve of genus 6. The security of curves of genus 4 for similar fields are far less secure. == Cover attack on a trace zero crypto-system == The attack published in shows, that the DLP in trace zero groups of genus 2 over finite fields of characteristic diverse than 2 or 3 and a field extension of degree 3 can be transformed into a DLP in a class group of degree 0 with genus of at most 6 over the base field. In this new class group the DLP can be attacked with the index calculus methods. This leads to a reduction of the bit length 1/6th.

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  Synonym (database)

  In databases, a synonym is an alias or alternate name for a table, view, sequence, or other schema object. They are used mainly to make it intuitive for users to access database objects owned by other users. They also hide the underlying object's identity and make it harder for a malicious program or user to target the underlying object (security through obscurity). Because a synonym is just an alternate name for an object, it requires no storage other than its definition. When an application uses a synonym, the DBMS forwards the request to the synonym's underlying base object. By coding your programs to use synonyms instead of database object names, you insulate yourself from any changes in the name, ownership, or object locations, at the cost of adding another layer that also needs to be maintained. Users can also have different needs, for example some may wish to use a shorter name to refer to database objects they often query, which can be done with aliases without having to rename the underlying object and alter the code referring to it. Synonyms are very powerful from the point of view of allowing users access to objects that do not lie within their schema. All synonyms have to be created explicitly with the CREATE SYNONYM command and the underlying objects can be located in the same database or in other databases that are connected by database links There are two major uses of synonyms: Object invisibility: Synonyms can be created to keep the original object hidden from the user. Location invisibility: Synonyms can be created as aliases for tables and other objects that are not part of the local database. When a table or a procedure is created, it is created in a particular schema, and other users can access it only by using that schema's name as a prefix to the object's name. The way around for this is for the schema owner creates a synonym with the same name as the table name. == Public synonyms == Public synonyms are owned by special schema in the Oracle Database called PUBLIC. As mentioned earlier, public synonyms can be referenced by all users in the database. Public synonyms are usually created by the application owner for the tables and other objects such as procedures and packages so the users of the application can see the objects The following code shows how to create a public synonym for the employee table: Now any user can see the table by just typing the original table name. If you wish, you could provide a different table name for that table in the CREATE SYNONYM statement. Remember that the DBA must create public synonyms. Just because you can see a table through public (or private) synonym doesn’t mean that you can also perform SELECT, INSERT, UPDATE or DELETE operations on the table. To be able to perform those operations, a user needs specific privileges for the underlying object, either directly or through roles from the application owner. == Private synonyms == A private synonym is a synonym within a database schema that a developer typically uses to mask the true name of a table, view stored procedure, or other database object in an application schema. Private synonyms, unlike public synonyms, can be referenced only by the schema that owns the table or object. You may want to create private synonyms when you want to refer to the same table by different contexts. Private synonym overrides public synonym definitions. You create private synonyms the same way you create public synonyms, but you omit the PUBLIC keyword in the CREATE statement. The following example shows how to create a private synonym called addresses for the locations table. Note that once you create the private synonym, you can refer to the synonym exactly as you would the original table name. == Drop a synonym == Synonyms, both private and public, are dropped in the same manner by using the DROP SYNONYM command, but there is one important difference. If you are dropping a public synonym; you need to add the keyword PUBLIC after the keyword DROP. The ALL_SYNONYMS (or DBA_SYNONYMS) view provides information on all synonyms in your database.

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  Scalable Coherent Interface

  The Scalable Coherent Interface or Scalable Coherent Interconnect (SCI), is a high-speed interconnect standard for shared memory multiprocessing and message passing. The goal was to scale well, provide system-wide memory coherence and a simple interface; i.e. a standard to replace existing buses in multiprocessor systems with one with no inherent scalability and performance limitations. The IEEE Std 1596-1992, IEEE Standard for Scalable Coherent Interface (SCI) was approved by the IEEE standards board on March 19, 1992. It saw some use during the 1990s, but never became widely used and has been replaced by other systems from the early 2000s. == History == Soon after the Fastbus (IEEE 960) follow-on Futurebus (IEEE 896) project in 1987, some engineers predicted it would already be too slow for the high performance computing marketplace by the time it would be released in the early 1990s. In response, a "Superbus" study group was formed in November 1987. Another working group of the standards association of the Institute of Electrical and Electronics Engineers (IEEE) spun off to form a standard targeted at this market in July 1988. It was essentially a subset of Futurebus features that could be easily implemented at high speed, along with minor additions to make it easier to connect to other systems, such as VMEbus. Most of the developers had their background from high-speed computer buses. Representatives from companies in the computer industry and research community included Amdahl, Apple Computer, BB&N, Hewlett-Packard, CERN, Dolphin Server Technology, Cray Research, Sequent, AT&T, Digital Equipment Corporation, McDonnell Douglas, National Semiconductor, Stanford Linear Accelerator Center, Tektronix, Texas Instruments, Unisys, University of Oslo, University of Wisconsin. The original intent was a single standard for all buses in the computer. The working group soon came up with the idea of using point-to-point communication in the form of insertion rings. This avoided the lumped capacitance, limited physical length/speed of light problems and stub reflections in addition to allowing parallel transactions. The use of insertion rings is credited to Manolis Katevenis who suggested it at one of the early meetings of the working group. The working group for developing the standard was led by David B. Gustavson (chair) and David V. James (Vice Chair). David V. James was a major contributor for writing the specifications including the executable C-code. Stein Gjessing’s group at the University of Oslo used formal methods to verify the coherence protocol and Dolphin Server Technology implemented a node controller chip including the cache coherence logic. Different versions and derivatives of SCI were implemented by companies like Dolphin Interconnect Solutions, Convex, Data General AViiON (using cache controller and link controller chips from Dolphin), Sequent and Cray Research. Dolphin Interconnect Solutions implemented a PCI and PCI-Express connected derivative of SCI that provides non-coherent shared memory access. This implementation was used by Sun Microsystems for its high-end clusters, Thales Group and several others including volume applications for message passing within HPC clustering and medical imaging. SCI was often used to implement non-uniform memory access architectures. It was also used by Sequent Computer Systems as the processor memory bus in their NUMA-Q systems. Numascale developed a derivative to connect with coherent HyperTransport. == The standard == The standard defined two interface levels: The physical level that deals with electrical signals, connectors, mechanical and thermal conditions The logical level that describes the address space, data transfer protocols, cache coherence mechanisms, synchronization primitives, control and status registers, and initialization and error recovery facilities. This structure allowed new developments in physical interface technology to be easily adapted without any redesign on the logical level. Scalability for large systems is achieved through a distributed directory-based cache coherence model. (The other popular models for cache coherency are based on system-wide eavesdropping (snooping) of memory transactions – a scheme which is not very scalable.) In SCI each node contains a directory with a pointer to the next node in a linked list that shares a particular cache line. SCI defines a 64-bit flat address space (16 exabytes) where 16 bits are used for identifying a node (65,536 nodes) and 48 bits for address within the node (256 terabytes). A node can contain many processors and/or memory. The SCI standard defines a packet switched network. === Topologies === SCI can be used to build systems with different types of switching topologies from centralized to fully distributed switching: With a central switch, each node is connected to the switch with a ringlet (in this case a two-node ring). In distributed switching systems, each node can be connected to a ring of arbitrary length and either all or some of the nodes can be connected to two or more rings. The most common way to describe these multi-dimensional topologies is k-ary n-cubes (or tori). The SCI standard specification mentions several such topologies as examples. The 2-D torus is a combination of rings in two dimensions. Switching between the two dimensions requires a small switching capability in the node. This can be expanded to three or more dimensions. The concept of folding rings can also be applied to the Torus topologies to avoid any long connection segments. === Transactions === SCI sends information in packets. Each packet consists of an unbroken sequence of 16-bit symbols. The symbol is accompanied by a flag bit. A transition of the flag bit from 0 to 1 indicates the start of a packet. A transition from 1 to 0 occurs 1 (for echoes) or 4 symbols before the packet end. A packet contains a header with address command and status information, payload (from 0 through optional lengths of data) and a CRC check symbol. The first symbol in the packet header contains the destination node address. If the address is not within the domain handled by the receiving node, the packet is passed to the output through the bypass FIFO. In the other case, the packet is fed to a receive queue and may be transferred to a ring in another dimension. All packets are marked when they pass the scrubber (a node is established as scrubber when the ring is initialized). Packets without a valid destination address will be removed when passing the scrubber for the second time to avoid filling the ring with packets that would otherwise circulate indefinitely. === Cache coherence === Cache coherence ensures data consistency in multiprocessor systems. The simplest form applied in earlier systems was based on clearing the cache contents between context switches and disabling the cache for data that were shared between two or more processors. These methods were feasible when the performance difference between the cache and memory were less than one order of magnitude. Modern processors with caches that are more than two orders of magnitude faster than main memory would not perform anywhere near optimal without more sophisticated methods for data consistency. Bus based systems use eavesdropping (snooping) methods since buses are inherently broadcast. Modern systems with point-to point links use broadcast methods with snoop filter options to improve performance. Since broadcast and eavesdropping are inherently non-scalable, these are not used in SCI. Instead, SCI uses a distributed directory-based cache coherence protocol with a linked list of nodes containing processors that share a particular cache line. Each node holds a directory for the main memory of the node with a tag for each line of memory (same line length as the cache line). The memory tag holds a pointer to the head of the linked list and a state code for the line (three states – home, fresh, gone). Associated with each node is also a cache for holding remote data with a directory containing forward and backward pointers to nodes in the linked list sharing the cache line. The tag for the cache has seven states (invalid, only fresh, head fresh, only dirty, head dirty, mid valid, tail valid). The distributed directory is scalable. The overhead for the directory based cache coherence is a constant percentage of the node’s memory and cache. This percentage is in the order of 4% for the memory and 7% for the cache. == Legacy == SCI is a standard for connecting the different resources within a multiprocessor computer system, and it is not as widely known to the public as for example the Ethernet family for connecting different systems. Different system vendors implemented different variants of SCI for their internal system infrastructure. These different implementations interface to very intricate mechanisms in processors and memory systems and each vendor has to preserve some degrees of

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  Internet

  The Internet (or internet) is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a network of networks that comprises private, public, academic, business, and government networks of local to global scope, linked by electronic, wireless, and optical networking technologies. The Internet carries a vast range of information services and resources, such as the interlinked hypertext documents and applications of the World Wide Web (WWW), electronic mail, discussion groups, internet telephony, streaming media and file sharing. Most traditional communication media, including telephone, radio, television, paper mail, newspapers, and print publishing, have been transformed by the Internet, giving rise to new media such as email, online music, digital newspapers, news aggregators, and audio and video streaming websites. The Internet has enabled and accelerated new forms of personal interaction through instant messaging, Internet forums, and social networking services. Online shopping has also grown to occupy a significant market across industries, enabling firms to extend brick and mortar presences to serve larger markets. Business-to-business and financial services on the Internet affect supply chains across entire industries. The origins of the Internet date back to research that enabled the time-sharing of computer resources, the development of packet switching, and the design of computer networks for data communication. The set of communication protocols to enable internetworking on the Internet arose from research and development commissioned in the 1970s by the Defense Advanced Research Projects Agency (DARPA) of the United States Department of Defense in collaboration with universities and researchers across the United States, United Kingdom and France. The Internet has no single centralized governance in either technological implementation or policies for access and usage. Each constituent network sets its own policies. The overarching definitions of the two principal name spaces on the Internet, the Internet Protocol address (IP address) space and the Domain Name System (DNS), are directed by a maintainer organization, the Internet Corporation for Assigned Names and Numbers (ICANN). The technical underpinning and standardization of the core protocols is an activity of the non-profit Internet Engineering Task Force (IETF). == Terminology == The word internetted was used as early as 1849, meaning interconnected or interwoven. The word Internet was used in 1945 by the United States War Department in a radio operator's manual, and in 1974 as the shorthand form of Internetwork. Today, the term Internet most commonly refers to the global system of interconnected computer networks, though it may also refer to any group of smaller networks. The word Internet may be capitalized as a proper noun, although this is becoming less common. This reflects the tendency in English to capitalize new terms and move them to lowercase as they become familiar. The word is sometimes still capitalized to distinguish the global internet from smaller networks, though many publications, including the AP Stylebook since 2016, recommend the lowercase form in every case. In 2016, the Oxford English Dictionary found that, based on a study of around 2.5 billion printed and online sources, "Internet" was capitalized in 54% of cases. The terms Internet and World Wide Web are often used interchangeably; it is common to speak of "going on the Internet" when using a web browser to view web pages. However, the World Wide Web, or the Web, is only one of a large number of Internet services. It is the global collection of web pages, documents and other web resources linked by hyperlinks and URLs. == History == === 1960s === In the 1960s, computer scientists began developing systems for time-sharing of computer resources. J. C. R. Licklider proposed the idea of a universal network while working at Bolt Beranek & Newman and, later, leading the Information Processing Techniques Office at the Advanced Research Projects Agency (ARPA) of the United States Department of Defense. Research into packet switching, one of the fundamental Internet technologies, started in the work of Paul Baran at RAND in the early 1960s and, independently, Donald Davies at the United Kingdom's National Physical Laboratory in 1965. After the Symposium on Operating Systems Principles in 1967, packet switching from the proposed NPL network was incorporated into the design of the ARPANET, an experimental resource sharing network proposed by ARPA. ARPANET development began with two network nodes which were interconnected between the University of California, Los Angeles and the Stanford Research Institute on 29 October 1969. The third site was at the University of California, Santa Barbara, followed by the University of Utah. === 1970s === By the end of 1971, 15 sites were connected to the young ARPANET. Thereafter, the ARPANET gradually developed into a decentralized communications network, connecting remote centers and military bases in the United States. Other user networks and research networks, such as the Merit Network and CYCLADES, were developed in the late 1960s and early 1970s. Early international collaborations for the ARPANET were rare. Connections were made in 1973 to Norway (NORSAR and, later, NDRE) and to Peter Kirstein's research group at University College London, which provided a gateway to British academic networks, the first internetwork for resource sharing. ARPA projects, the International Network Working Group and commercial initiatives led to the development of various protocols and standards by which multiple separate networks could become a single network, or a network of networks. In 1974, Vint Cerf at Stanford University and Bob Kahn at DARPA published a proposal for "A Protocol for Packet Network Intercommunication". Cerf and his graduate students used the term internet as a shorthand for internetwork in RFC 675. The Internet Experiment Notes and later RFCs repeated this use. The work of Louis Pouzin and Robert Metcalfe had important influences on the resulting TCP/IP design. National PTTs and commercial providers developed the X.25 standard and deployed it on public data networks. === 1980s === The ARPANET initially served as a backbone for the interconnection of regional academic and military networks in the United States to enable resource sharing. Access to the ARPANET was expanded in 1981 when the National Science Foundation (NSF) funded the Computer Science Network (CSNET). In 1982, the Internet Protocol Suite (TCP/IP) was standardized, which facilitated worldwide proliferation of interconnected networks. TCP/IP network access expanded again in 1986 when the National Science Foundation Network (NSFNet) provided access to supercomputer sites in the United States for researchers, first at speeds of 56 kbit/s and later at 1.5 Mbit/s and 45 Mbit/s. The NSFNet expanded into academic and research organizations in Europe, Australia, New Zealand and Japan in 1988–89. Although other network protocols such as UUCP and PTT public data networks had global reach well before this time, this marked the beginning of the Internet as an intercontinental network. Commercial Internet service providers emerged in 1989 in the United States and Australia. The ARPANET was decommissioned in 1990. === 1990s === The linking of commercial networks and enterprises by the early 1990s, as well as the advent of the World Wide Web, marked the beginning of the transition to the modern Internet. Steady advances in semiconductor technology and optical networking created new economic opportunities for commercial involvement in the expansion of the network in its core and for delivering services to the public. In mid-1989, MCI Mail and Compuserve established connections to the Internet, delivering email and public access products to the half million users of the Internet. Just months later, on 1 January 1990, PSInet launched an alternate Internet backbone for commercial use; one of the networks that added to the core of the commercial Internet of later years. In March 1990, the first high-speed T1 (1.5 Mbit/s) link between the NSFNET and Europe was installed between Cornell University and CERN, allowing much more robust communications than were capable with satellites. Later in 1990, Tim Berners-Lee began writing WorldWideWeb, the first web browser, after two years of lobbying CERN management. By Christmas 1990, Berners-Lee had built all the tools necessary for a working Web: the HyperText Transfer Protocol (HTTP) 0.9, the HyperText Markup Language (HTML), the first Web browser (which was also an HTML editor and could access Usenet newsgroups and FTP files), the first HTTP server software (later known as CERN httpd), the first web server, and the first Web pages that described the project itself. In 1991 the

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  ISO 15765-2

  ISO 15765-2, or ISO-TP (Transport Layer), is an international standard for sending data packets over a CAN bus. The protocol allows for the transport of messages that exceed the eight byte maximum payload of CAN frames. ISO-TP segments longer messages into multiple frames, adding metadata (CAN-TP Header) that allows the interpretation of individual frames and reassembly into a complete message packet by the recipient. It can carry up to 232-1 (4294967295) bytes of payload per message packet starting from the 2016 version. Prior versions were limited to a maximum payload size of 4095 bytes. In the OSI model, ISO-TP covers the layer 3 (network layer) and 4 (transport layer). The most common application for ISO-TP is the transfer of diagnostic messages with OBD-II equipped vehicles using KWP2000 and UDS, but is used broadly in other application-specific CAN implementations where one might need to send messages longer than what the CAN protocol physical layer allows (eight bytes for CAN, 64 bytes for CAN FD, and 2048 bytes for CAN-XL). ISO-TP can be operated with its own addressing as so-called Extended Addressing or without address using only the CAN ID (so-called Normal Addressing). Extended addressing uses the first data byte of each frame as an additional element of the address, reducing the application payload by one byte. For clarity the protocol description below is based on Normal Addressing with eight byte CAN frames. In total, six types of addressing are allowed by the ISO 15765-2 Protocol. ISO-TP prepends one or more metadata bytes to the payload data in the eight byte CAN frame, reducing the payload to seven or fewer bytes per frame. The metadata is called the Protocol Control Information, or PCI. The PCI is one, two or three bytes. The initial field is four bits indicating the frame type, and implicitly describing the PCI length. ISO 15765-2 is a part of ISO 15765 (headlined Road vehicles — Diagnostic communication over Controller Area Network (DoCAN)), which has the following parts: ISO 15765-1 Part 1: General information and use case definition ISO 15765-2 Part 2: Transport protocol and network layer services ISO 15765-3 Part 3: Implementation of unified diagnostic services (UDS on CAN) – replaced by ISO 14229-3 Road vehicles — Unified diagnostic services ISO 15765-4 Part 4: Requirements for emissions-related systems == List of protocol control information (PCI) field types == The ISO-TP defines four frame types: A message of seven bytes or less is sent in a single frame, with the initial byte containing the type (0) and payload length (1-7 bytes). With the 0 in the type field, this can also pass as a simpler protocol with a length-data format and is often misinterpreted as such. A message longer than 7 bytes requires segmenting the message packet over multiple frames. A segmented transfer starts with a First Frame. The PCI is two bytes in this case, with the first 4 bit field the type (type 1) and the following 12 bits the message length (excluding the type and length bytes). The recipient confirms the transfer with a flow control frame. The flow control frame has three PCI bytes specifying the interval between subsequent frames and how many consecutive frames may be sent (Block Size). For CAN FD, the ISO 15765-2 protocol has been extended for Single and First frame, to allow larger size values, but still backwards compatible with traditional ISO 15765. See CAN FD. The initial byte contains the type (type = 3) in the first four bits, and a flag in the next four bits indicating if the transfer is allowed (0 = Continue To Send, 1 = Wait, 2 = Overflow/abort). The next byte is the block size, the count of frames that may be sent before waiting for the next flow control frame. A value of zero allows the remaining frames to be sent without flow control or delay. The third byte is the minimum Separation Time (STmin), the minimum delay time between frames. STmin values up to 127 (0x7F) specify the minimum number of milliseconds to delay between frames, while values in the range 241 (0xF1) to 249 (0xF9) specify delays increasing from 100 to 900 microseconds. Note that the Separation Time is defined as the minimum time between the end of one frame to the beginning of the next. Robust implementations should be prepared to accept frames from a sender that misinterprets this as the frame repetition rate i.e. from start-of-frame to start-of-frame. Even careful implementations may fail to account for the minor effect of bit-stuffing in the physical layer. The sender transmits the rest of the message using Consecutive Frames. Each Consecutive Frame has a one byte PCI, with a four bit type (type = 2) followed by a 4-bit sequence number. The sequence number starts at 1 and increments with each frame sent (1, 2,..., F, 0, 1,...), with which lost or discarded frames can be detected. Each consecutive frame starts at 0, initially for the first set of data in the first frame will be considered as 0th data. So the first set of CF(Consecutive frames) start from 0x1. There afterwards when it reaches 0x2F, will be started from 0x20 (e.g. 0x21, 0x22, 0x23...0x2F, 0x20, 0x21...). The 12-bit length field (as indicated in the First Frame) allows up to 4095 bytes of user data in a segmented message, but in practice the typical application-specific limit is considerably lower because of receive buffer or hardware limitations. == Timing parameters == Timing parameters, such as P1 and P2 timers, have to be mentioned. == Standards == ISO 15765-2:2016 Road vehicles -- Diagnostic communication over Controller Area Network (DoCAN) -- Part 2: Transport protocol and network layer services

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  LIVAC Synchronous Corpus

  LIVAC is an uncommon language corpus dynamically maintained since 1995. Different from other existing corpora, LIVAC has adopted a rigorous and regular "Windows" approach in processing and filtering massive media texts from representative Chinese speech communities such as Beijing, Hong Kong, Macau, Taipei, Singapore, Shanghai, as well as Guangzhou, and Shenzhen. The contents are thus deliberately repetitive in most cases, represented by textual samples drawn from editorials, local and international news, cross-Taiwan Strait news, as well as news on finance, sports and entertainment. By 2023, more than 3 billion characters of news media texts have been filtered, of which 700 million characters have been processed and analyzed and have yielded an expanding Pan-Chinese dictionary of 2.5 million words from the Pan-Chinese printed media. Through rigorous analysis based on computational linguistic methodology, LIVAC has at the same time accumulated a large amount of accurate and meaningful statistical data on the Chinese language and on their diverse speech communities in the Pan-Chinese context, and the results show considerable and important long standing as well as evolving variations. The "Windows" approach is the most innovative feature of LIVAC and has enabled Pan-Chinese media texts to be quantitatively analyzed according to various attributes such as locations, time and subject domains. Thus, various types of comparative studies and applications in information technology as well as development of often related innovative applications have been possible. Moreover, LIVAC has allowed longitudinal developments to be taken into account, facilitating Key Word in Context (KWIC) search and comprehensive study of target words and their underlying concepts as well as linguistic structures over the past 25 years, based on the above mentioned variables of location, time and subject. Results from the extensive and accumulative data analysis contained in LIVAC have enabled the cultivation of textual databases of proper names, place names, organization names, new words, and bi-weekly and annual rosters of media figures. Related applications have included the establishment of verb and adjective databases, the formulation of sentiment indices, and related opinion mining, to measure and compare the popularity of global media figures in the Chinese media (LIVAC Annual Pan-Chinese Celebrity Rosters, later renamed as the Pan-Chinese Newsmaker Rosters). Notable among these are the decades long periodic reviews of the 25 years of annual pan-Chinese rosters since 2000 and compilation of new word databases (LIVAC Annual Pan-Chinese New Word Rosters). On this basis, the analysis of the emergence, diffusion and transformation of new words, and the publication of dictionaries of neologisms have been made possible. A recent focus is on the relative balance between disyllabic words and growing trisyllabic words in the Chinese language, and the comparative study of light verbs in three Chinese speech communities. as well as the link between the language use and use of language as a reflection of epochal change in China. A new LIVAC version 3.1 was launched in February 2024. == Corpus data processing == Accessing media texts, manual input, etc. Text unification including conversion from simplified to traditional Chinese characters, stored as Big5 and Unicode versions Automatic word segmentation Automatic alignment of parallel texts Manual verification, part-of-speech tagging Extraction of words and addition to regional sub-corpora Combination of regional sub-corpora to update the LIVAC corpus, and master lexical database == Labeling for data curation == Categories used include general terms and proper names, such as: general names, surnames, semi titles; geographical, organizations and commercial entities, etc.; time, prepositions, locations, etc.; stack-words; loanwords; case-word; numerals, etc. Construction of databases of proper names, place names, and specific terms, etc. Generate rosters: "new word rosters", "celebrity or media personality rosters", "place name rosters", compound words and matched words Other parts of speech tagging for sub-database, such as common nouns, numerals, numeral classifiers, different types of verbs, and of adjectives, pronouns, adverbs, prepositions, conjunctions, particles marking mood, onomatopoeia, interjection, etc. == Applications == Compilation of Pan-Chinese dictionaries or local dictionaries Information technology research, such as predictive Chinese text input for mobile phones, automatic speech to text conversion, opinion mining Comparative studies on linguistic and cultural developments in the Pan-Chinese regions, especially in a critical period of history in modern China. Language teaching and learning research, and speech-to-text conversion Customized service on linguistic research and lexical search for international corporations and government agencies The above applications are provided by the following functions: Word Segmentation Search Phrase Search Example Sentence Selection Multi-word Comparison Word Cloud

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  Completeness (cryptography)

  In cryptography, a boolean function is said to be complete if the value of each output bit depends on all input bits. This is a desirable property to have in an encryption cipher, so that if one bit of the input (plaintext) is changed, every bit of the output (ciphertext) has an average of 50% probability of changing. The easiest way to show why this is good is the following: consider that if we changed our 8-byte plaintext's last byte, it would only have any effect on the 8th byte of the ciphertext. This would mean that if the attacker guessed 256 different plaintext-ciphertext pairs, he would always know the last byte of every 8byte sequence we send (effectively 12.5% of all our data). Finding out 256 plaintext-ciphertext pairs is not hard at all in the internet world, given that standard protocols are used, and standard protocols have standard headers and commands (e.g. "get", "put", "mail from:", etc.) which the attacker can safely guess. On the other hand, if our cipher has this property (and is generally secure in other ways, too), the attacker would need to collect 264 (~1020) plaintext-ciphertext pairs to crack the cipher in this way.

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  Social film

  A social film is a type of interactive film that is presented through the lens of social media. A social film is distributed digitally and integrates with a social networking service, such as Facebook or YouTube. It combines features of web video, social network games and social media. == Key elements == Social films are a more recent phenomenon, and, in turn, there are few precedents for their format. Although there are not many examples of this genre of film, the medium has certain identifiable elements: Casual entertainment Social media User-generated content Game mechanics Using just one of these factors or a combination of them, a social film engages viewers to interact directly with the work. This can be done through usual social media functionality like comments and ranking or adding directly to the narrative itself. Just as with memes, social film distribution relies on the viral spread enabled by social media. This is based on the viral expansion loops model, in which a viewer benefits from sharing the application with friends, exponentially creating new viewers compelled to share the application. == History == One of the first social films to be created was from the YouTube channel lonelygirl15. This social film started in 2006 and was created by Miles Beckett , Mesh Flinders, and Greg Goodfried. They used YouTube posts to create an interactive video series about a fictional character who showcased her life in a vlog format. As the videos went on, more bizarre things would keep happening to the main character, Bree, before she just stopped uploading. This channel was not only the first viral social film, but went on to be one of the first viral YouTube channels to be created. It did take a few years to see any more films in this genre, but 2011 saw many people start to try their hand at making these films. The first social film in this year was a film called Him, Her and Them which was produced and released by Murmur in April 2011. It was distributed exclusively through Facebook and promoted as the first “Facebook film.” The film is composed of short video clips and interactive slideshows, integrating Facebook's Social Graph API. Users participate via text-based additions to the story, which are viewable only by friends within their social network. In May 2011, Canon and Ron Howard teamed up to create Project Imagin8ion, which was a photo contest where photographers submitted photos and the top 8 photos would be the inspiration for a short film. This short film was called "When You Find Me" and could be found exclusively on YouTube. In July 2011, Intel and Toshiba partnered together to create Hollywood's first Social Film experience, a thriller called Inside, directed by D.J. Caruso and starring Emmy Rossum. The project is broken up into several segments across multiple social media platforms including Facebook, YouTube, and Twitter. In this instance, the audience is challenged to help Emmy Rossum's character, Christina, safely make it out of the room she's been trapped in. This particular form of social film is a major undertaking in that it combines social media activity with A-list acting talent to create a user experience that all happens in real time. Although not quite the same idea, Hollywood also started experimenting with the idea of interactive and crowd-sourced films. One of the first examples of this was a short film called "Life In A Day" directed by Kevin Macdonald and produced by Ridley Scott. Kevin asked people from all over the world to submit videos onto YouTube of what they were doing on July 24th, 2010. They combined all of the best videos that were submitted together to create one film of people doing different things all around the world, no matter how boring or simple those things seemed. They took this short to film festivals before releasing it to the public on YouTube in 2011. In August 2012, Intel and Toshiba partnered again to create The Beauty Inside, directed by Drake Doremus, starring Mary Elizabeth Winstead and Topher Grace. It's Hollywood's first social film that gives everyone in the audience a chance to play Alex, the lead role. The experience will be broken up into six filmed episodes interspersed with real-time interactive storytelling that all takes place on Alex's Facebook timeline. In August 2013, Intel and Toshiba released their third entry into the category, The Power Inside, directed by Will Speck and Josh Gordon and starring Harvey Keitel, Analeigh Tipton, and Craig Roberts. It's Hollywood's first social film that asks the audience to audition to help save or destroy the world. The experience is broken up into six filmed episodes interspersed with user-generated content and interactive storytelling on the main character's Facebook timeline. In 2015, Intel partnered with Dell for their fourth entry, What Lives Inside directed by Robert Stromberg and starring Colin Hanks, Catherine O'Hara, and J. K. Simmons. The first of four episodes was released on Hulu on March 25, 2015.

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  Why We Post

  Why We Post is a research project funded by the European Research Council and launched in 2012 by Daniel Miller with the objective of examining the global impact of new social media. The study is based on ethnographic data collected through the course of 15 months in China, India, Turkey, Italy, United Kingdom, Trinidad, Chile and Brazil. The results of this project were released on 29 February 2016. This included the first three of eleven Open Access books (available via UCL Press), a five-week e-course (MOOC) on FutureLearn in English, also available in Chinese, Portuguese, Hindi, Tamil, Italian, Turkish, and Spanish on UCLeXtend. In addition a website containing key discoveries, stories and over 100 films is available in the same 8 languages.

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  Dailyhunt

  Dailyhunt (formerly Newshunt) is an Indian content and news aggregator application based in Bangalore, India that provides local language content in 14 Indian languages from multiple content providers. Viru serves as Founder of Dailyhunt with Co-founder Umang Bedi. == History == Dailyhunt, earlier called Newshunt, was created as a Symbian app in 2009 by two ex-Nokia employees Umesh Kulkarni and Chandrashekhar Sohoni. Later in 2011, Newshunt became available on the Android platform. It was by that time that Virendra Gupta, founder of Verse acquired the application. Virendra Gupta, better known as Viru, had started Verse in 2007 as a value-added service (VAS) company. In 2011, he acquired Newshunt from its owners Umesh and Chandrashekhar. Umesh became the CTO and stayed on to oversee its transition towards the smartphone era. In 2015, Viru renamed Newshunt as Dailyhunt. In early 2018, Viru roped in Umang Bedi, to be the President of Dailyhunt and lead the business with him while focusing on making the benefits of the platform available to a larger audience. Umang was elevated to co-founder in 2020. == Funding == In September 2014, Dailyhunt (then known as Newshunt) closed its Series B funding of INR 1 billion ( or approx $12 million in 2014) from Sequoia Capital India. The Series C funding round was led by Falcon Capital and was closed with $40 million in February 2015. In October 2016, the company received its Series D funding of $25 million from ByteDance and a Series E funding of $6.39 million from Falcon Edge Capital in September 2018. Additionally, Dailyhunt raised $3 Mn (INR 21.75 Cr) in a Series F funding round from Stonebridge Capital in August 2019. Other investors of Dailyhunt include Matrix Partners India, Omidyar Network, Goldman Sachs and Sofina. == Tie-ups and partnerships == In January 2021, Dailyhunt partnered with Twitter to bring ‘Twitter Moments’ to the Indian social app. Dailyhunt app now has a dedicated tab called “Twitter Moments India” to showcase curated tweets pertaining to news and other events. In January 2021, Dailyhunt announced the premiere of Season 2 of the popular show QuoteUnquote with KK (Kapil Khandelwal) on the app. It was the first podcast to have been launched on the Dailyhunt app. In September 2020, Dailyhunt signed up as an Associate Sponsor with Star Sports for Dream 11 IPL 2020. In May 2020, Snapdeal partnered with Dailyhunt to add new content on marketplace. In March 2019, Discovery Communications India, the factual entertainment network, entered into a multi-year partnership with Dailyhunt to showcase short-form content.

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  Forking lemma

  The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

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  Key Transparency

  Key Transparency allows communicating parties to verify public keys used in end-to-end encryption. In many end-to-end encryption services, to initiate communication a user will reach out to a central server and request the public keys of the user with which they wish to communicate. If the central server is malicious or becomes compromised, a man-in-the-middle attack can be launched through the issuance of incorrect public keys. The communications can then be intercepted and manipulated. Additionally, legal pressure could be applied by surveillance agencies to manipulate public keys and read messages. With Key Transparency, public keys are posted to a public log that can be universally audited. Communicating parties can verify public keys used are accurate.

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