A corpus language is a language that has no living speakers but for which numerous records produced by its native speakers survive. Examples of corpus languages are Ancient Greek, Latin, the Egyptian language, Old English, Old Norse, Elamite, and Sanskrit. Some corpus languages, such as Ancient Greek and Latin, left very large corpora and therefore can be fully reconstructed, even though some details of pronunciation may be unclear. Such languages can be used even today, as is the case with Sanskrit and Latin. Other languages have such limited corpora that some important words—e.g., some pronouns—are lacking in the corpora. Examples of these are Ugaritic and Gothic. Languages attested only by a few words, often names, and a few phrases, are called Trümmersprache (literally "rubble languages") in German linguistics. These can be reconstructed only in a very limited way, and often their genetic relationship to other languages remains unclear. Examples are Dalmatian, Etruscan, also known as Rasenna, Dadanitic, a Semitic language that may be close to classical Arabic, Lombardic, Burgundian, Vandalic, and Oscan, Umbrian, and Faliscan, all Italic languages that were related to Latin. Corpus languages are studied using the methods of corpus linguistics, but corpus linguistics can also be used (and is commonly used) for the study of the writings and other records of living languages. Not all extinct languages are corpus languages, since there are many extinct languages in which few or no writings or other records survive, as is the case in the vast majority of languages that have ever existed.
Zamzar
Zamzar is an online file converter and compressor, created by brothers Mike and Chris Whyley in England in 2006. It allows users to convert files online, without downloading a software tool, and supports over 1,200 different conversion types. Since its formation, the service has converted over 510 million files for users from 245 different countries. The service supports the conversion of documents, images, audio, video, e-Books, CAD files and compressed file formats. Users can type in a URL or upload one or more files (if they are all of the same format) from their computer; Zamzar will then convert the file(s) to another user-specified format, such as an Adobe PDF file to a Microsoft Word document. Once conversion is complete, users can immediately download the file from their web browser. Users can also choose to receive an email with a link to download the converted file. In February 2021 Zamzar expanded their tool and announced a new file compression service. The compressor is visually similar to the conversion tool with a drag and drop download feature. As with the converter, users have the option to subscribe for a paid plan if they wish to compress multiple or larger files than the free service permits == File conversion API == in 2015 Zamzar launched a file conversion API, allowing users to integrate file conversion capabilities into their own websites and applications. Sample code is provided to allow users to integrate file conversion capabilities in C#, Java, Node.js, PHP, Python and cURL. Zamzar also maintains a project on GitHub which allows users to perform file conversion from the command line on Linux, MacOS or Windows systems. == Email file conversion == It is also possible to send files for conversion by emailing them to Zamzar. Zamzar launched this capability in 2012, allowing users to email files to dedicated email addresses for the file to be automatically converted to a different format. A link is then emailed back to the end user to allow them to download their converted file. == User privilege levels == Zamzar is currently free to use, but there is a limit of two conversions per hour for files up to 100MB. Users can pay a monthly subscription in order to access preferential features, such as unlimited file conversions, online file management, shorter response and queuing times and other benefits. == Name == Its name comes from Franz Kafka's The Metamorphosis. Its main character is called Gregor Samsa and it is from his surname that Zamzar is derived. The founders of the service considered three other names – Konvertieren, Khamailen and Obrogo – before settling on Zamzar.
Dynamic knowledge repository
The dynamic knowledge repository (DKR) is a concept developed by Douglas C. Engelbart as a primary strategic focus for allowing humans to address complex problems. He has proposed that a DKR will enable us to develop a collective IQ greater than any individual's IQ. References and discussion of Engelbart's DKR concept are available at the Doug Engelbart Institute. == Definition == A knowledge repository is a computerized system that systematically captures, organizes and categorizes an organization's knowledge. The repository can be searched and data can be quickly retrieved. The effective knowledge repositories include factual, conceptual, procedural and meta-cognitive techniques. The key features of knowledge repositories include communication forums. A knowledge repository can take many forms to "contain" the knowledge it holds. A customer database is a knowledge repository of customer information and insights – or electronic explicit knowledge. A Library is a knowledge repository of books – physical explicit knowledge. A community of experts is a knowledge repository of tacit knowledge or experience. The nature of the repository only changes to contain/manage the type of knowledge it holds. A repository (as opposed to an archive) is designed to get knowledge out. It should therefore have some rules of structure, classification, taxonomy, record management, etc., to facilitate user engagement.
Social recruiting
Social recruiting (social hiring or social media recruitment) is recruiting candidates by using social platforms as talent databases or for advertising. Social recruiting uses social media profiles, blogs, and other Internet sites to find information on candidates. It also uses social media to advertise jobs either through HR vendors or through crowdsourcing where job seekers and others share job openings within their online social networks. Social recruiting's effectiveness and return on investment have been difficult to determine, since applicants do not usually apply through the social channels which first attracted them. In May 2013, Maximum Employment Marketing Group released the Social Recruitment Monitor, which ranks the reach, engagement, and interactivity of employers' social recruiting efforts around the world. == Social recruitment software == The social recruitment software market (a form of e-recruitment) is often included in the wider talent management software sector. Bersin & Associates valued the wider talent management market at over $2bn in 2007. Social recruitment increasingly sits at an intersection of a number of fast-moving areas including social networking, recruitment and now cloud computing. Additionally, mobile recruiting has become another hot topic, especially with the rise in tablet and smartphone usage. In 2012, there was a rise of tech companies using social recruiting applications to find and screen applicants. As more companies saw value in filling jobs by putting them on the social platforms where millions of people spend at least 37 minutes daily, there developed a much larger focus on social recruiting among the talent acquisition community. By mid-2013, many major enterprise companies such as Pepsi, Gap, AIG, and Oracle had begun effectively utilizing social recruiting software, making it clear that large corporations were open to automating or streamlining (and ultimately investing in) their social recruiting processes.
HKDF
HKDF is a multi-purpose key derivation function (KDF) based on the HMAC message authentication code. HKDF follows "extract-then-expand" paradigm, where the KDF logically consists of two modules: the first stage takes the input keying material and "extracts" from it a fixed-length pseudorandom key, and then the second stage "expands" this key into several additional, independent pseudorandom keys as the output of the KDF. == Mechanism == HKDF is the composition of two functions, HKDF-Extract and HKDF-Expand: HKDF(salt, IKM, info, length) = HKDF-Expand(HKDF-Extract(salt, IKM), info, length) === HKDF-Extract === HKDF-Extract (XTR) takes "input key material" or "source key material" (IKM or SKM) such as a shared secret generated using Diffie-Hellman; an optional, non-secret, random or pseudorandom salt (r); and generates a cryptographic key called the PRK ("pseudorandom key"). HKDF-Extract acts as a "randomness extractor", specifically a "computational extractor", taking a potentially non-uniform value of sufficient min-entropy and generating a value indistinguishable from a uniform random value (pseudorandom). Computational extractors assume attackers are computationally bounded and source entropy may only exist in a computational sense. Such extractors can be built using cryptographic functions under suitable assumptions, modeled as universal hash function (in the generic case) or a random oracle (in constrained scenarios like sources with weak entropy). Salt (r) acts as a "source-independent extractor", strengthening HKDF's security guarantees. Using a fixed public r is safe for multiple invocations of HKDF (on "independent" but secret IKMs which may or may not be derived from the same source), provided r isn't chosen or manipulated by an attacker. Ideally, r is a random string of hash function's output length. Even low quality r (weak entropy or shorter length) is recommended as they contribute "significantly" to the security of the OKM. Without or with a low-entropy, non-secret r, if an attacker can influence the IKMs source in a way that specifically exploits HKDF-Extract's underlying hash function (finding a collision or a specific bias), XTR provides no protection. A random r, even if fixed by the application (for example, random number generators using r as seed), would strengthen protections for that specific extractor session. In such a setting, sufficiently long IKMs also provide better entropy extraction. However, allowing the attacker to influence enough of the IKM after seeing r may result in a completely insecure KDF. HKDF-Extract is the result of HMAC with r as the key (all zeros up to length of the underlying extractor hash function, if not provided) and the IKM as the message. The underlying hash function used for HKDF-Extract step may be different to the one used by HKDF-Expand. It is recommended that HKDF-Extract uses strongest hash function available to the application, as it "concentrates" the entropy already present in IKM but may not necessarily "add" to it. Truncated output from a stronger underlying hash function for XTR (for example, SHA512/256) offers stronger extraction properties. The attacker is assumed to have partial knowledge about IKM (publicly known values in the case of Diffie-Hellman) or partial control over it (entropy pools). HKDF-Extract may be skipped if the IKM is itself a cryptographically strong key (and hence can assume the role of PRK), though it is recommended that HKDF-Extract be applied for the sake of compatibility with the general case, especially if r is available to the application. === HKDF-Expand === HKDF-Expand (PRF) takes the PRK (or any random key-derivation key if HKDF-Extract step is skipped), optional info (CTXinfo), and a length (L), to generate output key material (OKM) of length L. Multiple OKMs can be generated from a single PRK by using different values for CTXinfo, which must be "independent" of the IKM passed in HKDF-Extract. Even if an attacker, who knows r and some auxillary information about the secret IKM, can force the use of the same IKM (and PRK, by extension), in two or more HKDF-Expand contexts (represented by CTXinfo), the OKMs output are computationally independent (leak no useful information on each other). HKDF-Expand, acting as a variable-output-length pseudorandom function (PRF) keyed on PRK, calls HMAC on CTXinfo as the message (empty string, if unspecified) appended to a 8-bit counter i initialized to 1. Subsequent calls to HMAC are chained in "feedback mode" by prepending the previous HMAC output to CTXinfo and incrementing i. OKM is a function of the output size (k bits) of HMAC's underlying hash function; i.e., SHA-256 outputs OKM in segments of k=256 bits for up to a maximum of length i × k bits (255 × 256 bits = 8160 bytes) truncated to desired length L. HKDF-Expand may be skipped if PRK is at least desired length L, though it is recommended that HKDF-Expand be applied for additional "smoothing" of the OKM. == Standardization == HKDF was proposed as a building block in various protocols and applications, as well as to discourage the proliferation of multiple KDF mechanisms by its authors. It is formally described in RFC 5869 with detailed analysis in a paper published in 2010. NIST SP800-56Cr2 specifies a parameterizable extract-then-expand scheme, noting that RFC 5869 HKDF is a version of it and citing its paper for the rationale for the recommendations' extract-and-expand mechanisms. == Applications == HKDF is used in the Signal Protocol for end-to-end encrypted messaging where it generates the message keys, in conjunction with the triple Elliptic-curve Diffie-Hellman handshake (X3DH) key agreement protocol. Signal's "Secure Value Recovery" and "Sealed Sender" are based on HKDF. HKDF is a main component in the Noise Protocol Framework, Message Layer Security, and is used in widely deployed protocols like IPsec Internet Key Exchange and TLS 1.3. The "multi-purpose" nature of HKDF is meant to serve applications that require key extraction, key expansion, and key hierarchies in key wrapping, key exchange, PRNG, and password-based key derivation schemes. == Implementations == There are implementations of HKDF for C#, Go, Java, JavaScript, Perl, PHP, Python, Ruby, Rust, and other programming languages. RFC6234 lays out a reference C implementation of HKDF based on the Secure Hash Standard. === Example in Python ===
Hancom Office
Hancom Office is a proprietary office suite that includes a word processor, spreadsheet software, presentation software, and a PDF editor as well as their online versions accessible via a web browser. It is primarily addressed to Korean users. Hancom Office is written in Java and C++ that runs on Android, iOS, macOS and Windows platforms. == Products == Hangul - Hangul is a word processor developed by Hancom. It is a product that eliminates the inconvenience of the original Hangul word processor, which was limited to Hangul cards or PC models. Originally, the name was written using the '아래아' character, a vowel letter that is obsolete in modern Korean, and it was referred to as 'HWP' (an abbreviation for Hangul Word Processor), '아래아 한글' (Arae-a Hangul), '한/글' (Han/Geul), and so on. Hangul is currently the most widely used word processor in South Korea, often used alongside Microsoft Word. HanWord - word processor compatible with Word HanCell - spreadsheet program HanShow - presentation program Hancom Office Hanword Viewer - For viewing documents created by Hancom Office or Microsoft Office
Conditional disclosure of secrets
Conditional disclosure of secrets (CDS) is a primitive, studied in information-theoretic cryptography, that allows distributed, non-communicating parties to coordinate the release of information to a third party. CDS was initially introduced for use in the context of private information retrieval, and has been related to communication complexity and non-local quantum computation. == Definition of conditional disclosure of secrets == The conditional disclosure of secrets setting involves three players; Alice, Bob and the referee. Alice receives an input x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} and a secret z ∈ { 0 , 1 } {\displaystyle z\in \{0,1\}} , and Bob receives a string y ∈ { 0 , 1 } n {\displaystyle y\in \{0,1\}^{n}} . A choice of Boolean function f : { 0 , 1 } 2 n → { 0 , 1 } {\displaystyle f:\{0,1\}^{2n}\rightarrow \{0,1\}} is fixed in advance and known to all players. Alice and Bob cannot communicate with one another, but share a string of random bits which we label r {\displaystyle r} . Alice and Bob compute messages m A = m A ( x , z , r ) {\displaystyle m_{A}=m_{A}(x,z,r)} and m B = m B ( y , r ) {\displaystyle m_{B}=m_{B}(y,r)} , which they send to the referee. The referee knows ( x , y ) {\displaystyle (x,y)} . A CDS protocol consists of the encoding maps applied by Alice and Bob. A protocol is said to be ϵ {\displaystyle \epsilon } -correct if, for all ( x , y ) ∈ f − 1 ( 1 ) {\displaystyle (x,y)\in f^{-1}(1)} , the referee can determine z {\displaystyle z} with probability 1 − ϵ {\displaystyle 1-\epsilon } . A protocol is said to be δ {\displaystyle \delta } -secure if, for all ( x , y ) ∈ f − 1 ( 0 ) {\displaystyle (x,y)\in f^{-1}(0)} the distribution of the messages is δ {\displaystyle \delta } close to a simulator distribution (in total variation distance), where the simulator distribution is independent of z {\displaystyle z} . The communication complexity of a CDS protocol P is the total number of bits of message sent by Alice and Bob. The CDS communication cost of a function, C D S ϵ , δ ( f ) {\displaystyle CDS_{\epsilon ,\delta }(f)} is the minimal communication cost of an ϵ {\displaystyle \epsilon } -correct, δ {\displaystyle \delta } secure protocol that implements f {\displaystyle f} . The randomness complexity and randomness cost of implementing a function in the CDS model are defined similarly, but consider the number of bits of shared random bits held by Alice and Bob. == Basic properties of the primitive == === Amplification === Supposing we have an ϵ {\displaystyle \epsilon } -correct and δ {\displaystyle \delta } -secure CDS protocol, it is known that we can find a new protocol which reduces the correctness and privacy errors at the expense of an increased communication and randomness cost. More specifically, the following theorem has been proven Theorem (Amplification). A CDS protocol for f which supports a single-bit secret with privacy and correctness error of 1/3 can be transformed into a new CDS protocol with privacy and correctness error of 2 − Ω ( k ) {\displaystyle 2^{-\Omega (k)}} and communication/randomness complexity which are larger than those of the original protocol by a multiplicative factor of O(k). In fact, somewhat more than the above theorem is true in that the size of the secret can also be made to be of length k {\displaystyle k} , while simultaneously reducing the correctness and privacy errors as above. The proof involves first encoding the secret z {\displaystyle z} into a secret sharing scheme, and then running the original CDS protocol on each share of the resulting scheme. === Closure === If a CDS protocol for a function f {\displaystyle f} is known, then certain simple modifications of f {\displaystyle f} have CDS protocols with similar efficiency. The simplest case is to consider a CDS protocol for function f {\displaystyle f} and ask for a similarly efficient protocol for the negation of f {\displaystyle f} , labelled ¬ f {\displaystyle \neg f} . This is addressed by the following theorem Theorem (CDS is closed under complement). Suppose that f has a CDS protocol with randomness cost of ρ {\displaystyle \rho } bits, communication complexity of t {\displaystyle t} bits, and privacy and correctness errors δ = ϵ = 2 − k {\displaystyle \delta =\epsilon =2^{-k}} . Then ¬ f {\displaystyle \neg f} has a CDS scheme with similar privacy and correctness errors, and randomness and communication complexity of O ( k 3 ρ 2 t + k 3 ρ 3 ) {\displaystyle O(k^{3}\rho ^{2}t+k^{3}\rho ^{3})} . The cost of a CDS protocol is also closed under formula's, in the following sense. Consider two functions f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . Then, the communication and randomness costs of f 1 ∧ f 2 {\displaystyle f_{1}\wedge f_{2}} as well as f 1 ∨ f 2 {\displaystyle f_{1}\vee f_{2}} are not much larger than the sum of the costs for f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . See Applebaum et al. for a precise statement. == Upper and lower bounds on communication cost == Given a function f {\displaystyle f} we would like to understand the communication and randomness costs to implement f {\displaystyle f} in the CDS setting. Towards understanding this, protocols for implementing CDS have been developed (which give an upper bound on the cost) and lower bound strategies have been developed. For most functions, there is a large gap between the known upper and lower bound, so understanding the cost of CDS remains largely an open problem. This section presents some of what is known so far about the cost of CDS. === Secret sharing based upper bound === A subject with a close relationship to CDS is secret sharing. Secret sharing constructions provide an upper bound on the cost of CDS protocols. A secret sharing scheme encodes a secret, s {\displaystyle s} into a set of shares S 1 , . . . , S n {\displaystyle S_{1},...,S_{n}} . Associated to any secret sharing scheme is an access structure, which consists of a set of authorized sets A = A 1 , . . . , A k {\displaystyle {\mathcal {A}}={A_{1},...,A_{k}}} with A i ⊆ { S 1 , . . . , S n } {\displaystyle A_{i}\subseteq \{S_{1},...,S_{n}\}} . The authorized sets are those subsets of the A i {\displaystyle A_{i}} from which it is possible to recover the secret recorded into the scheme. A succinct way to describe an access structure is in terms of a function f A : { 0 , 1 } n → { 0 , 1 } {\displaystyle f_{\mathcal {A}}:\{0,1\}^{n}\rightarrow \{0,1\}} . Each subset of the shares K [ x ] ⊂ { S 1 , . . . , S n } {\displaystyle K[x]\subset \{S_{1},...,S_{n}\}} is labelled by a string x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} such that x i = 1 {\displaystyle x_{i}=1} if and only if S i ∈ K {\displaystyle S_{i}\in K} . Then we define f A {\displaystyle f_{\mathcal {A}}} to be such that f A ( x ) = 1 {\displaystyle f_{\mathcal {A}}(x)=1} if and only if K [ x ] ∈ A {\displaystyle K[x]\in {\mathcal {A}}} . In words, the function f A {\displaystyle f_{\mathcal {A}}} is 1 when given an authorized subset as input, and 0 otherwise. A basic result in the theory of secret sharing is that an access structure A {\displaystyle {\mathcal {A}}} can be realized in a secret sharing scheme if and only if f A {\displaystyle f_{\mathcal {A}}} is monotone. The size of a secret sharing scheme is defined as the total number of bits in the shares S i {\displaystyle S_{i}} . For monotone functions, there is an upper bound on the communication cost in CDS for any monotone function f {\displaystyle f} in terms of the size of any secret sharing scheme with access structure given by f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S h a r i n g S i z e ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SharingSize(f)} For some concrete classes of secret sharing schemes, this relationship can be extended to general (non-monotone) Boolean functions. This leads to an upper bound on CDS cost in terms of the size of any span program that computes f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S P k ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SP_{k}(f)} The class of problems with efficient (polynomial size) span program is the complexity class M o d k L {\displaystyle Mod_{k}L} , so problems in this class have efficient CDS protocols. === Sub-exponential upper bounds for all functions === Using a matching vector family based construction, it has been proven that ∀ f , C D S ϵ = 0 , δ = 0 ( f ) ≤ 2 O ( n log n ) {\displaystyle \forall f,\,\,\,\,\,\,CDS_{\epsilon =0,\delta =0}(f)\leq 2^{O({\sqrt {n\log n}})}} . The technique for this proof is similar to one used to prove upper bounds on private information retrieval. This upper bound on CDS also leads to sub-exponential upper bounds on the size of a large class of secret sharing schemes. === Lower bounds from communication complexity === In a CDS protocol, the referee is given the inputs ( x , y ) {\displaystyle (x,y)} . This means it is not clear if the messages sent by Alice a