AI Email Write Up

AI Email Write Up — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Keyword extraction

    Keyword extraction

    Keyword extraction is tasked with the automatic identification of terms that best describe the subject of a document. Key phrases, key terms, key segments or just keywords are the terminology which is used for defining the terms that represent the most relevant information contained in the document. Although the terminology is different, function is the same: characterization of the topic discussed in a document. The task of keyword extraction is an important problem in text mining, information extraction, information retrieval and natural language processing (NLP). == Keyword assignment vs. extraction == Keyword assignment methods can be roughly divided into: keyword assignment (keywords are chosen from controlled vocabulary or taxonomy) and keyword extraction (keywords are chosen from words that are explicitly mentioned in original text). Methods for automatic keyword extraction can be supervised, semi-supervised, or unsupervised. Unsupervised methods can be further divided into simple statistics, linguistics or graph-based, or ensemble methods that combine some or most of these methods.

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  • Data verification

    Data verification

    Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. In some domains it is referred to Source Data Verification (SDV), such as in clinical trials. Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss. Methods for data verification include double data entry, proofreading and automated verification of data. Proofreading data involves someone checking the data entered against the original document. This is also time-consuming and costly. Automated verification of data can be achieved using one way hashes locally or through use of a SaaS based service such as Q by SoLVBL to provide immutable seals to allow verification of the original data.

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  • Myrinet

    Myrinet

    Myrinet, ANSI/VITA 26-1998, is a high-speed local area networking system designed by the company Myricom to be used as an interconnect between multiple machines to form computer clusters. == Description == Myrinet was promoted as having lower protocol overhead than standards such as Ethernet, and therefore better throughput, less interference, and lower latency while using the host CPU. Although it can be used as a traditional networking system, Myrinet is often used directly by programs that "know" about it, thereby bypassing a call into the operating system. Earlier versions of Myrinet used a variety of media and connectors: Generation 2 used copper media with DC-37 (Myrinet-LAN, M2L- controllers and switches) or microribbon (Myrinet-SAN, M2M-) connectors. Generation 3 used copper media with HSSDC (Myrinet-Serial, M3S-) or microribbon (Myrinet-SAN, M3M-) connectors, or fiber with LC-connectors (Myrinet-Fiber, M3F-). The later versions of Myrinet physically consist of two fibre optic cables, upstream and downstream, connected to the host computers with a single connector. Machines are connected via low-overhead routers and switches, as opposed to connecting one machine directly to another. Myrinet includes a number of fault-tolerance features, mostly backed by the switches. These include flow control, error control, and "heartbeat" monitoring on every link. The "fourth-generation" Myrinet, called Myri-10G, supported a 10 Gbit/s data rate and can use 10 Gigabit Ethernet on PHY, the physical layer (cables, connectors, distances, signaling). Myri-10G started shipping at the end of 2005. Myrinet was approved in 1998 by the American National Standards Institute for use on the VMEbus as ANSI/VITA 26-1998. One of the earliest publications on Myrinet is a 1995 IEEE article. === Performance === Myrinet is a lightweight protocol with little overhead that allows it to operate with throughput close to the basic signaling speed of the physical layer. For supercomputing, the low latency of Myrinet is even more important than its throughput performance, since, according to Amdahl's law, a high-performance parallel system tends to be bottlenecked by its slowest sequential process, which in all but the most embarrassingly parallel supercomputer workloads is often the latency of message transmission across the network. === Deployment === According to Myricom, 141 (28.2%) of the June 2005 TOP500 supercomputers used Myrinet technology. In the November 2005 TOP500, the number of supercomputers using Myrinet was down to 101 computers, or 20.2%, in November 2006, 79 (15.8%), and by November 2007, 18 (3.6%), a long way behind gigabit Ethernet at 54% and InfiniBand at 24.2%. In the June 2014 TOP500 list, the number of supercomputers using Myrinet interconnect was 1 (0.2%). In November 2013, the assets of Myricom (including the Myrinet technology) were acquired by CSP Inc. In 2016, it was reported that Google had also offered to buy the company.

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  • OARnet

    OARnet

    The Ohio Academic Resources Network (OARnet) is a state-funded IT organization that provides member organizations with intrastate networking, virtualization and cloud computing applications, advanced videoconferencing, connections to regional and international research networks and the commodity Internet, colocation services, and emergency web-hosting. The OARnet network (known for a time as Third Frontier Network and later, OSCnet) is a dedicated, statewide, high-speed fiber-optic network that serves Ohio K-12 schools, college and university campuses, academic medical centers, public broadcasting stations and state and local/state government. OARnet is connected in Cleveland and Cincinnati to Internet2, the United States' most advanced nationwide research and education network. OARnet also maintains direct connections to Michigan's Merit network and OmniPoP in Chicago. OARnet offices are located on the West Campus of Ohio State University in Columbus, Ohio, United States. OARnet additionally serves as the delegated registrar for many third-level domains (both generic and locality-based) under .oh.us and some under .in.us and .ky.us. == History == A member-organization of the Ohio Technology Consortium, the technology and information division of the Ohio Board of Regents (now the Ohio Department of Higher Education), OARnet was created by the Ohio General Assembly in 1987 to provide Ohio researchers with network connectivity to the resources of the Ohio Supercomputer Center (OSC). It was recognized at the time that the network would serve a much broader audience, so when a network name was selected in early 1988, OARnet was chosen to emphasize the many uses of the network. The initial plan (1987) was to make use of a number of existing BITNET and CCnet (regional DECnet network) connections to get started. Three network (compatible) protocols were used, NJE, DECnet, and TCP/IP. The first OARnet-funded line was installed between Case Western Reserve University and John Carroll University in June 1987. Many subsequent lines at 9.6 kbit/s, 56 kbit/s, and T1 (1.544 Mbit/s) were installed with the aid of an Ohio Department of Administrative Services contract with Litel Corp. Internet (then NSFNET) connections were obtained in the spring of 1988. The non-TCP/IP protocols were soon phased out, and a process of upgrading connections took place regularly. In 1991, it was decided that OARnet would accept commercial business, at appropriate rates, for Internet connection services. Thus OARnet became one of the first Internet service providers (ISPs) in Ohio. After commercial ISPs entered the business extensively, OARnet stopped seeking new commercial accounts. A very large increase in backbone capacity occurred (planning 2000–02, installation 2003–04) when it became possible to lease optical fiber lines themselves ("dark fiber"). A new network backbone of 1,850 miles was installed at much higher capacity, and the eTech Ohio Commission and the Ohio Department of Education joined in funding and using OARnet. The fiber-optic backbone was launched in November 2004. In 2006, OARnet provided one of the first networks for delivery of live TV via Internet Protocol, known today as IPTV. OARnet served as the backbone for Ohio News Network to transmit Miami Redhawks hockey. The team finished the 2008-2009 season at the Frozen Four with a 4-3 OT loss to Boston University in the championship. It was one of the first live sports transmission deliveries over IPTV in the US. Another sharp jump in capacity occurred in 2012, when the State of Ohio funded an upgrade of the OARnet backbone to 100 Gigabits per second. Today, more than 1,500 miles of Ohio’s network backbone runs at an ultra-fast 100 Gbit/s, which was recognized by ComputerWorld in the Emerging Technology category of their 2013 Computerworld Honors Laureates program. In November 2012, Case Western Reserve University became the first member institution to connect at 100 Gbit/s to the OARnet backbone. The OARnet leaders have been: Russell M. Pitzer, director, 1987–88 Alison Brown, director, 1988–94 John Ritter, acting director, 1995 Larry Buell, acting director, 1996–97 Douglas Gale, director, 1998–2002 Alvin Stutz, director, 2002–05 Pankaj Shah, executive director, 2005–15 Paul Schopis, interim executive director, 2015–2018, executive director 2018–19 Denis Walsh, interim executive director, 2019–20 Pankaj Shah, executive director, 2020–

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  • Augment (app)

    Augment (app)

    Augment is an augmented reality SaaS platform that allows users to visualize their products in 3D in real environment and in real-time through tablets or smartphones. The software can be used for retail, e-commerce, architecture, and other purposes. Augment created a mobile app of the same name, used to visualize 3D models in augmented reality and a web application called Augment Manager for 3D content management. The company is based in Paris, France, and was founded in October 2011 by Jean-François Chianetta, Cyril Champier, and Mickaël Jordan. In March 2016, Augment announced €3 million in its series-A round from Salesforce Ventures, which bringing the total funding since launch to $4.7 million. Augment lets businesses and 3D professionals visualize projects in their actual size and environment, on iPhone, iPad, and Android, using the power of augmented reality. Users can print the Augment tracker or create their own tracker to place the 3D models in space and at scale in real time. Common uses of the technology include product presentations, interactive print campaigns and e-Commerce product visualization. Augment has just released its augmented reality SDK solutions for retail and augmented commerce. The SDK solutions, available for both native mobile app and web integrations, allow companies to embed augmented reality product visualization in their existing eCommerce platforms. == Technology == Augment uses the following 3D technologies: Vuforia Augmented Reality SDK OpenGL == Customer cases == Companies such as Coca-Cola, Siemens, Nokia, Nestle, and Boeing are using Augment's solutions. == History == Augment was first created by Jean-François Chianetta in October 2011. Chianetta later teamed up with Cyril Champier and Mickaël Jordan for further development. The co-founding team was among the 12 startups of Season 3 of French accelerator Le Camping. The team raised one million euros (US$1,300,000) in April 2013 and moved its office to Paris. In March 2016, Augment raised US$3M Series A funding from Salesforce and other investors. In 2013, Augment's first service, Boost Business Catalog, was made available to help businesses catalogue and display their product models. Customers can rotate the images in 3D and view augmented content before deciding what to buy. == Awards == "Best Innovation" at Ecommerce Mag Trophy 2013

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  • InfiniBand

    InfiniBand

    InfiniBand (IB) is a computer networking standard used in high-performance computing that features very high throughput and very low latency. It is used for data interconnect both among and within computers. InfiniBand is also used as either a direct or switched interconnect between servers and storage systems, as well as an interconnect between storage systems. It is designed to be scalable and uses a switched fabric network topology. Between 2014 and June 2016, it was the most commonly used interconnect in the TOP500 list of supercomputers. Mellanox (acquired by Nvidia) manufactures InfiniBand host bus adapters and network switches, which are used by large computer system and database vendors in their product lines. As a computer cluster interconnect, IB competes with Ethernet, Fibre Channel, and Intel Omni-Path. The technology is promoted by the InfiniBand Trade Association. == History == InfiniBand originated in 1999 from the merger of two competing designs: Future I/O and Next Generation I/O (NGIO). NGIO was led by Intel, with a specification released in 1998, and joined by Sun Microsystems and Dell. Future I/O was backed by Compaq, IBM, and Hewlett-Packard. This led to the formation of the InfiniBand Trade Association (IBTA), which included both sets of hardware vendors as well as software vendors such as Microsoft. At the time it was thought some of the more powerful computers were approaching the interconnect bottleneck of the PCI bus, in spite of upgrades like PCI-X. Version 1.0 of the InfiniBand Architecture Specification was released in 2000. Initially the IBTA vision for IB was simultaneously a replacement for PCI in I/O, Ethernet in the machine room, cluster interconnect and Fibre Channel. IBTA also envisaged decomposing server hardware on an IB fabric. Mellanox had been founded in 1999 to develop NGIO technology, but by 2001 shipped an InfiniBand product line called InfiniBridge at 10 Gbit/second speeds. Following the burst of the dot-com bubble there was hesitation in the industry to invest in such a far-reaching technology jump. By 2002, Intel announced that instead of shipping IB integrated circuits ("chips"), it would focus on developing PCI Express, and Microsoft discontinued IB development in favor of extending Ethernet. Sun Microsystems and Hitachi continued to support IB. In 2003, the System X supercomputer built at Virginia Tech used InfiniBand in what was estimated to be the third largest computer in the world at the time. The OpenIB Alliance (later renamed OpenFabrics Alliance) was founded in 2004 to develop an open set of software for the Linux kernel. By February, 2005, the support was accepted into the 2.6.11 Linux kernel. In November 2005 storage devices finally were released using InfiniBand from vendors such as Engenio. Cisco, desiring to keep technology superior to Ethernet off the market, adopted a "buy to kill" strategy. Cisco successfully killed InfiniBand switching companies such as Topspin via acquisition. Of the top 500 supercomputers in 2009, Gigabit Ethernet was the internal interconnect technology in 259 installations, compared with 181 using InfiniBand. In 2010, market leaders Mellanox and Voltaire merged, leaving just one other IB vendor, QLogic, primarily a Fibre Channel vendor. At the 2011 International Supercomputing Conference, links running at about 56 gigabits per second (known as FDR, see below), were announced and demonstrated by connecting booths in the trade show. In 2012, Intel acquired QLogic's InfiniBand technology, leaving only one independent supplier. By 2014, InfiniBand was the most popular internal connection technology for supercomputers, although within two years, 10 Gigabit Ethernet started displacing it. In 2016, it was reported that Oracle Corporation (an investor in Mellanox) might engineer its own InfiniBand hardware. In 2019 Nvidia acquired Mellanox, the last independent supplier of InfiniBand products. == Specification == Specifications are published by the InfiniBand trade association. === Performance === Original names for speeds were single-data rate (SDR), double-data rate (DDR) and quad-data rate (QDR) as given below. Subsequently, other three-letter initialisms were added for even higher data rates. Notes Each link is duplex. Links can be aggregated: most systems use a 4 link/lane connector (QSFP). HDR often makes use of 2x links (aka HDR100, 100 Gb link using 2 lanes of HDR, while still using a QSFP connector). NDR introduced OSFP connectors which host one or two links at 2x (NDR200) or 4x (NDR400). They are not logically configured as a single 8x link, even when connecting switches together with an OSFP cable. InfiniBand provides remote direct memory access (RDMA) capabilities for low CPU overhead. === Topology === InfiniBand uses a switched fabric topology, as opposed to early shared medium Ethernet. All transmissions begin or end at a channel adapter. Each processor contains a host channel adapter (HCA) and each peripheral has a target channel adapter (TCA). These adapters can also exchange information for security or quality of service (QoS). === Messages === InfiniBand transmits data in packets of up to 4 KB that are taken together to form a message. A message can be: a remote direct memory access read or write a channel send or receive a transaction-based operation (that can be reversed) a multicast transmission an atomic operation === Physical interconnection === In addition to a board form factor connection, it can use both active and passive copper (up to 10 meters) and optical fiber cable (up to 10 km). QSFP connectors are used. The InfiniBand Association also specified the CXP connector system for speeds up to 120 Gbit/s over copper, active optical cables, and optical transceivers using parallel multi-mode fiber cables with 24-fiber MPO connectors. === Software interfaces === Mellanox operating system support is available for Solaris, FreeBSD, Red Hat Enterprise Linux, SUSE Linux Enterprise Server (SLES), Windows, HP-UX, VMware ESX, and AIX. InfiniBand has no specific standard application programming interface (API). The standard only lists a set of verbs such as ibv_open_device or ibv_post_send, which are abstract representations of functions or methods that must exist. The syntax of these functions is left to the vendors. Sometimes for reference this is called the verbs API. The de facto standard software is developed by OpenFabrics Alliance and called the Open Fabrics Enterprise Distribution (OFED). It is released under two licenses GPL2 or BSD license for Linux and FreeBSD, and as Mellanox OFED for Windows (product names: WinOF / WinOF-2; attributed as host controller driver for matching specific ConnectX 3 to 5 devices) under a choice of BSD license for Windows. It has been adopted by most of the InfiniBand vendors, for Linux, FreeBSD, and Microsoft Windows. IBM refers to a software library called libibverbs, for its AIX operating system, as well as "AIX InfiniBand verbs". The Linux kernel support was integrated in 2005 into the kernel version 2.6.11. === Ethernet over InfiniBand === Ethernet over InfiniBand, abbreviated to EoIB, is an Ethernet implementation over the InfiniBand protocol and connector technology. EoIB enables multiple Ethernet bandwidths varying on the InfiniBand (IB) version. Ethernet's implementation of the Internet Protocol Suite, usually referred to as TCP/IP, is different in some details compared to the direct InfiniBand protocol in IP over IB (IPoIB).

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  • Manufacturing Automation Protocol

    Manufacturing Automation Protocol

    Manufacturing Automation Protocol (MAP) was a computer network standard released in 1982 for interconnection of devices from multiple manufacturers. It was developed by General Motors to combat the proliferation of incompatible communications standards used by suppliers of automation products such as programmable controllers. By 1985 demonstrations of interoperability were carried out and 21 vendors offered MAP products. In 1986 the Boeing corporation merged its Technical Office Protocol with the MAP standard, and the combined standard was referred to as "MAP/TOP". The standard was revised several times between the first issue in 1982 and MAP 3.0 in 1987, with significant technical changes that made interoperation between different revisions of the standard difficult. Although promoted and used by manufacturers such as General Motors, Boeing, and others, it lost market share to the contemporary Ethernet standard and was not widely adopted. Difficulties included changing protocol specifications, the expense of MAP interface links, and the speed penalty of a token-passing network. The token bus network protocol used by MAP became standardized as IEEE standard 802.4 but this committee disbanded in 2004 due to lack of industry attention.

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  • Format-preserving encryption

    Format-preserving encryption

    In cryptography, format-preserving encryption (FPE), refers to encrypting in such a way that the output (the ciphertext) is in the same format as the input (the plaintext). The meaning of "format" varies. Typically only finite sets of characters are used; numeric, alphabetic or alphanumeric. For example: Encrypting a 16-digit credit card number so that the ciphertext is another 16-digit number. Encrypting an English word so that the ciphertext is another English word. Encrypting an n-bit number so that the ciphertext is another n-bit number (this is the definition of an n-bit block cipher). For such finite domains, and for the purposes of the discussion below, the cipher is equivalent to a permutation of N integers {0, ... , N−1} where N is the size of the domain. == Motivation == === Restricted field lengths or formats === One motivation for using FPE comes from the problems associated with integrating encryption into existing applications, with well-defined data models. A typical example would be a credit card number, such as 1234567812345670 (16 bytes long, digits only). Adding encryption to such applications might be challenging if data models are to be changed, as it usually involves changing field length limits or data types. For example, output from a typical block cipher would turn credit card number into a hexadecimal (e.g.0x96a45cbcf9c2a9425cde9e274948cb67, 34 bytes, hexadecimal digits) or Base64 value (e.g. lqRcvPnCqUJc3p4nSUjLZw==, 24 bytes, alphanumeric and special characters), which will break any existing applications expecting the credit card number to be a 16-digit number. Apart from simple formatting problems, using AES-128-CBC, this credit card number might get encrypted to the hexadecimal value 0xde015724b081ea7003de4593d792fd8b695b39e095c98f3a220ff43522a2df02. In addition to the problems caused by creating invalid characters and increasing the size of the data, data encrypted using the CBC mode of an encryption algorithm also changes its value when it is decrypted and encrypted again. This happens because the random seed value that is used to initialize the encryption algorithm and is included as part of the encrypted value is different for each encryption operation. Because of this, it is impossible to use data that has been encrypted with the CBC mode as a unique key to identify a row in a database. FPE attempts to simplify the transition process by preserving the formatting and length of the original data, allowing a drop-in replacement of plaintext values with their ciphertexts in legacy applications. == Comparison to truly random permutations == Although a truly random permutation is the ideal FPE cipher, for large domains it is infeasible to pre-generate and remember a truly random permutation. So the problem of FPE is to generate a pseudorandom permutation from a secret key, in such a way that the computation time for a single value is small (ideally constant, but most importantly smaller than O(N)). == Comparison to block ciphers == An n-bit block cipher technically is a FPE on the set {0, ..., 2n-1}. If an FPE is needed on one of these standard sized sets (for example, n = 64 for DES and n = 128 for AES) a block cipher of the right size can be used. However, in typical usage, a block cipher is used in a mode of operation that allows it to encrypt arbitrarily long messages, and with an initialization vector as discussed above. In this mode, a block cipher is not an FPE. == Definition of security == In cryptographic literature (see most of the references below), the measure of a "good" FPE is whether an attacker can distinguish the FPE from a truly random permutation. Various types of attackers are postulated, depending on whether they have access to oracles or known ciphertext/plaintext pairs. == Algorithms == In most of the approaches listed here, a well-understood block cipher (such as AES) is used as a primitive to take the place of an ideal random function. This has the advantage that incorporation of a secret key into the algorithm is easy. Where AES is mentioned in the following discussion, any other good block cipher would work as well. === The FPE constructions of Black and Rogaway === Implementing FPE with security provably related to that of the underlying block cipher was first undertaken in a paper by cryptographers John Black and Phillip Rogaway, which described three ways to do this. They proved that each of these techniques is as secure as the block cipher that is used to construct it. This means that if the AES algorithm is used to create an FPE algorithm, then the resulting FPE algorithm is as secure as AES because an adversary capable of defeating the FPE algorithm can also defeat the AES algorithm. Therefore, if AES is secure, then the FPE algorithms constructed from it are also secure. In all of the following, E denotes the AES encryption operation that is used to construct an FPE algorithm and F denotes the FPE encryption operation. ==== FPE from a prefix cipher ==== One simple way to create an FPE algorithm on {0, ..., N-1} is to assign a pseudorandom weight to each integer, then sort by weight. The weights are defined by applying an existing block cipher to each integer. Black and Rogaway call this technique a "prefix cipher" and showed it was provably as good as the block cipher used. Thus, to create an FPE on the domain {0,1,2,3}, given a key K apply AES(K) to each integer, giving, for example, weight(0) = 0x56c644080098fc5570f2b329323dbf62 weight(1) = 0x08ee98c0d05e3dad3eb3d6236f23e7b7 weight(2) = 0x47d2e1bf72264fa01fb274465e56ba20 weight(3) = 0x077de40941c93774857961a8a772650d Sorting [0,1,2,3] by weight gives [3,1,2,0], so the cipher is F(0) = 3 F(1) = 1 F(2) = 2 F(3) = 0 This method is only useful for small values of N. For larger values, the size of the lookup table and the required number of encryptions to initialize the table gets too big to be practical. ==== FPE from cycle walking ==== If there is a set M of allowed values within the domain of a pseudorandom permutation P (for example P can be a block cipher like AES), an FPE algorithm can be created from the block cipher by repeatedly applying the block cipher until the result is one of the allowed values (within M). CycleWalkingFPE(x) { if P(x) is an element of M then return P(x) else return CycleWalkingFPE(P(x)) } The recursion is guaranteed to terminate. (Because P is one-to-one and the domain is finite, repeated application of P forms a cycle, so starting with a point in M the cycle will eventually terminate in M.) This has the advantage that the elements of M do not have to be mapped to a consecutive sequence {0,...,N-1} of integers. It has the disadvantage, when M is much smaller than P's domain, that too many iterations might be required for each operation. If P is a block cipher of a fixed size, such as AES, this is a severe restriction on the sizes of M for which this method is efficient. For example, an application may want to encrypt 100-bit values with AES in a way that creates another 100-bit value. With this technique, AES-128-ECB encryption can be applied until it reaches a value which has all of its 28 highest bits set to 0, which will take an average of 228 iterations to happen. ==== FPE from a Feistel network ==== It is also possible to make a FPE algorithm using a Feistel network. A Feistel network needs a source of pseudo-random values for the sub-keys for each round, and the output of the AES algorithm can be used as these pseudo-random values. When this is done, the resulting Feistel construction is good if enough rounds are used. One way to implement an FPE algorithm using AES and a Feistel network is to use as many bits of AES output as are needed to equal the length of the left or right halves of the Feistel network. If a 24-bit value is needed as a sub-key, for example, it is possible to use the lowest 24 bits of the output of AES for this value. This may not result in the output of the Feistel network preserving the format of the input, but it is possible to iterate the Feistel network in the same way that the cycle-walking technique does to ensure that format can be preserved. Because it is possible to adjust the size of the inputs to a Feistel network, it is possible to make it very likely that this iteration ends very quickly on average. In the case of credit card numbers, for example, there are 1015 possible 16-digit credit card numbers (accounting for the redundant check digit), and because the 1015 ≈ 249.8, using a 50-bit wide Feistel network along with cycle walking will create an FPE algorithm that encrypts fairly quickly on average. === The Thorp shuffle === A Thorp shuffle is like an idealized card-shuffle, or equivalently a maximally-unbalanced Feistel cipher where one side is a single bit. It is easier to prove security for unbalanced Feistel ciphers than for balanced ones. === VIL mode === For domain sizes that are a power of two, and an existing block cipher with a smaller bl

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  • Machine translation software usability

    Machine translation software usability

    The sections below give objective criteria for evaluating the usability of machine translation software output. == Stationarity or canonical form == Do repeated translations converge on a single expression in both languages? I.e. does the translation method show stationarity or produce a canonical form? Does the translation become stationary without losing the original meaning? This metric has been criticized as not being well correlated with BLEU (BiLingual Evaluation Understudy) scores. == Adaptive to colloquialism, argot or slang == Is the system adaptive to colloquialism, argot or slang? The French language has many rules for creating words in the speech and writing of popular culture. Two such rules are: (a) The reverse spelling of words such as femme to meuf. (This is called verlan.) (b) The attachment of the suffix -ard to a noun or verb to form a proper noun. For example, the noun faluche means "student hat". The word faluchard formed from faluche colloquially can mean, depending on context, "a group of students", "a gathering of students" and "behavior typical of a student". The Google translator as of 28 December 2006 doesn't derive the constructed words as for example from rule (b), as shown here: Il y a une chorale falucharde mercredi, venez nombreux, les faluchards chantent des paillardes! ==> There is a choral society falucharde Wednesday, come many, the faluchards sing loose-living women! French argot has three levels of usage: familier or friendly, acceptable among friends, family and peers but not at work grossier or swear words, acceptable among friends and peers but not at work or in family verlan or ghetto slang, acceptable among lower classes but not among middle or upper classes The United States National Institute of Standards and Technology conducts annual evaluations [1] Archived 2009-03-22 at the Wayback Machine of machine translation systems based on the BLEU-4 criterion [2]. A combined method called IQmt which incorporates BLEU and additional metrics NIST, GTM, ROUGE and METEOR has been implemented by Gimenez and Amigo [3]. == Well-formed output == Is the output grammatical or well-formed in the target language? Using an interlingua should be helpful in this regard, because with a fixed interlingua one should be able to write a grammatical mapping to the target language from the interlingua. Consider the following Arabic language input and English language translation result from the Google translator as of 27 December 2006 [4]. This Google translator output doesn't parse using a reasonable English grammar: وعن حوادث التدافع عند شعيرة رمي الجمرات -التي كثيرا ما يسقط فيها العديد من الضحايا- أشار الأمير نايف إلى إدخال "تحسينات كثيرة في جسر الجمرات ستمنع بإذن الله حدوث أي تزاحم". ==> And incidents at the push Carbuncles-throwing ritual, which often fall where many of the victims - Prince Nayef pointed to the introduction of "many improvements in bridge Carbuncles God would stop the occurrence of any competing." == Semantics preservation == Do repeated re-translations preserve the semantics of the original sentence? For example, consider the following English input passed multiple times into and out of French using the Google translator as of 27 December 2006: Better a day earlier than a day late. ==> Améliorer un jour plus tôt qu'un jour tard. ==> To improve one day earlier than a day late. ==> Pour améliorer un jour plus tôt qu'un jour tard. ==> To improve one day earlier than a day late. As noted above and in, this kind of round-trip translation is a very unreliable method of evaluation. == Trustworthiness and security == An interesting peculiarity of Google Translate as of 24 January 2008 (corrected as of 25 January 2008) is the following result when translating from English to Spanish, which shows an embedded joke in the English-Spanish dictionary which has some added poignancy given recent events: Heath Ledger is dead ==> Tom Cruise está muerto This raises the issue of trustworthiness when relying on a machine translation system embedded in a Life-critical system in which the translation system has input to a Safety Critical Decision Making process. Conjointly it raises the issue of whether in a given use the software of the machine translation system is safe from hackers. It is not known whether this feature of Google Translate was the result of a joke/hack or perhaps an unintended consequence of the use of a method such as statistical machine translation. Reporters from CNET Networks asked Google for an explanation on January 24, 2008; Google said only that it was an "internal issue with Google Translate". The mistranslation was the subject of much hilarity and speculation on the Internet. If it is an unintended consequence of the use of a method such as statistical machine translation, and not a joke/hack, then this event is a demonstration of a potential source of critical unreliability in the statistical machine translation method. In human translations, in particular on the part of interpreters, selectivity on the part of the translator in performing a translation is often commented on when one of the two parties being served by the interpreter knows both languages. This leads to the issue of whether a particular translation could be considered verifiable. In this case, a converging round-trip translation would be a kind of verification.

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  • Data verification

    Data verification

    Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. In some domains it is referred to Source Data Verification (SDV), such as in clinical trials. Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss. Methods for data verification include double data entry, proofreading and automated verification of data. Proofreading data involves someone checking the data entered against the original document. This is also time-consuming and costly. Automated verification of data can be achieved using one way hashes locally or through use of a SaaS based service such as Q by SoLVBL to provide immutable seals to allow verification of the original data.

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  • Perfectly Imperfect (platform)

    Perfectly Imperfect (platform)

    Perfectly Imperfect is an online newsletter and social media platform. It was initially founded in 2020 as a biweekly email newsletter that focused on recommendations. In January 2024, Perfectly Imperfect launched PI.FYI, a social media platform. The platform is based around sharing recommendations. Its main feed is presented in reverse chronological order and is not algorithmically curated. == History == Perfectly Imperfect was started during the COVID-19 pandemic by Tyler Bainbridge, alongside college friends Alex Cushing and Serey Morm, whom he met at UMass Lowell; Morm later departed. Motivated by a dissatisfaction with algorithm-driven recommendation culture, they launched on Substack in September 2020. Its early newsletter format, PI, published brief recommendation lists and personal notes from contributors. Contributors have included a mix of underground artists and more established creative figures, such as Charli XCX, Chloe Cherry, Chloe Wise, and Meetka Otto. In October 2024, PI announced it was leaving Substack to launch its own site. == Overview == The current platform, PI.FYI, features both editorial content (guest columns, long-form essays, staff picks) and user-generated recommendations. The platform also supports "Ask" posts, where users can solicit recommendations from the community, and allows commenting, liking, and profile customization. In August 2025, it launched an events feature. In 2022, Perfectly Imperfect hosted their first offline event at Baby's All Right in Brooklyn, with a performance by The Dare. They have since expanded their event promotion/sponsorship to markets such as Los Angeles, San Francisco, and even Auckland.

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  • Multiple encryption

    Multiple encryption

    Multiple encryption is the process of encrypting an already encrypted message one or more times, either using the same or a different algorithm. It is also known as cascade encryption, cascade ciphering, cipher stacking, multiple encryption, and superencipherment. Superencryption refers to the outer-level encryption of a multiple encryption. Some cryptographers, like Matthew Green of Johns Hopkins University, say multiple encryption addresses a problem that mostly doesn't exist: Modern ciphers rarely get broken... You’re far more likely to get hit by malware or an implementation bug than you are to suffer a catastrophic attack on Advanced Encryption Standard (AES). However, from the previous quote an argument for multiple encryption can be made, namely poor implementation. Using two different cryptomodules and keying processes from two different vendors requires both vendors' wares to be compromised for security to fail completely. == Independent keys == Picking any two ciphers, if the key used is the same for both, the second cipher could possibly undo the first cipher, partly or entirely. This is true of ciphers where the decryption process is exactly the same as the encryption process (a reciprocal cipher) – the second cipher would completely undo the first. If an attacker were to recover the key through cryptanalysis of the first encryption layer, the attacker could possibly decrypt all the remaining layers, assuming the same key is used for all layers. To prevent that risk, one can use keys that are statistically independent for each layer (e.g. independent RNGs). Ideally each key should have separate and different generation, sharing, and management processes. == Independent Initialization Vectors == For en/decryption processes that require sharing an Initialization Vector (IV) / nonce these are typically, openly shared or made known to the recipient (and everyone else). Its good security policy never to provide the same data in both plaintext and ciphertext when using the same key and IV. Therefore, its recommended (although at this moment without specific evidence) to use separate IVs for each layer of encryption. == Importance of the first layer == With the exception of the one-time pad, no cipher has been theoretically proven to be unbreakable. Furthermore, some recurring properties may be found in the ciphertexts generated by the first cipher. Since those ciphertexts are the plaintexts used by the second cipher, the second cipher may be rendered vulnerable to attacks based on known plaintext properties (see references below). This is the case when the first layer is a program P that always adds the same string S of characters at the beginning (or end) of all ciphertexts (commonly known as a magic number). When found in a file, the string S allows an operating system to know that the program P has to be launched in order to decrypt the file. This string should be removed before adding a second layer. To prevent this kind of attack, one can use the method provided by Bruce Schneier: Generate a random pad R of the same size as the plaintext. Encrypt R using the first cipher and key. XOR the plaintext with the pad, then encrypt the result using the second cipher and a different (!) key. Concatenate both ciphertexts in order to build the final ciphertext. A cryptanalyst must break both ciphers to get any information. This will, however, have the drawback of making the ciphertext twice as long as the original plaintext. Note, however, that a weak first cipher may merely make a second cipher that is vulnerable to a chosen plaintext attack also vulnerable to a known plaintext attack. However, a block cipher must not be vulnerable to a chosen plaintext attack to be considered secure. Therefore, the second cipher described above is not secure under that definition, either. Consequently, both ciphers still need to be broken. The attack illustrates why strong assumptions are made about secure block ciphers and ciphers that are even partially broken should never be used. == The Rule of Two == The Rule of Two is a data security principle from the NSA's Commercial Solutions for Classified Program (CSfC). It specifies two completely independent layers of cryptography to protect data. For example, data could be protected by both hardware encryption at its lowest level and software encryption at the application layer. It could mean using two FIPS-validated software cryptomodules from different vendors to en/decrypt data. The importance of vendor and/or model diversity between the layers of components centers around removing the possibility that the manufacturers or models will share a vulnerability. This way if one components is compromised there is still an entire layer of encryption protecting the information at rest or in transit. The CSfC Program offers solutions to achieve diversity in two ways. "The first is to implement each layer using components produced by different manufacturers. The second is to use components from the same manufacturer, where that manufacturer has provided NSA with sufficient evidence that the implementations of the two components are independent of one another." The principle is practiced in the NSA's secure mobile phone called Fishbowl. The phones use two layers of encryption protocols, IPsec and Secure Real-time Transport Protocol (SRTP), to protect voice communications. The Samsung Galaxy S9 Tactical Edition is also an approved CSfC Component.

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  • Indic computing

    Indic computing

    Indic Computing means "computing in Indic", i.e., Indian Scripts and Languages. It involves developing software in Indic Scripts/languages, Input methods, Localization of computer applications, web development, Database Management, Spell checkers, Speech to Text and Text to Speech applications and OCR in Indian languages. Unicode standard version 15.0 specifies codes for 9 Indic scripts in Chapter 12 titled "South and Central Asia-I, Official Scripts of India". The 9 scripts are Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil and Telugu. A lot of Indic Computing projects are going on. They involve some government sector companies, some volunteer groups and individual people. == Government sector == Indian Union Government made it mandatory for Mobile phone companies whose handsets manufactured, stored, sold and distributed in India to have support for displaying and typing text using fonts for all 22 languages. This move has seen rise in use of Indian languages by millions of users. === TDIL === The Department of Electronics and Information Technology, India initiated the TDIL (Technology Development for Indian Languages) with the objective of developing Information Processing Tools and Techniques to facilitate human-machine interaction without a language barrier; creating and accessing multilingual knowledge resources; and integrating them to develop innovative user products and services. In 2005, it started distributing language software tools developed by Government/Academic/Private companies in the form of CD for non commercial use. Some of the outcomes of TDIL program have been deployed on Indian Language Technology Proliferation & Deployment Centre. This Centre disseminates all the linguistic resources, tools & applications which have been developed under TDIL funding. This programme took to exponential expansion under the leadership of Dr. Swaran Lata who also created international foot-print of the programme. She has now retired. === C-DAC === C-DAC is an India based government software company which is involved in developing language related software. It is best known for developing InScript Keyboard, the standard keyboard for Indian languages. It has also developed lot of Indic language solutions including Word Processors, typing tools, text to speech software, OCR in Indian languages etc. ==== BharateeyaOO.org ==== The work developed out of CDAC, Bangalore (earlier known as NCST, Bangalore) became BharateeyaOO. OpenOffice 2.1 had support for over 10 Indian languages. ==== BOSS ==== BOSS linux was developed by the Centre for Development of Advanced Computing (CDAC) to promote use of open-source software in India. == NGO and Volunteer groups == === Indlinux === Indlinux organisation helped organise the individual volunteers working on different indic language versions of Linux and its applications. === Sarovar === Sarovar.org is India's first portal to host projects under Free/Open source licenses. It is located in Trivandrum, India and hosted at Asianet data center. Sarovar.org is customised, installed and maintained by Linuxense as part of their community services and sponsored by River Valley Technologies. Sarovar.org is built on Debian Etch and GForge and runs off METTLE. === Pinaak === Pinaak is a non-government charitable society devoted to Indic language computing. It works for software localization, developing language software, localizing open source software, enriching online encyclopedias etc. In addition to this Pinaak works for educating people about computing, ethical use of Internet and use of Indian languages on Internet. === Ankur Group === Ankur Group is working toward supporting Bengali language (Bengali) on Linux operating system including localized Bengali GUI, Live CD, English-to-Bengali translator, Bengali OCR and Bengali Dictionary etc. === BhashaIndia === === SMC === SMC is a free software group, working to bridge the language divide in Kerala in the technology front and is today the biggest language computing community in India. == Input methods == === Full size keyboards === With the advent of Unicode inputting Indic text on computer has become very easy. A number of methods exist for this purpose, but the main ones are:- ==== InScript ==== Inscript is the standard keyboard for Indian languages. Developed by C-DAC and standardized by Government of India. Nowadays it comes inbuilt in all major operating systems including Microsoft Windows (2000, XP, Vista, 7), Linux and Macintosh. ==== Phonetic transliteration ==== This is a typing method in which, for instance, the user types text in an Indian language using Roman characters and it is phonetically converted to equivalent text in Indian script in real time. This type of conversion is done by phonetic text editors, word processors and software plugins. Building up on the idea, one can use phonetic IME tools that allow Indic text to be input in any application. Some examples of phonetic transliterators are Xlit, Google Indic Transliteration, BarahaIME, Indic IME, Rupantar, SMC's Indic Keyboard and Microsoft Indic Language Input Tool. SMC's Indic Keyboard has support for as many as 23 languages whereas Google Indic Keyboard only supports 11 Indian languages. They can be broadly classified as: Fixed transliteration scheme based tools – They work using a fixed transliteration scheme to convert text. Some examples are Indic IME, Rupantar and BarahaIME. Intelligent/Learning based transliteration tools – They compare the word with a dictionary and then convert it to the equivalent words in the target language. Some of the popular ones are Google Indic Transliteration, Xlit, Microsoft Indic Language Input Tool and QuillPad. ==== Remington (typewriter) ==== This layout was developed when computers had not been invented or deployed with Indic languages, and typewriters were the only means to type text in Indic scripts. Since typewriters were mechanical and could not include a script processor engine, each character had to be placed on the keyboard separately, which resulted in a very complex and difficult to learn keyboard layout. With the advent of Unicode, the Remington layout was added to various typing tools for sake of backward compatibility, so that old typists did not have to learn a new keyboard layout. Nowadays this layout is only used by old typists who are used to this layout due to several years of usage. One tool to include Remington layout is Indic IME. A font that is based on the Remington keyboard layout is Kruti Dev. Another online tool that very closely supports the old Remington keyboard layout using Kruti Dev is the Remington Typing tool. === Braille === IBus Sharada Braille, which supports seven Indian languages was developed by SMC. === Mobile phones with Numeric keyboards === Mobile/Hand/cell phone basic models have 12 keys like the plain old telephone keypad. Each key is mapped to 3 or 4 English letters to facilitate data entry in English. For inputting Indian languages with this kind of keypad, there are two ways to do so. First is the Multi-tap Method and second uses visual help from the screen like Panini Keypad. The primary usage is SMS. 140 characters size used for English/Roman languages can be used to accommodate only about 70 language characters when Unicode Proprietary compression is used some times to increase the size of single message for Complex script languages like Hindi. A research study of the available methods and recommendations of proposed standard was released by Broadband Wireless Consortium of India (BWCI). ==== Transliteration/Phonetic methods ==== English is used to type in Indian languages. QuillPad IndiSMS ==== Native methods ==== In native methods, the letters of the language are displayed on the screen corresponding to the numeral keys based on the probabilities of those letters for that language. Additional letters can be accessed by using a special key. When a word is partially typed, options are presented from which the user can make a selection. === Smart phones with Qwerty keyboards === Most smart phones have about 35 keys catering primarily to the English language. Numerals and some symbols are accessed with a special key called Alt. Indic input methods are yet to evolve for these types of phones, as support of Unicode for rendering is not widely available. === For Smart Phones with Soft/Virtual keyboards === Inscript is being adopted for smart phone usage. For Android phones which can render Indic languages, Swalekh Multilingual Keypad Multiling Keyboard app are available. Gboard offers support for several Indian languages. == Localization == Localization means translating software, operating systems, websites etc. various applications in Indian language. Various volunteers groups are working in this direction. === Mandrake Tamil Version === A notable example is the Tamil version of Mandrake linux(defunct since 2011). Tamil speakers in Toronto (Canada) released Mandrake,

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  • Data lineage

    Data lineage

    Data lineage refers to the process of tracking how data is generated, transformed, transmitted and used across systems over time. It documents data's origins, transformations and movements, providing detailed visibility into its life cycle. This process simplifies the identification of errors in data analytics workflows, by enabling users to trace issues back to their root causes. Data lineage facilitates the ability to replay specific segments or inputs of the dataflow. This can be used in debugging or regenerating lost outputs. In database systems, this concept is closely related to data provenance, which involves maintaining records of inputs, entities, systems and processes that influence data. Data provenance provides a historical record of data origins and transformations. It supports forensic activities such as data-dependency analysis, error/compromise detection, recovery, auditing and compliance analysis: "Lineage is a simple type of why provenance." Data governance plays a critical role in managing metadata by establishing guidelines, strategies and policies. Enhancing data lineage with data quality measures and master data management adds business value. Although data lineage is typically represented through a graphical user interface (GUI), the methods for gathering and exposing metadata to this interface can vary. Based on the metadata collection approach, data lineage can be categorized into three types: Those involving software packages for structured data, programming languages and Big data systems. Data lineage information includes technical metadata about data transformations. Enriched data lineage may include additional elements such as data quality test results, reference data, data models, business terminology, data stewardship information, program management details and enterprise systems associated with data points and transformations. Data lineage visualization tools often include masking features that allow users to focus on information relevant to specific use cases. To unify representations across disparate systems, metadata normalization or standardization may be required. == Representation of data lineage == Representation broadly depends on the scope of the metadata management and reference point of interest. Data lineage provides sources of the data and intermediate data flow hops from the reference point with backward data lineage, leading to the final destination's data points and its intermediate data flows with forward data lineage. These views can be combined with end-to-end lineage for a reference point that provides a complete audit trail of that data point of interest from sources to their final destinations. As the data points or hops increase, the complexity of such representation becomes incomprehensible. Thus, the best feature of the data lineage view is the ability to simplify the view by temporarily masking unwanted peripheral data points. Tools with the masking feature enable scalability of the view and enhance analysis with the best user experience for both technical and business users. Data lineage also enables companies to trace sources of specific business data to track errors, implement changes in processes and implementing system migrations to save significant amounts of time and resources. Data lineage can improve efficiency in business intelligence BI processes. Data lineage can be represented visually to discover the data flow and movement from its source to destination via various changes and hops on its way in the enterprise environment. This includes how the data is transformed along the way, how the representation and parameters change and how the data splits or converges after each hop. A simple representation of the Data Lineage can be shown with dots and lines, where dots represent data containers for data points, and lines connecting them represent transformations the data undergoes between the data containers. Data lineage can be visualized at various levels based on the granularity of the view. At a very high-level, data lineage is visualized as systems that the data interacts with before it reaches its destination. At its most granular, visualizations at the data point level can provide the details of the data point and its historical behavior, attribute properties and trends and data quality of the data passed through that specific data point in the data lineage. The scope of the data lineage determines the volume of metadata required to represent its data lineage. Usually, data governance and data management of an organization determine the scope of the data lineage based on their regulations, enterprise data management strategy, data impact, reporting attributes and critical data elements of the organization. == Rationale == Distributed systems like Google Map Reduce, Microsoft Dryad, Apache Hadoop (an open-source project) and Google Pregel provide such platforms for businesses and users. However, even with these systems, Big Data analytics can take several hours, days or weeks to run, simply due to the data volumes involved. For example, a ratings prediction algorithm for the Netflix Prize challenge took nearly 20 hours to execute on 50 cores, and a large-scale image processing task to estimate geographic information took 3 days to complete using 400 cores. "The Large Synoptic Survey Telescope is expected to generate terabytes of data every night and eventually store more than 50 petabytes, while in the bioinformatics sector, the 12 largest genome sequencing houses in the world now store petabytes of data apiece. It is very difficult for a data scientist to trace an unknown or an unanticipated result. === Big data debugging === Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Machine learning, among other algorithms, is used to transform and analyze the data. Due to the large size of the data, there could be unknown features in the data. The massive scale and unstructured nature of data, the complexity of these analytics pipelines, and long runtimes pose significant manageability and debugging challenges. Even a single error in these analytics can be extremely difficult to identify and remove. While one may debug them by re-running the entire analytics through a debugger for stepwise debugging, this can be expensive due to the amount of time and resources needed. Auditing and data validation are other major problems due to the growing ease of access to relevant data sources for use in experiments, the sharing of data between scientific communities and use of third-party data in business enterprises. As such, more cost-efficient ways of analyzing data intensive scale-able computing (DISC) are crucial to their continued effective use. === Challenges in Big Data debugging === ==== Massive scale ==== According to an EMC/IDC study, 2.8 ZB of data were created and replicated in 2012. Furthermore, the same study states that the digital universe will double every two years between now and 2020, and that there will be approximately 5.2 TB of data for every person in 2020. Based on current technology, the storage of this much data will mean greater energy usage by data centers. ==== Unstructured data ==== Unstructured data usually refers to information that doesn't reside in a traditional row-column database. Unstructured data files often include text and multimedia content, such as e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages and many other kinds of business documents. While these types of files may have an internal structure, they are still considered "unstructured" because the data they contain doesn't fit neatly into a database. The amount of unstructured data in enterprises is growing many times faster than structured databases are growing. Big data can include both structured and unstructured data, but IDC estimates that 90 percent of Big Data is unstructured data. The fundamental challenge of unstructured data sources is that they are difficult for non-technical business users and data analysts alike to unbox, understand and prepare for analytic use. Beyond issues of structure, the sheer volume of this type of data contributes to such difficulty. Because of this, current data mining techniques often leave out valuable information and make analyzing unstructured data laborious and expensive. In today's competitive business environment, companies have to find and analyze the relevant data they need quickly. The challenge is going through the volumes of data and accessing the level of detail needed, all at a high speed. The challenge only grows as the degree of granularity increases. One possible solution is hardware. Some vendors are using increased memory and parallel processing to crunch large volumes of data quickly. Another method is putting data in-memory but using a grid

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  • Signatures with efficient protocols

    Signatures with efficient protocols

    Signatures with efficient protocols are a form of digital signature invented by Jan Camenisch and Anna Lysyanskaya in 2001. In addition to being secure digital signatures, they need to allow for the efficient implementation of two protocols: A protocol for computing a digital signature in a secure two-party computation protocol. A protocol for proving knowledge of a digital signature in a zero-knowledge protocol. In applications, the first protocol allows a signer to possess the signing key to issue a signature to a user (the signature owner) without learning all the messages being signed or the complete signature. The second protocol allows the signature owner to prove that he has a signature on many messages without revealing the signature and only a (possibly) empty subset of the messages. The combination of these two protocols allows for the implementation of digital credential and ecash protocols.

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