AI For Work Kore

AI For Work Kore — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Swizzling (computer graphics)

  In computer graphics, swizzles are a class of operations that transform vectors by rearranging components. Swizzles can also project from a vector of one dimensionality to a vector of another dimensionality, such as taking a three-dimensional vector and creating a two-dimensional or five-dimensional vector using components from the original vector. For example, if A = {1,2,3,4}, where the components are x, y, z, and w respectively, one could compute B = A.wwxy, whereupon B would equal {4,4,1,2}. Additionally, one could create a two-dimensional vector with A.wx or a five-dimensional vector with A.xyzwx. Combining vectors and swizzling can be employed in various ways. This is common in GPGPU applications. In terms of linear algebra, this is equivalent to multiplying by a matrix whose rows are standard basis vectors. If A = ( 1 , 2 , 3 , 4 ) T {\displaystyle A=(1,2,3,4)^{T}} , then swizzling A {\displaystyle A} as above looks like A . w w x y = [ 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 ] [ 1 2 3 4 ] = [ 4 4 1 2 ] . {\displaystyle A.\!wwxy={\begin{bmatrix}0&0&0&1\\0&0&0&1\\1&0&0&0\\0&1&0&0\end{bmatrix}}{\begin{bmatrix}1\\2\\3\\4\end{bmatrix}}={\begin{bmatrix}4\\4\\1\\2\end{bmatrix}}.}

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  WYSIWYS

  In cryptography, What You See Is What You Sign (WYSIWYS) is a property of digital signature systems that ensures the semantic content of signed messages can not be changed, either by accident or intent. == Mechanism of WYSIWYS == When digitally signing a document, the integrity of the signature relies not just on the soundness of the digital signature algorithms that are used, but also on the security of the computing platform used to sign the document. The WYSIWYS property of digital signature systems aims to tackle this problem by defining a desirable property that the visual representation of a digital document should be consistent across computing systems, particularly at the points of digital signature and digital signature verification. It is relatively easy to change the interpretation of a digital document by implementing changes on the computer system where the document is being processed, and the greater the semantic distance, the easier it gets. From a semantic perspective this creates uncertainty about what exactly has been signed. WYSIWYS is a property of a digital signature system that ensures that the semantic interpretation of a digitally signed message cannot be changed, either by accident or by intent. This property also ensures that a digital document to be signed can not contain hidden semantic content that can be revealed after the signature has been applied. Though a WYSIWYS implementation is only as secure as the computing platform it is running on, various methods have been proposed to make WYSIWYS more robust. The term WYSIWYS was coined by Peter Landrock and Torben Pedersen to describe some of the principles in delivering secure and legally binding digital signatures for Pan-European projects.

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  Key (cryptography)

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

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  Cryptographic Service Provider

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

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  Automatic summarization

  Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence (AI) algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content. == Commercial products == In 2022 Google Docs released an automatic summarization feature. == Approaches == There are two general approaches to automatic summarization: extraction and abstraction. === Extraction-based summarization === Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above. For text, extraction is analogous to the process of skimming, where the summary (if available), headings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail. Other examples of extraction that include key sequences of text in terms of clinical relevance (including patient/problem, intervention, and outcome). === Abstractive-based summarization === Abstractive summarization methods generate new text that did not exist in the original text. This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive. === Aided summarization === Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate. == Applications and systems for summarization == There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs. An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary. Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured. At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc. === Keyphrase extraction === The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below. Consider the example text from a news article: "The Army Corps of Engineers, rushing to meet President Bush's promise to protect New Orleans by the start of the 2006 hurricane season, installed defective flood-control pumps last year despite warnings from its own expert that the equipment would fail during a storm, according to documents obtained by The Associated Press". A keyphrase extractor might select "Army Corps of Engineers", "President Bush", "New Orleans", and "defective flood-control pumps" as keyphrases. These are pulled directly from the text. In contrast, an abstractive keyphrase system would somehow internalize the content and generate keyphrases that do not appear in the text, but more closely resemble what a human might produce, such as "political negligence" or "inadequate protection from floods". Abstraction requires a deep understanding of the text, which makes it difficult for a computer system. Keyphrases have many applications. They can enable document browsing by providing a short summary, improve information retrieval (if documents have keyphrases assigned, a user could search by keyphrase to produce more reliable hits than a full-text search), and be employed in generating index entries for a large text corpus. Depending on the different literature and the definition of key terms, words or phrases, keyword extraction is a highly related theme. ==== Supervised learning approaches ==== Beginning with the work of Turney, many researchers have approached keyphrase extraction as a supervised machine learning problem. Given a document, we construct an example for each unigram, bigram, and trigram found in the text (though other text units are also possible, as discussed below). We then compute various features describing each example (e.g., does the phrase begin with an upper-case letter?). We assume there are known keyphrases available for a set of training documents. Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a binary classification for a test example, while others assign a probability of being a keyphrase. For ins

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  Pinoy baiting

  Pinoy baiting is a phrase that has been used to refer to acts by non-Filipino individuals, usually celebrities or YouTubers, of posting content online purportedly with the intention of getting the attention of Filipinos, by being surprised about the Philippines or its people. Pinoy baiters are defined as giving superficial and allegedly insincere praises and similar reactions that give recognition to the Philippines or its people. Subsequent responses by Filipinos to what have been referred to as acts of Pinoy baiting have been criticized as a form of cultural cringe. This criticism would subsequently give the advice that Filipinos should not constantly require validation from non-Filipinos about themselves or their country. == Pinoy baiting mediums == === Reaction videos === On social media such as YouTube, channels with specific focus on showing their reaction towards and opinions about certain videos or topics are called reaction channels. Reaction videos are very popular and require minimal effort to create, and thus made it easy for alleged Pinoy baiting to thrive within this video-making genre. === Travel vlogs === Vlogging, short for video blogging, grew in popularity in the 2020s. Most of the popular alleged Pinoy-baiting channels tend to be vlog channels, normally following the same script under such titles as "The Philippines changed us/me", "First impression of the Philippines", "Is this really Manila?" and "Filipinos are such Kind/Good People!", and made while travelling to touristy areas such as Boracay or Bonifacio Global City and taste-testing the fast food chain Jollibee, among others. == Criticism of the phrase == Philippines-based Korean vlogger Jessica Lee had been accused by some YouTube viewers of engaging in Pinoy baiting. In a response vlog, Lee acknowledged that there may be individuals engaging in this "business strategy" of gaining views and subscribers from one of the largest communities online. However, she questioned the objectivity of some use of the phrase, citing any vlogging subject as fair game for a negative impression of being a "baiting" tool for the vlogger treating of that subject. She also invoked vloggers' freedom to choose whatever subject they want to talk about in a deep or shallow manner, while enjoining citizens to exercise their free-market right to unfollow vloggers they hate and follow those vloggers that "make them happy". She also gave her critics an explanation why she ended up vlogging about Philippine and Filipino subjects.

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  Private message

  In computer networking, a private message (PM), or direct message (DM), refers to a private communication, often text-based, sent or received by a user of a private communication channel on any given platform. Unlike public posts, PMs are only viewable by the participants. Long a function present on IRCs and Internet forums, private channels for PMs have also been prevalent features on instant messaging (IM) and on social media networks. It may be either synchronous (e.g. on an IM) or asynchronous (e.g. on an Internet forum). The term private message (PM) originated as a feature on internet forums, while the term direct message (DM) originated as a feature on Twitter. Due to the popularity of the latter service, DM has since been appropriated by other platforms, such as Instagram, and is often genericized in popular usage. == Overview == There are two main types of private messages, and one obscure type: One type includes those found on IRCs and Internet forums, as well as on social media services like Twitter, Facebook, and Instagram, where the focus is public posting, PMs allow users to communicate privately without leaving the platform. The second type are those relayed through instant messaging platforms such as WhatsApp and Snapchat, where users join the networks primarily to exchange PMs. A third type, peer-to-peer messaging, occurs when users create and own the infrastructure used to transmit and store the messages; while features vary depending on application, they give the user full control over the data they transmit. An example of software that enables this kind of messaging is Classified-ads. Besides serving as a tool to connect privately with friends and family, PMs have gained momentum in the workplace. Working professionals use PMs to reach coworkers in other spaces and increase efficiency during meetings. Although useful, using PMs in the workplace may blur the boundary between work and private lives. Some common forms of private messaging today include Facebook messaging (sometimes referred to as "inboxing"), Twitter direct messaging, and Instagram direct messaging. These forms of private messaging provide a private space on a usually public site. For instance, most activity on Twitter is public, but Twitter DMs provide a private space for communication between two users. This differs from mediums like email, texting, and Snapchat, where most or all activity is always private. Modern forms of private messaging may include multimedia messages, such as pictures or videos. == History == Email was first developed to send messages between different computers on ARPANET in 1971. Access to ARPANET was primarily limited to universities and other research institutions. Starting in 1983 or 1984, FidoNet allowed home computer users to send and receive email via bulletin board systems. Information services such as CompuServe, America Online, and Prodigy also helped to popularizes online messaging. The advent of the public World Wide Web in 1993 increased access to email via internet service providers, and later via webmail. Instant messaging systems became popular in the mid 1990s, as Internet access improved and personal computers became more common. The introduction of Skype in 2003 popularized Internet-based voice and video messaging. Direct messaging is now a feature of all major social networking services. == Privacy concerns == In January 2014, Matthew Campbell and Michael Hurley filed a class-action lawsuit against Facebook for breaching the Electronic Communications Privacy Act. They alleged that private messages which contained URLs were being read and used to generate profit, through data mining and user profiling, and that it was misleading for Facebook to refer to the functionality as "private" with the implication that the communication was "free from surveillance". In 2012, some Facebook users misinterpreted a redesign of the Facebook wall as publicly sharing private messages from 2008–2009. These were found to be public wall posts from those years, made at a time when it was not possible to like or comment on a wall post, making the notes look like private messages.

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  Hilscher netx network controller

  The netX network controller family (based on ASICs), developed by Hilscher Gesellschaft für Systemautomation mbH, is a solution for implementing all proven Fieldbus and Real-Time Ethernet systems. It was the first Multi-Protocol ASIC which combines Real-Time-Ethernet and Fieldbus System in one solution. The Multiprotocol functionality is done over a flexible cpu sub system called XC. Through exchanging some microcode the XC is able to realize beside others a PROFINET IRT Switch, EtherCAT Slave, Ethernet Powerlink HUB, PROFIBUS, CAN bus, CC-Link Industrial Networks Interface. == The Hilscher netX family == === Multiplex Matrix IOs (MMIO) === The Multiplex Matrix is a set of PINs which could be configured freely with peripheral functions. Options are CAN, UART, SPI, I2C, GPIOs, PIOs and SYNC Trigger. === GPIOs === The GPIOs from Hilscher are able to generate Interrupts, could count level or flags, or could be connected to a timer unit to auto generate a PWM. The Resolution of the PWM is normally 10ns. In some netX ASICS is a dedicated Motion unit with a resolution if 1ns is available.

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  BLOOM (language model)

  The BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is an open-access large language model (LLM) released in 2022. It was created by a volunteer-driven research effort to provide a transparently-created alternative to proprietary AI models. With 176 billion parameters, BLOOM is a transformer-based autoregressive model designed to generate text in 46 natural languages and 13 programming languages. The model is distributed under the project's "Responsible AI License". == Development == BLOOM is the main outcome of the BigScience initiative, a one-year-long research workshop. The project was coordinated by Hugging Face using funding from the French government and involved several hundred volunteer researchers and engineers from academia and the private sector. The model was trained between March and July 2022 on the Jean Zay public supercomputer in France, managed by GENCI and IDRIS (CNRS). Unlike GPT-3, BLOOM was trained to be multilingual. The source code is released under the Apache 2.0 license. The model's parameters are released under BigScience's "Responsible AI License" (RAIL), which grants open access and reuse rights but with some usage restrictions. BLOOM was used in the chatbots BLOOMChat and HuggingChat due to its multilingual abilities. BLOOM's training corpus, named ROOTS, combines data extracted from the then-latest version of the web-based OSCAR corpus (38% of ROOTS) and newly collected data extracted from a manually selected and documented list of language data sources. In total, the model was trained on approximately 366 billion (1.6TB) tokens. It was developed using the open-source libraries DeepSpeed Megatron. BigScience then released xP3, a multilingual dataset for LLM supervised learning. It also released BLOOMZ, a variant of BLOOM fine-tuned on xP3 to follow instructions.

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  Data refuge

  Data Refuge is a public and collaborative project designed to address concerns about federal climate and environmental data that is in danger of being lost. In particular, the initiative addresses five main concerns: What are the best ways to safeguard data? How do federal agencies play a crucial role in collecting, managing, and distributing data? How do government priorities impact data's accessibility? Which projects and research fields depend on federal data? Which data sets are of value to research and local communities, and why? Data Refuge began as a grassroots organization in opposition to government data on climate change and the environment not being archived systemically. Data Refuge's main goal is to collect and allocate data in multiple safe locations to create a sustainable way of archiving old and new data. Data Refuge was initiated in 2016 to protect federal climate and environmental data that is vulnerable under an administration that denies climate change. The system aims to make public research-quality copies of federal climate and environmental data. Data Refuge is supported by the National Geographic Foundation, private donors, Libraries+ Network, Preserving Electronic Governance Initiative (PEGI), the Union of Concerned Scientists (USC), and the Penn Program in Environmental Humanities (PPEH). == Types of data == Data Refuge collects public federal data on the climate and environment in the form of satellite imagery, PDFs, and stories. The data are stored in multiple trusted locations as they are less vulnerable if in only one location, and to ensure accessibility for researchers. Through the Data Rescue events, Data Refuge has accumulated 4 terabytes of data, 30,000 URLs, and 800 participants. === Storytelling === Data Refuge collects stories on vulnerable federal climate and environmental data through: surveys, oral history, photo essays, maps, video shorts, and animations. The stories are archived in a public bank that showcase how federal environmental data support health and safety in communities. Data Stories are collected at Data Rescue events, which are partnered with universities, city and town halls, and advocacy groups. Data stories are collected and used to emphasize the importance of Data Refuge, in how the data on climate change and the environment are being used by people in the United States and across the world for meaningful practices.

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  Clustered file system

  A clustered file system (CFS) is a file system which is shared by being simultaneously mounted on multiple servers. There are several approaches to clustering, most of which do not employ a clustered file system (only direct attached storage for each node). Clustered file systems can provide features like location-independent addressing and redundancy which improve reliability or reduce the complexity of the other parts of the cluster. Parallel file systems are a type of clustered file system that spread data across multiple storage nodes, usually for redundancy or performance. == Shared-disk file system == A shared-disk file system uses a storage area network (SAN) to allow multiple computers to gain direct disk access at the block level. Access control and translation from file-level operations that applications use to block-level operations used by the SAN must take place on the client node. The most common type of clustered file system, the shared-disk file system – by adding mechanisms for concurrency control – provides a consistent and serializable view of the file system, avoiding corruption and unintended data loss even when multiple clients try to access the same files at the same time. Shared-disk file-systems commonly employ some sort of fencing mechanism to prevent data corruption in case of node failures, because an unfenced device can cause data corruption if it loses communication with its sister nodes and tries to access the same information other nodes are accessing. The underlying storage area network may use any of a number of block-level protocols, including SCSI, iSCSI, HyperSCSI, ATA over Ethernet (AoE), Fibre Channel, network block device, and InfiniBand. There are different architectural approaches to a shared-disk filesystem. Some distribute file information across all the servers in a cluster (fully distributed). === Examples === == Distributed file systems == Distributed file systems do not share block level access to the same storage but use a network protocol. These are commonly known as network file systems, even though they are not the only file systems that use the network to send data. Distributed file systems can restrict access to the file system depending on access lists or capabilities on both the servers and the clients, depending on how the protocol is designed. The difference between a distributed file system and a distributed data store is that a distributed file system allows files to be accessed using the same interfaces and semantics as local files – for example, mounting/unmounting, listing directories, read/write at byte boundaries, system's native permission model. Distributed data stores, by contrast, require using a different API or library and have different semantics (most often those of a database). === Design goals === Distributed file systems may aim for "transparency" in a number of aspects. That is, they aim to be "invisible" to client programs, which "see" a system which is similar to a local file system. Behind the scenes, the distributed file system handles locating files, transporting data, and potentially providing other features listed below. Access transparency: clients are unaware that files are distributed and can access them in the same way as local files are accessed. Location transparency: a consistent namespace exists encompassing local as well as remote files. The name of a file does not give its location. Concurrency transparency: all clients have the same view of the state of the file system. This means that if one process is modifying a file, any other processes on the same system or remote systems that are accessing the files will see the modifications in a coherent manner. Failure transparency: the client and client programs should operate correctly after a server failure. Heterogeneity: file service should be provided across different hardware and operating system platforms. Scalability: the file system should work well in small environments (1 machine, a dozen machines) and also scale gracefully to bigger ones (hundreds through tens of thousands of systems). Replication transparency: Clients should not have to be aware of the file replication performed across multiple servers to support scalability. Migration transparency: files should be able to move between different servers without the client's knowledge. === History === The Incompatible Timesharing System used virtual devices for transparent inter-machine file system access in the 1960s. More file servers were developed in the 1970s. In 1976, Digital Equipment Corporation created the File Access Listener (FAL), an implementation of the Data Access Protocol as part of DECnet Phase II which became the first widely used network file system. In 1984, Sun Microsystems created the file system called "Network File System" (NFS) which became the first widely used Internet Protocol based network file system. Other notable network file systems are Andrew File System (AFS), Apple Filing Protocol (AFP), NetWare Core Protocol (NCP), and Server Message Block (SMB) which is also known as Common Internet File System (CIFS). In 1986, IBM announced client and server support for Distributed Data Management Architecture (DDM) for the System/36, System/38, and IBM mainframe computers running CICS. This was followed by the support for IBM Personal Computer, AS/400, IBM mainframe computers under the MVS and VSE operating systems, and FlexOS. DDM also became the foundation for Distributed Relational Database Architecture, also known as DRDA. There are many peer-to-peer network protocols for open-source distributed file systems for cloud or closed-source clustered file systems, e. g.: 9P, AFS, Coda, CIFS/SMB, DCE/DFS, WekaFS, Lustre, PanFS, Google File System, Mnet, Chord Project. === Examples === == Network-attached storage == Network-attached storage (NAS) provides both storage and a file system, like a shared disk file system on top of a storage area network (SAN). NAS typically uses file-based protocols (as opposed to block-based protocols a SAN would use) such as NFS (popular on UNIX systems), SMB/CIFS (Server Message Block/Common Internet File System) (used with MS Windows systems), AFP (used with Apple Macintosh computers), or NCP (used with OES and Novell NetWare). == Design considerations == === Avoiding single point of failure === The failure of disk hardware or a given storage node in a cluster can create a single point of failure that can result in data loss or unavailability. Fault tolerance and high availability can be provided through data replication of one sort or another, so that data remains intact and available despite the failure of any single piece of equipment. For examples, see the lists of distributed fault-tolerant file systems and distributed parallel fault-tolerant file systems. === Performance === A common performance measurement of a clustered file system is the amount of time needed to satisfy service requests. In conventional systems, this time consists of a disk-access time and a small amount of CPU-processing time. But in a clustered file system, a remote access has additional overhead due to the distributed structure. This includes the time to deliver the request to a server, the time to deliver the response to the client, and for each direction, a CPU overhead of running the communication protocol software. === Concurrency === Concurrency control becomes an issue when more than one person or client is accessing the same file or block and want to update it. Hence updates to the file from one client should not interfere with access and updates from other clients. This problem is more complex with file systems due to concurrent overlapping writes, where different writers write to overlapping regions of the file concurrently. This problem is usually handled by concurrency control or locking which may either be built into the file system or provided by an add-on protocol. == History == IBM mainframes in the 1970s could share physical disks and file systems if each machine had its own channel connection to the drives' control units. In the 1980s, Digital Equipment Corporation's TOPS-20 and OpenVMS clusters (VAX/ALPHA/IA64) included shared disk file systems.

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  Intent-based network

  Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.

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  AI Overviews

  AI Overviews is an artificial intelligence (AI) feature integrated into Google Search that produces AI-generated summaries of search results. The feature has been criticized for its inaccuracy and for reducing website traffic. == History and development == AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024 at Google I/O 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a strategic move to compete with other generative AI advancements, including OpenAI's ChatGPT. By August 2024, AI Overviews was rolled out to several other countries, including the United Kingdom, India, Japan, Brazil, Mexico, and Indonesia, with support for multiple languages. In October 2024, Google expanded the feature globally, making it available in over 100 countries. In December 2024, Botify x Demandsphere released findings stating that when AI Overviews and featured snippets appear together on the search engine results page, they take up approximately 67.1% of the screen on desktop and 75.7% on mobile. Even if content is ranking in the #1 position, it may not be visible to consumers if other visual elements on the results page are more prominent. In March 2025, Google started testing an "AI Mode", where the search results page is AI-generated. The company was also considering adding advertisements to the AI Mode, as they already exist in AI Overviews. As of May 2025, AI Overviews are available in over 200 countries and territories and in more than 40 languages. As of March 2026, Google AI Overviews appear on more than 48% of total Google Search queries, compared to just 6.49% in the previous year (58% year-over-year growth). == Functionality == The AI Overviews feature uses large language models to generate summaries from web content. The overviews are designed to be concise, providing a snapshot of relevant information about the queried topic. Google allows users to adjust the language complexity in summaries, offering both simplified and detailed options. The overviews also include links to sources. According to a June 2025 study by Semrush, the most cited source is Quora, followed by Reddit. == Reception == The feature has faced criticism for inaccuracies, including instances where erroneous or nonsensical content was generated. Depending on what is searched for, the overview may also consist of hallucinated content, such as when searching for idioms that do not exist. In May 2024, Google temporarily restricted the AI tool after it provided suggestions that were seen as nonsensical and harmful, such as telling users to eat rocks or apply glue on pizza. Concerns were also raised by content publishers, who feared a decline in web traffic as users relied on the summaries instead of visiting source websites. A Google patent from 2026 raised the concern of webmasters that Google could entirely replace the landing page of websites by an AI optimized copy of the website in its results. There is also apprehension about the ethical implications of AI-driven content aggregation, including its impact on intellectual property rights and the visibility of smaller content providers. The European Commission announced in December 2025 that they were investigating whether AI Overviews breached European competition law. In response, Google has stated its commitment to improve content validation and refine the algorithms used to filter unreliable information. Google implemented measures to prioritize link placement within AI Overviews, aiming to balance user convenience with the needs of content creators. In January 2026, Google restricted AI Overviews on certain health-related searches following an investigation by The Guardian. == Lawsuits == On February 24, 2025, Chegg sued Alphabet over the AI Overviews feature, claiming that it was leading to students preferring "low-quality, unverified AI summaries", thus violating antitrust law. Chegg also said it was considering either a sale or a take-private transaction. In September 2025, Penske Media Corporation, the publisher of Rolling Stone and The Hollywood Reporter, sued Google, claiming that AI Overviews illegally regurgitate content from their websites and drive off potential site visitors by always appearing on top of the search results while leaving little incentive to see the linked sources. The company stated that "the future of digital media and [...] its integrity [...] is threatened by Google's current actions", alleging that 20% of searches that link to Penske-owned websites show AI Overviews and that the figure is expected to rise. Google spokesperson José Castañeda called the claims "meritless" and stated that "AI Overviews send traffic to a greater diversity of sites." In 2026, Canadian musician Ashley MacIsaac filed a lawsuit against Google claiming that the AI Overview feature had wrongly stated that MacIsaac had been convicted of numerous criminal offences and was on the sex offender registry. He claims this incorrect information led to the cancellation of a December 2025 gig organized by the Sipekne'katik First Nation.

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  Data governance

  Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate/organizational data governance. Data governance involves delegating authority over data and exercising that authority through decision-making processes. It plays a role in enhancing the value of data assets. == Macro level == Data governance at the macro level involves regulating cross-border data flows among countries, which is more precisely termed international data governance. This field was first formed in the early 2000s, and consists of "norms, principles and rules governing various types of data." There have been several international groups established by research organizations that aim to grant access to their data. These groups that enable an exchange of data are, as a result, exposed to domestic and international legal interpretations that ultimately decide how data is used. However, as of 2023, there are no international laws or agreements specifically focused on data protection. == Data governance (Data Management) == Data governance is the set of principles, policies, and processes that guide the effective and responsible use of data within an organization. It creates a framework for decision making, accountability, and oversight across the data lifecycle, from creation and storage to sharing and disposal. Data governance is closely linked with data management, which provides the practical methods to carry out governance objectives. These methods include data quality assurance, metadata management, master data management, security controls, and compliance monitoring. Together, governance and management aim to maximize the value of data as a strategic asset, reduce risks from misuse or inaccuracy, and ensure compliance with regulatory, ethical, and business requirements. The importance of this discipline has grown with the rise of big data, cloud computing, and artificial intelligence, where consistent standards and stewardship are essential for privacy protection, interoperability, and informed decision making. == Data governance drivers == While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by the CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. Examples of these regulations include Sarbanes–Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP, and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers that are meaningful to both supervisory and executive leadership. Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. Methods to manage these risks vary from industry to industry. Examples of commonly referenced best practices and guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges. == Data governance initiatives (Dimensions) == Data governance initiatives improve the quality of data by assigning a team responsible for data's accuracy, completeness, consistency, timeliness, validity, and uniqueness. This team usually consists of executive leadership, project management, line-of-business managers, and data stewards. The team usually employs a methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and monitoring data. Data governance initiatives may be aimed at achieving a number of objectives including offering better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or to aid the efficiency of enterprise knowledge workers by reducing confusion and error and increasing their scope of knowledge. Many data governance initiatives are also inspired by past attempts to fix information quality at the departmental level, which can lead to incongruent and redundant data quality processes. Most large companies have many applications and databases that can not easily share information. Therefore, knowledge workers within large organizations may not have access to the data they need to best do their jobs. When they do have access to the data, the data quality may be poor. By setting up a data governance practice or corporate data authority (individual or area responsible for determining how to proceed, in the best interest of the business, when a data issue arises), these problems can be mitigated. == Implementation == Implementation of a data governance initiative may vary in scope as well as origin. Sometimes, an executive mandate will arise to initiate an enterprise-wide effort. Sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at either resolving existing issues or demonstrating value. Sometimes, an initiative originates from lower down in the organization's hierarchy and will be deployed in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can vary greatly as well, from review of a one-off IT system to a cross-organization initiative. == Data governance tools == Leaders of successful data governance programs declared at the Data Governance Conference in Orlando, FL, in December 2006, that data governance is about 80 to 95 percent communication. That stated, it is a given that many of the objectives of a data governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as data governance tools. Due to the different focus areas of various data governance initiatives, a given tool may or may not be appropriate. Additionally, many tools that are not marketed as governance tools address governance needs and demands.

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  Tropical cryptography

  In tropical analysis, tropical cryptography refers to the study of a class of cryptographic protocols built upon tropical algebras. In many cases, tropical cryptographic schemes have arisen from adapting classical (non-tropical) schemes to instead rely on tropical algebras. The case for the use of tropical algebras in cryptography rests on at least two key features of tropical mathematics: in the tropical world, there is no classical multiplication (a computationally expensive operation), and the problem of solving systems of tropical polynomial equations has been shown to be NP-hard. == Basic Definitions == The key mathematical object at the heart of tropical cryptography is the tropical semiring ( R ∪ { ∞ } , ⊕ , ⊗ ) {\displaystyle (\mathbb {R} \cup \{\infty \},\oplus ,\otimes )} (also known as the min-plus algebra), or a generalization thereof. The operations are defined as follows for x , y ∈ R ∪ { ∞ } {\displaystyle x,y\in \mathbb {R} \cup \{\infty \}} : x ⊕ y = min { x , y } {\displaystyle x\oplus y=\min\{x,y\}} x ⊗ y = x + y {\displaystyle x\otimes y=x+y} It is easily verified that with ∞ {\displaystyle \infty } as the additive identity, these binary operations on R ∪ { ∞ } {\displaystyle \mathbb {R} \cup \{\infty \}} form a semiring.

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