AI Chatbots and Assistants

Explore the best AI Chatbots and Assistants — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

Nobody (username)

In many Unix variants, "nobody" is the conventional name of a user identifier which owns no files, is in no privileged groups, and has no abilities except those which every other user has. It is normally not enabled as a user account, i.e. has no home directory or login credentials assigned. Some systems also define an equivalent group "nogroup". == Uses == The pseudo-user "nobody" and group "nogroup" are used, for example, in the NFSv4 implementation of Linux by idmapd, if a user or group name in an incoming packet does not match any known username on the system. It was once common to run daemons as nobody, especially on servers, in order to limit the damage that could be done by a malicious user who gained control of them. However, the usefulness of this technique is reduced if more than one daemon is run like this, because then gaining control of one daemon would provide control of them all. The reason is that processes owned by the same user have the ability to send signals to each other and use debugging facilities to read or even modify each other's memory. Modern practice, as recommended by the Linux Standard Base, is to create a separate user account for each daemon.
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Public Services Network

The Public Services Network (PSN) is a UK government's high-performance network, which helps public sector organisations work together, reduce duplication and share resources. It unified the provision of network infrastructure across the United Kingdom public sector into an interconnected "network of networks" to increase efficiency and reduce overall public expenditure. It is now a legacy network and public sector organisations are being migrated to using services on the public internet. == Origins == The Public Services Network (PSN) was launched officially as part of the Transformational Government Strategy commencing in 2005, under the original name of the Public Sector Network. Prior to this, some parts of local government had already successfully implemented the concept. The Hampshire Public Services Network (HPSN) was the first PSN, launched in 1999, followed closely by Kent County Councils partnerships with the KPSN. The HPSN, encompassing all of the borough, district and unitary councils, with the County Council, as well as the Fire Services, the Isle of Wight Council and 540 schools. National PSN technical and architecture compliance criteria were established from 2007, by GDS working with local government leaders from Socitm (the Society of Information Technology Management) on the National CIO Council and the Local CIO Council. The PSN's aim was to bring public services organisations with a common interest onto a single, coherent and standards-based ‘network of networks’. This would create influence, economies of scale and a commonality of standards for secure and easy inter-connection between public service organisations. The original concept of a network of networks strategy was based upon the work already undertaken in local government and recognition of Communities of Interest (COI) within the Criminal Justice Sector during work by the Office for Criminal Justice Reform (OCJR) between 2005 and 2007 to enable data sharing across business units. In this context a COI was defined as groups of Government departments and external partners who in combination provided services within a specific area of operation and used the same data, with a similar risk profile, shared risk appetite and common governance framework. Historically each group member had implemented their own networks and standards of operation in isolation with little or no consideration as to how services and data may be shared and resulting in increased costs of operation. The Network of Networks strategy proposed within OCJR recommended the creation of specific networks based upon these Communities of Interest which were joined together through data interchange gateways supporting common standards. Under this approach networks would be arranged by data type and business functions such as Criminal Justice, Health and Social Care, Defence and Intelligence or Public Finance rather than solely on established departmental boundaries. Within a COI, trust relationships and data interchange are readily supported, enabling data sharing without a need to cross network boundaries and providing benefits of scale without the challenges and compromises intrinsic to homogeneous cross sector networks. Data is made available without a need to transport it between organisations and control is retained by the data originator. In early 2007 a group of UK Government department CTOs in conjunction with the Office for Government Commerce Buying Solutions (OGC BS) established the vision for a single commonly provided, procured and managed public sector voice and data network infrastructure to replace the multitude of separately procured and managed networks serving various segments of the UK public sector; Education, Health, Central Government, Local Government etc. In 2008 an Industry Working Group was established to document the objectives and requirements more clearly. Their report set out the architectural and commercial principles as well as anticipated security, service management, governance and transition arrangements. == Architecture == The PSN comprises a core network, the Government Conveyancing Network or GCN provided by GCN Service Providers or GCNSPs. The GCN interconnects multiple operator networks, termed Direct Network Service Providers or DNSPs. Subscriber organisations contract to a connection from a local participating DNSP, connect via that to GCN and hence onwards to other interconnected networks and services. The GCN network is entirely based on IPv4 and MPLS and the GCNSPs are not currently mandated to provide IPv6, though they should have a roadmap to implementing it if and when required. == Commercial framework == In 2010 Virgin Media Business, BT, Cable & Wireless and Global Crossing signed Deeds of Undertaking (DoU) and subsequently achieved accreditation for providing GCN and IP VPN services. In March 2012, BT, Cable & Wireless, Capita Business Services, Eircom, Fujitsu, Kcom, Level 3, Logicalis, MDNX, Thales, Updata and Virgin Media Business were successful bidders for the initial two-year PSN Connectivity framework. In June 2012, 29 companies were confirmed as suppliers of ICT services to the UK public sector under the Government's PSN Services framework contract. Apart from most of the previous suppliers, additional companies also included 2e2, Airwave Solutions, Azzurri Communications, Cassidian, CSC Computer Sciences, Computacenter, Daisy Communications, Easynet Global Services, EE, Freedom Communications, Icom Holdings, NextiraOne, PageOne Communications, Phoenix IT Group, Siemens Communications, Specialist Computer Centres, Telefónica, telent Technology Services, Uniworld Communications and Vodafone. == Governance == The PSN is managed within the Cabinet Office where it is part of the Government Digital Service. == Early implementations == There were already notable initiatives in progress in county council areas, demonstrating public sector network integration in both the Hampshire HPSN2 network and in Kent's community network. Project Pathway was established as a pilot linking these two county-wide networks, with Virgin Media Business and Global Crossing the subscriber and GCN network elements. Staffordshire County Council was the first council in England to establish a PSN that included the county's NHS Health partners. Other county councils have since followed the leads of these councils. == Transition == Centrally procured public sector networks are expected to migrate across to the PSN framework as they reach the end of their contract terms, either through an interim framework or directly. The Government Secure Intranet (GSi) contracts expired in September 2011, running on to 12 February 2012 and were replaced by the transitional Government Secure Intranet Convergence Framework (GCF). The Managed Telephony Service (MTS) contract expired on 31 December 2011 and was replaced by the Managed Telephony Convergence Framework (MTCF). == Future plan == In a blog post published on 20 January 2017, Government Digital Service announced that the Technology Leaders Network (TLN) had agreed that government was starting a journey away from the PSN. This was because using the Internet was considered suitable for the vast majority of the work that the public sector does. The blog post confirmed that the 'move was not going to happen immediately' and stated that 'there's quite a bit of work to do across the public sector to prepare for the changes'. It also stated that it was too early for a full timeline to be provided, although all PSN-connected organisations would be updated as the process evolved. The blog post confirmed that organisations that need to access services that are only available on the PSN would still need to connect to it for the time being and continue to meet its assurance requirements. In a blog post published on 16 March 2017, Government Digital Service (GDS) set out its plans for PSN assurance. The blog post confirmed that the PSN compliance process wasn't 'going anywhere, certainly for a while yet'. It explained that the TLN agreed that – as one of the only recognised, externally accredited, cross-government common assurance standards – it 'needs to live on far beyond the end of the physical PSN network'. Government Digital Service, along with the National Cyber Security Centre (NCSC) and the Cyber and Government Security Directorate, are now looking at ways to expand and reframe PSN compliance in a new context that, while retaining the assurance principles that are the basis of the existing process, will aim to improve the process. A GDS blog post titled 'The road to closing down the PSN' published on 8 September 2020 describes how the public sector will migrate away from the PSN. The Cabinet Office has set up a programme called Future Networks for Government (FN4G) to help organisations move away from the PSN.
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Weird SoundCloud

Weird SoundCloud, or SoundClown, is a mashup parody music scene taking place on the online distribution platform SoundCloud. The scene has been described by its producers and music journalists to be a satirical take on electronic dance music, and useless, throwaway internet content. One critic, Audra Schroeder, categorized it as an in-joke that is "deconstructing and reshaping memes and popular music, recontextualizing the sacred texts of millennial chat rooms." == Origins == In a January 2014 interview, DJ Kevin Wang suggested that the Weird SoundCloud has "been around in the last one to two years", but started to gain much more popularity the previous year through electronic dance music internet blogs. Weird SoundCloud producer Ideaot suggested that some in the phenomenon came from the YouTube poop scene. Another producer in the community, DJ @@ (AT-AT), reasoned that producers joining the scene "want to express their musicality, see it as a more mature form of YouTube Poop," or are "just looking for recognition on social media sites." AT-AT said that it was "a fun thing to do, and after I stopped making proper music I felt I needed a bit of an outlet for my creativity. The fact that people enjoyed it and/or treated it as a travesty (Direct quote from one of my tracks) spurs me on." == Characteristics == Weird SoundCloud is a mash-up and parody music genre labeled by journalist Audra Schroeder as an in-joke that is "deconstructing and reshaping memes and popular music, recontextualizing the sacred texts of millennial chat rooms." Most tracks range from around 30 seconds to one minute in length. The people who make weird SoundCloud are known as SoundClowns, a term coined by producer Dicksoak. Ideaot described the weird SoundCloud community as "largely just people who are friends with each other." Noisey critic Ryan Bassil spotlight the variety of music coming out of the weird SoundCloud landscape: "One minute you could be listening to the Seinfeld theme reimagined as an aneurysm inducing dubstep corker, the next, you're recovering from hearing a version of Tenacious D's "Tribute" that's akin to having a stroke." Bassil analyzes that the tracks "often take the past and repurpose it into something that, although not altogether useful, sounds fresh and reflective of the abstract, confusing panoramic that encapsulates the modern internet." Bassil compared the lexicon of SoundClown's track titles to that of Reddit and Twitter users. According to Dicksoak, most works of the style are critiques of EDM or "are just uploaded because they sound funny." However, Bassil disagreed, writing that there are also many tracks that keep repurposing a certain meme, such as "mom's spaghetti" or the re-use of vocals from recordings by hip hop group Death Grips. He describe the scene's re-use of memes as a satirical take on pointless online content that is only on the internet to "do nothing other than fill the void": They're changing the format of the original work's intended message or audience - a technique often employed by top-tier digital media companies - and in doing so they're sarcastically, ironically, taking the piss out of what Web 2.0's turned into - an open arena where the most ridiculous, unashamed, often pointless piggy-back content can rack up thousands and thousands of clicks. == Notable examples == There are mash-ups that "disrupt the flow of popular music", in the words of writer Schroeder, such as a "flutedrop" remix of the Miley Cyrus song "Wrecking Ball" and Shaliek's mashup of music by Bruno Mars and Korn. In November 2013, Wang released a set of mp3 files on SoundCloud named Best Drops Ever, which included tracks like "A Drop So Epic a Bunch of NYU Bros Already Bought a 3-Day Weekend Pass for It" and "A Drop So Crazy You'll Kill Your Family". All of the tracks start as normal electronic dance music build-ups, before they drop into a "bait and switch" audio or film clip such as Filet-O-Fish commercials, the Whitney Houston song "I Will Always Love You" and the film Bambi (1942) that ruins the anticipation. The collection is a parody of the over-importance and over-focus of the drop and lack of care of the overall quality of a song common in the modern electronic dance music scene. Wang has released more than 45 tracks in the weird SoundCloud, some of them receiving around a million plays. Subgenres of Weird SoundCloud include Macklecore, mash-ups and remixes that include the works of American hip-hop recording artist Macklemore, and Biggiewave, which include samples of songs from the album Ready to Die (1994) by The Notorious B.I.G. Common audio and meme sources used include Skrillex, the Martin Garrix track "Animals", Thomas the Tank Engine, Shrek, Macklemore, "Gangnam Style", the Bruno Mars track "Uptown Funk", the Disturbed track "Down with the Sickness", Space Jam, the Childish Gambino track "Bonfire", the Death Grips track "Takyon" and air horn sound effects. == Reception == Bassil praised the SoundClown scene as "loveable and strangely honest", reasoning that it "just reminds me that we're all humans on the internet, all searching for #content that means something, something to connect with, but usually only dredging up bastardised versions of things we've already read, seen, or watched before." Bassil also described the weird SoundCloud as a more successful version of a similar scene known as weird YouTube; the reason for the success of SoundClowns is due to SoundCloud's discovery algorithm: "Small collectives and trends are able to form, and there's an abundance of tracks from artists who are almost forging careers out of it, as opposed to uploading one viral hit." Publications have made lists of weird SoundCloud works, such as BuzzFeed's "23 Of The Weirdest Songs On Soundcloud", Obsev's "Weird SoundCloud Mashups That Must've Been Made While Drunk", and Thump's "9 of the Best and Most Upsetting Soundclowns we Could Find", where writer Isabelle Hellyer called it the "most influential genre of music in human history." A Your EDM writer called it "oddly addicting."
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MIME Object Security Services

MIME Object Security Services (MOSS) is a protocol that uses the multipart/signed and multipart/encrypted framework to apply digital signature and encryption services to MIME objects. == Details == The services are offered through the use of end-to-end cryptography between an originator and a recipient at the application layer. Asymmetric (public key) cryptography is used in support of the digital signature service and encryption key management. Symmetric (secret key) cryptography is used in support of the encryption service. The procedures are intended to be compatible with a wide range of public key management approaches, including both ad hoc and certificate-based schemes. Mechanisms are provided to support many public key management approaches. == Spreading == MOSS was never widely deployed and is now abandoned, largely due to the popularity of PGP.
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XLNet

The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words. It was released on 19 June 2019, under the Apache 2.0 license. It achieved state-of-the-art results on a variety of natural language processing tasks, including language modeling, question answering, and natural language inference. == Architecture == The main idea of XLNet is to model language autoregressively like the GPT models, but allow for all possible permutations of a sentence. Concretely, consider the following sentence:My dog is cute.In standard autoregressive language modeling, the model would be tasked with predicting the probability of each word, conditioned on the previous words as its context: We factorize the joint probability of a sequence of words x 1 , … , x T {\displaystyle x_{1},\ldots ,x_{T}} using the chain rule: Pr ( x 1 , … , x T ) = Pr ( x 1 ) Pr ( x 2 | x 1 ) Pr ( x 3 | x 1 , x 2 ) … Pr ( x T | x 1 , … , x T − 1 ) . {\displaystyle \Pr(x_{1},\ldots ,x_{T})=\Pr(x_{1})\Pr(x_{2}|x_{1})\Pr(x_{3}|x_{1},x_{2})\ldots \Pr(x_{T}|x_{1},\ldots ,x_{T-1}).} For example, the sentence "My dog is cute" is factorized as: Pr ( My , dog , is , cute ) = Pr ( My ) Pr ( dog | My ) Pr ( is | My , dog ) Pr ( cute | My , dog , is ) . {\displaystyle \Pr({\text{My}},{\text{dog}},{\text{is}},{\text{cute}})=\Pr({\text{My}})\Pr({\text{dog}}|{\text{My}})\Pr({\text{is}}|{\text{My}},{\text{dog}})\Pr({\text{cute}}|{\text{My}},{\text{dog}},{\text{is}}).} Schematically, we can write it as → My → My dog → My dog is → My dog is cute . {\displaystyle {\texttt {}}{\texttt {}}{\texttt {}}{\texttt {}}\to {\text{My }}{\texttt {}}{\texttt {}}{\texttt {}}\to {\text{My dog }}{\texttt {}}{\texttt {}}\to {\text{My dog is }}{\texttt {}}\to {\text{My dog is cute}}.} However, for XLNet, the model is required to predict the words in a randomly generated order. Suppose we have sampled a randomly generated order 3241, then schematically, the model is required to perform the following prediction task: → is → dog is → dog is cute → My dog is cute {\displaystyle {\texttt {}}{\texttt {}}{\texttt {}}{\texttt {}}\to {\texttt {}}{\texttt {}}{\text{is }}{\texttt {}}\to {\texttt {}}{\text{dog is }}{\texttt {}}\to {\texttt {}}{\text{dog is cute}}\to {\text{My dog is cute}}} By considering all permutations, XLNet is able to capture longer-range dependencies and better model the bidirectional context of words. === Two-Stream Self-Attention === To implement permutation language modeling, XLNet uses a two-stream self-attention mechanism. The two streams are: Content stream: This stream encodes the content of each word, as in standard causally masked self-attention. Query stream: This stream encodes the content of each word in the context of what has gone before. In more detail, it is a masked cross-attention mechanism, where the queries are from the query stream, and the key-value pairs are from the content stream. The content stream uses the causal mask M causal = [ 0 − ∞ − ∞ … − ∞ 0 0 − ∞ … − ∞ 0 0 0 … − ∞ ⋮ ⋮ ⋮ ⋱ ⋮ 0 0 0 … 0 ] {\displaystyle M_{\text{causal}}={\begin{bmatrix}0&-\infty &-\infty &\dots &-\infty \\0&0&-\infty &\dots &-\infty \\0&0&0&\dots &-\infty \\\vdots &\vdots &\vdots &\ddots &\vdots \\0&0&0&\dots &0\end{bmatrix}}} permuted by a random permutation matrix to P M causal P − 1 {\displaystyle PM_{\text{causal}}P^{-1}} . The query stream uses the cross-attention mask P ( M causal − ∞ I ) P − 1 {\displaystyle P(M_{\text{causal}}-\infty I)P^{-1}} , where the diagonal is subtracted away specifically to avoid the model "cheating" by looking at the content stream for what the current masked token is. Like the causal masking for GPT models, this two-stream masked architecture allows the model to train on all tokens in one forward pass. == Training == Two models were released: XLNet-Large, cased: 110M parameters, 24-layer, 1024-hidden, 16-heads XLNet-Base, cased: 340M parameters, 12-layer, 768-hidden, 12-heads. It was trained on a dataset that amounted to 32.89 billion tokens after tokenization with SentencePiece. The dataset was composed of BooksCorpus, and English Wikipedia, Giga5, ClueWeb 2012-B, and Common Crawl. It was trained on 512 TPU v3 chips, for 5.5 days. At the end of training, it still under-fitted the data, meaning it could have achieved lower loss with more training. It took 0.5 million steps with an Adam optimizer, linear learning rate decay, and a batch size of 8192.
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CrySyS Lab

CrySyS Lab (Hungarian pronunciation: [ˈkriːsis]) is part of the Department of Telecommunications at the Budapest University of Technology and Economics. The name is derived from "Laboratory of Cryptography and System Security", the full Hungarian name is CrySys Adat- és Rendszerbiztonság Laboratórium. == History == CrySyS Lab. was founded in 2003 by a group of security researchers at the Budapest University of Technology and Economics. Currently, it is located in the Infopark Budapest. The heads of the lab were Dr. István Vajda (2003–2010) and Dr. Levente Buttyán (2010-now). Since its establishment, the lab participated in several research and industry projects, including successful EU FP6 and FP7 projects (SeVeCom, a UbiSecSens and WSAN4CIP). == Research results == CrySyS Lab is recognized in research for its contribution to the area of security in wireless embedded systems. In this area, the members of the lab produced 5 books 4 book chapters 21 journal papers 47 conference papers 3 patents 2 Internet Draft The above publications had an impact factor of 30+ and obtained more than 7500 references. Several of these publications appeared in highly cited journals (e.g., IEEE Transactions on Dependable and Secure Systems, IEEE Transactions on Mobile Computing). == Forensics analysis of malware incidents == The laboratory was involved in the forensic analysis of several high-profile targeted attacks. In October 2011, CrySyS Lab discovered the Duqu malware; pursued the analysis of the Duqu malware and as a result of the investigation, identified a dropper file with an MS 0-day kernel exploit inside; and finally released a new open-source Duqu Detector Toolkit to detect Duqu traces and running Duqu instances. In May 2012, the malware analysis team at CrySyS Lab participated in an international collaboration aiming at the analysis of an as yet unknown malware, which they call sKyWIper. At the same time Kaspersky Lab analyzed the malware Flame and Iran National CERT (MAHER) the malware Flamer. Later, they turned out to be the same. Other analysis published by CrySyS Lab include the password analysis of the Hungarian ISP, Elender, and a thorough Hungarian security survey of servers after the publications of the Kaminsky DNS attack.
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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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SCinet

SCinet is the high-performance network built annually by volunteers in support of SC (formerly Supercomputing, the International Conference for High Performance Computing, Networking, Storage and Analysis). SCinet is the primary network for the yearly conference and is used by attendees and exhibitors to demonstrate and test high-performance computing and networking applications. == International Community == SCinet is also a hub for the international networking community. It provides a platform to share the latest research, technologies, and demonstrations for networks, network technology providers, and even software developers who are in charge of supporting HPC communities at their own institutions or organizations. == Volunteers == Nearly 200 volunteers from educational institutions, high performance computing sites, equipment vendors, research and education networks, government agencies and telecommunications carriers collaborate via technology and in-person to design, build and operate SCinet. While many of these credentialed individuals have volunteered at SCinet for years, first timers join the team each year. They include international students and participants in the National Science Foundation-funded Women in IT Networking at SC (WINS) program. The 2017 SCinet team included women and men from high performance computing institutions in the U.S. and throughout the world. == History == Originated in 1991 as an initiative within the SC conference to provide networking to attendees, SCinet has grown to become the "World's Fastest Network" during the duration of the conference. For 29 years, SCinet has provided SC attendees and the high performance computing (HPC) community with the innovative network platform necessary to internationally interconnect, transport, and display HPC research during SC. Historically, SCinet has been used as a platform to test networking technology and applications which have found their way into common use. == Research and development == In the past years, SCinet deployed conference wide networking technologies such as ATM, FDDI, HiPPi before they were deployed commercially.
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Data cube

In computer programming, a data cube (or datacube) is a multi-dimensional array of values. Typically, the term "data cube" is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data. Even though it is called a cube, a data cube generally is a multi-dimensional concept which can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. The data cube is used to represent data (sometimes called facts) along some dimensions of interest. In satellite image timeseries, dimensions would be latitude and longitude coordinates and time; a fact (sometimes called measure) would be a pixel at a given space and time as taken by the satellite. For example, in online analytical processing, an OLAP cube about a company would have dimensions that could be the company subsidiaries, the company products, and time; in this setup, a fact would be a sales event where a particular product has been sold in a particular subsidiary at a particular time. In any case, every dimension divides data into groups of cells whereas each cell in the cube represents a single measure of interest. Sometimes cubes hold only a few values with the rest being empty, i.e. undefined, while sometimes most or all cube coordinates hold a cell value. In the first case such data are called sparse, and in the second case they are called dense, although there is no hard delineation between the two. Data cubes may be stored in database management systems (DBMS) as part of array DBMS. Spatio-temporal databases and geospatial databases may also be represented as coverage data. == History == Multi-dimensional arrays have long been familiar in programming languages. Fortran offers arbitrarily-indexed 1-D arrays and arrays of arrays, which allows the construction of higher-dimensional arrays, up to 15 dimensions. APL supports n-D arrays with a rich set of operations. All these have in common that arrays must fit into the main memory and are available only while the particular program maintaining them (such as image processing software) is running. A series of data exchange formats support storage and transmission of data cube-like data, often tailored towards particular application domains. Examples include MDX for statistical (in particular, business) data, Zarr and Hierarchical Data Format for general scientific data, and TIFF for imagery. In 1992, Peter Baumann introduced management of massive data cubes with high-level user functionality combined with an efficient software architecture. Datacube operations include subset extraction, processing, fusion, and in general queries in the spirit of data manipulation languages like SQL. Some years after, the data cube concept was applied to describe time-varying business data as data cubes by Jim Gray, et al., and by Venky Harinarayan, Anand Rajaraman and Jeff Ullman. Around that time, a working group on Multi-Dimensional Databases ("Arbeitskreis Multi-Dimensionale Datenbanken") was established at German Gesellschaft für Informatik. Datacube Inc. was an image processing company selling hardware and software applications for the PC market in 1996, however without addressing data cubes as such. The EarthServer initiative has established geo data cube service requirements. == Standardization == In 2018, the ISO SQL database language was extended with data cube functionality as "SQL – Part 15: Multi-dimensional arrays (SQL/MDA)". Web Coverage Processing Service is a geo data cube analytics language issued by the Open Geospatial Consortium in 2008. In addition to the common data cube operations, the language knows about the semantics of space and time and supports both regular and irregular grid data cubes, based on the concept of coverage data. An industry standard for querying business data cubes, originally developed by Microsoft, is MultiDimensional eXpressions. == Implementation == Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which Fortran, APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional data cubes. This database category has been pioneered by the rasdaman system since 1994. == Applications == Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a data cube. === Mathematics === In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. === Science and engineering === For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo data cube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. Earth observation data cubes combine satellite imagery such as Landsat 8 and Sentinel-2 with Geographic information system analytics. === Business intelligence === In online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
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Scalable Coherent Interface

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

Strong cryptography or cryptographically strong are general terms used to designate the cryptographic algorithms that, when used correctly, provide a very high (usually insurmountable) level of protection against any eavesdropper, including the government agencies. There is no precise definition of the boundary line between the strong cryptography and (breakable) weak cryptography, as this border constantly shifts due to improvements in hardware and cryptanalysis techniques. These improvements eventually place the capabilities once available only to the NSA within the reach of a skilled individual, so in practice there are only two levels of cryptographic security, "cryptography that will stop your kid sister from reading your files, and cryptography that will stop major governments from reading your files" (Bruce Schneier). The strong cryptography algorithms have high security strength, for practical purposes usually defined as a number of bits in the key. For example, the United States government, when dealing with export control of encryption, considered as of 1999 any implementation of the symmetric encryption algorithm with the key length above 56 bits or its public key equivalent to be strong and thus potentially a subject to the export licensing. To be strong, an algorithm needs to have a sufficiently long key and be free of known mathematical weaknesses, as exploitation of these effectively reduces the key size. At the beginning of the 21st century, the typical security strength of the strong symmetrical encryption algorithms is 128 bits (slightly lower values still can be strong, but usually there is little technical gain in using smaller key sizes). Demonstrating the resistance of any cryptographic scheme to attack is a complex matter, requiring extensive testing and reviews, preferably in a public forum. Good algorithms and protocols are required (similarly, good materials are required to construct a strong building), but good system design and implementation is needed as well: "it is possible to build a cryptographically weak system using strong algorithms and protocols" (just like the use of good materials in construction does not guarantee a solid structure). Many real-life systems turn out to be weak when the strong cryptography is not used properly, for example, random nonces are reused A successful attack might not even involve algorithm at all, for example, if the key is generated from a password, guessing a weak password is easy and does not depend on the strength of the cryptographic primitives. A user can become the weakest link in the overall picture, for example, by sharing passwords and hardware tokens with the colleagues. == Background == The level of expense required for strong cryptography originally restricted its use to the government and military agencies, until the middle of the 20th century the process of encryption required a lot of human labor and errors (preventing the decryption) were very common, so only a small share of written information could have been encrypted. US government, in particular, was able to keep a monopoly on the development and use of cryptography in the US into the 1960s. In the 1970, the increased availability of powerful computers and unclassified research breakthroughs (Data Encryption Standard, the Diffie-Hellman and RSA algorithms) made strong cryptography available for civilian use. Mid-1990s saw the worldwide proliferation of knowledge and tools for strong cryptography. By the 21st century the technical limitations were gone, although the majority of the communication were still unencrypted. At the same the cost of building and running systems with strong cryptography became roughly the same as the one for the weak cryptography. The use of computers changed the process of cryptanalysis, famously with Bletchley Park's Colossus. But just as the development of digital computers and electronics helped in cryptanalysis, it also made possible much more complex ciphers. It is typically the case that use of a quality cipher is very efficient, while breaking it requires an effort many orders of magnitude larger - making cryptanalysis so inefficient and impractical as to be effectively impossible. == Cryptographically strong algorithms == This term "cryptographically strong" is often used to describe an encryption algorithm, and implies, in comparison to some other algorithm (which is thus cryptographically weak), greater resistance to attack. But it can also be used to describe hashing and unique identifier and filename creation algorithms. See for example the description of the Microsoft .NET runtime library function Path.GetRandomFileName. In this usage, the term means "difficult to guess". An encryption algorithm is intended to be unbreakable (in which case it is as strong as it can ever be), but might be breakable (in which case it is as weak as it can ever be) so there is not, in principle, a continuum of strength as the idiom would seem to imply: Algorithm A is stronger than Algorithm B which is stronger than Algorithm C, and so on. The situation is made more complex, and less subsumable into a single strength metric, by the fact that there are many types of cryptanalytic attack and that any given algorithm is likely to force the attacker to do more work to break it when using one attack than another. There is only one known unbreakable cryptographic system, the one-time pad, which is not generally possible to use because of the difficulties involved in exchanging one-time pads without them being compromised. So any encryption algorithm can be compared to the perfect algorithm, the one-time pad. The usual sense in which this term is (loosely) used, is in reference to a particular attack, brute force key search — especially in explanations for newcomers to the field. Indeed, with this attack (always assuming keys to have been randomly chosen), there is a continuum of resistance depending on the length of the key used. But even so there are two major problems: many algorithms allow use of different length keys at different times, and any algorithm can forgo use of the full key length possible. Thus, Blowfish and RC5 are block cipher algorithms whose design specifically allowed for several key lengths, and who cannot therefore be said to have any particular strength with respect to brute force key search. Furthermore, US export regulations restrict key length for exportable cryptographic products and in several cases in the 1980s and 1990s (e.g., famously in the case of Lotus Notes' export approval) only partial keys were used, decreasing 'strength' against brute force attack for those (export) versions. More or less the same thing happened outside the US as well, as for example in the case of more than one of the cryptographic algorithms in the GSM cellular telephone standard. The term is commonly used to convey that some algorithm is suitable for some task in cryptography or information security, but also resists cryptanalysis and has no, or fewer, security weaknesses. Tasks are varied, and might include: generating randomness encrypting data providing a method to ensure data integrity Cryptographically strong would seem to mean that the described method has some kind of maturity, perhaps even approved for use against different kinds of systematic attacks in theory and/or practice. Indeed, that the method may resist those attacks long enough to protect the information carried (and what stands behind the information) for a useful length of time. But due to the complexity and subtlety of the field, neither is almost ever the case. Since such assurances are not actually available in real practice, sleight of hand in language which implies that they are will generally be misleading. There will always be uncertainty as advances (e.g., in cryptanalytic theory or merely affordable computer capacity) may reduce the effort needed to successfully use some attack method against an algorithm. In addition, actual use of cryptographic algorithms requires their encapsulation in a cryptosystem, and doing so often introduces vulnerabilities which are not due to faults in an algorithm. For example, essentially all algorithms require random choice of keys, and any cryptosystem which does not provide such keys will be subject to attack regardless of any attack resistant qualities of the encryption algorithm(s) used. == Legal issues == Widespread use of encryption increases the costs of surveillance, so the government policies aim to regulate the use of the strong cryptography. In the 2000s, the effect of encryption on the surveillance capabilities was limited by the ever-increasing share of communications going through the global social media platforms, that did not use the strong encryption and provided governments with the requested data. Murphy talks about a legislative balance that needs to be struck between the power of the government that are broad enough to be able to follow the qui
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Data dictionary

A data dictionary, or metadata repository, as defined in the IBM Dictionary of Computing, is a "centralized repository of information about data such as meaning, relationships to other data, origin, usage, and format". Oracle defines it as a collection of tables with metadata. The term can have one of several closely related meanings pertaining to databases and database management systems (DBMS): A document describing a database or collection of databases An integral component of a DBMS that is required to determine its structure A piece of middleware that extends or supplants the native data dictionary of a DBMS == Documentation == The terms data dictionary and data repository indicate a more general software utility than a catalogue. A catalogue is closely coupled with the DBMS software. It provides the information stored in it to the user and the DBA, but it is mainly accessed by the various software modules of the DBMS itself, such as DDL and DML compilers, the query optimiser, the transaction processor, report generators, and the constraint enforcer. On the other hand, a data dictionary is a data structure that stores metadata, i.e., (structured) data about information. The software package for a stand-alone data dictionary or data repository may interact with the software modules of the DBMS, but it is mainly used by the designers, users and administrators of a computer system for information resource management. These systems maintain information on system hardware and software configuration, documentation, application and users as well as other information relevant to system administration. If a data dictionary system is used only by the designers, users, and administrators and not by the DBMS Software, it is called a passive data dictionary. Otherwise, it is called an active data dictionary or data dictionary. When a passive data dictionary is updated, it is done so manually and independently from any changes to a DBMS (database) structure. With an active data dictionary, the dictionary is updated first and changes occur in the DBMS automatically as a result. Database users and application developers can benefit from an authoritative data dictionary document that catalogs the organization, contents, and conventions of one or more databases. This typically includes the names and descriptions of various tables (records or entities) and their contents (fields), plus additional details, like the type and length of each data element. Another important piece of information that a data dictionary can provide is the relationship between tables. This is sometimes referred to in entity-relationship diagrams (ERDs), or if using set descriptors, identifying which sets database tables participate in. In an active data dictionary constraints may be placed upon the underlying data. For instance, a range may be imposed on the value of numeric data in a data element (field), or a record in a table may be forced to participate in a set relationship with another record-type. Additionally, a distributed DBMS may have certain location specifics described within its active data dictionary (e.g. where tables are physically located). The data dictionary consists of record types (tables) created in the database by systems generated command files, tailored for each supported back-end DBMS. Oracle has a list of specific views for the "sys" user. This allows users to look up the exact information that is needed. Command files contain SQL Statements for CREATE TABLE, CREATE UNIQUE INDEX, ALTER TABLE (for referential integrity), etc., using the specific statement required by that type of database. There is no universal standard as to the level of detail in such a document. == Middleware == In the construction of database applications, it can be useful to introduce an additional layer of data dictionary software, i.e. middleware, which communicates with the underlying DBMS data dictionary. Such a "high-level" data dictionary may offer additional features and a degree of flexibility that goes beyond the limitations of the native "low-level" data dictionary, whose primary purpose is to support the basic functions of the DBMS, not the requirements of a typical application. For example, a high-level data dictionary can provide alternative entity-relationship models tailored to suit different applications that share a common database. Extensions to the data dictionary also can assist in query optimization against distributed databases. Additionally, DBA functions are often automated using restructuring tools that are tightly coupled to an active data dictionary. Software frameworks aimed at rapid application development sometimes include high-level data dictionary facilities, which can substantially reduce the amount of programming required to build menus, forms, reports, and other components of a database application, including the database itself. For example, PHPLens includes a PHP class library to automate the creation of tables, indexes, and foreign key constraints portably for multiple databases. Another PHP-based data dictionary, part of the RADICORE toolkit, automatically generates program objects, scripts, and SQL code for menus and forms with data validation and complex joins. For the ASP.NET environment, Base One's data dictionary provides cross-DBMS facilities for automated database creation, data validation, performance enhancement (caching and index utilization), application security, and extended data types. Visual DataFlex features provides the ability to use DataDictionaries as class files to form middle layer between the user interface and the underlying database. The intent is to create standardized rules to maintain data integrity and enforce business rules throughout one or more related applications. Some industries use generalized data dictionaries as technical standards to ensure interoperability between systems. The real estate industry, for example, abides by a RESO's Data Dictionary to which the National Association of REALTORS mandates its MLSs comply with through its policy handbook. This intermediate mapping layer for MLSs' native databases is supported by software companies which provide API services to MLS organizations. == Platform-specific examples == Developers use a data description specification (DDS) to describe data attributes in file descriptions that are external to the application program that processes the data, in the context of an IBM i. The sys.ts$ table in Oracle stores information about every table in the database. It is part of the data dictionary that is created when the Oracle Database is created. Developers may also use DDS context from free and open-source software (FOSS) for structured and transactional queries in open environments. == Typical attributes == Here is a non-exhaustive list of typical items found in a data dictionary for columns or fields: Entity or form name or their ID (EntityID or FormID). The group this field belongs to. Field name, such as RDBMS field name Displayed field title. May default to field name if blank. Field type (string, integer, date, etc.) Measures such as min and max values, display width, or number of decimal places. Different field types may interpret this differently. An alternative is to have different attributes depending on field type. Field display order or tab order Coordinates on screen (if a positional or grid-based UI) Default value Prompt type, such as drop-down list, combo-box, check-boxes, range, etc. Is-required (Boolean) - If 'true', the value cannot be blank, null, or only white-spaces Is-read-only (Boolean) Reference table name, if a foreign key. Can be used for validation or selection lists. Various event handlers or references to. Example: "on-click", "on-validate", etc. See event-driven programming. Format code, such as a regular expression or COBOL-style "PIC" statements Description or synopsis Database index characteristics or specification
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Adobe PhotoDeluxe

PhotoDeluxe was a consumer-oriented image editing software line published by Adobe Systems from 1996 until July 8, 2002. At that time it was replaced by Adobe's newly launched consumer-oriented image editing software Photoshop Elements. Adobe no longer provides technical support for the PhotoDeluxe software line. PhotoDeluxe had a range of image processing capabilities for the home photographer and image handler. These included removing red-eye, cropping, and adjusting brightness, contrast, and sharpness. It also included software to extract pictures from an image scanner. Among the functionality included was the ability to dynamically resize photos and export them in a wide range of formats. It also had a range of printing options including printing multiple copies of an image on the same page. It was often bundled free with Epson scanners or as free software with new computers. == Features == Despite the critical concerns regarding the quality of the setup, Photo Deluxe supports layering, blurs, sharpening, cloning, gradient fills, color and background switches, color variations, resizing options, and many other features. Another drawback of PhotoDeluxe was that it was designed for Mac computers, so working on Windows PC was a problem for those who were unable to customize their preferences. == Versions == === Adobe PhotoDeluxe 1.0 === The first version was released in 1996 for Windows and Macintosh computers. In one year, it sold over one million copies. === Adobe PhotoDeluxe 2.0 === The new version was released in 1997 and had added features such as a Clone Tool, red-eye removal, and sample templates for making posters, cards, and calendars. It also had new special effect features. === Adobe PhotoDeluxe 3.0 === The 3rd version was released in 1998. The new features included customizable clipart settings, the ability to import photos on the web, enhanced repair activities following Guided Activities, and Adobe Connectables to add new activities. === Adobe PhotoDeluxe Home Edition (4.0) === Version 4.0 was created by the makers of Photoshop. It had advanced abilities such as tools to add animation, voice, and music to a picture. It also had features to restore photos to their original position. == History == Adobe PhotoDeluxe 1.0 was released in 1996 for Macintosh computers, initially retailing for an MSRP of $49. The software did quite well, reportedly selling over a million copies by February of the next year, primarily due to bundles with companies like Apple and Hewlett-Packard. PhotoDeluxe was primarily advertised to consumers as a way to do basic photo manipulation, such as cropping and rotating images, or creating simple cards and calendars. PhotoDeluxe 2.0 was released in 1997, and was the last version of PhotoDeluxe that Adobe made that worked on Macs. PhotoDeluxe 2.0 became the "number one selling consumer photo-editing software product in the world." PhotoDeluxe 3.0 was released in 1998, where it was rebranded as "3.0 Home Edition", as Adobe released PhotoDeluxe Business Edition later that year for a higher price. PhotoDeluxe Home Edition, unofficially called PhotoDeluxe 4.0, was released in 1999 and was the last version of PhotoDeluxe to be released. Adobe officially cancelled PhotoDeluxe on July 8, 2002, citing the presence of Photoshop and Photoshop Elements, with support being officially cancelled in mid-2003. No version of PhotoDeluxe is compatible with Windows 10, rendering the program obsolete. == Pricing == All home versions of PhotoDeluxe retailed for an MSRP of $49. PhotoDeluxe 2.0 and onwards allowed users to upgrade from a previous version of PhotoDeluxe or a competing piece of graphics software for $39. Additionally PhotoDeluxe Business Edition allowed a similar deal, allowing users to upgrade from other versions of PhotoDeluxe or a competing software for $59, instead of its normal price of $99. Adobe also offered a bundle allowing users of 1.0 or 2.0 to get 3.0 and Business Edition for $79.
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Thirst trap

A thirst trap is a type of social media post intended to entice viewers sexually. It refers to a viewer's "thirst", a colloquialism likening sexual frustration to dehydration, implying desperation, with the afflicted individual being described as "thirsty". The phrase entered into the lexicon in the late 1990s, but is most related to Internet slang that developed in the early 2010s. Its meaning has changed over time, previously referring to a graceless need for approval, affection or attention. == History == The term thirst trap originated within selfie culture, though its precise origins remain unclear. An early use of the phrase with reference to dehydration appears in the 1999 book Running for Dummies by Florence Griffith Joyner and John Hanc, where it referred to the deceptive sensation of thirst being quenched after initial fluid intake, advising continued hydration to avoid the so-called "thirst trap." The modern usage of thirst trap resurfaced around 2011 on platforms such as Twitter and Urban Dictionary, coinciding with the growing popularity of Snapchat, Instagram, and dating apps like Tinder and Grindr. In 2011, Urban Dictionary defined it as "any statement used to intentionally create attention or 'thirst'." By 2018, the term had entered mainstream discourse, appearing in outlets such as The New York Times and GQ without the need for explanation. == Usage of the term == Often, the term thirst trap describes an attractive picture of an individual that they post online. Thirst trap can also describe a digital heartthrob. For instance, former Canadian prime minister Justin Trudeau has been described as a political thirst trap. It has also been described as a modern form of "fishing for compliments". == Motivation == Thirst trapping may be driven by a variety of motives. Individuals often seek attention through "likes" and comments on social media, which can offer a temporary sense of validation and improved self-esteem. It can also serve as an outlet for expressing one's sexuality or enhancing a personal brand. In some cases, sharing such content may provide financial gain. Others might post thirst traps to cope with emotional distress, such as after breakup, or to spite a former lover. Sharing a thirst trap has also been used as a way to connect in times of social isolation (e.g. COVID-19 pandemic). From a physiological standpoint, endorphins and neurotransmitters like oxytocin and dopamine are released during sexual contact. It has been speculated outside of the academic setting that sharing and engaging with thirst traps may elicit similar pleasure responses. == Methodology == Methodologies have developed to take an optimal thirst trap photo. Reporting for Vice magazine, Graham Isador found several of his social network contacts spent a lot of time considering how to take the best photo and what text they should use. They considered angles and lighting. Sometimes they made use of the self-timer feature available on some cameras. Often, body parts are put on display without being too explicit (e.g. bulges of male genitalia, breast cleavage, abdominal muscles, pectoral muscles, backs, buttocks). Often, the thirst trap is accompanied by a caption. For instance, in October 2019, actress Tracee Ellis Ross posted bikini pictures on Instagram with a caption that included the message: "I've worked so hard to feel good in my skin and to build a life that truly matches me and I'm in it and it feels good. ... No filter, no retouch 47 year old thirst trap! Boom!" On Instagram, #ThirstTrapThursdays is a popular tag. Followers reply in turn after a posting. == Variations == "Gatsbying" is a variation of the thirst trap, where one puts posts on social media to attract the attention of a particular individual. The term alludes to the novel The Great Gatsby where the character Jay Gatsby would throw extravagant parties to attract the attention of his love interest, Daisy. "Instagrandstanding" is an alternative name for this. "Wholesome trapping" has developed, where one posts pictures of more meaningful aspects of life, such as spending time with friends or doing outdoor activities. == Criticism == Psychotherapist Lisa Brateman has criticized thirst traps as an unhealthy method of receiving external validation. This desire for external validation can be addictive. Thirst traps can cause pressure to maintain a good physical appearance, and therefore cause self-esteem issues. Additionally, thirst traps are often highly choreographed and thus present a distorted perception of reality. The manufacturing of thirst traps can be limited when one enters a relationship or with time as the body ages. In some cases, thirst traps can lead to harassment and online bullying. In April 2020, model Chrissy Teigen posted a video of herself wearing a black one-piece swimsuit, and she received a multitude of negative comments that constituted bullying and body shaming.
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Bus encryption

Bus encryption is the use of encrypted program instructions on a data bus in a computer that includes a secure cryptoprocessor for executing the encrypted instructions. Bus encryption is used primarily in electronic systems that require high security, such as automated teller machines, TV set-top boxes, and secure data communication devices such as two-way digital radios. Bus encryption can also mean encrypted data transmission on a data bus from one processor to another processor. For example, from the CPU to a GPU which does not require input of encrypted instructions. Such bus encryption is used by Windows Vista and newer Microsoft operating systems to protect certificates, BIOS, passwords, and program authenticity. PVP-UAB (Protected Video Path) provides bus encryption of premium video content in PCs as it passes over the PCIe bus to graphics cards to enforce digital rights management. The need for bus encryption arises when multiple people have access to the internal circuitry of an electronic system, either because they service and repair such systems, stock spare components for the systems, own the system, steal the system, or find a lost or abandoned system. Bus encryption is necessary not only to prevent tampering of encrypted instructions that may be easily discovered on a data bus or during data transmission, but also to prevent discovery of decrypted instructions that may reveal security weaknesses that an intruder can exploit. In TV set-top boxes, it is necessary to download program instructions periodically to customer's units to provide new features and to fix bugs. These new instructions are encrypted before transmission, but must also remain secure on data buses and during execution to prevent the manufacture of unauthorized cable TV boxes. This can be accomplished by secure crypto-processors that read encrypted instructions on the data bus from external data memory, decrypt the instructions in the cryptoprocessor, and execute the instructions in the same cryptoprocessor.
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