AI For Psychology Students

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

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  Jais (language model)

  Jais is an open-source large language model launched in August 2023. Developed as a collaboration between Emirati AI company G42, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and US-based Cerebras Systems, Jais was designed to produce high-quality Arabic text and was also trained on English data. The model's creation was motivated by the underrepresentation of the Arabic language in the field of generative artificial intelligence. It aims to provide a more culturally and linguistically accurate model for the world's 400 million Arabic speakers. Its name is a reference to Jebel Jais, the highest mountain in the UAE. == Background and development == Jais was developed in response to the limited availability of advanced generative artificial intelligence models for the Arabic language, despite it being spoken by over 400 million people. Existing models were often trained on limited or low-quality Arabic web content, resulting in poor performance. The project represents a significant investment by the United Arab Emirates in the field of AI as part of its national strategy. The model was created through a partnership between Inception (now Core42), a subsidiary of the Abu Dhabi-based AI company G42; the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); and Cerebras Systems, a US company specializing in AI hardware. The model is named after Jebel Jais, the highest peak in the UAE. == Training == The initial version of Jais released in August 2023 had 13 billion parameters. In November 2023, Core42 released Jais 30B, an improved version with 30 billion parameters. Both models were trained on a subset of the Cerebras Condor Galaxy 1 supercomputer. The training dataset consisted of a mix of Arabic, English, and computer code. According to Timothy Baldwin, a professor of natural language processing at MBZUAI, training the model on a diverse Arabic dataset allows it to switch between dialects. == Features == Jais is designed to generate text in both English and Arabic. The project has also released instruction-tuned "Chat" variants for both the 13B and 30B models, which are specifically optimized for conversational applications. Additional functionality for working with images, graphs, and tabular data is planned for future releases.

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  Viber

  Rakuten Viber, commonly known as Viber, is a cross-platform voice over IP (VoIP) and instant messaging (IM) software application owned by the Japanese technology company Rakuten Group. The service is available as freeware for Android, iOS, Microsoft Windows, macOS and Linux. Users are registered and identified through a mobile phone number, although the service can also be accessed on desktop platforms without mobile connectivity. In addition to instant messaging, the platform allows users to exchange media such as images, videos and files, and provides a paid international calling service called Viber Out. The software was launched in 2010 by the company Viber Media, founded by Talmon Marco and Igor Magazinnik. Rakuten acquired Viber Media in 2014 and later renamed the company Rakuten Viber. The company is headquartered in Cyprus and maintains offices in London, Manila, Paris, San Francisco, Singapore, Tokyo and Beijing. == History == === Founding (2010) === Viber Media was founded in Tel Aviv, Israel, in 2010 by Talmon Marco and Igor Magazinnik. Marco and Magazinnik are also co-founders of the peer-to-peer media and file-sharing client iMesh. The company was run from Israel and was registered in Cyprus. Sani Maroli and Ofer Smocha soon joined the company as well. Marco said Viber allows instant calling and synchronization with contacts because the ID is the user's cell number. In its early days, Viber relied on a patchwork of outsourcing partners from different countries, commissioning specific solutions from external vendors — including teams based in Cyprus and Belarus. According to the company's statements, development of Viber's core functionality historically originated from its Tel Aviv office — a testament to its roots — even though the legal entity was registered elsewhere. === Early monetisation (2011) === In its first two years of availability, Viber did not generate revenues. It began doing so in 2013, via user payments for Viber Out voice calling and the Viber graphical messaging "sticker market". The company was originally funded by individual investors, described by Marco as "friends and family". They invested $20 million in the company, which had 120 employees as of May 2013. On 24 July 2013, Viber's support system was defaced by the Syrian Electronic Army. According to Viber, no sensitive user information was accessed. By the time Rakuten came forward with its acquisition deal in 2014, Viber had already stopped working with external vendors, choosing instead to consolidate development under its own offices. === Rakuten acquires Viber (2014) === On 13 February 2014, Rakuten announced they had acquired Viber Media for $900 million, and since then Viber has been owned by Rakuten, Inc., an e-commerce conglomerate headquartered in Tokyo. The sale of Viber earned the Shabtai family (Benny, his brother Gilad, and Gilad's son Ofer) some $500 million from their 55.2% stake in the company. At that sale price, the founders each realized over 30 times return on their investments. Later that year, the company established a UK presence with the incorporation of Viber UK Limited in London. Djamel Agaoua became Viber Media CEO in February 2017, replacing co-founder Marco who left in 2015. In July 2017 the corporate name of Viber Media was changed to Rakuten Viber and a new wordmark logo was introduced. Its legal name remains Viber Media, S.à r.l. based in Luxembourg. === Post-acquisition === In August 2015 Viber opened a regional office for Central and Eastern Europe in Sofia to support growth in the region. In 2017, Rakuten Viber and the World Wildlife Fund engaged in a commercial transaction aimed at raising awareness and protecting wildlife. After first using Viber to spread its message in June 2020, the International Federation of the Red Cross launched an official chatbot and community on the messaging app to combat the spread of false information, which they termed an infodemic, about COVID-19. The chatbot is still active as of June 2022, with over 1.4 million subscribers. In 2020, Rakuten Viber and the World Health Organization (the WHO) engaged in a commercial transaction for a chatbot to inform users of issues such as women's health. and an anti-smoking campaign. In the wake of the July–August 2020 Belarusian election protests, to avoid sanctions and harassment from monopolies the company closed its office in Minsk. In 2022, Ofir Eyal became Viber CEO, replacing Djamel Agaoua. Eyal is a Viber veteran; he worked as Vice President of Product in 2014 before his promotion to Chief Operating Officer in 2019. Shortly after the appointment of a new CEO, Viber continued its international expansion. In March 2022, Rakuten announced the opening of a development center in Tbilisi, Georgia, intended to support work on mobile applications and technology projects in the region. In July 2022, Rakuten Viber partnered with Rapyd to launch instant cross-border P2P payments. The company launched payments on the Viber app first in Greece and Germany, and then in other countries. In August, Mineski teamed up with Viber to develop a social minigame platform that can play off Viber's application. In May 2022, Rakuten Viber launched the premium chat service Viber Plus that offers exclusive features, including sticker market privileges, ad-free use, priority Viber support, exclusive badge, unique Viber icon, large file sharing, and more. In 2022, Viber joined the European Union’s Code of Conduct on countering illegal hate speech online. As part of this framework, the company undertook to review reported content and remove material identified as hate speech in accordance with the Code and its platform rules. In January 2024 Rakuten (the company behind Viber) established an office in Kyiv to bring together engineering and marketing departments. Alongside launching its Kyiv office the company joined Diia.City as a resident. Subsequently in October 2024 Rakuten Viber inaugurated an office in Manila to broaden its operations, in the Philippines. The company’s legal entity remains Viber Media S.à r.l., registered in Luxembourg. Viber’s engineering work has been carried out across multiple countries and through external partners, including outsourcing and near-shore vendors. As a result, its development operations are distributed internationally rather than concentrated in a single location. In December 2024, Viber was blocked in Russia. Roskomnadzor announced the nationwide blocking of the messaging app due to non-compliance with local legal requirements. == Security audit == On 4 November 2014, Viber scored 1 out of 7 points on the Electronic Frontier Foundation's "Secure Messaging Scorecard". Viber received a point for encryption during transit but lost points because communications were not encrypted with keys that the provider did not have access to (i.e. the communications were not end-to-end encrypted), users could not verify contacts' identities, past messages were not secure if the encryption keys were stolen (i.e. the service did not provide forward secrecy), the code was not open to independent review (i.e. the code was not open-source), the security design was not properly documented, and there had not been a recent independent security audit. On 14 November 2014, the EFF changed Viber's score to 2 out of 7 after it had received an external security audit from Ernst & Young's Advanced Security Centre. On 19 April 2016, with the announcement of Viber version 6.0, Rakuten added end-to-end encryption to their service. The company said that the encryption protocol had only been audited internally, and promised to commission external audits "in the coming weeks". In May 2016, Viber published an overview of their encryption protocol, saying that it is a custom implementation that "uses the same concepts" as the Signal Protocol. In 2022, Rakuten Viber won a Security Award, by test.de, a tech firm based in Germany where there are over 3 million Viber users. In 2024, Rakuten Viber received SOC certification following an audit conducted by Ernst & Young. The certification relates to the company’s controls for data protection and information security. == Market share == As of December 2016, Viber had 800 million registered users. According to Statista, there are 260 million monthly active users as of January 2019. The Viber messenger is very popular in the Philippines, Greece, Eastern Europe, Russia, the Middle East, and some Asian markets. India was the largest market for Viber as of December 2014 with 33 million registered users, the fifth most popular instant messenger in the country. At the same time there were 30 million users in the United States, 28 million in Russia and 18 million in Brazil. Viber is particularly popular in Eastern Europe, being the most downloaded messaging app on Android in Belarus, Moldova and Ukraine as of 2016. It is also popular in Iraq, Libya and Nepal. Viber is translated in 44 languages and used in more than 190 co

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  Data deduplication

  In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs. It can also be applied to network data transfers to reduce the number of bytes that must be sent. The deduplication process requires comparison of data 'chunks' (also known as 'byte patterns') which are unique, contiguous blocks of data. These chunks are identified and stored during a process of analysis, and compared to other chunks within existing data. Whenever a match occurs, the redundant chunk is replaced with a small reference that points to the stored chunk. Given that the same byte pattern may occur dozens, hundreds, or even thousands of times (the match frequency is dependent on the chunk size), the amount of data that must be stored or transferred can be greatly reduced. A related technique is single-instance (data) storage, which replaces multiple copies of content at the whole-file level with a single shared copy. While possible to combine this with other forms of data compression and deduplication, it is distinct from newer approaches to data deduplication (which can operate at the segment or sub-block level). Deduplication is different from data compression algorithms, such as LZ77 and LZ78. Whereas compression algorithms identify redundant data inside individual files and encodes this redundant data more efficiently, the intent of deduplication is to inspect large volumes of data and identify large sections – such as entire files or large sections of files – that are identical, and replace them with a shared copy. == Functioning principle == For example, a typical email system might contain 100 instances of the same 1 MB (megabyte) file attachment. Each time the email platform is backed up, all 100 instances of the attachment are saved, requiring 100 MB storage space. With data deduplication, only one instance of the attachment is actually stored; the subsequent instances are referenced back to the saved copy for deduplication ratio of roughly 100 to 1. Deduplication is often paired with data compression for additional storage saving: Deduplication is first used to eliminate large chunks of repetitive data, and compression is then used to efficiently encode each of the stored chunks. In computer code, deduplication is done by, for example, storing information in variables so that they don't have to be written out individually but can be changed all at once at a central referenced location. Examples are CSS classes and named references in MediaWiki. == Benefits == Storage-based data deduplication reduces the amount of storage needed for a given set of files. It is most effective in applications where many copies of very similar or even identical data are stored on a single disk. In the case of data backups, which routinely are performed to protect against data loss, most data in a given backup remain unchanged from the previous backup. Common backup systems try to exploit this by omitting (or hard linking) files that haven't changed or storing differences between files. Neither approach captures all redundancies, however. Hard-linking does not help with large files that have only changed in small ways, such as an email database; differences only find redundancies in adjacent versions of a single file (consider a section that was deleted and later added in again, or a logo image included in many documents). In-line network data deduplication is used to reduce the number of bytes that must be transferred between endpoints, which can reduce the amount of bandwidth required. See WAN optimization for more information. Virtual servers and virtual desktops benefit from deduplication because it allows nominally separate system files for each virtual machine to be coalesced into a single storage space. At the same time, if a given virtual machine customizes a file, deduplication will not change the files on the other virtual machines—something that alternatives like hard links or shared disks do not offer. Backing up or making duplicate copies of virtual environments is similarly improved. == Classification == === Post-process versus in-line deduplication === Deduplication may occur "in-line", as data is flowing, or "post-process" after it has been written. With post-process deduplication, new data is first stored on the storage device and then a process at a later time will analyze the data looking for duplication. The benefit is that there is no need to wait for the hash calculations and lookup to be completed before storing the data, thereby ensuring that store performance is not degraded. Implementations offering policy-based operation can give users the ability to defer optimization on "active" files, or to process files based on type and location. One potential drawback is that duplicate data may be unnecessarily stored for a short time, which can be problematic if the system is nearing full capacity. Alternatively, deduplication hash calculations can be done in-line: synchronized as data enters the target device. If the storage system identifies a block which it has already stored, only a reference to the existing block is stored, rather than the whole new block. The advantage of in-line deduplication over post-process deduplication is that it requires less storage and network traffic, since duplicate data is never stored or transferred. On the negative side, hash calculations may be computationally expensive, thereby reducing the storage throughput. However, certain vendors with in-line deduplication have demonstrated equipment which performs in-line deduplication at high rates. Post-process and in-line deduplication methods are often heavily debated. === Data formats === The SNIA Dictionary identifies two methods: Content-agnostic data deduplication – a data deduplication method that does not require awareness of specific application data formats. Content-aware data deduplication – a data deduplication method that leverages knowledge of specific application data formats. === Source versus target deduplication === Another way to classify data deduplication methods is according to where they occur. Deduplication occurring close to where data is created, is referred to as "source deduplication". When it occurs near where the data is stored, it is called "target deduplication". Source deduplication ensures that data on the data source is deduplicated. This generally takes place directly within a file system. The file system will periodically scan new files creating hashes and compare them to hashes of existing files. When files with same hashes are found then the file copy is removed and the new file points to the old file. Unlike hard links however, duplicated files are considered to be separate entities and if one of the duplicated files is later modified, then using a system called copy-on-write a copy of that changed file or block is created. The deduplication process is transparent to the users and backup applications. Backing up a deduplicated file system will often cause duplication to occur resulting in the backups being bigger than the source data. Source deduplication can be declared explicitly for copying operations, as no calculation is needed to know that the copied data is in need of deduplication. This leads to a new form of link on file systems, called a reference-counted link, or reflink, in some systems (e.g. Linux), or a cloned file on macOS, where one or more inodes (file information entries) are made to share some or all of their data. It is named analogously to hard links, which work at the inode level, and symbolic links, which work at the filename level.The individual entries have a copy-on-write behavior that is non-aliasing, i.e. changing one copy afterwards will not affect other copies. Microsoft's ReFS also supports this operation. Target deduplication is the process of removing duplicates when the data was not generated at that location. Example of this would be a server connected to a SAN/NAS, The SAN/NAS would be a target for the server (target deduplication). The server is not aware of any deduplication, the server is also the point of data generation. A second example would be backup. Generally this will be a backup store such as a data repository or a virtual tape library. === Deduplication methods === One of the most common forms of data deduplication implementations works by comparing chunks of data to detect duplicates. For that to happen, each chunk of data is assigned an identification, calculated by the software, typically using cryptographic hash functions. In many implementations, the assumption is made that if the identification is identical, the data is identical, even though this cannot be true in all cases due to the pigeonhole principle; other implementations do not as

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  Cypherpunks (book)

  Cypherpunks: Freedom and the Future of the Internet is a 2012 book by Julian Assange, in discussion with Internet activists and cypherpunks Jacob Appelbaum, Andy Müller-Maguhn and Jérémie Zimmermann. Its primary topic is society's relationship with information security. In the book, the authors warn that the Internet has become a tool of the police state, and that the world is inadvertently heading toward a form of totalitarianism. They promote the use of cryptography to protect against state surveillance. In the introduction, Assange says that the book is "not a manifesto [...] [but] a warning". He told Guardian journalist Decca Aitkenhead: A well-defined mathematical algorithm can encrypt something quickly, but to decrypt it would take billions of years – or trillions of dollars' worth of electricity to drive the computer. So cryptography is the essential building block of independence for organisations on the Internet, just like armies are the essential building blocks of states, because otherwise one state just takes over another. There is no other way for our intellectual life to gain proper independence from the security guards of the world, the people who control physical reality. Assange later wrote in The Guardian: "Strong cryptography is a vital tool in fighting state oppression." saying that was the message of his book, Cypherpunks. Cypherpunks is published by OR Books. It is primarily a transcript of World Tomorrow episode eight, a two-part interview between Assange, Jacob Appelbaum, Andy Müller-Maguhn, and Jérémie Zimmermann. In the foreword, Assange said, "the Internet, our greatest tool for emancipation, has been transformed into the most dangerous facilitator of totalitarianism we have ever seen".

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  EfficientNet

  EfficientNet is a family of convolutional neural networks (CNNs) for computer vision published by researchers at Google AI in 2019. Its key innovation is compound scaling, which uniformly scales all dimensions of depth, width, and resolution using a single parameter. EfficientNet models have been adopted in various computer vision tasks, including image classification, object detection, and segmentation. == Compound scaling == EfficientNet introduces compound scaling, which, instead of scaling one dimension of the network at a time, such as depth (number of layers), width (number of channels), or resolution (input image size), uses a compound coefficient ϕ {\displaystyle \phi } to scale all three dimensions simultaneously. Specifically, given a baseline network, the depth, width, and resolution are scaled according to the following equations: depth multiplier: d = α ϕ width multiplier: w = β ϕ resolution multiplier: r = γ ϕ {\displaystyle {\begin{aligned}{\text{depth multiplier: }}d&=\alpha ^{\phi }\\{\text{width multiplier: }}w&=\beta ^{\phi }\\{\text{resolution multiplier: }}r&=\gamma ^{\phi }\end{aligned}}} subject to α ⋅ β 2 ⋅ γ 2 ≈ 2 {\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2} and α ≥ 1 , β ≥ 1 , γ ≥ 1 {\displaystyle \alpha \geq 1,\beta \geq 1,\gamma \geq 1} . The α ⋅ β 2 ⋅ γ 2 ≈ 2 {\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2} condition is such that increasing ϕ {\displaystyle \phi } by a factor of ϕ 0 {\displaystyle \phi _{0}} would increase the total FLOPs of running the network on an image approximately 2 ϕ 0 {\displaystyle 2^{\phi _{0}}} times. The hyperparameters α {\displaystyle \alpha } , β {\displaystyle \beta } , and γ {\displaystyle \gamma } are determined by a small grid search. The original paper suggested 1.2, 1.1, and 1.15, respectively. Architecturally, they optimized the choice of modules by neural architecture search (NAS), and found that the inverted bottleneck convolution (which they called MBConv) used in MobileNet worked well. The EfficientNet family is a stack of MBConv layers, with shapes determined by the compound scaling. The original publication consisted of 8 models, from EfficientNet-B0 to EfficientNet-B7, with increasing model size and accuracy. EfficientNet-B0 is the baseline network, and subsequent models are obtained by scaling the baseline network by increasing ϕ {\displaystyle \phi } . == Variants == EfficientNet has been adapted for fast inference on edge TPUs and centralized TPU or GPU clusters by NAS. EfficientNet V2 was published in June 2021. The architecture was improved by further NAS search with more types of convolutional layers. It also introduced a training method, which progressively increases image size during training, and uses regularization techniques like dropout, RandAugment, and Mixup. The authors claim this approach mitigates accuracy drops often associated with progressive resizing.

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  Intranet

  An intranet is a computer network for sharing information, easier communication, collaboration tools, operational systems, and other computing services within an organization, usually to the exclusion of access by outsiders. The term is used in contrast to public networks, such as the Internet, but uses the same technology based on the Internet protocol suite. An organization-wide intranet can constitute a focal point of internal communication and collaboration, and provide a single starting point to access internal and external resources. In its simplest form, an intranet is established with the technologies for local area networks (LANs) and wide area networks (WANs). Many modern intranets have search engines, user profiles, blogs, mobile apps with notifications, and events planning within their infrastructure. An intranet is sometimes contrasted to an extranet. While an intranet is generally restricted to employees of the organization, extranets may also be accessed by customers, suppliers, or other approved parties. Extranets extend a private network onto the Internet with special provisions for authentication, authorization and accounting (AAA protocol). == Uses == Intranets are increasingly being used to deliver tools, such as for collaboration (to facilitate working in groups and teleconferencing) or corporate directories, sales and customer relationship management, or project management. Intranets are also used as corporate culture-change platforms. For example, a large number of employees using an intranet forum application to host a discussion about key issues could come up with new ideas related to management, productivity, quality, and other corporate issues. In large intranets, website traffic is often similar to public website traffic and can be better understood by using web metrics software to track overall activity. User surveys also improve intranet website effectiveness. Larger businesses allow users within their intranet to access public internet through firewall servers. They have the ability to screen incoming and outgoing messages, keeping security intact. When part of an intranet is made accessible to customers and others outside the business, it becomes part of an extranet. Businesses can send private messages through the public network using special encryption/decryption and other security safeguards to connect one part of their intranet to another. Intranet user-experience, editorial, and technology teams work together to produce in-house sites. Most commonly, intranets are managed by the communications, HR or CIO departments of large organizations, or some combination of these. Because of the scope and variety of content and the number of system interfaces, the intranets of many organizations are much more complex than their respective public websites. Intranets and the use of intranets are growing rapidly. According to the Intranet Design Annual 2007 from Nielsen Norman Group, the number of pages on participants' intranets averaged 200,000 over the years 2001 to 2003 and has grown to an average of 6 million pages over 2005–2007. == Benefits == Intranets can help users locate and view information faster and use applications relevant to their roles and responsibilities. With a web browser interface, users can access data held in any database the organization wants to make available at any time and — subject to security provisions — from anywhere within company workstations, increasing employees' ability to perform their jobs faster, more accurately, and with confidence that they have the right information. It also helps improve services provided to users. Using hypermedia and Web technology, Web publishing allows for the maintenance of and easy access to cumbersome corporate knowledge, such as employee manuals, benefits documents, company policies, business standards, news feeds, and even training, all of which can be accessed throughout a company using common Internet standards (Acrobat files, Flash files, CGI applications). Because each business unit can update the online copy of a document, the most recent version is usually available to employees using the intranet. Intranets are also used as a platform for developing and deploying applications to support business operations and decisions across the internetworked enterprise. Information is easily accessible to all authorised users, enabling collaboration. Being able to communicate in real-time through integrated third-party tools, such as an instant messenger, promotes the sharing of ideas and removes blockages to communication to help boost a business's productivity. Intranets can serve as powerful tools for communicating (such as through chat, email and/or blogs) within a given organization about vertically strategic initiatives that have a global reach throughout said organization. The type of information that can easily be conveyed is the purpose of the initiative and what it is aiming to achieve, who is driving it, results achieved to date, and whom to speak to for more information. By providing this information on the intranet, staff can keep up-to-date with the strategic focus of their organization. For example, when Nestlé had a number of food processing plants in Scandinavia, their central support system had to deal with a number of queries every day. When Nestlé decided to invest in an intranet, they quickly realized the savings. Gerry McGovern says that the savings from the reduction in query calls was substantially greater than the investment in the intranet. Users can view information and data via a web browser rather than maintaining physical documents such as procedure manuals, internal phone list and requisition forms. This can potentially save the business money on printing, duplicating documents, and the environment, as well as document maintenance overhead. For example, the HRM company PeopleSoft "derived significant cost savings by shifting HR processes to the intranet". McGovern goes on to say the manual cost of enrolling in benefits was found to be US$109.48 per enrollment. "Shifting this process to the intranet reduced the cost per enrollment to $21.79; a saving of 80 percent". Another company that saved money on expense reports was Cisco. "In 1996, Cisco processed 54,000 reports and the amount of dollars processed was USD19 million". Many companies dictate computer specifications which, in turn, may allow Intranet developers to write applications that only have to work on one browser such that there are no cross-browser compatibility issues. Being able to specifically address one's "viewer" is a great advantage. Since intranets are user-specific (requiring database/network authentication prior to access), users know exactly who they are interfacing with and can personalize their intranet based on role (job title, department) or individual ("Congratulations Jane, on your 3rd year with our company!"). Since "involvement in decision making" is one of the main drivers of employee engagement, offering tools (like forums or surveys) that foster peer-to-peer collaboration and employee participation can make employees feel more valued and involved. == Planning and creation == Most organizations devote considerable resources into the planning and implementation of their intranet as it is of strategic importance to the organization's success. Some of the planning would include topics such as determining the purpose and goals of the intranet, identifying persons or departments responsible for implementation and management and devising functional plans, page layouts and designs. The appropriate staff would also ensure that implementation schedules and phase-out of existing systems were organized, while defining and implementing security of the intranet and ensuring it lies within legal boundaries and other constraints. In order to produce a high-value end product, systems planners should determine the level of interactivity (e.g. wikis, on-line forms) desired. Planners may also consider whether the input of new data and updating of existing data is to be centrally controlled or devolve. These decisions sit alongside to the hardware and software considerations (like content management systems), participation issues (like good taste, harassment, confidentiality), and features to be supported. Intranets are often static sites; they are a shared drive, serving up centrally stored documents alongside internal articles or communications (often one-way communication). By leveraging firms which specialise in 'social' intranets, organisations are beginning to think of how their intranets can become a 'communication hub' for their entire team. The actual implementation would include steps such as securing senior management support and funding, conducting a business requirement analysis and identifying users' information needs. From the technical perspective, there would need to be a coordinated installation of the web server and user access netw

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  Completeness (cryptography)

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

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  Critical data studies

  Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of critical theory, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. Interest in this unique field of critical data studies began in 2011 with scholars danah boyd and Kate Crawford posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and Phoebe Sengers focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. == Motivation == In their article in which they coin the term 'critical data studies,' Dalton and Thatcher also provide several justifications as to why data studies is a discipline worthy of a critical approach. First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. Big data is important to critical data studies because it is the type of data used within this field. Big data does not necessarily refer to a large data set, it can have a data set with millions of rows, but also a data set that just has a wide variety and expansive scope of data with a smaller type of dataset. As well as having whole populations in the data set and not just sample sizes. Furthermore, big data as a technological tool and the information that it yields are not neutral, according to Dalton and Thatcher, making it worthy of critical analysis in order to identify and address its biases. Building off this idea, another justification for a critical approach is that the relationship between big data and society is an important one, and therefore worthy of study. Ribes et. al. argue there is a need for an interdisciplinary understanding of data as a historical artifact as a motivating aspect of critical data studies.The overarching consensus in the Computer-Supported Cooperative Work (CSCW) field, is that people should speak for the data, and not let the data speak for itself. The sources of big data and it’s relationship to varied metadata can be a complicated one, which leads to data disorder and a need for an ethical analysis. Additionally, Iliadis and Russo (2016) have called for studying data assemblages. This is to say, data has innate technological, political, social, and economic histories that should be taken into consideration. Kitchin argues data is almost never raw, and it is almost always cooked, meaning that it is always spoken for by the data scientists utilizing it. Thus, Big Data should be open to a variety of perspectives, especially those of cultural and philosophical nature. Further, data contains hidden histories, ideologies, and philosophies. Big data technology can cause significant changes in society's structure and in the everyday lives of people, and, being a product of society, big data technology is worthy of sociological investigation. Moreover, data sets are almost never completely without any influence. Rather, data are shaped by the vision or goals of those gathering the data, and during the data collection process, certain things are quantified, stored, sorted and even discarded by the research team. A critical approach is thus necessary in order to understand and reveal the intent behind the information being presented.One of these critical approaches has been through feminist data studies. This method applies feminist principles to critical studies and data collecting and analysis. The goal of this is to address the power imbalance in data science and society. According to Catherine D’Ignazio and Lauren F. Klein, a power analysis can be performed by examining power, challenging power, evaluating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Feminist data studies is part of the movement towards making data to benefit everyone and not to increase existing inequalities. Moreover, data alone cannot speak for themselves; in order to possess any concrete meaning, data must be accompanied by theoretical insight or alternative quantitative or qualitative research measures. Based on different social topics such as anti-racist data studies, critical data studies give a focus on those social issues concerning data. Specifically in anti-racist data studies they use a classification approach to get representation for those within that community. Desmond Upton Patton and others used their own classification system in the communities of Chicago to help target and reduce violence with young teens on twitter. They had students in those communities help them to decipher the terminology and emojis of these teens to target the language used in tweets that followed with violence outside of the computer screens. This is just one real world example of critical data studies and its application. Dalton and Thatcher argue that if one were to only think of data in terms of its exploitative power, there is no possibility of using data for revolutionary, liberatory purposes. Finally, Dalton and Thatcher propose that a critical approach in studying data allows for 'big data' to be combined with older, 'small data,' and thus create more thorough research, opening up more opportunities, questions and topics to be explored. == Issues and concerns for critical data scholars == Data plays a pivotal role in the emerging knowledge economy, driving productivity, competitiveness, efficiency, sustainability, and capital accumulation. The ethical, political, and economic dimensions of data dynamically evolve across space and time, influenced by changing regimes, technologies, and priorities. Technically, the focus lies on handling, storing, and analyzing vast data sets, utilizing machine learning-based data mining and analytics. This technological advancement raises concerns about data quality, encompassing validity, reliability, authenticity, usability, and lineage. The use of data in modern society brings about new ways of understanding and measuring the world, but also brings with it certain concerns or issues. Data scholars attempt to bring some of these issues to light in their quest to be critical of data. Technical and organizational issues could include the scope of the data set, meaning there is too little or too much data to work with, leading to inaccurate results. It becomes crucial for critical data scholars to carefully consider the adequacy of data volume for their analyses. The quality of the data itself is another facet of concern. The data itself could be of poor quality, such as an incomplete or messy data set with missing or inaccurate data values. This would lead researchers to have to make edits and assumptions about the data itself. Addressing these issues often requires scholars to make edits and assumptions about the data to ensure its reliability and relevance. Data scientists could have improper access to the actual data set, limiting their abilities to analyze it. Linnet Taylor explains how gaps in data can arise when people of varying levels of power have certain rights to their data sources. These people in power can control what data is collected, how it is displayed and how it is analyzed. The capabilities of the research team also play a crucial role in the quality of data analytics. The research team may have inadequate skills or organizational capabilities which leads to the actual analytics performed on the dataset to be biased. This can also lead to ecological fallacies, meaning an assumption is made about an individual based on data or results from a larger group of people. These technical and organizational challenges highlight the complexity of working with data and

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  Automated attendant

  In telephony, an automated attendant (also auto attendant, auto-attendant, autoattendant, automatic phone menus, AA, or virtual receptionist) allows callers to be automatically transferred to an extension without the intervention of an operator/receptionist. Many AAs will also offer a simple menu system ("for sales, press 1, for service, press 2," etc.). An auto attendant may also allow a caller to reach a live operator by dialing a number, usually "0". Typically the auto attendant is included in a business's phone system such as a PBX, but some services allow businesses to use an AA without such a system. Modern AA services (which now overlap with more complicated interactive voice response or IVR systems) can route calls to mobile phones, VoIP virtual phones, other AAs/IVRs, or other locations using traditional land-line phones or voice message machines. == Feature description == Telephone callers will recognize an automated attendant system as one that greets calls incoming to an organization with a recorded greeting of the form, "Thank you for calling .... If you know your party's extension, you may dial it any time during this message." Callers who have a touch-tone (DTMF) phone can dial an extension number or, in most cases, wait for operator ("attendant") assistance. Since the telephone network does not transmit the DC signals from rotary dial telephones (except for audible clicks), callers who have rotary dial phones have to wait for assistance. On a purely technical level it could be argued that an automated attendant is a very simple kind of IVR however, in the telecom industry the terms IVR and auto attendant are generally considered distinct. An automated attendant serves a very specific purpose (replace live operator and route calls), whereas an IVR can perform all sorts of functions (telephone banking, account inquiries, etc.). An AA will often include a directory which will allow a caller to dial by name in order to find a user on a system. There is no standard format to these directories, and they can use combinations of first name, last name, or both. The following lists common routing steps that are components of an automated attendant: Transfer to extension Transfer to voicemail Play message (i.e., "our address is ...") Go to a sub-menu Repeat choices In addition, an automated attendant would be expected to have values for the following: '0' – where to go when the caller dials '0' Timeout – what to do if the caller does nothing (usually go to the same place as '0') Default mailbox – where to send calls if '0' is not answered (or is not pointing to a live person) == Background == PBXs (private branch exchanges) or PABXs (private automatic branch exchanges) are telephone systems that serve an organization that has many telephone extensions but fewer telephone lines (sometimes called "trunks") that connect that organization to the rest of the global telecommunications network. While persons within an enterprise served by a PBX can call each other by dialing their extension numbers, incoming calls, i.e., calls originating from a telephone not served by the PBX but intended for a party served by the PBX, required assistance from a switchboard operator (also called a "switchboard attendant") or a telephone service called DID ("direct inward dialing"). Direct inward dialing has advantages such as rapid connection to the destination party and disadvantages including cost, lack of identification of the called organization and use of ten-digit telephone numbers. Automated attendants provide, among many other things, a way for an external caller to be directed to an extension or department served by a PBX system without using direct inward dialing or without switchboard attendant assistance. == History == Automated attendants are not part of voicemail systems. Voice messaging (or voicemail or VM) technology has existed since the late 1970s; in the early 1980s companies provided voice-prompting systems that allowed callers to reach (route the call) to an intended party, not necessarily to leave a message. Automated attendant systems are also referred to as automated menu systems and much early work in this field was done by Michael J. Freeman, Ph.D. == Time-based routing == Many auto attendants will have options to allow for time-of-day routing, as well as weekend and holiday routing. The specifics of these features will depend entirely on the particular automated attendant, but typically there would be a normal greeting and routing steps that would take place during normal business hours, and a different greeting and routing for non-business hours.

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  Forward anonymity

  Forward anonymity is a property of a cryptographic system which prevents an attacker who has recorded past encrypted communications from discovering its contents and participants in the future. This property is analogous to forward secrecy. An example of a system which uses forward anonymity is a public key cryptography system, where the public key is well-known and used to encrypt a message, and an unknown private key is used to decrypt it. In this system, one of the keys is always said to be compromised, but messages and their participants are still unknown by anyone without the corresponding private key. In contrast, an example of a system which satisfies the perfect forward secrecy property is one in which a compromise of one key by an attacker (and consequent decryption of messages encrypted with that key) does not undermine the security of previously used keys. Forward secrecy does not refer to protecting the content of the message, but rather to the protection of keys used to decrypt messages. == History == Originally introduced by Whitfield Diffie, Paul van Oorschot, and Michael James Wiener to describe a property of STS (station-to-station protocol) involving a long term secret, either a private key or a shared password. == Public Key Cryptography == Public Key Cryptography is a common form of a forward anonymous system. It is used to pass encrypted messages, preventing any information about the message from being discovered if the message is intercepted by an attacker. It uses two keys, a public key and a private key. The public key is published, and is used by anyone to encrypt a plaintext message. The Private key is not well known, and is used to decrypt cyphertext. Public key cryptography is known as an asymmetric decryption algorithm because of different keys being used to perform opposing functions. Public key cryptography is popular because, while it is computationally easy to create a pair of keys, it is extremely difficult to determine the private key knowing only the public key. Therefore, the public key being well known does not allow messages which are intercepted to be decrypted. This is a forward anonymous system because one compromised key (the public key) does not compromise the anonymity of the system. == Web of Trust == A variation of the public key cryptography system is a Web of trust, where each user has both a public and private key. Messages sent are encrypted using the intended recipient's public key, and only this recipient's private key will decrypt the message. They are also signed with the senders private key. This creates added security where it becomes more difficult for an attacker to pretend to be a user, as the lack of a private key signature indicates a non-trusted user. == Limitations == A forward anonymous system does not necessarily mean a wholly secure system. A successful cryptanalysis of a message or sequence of messages can still decode the information without the use of a private key or long term secret. == News == Forward anonymity, along with other privacy-protecting measures, received a burst of media attention after the leak of classified information by Edward Snowden, beginning in June, 2013, which indicated that the NSA and FBI, through specially crafted backdoors in software and computer systems, were conducting mass surveillance over large parts of the population of both the United States (see Mass surveillance in the United States), Europe, Asia, and other parts of the world. They justified this practice as an aid to catch predatory pedophiles. Opponents to this practice argue that leaving in a back door to law enforcement increases the risk of attackers being able to decrypt information, as well as questioning its legality under the US Constitution, specifically being a form of illegal Search and Seizure.

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  Code (cryptography)

  In cryptology, a code is a method used to encrypt a message that operates at the level of meaning; that is, words or phrases are converted into something else. A code might transform "change" into "CVGDK" or "cocktail lounge". The U.S. National Security Agency defined a code as "A substitution cryptosystem in which the plaintext elements are primarily words, phrases, or sentences, and the code equivalents (called "code groups") typically consist of letters or digits (or both) in otherwise meaningless combinations of identical length." A codebook is needed to encrypt, and decrypt the phrases or words. By contrast, ciphers encrypt messages at the level of individual letters, or small groups of letters, or even, in modern ciphers, individual bits. Messages can be transformed first by a code, and then by a cipher. Such multiple encryption, or "superencryption" aims to make cryptanalysis more difficult. Another comparison between codes and ciphers is that a code typically represents a letter or groups of letters directly without the use of mathematics. As such the numbers are configured to represent these three values: 1001 = A, 1002 = B, 1003 = C, ... . The resulting message, then would be 1001 1002 1003 to communicate ABC. Ciphers, however, utilize a mathematical formula to represent letters or groups of letters. For example, A = 1, B = 2, C = 3, ... . Thus the message ABC results by multiplying each letter's value by 13. The message ABC, then would be 13 26 39. Codes have a variety of drawbacks, including susceptibility to cryptanalysis and the difficulty of managing the cumbersome codebooks, so ciphers are now the dominant technique in modern cryptography. In contrast, because codes are representational, they are not susceptible to mathematical analysis of the individual codebook elements. In the example, the message 13 26 39 can be cracked by dividing each number by 13 and then ranking them alphabetically. However, the focus of codebook cryptanalysis is the comparative frequency of the individual code elements matching the same frequency of letters within the plaintext messages using frequency analysis. In the above example, the code group, 1001, 1002, 1003, might occur more than once and that frequency might match the number of times that ABC occurs in plain text messages. (In the past, or in non-technical contexts, code and cipher are often used to refer to any form of encryption). == One- and two-part codes == Codes are defined by "codebooks" (physical or notional), which are dictionaries of codegroups listed with their corresponding plaintext. Codes originally had the codegroups assigned in 'plaintext order' for convenience of the code designed, or the encoder. For example, in a code using numeric code groups, a plaintext word starting with "a" would have a low-value group, while one starting with "z" would have a high-value group. The same codebook could be used to "encode" a plaintext message into a coded message or "codetext", and "decode" a codetext back into plaintext message. In order to make life more difficult for codebreakers, codemakers designed codes with no predictable relationship between the codegroups and the ordering of the matching plaintext. In practice, this meant that two codebooks were now required, one to find codegroups for encoding, the other to look up codegroups to find plaintext for decoding. Such "two-part" codes required more effort to develop, and twice as much effort to distribute (and discard safely when replaced), but they were harder to break. The Zimmermann Telegram in January 1917 used the German diplomatic "0075" two-part code system which contained upwards of 10,000 phrases and individual words. == One-time code == A one-time code is a prearranged word, phrase or symbol that is intended to be used only once to convey a simple message, often the signal to execute or abort some plan or confirm that it has succeeded or failed. One-time codes are often designed to be included in what would appear to be an innocent conversation. Done properly they are almost impossible to detect, though a trained analyst monitoring the communications of someone who has already aroused suspicion might be able to recognize a comment like "Aunt Bertha has gone into labor" as having an ominous meaning. Famous example of one time codes include: In the Bible, Jonathan prearranges a code with David, who is going into hiding from Jonathan's father, King Saul. If, during archery practice, Jonathan tells the servant retrieving arrows "the arrows are on this side of you," David may safely return to court; if the command is "the arrows are beyond you," David must flee. "One if by land; two if by sea" in "Paul Revere's Ride" made famous in the poem by Henry Wadsworth Longfellow "Climb Mount Niitaka" - the signal to Japanese planes to begin the attack on Pearl Harbor During World War II the British Broadcasting Corporation's overseas service frequently included "personal messages" as part of its regular broadcast schedule. The seemingly nonsensical stream of messages read out by announcers were actually one time codes intended for Special Operations Executive (SOE) agents operating behind enemy lines. An example might be "The princess wears red shoes" or "Mimi's cat is asleep under the table". Each code message was read out twice. By such means, the French Resistance were instructed to start sabotaging rail and other transport links the night before D-day. "Over all of Spain, the sky is clear" was a signal (broadcast on radio) to start the nationalist military revolt in Spain on July 17, 1936. Sometimes messages are not prearranged and rely on shared knowledge hopefully known only to the recipients. An example is the telegram sent to U.S. President Harry Truman, then at the Potsdam Conference to meet with Soviet premier Joseph Stalin, informing Truman of the first successful test of an atomic bomb. "Operated on this morning. Diagnosis not yet complete but results seem satisfactory and already exceed expectations. Local press release necessary as interest extends great distance. Dr. Groves pleased. He returns tomorrow. I will keep you posted." == Idiot code == An idiot code is a code that is created by the parties using it. This type of communication is akin to the hand signals used by armies in the field. Example: Any sentence where 'day' and 'night' are used means 'attack'. The location mentioned in the following sentence specifies the location to be attacked. Plaintext: Attack X. Codetext: We walked day and night through the streets but couldn't find it! Tomorrow we'll head into X. An early use of the term appears to be by George Perrault, a character in the science fiction book Friday by Robert A. Heinlein: The simplest sort [of code] and thereby impossible to break. The first ad told the person or persons concerned to carry out number seven or expect number seven or it said something about something designated as seven. This one says the same with respect to code item number ten. But the meaning of the numbers cannot be deduced through statistical analysis because the code can be changed long before a useful statistical universe can be reached. It's an idiot code... and an idiot code can never be broken if the user has the good sense not to go too often to the well. Terrorism expert Magnus Ranstorp said that the men who carried out the September 11 attacks on the United States used basic e-mail and what he calls "idiot code" to discuss their plans. == Cryptanalysis of codes == While solving a monoalphabetic substitution cipher is easy, solving even a simple code is difficult. Decrypting a coded message is a little like trying to translate a document written in a foreign language, with the task basically amounting to building up a "dictionary" of the codegroups and the plaintext words they represent. One fingerhold on a simple code is the fact that some words are more common than others, such as "the" or "a" in English. In telegraphic messages, the codegroup for "STOP" (i.e., end of sentence or paragraph) is usually very common. This helps define the structure of the message in terms of sentences, if not their meaning, and this is cryptanalytically useful. Further progress can be made against a code by collecting many codetexts encrypted with the same code and then using information from other sources spies newspapers diplomatic cocktail party chat the location from where a message was sent where it was being sent to (i.e., traffic analysis) the time the message was sent, events occurring before and after the message was sent the normal habits of the people sending the coded messages etc. For example, a particular codegroup found almost exclusively in messages from a particular army and nowhere else might very well indicate the commander of that army. A codegroup that appears in messages preceding an attack on a particular location may very well stand for that location. Cribs can be an immediate giveaway to the definiti

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  Data preservation

  Data preservation is the act of conserving and maintaining both the safety and integrity of data. Preservation is done through formal activities that are governed by policies, regulations and strategies directed towards protecting and prolonging the existence and authenticity of data and its metadata. Data can be described as the elements or units in which knowledge and information is created, and metadata are the summarizing subsets of the elements of data; or the data about the data. The main goal of data preservation is to protect data from being lost or destroyed and to contribute to the reuse and progression of the data. == History == Most historical data collected over time has been lost or destroyed. War and natural disasters combined with the lack of materials and necessary practices to preserve and protect data has caused this. Usually, only the most important data sets were saved, such as government records and statistics, legal contracts and economic transactions. Scientific research and doctoral theses data have mostly been destroyed from improper storage and lack of data preservation awareness and execution. Over time, data preservation has evolved and has generated importance and awareness. We now have many different ways to preserve data and many different important organizations involved in doing so. The first digital data preservation storage solutions appeared in the 1950s, which were usually flat or hierarchically structured. While there were still issues with these solutions, it made storing data much cheaper, and more easily accessible. In the 1970s relational databases as well as spreadsheets appeared. Relational data bases structure data into tables using structured query languages which made them more efficient than the preceding storage solutions, and spreadsheets hold high volumes of numeric data which can be applied to these relational databases to produce derivative data. More recently, non-relational (non-structured query language) databases have appeared as complements to relational databases which hold high volumes of unstructured or semi-structured data. == Importance == The scope of data preservation is vast. Everything from governmental to business records to art essentially can be represented as data, and is amenable to be lost. This then leads to loss of human history, for perpetuity. Data can be lost on a small or independent scale whether it's personal data loss, or data loss within businesses and organizations, as well as on a larger or national or global scale which can negatively and potentially permanently affect things such as environmental protection, medical research, homeland security, public health and safety, economic development and culture. The mechanisms of data loss are also as many as they are varied, spanning from disaster, wars, data breaches, negligence, all the way through simple forgetting to natural decay. Ways in which data collections can be used when preserved and stored properly can be seen through the U.S. Geological Survey, which stores data collections on natural hazards, natural resources, and landscapes. The data collected by the Survey is used by federal and state land management agencies towards land use planning and management, and continually needs access to historical reference data. == Related Concepts == In contrast, data holdings are collections of gathered data that are informally kept, and not necessarily prepared for long-term preservation. For example, a collection or back-up of personal files. Data holdings are generally the storage methods used in the past when data has been lost due to environmental and other historical disasters. Furthermore, data retention differs from data preservation in the sense that by definition, to retain an object (data) is to hold or keep possession or use of the object. To preserve an object is to protect, maintain and keep up for future use. Retention policies often circle around when data should be deleted on purpose as well, and held from public access, while preservation prioritizes permanence and more widely shared access. Thus, data preservation exceeds the concept of having or possessing data or back up copies of data. Data preservation ensures reliable access to data by including back-up and recovery mechanisms that precede the event of a disaster or technological change. == Methods == === Digital === Digital preservation, is similar to data preservation, but is mainly concerned with technological threats, and solely digital data. Essentially digital data is a set of formal activities to enable ongoing or persistent use and access of digital data exceeding the occurrence of technological malfunction or change. Digital preservation is aware of the inevitable change in technology and protocols, and prepares for data that will need to be accessible across new types of technologies and platforms while the integrity of the data and metadata are being conserved. Technology, while providing great process in conserving data that may not have been possible in the past, is also changing at such a quick rate that digital data may not be accessible anymore due to the format being incompatible with new software. Without the use of data preservation much of our existing digital data is at risk. The majority of methods used towards data preservation today are digital methods, which are so far the most effective methods that exist. === Archives === Archives are a collection of historical documents and records. Archives contribute and work towards the preservation of data by collecting data that is well organized, while providing the appropriate metadata to confirm it. An example of an important data archive is The LONI Image Data Archive, which is an archive that collects data regarding clinical trials and clinical research studies. === Catalogues, directories and portals === Catalogues, directories and portals are consolidated resources which are kept by individual institutions, and are associated with data archives and holdings. In other words, the data is not presented on the site, but instead might act as metadata and aggregators, and may administer thorough inventories. === Repositories === Repositories are places where data archives and holdings can be accessed and stored. The goal of repositories is to make sure that all requirements and protocols of archives and holdings are being met, and data is being certified to ensure data integrity and user trust. Single-site Repositories A repository that holds all data sets on a single site. An example of a major single-site repository the Data Archiving and Networking Services which is a repository which provides ongoing access to digital research resources for the Netherlands. Multi-Site Repositories A repository that hosts data set on multiple institutional sites. An example of a well known multi-site repository is OpenAIRE which is a repository that hosts research data and publications collaborating all of the EU countries and more. OpenAIRE promotes open scholarship and seeks to improves discover-ability and re-usability of data. Trusted Digital Repository A repository that seeks to provide reliable, trusted access over a long period of time. The repository can be single or multi-sited but must cooperate with the Reference Model for an Open Archival Information System, as well as adhere to a set of rules or attributes that contribute to its trust such as having persistent financial responsibility, organizational buoyancy, administrative responsibility security and safety. An example of a trusted digital repository is The Digital Repository of Ireland (DRI) which is a multi-site repository that hosts Ireland's humanity and social science data sets. === Cyber Infrastructures === Cyber infrastructures which consists of archive collections which are made available through the system of hardware, technologies, software, policies, services and tools. Cyber infrastructures are geared towards the sharing of data supporting peer-to-peer collaborations and a cultural community. An example of a major cyber-infrastructure is The Canadian Geo-spatial Data Infrastructure which provides access to spatial data in Canada.

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  Brave Leo

  Brave Leo is a large language model-based chatbot developed by Brave Software and included with the Brave browser. == History == In November 2023, the company said versions for iOS and Android would be available "in the coming months". == Features == Since January 2024, Leo has used the open-source Mixtral 8x7B from Mistral AI as its default large language model, in addition to LLaMA 2 from Meta Platforms and Claude from Anthropic, both of which have been used previously. Leo can suggest follow-up questions, and summarize webpages, PDFs, and videos. Leo has a $15 (US) per month premium version that enables more requests and uses larger LLMs. == Privacy == The answers given by Leo are not saved. Brave uses the slogan Love Privacy to emphasize its focus on user privacy and data protection. The phrase has been featured in Brave's official marketing campaigns and has been cited in media coverage of the browser's privacy-first approach. == Controversies == In 2023, PC World reported that Leo evades questions about US elections.

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  Virtual influencer

  A virtual influencer, sometimes described as a virtual persona or virtual model, is a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of online human "influencers". Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations. Common derivatives of virtual influencers include VTubers, which broadly refer to online entertainers and YouTubers who represent themselves using virtual avatars instead of their physical selves. == History == Virtual influencers are fundamentally synonymous with virtual idols, which originate from Japan's anime and Japanese idol culture that dates back to the 1980s. The first virtual idol created was Lynn Minmay, a fictional singer and main character of the anime television series Super Dimension Fortress Macross (1982) and the animated film adaptation Macross: Do You Remember Love? (1984). Minmay's success led to the production of more Japanese virtual idols, such as EVE from the Japanese cyberpunk anime Megazone 23 (1985), and Sharon Apple in Macross Plus (1994). Virtual idols were not always well received – in 1995, Japanese talent agency Horipro created Kyoko Date, which was inspired by the Macross franchise and dating sim games such as Tokimeki Memorial (1994). Date failed to gain commercial success despite drawing headlines for her debut as a CGI idol, largely due to technical limitations leading to issues such as unnatural movements, an issue also known as the uncanny valley. Since their inception, many virtual idols created have achieved continual success, with notable names including the Vocaloid singer Hatsune Miku, and the VTuber Kizuna AI. Technological advancements have also enabled production teams to use artificial intelligence and advanced techniques to customize the personalities and behavior of virtual idols. Due to modern-day advancements in technology, many virtual idols have held real-life tours and events. Notable ones include Hatsune Miku's titular tour Miku Expo and Hololive's concerts with many of their idols from their English, Japanese and Indonesian branches. Some notable events including virtual singers and influencers have included: Hatsune Miku opening for Lady Gaga in 2014 and Hoshimachi Suisei's concerts at the famous Budokan venue in Japan and her addition to the Forbes Japan list of '30 Under 30' individuals who are changing the world in their respective fields. == Benefits and criticism == From a branding perspective, virtual influencers are perceived to be much less likely to be mired in scandals. In China, celebrities caught in bad publicity such as singer Wang Leehom and entertainer Kris Wu have heightened the appeal of virtual influencers, since their existence relies entirely on computer-generated imagery and they are therefore unlikely to cause any damage to a brand's image by association. Some studies have also suggested that Generation Z consumers have a unique appetite for virtual idols and influencers, since they grew up in the age of the internet. Studies also show that human-like appearance of virtual influencers show higher message credibility than anime-like virtual influencers. Scholars and commentators have also questioned the ethics and cultural impact of virtual influencers, arguing that computer-generated personas can entrench unrealistic beauty standards while diffusing accountability for labor, identity, and consent. Business and marketing analysts have also warned that disclosure and governance remain inconsistent, recommending clearer guardrails and transparency when brands deploy synthetic spokespeople. In 2025, reporting highlighted concerns that AI-driven "virtual humans" could displace human creators and sales workers, intensifying debates over the future of creative labor and authenticity online. == Notable examples == === Virtual bands === Eternity - A South Korean virtual idol group formed by Pulse9. Gorillaz - A virtual band formed in 1998. K/DA - A virtual K-pop girl group created as part of the League of Legends video game franchise. MAVE: - A South Korean virtual girl group formed in 2023 by Metaverse Entertainment. Pentakill - A virtual heavy metal band created as part of the League of Legends video game franchise. Plave (band) - A South Korean virtual boy band formed by VLast. Squid Sisters and Off the Hook - Two virtual pop idol duos as part of the Splatoon series. Studio Killers - A Finnish-Danish-British virtual band formed in 2011. === Vocaloids === Hatsune Miku (modeled after Saki Fujita) Kagamine Rin/Len (modeled after Asami Shimoda) Megurine Luka (modeled after Yū Asakawa) Meiko (modeled after Meiko Haigō) Kaito (modeled after Naoto Fūga) === VTubers === Kano Kizuna AI Neuro-sama VShojo Ironmouse Projekt Melody Nijisanji Hololive Akai Haato Gawr Gura Hoshimachi Suisei Natsuiro Matsuri === Other examples === Ami Yamato Crazy Frog FN Meka IA Kuki AI Kyoko Date Kyra Miquela Naevis Shudu Gram

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  Superincreasing sequence

  In mathematics, a sequence of positive real numbers ( s 1 , s 2 , . . . ) {\displaystyle (s_{1},s_{2},...)} is called superincreasing if every element of the sequence is greater than the sum of all previous elements in the sequence. Formally, this condition can be written as s n + 1 > ∑ j = 1 n s j {\displaystyle s_{n+1}>\sum _{j=1}^{n}s_{j}} for all n ≥ 1. == Program == The following Python source code tests a sequence of numbers to determine if it is superincreasing: This produces the following output: Sum: 0 Element: 1 Sum: 1 Element: 3 Sum: 4 Element: 6 Sum: 10 Element: 13 Sum: 23 Element: 27 Sum: 50 Element: 52 Is it a superincreasing sequence? True == Examples == (1, 3, 6, 13, 27, 52) is a superincreasing sequence, but (1, 3, 4, 9, 15, 25) is not. The series a^x for a>=2 == Properties == Multiplying a superincreasing sequence by a positive real constant keeps it superincreasing.

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