Group concept mapping is a structured methodology for organizing the ideas of a group on any topic of interest and representing those ideas visually in a series of interrelated maps. It is a type of integrative mixed method, combining qualitative and quantitative approaches to data collection and analysis. Group concept mapping allows for a collaborative group process with groups of any size, including a broad and diverse array of participants. Since its development in the late 1980s by William M.K. Trochim at Cornell University, it has been applied to various fields and contexts, including community and public health, social work, health care, human services,, instructional interventions, and biomedical research and evaluation. == Overview == Group concept mapping integrates qualitative group processes with multivariate analysis to help a group organize and visually represent its ideas on any topic of interest through a series of related maps. It combines the ideas of diverse participants to show what the group thinks and values in relation to the specific topic of interest. It is a type of structured conceptualization used by groups to develop a conceptual framework, often to help guide evaluation and planning efforts. Group concept mapping is participatory in nature, allowing participants to have an equal voice and to contribute through various methods. A group concept map visually represents all the ideas of a group and how they relate to each other, and depending on the scale, which ideas are more relevant, important, or feasible. == Process == Group concept mapping involves a structured multi-step process, including brainstorming, sorting and rating, multidimensional scaling and cluster analysis, and the generation and interpretation of multiple maps. The first step requires participants to brainstorm a large set of statements relevant to the topic of interest, usually in response to a focus prompt. Participants are then asked to individually sort those statements into categories based on their perceived similarity and rate each statement on one or more scales, such as importance or feasibility. The data is then analyzed using The Concept System software, which creates a series of interrelated maps using multidimensional scaling (MDS) of the sort data, hierarchical clustering of the MDS coordinates applying Ward's method, and the computation of average ratings for each statement and cluster of statements. The resulting maps display the individual statements in two-dimensional space with more similar statements located closer to each other, and grouped into clusters that partition the space on the map. The Concept System software also creates other maps that show the statements in each cluster rated on one or more scales, and absolute or relative cluster ratings between two cluster sets. As a last step in the process, participants are led through a structured interpretation session to better understand and label all the maps. == History == Group concept mapping was developed as a methodology in the late 1980s by William M.K. Trochim at Cornell University. Trochim is considered to be a leading evaluation expert, and he has taught evaluation and research methods at Cornell since 1980. Originally called "concept mapping", the methodology has evolved since its inception with the maturation of the field and the continued advancement of the software, which is now a Web application. == Uses == Group concept mapping can be used with any group for any topic of interest. It is often used by government agencies, academic institutions, national associations, not-for-profit and community-based organizations, and private businesses to help turn the ideas of the group into measurable actions. This includes in the areas of organizational development, strategic planning, needs assessment, curriculum development, research, and evaluation. Group concept mapping is well-documented, well-established methodology, and it has been used in hundreds of published papers. == Versus concept mapping and mind mapping == More generally, concept mapping is any process used for visually representing relationships between ideas in pictures or diagrams. A concept map is typically a diagram of multiple ideas, often represented as boxes or circles, linked in a graph (network) structure through arrows and words where each idea is connected to another. The technique was originally developed in the 1970s by Joseph D. Novak at Cornell University. Concept mapping may be done by an individual or a group. A mind map is a diagram used to visually represent information, centering on one word or idea with categories and sub-categories radiating off of it in a tree structure. Popularized by Tony Buzan in the 1970s, mind mapping is often a spontaneous exercise done by an individual or group to gather information about what they think around a single topic. Unlike Novak's concept maps and Buzan's mind maps, group concept mapping has a structured mathematical process (sorting and rating, multidimensional scaling and cluster analysis) for organizing and visually representing multiple ideas of a group through a series of specific steps. In other words, in group concept mapping, the resulting visual representations are mathematically generated from mixed (qualitative and quantitative) data collected from a group of research subjects, whereas in Novak's concept maps and Buzan's mind maps the visual representations are drawn directly by the subjects resulting in diagrams that are qualitative data and final product at the same time.
Misskey
Misskey (Japanese: ミスキー, romanized: Misukī) is an open source, federated, social networking service created in 2014 by Japanese software engineer Eiji "syuilo" Shinoda. Misskey uses the ActivityPub protocol for federation, allowing users to interact between independent Misskey instances, and other ActivityPub compatible platforms. Misskey is generally considered to be part of the Fediverse. Despite being a decentralized service, Misskey is not philosophically opposed to centralization. The name Misskey comes from the lyrics of Brain Diver, a song by the Japanese singer May'n. == History == Misskey was initially developed as a BBS-style internet forum by high school student Eiji Shinoda in 2014. After introducing a timeline feature, Misskey gained popularity as the microblogging platform it is today. In 2018, Misskey added support for ActivityPub, becoming a federated social media platform. The flagship Misskey server, Misskey.io, was started on April 15, 2019. Misskey, alongside Mastodon and Bluesky, has received attention as a potential replacement for Twitter following Twitter's acquisition by Elon Musk in 2022. On April 8, 2023, Misskey.io incorporated as MisskeyHQ K.K. As of February 2024, over 450,000 users were registered, making it the largest instance of Misskey. Misskey.io is crowdfunded. The administrator of Misskey.io is Japanese system administrator Yoshiki Eto, who operates under the alias Murakami-san. Eiji Shinoda serves as director. In July 2023, Twitter introduced extreme restrictions on their API in order to combat scraping from bots. Some users were critical of the changes, and as a result migrated to other social networks. The number of users registering on Misskey.io, Misskey's official instance and the largest one, increased rapidly, with other Misskey instances also receiving a spike in signups. In response to this trend, Skeb, a platform for sharing art, announced on July 14, 2023 that it would sponsor the Misskey development team. In early 2024, Misskey was targeted by a spam attack from Japan. The cause of the attack is believed to be a dispute between rival groups on a Japanese hacker forum and a DDoS attack on a Discord bot. Mastodon instances with open registration were used in the attack. In November 2025, Eto announced intentions to replace ActivityPub with Misskey's own low-overhead federation system in "a few years". Shinoda later said that this was "fake news". == Development == Misskey is open source software and is licensed under the AGPLv3. The Misskey API is publicly available and is documented using the OpenAPI Specification, which allows users to build automated accounts and use it on any Misskey instance. The service is translated using Crowdin. Misskey is developed using Node.js. TypeScript is used on both the frontend and backend. PostgreSQL is used as its database. Vue.js is used for the frontend. == Functionality == Posts on Misskey are called "notes". Notes are limited to a maximum of 3,000 characters (a limit which can be customized by instances), and can be accompanied by any file, including polls, images, videos, and audio. Notes can be reposted, either by themselves or with another "quote" note. Misskey comes with multiple timelines to sort through the notes that an instance has available, and are displayed in reverse chronological order. The Home timeline shows notes from users that you follow, the Local timeline shows all notes from the instance in use, the Social timeline shows both the Home and Local timeline, and the Global timeline shows every public note that the instance knows about. Notes have customizable privacy settings to control what users can see a note, similar to Mastodon's post visibility ranges. Public notes show up on all timelines, while Home notes only show on a user's Home timeline. Notes can also be set to be available only for followers. Direct messages using notes can be sent to users.
Branch number
In cryptography, the branch number is a numerical value that characterizes the amount of diffusion introduced by a vectorial Boolean function F that maps an input vector a to output vector F ( a ) {\displaystyle F(a)} . For the (usual) case of a linear F the value of the differential branch number is produced by: applying nonzero values of a (i.e., values that have at least one non-zero component of the vector) to the input of F; calculating for each input value a the Hamming weight W {\displaystyle W} (number of nonzero components), and adding weights W ( a ) {\displaystyle W(a)} and W ( F ( a ) ) {\displaystyle W(F(a))} together; selecting the smallest combined weight across for all nonzero input values: B d ( F ) = min a ≠ 0 ( W ( a ) + W ( F ( a ) ) ) {\displaystyle B_{d}(F)={\underset {a\neq 0}{\min }}(W(a)+W(F(a)))} . If both a and F ( a ) {\displaystyle F(a)} have s components, the result is obviously limited on the high side by the value s + 1 {\displaystyle s+1} (this "perfect" result is achieved when any single nonzero component in a makes all components of F ( a ) {\displaystyle F(a)} to be non-zero). A high branch number suggests higher resistance to the differential cryptanalysis: the small variations of input will produce large changes on the output and in order to obtain small variations of the output, large changes of the input value will be required. The term was introduced by Daemen and Rijmen in early 2000s and quickly became a typical tool to assess the diffusion properties of the transformations. == Mathematics == The branch number concept is not limited to the linear transformations, Daemen and Rijmen provided two general metrics: differential branch number, where the minimum is obtained over inputs of F that are constructed by independently sweeping all the values of two nonzero and unequal vectors a, b ( ⊕ {\displaystyle \oplus } is a component-by-component exclusive-or): B d ( F ) = min a ≠ b ( W ( a ⊕ b ) + W ( F ( a ) ⊕ F ( b ) ) {\displaystyle B_{d}(F)={\underset {a\neq b}{\min }}(W(a\oplus b)+W(F(a)\oplus F(b))} ; for linear branch number, the independent candidates α {\displaystyle \alpha } and β {\displaystyle \beta } are independently swept; they should be nonzero and correlated with respect to F (the L A T ( α , β ) {\displaystyle LAT(\alpha ,\beta )} coefficient of the linear approximation table of F should be nonzero): B l ( F ) = min α ≠ 0 , β , L A T ( α , β ) ≠ 0 ( W ( α ) + W ( β ) ) {\displaystyle B_{l}(F)={\underset {\alpha \neq 0,\beta ,LAT(\alpha ,\beta )\neq 0}{\min }}(W(\alpha )+W(\beta ))} .
Death of Molly Russell
In November 2017, Molly Russell, a fourteen-year-old British schoolgirl from Harrow, London, was found dead in her bedroom by her parents. In an inquest, the coroner stated that she had died from an act of self-harm following depression and the results of social media consumption, including material on Instagram and Pinterest. She also had a Twitter account in which she documented her growing depression. == Life == Russell had been a pupil at Hatch End High School. At the inquest, the school's head teacher expressed shock that she was able to access distressing online content. Her parents stated that she had never shown any previous signs of struggle and was doing very well in school. It was revealed at the inquest that in the six months prior to her death, 2,100 of 16,300 pieces of content she had interacted with on Instagram were on topics such as self-harm, depression, and suicide. It was also noted that throughout her experience on social media, there were never any warning signs about the information she viewed on these platforms. == Subsequent events == Dr. Navin Venugopal, the child psychiatrist assigned to the case investigating her death, called the material she viewed "disturbing and distressing" and said he was unable to sleep well for weeks after viewing it. The coroner Andrew Walker concluded that Molly's death was "an act of self harm suffering from depression and the negative effects of online content". He issued a prevention of future deaths report regarding her death, in which he made a number of recommendations for operators of online platforms, including: separating platforms for adults and children age verification changes in policy on filtering of age-specific content adding features for parental supervision and control data retention of material viewed by children He suggested that this could be accomplished by either legislation or self-regulation. The lawyer representing her family at the inquest stated that the findings "captured all of the elements of why this material is so harmful." The case has been cited as a motivator for the passage of the Online Safety Act. A charity, the Molly Rose Foundation, was set up in her memory, with the goal of suicide prevention for young people. Meta and Pinterest are believed to have made substantial donations to the charity.
Web presence
A web presence is a location on the World Wide Web where a person, business, or some other entity is represented (see also web property and point of presence). Examples of a web presence for a person could be a personal website, a blog, a profile page, a wiki page, or a social media point of presence (e.g. a LinkedIn profile, a Facebook account, or a Twitter account). Examples of a web presence for a business or some other entity could be a corporate website, a microsite, a page on a review site, a wiki page, or a social media point of presence (e.g., a LinkedIn company page and/or group, a Facebook business/brand/product page, or a Twitter account). Every web presence is associated with a unique web address to distinguish one point of presence from another. == Owned vs. unowned == Web presence can either be owned or unowned. Owned media exists when a single person or group can control the content that is published on its web presence (e.g. a corporate website or a personal Twitter account). However, when a single person or group cannot solely control the content, the creator is different from the owner. This is considered unowned media (see earned media). A Wikipedia page or a Yelp page about a person, company, or product would be an example of a known (or "earned") web presence. Occasionally, a first form of media known as "paid media" is often included in the discussion of media types: "earned vs. owned vs. paid". Paid media is commonly found in the form of advertisements, but it is not considered a form of web presence. == Management == Web presence management is the process of establishing and maintaining a digital footprint on the web. The three factors that are considered include the following: where a person or business has web presence; how each web presence represents its enterprise; and what is published at a point of presence. Web presence management is the discipline of determining and governing: the distribution of policy documents which platforms are most appropriate (e.g. internal vs. external blog, YouTube vs. Vimeo) the single inventory of personal or corporate web presence (e.g. partners or advocates) where on the web a business and any relatable assets are represented where on the web a business and any relatable assets are impersonated or pirated web properties with the particular entities they represent who has control over which web properties new web properties which are not in the personal or corporate inventory (e.g. someone creates a new presence) authorized and unauthorized changes to the creation (e.g. branding) of a web presence a workflow for creating a web property that follows its corporate standards === Management system === The purpose of a web presence management system is to manage the web presence of a person or business. This includes the collection of domain names, websites, social media, and other web pages where he, she, or it is being represented. The tool generally offers the following key functions: new presence discovery, inventory management, change detection, access control, stakeholder coordination, and compliance workflow. A web presence management system is meant to have a broader reach so that it emphasizes where a presence has been established, will be established, must be maintained, or must be remediated. An example of a web presence management system is the Brandle Presence Manager. In order to publish content to the various points of web presence, multiple content management systems and sometimes even social media management systems are often used. The primary focus of most content and social media management systems is limited to their specific web platforms. === Domain names === Another aspect of web presence management is managing the collection of domain names registered to the person or business. Any entity may register multiple domain names for the same property. As a result, they can link alternative spellings, different top-level domains, aliases, brands, or products to the same website. Similarly, negative or derogatory domain names may also be registered. This is done to prevent certain domain names from being used against the person or business. It is common for a larger business to have domain names registered by multiple employees at multiple domain name registrars, possibly a result of organizational or geographical requirements. Consequently, a web presence management system can be used to monitor all domain names registered by the business, regardless of the registrars used. == Discovery == Web presence discovery is the process of monitoring the web for a new point of presence about a person or business. Web presence discovery is often included in a web presence management system. Whether a new domain is registered, a new website is published, or a new social media account is established, it occurs outside of the person's or business’ control. As a result, its purpose is to assess a new point of presence and appropriately handle any violations. Web presence discovery differs from content listening. The former involves looking for new properties on the web, whereas the latter refers to analyzing content that already exists to hear how a person or business is seen often in near real time. Examples of content listening systems include Sysomos and Radian6, which is now a subsidiary of Salesforce.com. === Brand protection === A person or business may choose to watch for a new web presence that might appear to misrepresent or mislead an audience, such as counterfeiters, spoofers, or malicious hackers. One of the early software in the online brand protection marketplace was MarkMonitor, now part of Thomson Reuters. This software helped detect rogue domain names and websites. However, the modern day growth of social media has seen a rise in the number of fraudulent brand impersonations. It has become much easier for a new web presence to be created on those platforms, which results in a greater frequency of them today. As a preventive measure, online brand protection providers are now adding social media to their domain and website discovery options. === Security === The widespread growth of social media has also made it easier for unauthorized individuals to impersonate an employee. Consequently, social media has now become a recognized threat vector in that it can be used to socially engineer an attack on a business. To counter this, companies are able to use web presence monitoring tools to detect new points of presence on the web and thereby defend against socially engineered attacks. === Distributed inventory management === A web presence monitoring system can be used by a business to associate a new web property with its corporate inventory. It is designed to address autonomous, distributed behaviors. This usually applies to larger businesses whose geographically diverse employees are more prone to creating new points of presence on the web. For example, a retail chain may allow each local store to create and manage their web presence to market to and communicate with their local customer base. Similarly, a global business may have teams in each country or region who create and manage a web presence to adapt to local languages or cultures. == Monitoring == Web presence monitoring is the process of monitoring a known inventory of web presence to detect any changes that are made. Web presence monitoring is often included in a web presence management system and can serve multiple purposes for both larger corporations and certain individuals, such as celebrities. It is important to note that presence monitoring differs from content listening. The former involves monitoring the properties (e.g. branding) of a web property in an established inventory, whereas the latter refers to analyzing content that already exists to hear how a person or business is seen often in near real time. Additionally, presence monitoring focuses on owned media and content listening on earned media. === Corporate, brand, and regulatory compliance === Many companies ensure that certain standards are met for a property on the web that represents their business. For companies in regulated industries, such as finance and healthcare, the company may be required by law to ensure that all publicized content, regardless of platform or technology, follow specific requirements. The widespread growth of social media has seen a rise in the number of fraudulent corporate impersonations. It has become much easier for a new web presence to be created on these platforms, and so these are much more prevalent than they used to be. As a preventive measure, a web presence monitoring system alerts the company when a known property is changed, allowing for the property to be reviewed and amended so that it follows the proper standards. . A web presence monitoring system helps alert the company when a known property is changed, so it can be reviewed and brought back, if necessary, into compliance with the appro
Apptek
Applications Technology (AppTek) is a U.S. company headquartered in McLean, Virginia that specializes in artificial intelligence and machine learning for human language technologies. The company provides both managed and professional services for natural language processing (NLP) technologies including automatic speech recognition (ASR), neural machine translation (MT), natural-language understanding (NLU) and neural speech synthesis. AppTek's Head of Science, Prof. Dr. -Ing Hermann Ney, was awarded the IEEE James L. Flanagan Speech and Audio Processing Award in 2019 and the ISCA Medal for Scientific Achievement in 2021 for his work in natural language processing. == History == AppTek was acquired in 1998 by Lernout & Hauspie (at the time a NASDAQ publicly traded company), AppTek organized a management buy-out and went private again in 2001. In 2014, the company sold its hybrid machine translation technology to eBay and has since rebuilt the platform to modern neural-based approaches for machine translation. In 2020, SOSi acquired non-controlling interest in AppTek and became an exclusive reseller of AppTek products for U.S. federal, state, and local government entities.
PGP word list
The PGP Word List ("Pretty Good Privacy word list", also called a biometric word list for reasons explained below) is a list of words for conveying data bytes in a clear unambiguous way via a voice channel. They are analogous in purpose to the NATO phonetic alphabet, except that a longer list of words is used, each word corresponding to one of the 256 distinct numeric byte values. == History and structure == The PGP Word List was designed in 1995 by Patrick Juola, a computational linguist, and Philip Zimmermann, creator of PGP. The words were carefully chosen for their phonetic distinctiveness, using genetic algorithms to select lists of words that had optimum separations in phoneme space. The candidate word lists were randomly drawn from Grady Ward's Moby Pronunciator list as raw material for the search, successively refined by the genetic algorithms. The automated search converged to an optimized solution in about 40 hours on a DEC Alpha, a particularly fast machine in that era. The Zimmermann–Juola list was originally designed to be used in PGPfone, a secure VoIP application, to allow the two parties to verbally compare a short authentication string to detect a man-in-the-middle attack (MiTM). It was called a biometric word list because the authentication depended on the two human users recognizing each other's distinct voices as they read and compared the words over the voice channel, binding the identity of the speaker with the words, which helped protect against the MiTM attack. The list can be used in many other situations where a biometric binding of identity is not needed, so calling it a biometric word list may be imprecise. Later, it was used in PGP to compare and verify PGP public key fingerprints over a voice channel. This is known in PGP applications as the "biometric" representation. When it was applied to PGP, the list of words was further refined, with contributions by Jon Callas. More recently, it has been used in Zfone and the ZRTP protocol, the successor to PGPfone. The list is actually composed of two lists, each containing 256 phonetically distinct words, in which each word represents a different byte value between 0 and 255. Two lists are used because reading aloud long random sequences of human words usually risks three kinds of errors: 1) transposition of two consecutive words, 2) duplicate words, or 3) omitted words. To detect all three kinds of errors, the two lists are used alternately for the even-offset bytes and the odd-offset bytes in the byte sequence. Each byte value is actually represented by two different words, depending on whether that byte appears at an odd or an even offset from the beginning of the byte sequence. The two lists are readily distinguished by the number of syllables; the odd list has words of three syllables, the even list has two. The two lists have a maximum word length of 11 and 9 letters, respectively. Using a two-list scheme was suggested by Zhahai Stewart. == Examples == Each byte in a bytestring is encoded as a single word. A sequence of bytes is rendered in network byte order, from left to right. For example, the leftmost (i.e. byte 0) is considered "even" and is encoded using the PGP Even Word table. The next byte to the right (i.e. byte 1) is considered "odd" and is encoded using the PGP Odd Word table. This process repeats until all bytes are encoded. Thus, "E582" produces "topmost Istanbul", whereas "82E5" produces "miser travesty". A PGP public key fingerprint that displayed in hexadecimal as E582 94F2 E9A2 2748 6E8B 061B 31CC 528F D7FA 3F19 would display in PGP Words (the "biometric" fingerprint) as topmost Istanbul Pluto vagabond treadmill Pacific brackish dictator goldfish Medusa afflict bravado chatter revolver Dupont midsummer stopwatch whimsical cowbell bottomless The order of bytes in a bytestring depends on endianness. == Other word lists for data == There are several other word lists for conveying data in a clear unambiguous way via a voice channel: the NATO phonetic alphabet maps individual letters and digits to individual words the S/KEY system maps 64 bit numbers to 6 short words of 1 to 4 characters each from a publicly accessible 2048-word dictionary. The same dictionary is used in RFC 1760 and RFC 2289. the Diceware system maps five base-6 random digits (almost 13 bits of entropy) to a word from a dictionary of 7,776 distinct words. the Electronic Frontier Foundation has published a set of improved word lists based on the same concept FIPS 181: Automated Password Generator converts random numbers into somewhat pronounceable "words". mnemonic encoding converts 32 bits of data into 3 words from a vocabulary of 1626 words. what3words encodes geographic coordinates in 3 dictionary words. the BIP39 standard permits encoding a cryptographic key of fixed size (128 or 256 bits, usually the unencrypted master key of a Cryptocurrency wallet) into a short sequence of readable words known as the seed phrase, for the purpose of storing the key offline. This is used in cryptocurrencies such as Bitcoin or Monero. Like the PGP word list, the Bytewords standard maps each possible byte to a word. There is only one list, rather than two. The words are uniformly four letters long and can be uniquely identified by their first and last letters