Unique negative dimension (UND) is a complexity measure for the model of learning from positive examples. The unique negative dimension of a class C {\displaystyle C} of concepts is the size of the maximum subclass D ⊆ C {\displaystyle D\subseteq C} such that for every concept c ∈ D {\displaystyle c\in D} , we have ∩ ( D ∖ { c } ) ∖ c {\displaystyle \cap (D\setminus \{c\})\setminus c} is nonempty. This concept was originally proposed by M. Gereb-Graus in "Complexity of learning from one-side examples", Technical Report TR-20-89, Harvard University Division of Engineering and Applied Science, 1989.
Content Threat Removal
Content Threat Removal (CTR) is a cybersecurity technology intended to defeat the threat posed by handling digital content in the cyberspace. Unlike other defenses, including antivirus software and sandboxed execution, CTR does not rely on being able to detect threats. Similar to Content Disarm and Reconstruction, CTR is designed to remove the threat without knowing whether it has done so and acts without knowing if data contains a threat or not. Detection strategies work by detecting unsafe content, and then blocking or removing that content. Content that is deemed safe is delivered to its destination. In contrast, Content Threat Removal assumes all data is hostile and delivers none of it to the destination, regardless of whether it is actually hostile. Although no data is delivered, the business information carried by the data is delivered using new data created for the purpose. == Threat == Advanced attacks continuously defeat defenses that are based on detection. These are often referred to as zero-day attacks, because as soon as they are discovered attack detection mechanisms must be updated to identify and neutralize the attack, and until they are, all systems are unprotected. These attacks succeed because attackers find new ways of evading detection. Polymorphic code can be used to evade the detection of known unsafe data and sandbox detection allows attacks to evade dynamic analysis. == Method == A Content Threat Removal defence works by intercepting data on its way to its destination. The business information carried by the data is extracted and the data is discarded. Then entirely new, clean and safe data is built to carry the information to its destination. The effect of building new data to carry the business information is that any unsafe elements of the original data are left behind and discarded. This includes executable data, macros, scripts and malformed data that trigger vulnerabilities in applications. While CTR is a form of content transformation, not all transformations provide a complete defence against the content threat. == Applicability == CTR is applicable to user-to-user traffic, such as email and chat, and machine-to-machine traffic, such as web services. Data transfers can be intercepted by in-line application layer proxies and these can transform the way information content is delivered to remove any threat. CTR works by extracting business information from data and it is not possible to extract information from executable code. This means CTR is not directly applicable to web browsing, since most web pages are code. It can, however, be applied to content that is downloaded from, and uploaded to, websites. Although most web pages cannot be transformed to render them safe, web browsing can be isolated and the remote access protocols used to reach the isolated environment can be subjected to CTR. CTR provides a solution to the problem of stegware. It naturally removes detectable steganography and eliminates symbiotic and permutation steganography through normalisation.
Proof of authority
Proof of authority (PoA) is a category of consensus protocols used with blockchains based on reputation and identity as a stake that delivers comparatively fast and efficient transactions (compared to proof-of-work and proof-of-stake). The most notable platforms using PoA are VeChain, Bitgert, Palm Network and Xodex. == Description == Proof-of-authority is a category of consensus protocols for networks and blockchains where transactions and blocks are built and validated by approved entities known as validators. Their permissions are often granted through a centralized authority, but they can also be granted through a council or decentralized organization. The term "proof-of-authority" was coined by Gavin Wood, co-founder of Ethereum and Parity Technologies. With PoA, validators are incentivized to maintain good behavior and honesty when validating blocks to avoid developing a negative reputation. PoA can have higher security than PoW and even PoS due to validators wanting to avoid damaging their reputation. Because PoA is permissioned, it is not fully trustless. Validators without good reputation may risk having their validator permissions removed. PoA is generally more efficient than PoW and PoS because it operates with fewer nodes and validators, thus requiring fewer duplicated resources.
Enterprise information integration
Enterprise information integration (EII) is the ability to support a unified view of data and information for an entire organization. The goal of EII is to get a large set of heterogeneous data sources to appear to a user or system as a single, homogeneous data source. In a data virtualization application of EII, there is a process of information integration, using data abstraction to provide a unified interface (known as uniform data access) for viewing all the data within an organization, and a single set of structures and naming conventions (known as uniform information representation) to represent this data. == Overview == Data within an enterprise can be stored in heterogeneous formats, including relational databases (which themselves come in a large number of varieties), text files, XML files, spreadsheets and a variety of proprietary storage methods, each with their own indexing and data access methods. Standardized data access APIs have emerged that offer a specific set of commands to retrieve and modify data from a generic data source. Many applications exist that implement these APIs' commands across various data sources, most notably relational databases. Such APIs include ODBC, JDBC, XQJ, OLE DB, and more recently ADO.NET. There are also standard formats for representing data within a file that are very important to information integration. The best-known of these is XML, which has emerged as a standard universal representation format. There are also more specific XML "grammars" defined for specific types of data such as Geography Markup Language for expressing geographical features and Directory Service Markup Language for holding directory-style information. In addition, non-XML standard formats exist such as iCalendar for representing calendar information and vCard for business card information. Enterprise Information Integration (EII) applies data integration commercially. Despite the theoretical problems described above, the private sector shows more concern with the problems of data integration as a viable product. EII emphasizes neither correctness nor tractability, but speed and simplicity. === Uniform data access === Uniform data access means connectivity and controllability across numerous target data sources. Necessary to fields such as EII and Electronic Data Interchange (EDI), it is most often used regarding analysis of disparate data types and data sources, which must be rendered into a uniform information representation, and generally must appear homogenous to the analysis tools—when the data being analyzed is typically heterogeneous and widely varying in size, type, and original representation. === Uniform information representation === Uniform information representation allows information from several realms or disciplines to be displayed and worked with as if it came from the same realm or discipline. It takes information from a number of sources, which may have used different methodologies and metrics in their data collection, and builds a single large collection of information, where some records may be more complete than others across all fields of data Uniform information representation is particularly important in EII and Electronic Data Interchange (EDI), where different departments of a large organization may have collected information for different purposes, with different labels and units, until one department realized that data already collected by those other departments could be re-purposed for their own needs—saving the enterprise the effort and cost of re-collecting the same information. === Combining disparate data sets === Each data source is disparate and as such is not designed to support EII. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. === Simplicity of deployment === Even if recognized as a solution to a problem, EII as of 2009 currently takes time to apply and offers complexities in deployment. Proposed schema-less solutions include "Lean Middleware". === Handling higher-order information === Analysts experience difficulty—even with a functioning information integration system—in determining whether the sources in the database will satisfy a given application. Answering these kinds of questions about a set of repositories requires semantic information like metadata and/or ontologies. == Applications == EII products enable loose coupling between homogeneous-data consuming client applications and services and heterogeneous-data stores. Such client applications and services include Desktop Productivity Tools (spreadsheets, word processors, presentation software, etc.), development environments and frameworks (Java EE, .NET, Mono, SOAP or RESTful Web services, etc.), business intelligence (BI), business activity monitoring (BAM) software, enterprise resource planning (ERP), Customer relationship management (CRM), business process management (BPM and/or BPEL) Software, and web content management (CMS). == Data access technologies == Service Data Objects (SDO) for Java, C++ and .Net clients and any type of data source XQuery and XQuery API for Java
Leiden algorithm
The Leiden algorithm is a community detection algorithm developed by Traag et al at Leiden University. It was developed as a modification of the Louvain method. Like the Louvain method, the Leiden algorithm attempts to optimize modularity in extracting communities from networks; however, it addresses key issues present in the Louvain method, namely poorly connected communities and the resolution limit of modularity. == Improvement over Louvain method == Broadly, the Leiden algorithm uses the same two primary phases as the Louvain algorithm: a local node moving step (though, the method by which nodes are considered in Leiden is more efficient) and a graph aggregation step. However, to address the issues with poorly-connected communities and the merging of smaller communities into larger communities (the resolution limit of modularity), the Leiden algorithm employs an intermediate refinement phase in which communities may be split to guarantee that all communities are well-connected. Consider, for example, the following graph: Three communities are present in this graph (each color represents a community). Additionally, the center "bridge" node (represented with an extra circle) is a member of the community represented by blue nodes. Now consider the result of a node-moving step which merges the communities denoted by red and green nodes into a single community (as the two communities are highly connected): Notably, the center "bridge" node is now a member of the larger red community after node moving occurs (due to the greedy nature of the local node moving algorithm). In the Louvain method, such a merging would be followed immediately by the graph aggregation phase. However, this causes a disconnection between two different sections of the community represented by blue nodes. In the Leiden algorithm, the graph is instead refined: The Leiden algorithm's refinement step ensures that the center "bridge" node is kept in the blue community to ensure that it remains intact and connected, despite the potential improvement in modularity from adding the center "bridge" node to the red community. == Graph components == Before defining the Leiden algorithm, it will be helpful to define some of the components of a graph. === Vertices and edges === A graph is composed of vertices (nodes) and edges. Each edge is connected to two vertices, and each vertex may be connected to zero or more edges. Edges are typically represented by straight lines, while nodes are represented by circles or points. In set notation, let V {\displaystyle V} be the set of vertices, and E {\displaystyle E} be the set of edges: V := { v 1 , v 2 , … , v n } E := { e i j , e i k , … , e k l } {\displaystyle {\begin{aligned}V&:=\{v_{1},v_{2},\dots ,v_{n}\}\\E&:=\{e_{ij},e_{ik},\dots ,e_{kl}\}\end{aligned}}} where e i j {\displaystyle e_{ij}} is the directed edge from vertex v i {\displaystyle v_{i}} to vertex v j {\displaystyle v_{j}} . We can also write this as an ordered pair: e i j := ( v i , v j ) {\displaystyle {\begin{aligned}e_{ij}&:=(v_{i},v_{j})\end{aligned}}} === Community === A community is a unique set of nodes: C i ⊆ V C i ⋂ C j = ∅ ∀ i ≠ j {\displaystyle {\begin{aligned}C_{i}&\subseteq V\\C_{i}&\bigcap C_{j}=\emptyset ~\forall ~i\neq j\end{aligned}}} and the union of all communities must be the total set of vertices: V = ⋃ i = 1 C i {\displaystyle {\begin{aligned}V&=\bigcup _{i=1}C_{i}\end{aligned}}} === Partition === A partition is the set of all communities: P = { C 1 , C 2 , … , C n } {\displaystyle {\begin{aligned}{\mathcal {P}}&=\{C_{1},C_{2},\dots ,C_{n}\}\end{aligned}}} == Partition quality == How communities are partitioned is an integral part on the Leiden algorithm. How partitions are decided can depend on how their quality is measured. Additionally, many of these metrics contain parameters of their own that can change the outcome of their communities. === Modularity === Modularity is a highly used quality metric for assessing how well a set of communities partition a graph. The equation for this metric is defined for an adjacency matrix, A, as: Q = 1 2 m ∑ i j ( A i j − k i k j 2 m ) δ ( c i , c j ) {\displaystyle Q={\frac {1}{2m}}\sum _{ij}(A_{ij}-{\frac {k_{i}k_{j}}{2m}})\delta (c_{i},c_{j})} where: A i j {\displaystyle A_{ij}} represents the edge weight between nodes i {\displaystyle i} and j {\displaystyle j} ; see Adjacency matrix; k i {\displaystyle k_{i}} and k j {\displaystyle k_{j}} are the sum of the weights of the edges attached to nodes i {\displaystyle i} and j {\displaystyle j} , respectively; m {\displaystyle m} is the sum of all of the edge weights in the graph; c i {\displaystyle c_{i}} and c j {\displaystyle c_{j}} are the communities to which the nodes i {\displaystyle i} and j {\displaystyle j} belong; and δ {\displaystyle \delta } is Kronecker delta function: δ ( c i , c j ) = { 1 if c i and c j are the same community 0 otherwise {\displaystyle {\begin{aligned}\delta (c_{i},c_{j})&={\begin{cases}1&{\text{if }}c_{i}{\text{ and }}c_{j}{\text{ are the same community}}\\0&{\text{otherwise}}\end{cases}}\end{aligned}}} === Reichardt Bornholdt Potts Model (RB) === One of the most well used metrics for the Leiden algorithm is the Reichardt Bornholdt Potts Model (RB). This model is used by default in most mainstream Leiden algorithm libraries under the name RBConfigurationVertexPartition. This model introduces a resolution parameter γ {\displaystyle \gamma } and is highly similar to the equation for modularity. This model is defined by the following quality function for an adjacency matrix, A, as: Q = ∑ i j ( A i j − γ k i k j 2 m ) δ ( c i , c j ) {\displaystyle Q=\sum _{ij}(A_{ij}-\gamma {\frac {k_{i}k_{j}}{2m}})\delta (c_{i},c_{j})} where: γ {\displaystyle \gamma } represents a linear resolution parameter === Constant Potts Model (CPM) === Another metric similar to RB is the Constant Potts Model (CPM). This metric also relies on a resolution parameter γ {\displaystyle \gamma } The quality function is defined as: H = − ∑ i j ( A i j w i j − γ ) δ ( c i , c j ) {\displaystyle H=-\sum _{ij}(A_{ij}w_{ij}-\gamma )\delta (c_{i},c_{j})} === Understanding Potts Model resolution parameters/Resolution limit === Typically Potts models such as RB or CPM include a resolution parameter in their calculation. Potts models are introduced as a response to the resolution limit problem that is present in modularity maximization based community detection. The resolution limit problem is that, for some graphs, maximizing modularity may cause substructures of a graph to merge and become a single community and thus smaller structures are lost. These resolution parameters allow modularity adjacent methods to be modified to suit the requirements of the user applying the Leiden algorithm to account for small substructures at a certain granularity. The figure on the right illustrates why resolution can be a helpful parameter when using modularity based quality metrics. In the first graph, modularity only captures the large scale structures of the graph; however, in the second example, a more granular quality metric could potentially detect all substructures in a graph. == Algorithm == The Leiden algorithm starts with a graph of disorganized nodes (a) and sorts it by partitioning them to maximize modularity (the difference in quality between the generated partition and a hypothetical randomized partition of communities). The method it uses is similar to the Louvain algorithm, except that after moving each node it also considers that node's neighbors that are not already in the community it was placed in. This process results in our first partition (b), also referred to as P {\displaystyle {\mathcal {P}}} . Then the algorithm refines this partition by first placing each node into its own individual community and then moving them from one community to another to maximize modularity. It does this iteratively until each node has been visited and moved, and each community has been refined - this creates partition (c), which is the initial partition of P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} . Then an aggregate network (d) is created by turning each community into a node. P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} is used as the basis for the aggregate network while P {\displaystyle {\mathcal {P}}} is used to create its initial partition. Because we use the original partition P {\displaystyle {\mathcal {P}}} in this step, we must retain it so that it can be used in future iterations. These steps together form the first iteration of the algorithm. In subsequent iterations, the nodes of the aggregate network (which each represent a community) are once again placed into their own individual communities and then sorted according to modularity to form a new P refined {\displaystyle {\mathcal {P}}_{\text{refined}}} , forming (e) in the above graphic. In the case depicted by the graph, the nodes were already sorted optimally, so no change too
Game Jolt
Game Jolt is a social community platform for video games, gamers and content creators. Founded by Yaprak and David DeCarmine, it is available on iOS, Android, and on the web and as a desktop app for Windows and Linux. Users share interactive content through a variety of formats including images, videos, live streams, chat rooms, and virtual events. == Features == === Crowd streaming === In 2021 Game Jolt revealed their own live streaming feature called Firesides. Firesides allowed multiple users to simultaneously livestream together with nearly no delay. The feature launched with a virtual concert showcasing its ability to accommodate multiple streamers. On October 16, 2023, Firesides were removed from Game Jolt. === Mobile app === Game Jolt Social by Game Jolt Inc. launched on both the Apple App Store and Google Play Store in March 2022. "It's clear to us that Gen Z is tired of generic social media and they want a place specifically for gaming that supports all types of content they're creating–art, videos, thoughts, and livestreams all in one place." said Game Jolt founder and CEO Yaprak DeCarmine, in a statement to VentureBeat. === Game API === The Game Jolt Application Programming Interface (usually known as the Game Jolt Game API) allows any developer using a game development platform that supports HTTP operations and MD5 or SHA-1. Game Jolt advertises that the API can: Create multiple "scoreboards" which collect high scores from players made publicly available on the game's profile and give user accounts EXP Award player's trophies which give user accounts EXP Store game data on Game Jolt's data servers Log whether a user is currently playing a game they're logged into via the GJAPI == Game jams and competitions == Game Jolt regularly hosts game jams where participants are encouraged to develop games for a chance to win prizes. They hosted their first game jam in 2009, Shocking Contest. In November 2014, Game Jolt announced the "Indies vs PewDiePie" game jam, partnering with the popular YouTuber Felix "PewDiePie" Kjellberg. Developers were given a weekend (21–24 November) to create a game with the theme of "fun to play, fun to watch" to suit the Let's Plays entertainment style. Users could rate entries afterwards until December 1 when the scores were counted up. The prize to the top 10 rated games was Felix playing the games on his channel as a means of promotion for the developers, although later he played other entries. One of the participants of the jam, now known as Outerminds Inc. was discovered and hired by PewDiePie to develop his mobile game, Legend of the Brofist. Game Jolt partnered with Felix, Sean "Jacksepticeye" McLoughlin and Mark "Markiplier" Fischbach to host "Indies vs Gamers" in July 2015. The requirements for entries were arcade games using the Game Jolt Game API highscore tables, to be made between the July 17–20 and the top 5 games were played on the partner's YouTube channels. Following the "Indies vs PewDiePie" game jam in 2014, Game Jolt released their internal jam hosting tools public for all users to use as a service, to create their own game jams that integrated with the main site. Today, Game Jolt focuses on hosting and co-hosting game competitions with established brands in order to bring monetary and educational opportunities to their users. On April 15, 2024, an announcement was made about a collaboration with Pocket Worlds for the "HighRise Game Jam". Pocket Worlds had sold NFTs up until roughly 2022, causing a community outburst. The situation was addressed, and the situation started to disperse. == Contests == == Events == Game Jolt hosts both physical and virtual events to entertain and prank its users, which consists of the following: == History == Game Jolt has supported independent creators with a central platform to manage their content and communities since its start in 2003. David DeCarmine began development of Game Jolt at the age of 14 for a group of hobbyists, making games and sharing on forums in an early iteration known as Holo World. The original intention was to create a platform for gamers where new games could be discoverable and quickly playable, and where feedback could be provided directly to the creators, allowing them to continue improving their games. In 2008, Game Jolt was registered as an LLC, then incorporated as Game Jolt Inc. in September 2020. A new site launched in 2015 featuring a responsive design, automated curation for both games and game news articles which weighs how recent a game was uploaded and how popular it is ("hot") and filtering options on game listings for platform, maturity rating and development status. In March 2022, Game Jolt launched a mobile application simultaneously on the Google Play Store and Apple App Store targeted at Gen Z gamers and creators. While in beta, the mobile app had 100,000 installs pre-launch. === Game store === Game Jolt continues to host a large library of independent games. Game developers can upload their games directly to the site to share or sell. They would allow distribution for downloadable games, later adding support for Adobe Flash, Unity and Java games which allowed support for browser based games. In February 2013, Game Jolt built support for browser-based HTML5 games as well. A user levelling system was released into public beta in April 2013, incorporating the GJAPI trophies and highscores, as well as site activity, to generate 'EXP' (experience points). Game Jolt Jams released in early 2014 as a service to allow users to create their own game jams that integrated with the main site. In April 2016, an online marketplace was announced and released the following month with an exclusive set of game titles, including Bendy and the Ink Machine, allowing developers to sell their games on the site. In January 2016, Game Jolt released source code of the client and site's front end on GitHub under MIT license. In January 2022, Game Jolt banned adult games from appearing on the site, stating in an email to developers that the site had become a "social media platform" and they "had to make decisions around the direction and future of the brand which has now included the removal of hosted games with explicitly adult content." In response to a tweet by Itch.io saying the site is not for prudes, they wrote in their own tweet: "Game Jolt is a platform with a large audience of 13-16 year olds. Our users asked us to clean up, so here we are." == Investments == After bootstrapping Game Jolt with revenue earned from ads on the website for years, the DeCarmines secured venture capital in 2020 from SoftBank, doing so again in 2021 from founders of Twitch, Rec Room, Modio and more.
Emotion Markup Language
An Emotion Markup Language (EML or EmotionML) has first been defined by the W3C Emotion Incubator Group (EmoXG) as a general-purpose emotion annotation and representation language, which should be usable in a large variety of technological contexts where emotions need to be represented. Emotion-oriented computing (or "affective computing") is gaining importance as interactive technological systems become more sophisticated. Representing the emotional states of a user or the emotional states to be simulated by a user interface requires a suitable representation format; in this case a markup language is used. EmotionML version 1.0 was published by the group in May 2014. == Example == Here is an example of an EmotionML document describing emotions expressed in a video recording of the interaction between a teacher, Alice, and a student, Bob. == History == In 2006, a first W3C Incubator Group, the Emotion Incubator Group (EmoXG), was set up "to investigate a language to represent the emotional states of users and the emotional states simulated by user interfaces" with the final Report published on 10 July 2007. In 2007, the Emotion Markup Language Incubator Group (EmotionML XG) was set up as a follow-up to the Emotion Incubator Group, "to propose a specification draft for an Emotion Markup Language, to document it in a way accessible to non-experts, and to illustrate its use in conjunction with a number of existing markups." The final report of the Emotion Markup Language Incubator Group, Elements of an EmotionML 1.0, was published on 20 November 2008. The work then was continued in 2009 in the frame of the W3C's Multimodal Interaction Activity, with the First Public Working Draft of "Emotion Markup Language (EmotionML) 1.0" being published on 29 October 2009. The Last Call Working Draft of "Emotion Markup Language 1.0", was published on 7 April 2011. The Last Call Working Draft addressed all open issues that arose from feedback of the community on the First Call Working Draft as well as results of a workshop held in Paris in October 2010. Along with the Last Call Working Draft, a list of vocabularies for EmotionML has been published to aid developers using common vocabularies for annotating or representing emotions. Annual draft updates were published until the 1.0 version was finished in 2014. == Reasons for defining an emotion markup language == A standard for an emotion markup language would be useful for the following purposes: To enhance computer-mediated human-human or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states. To enhance systems' processing efficiency. Emotion and intelligence are strongly interconnected. The modeling of human emotions in computer processing can help to build more efficient systems, e.g. using emotional models for time-critical decision enforcement. To allow the analysis of non-verbal behavior, emotion, mental states that can be provided using web services to enable data collection, analysis, and reporting. Concrete examples of existing technology that could apply EmotionML include: Opinion mining / sentiment analysis in Web 2.0, to automatically track customer's attitude regarding a product across blogs; Affective monitoring, such as ambient assisted living applications, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction; Wellness technologies that provide assistance according to a person's emotional state with the goal to improve the person's well-being; Character design and control for games and virtual worlds; Building web services to capture, analysis, and report data of non-verbal behavior, emotion and mental states of an individual or group across the internet using standard web technologies such as HTML5 and JSON. Social robots, such as guide robots engaging with visitors; Expressive speech synthesis, generating synthetic speech with different emotions, such as happy or sad, friendly or apologetic; expressive synthetic speech would for example make more information available to blind and partially sighted people, and enrich their experience of the content; Emotion recognition (e.g., for spotting angry customers in speech dialog systems, to improve computer games or e-Learning applications); Support for people with disabilities, such as educational programs for people with autism. EmotionML can be used to make the emotional intent of content explicit. This would enable people with learning disabilities (such as Asperger syndrome) to realise the emotional context of the content; EmotionML can be used for media transcripts and captions. Where emotions are marked up to help deaf or hearing impaired people who cannot hear the soundtrack, more information is made available to enrich their experience of the content. The Emotion Incubator Group has listed 39 individual use cases for an Emotion markup language. A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases, and the standard can be used to ease a market of providers for sub-modules of emotion processing systems, e.g. a web service for the recognition of emotion from text, speech or multi-modal input. == The challenge of defining a generally usable emotion markup language == Any attempt to standardize the description of emotions using a finite set of fixed descriptors is doomed to failure, as there is no consensus on the number of relevant emotions, on the names that should be given to them or how else best to describe them. For example, the difference between ":)" and "(:" is small, but using a standardized markup it would make one invalid. Even more basically, the list of emotion-related states that should be distinguished varies depending on the application domain and the aspect of emotions to be focused. Basically, the vocabulary needed depends on the context of use. On the other hand, the basic structure of concepts is less controversial: it is generally agreed that emotions involve triggers, appraisals, feelings, expressive behavior including physiological changes, and action tendencies; emotions in their entirety can be described in terms of categories or a small number of dimensions; emotions have an intensity, and so on. For details, see the Scientific Descriptions of Emotions in the Final Report of the Emotion Incubator Group. Given this lack of agreement on descriptors in the field, the only practical way of defining an emotion markup language is the definition of possible structural elements and to allow users to "plug in" vocabularies that they consider appropriate for their work. An additional challenge lies in the aim to provide a markup language that is generally usable. The requirements that arise from different use cases are rather different. Whereas manual annotation tends to require all the fine-grained distinctions considered in the scientific literature, automatic recognition systems can usually distinguish only a very small number of different states and affective avatars need yet another level of detail for expressing emotions in an appropriate way. For the reasons outlined here, it is clear that there is an inevitable tension between flexibility and interoperability, which need to be weighed in the formulation of an EmotionML. The guiding principle in the following specification has been to provide a choice only where it is needed, and to propose reasonable default options for every choice. == Applications and web services benefiting from an emotion markup language == There are a range of existing projects and applications to which an emotion markup language will enable the building of webservices to measure capture data of individuals non-verbal behavior, mental states, and emotions and allowing results to be reported and rendered in a standardized format using standard web technologies such as JSON and HTML5. One such project is measuring affect data across the Internet using EyesWeb.