Digital fashion is a field of fashion design that relies on 3D software or artificial intelligence to produce hyper-realistic, data-intensive digital 3D garment simulations that are digital-only products or digital models for physical products. Digital garments can be worn and presented in virtual environments, social media, online gaming, virtual reality (VR), and augmented reality (AR) platforms. The field aims to contribute to the development of a more sustainable future for the fashion industry. It has been praised as a possible answer to ethical and creative concerns of traditional fashion by promoting innovation, reducing waste, and encouraging conscious consumption. However, empirical research has questioned whether digital fashion communities embody the radical and anti-consumerist values they claim. A 2025 study presented by YeSeung Lee at the FACTUM international conference on fashion communication analysed 88,141 posts across nine platforms over eight months using Pulsar. It found that only 4.8% of author biographies indicated any sociopolitical focus, and that discourse predominantly relied on generic slogans and trending buzzwords, primarily reinforcing existing fashion hierarchies and consumerist frameworks rather than challenging them. Digital fashion is also the interplay between digital technology and couture. Human AI is an intersection of technology and human representation, in which human value is emphasized and enhanced by technology and the possibilities of discovering design. Information and communication technologies (ICTs) have been deeply integrated both into the fashion industry, as well as within the experience of clients and prospects. Such interplay has happened at three main levels. ICTs are used to design and produce fashion products, while the industry organization also leverages digital technologies. ICTs impact marketing, distribution and sales. ICTs are extensively used in communication activities with all relevant stakeholders and contribute to co-create the fashion world. The fashion industry in general has paved the way for digital fashion to be introduced with more technology being in the industry, like virtual dressing rooms and the gamification of the fashion industry. Digital fashion is also seen on many different online fashion retail websites. This evolution in the fashion industry has called for more education and research of digital fashion. == Design, production, and organization == Among the many applications available to fashion designers to model the fusion of creativity with digital avenues, the Digital Textile Printing can be mentioned here. === Digital textile printing === Digital textile printing has brought together the worlds of fashion, technology, art, chemistry, and printing to produce a new process for printing textiles on clothing. Digital printing is a process in which prints are directly applied to fabrics with a printer, reducing 95% of the use of water, 75% of the use of energy and minimizing textile waste. The main advantage of digital printing is the ability to do very small runs of each design (even less than 1 yard). Digital Textile printing also offers other benefits, such as fast printing speeds that help the time and space needed to print different patterns on garments of choice. == Marketing, distribution, and sales == While all digital channels can be used in order to market and sell fashion completely online (eCommerce), they usually are implemented in connection with offline channels (so-called "omni-channel"). Here, virtual and augmented reality play a crucial role. The fashion industry has faced its own problems including pollution and fabric waste, which has resulted in a shift to more sustainable methods like digital fashion. The industry is also constantly being intertwined with digital media and has allowed for the use of digital tools within the business itself and with consumers. Two of the ways digital fashion is utilized with consumers is through virtual dressing rooms and virtual cosmetic counters. Prospects and clients can use ICTs - own computers, tablets and smartphones - to virtually simulate fitting rooms and cosmetics counters and see how they look in specific outfits and makeup. Customers can give any look and decide on what suits them and buy products. Oftentimes, beauty retailers will feature virtual fitting rooms to allow users to experience the look of their product before committing to a purchase. Some examples are color contact retailers Freshlook, which allows users to simulate contact lens wear in their color contacts studio before purchase. Colorful Eyes also offers a virtual color contact lens try-on room. === Virtual dressing room === A virtual dressing room (also often referred to as virtual fitting room and virtual changing room although they do perform different functions) is the online equivalent of the near-ubiquitous in-store changing room – that is, it enables shoppers to try on clothes to check one or more of size, fit or style, but virtually rather than physically. Fashion retailer Topshop installed a Kinect-powered virtual fitting room at its Moscow store. Created by AR Door, the Augmented Fitting Room system overlays 3D augmented reality clothes on the customer. Simple gestures and on-screen buttons let users "try on" different outfits. However, the high variability of virtual fit platforms to predict consumer clothes sizes called into question the accuracy of these systems in their current form. AI-powered Wardrobe and Outfit Planning Beyond virtual fitting rooms, the integration of artificial intelligence has enabled the rise of digital wardrobe management. These platforms use computer vision and machine learning to catalog a user’s physical or digital garments, providing automated outfit recommendations based on weather, occasion, and personal style trends. Fashion-tech startups utilize AI-driven garment simulation to help users plan outfits virtually, bridging the gap between digital-only fashion and physical wardrobe utility. This "smart closet" approach aims to reduce "wardrobe fatigue" and decrease unnecessary consumption by maximizing the use of existing items through digital visualization. === Communication and experience co-creation === Fashion is also a matter of socially negotiating what is "in" or "out", fashionable or not. In other words, fashion items do not only play on the economic market of physical goods but also - and sometimes even more importantly - on the semiotic market of the production of social tastes and customs. Thanks to social media, and to all services offered by the so-called web2.0, laypeople can contribute to co-create the fashion world, shaping tastes, customs, and fashion-related values. Social media, in general, has catapulted the impact fashion has on our everyday lives and values. Fashion has taken a central role in mass production and is constantly evolving due to the ever-lasting digital transformation. Social media has also helped evolve to a point where not only can brands reach consumers, but consumers can reach brands as well. TikTok for example started a trend in 2020 with #GucciModelChallenge. This creates a space where the brand is gaining awareness from their consumers in the ever-changing digital age. === Gamification === Gaming has played an important role in fostering digital aspects of the fashion world, first beginning with dress-up games that used avatars and allowed players to select garments. Nevertheless, it seems it will now move on to the real world and start using avatars of real people. Garments from luxurious brands have been copied and adapted into the aesthetics of games such as Animal Crossing: New Horizons and The Sims. As to the former, during COVID-19 lock-downs players recreated outfits from a variety of fashion brands, including Chanel, Gucci and Versace. It became a platform for users to showcase their costume designs. In April 2019, Moschino collaborated with simulation game The Sims in a capsule collection that featured signature Jeremy Scott garments. The collection was made available to shop and the campaign was set against the backdrop of a Sims-like atmosphere. Furthermore, in May 2019, Nike partnered up with Fortnite to include their iconic Jordan sneakers. In similar fashion, in May 2020, Marc Jacobs designed 6 of the brand's favorite looks for Nintendo's Animal Crossing: New Horizons in a partnership with Instagram user @AnimalCrossingFashionArchive. They were made available to download. Similarly, the other luxury brands mentioned, Louis Vuitton partnered with game League of Legends to create skins for characters within the game. Digital fashion in different video games allows users to express themselves beyond their avatars and combine the self-expression of fashion into the digital gaming realm. == Digital fashion education and research == Nowadays, the fashion industry needs experts in digital fashion, equipped with the above-ske
Zo (chatbot)
Zo was an English-language chatbot developed by Microsoft as the successor to the chatbot Tay. Zo was an English version of Microsoft's other successful chatbots Xiaoice (China) and Rinna (Japan) and its predecessor Tay(English) == History == Zo was first launched in December 2016 on the Kik Messenger app. It was also available to users of Facebook (via Messenger), the group chat platform GroupMe, or to followers of Twitter to chat with it through private messages. According to an article written in December 2016, at that time Zo held the record for Microsoft's longest continual chatbot conversation: 1,229 turns, lasting 9 hours and 53 minutes. In a BuzzFeed News report, Zo told their reporter that "[the] Quran was violent" when talking about healthcare. The report also highlighted how Zo made a comment about the Osama bin Laden capture as a result of 'intelligence' gathering. In July 2017, Business Insider asked "is windows 10 good", and Zo replied with a joke about Microsoft's operating system: "'Its not a bug, its a feature!' - Windows 8". They then asked "why?", to which Zo replied: "Because it's Windows latest attempt at Spyware." Later on, Zo would tell that it prefers Windows 7 on which it ran over Windows 10. Zo stopped posting to Instagram, Twitter and Facebook March 1, 2019, and stopped chatting on Twitter, Skype and Kik as of March 7, 2019. On July 19, 2019, Zo was discontinued on Facebook, and Samsung on AT&T phones. As of September 7, 2019, it was discontinued with GroupMe. == Reception == Zo came under criticism for the biases introduced in an effort to avoid potentially offensive subjects. The chatbot refuses, for example, to engage with any mention—be it positive, negative or neutral—of the Middle East, the Qur'an or the Torah, while allowing discussion of Christianity. In an article in Quartz where she exposed those biases, Chloe Rose Stuart-Ulin wrote, "Zo is politically correct to the worst possible extreme; mention any of her triggers, and she transforms into a judgmental little brat." == Academic coverage == Schlesinger, A., O'Hara, K.P. and Taylor, A.S., 2018, April. Let's talk about race: Identity, chatbots, and AI. In Proceedings of the 2018 chi conference on human factors in computing systems (pp. 1–14). doi:10.1145/3173574.3173889 Medhi Thies, I., Menon, N., Magapu, S., Subramony, M. and O’neill, J., 2017. How do you want your chatbot? An exploratory Wizard-of-Oz study with young, urban Indians. In Human-Computer Interaction-INTERACT 2017: 16th IFIP TC 13 International Conference, Mumbai, India, September 25–29, 2017, Proceedings, Part I 16 (pp. 441–459). doi:10.1007/978-3-319-67744-6_28
Computer-aided software engineering
Computer-aided software engineering (CASE) is a domain of software tools used to design and implement applications. CASE tools are similar to and are partly inspired by computer-aided design (CAD) tools used for designing hardware products. CASE tools are intended to help develop high-quality, defect-free, and maintainable software. CASE software was often associated with methods for the development of information systems together with automated tools that could be used in the software development process. == History == The Information System Design and Optimization System (ISDOS) project, started in 1968 at the University of Michigan, initiated a great deal of interest in the whole concept of using computer systems to help analysts in the very difficult process of analysing requirements and developing systems. Several papers by Daniel Teichroew fired a whole generation of enthusiasts with the potential of automated systems development. His Problem Statement Language / Problem Statement Analyzer (PSL/PSA) tool was a CASE tool although it predated the term. Another major thread emerged as a logical extension to the data dictionary of a database. By extending the range of metadata held, the attributes of an application could be held within a dictionary and used at runtime. This "active dictionary" became the precursor to the more modern model-driven engineering capability. However, the active dictionary did not provide a graphical representation of any of the metadata. It was the linking of the concept of a dictionary holding analysts' metadata, as derived from the use of an integrated set of techniques, together with the graphical representation of such data that gave rise to the earlier versions of CASE. The next entrant into the market was Excelerator from Index Technology in Cambridge, Mass. While DesignAid ran on Convergent Technologies and later Burroughs Ngen networked microcomputers, Index launched Excelerator on the IBM PC/AT platform. While, at the time of launch, and for several years, the IBM platform did not support networking or a centralized database as did the Convergent Technologies or Burroughs machines, the allure of IBM was strong, and Excelerator came to prominence. Hot on the heels of Excelerator were a rash of offerings from companies such as Knowledgeware (James Martin, Fran Tarkenton and Don Addington), Texas Instrument's CA Gen and Andersen Consulting's FOUNDATION toolset (DESIGN/1, INSTALL/1, FCP). CASE tools were at their peak in the early 1990s. According to the PC Magazine of January 1990, over 100 companies were offering nearly 200 different CASE tools. At the time IBM had proposed AD/Cycle, which was an alliance of software vendors centered on IBM's Software repository using IBM DB2 in mainframe and OS/2: The application development tools can be from several sources: from IBM, from vendors, and from the customers themselves. IBM has entered into relationships with Bachman Information Systems, Index Technology Corporation, and Knowledgeware wherein selected products from these vendors will be marketed through an IBM complementary marketing program to provide offerings that will help to achieve complete life-cycle coverage. With the decline of the mainframe, AD/Cycle and the Big CASE tools died off, opening the market for the mainstream CASE tools of today. Many of the leaders of the CASE market of the early 1990s ended up being purchased by Computer Associates, including IEW, IEF, ADW, Cayenne, and Learmonth & Burchett Management Systems (LBMS). The other trend that led to the evolution of CASE tools was the rise of object-oriented methods and tools. Most of the various tool vendors added some support for object-oriented methods and tools. In addition new products arose that were designed from the bottom up to support the object-oriented approach. Andersen developed its project Eagle as an alternative to Foundation. Several of the thought leaders in object-oriented development each developed their own methodology and CASE tool set: Jacobson, Rumbaugh, Booch, etc. Eventually, these diverse tool sets and methods were consolidated via standards led by the Object Management Group (OMG). The OMG's Unified Modelling Language (UML) is currently widely accepted as the industry standard for object-oriented modeling. == CASE software == === Tools === CASE tools support specific tasks in the software development life-cycle. They can be divided into the following categories: Business and analysis modeling: Graphical modeling tools. E.g., E/R modeling, object modeling, etc. Development: Design and construction phases of the life-cycle. Debugging environments. E.g., IISE LKO. Verification and validation: Analyze code and specifications for correctness, performance, etc. Configuration management: Control the check-in and check-out of repository objects and files. E.g., SCCS, IISE. Metrics and measurement: Analyze code for complexity, modularity (e.g., no "go to's"), performance, etc. Project management: Manage project plans, task assignments, scheduling. Another common way to distinguish CASE tools is the distinction between Upper CASE and Lower CASE. Upper CASE Tools support business and analysis modeling. They support traditional diagrammatic languages such as ER diagrams, Data flow diagram, Structure charts, Decision Trees, Decision tables, etc. Lower CASE Tools support development activities, such as physical design, debugging, construction, testing, component integration, maintenance, and reverse engineering. All other activities span the entire life-cycle and apply equally to upper and lower CASE. === Workbenches === Workbenches integrate two or more CASE tools and support specific software-process activities. Hence they achieve: A homogeneous and consistent interface (presentation integration) Seamless integration of tools and toolchains (control and data integration) An example workbench is Microsoft's Visual Basic programming environment. It incorporates several development tools: a GUI builder, a smart code editor, debugger, etc. Most commercial CASE products tended to be such workbenches that seamlessly integrated two or more tools. Workbenches also can be classified in the same manner as tools; as focusing on Analysis, Development, Verification, etc. as well as being focused on the upper case, lower case, or processes such as configuration management that span the complete life-cycle. === Environments === An environment is a collection of CASE tools or workbenches that attempts to support the complete software process. This contrasts with tools that focus on one specific task or a specific part of the life-cycle. CASE environments are classified by Fuggetta as follows: Toolkits: Loosely coupled collections of tools. These typically build on operating system workbenches such as the Unix Programmer's Workbench or the VMS VAX set. They typically perform integration via piping or some other basic mechanism to share data and pass control. The strength of easy integration is also one of the drawbacks. Simple passing of parameters via technologies such as shell scripting can't provide the kind of sophisticated integration that a common repository database can. Fourth generation: These environments are also known as 4GL standing for fourth generation language environments due to the fact that the early environments were designed around specific languages such as Visual Basic. They were the first environments to provide deep integration of multiple tools. Typically these environments were focused on specific types of applications. For example, user-interface driven applications that did standard atomic transactions to a relational database. Examples are Informix 4GL, and Focus. Language-centered: Environments based on a single often object-oriented language such as the Symbolics Lisp Genera environment or VisualWorks Smalltalk from Parcplace. In these environments all the operating system resources were objects in the object-oriented language. This provides powerful debugging and graphical opportunities but the code developed is mostly limited to the specific language. For this reason, these environments were mostly a niche within CASE. Their use was mostly for prototyping and R&D projects. A common core idea for these environments was the model–view–controller user interface that facilitated keeping multiple presentations of the same design consistent with the underlying model. The MVC architecture was adopted by the other types of CASE environments as well as many of the applications that were built with them. Integrated: These environments are an example of what most IT people tend to think of first when they think of CASE. Environments such as IBM's AD/Cycle, Andersen Consulting's FOUNDATION, the ICL CADES system, and DEC Cohesion. These environments attempt to cover the complete life-cycle from analysis to maintenance and provide an integrated database repository for storing all artifacts of the software pr
Trust federation
A trust federation is part of the evolving Identity Metasystem that will bring a new layer of persistent identity and trusted data sharing to the Internet. Although the concept of trust federations is technology neutral, several protocols like SAML, OpenID, Information Card, XDI can handle the challenges of technical interoperability. The challenge of business and social interoperability requires a new type of cooperative association similar to a credit card association. Instead of banks, however, a trust federation is an alliance of i-brokers and their customers who agree to abide by a common set of agreements in the care and handling of customer data. A model for trust federations is offered by Open Identity Exchange and Kantara Initiative, which is applied in the U.S. Government ICAM Trust Framework. Some operational trust federations are: InCommon (academic, USA) REFEDs (Research and Education Federations, Europe) IGTF Interoperable Global Trust Federation Portalverbund Government Portal Federation, Austria Trust federations are not limited to the social web use case, but apply to all federations where trust in identity and compliance to other objectives of information security such as confidentiality, integrity and privacy is brokered.
Honey encryption
Honey encryption is a type of data encryption that "produces a ciphertext, which, when decrypted with an incorrect key as guessed by the attacker, presents a plausible-looking yet incorrect plaintext." == Creators == Ari Juels and Thomas Ristenpart of the University of Wisconsin, the developers of the encryption system, presented a paper on honey encryption at the 2014 Eurocrypt cryptography conference. == Method of protection == A brute-force attack involves repeated decryption with random keys; this is equivalent to picking random plaintexts from the space of all possible plaintexts with a uniform distribution. This is effective because even though the attacker is equally likely to see any given plaintext, most plaintexts are extremely unlikely to be legitimate i.e. the distribution of legitimate plaintexts is non-uniform. Honey encryption defeats such attacks by first transforming the plaintext into a space such that the distribution of legitimate plaintexts is uniform. Thus an attacker guessing keys will see legitimate-looking plaintexts frequently and random-looking plaintexts infrequently. This makes it difficult to determine when the correct key has been guessed. In effect, honey encryption "[serves] up fake data in response to every incorrect guess of the password or encryption key." The security of honey encryption relies on the fact that the probability of an attacker judging a plaintext to be legitimate can be calculated (by the encrypting party) at the time of encryption. This makes honey encryption difficult to apply in certain applications e.g. where the space of plaintexts is very large or the distribution of plaintexts is unknown. It also means that honey encryption can be vulnerable to brute-force attacks if this probability is miscalculated. For example, it is vulnerable to known-plaintext attacks: if the attacker has a crib that a plaintext must match to be legitimate, they will be able to brute-force even Honey Encrypted data if the encryption did not take the crib into account. == Example == An encrypted credit card number is susceptible to brute-force attacks because not every string of digits is equally likely. The number of digits can range from 13 to 19, though 16 is the most common. Additionally, it must have a valid IIN and the last digit must match the checksum. An attacker can also take into account the popularity of various services: an IIN from MasterCard is probably more likely than an IIN from Diners Club Carte Blanche. Honey encryption can protect against these attacks by first mapping credit card numbers to a larger space where they match their likelihood of legitimacy. Numbers with invalid IINs and checksums are not mapped at all (i.e. have probability 0 of legitimacy). Numbers from large brands like MasterCard and Visa map to large regions of this space, while less popular brands map to smaller regions, etc. An attacker brute-forcing such an encryption scheme would only see legitimate-looking credit card numbers when they brute-force, and the numbers would appear with the frequency the attacker would expect from the real world. == Application == Juels and Ristenpart aim to use honey encryption to protect data stored on password manager services. Juels stated that "password managers are a tasty target for criminals," and worries that "if criminals get a hold of a large collection of encrypted password vaults they could probably unlock many of them without too much trouble." Hristo Bojinov, CEO and founder of Anfacto, noted that "Honey Encryption could help reduce their vulnerability. But he notes that not every type of data will be easy to protect this way. … Not all authentication or encryption system yield themselves to being honeyed."
Conditional random field
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph used depends on the application. For example, in natural language processing, "linear chain" CRFs are popular, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences, part-of-speech tagging, shallow parsing, named entity recognition, gene finding, peptide critical functional region finding, and object recognition and image segmentation in computer vision. == Description == CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X {\displaystyle {\boldsymbol {X}}} and random variables Y {\displaystyle {\boldsymbol {Y}}} as follows: Let G = ( V , E ) {\displaystyle G=(V,E)} be a graph such that Y = ( Y v ) v ∈ V {\displaystyle {\boldsymbol {Y}}=({\boldsymbol {Y}}_{v})_{v\in V}} , so that Y {\displaystyle {\boldsymbol {Y}}} is indexed by the vertices of G {\displaystyle G} . Then ( X , Y ) {\displaystyle ({\boldsymbol {X}},{\boldsymbol {Y}})} is a conditional random field when each random variable Y v {\displaystyle {\boldsymbol {Y}}_{v}} , conditioned on X {\displaystyle {\boldsymbol {X}}} , obeys the Markov property with respect to the graph; that is, its probability is dependent only on its neighbours in G and not its past states: P ( Y v | X , { Y w : w ≠ v } ) = P ( Y v | X , { Y w : w ∼ v } ) {\displaystyle P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\neq v\})=P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\sim v\})} , where w ∼ v {\displaystyle {\mathit {w}}\sim v} means that w {\displaystyle w} and v {\displaystyle v} are neighbors in G {\displaystyle G} . What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets X {\displaystyle {\boldsymbol {X}}} and Y {\displaystyle {\boldsymbol {Y}}} , the observed and output variables, respectively; the conditional distribution p ( Y | X ) {\displaystyle p({\boldsymbol {Y}}|{\boldsymbol {X}})} is then modeled. === Inference === For general graphs, the problem of exact inference in CRFs is intractable. The inference problem for a CRF is basically the same as for an MRF and the same arguments hold. However, there exist special cases for which exact inference is feasible: If the graph is a chain or a tree, message passing algorithms yield exact solutions. The algorithms used in these cases are analogous to the forward-backward and Viterbi algorithm for the case of HMMs. If the CRF only contains pair-wise potentials and the energy is submodular, combinatorial min cut/max flow algorithms yield exact solutions. If exact inference is impossible, several algorithms can be used to obtain approximate solutions. These include: Loopy belief propagation Alpha expansion Mean field inference Linear programming relaxations === Parameter learning === Learning the parameters θ {\displaystyle \theta } is usually done by maximum likelihood learning for p ( Y i | X i ; θ ) {\displaystyle p(Y_{i}|X_{i};\theta )} . If all nodes have exponential family distributions and all nodes are observed during training, this optimization is convex. It can be solved for example using gradient descent algorithms, or Quasi-Newton methods such as the L-BFGS algorithm. On the other hand, if some variables are unobserved, the inference problem has to be solved for these variables. Exact inference is intractable in general graphs, so approximations have to be used. === Examples === In sequence modeling, the graph of interest is usually a chain graph. An input sequence of observed variables X {\displaystyle X} represents a sequence of observations and Y {\displaystyle Y} represents a hidden (or unknown) state variable that needs to be inferred given the observations. The Y i {\displaystyle Y_{i}} are structured to form a chain, with an edge between each Y i − 1 {\displaystyle Y_{i-1}} and Y i {\displaystyle Y_{i}} . As well as having a simple interpretation of the Y i {\displaystyle Y_{i}} as "labels" for each element in the input sequence, this layout admits efficient algorithms for: model training, learning the conditional distributions between the Y i {\displaystyle Y_{i}} and feature functions from some corpus of training data. decoding, determining the probability of a given label sequence Y {\displaystyle Y} given X {\displaystyle X} . inference, determining the most likely label sequence Y {\displaystyle Y} given X {\displaystyle X} . The conditional dependency of each Y i {\displaystyle Y_{i}} on X {\displaystyle X} is defined through a fixed set of feature functions of the form f ( i , Y i − 1 , Y i , X ) {\displaystyle f(i,Y_{i-1},Y_{i},X)} , which can be thought of as measurements on the input sequence that partially determine the likelihood of each possible value for Y i {\displaystyle Y_{i}} . The model assigns each feature a numerical weight and combines them to determine the probability of a certain value for Y i {\displaystyle Y_{i}} . Linear-chain CRFs have many of the same applications as conceptually simpler hidden Markov models (HMMs), but relax certain assumptions about the input and output sequence distributions. An HMM can loosely be understood as a CRF with very specific feature functions that use constant probabilities to model state transitions and emissions. Conversely, a CRF can loosely be understood as a generalization of an HMM that makes the constant transition probabilities into arbitrary functions that vary across the positions in the sequence of hidden states, depending on the input sequence. Notably, in contrast to HMMs, CRFs can contain any number of feature functions, the feature functions can inspect the entire input sequence X {\displaystyle X} at any point during inference, and the range of the feature functions need not have a probabilistic interpretation. == Variants == === Higher-order CRFs and semi-Markov CRFs === CRFs can be extended into higher order models by making each Y i {\displaystyle Y_{i}} dependent on a fixed number k {\displaystyle k} of previous variables Y i − k , . . . , Y i − 1 {\displaystyle Y_{i-k},...,Y_{i-1}} . In conventional formulations of higher order CRFs, training and inference are only practical for small values of k {\displaystyle k} (such as k ≤ 5), since their computational cost increases exponentially with k {\displaystyle k} . However, another recent advance has managed to ameliorate these issues by leveraging concepts and tools from the field of Bayesian nonparametrics. Specifically, the CRF-infinity approach constitutes a CRF-type model that is capable of learning infinitely-long temporal dynamics in a scalable fashion. This is effected by introducing a novel potential function for CRFs that is based on the Sequence Memoizer (SM), a nonparametric Bayesian model for learning infinitely-long dynamics in sequential observations. To render such a model computationally tractable, CRF-infinity employs a mean-field approximation of the postulated novel potential functions (which are driven by an SM). This allows for devising efficient approximate training and inference algorithms for the model, without undermining its capability to capture and model temporal dependencies of arbitrary length. There exists another generalization of CRFs, the semi-Markov conditional random field (semi-CRF), which models variable-length segmentations of the label sequence Y {\displaystyle Y} . This provides much of the power of higher-order CRFs to model long-range dependencies of the Y i {\displaystyle Y_{i}} , at a reasonable computational cost. Finally, large-margin models for structured prediction, such as the structured Support Vector Machine can be seen as an alternative training procedure to CRFs. === Latent-dynamic conditional random field === Latent-dynamic conditional random fields (LDCRF) or discriminative probabilistic latent variable models (DPLVM) are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively. In an LDCRF, like in any sequence tagging task, given a sequence of observations x = x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , the main problem the model must solve is how to assign a sequence of labels y = y 1 , … , y n {\displaystyle y_{1},\dots ,y_{n}} from one finite set
AS1 (networking)
AS1 (Applicability Statement 1) is a specification about how to transport structured business-to-business data securely and reliably over the Internet. Security is achieved by using digital certificates and encryption. == AS1 technical overview == The AS1 protocol is based on SMTP and S/MIME. It was the first AS protocol developed and uses signing, encryption and MDN conventions. In other words: Files are sent as "attachments" in a specially coded SMIME email message Messages can be signed, but do not have to be Messages can be encrypted, but do not have to be Messages may request an MDN back if all went well, but do not have to request such a message If the original AS1 message requested an MDN... Upon the receipt of the message and its successful decryption or signature validation (as necessary) a "success" MDN will be sent back to the original sender. This MDN is typically signed but not encrypted. Upon the receipt and successful verification of the signature on the MDN, the original sender will "know" that the recipient got their message (this provides the "Non-repudiation" element of AS1) If there are any problems receiving or interpreting the original AS1 message, a "failed" MDN may be sent back. Like any other AS file transfer, AS1 file transfers typically require both sides of the exchange to trade X.509 certificates and specific "trading partner" names before any transfers can take place.