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
Gcore
Gcore is an edge AI, cloud, network, and security company headquartered in Luxembourg. Founded in 2014, the company provides low-latency services to industries including finance, healthcare, manufacturing, gaming, media and telecommunications internationally. As of March 2024, its global network includes over 180 Points of Presence (PoPs) across six continents. == History == Gcore was founded in 2014 in Luxembourg. The company built its own content delivery network, originally designed for the needs of the gaming industry. In 2016, Gcore's infrastructure expanded to multiple regions that were underserved by hyperscale cloud providers. In 2020, the company formed partnerships with Intel and Equinix. In 2022, Gcore launched the European AI Cloud, providing access to infrastructure for machine learning tasks. In March 2024, Gcore announced the acquisition of a web application and API protection (WAAP) solution from StackPath. In April 2024, Gcore received a commendation in the Industry Innovation category at the NVIDIA Partner Network Awards EMEA for developing the first speech-to-text technology for Luxembourgish, using the LuxemBERT AI model. In May 2024, Philipp Rösler, former vice-chancellor of Germany and federal minister of health joined the Gcore board. In July 2024, Gcore raised $60 million in a Series A funding round, marking the company's first external investment since its founding. In August 2024, Gcore was recognized as a Major Player in the IDC MarketScape report for European public cloud Infrastructure (IaaS) 2024 by IDC, the global market intelligence firm. In May 2025, Feiyu Xu became a member of the Gcore advisory board. == Network infrastructure == According to the company's website, Gcore has network locations in six continents: Europe, North America, Asia, South America, Africa, and Australia with over 14,000 peering partners and a network capacity exceeding 200 Tbps. According to a 2025 review by Geekflare, Gcore's CDN achieved an average global response time of around 30 milliseconds. Gcore offers AI cloud clusters, including a generative AI cluster with Nvidia GPUs in Luxembourg and additional sites in the Netherlands and Wales, as part of its European AI infrastructure. == Products and services == Gcore offers a range of services, including content delivery network (CDN), cloud computing,virtual machines, bare-metal servers, object storage AI infrastructure and inference, Kubernetes, video streaming, DDoS mitigation, web application and API protection (WAAP), Domain Name System (DNS). Gcore provides AI services and GPU cloud infrastructure to support model development, training, fine-tuning, and inference. In January 2025, the company introduced Everywhere Inference, a serverless inference solution that enables AI model deployment. == Controversies == Correctiv and Tageszeitung reported that Gcore supported the distribution of the TV network RT until April 2023, which has been under sanctions by the EU since March 2022. However, Gcore denies these allegations. == Collaborations == In 2024, Gcore and Qareeb Data Centres, a data center provider in the Middle East, launched a collaboration to integrate Gcore's AI, cloud and edge services across data centers in multiple Middle Eastern countries. In June 2025, Gcore joined the SmartSpires initiative, a €3.1 million smart city project co-funded by the Connecting Europe Facility. The three-year programme is coordinated by a public–private consortium including 5SKYE, the Luxembourg Institute of Science and Technology (LIST), Orange Luxembourg, and Gcore. The project aims to transform the Belval campus into a smart city by deploying 5G-enabled smart towers that integrate edge computing, artificial intelligence and IoT services. Within the consortium, Gcore acts as project coordinator and is responsible for the deployment of the edge infrastructure.
Content reference identifier
A content reference identifier or CRID is a concept from the standardization work done by the TV-Anytime forum. It is or closely matches the concept of the Uniform Resource Locator, or URL, as used on the World-Wide Web: A unit of content, in a broadcast stream, can be referred to by its globally unique CRID in the same way that a webpage can be referred to by its globally unique URL on the web. The concept of CRID permits referencing contents unambiguously, regardless of their location, i.e., without knowing specific broadcast information (time, date and channel) or how to obtain them through a network, for instance, by means of a streaming service or by downloading a file from an Internet server. The receiver must be capable of resolving these unambiguous references, i.e. of translating them into specific data that will allow it to obtain the location of that content in order to acquire it. This makes it possible for recording processes to take place without knowing that information, and even without knowing beforehand the duration of the content to be recorded: a complete series by a simple click, a program that has not been scheduled yet, a set of programs grouped by a specific criterion... This framework allows for the separation between the reference to a given content (the CRID) and the necessary information to acquire it, which is called a “locator”. Each CRID may lead to one or more locators which will represent different copies of the same content. They may be identical copies broadcast in different channels or dates, or cost different prices. They may also be distinct copies with different technical parameters such as format or quality. It may also be the case that the resolution process of a CRID provides another CRID as a result (for example, its reference in a different network, where it has an alternative identifier assigned by a different operator) or a set of CRIDs (for instance, if the original CRID represents a TV series, in which case the resolution process would result in the list of CRIDs representing each episode). From the above it can be concluded that provided that a given content can belong to many groups (each possibly defined by distinctive qualities), it is possible that many CRIDs carry the same content. That is, several CRIDs may be resolved into the same locator. A CRID is not exactly a universal, unique and exclusive identifier for a given content. It is closely related to the authority that creates it, to the resolution service provider, and to the content provider in such a way that the same content may have different CRIDs depending on the field in which they are used (for example, a different one for each television operator that has the rights to broadcast the content). == Format == A CRID is specified much like URLs. In fact, a CRID is a so-called URI. Typically, the content creator, the broadcaster or a third party will use their DNS-names in a combination with a product-specific name to create globally unique CRIDs. That is, the syntax of a CRID is: crid://authority/data The authority field represents the entity that created the CRID and its format is that of a DNS name. The data field represents a string of characters that will unambiguously identify the content within the authority scope (it is a string of characters assigned by the authority itself). As an example, let's assume that BBC wanted to make a CRID for (all the programs of) the Olympics in China. It may have looked something like this crid://bbc.co.uk/olympics/2008/ This would be a group CRID, that is, a CRID representing a group of contents. Then, to refer to a specific event – such as the women's shot-put final – they could have used the following inside their metadata. crid://bbc.co.uk/olympics/2008/final/shotput/women Currently, four types of CRIDs are playing a major role in some unidirectional television networks: programme CRID, series CRID, group CRID, and recommendation CRID. One of the most important applications of CRIDs is the so-called series link recording function (SL) of modern digital video recorders (DVR, PVR). In turn, a locator is a string of characters that contains all the necessary information for a receiver to find and acquire a given content, whether it is received through a transport stream, located in local storage, downloaded as a file from an Internet server, or through a streaming service. For example, a DVB locator will include all the necessary parameters to identify a specific content within a transport stream: network, transport stream, service, table and/or event identifiers. The locators' format, as established in TV-Anytime, is quite generic and simple, and corresponds to: [transport-mechanism]:[specific-data] The first part of the locator's format (the transport mechanism) must be a string of characters that is unique for each mechanism (transport stream, local file, HTTP Internet access...). The second part must be unambiguous only within the scope of a given transport mechanism and will be standardized by the organism in charge of the regulation of the mechanism itself. For instance, a DVB locator to identify a content within the transport stream of networks that follow this standard would be: dvb://112.4a2.5ec;2d22~20121212T220000Z—PT01H30M which would indicate a content (identified by the string “2d22”) that airs on a channel available on a DVB network identified by the address “112.4a2.5ec” (network “112”, transport stream “4a2” and service “5ec”), on 12 December 2012 at 10 p.m. and with a duration of 90 minutes. == The location resolution process == The location resolution process is the procedure by which, starting from the CRID of a given content, one or several locators of that content are obtained. Resolving a CRID can be a direct process, which leads immediately to one or many locators, or it may also happen that in the first place one or many intermediate CRIDs are returned, which must undergo the same procedure to finally obtain one or several locators. This procedure involves some information elements, among which we find two structures named resolving authority record (RAR) and ContentReferencingTable, respectively. Consulting them repeatedly will take the receiver from a CRID to one or many locators that will allow it to acquire the content. The RAR table The RAR table is one or many data structures that provide the receiver, for each authority that submits CRIDs, information on the corresponding resolution service provider. Among other things, it informs about which mechanism is used to provide information to resolve the CRIDs from each authority. That is, one or many RAR records must exist for each authority that indicate the receiver where it has to go to resolve the CRIDs of that particular authority. For example, in the record of the figure (expressed by means of a XML structure, according to the XML Schema defined in the TV-Anytime) there is an authority called “tve.es”, whose resolution service provider is the entity “rtve.es”, available on the URL "http://tva.rtve.es/locres/tve", which means there is resolution information in that URL. These RAR records will have reached the receiver in an indefinite form, unimportant for the TV-Anytime specification, which will depend on the specific transport mechanism of the network to which the receiver is connected. Each family of standards that regulates distribution networks (DVB, ATSC, ISDB, IPTV...) will have previously defined such procedure, which will be used by devices certified according to those standards. The ContentReferencingTable table The second structure involved in the location resolution process is a proper resolution table which, given a content's CRID, returns one or several locators that enable the receiver to access an instance of that content, or one or many CRIDs that allow it to move forward in the resolution process. The figure shows an example of this second structure, an XML document according to the specifications of the XML Schema defined in TV-Anytime. In it, several sections are included (
Bare machine
In information technology, a bare machine (or bare-metal computer) is a computer which has no operating system. The software executed by a bare machine, commonly called a bare metal program or bare metal application, is designed to interact directly with hardware. Bare machines are widely used in embedded systems, particularly in cases where resources are limited or high performance is required. == Bare machine computing == Bare Machine Computing is a computing paradigm in which application software runs directly on a bare machine as a single, stand-alone executable, without an operating system or device drivers. The application software has direct access to hardware resources, and there is typically no distinction between user and kernel mode. It is self-managed software that boots, loads and runs without using any other software components. Bare metal programs are typically written in a close-to-hardware language such as C or assembly language. == Advantages == Typically, a bare-metal application will run faster, use less memory and be more power efficient than an equivalent program that relies on an operating system, due to the inherent overhead imposed by system calls. For example, hardware inputs and outputs are directly accessible to bare metal software, whereas they must usually be accessed through system calls when using an OS. It has no OS and therefore has no OS-related vulnerabilities. == Disadvantages == Bare metal applications typically require more effort to develop because operating system services such as memory management and task scheduling are not available. Debugging a bare-metal program may be complicated by factors such as: Lack of a standard output. The target machine may differ from the hardware used for program development (e.g., emulator, simulator). This forces setting up a way to load the bare-metal program onto the target (flashing), start the program execution and access the target resources. == Examples == === Early computers === Early computers, such as the PDP-11, allowed programmers to load a program, supplied in machine code, to RAM. The resulting operation of the program could be monitored by lights, and output derived from magnetic tape, print devices, or storage. Amdahl UTS's performance improves by 25% when run on bare metal without VM, the company said in 1986. === Embedded systems === Bare machine programming is a common practice in embedded systems, in which microcontrollers or microprocessors boot directly into monolithic, single-purpose software without loading an operating system. Such embedded software can vary in structure. For example, one such program paradigm, known as foreground-background or superloop architecture, consists of an infinite main loop in which each task is executed sequentially and must voluntarily return control back to the loop. The loop runs these cooperative background processes that are not time-critical, while interrupt service routines momentarily interrupt the loop to handle time-critical foreground tasks.
Awwwards
Awwwards (Awwwards Online SL) is an organization that hosts web design competitions and conferences across Europe and the United States. Website owners and developers can participate by submitting their websites for review. Submissions are assessed by a jury, and top entries are presented and awarded prizes on a rotational basis. == Nomination process == Web designers submit their websites through Awwwards' platform for consideration for the Site of the Day. A jury, composed of industry professionals, and the Awwwards community evaluate the entries. The best daily sites are published annually in "The 365 Best Websites Around the World" book. == Jury == The jury consists of international designers, developers, and agencies who assess the creativity, technical skills, and insight of the submitted web projects. The panel's expertise ensures a comprehensive review process. === Developer Award === Awwwards, in partnership with Microsoft, created the Developer Award to recognize web developers who demonstrate excellence in creating websites that meet modern standards. The award highlights websites that work seamlessly across various platforms and devices, using best practices in HTML5, JavaScript, and CSS. == Annual winners == Some prominent Site of the Year winners include Mercedes-Benz, Bloomberg L.P., Bose Corporation, Warner Brothers, Volkswagen, Uber, and Google. == Awwwards conference == Awwwards also organizes two-day conferences featuring speakers from major tech companies and industry leaders such as Microsoft, Google, Spotify, Adobe, Opera, and Smashing Magazine. These events focus on the latest trends in web design and development. Speakers at Awwwards conferences have included notable figures in the design and technology industry such as Stefan Sagmeister, Paula Scher, and design leaders from companies including Wix. == Corporate affairs == === Platform === Awwwards operates an online platform where web designers and developers submit websites for evaluation and awards. Submitted projects are reviewed by a jury based on design, usability, creativity, and content. The platform also serves as a community hub for discovering digital trends, showcasing work, and accessing educational resources including talks and interviews. Design professionals from international companies have participated in Awwwards events and platform content. For example, Wix, a cloud-based web development company known for its website builder tools, has featured prominently in Awwwards conferences, with its design leadership contributing to discussions on design trends and creative thinking.
Microapp
A microapp is a super-specialized application designed to perform one task or use case with the only objective of doing it well. They follow the single responsibility principle, which states that "a class should have one and only one reason to change." Micro applications help developers create less complex applications while reducing costs by breaking down monolithic systems into groups of independent services acting as one system. A good example of Microapps would be https://docs.citrix.com/en-us/legacy-archive/downloads/microapps.pdfthat provide single purpose action from Salesforce and over 40 applications on its workspace. == Requirements and characteristics == Microapps usually are accessible on any device, display, or operating system without installation on the viewer's device. To qualify as a microapp, the entity must: be built and deployed as an independent software module bring together various media types into a single experience have advanced security and compliance features be functionally-extensible comply with granular data demands be agnostic single use case oriented Microapps differentiate from traditional web or mobile applications by how the end-user interacts with them. Consequently, they can be embedded in websites or viewed online to bypass app stores and are typically built to provide a focused experience to the user. == Usage == Microapps are typically used for commercial purposes to reduce development costs for projects not requiring the large scope of a traditional web or mobile application. In addition, they are often used to showcase in-depth information or enrich marketing material with interactivity. Lately, micro apps are being used to boost productivity by providing quick tools to people to reuse best practices. Users have been interacting with microapps for a while with suites like Microsoft 365 and Google Workspace, where each one of their end-user services could be considered as a microapp. All these microapps share a unique identity manager to provide a unified user experience. == Benefits == Replacing monolith systems with microapps provide several advantages like: Reduce complexity for developers and users. Smaller, more cohesive, and maintainable codebases Scalable organizations with decoupled, autonomous teams Allows for hyper-specialization Independent deployment Multi-stack == Cloud-native microapps == Technologies like Kubernetes, or OpenShift, allow companies to replace their monolith and legacy systems with modular software taking advantage of microapps on reducing costs and improve reliability and security. == Microapps vs. microservices == There is a widespread misunderstanding between these two concepts, which is the key difference. Microservices is an architectural style that is systems-centric, meaning it decouples the presentation and data layer using web services APIs. On the other side, micro apps behave more as a super-architecture style (that embraces microservices among other types), and it is user-centric, meaning they decouple the whole monolith system onto modules that are designed to interact with final users. Both architectural styles rely on modularity to provide high performance, scalability, and resilience. == Considerations == Developing Micro apps requires a different approach than traditional software, and user experience is crucial. The following considerations are essential for switching to microapps. To run multiple microapps is required a single identity management system. Microservices are well suited to make microapps more powerful Apps with different levels of maturity might create a non-unified user experience. Duplication of dependencies can create security issues and inefficiencies. Suitable for well-organized teams
Digital studio
A digital studio provides both a technology-equipped space and technological/rhetorical support to students (commonly at a university) working individually or in groups on a variety of digital projects, such as designing a website, developing an electronic portfolio for a class, creating a blog, making edits, selecting images for a visual essay, or writing a script for a podcast. == History/theory == === Overview === Digital Studios are places with different names but similar objectives. They have risen in response to the need for resources dedicated to improving students' interactions with digital technologies for rhetorical ends. Digital Studios have often been theoretically and administratively linked to writing centers, which are sites where students can seek assistance with their text-based projects. The academic term that has been used for this kind of site (i.e. a writing center with a focus on digital and new media) is multiliteracy center. Besides having a multimodal focus, Digital Studios also make a departure from writing center model in allowing students the freedom to work in the Studio without one-on-one interaction with a writing tutor. === The rise of technology === ==== Computer literacy in popular culture ==== As early as 1983, computer literacy was being hailed in The New York Times as the "new goal in schools." As computer technology became more ubiquitous, and the World Wide Web became more popular and accessible, and as the teaching of computer skills became official US policy with the enactment of the "Technology Literacy Challenge" by the Clinton Administration in 1996, educators across disciplines began to investigate with renewed vigor the role of computer technology in curriculum as both a means and an end. ==== Scholarly interest in 'multiliteracies' ==== The same year that President Clinton initiated the "Technology Literacy Challenge", the New London Group (NLG) issued a call for scholars of literacy pedagogy to account for the burgeoning variety of text forms associated with information and multimedia technologies. This includes understanding and competent control of representational forms that are becoming increasingly significant in the overall communications environment, such as visual images and their relationship to the written word – for instance, visual design in desktop publishing or the interface of visual and linguistic meaning in multimedia. This account for new text forms, combined with a similar account for "increasingly globalized societies," is called 'multiliteracies' by the NLG. ==== Technological literacy in rhetoric and composition ==== Two years later, during the 1998 CCCC Chair's Address, Cynthia Selfe (who founded the peer-reviewed journal Computers and Composition in 1983) addressed professionals in the field of Rhetoric and Composition with an objective similar to that of the NLG, arguing that as a field, composition scholars had "paid technology issues precious little focused attention over the years." She called this lack of attention "dangerously shortsighted." What was needed, Selfe claimed, was for teachers to "pay attention" to "how technology is now inextricably linked with literacy and literacy education in this country." In a way, Selfe's call marked the beginning of a new scholarly interest in what Selfe called "critical technological literacy": Composition teachers, language arts teachers, and other literacy specialists need to recognize that the relevance of technology in the English studies disciplines is not simply a matter of helping students work effectively with communication software and hardware, but, rather, also a matter of helping them to understand and to be able to assess – to pay attention to – the social, economic, and pedagogical implications of new communication technologies and technological initiatives that affect their lives. Scholars who took up this call included Barbara Blakely Duffelmeyer, who conducted studies involving the incorporation of "critical computer literacy" (an adaptation of Selfe's term) into first-year composition. ==== Communications across media, inside and outside school ==== The years following Selfe's address saw more rapid advancements in mobile technologies, social media, and Web 2.0, creating even more new venues of composing for teachers to pay attention to. In her own CCCC Chair's Address in 2004, Kathleen Blake Yancey cited these new venues in her argument as a "new curriculum for the 21st century," one that would bring "together the writing outside of school and that of inside." Such a curriculum, she said: is located in a new vocabulary, a new set of practices, and a new set of outcomes; it will focus our research in new and provocative ways; it has as its goal the creation of thoughtful, informed, technologically adept writing publics. A professor at Clemson at the time of her speech, Yancey also argued for the creation of an undergraduate major in composition and rhetoric. She soon moved to Florida State University, where she helped to establish a new major in line with the one she argued for at CCCC called Editing, Writing, and Media (EWM). As teachers and administrators across the country looked to incorporate more digital technology into their curriculum, the need for spaces for digital composition and for support with the innumerable digital composing platforms became apparent. A Digital Studio is one such space. === Link with writing centers === With the need for support for students who would engage with digital writing and multimedia projects, professionals involved with work in writing centers began to draw comparisons between their traditional work — assisting students with alphabetic texts on the page — and a new kind of work: assisting students with their multimedia projects on the screen. John Trimbur predicted in 2000: My guess is that writing centers will more and more define themselves as multiliteracy centers. Many are already doing so – tutoring oral presentations, adding online tutorials, offering workshops in evaluating web sources, and being more conscious of document design. To my mind, new digital literacies will increasingly be incorporated into writing centers not just as sources of information or delivery systems for tutoring but as productive arts in their own right, and writing center work will, if anything, become more rhetorical in paying attention to the practices and effects of design in written and visual communication — more product-oriented and perhaps less like the composing conferences of the process movement. Later, just months before Yancey delivered her CCCC Chair's Address, Michael Pemberton, writing in the Writing Center Journal, asked: As we enter an era when electronic publishing and computer-mediated discourse are the norm, an era when new literary genres and new forms of communication emerge on, seemingly, a weekly basis, we must ask ourselves whether writing centers should continue to dwell exclusively in the linear, non-linked world of the printed page or whether they should plan to redefine themselves – and retrain themselves – to take residence in the emerging world of multimedia, hyperlinked, digital documents. To put it plainly, should we be preparing tutors to conference with students about hypertexts? Pemberton also surveyed (by his account) the forty-year history of how "writing centers [have] viewed new technologies," observing that "the relationship between writing centers and computer technology has been, overall, only a cordial one." Pemberton's article is evidence of the continuing discussion among writing center professionals about the need for support for students' digital creations, support which they saw as analogous to work in writing centers. In 2010, a collection edited by David Sheridan and James Inman, Multiliteracy Centers: Writing Center Work, New Media, and Multimodal Rhetoric, was published. Many of the chapters therein cite the above Trimbur and Pemberton quotes as they work to explain the exigence for the collection, the instances in which multiliteracy centers have been established (the founding of the Clemson Class of 1941 Studio for Student Communication is the subject of two chapters), and both theoretical and practical analyses of potential futures of such work. === 'Studio' vs. 'Center:' A break from the model === The conflation of digital studios and writing centers into multiliteracy centers is helpful in some respects, for example, administratively the two may be managed in similar ways and staffed by the same people. In other respects, it has been said that it is better to separate them into two distinct kinds of facilities. The very choice of naming a "writing center" or "digital studio" by either (or another) title, for instance, ought (according to some) to be informed by what kinds of student-activities are expected to take place there. A writing center is a place for individual students to seek help from individual writing