An inauthentic text is a computer-generated expository document meant to appear as genuine, but which is actually meaningless. Frequently they are created in order to be intermixed with genuine documents and thus manipulate the results of search engines, as with Spam blogs. They are also carried along in email in order to fool spam filters by giving the spam the superficial characteristics of legitimate text. Sometimes nonsensical documents are created with computer assistance for humorous effect, as with Dissociated press or Flarf poetry. They have also been used to challenge the veracity of a publication—MIT students submitted papers generated by a computer program called SCIgen to a conference, where they were initially accepted. This led the students to claim that the bar for submissions was too low. With the amount of computer generated text outpacing the ability of people to humans to curate it, there needs some means of distinguishing between the two. Yet automated approaches to determining absolutely whether a text is authentic or not face intrinsic challenges of semantics. Noam Chomsky coined the phrase "Colorless green ideas sleep furiously" giving an example of grammatically correct, but semantically incoherent sentence; some will point out that in certain contexts one could give this sentence (or any phrase) meaning. The first group to use the expression in this regard can be found below from Indiana University. Their work explains in detail an attempt to detect inauthentic texts and identify pernicious problems of inauthentic texts in cyberspace. The site has a means of submitting text that assesses, based on supervised learning, whether a corpus is inauthentic or not. Many users have submitted incorrect types of data and have correspondingly commented on the scores. This application is meant for a specific kind of data; therefore, submitting, say, an email, will not return a meaningful score.
Self-management (computer science)
Self-management is the process by which computer systems manage their own operation without human intervention. Self-management technologies are expected to pervade the next generation of network management systems. The growing complexity of modern networked computer systems is a limiting factor in their expansion. The increasing heterogeneity of corporate computer systems, the inclusion of mobile computing devices, and the combination of different networking technologies like WLAN, cellular phone networks, and mobile ad hoc networks make the conventional, manual management difficult, time-consuming, and error-prone. More recently, self-management has been suggested as a solution to increasing complexity in cloud computing. An industrial initiative towards realizing self-management is the Autonomic Computing Initiative (ACI) started by IBM in 2001. The ACI defines the following four functional areas: Self-configuration Auto-configuration of components Self-healing Automatic discovery, and correction of faults; automatically applying all necessary actions to bring system back to normal operation Self-optimization Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements Self-protection Proactive identification and protection from arbitrary attacks
JDoodle
JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==
SWILE
SWILE (formerly: Lunchr) is a French app-based company that focuses on improving the employee experience. Among others, the platform offers meal vouchers, gift vouchers, mobility vouchers, and business travel solutions. In March 2020, it was renamed SWILE and entered the lunch break and meal voucher market. == History == The company was founded as Lunchr by Loïc Soubeyrand in 2016. Originally, Lunchr was an app for pre-ordering lunch on the spot or to go. In January 2017, the company raised €2.5 million in seed funding from Daphni. In 2018, the company raised €11 million (series A) from Idinvest, followed by another €30 million in February 2019 (series B), notably from Index Ventures and Kima Ventures. In January 2020, Lunchr became one of the first startups to join the French Tech 120. A few months later, in March, Lunchr diversified its services, adding team life management tools and changing its brand name to Swile. In June 2020, the company raised €70 million more in a new round of financing (Series C) from the same investors and the BPI. In November 2020, Swile acquired Briq, a startup specializing in employee engagement. In January 2021, Swile won a tender with Carrefour and distributed 62,000 Swile cards to its employees. In early October 2021, a new $200 million (€175 million) fundraising round, in which Japanese Softbank joined other investors, allowed Swile to capitalize on $1 billion. President Emmanuel Macron cited the company as "a further proof that FrenchTech is at the forefront internationally." In May 2022, the company acquired the travel management start-up Okarito for €6 million. == Overview == Swile operates in two countries (France and Brazil) and has a total of 1000 employees, 5.5 million users and 85,000 corporate customers, including Carrefour, Le Monde, JCDECAUX, PSG, Airbnb, Spotify, Red Bull, and TikTok in the private sector, as well as numerous local authorities and ministerial references in the public sector.
Language Server Protocol
The Language Server Protocol (LSP) is an open, JSON-RPC-based protocol for use between source-code editors or integrated development environments (IDEs) and servers that provide "language intelligence tools": programming language-specific features like code completion, syntax highlighting and marking of warnings and errors, as well as refactoring routines. The goal of the protocol is to allow programming language support to be implemented and distributed independently of any given editor or IDE. In the early 2020s, LSP quickly became a "norm" for language intelligence tools providers. == History == LSP was originally developed for Microsoft Visual Studio Code and is now an open standard. On June 27, 2016, Microsoft announced a collaboration with Red Hat and Codenvy to standardize the protocol's specification. Its specification is hosted and developed on GitHub. == Background == Modern IDEs provide programmers with sophisticated features like code completion, refactoring, navigating to a symbol's definition, syntax highlighting, and error and warning markers. For example, in a text-based programming language, a programmer might want to rename a method read. The programmer could either manually edit the respective source code files and change the appropriate occurrences of the old method name into the new name, or instead use an IDE's refactoring capabilities to make all the necessary changes automatically. To be able to support this style of refactoring, an IDE needs a sophisticated understanding of the programming language that the program's source is written in. A programming tool without such an understanding—for example, one that performs a naive search-and-replace instead—could introduce errors. When renaming a read method, for example, the tool should not replace the partial match in a variable that might be called readyState, nor should it replace the portion of a code comment containing the word "already". Neither should renaming a local variable read, for example, end up altering identically-named variables in other scopes. Conventional compilers or interpreters for a specific programming language are typically unable to provide these language services, because they are written with the goal of either transforming the source code into object code or immediately executing the code. Additionally, language services must be able to handle source code that is not well-formed, e.g. because the programmer is in the middle of editing and has not yet finished typing a statement, procedure, or other construct. Additionally, small changes to a source code file which are done during typing usually change the semantics of the program. In order to provide instant feedback to the user, the editing tool must be able to very quickly evaluate the syntactical and semantical consequences of a specific modification. Compilers and interpreters therefore provide a poor candidate for producing the information needed for an editing tool to consume. Prior to the design and implementation of the Language Server Protocol for the development of Visual Studio Code, most language services were generally tied to a given IDE or other editor. In the absence of the Language Server Protocol, language services are typically implemented by using a tool-specific extension API. Providing the same language service to another editing tool requires effort to adapt the existing code so that the service may target the second editor's extension interfaces. The Language Server Protocol allows for decoupling language services from the editor so that the services may be contained within a general-purpose language server. Any editor can inherit sophisticated support for many different languages by making use of existing language servers. Similarly, a programmer involved with the development of a new programming language can make services for that language available to existing editing tools. Making use of language servers via the Language Server Protocol thus also reduces the burden on vendors of editing tools, because vendors do not need to develop language services of their own for the languages the vendor intends to support, as long as the language servers have already been implemented. The Language Server Protocol also enables the distribution and development of servers contributed by an interested third party, such as end users, without additional involvement by either the vendor of the compiler for the programming language in use or the vendor of the editor to which the language support is being added. LSP is not restricted to programming languages. It can be used for any kind of text-based language, like specifications or domain-specific languages (DSL). == Technical overview == When a user edits one or more source code files using a language server protocol-enabled tool, the tool acts as a client that consumes the language services provided by a language server. The tool may be a text editor or IDE and the language services could be refactoring, code completion, etc. The client informs the server about what the user is doing, e.g., opening a file or inserting a character at a specific text position. The client can also request the server to perform a language service, e.g. to format a specified range in the text document. The server answers a client's request with an appropriate response. For example, the formatting request is answered either by a response that transfers the formatted text to the client or by an error response containing details about the error. The Language Server Protocol defines the messages to be exchanged between client and language server. They are JSON-RPC preceded by headers similar to HTTP. Messages may originate from the server or client. The protocol does not make any provisions about how requests, responses and notifications are transferred between client and server. For example, client and server could be components within the same process exchanging JSON strings via method calls. They could also be different processes on the same or on different machines communicating via network sockets. == Registry == There are lists of LSP-compatible implementations, maintained by the community-driven Langserver.org or Microsoft.
Content determination
Content determination is the subtask of natural language generation (NLG) that involves deciding on the information to be communicated in a generated text. It is closely related to the task of document structuring. == Example == Consider an NLG system which summarises information about sick babies. Suppose this system has four pieces of information it can communicate The baby is being given morphine via an IV drop The baby's heart rate shows bradycardia's (temporary drops) The baby's temperature is normal The baby is crying Which of these bits of information should be included in the generated texts? == Issues == There are three general issues which almost always impact the content determination task, and can be illustrated with the above example. Perhaps the most fundamental issue is the communicative goal of the text, i.e. its purpose and reader. In the above example, for instance, a doctor who wants to make a decision about medical treatment would probably be most interested in the heart rate bradycardias, while a parent who wanted to know how her child was doing would probably be more interested in the fact that the baby was being given morphine and was crying. The second issue is the size and level of detail of the generated text. For instance, a short summary which was sent to a doctor as a 160 character SMS text message might only mention the heart rate bradycardias, while a longer summary which was printed out as a multipage document might also mention the fact that the baby is on a morphine IV. The final issue is how unusual and unexpected the information is. For example, neither doctors nor parents would place a high priority on being told that the baby's temperature was normal, if they expected this to be the case. Regardless, content determination is very important to users, indeed in many cases the quality of content determination is the most important factor (from the user's perspective) in determining the overall quality of the generated text. == Techniques == There are three basic approaches to document structuring: schemas (content templates), statistical approaches, and explicit reasoning. Schemas are templates which explicitly specify the content of a generated text (as well as document structuring information). Typically, they are constructed by manually analysing a corpus of human-written texts in the target genre, and extracting a content template from these texts. Schemas work well in practice in domains where content is somewhat standardised, but work less well in domains where content is more fluid (such as the medical example above). Statistical techniques use statistical corpus analysis techniques to automatically determine the content of the generated texts. Such work is in its infancy, and has mostly been applied to contexts where the communicative goal, reader, size, and level of detail are fixed. For example, generation of newswire summaries of sporting events. Explicit reasoning approaches have probably attracted the most attention from researchers. The basic idea is to use AI reasoning techniques (such as knowledge-based rules, planning, pattern detection, case-based reasoning, etc.) to examine the information available to be communicated (including how unusual/unexpected it is), the communicative goal and reader, and the characteristics of the generated text (including target size), and decide on the optimal content for the generated text. A very wide range of techniques has been explored, but there is no consensus as to which is most effective.
Fabric Connect
Fabric Connect, in computer networking usage, is the name used by Extreme Networks to market an extended implementation of the IEEE 802.1aq and IEEE 802.1ah-2008 standards. The Fabric Connect technology was originally developed by the Enterprise Solutions R&D department within Nortel Networks. In 2009, Avaya, Inc acquired Nortel Networks Enterprise Business Solutions; this transaction included the Fabric Connect intellectual property together with all of the Ethernet Switching platforms that supported it. Subsequently, the Fabric Connect technology became part of the Extreme Networks portfolio by virtue of their 2017 purchase of the Avaya Networking business and assets. It was during the Avaya era that this technology was promoted as the lead element of the Virtual Enterprise Network Architecture (VENA). == Technologies == === Fabric Connect === Fabric Connect's provides network-wide, end-to-end, multi-layer virtualization. A network virtualization capability, based on an enhanced implementation of the IEEE 802.1aq Shortest Path Bridging (SPB) standard, Fabric Connect offers the ability to create a simplified network that can dynamically virtualize elements to efficiently provision and utilize resources, thus reducing the strain on the network and personnel. Extreme Networks base the Fabric Connect technology on the SPB standard, including support for RFC 6329, and have integrated IP Routing and IP Multicast support; this unified technology allows for the replacement of multiple conventional protocols such as Spanning Tree, RIP and/or OSPF, ECMP, and PIM. === Fabric Attach === An adjunct to the Fabric Connect technology, Fabric Attach allows network operators to extend network virtualization directly into conventional wiring closets (using existing non-Fabric Ethernet switches) and automate the provisioning of devices to their appropriate virtual network. This is particularly relevant for the mass of unattended network end-point that are now appearing, such as IP Phones, Wireless Access Points, and IP Cameras. Fabric Attach standardized protocols such as 802.1AB LLDP to exchange credentials and obtain provisioning information that allows "Client" Switches to be automatically re-configured on the fly with parameters that let Traffic Flows Map through to Fabric Connect Edge Switches (aka "Backbone Edge Bridge" in SPB definition) functioning as a Fabric Attach "Server" Switch. This method is described by an IETF "Internet Draft", pending further standardization activity. Fabric Attach is typically used to automate Wiring Closet connectivity, but has the potential to be extensible for use in the Data Center, with Virtual Machines being able to dynamically request VLAN/VSN (Virtual Service Network) assignment based upon application requirements. == Hardware products == === Virtual Services Platform 9000 Series === A range of modular chassis-based products, featuring a carrier-grade Linux operation system, and designed for high-performance deployment scenarios that need to scale to multiple terabits of switching capacity and support 10 and 40 gigabit Ethernet connections, and is designed eventually to support 100 gigabit Ethernet. === Virtual Services Platform 8000 Series === A compact form-factor platform delivering high-density 10/40 gigabit Ethernet connectivity, and targeted at mid-market through to mid-size enterprise core switch applications. === Virtual Services Platform 7000 Series === A range of high-end 10 gigabit Ethernet stackable switches that extend fabric-based networking to the data center top-of-rack. They support 40 gigabit Ethernet via the MDA Slot. === Virtual Services Platform 4000 Series === A range of high-end gigabit Ethernet stackable switches that extend Fabric-based networking to branch and metro locations. === Ethernet Routing Switch 5000 Series === A range of high-end gigabit Ethernet stackable switches that provides enterprise-class desktop features, including PoE, and offers 10 Gbit/s uplink connections. Each Switch supports up to 144 Gbit/s of virtual backplane capacity, delivering up to 1.152 Tbit/s for a system of eight, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 4000 Series === A range of gigabit Ethernet stackable switches that provide enterprise-class desktop features, including PoE/PoE+, and offer 1/10 Gbit/s uplink connections. Each switch supports up to 48 Gbit/s of virtual backplane capacity, delivering up to 384 Gbit/s for a system of 8, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 3500 Series === These entry-level gigabit Ethernet stackable switches provide enterprise-class desktop features, including PoE/PoE+, and 1 Gbit/s uplink connections.