A static web page, sometimes called a flat page or a stationary page, is a web page that is delivered to a web browser exactly as stored, in contrast to dynamic web pages which are generated by a web application. Consequently, a static web page displays the same information for all users, from all contexts, subject to modern capabilities of a web server to negotiate content-type or language of the document where such versions are available and the server is configured to do so. However, a webpage's JavaScript can introduce dynamic functionality which may make the static web page dynamic. == Overview == Static web pages are often HTML documents, stored as files in the file system and made available by the web server over HTTP (nevertheless URLs ending with ".html" are not always static). However, loose interpretations of the term could include web pages stored in a database, and could even include pages formatted using a template and served through an application server, as long as the page served is unchanging and presented essentially as stored. The content of static web pages remains stationary irrespective of the number of times it is viewed. Such web pages are suitable for the contents that rarely need to be updated, though modern web template systems are changing this. Maintaining large numbers of static pages as files can be impractical without automated tools, such as static site generators. Any personalization or interactivity has to run client-side, which is restricting. Cloud-based website builders, including Wix, Weebly, and Duda, offer no-code platforms for creating static and dynamic web pages through graphical interfaces, without requiring programming expertise. === Advantages === Provide improved security over dynamic websites (dynamic websites are at risk to web shell attacks if a vulnerability is present) Improved performance for end users compared to dynamic websites Fewer or no dependencies on systems such as databases or other application servers Cost savings from utilizing cloud storage, as opposed to a hosted environment Security configurations are easy to set up, which makes it more secure Static files can be cached by content delivery networks (CDNs) and other intermediate caches, which both reduces page load times at the user and also reduces load on the origin server. Static websites can have improved uptime, since they are still available through any available CDN exit node even when other CDN nodes or the origin webserver are temporarily offline. === Disadvantages === Dynamic functionality must be performed on the client side. After each update of a static website, some or all users may see old, stale, outdated previous versions instead of the latest version until the old version is flushed from CDNs and other caches. == Static site generators == Static site generators are applications that compile static websites - typically populating HTML templates in a predefined folder and file structure, with content supplied in a format such as Markdown or AsciiDoc. === Implementations === Jekyll (powers GitHub Pages) Middleman Hugo Next.js Astro.build Pelican Franklin
GNU social
GNU social (and its predecessor StatusNet) is a largely defunct free and open-source microblogging social networking service that implements the OStatus and ActivityPub standards for interoperability between installations. While offering similar functionality to social networks such as Twitter, GNU social seeks to provide the ability for open and federated communication between different microblogging communities, known as 'instances'. Both enterprises and individuals can install and control their own instances and user data. At its peak in popularity, GNU social had been deployed on hundreds of interconnected instances, however has since fallen into disuse as competing software like Mastodon and Pleroma have taken its position as the dominant federated microblogging services. Later on in its lifespan, the project split into two separate branches, with "v2" being a continuation of the original codebase for maintenance of existing instances, with "v3" being a complete redesign of the project meant to integrate further ActivityPub support and modernization of the user experience and its technological back-end. As of August 15, 2022, there had been no new commits to the v2 branch, with the v3 branch also no longer being actively developed not long after by November 25, 2022, with the project essentially abandoned. Despite its modern obsolescence and dated design compared to modern platforms, GNU social and StatusNet is regarded to be the origin of the Fediverse network and has had a major influence on the design of more modern decentralized social networks that succeeded it. == History == While being the main project within its lineage, GNU social originally began as a fork of StatusNet. The software was first developed for a service called identi.ca from Evan Prodromou, which offered free microblogging accounts to the public. The software quickly became one of the first popular examples of a decentralized social network, as identi.ca allowed any other server that was running the software to communicate with it, something which had not previously been attempted before in social media at such a large scale. === StatusNet === Originally, StatusNet (named Laconica at the time) was launched with a communication protocol designed specifically for the project called OpenMicroBlogging (OMB). With version 0.8.1, the name of the software was changed to StatusNet. Version 0.9.0 was released soon after in March 3, 2010, with the developers implementing a newly designed protocol dubbed OStatus, with support for OMB being dropped not long after. Compared to OpenMicroBlogging, OStatus could handle and federate more events and actions than the basic plaintext communication that OMB provided and was based on a variety of other web technologies, allowing for easier adoption of new implementations of the protocol for servers and clients compared to the fully custom architecture of OMB. With the StatusNet name change, the company developing both the software and OStatus as well as managing identi.ca rebranded from Control Yourself to StatusNet Inc. In August 2010, the company raised a new round of venture capital funds to establish a hosting service under the status.net domain from sources such as First Mark Capital, BOLDstart Ventures, iNovia Capital and Montreal Start Up, raising over $2.3 million in funding up to that point. The hosting service allowed anyone to establish their own StatusNet instance without maintaining a server, similar to WordPress.com and other blogging platforms. New registrations on identi.ca along with the ability to create new status.net instances was disabled in December 2012, in preparation for a migration to pump.io that has since been named by users of StatusNet and OStatus as "the Pumpocalypse". pump.io was a brand new software package like StatusNet, but with a new protocol designed for general purpose activity streams outside of microblogging and ease-of-use for developers building on the technology, much like the transition from OMB to OStatus. The announcement was seen as unexpected among identi.ca users, who were concerned about the possibility of their statuses being deleted with the transition. At the same time, server administrators running third-party instances and their users who were left behind on StatusNet were also worried, as it was unclear at the time whether future development of the software would be picked up by a new maintainer. The transition for identi.ca users to pump.io was completed on 12 July 2013. ==== Previous names ==== The original name of StatusNet was Laconica, a reference to the Laconic phrase; a particularly brief statement commonly attributed to the leaders of Sparta (Laconia being the Greek region containing Sparta). In microblogging, all messages are designed to be very short due to the traditional 140-character limit on message size, a limitation imported from SMS. Beginning with version 0.8.1, the name was changed to StatusNet. The developers said that the new name "simply reflects what our software does: send status updates into your social network." === GNU social === GNU social originally began as a side project of GNU FM (Libre.fm) maintainer Matt Lee, with the goal of being able to federate messages between Last.fm and other instances of GNU FM using StatusNet plugins. Around the same time, a developer named Mikael Nordfeldth forked StatusNet with the intention of maintaining it as a personal project, dubbing it "Free Social". However, following identi.ca's transition to pump.io and its developers' sudden abandonment of StatusNet, the projects received more attention from server administrators and other users looking for an actively updated alternative. Shortly after LibrePlanet 2012, a plan was formed to merge all three projects into a single service. On June 8, 2013, it was announced that along with Free Social, StatusNet would be merged into the GNU social project and stewarded by the Free Software Foundation, with the project since becoming the dominant variant of StatusNet. During GNU social's lifespan, a popular theme for the user interface named Quitter was used, which was similar to an earlier Twitter interface. Many instances were made specifically using the name Quitter such as Quitter.se, an instance created by the developer of the theme. Before the establishment of Mastodon's popularity and dominance within the network, Quitter was noted as a frequent location for users of Twitter to migrate to when users disagreed with moderation policies or feature updates, such as when an algorithmic feed was added to Twitter. A fork of GNU social was made called postActiv, which planned to rewrite the backend and user interface of GNU social, as well as to add compatibility for Diaspora's protocol. == Features == A basic GNU social instance takes the form of a microblogging service with a reverse chronological timeline that features status updates and small messages from followed accounts, similar to other services such as Twitter or Weibo. While users could see their own customized timeline, they could access another timeline that showcased every message that the instance knows of, including from other instances that were connected to each other if someone on the instance followed an account from it. Users could also create and join groups, which allows for discussion and collaboration on specific topics. Administrators can also customize their server via the plugin system, which allows developers to create new features or modify existing plugins to suit the needs of the instance via PHP. A notable plugin built for GNU social was Quitter, a revamp of the user interface that resembles an earlier version of Twitter's user interface.
Parity benchmark
Parity problems are widely used as benchmark problems in genetic programming but inherited from the artificial neural network community. Parity is calculated by summing all the binary inputs and reporting if the sum is odd or even. This is considered difficult because: a very simple artificial neural network cannot solve it, and all inputs need to be considered and a change to any one of them changes the answer.
Neural Networks (journal)
Neural Networks is a monthly peer-reviewed scientific journal and an official journal of the International Neural Network Society, European Neural Network Society, and Japanese Neural Network Society. == History == The journal was established in 1988 and is published by Elsevier. It covers all aspects of research on artificial neural networks. The founding editor-in-chief was Stephen Grossberg (Boston University). The current editors-in-chief are DeLiang Wang (Ohio State University) and Taro Toyoizumi (RIKEN Center for Brain Science). == Abstracting and indexing == The journal is abstracted and indexed in Scopus and the Science Citation Index Expanded. According to the Journal Citation Reports, the journal has a 2022 impact factor of 7.8.
Population model (evolutionary algorithm)
The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population is the set of all proposed solutions of an EA considered in one iteration, which are also called individuals according to the biological role model. The individuals of a population can generate further individuals as offspring with the help of the genetic operators of the procedure. The simplest and widely used population model in EAs is the global or panmictic model, which corresponds to an unstructured population. It allows each individual to choose any other individual of the population as a partner for the production of offspring by crossover, whereby the details of the selection are irrelevant as long as the fitness of the individuals plays a significant role. Due to global mate selection, the genetic information of even slightly better individuals can prevail in a population after a few generations (iteration of an EA), provided that no better other offspring have emerged in this phase. If the solution found in this way is not the optimum sought, that is called premature convergence. This effect can be observed more often in panmictic populations. In nature global mating pools are rarely found. What prevails is a certain and limited isolation due to spatial distance. The resulting local neighbourhoods initially evolve independently and mutants have a higher chance of persisting over several generations. As a result, genotypic diversity in the gene pool is preserved longer than in a panmictic population. It is therefore obvious to divide the previously global population by substructures. Two basic models were introduced for this purpose, the island models, which are based on a division of the population into fixed subpopulations that exchange individuals from time to time, and the neighbourhood models, which assign individuals to overlapping neighbourhoods, also known as cellular genetic or evolutionary algorithms (cGA or cEA). The associated division of the population also suggests a corresponding parallelization of the procedure. For this reason, the topic of population models is also frequently discussed in the literature in connection with the parallelization of EAs. == Island models == In the island model, also called the migration model or coarse grained model, evolution takes place in strictly divided subpopulations. These can be organised panmictically, but do not have to be. From time to time an exchange of individuals takes place, which is called migration. The time between an exchange is called an epoch and its end can be triggered by various criteria: E.g. after a given time or given number of completed generations, or after the occurrence of stagnation. Stagnation can be detected, for example, by the fact that no fitness improvement has occurred in the island for a given number of generations. Island models introduce a variety of new strategy parameters: Number of subpopulations Size of the subpopulations Neighbourhood relations between islands: they determine which islands are considered neighbouring and can thus exchange individuals, see picture of a simple unidirectional ring (black arrows) and its extension by additional bidirectional neighbourhood relations (additional green arrows) Criteria for the termination of an epoch, synchronous or asynchronous migration Migration rate: number or proportion of individuals involved in migration. Migrant selection: There are many alternatives for this. E.g. the best individuals can replace the worst or randomly selected ones. Depending on the migration rate, this can affect one or more individuals at a time. With these parameters, the selection pressure can be influenced to a considerable extent. For example, it increases with the interconnectedness of the islands and decreases with the number of subpopulations or the epoch length. == Neighbourhood models or cellular evolutionary algorithms == The neighbourhood model, also called diffusion model or fine grained model, defines a topological neighbouhood relation between the individuals of a population that is independent of their phenotypic properties. The fundamental idea of this model is to provide the EA population with a special structure defined as a connected graph, in which each vertex is an individual that communicates with its nearest neighbours. Particularly, individuals are conceptually set in a toroidal mesh, and are only allowed to recombine with close individuals. This leads to a kind of locality known as isolation by distance. The set of potential mates of an individual is called its neighbourhood or deme. The adjacent figure illustrates that by showing two slightly overlapping neighbourhoods of two individuals marked yellow, through which genetic information can spread between the two demes. It is known that in this kind of algorithm, similar individuals tend to cluster and create niches that are independent of the deme boundaries and, in particular, can be larger than a deme. There is no clear borderline between adjacent groups, and close niches could be easily colonized by competitive ones and maybe merge solution contents during this process. Simultaneously, farther niches can be affected more slowly. EAs with this type of population are also well known as cellular EAs (cEA) or cellular genetic algorithms (cGA). A commonly used structure for arranging the individuals of a population is a 2D toroidal grid, although the number of dimensions can be easily extended (to 3D) or reduced (to 1D, e.g. a ring, see the figure on the right). The neighbourhood of a particular individual in the grid is defined in terms of the Manhattan distance from it to others in the population. In the basic algorithm, all the neighbourhoods have the same size and identical shapes. The two most commonly used neighbourhoods for two-dimensional cEAs are L5 and C9, see the figure on the left. Here, L stands for Linear while C stands for Compact. Each deme represents a panmictic subpopulation within which mate selection and the acceptance of offspring takes place by replacing the parent. The rules for the acceptance of offspring are local in nature and based on the neighbourhood: for example, it can be specified that the best offspring must be better than the parent being replaced or, less strictly, only better than the worst individual in the deme. The first rule is elitist and creates a higher selective pressure than the second non-elitist rule. In elitist EAs, the best individual of a population always survives. In this respect, they deviate from the biological model. The overlap of the neighbourhoods causes a mostly slow spread of genetic information across the neighbourhood boundaries, hence the name diffusion model. A better offspring now needs more generations than in panmixy to spread in the population. This promotes the emergence of local niches and their local evolution, thus preserving genotypic diversity over a longer period of time. The result is a better and dynamic balance between breadth and depth search adapted to the search space during a run. Depth search takes place in the niches and breadth search in the niche boundaries and through the evolution of the different niches of the whole population. For the same neighbourhood size, the spread of genetic information is larger for elongated figures like L9 than for a block like C9, and again significantly larger than for a ring. This means that ring neighbourhoods are well suited for achieving high quality results, even if this requires comparatively long run times. On the other hand, if one is primarily interested in fast and good, but possibly suboptimal results, 2D topologies are more suitable. == Comparison == When applying both population models to genetic algorithms, evolutionary strategy and other EAs, the splitting of a total population into subpopulations usually reduces the risk of premature convergence and leads to better results overall more reliably and faster than would be expected with panmictic EAs. Island models have the disadvantage compared to neighbourhood models that they introduce a large number of new strategy parameters. Despite the existing studies on this topic in the literature, a certain risk of unfavourable settings remains for the user. With neighbourhood models, on the other hand, only the size of the neighbourhood has to be specified and, in the case of the two-dimensional model, the choice of the neighbourhood figure is added. == Parallelism == Since both population models imply population partitioning, they are well suited as a basis for parallelizing an EA. This applies even more to cellular EAs, since they rely only on locally available information about the members of their respective demes. Thus, in the extreme case, an independent execution thread can be assigned to each individual, so that the entire cEA can run on a parallel hardware platform. The island model also supports p
VACUUM
VACUUM is a set of normative guidance principles for achieving training and test dataset quality for structured datasets in data science and machine learning. The garbage-in, garbage out principle motivates a solution to the problem of data quality but does not offer a specific solution. Unlike the majority of the ad-hoc data quality assessment metrics often used by practitioners VACUUM specifies qualitative principles for data quality management and serves as a basis for defining more detailed quantitative metrics of data quality. VACUUM is an acronym that stands for: valid accurate consistent uniform unified model
Ni1000
The Ni1000 is an artificial neural network chip developed by Nestor Corporation and Intel, developed in the 1990s. It is Intel's second-generation neural network chip, but the first all-digital chip. The chip is aimed at image analysis applications– containing more than 3 million transistors – and can analyze 40,000 patterns per second. Prototypes running Nestor's OCR software in 1994 were capable of recognizing around 100 handwritten characters per second. The development was funded with money from DARPA and Office of Naval Research.