Microsoft Sway is a presentation program and is part of the Microsoft 365 family of products. Sway was offered for general release by Microsoft in August 2015. It allows users who have a Microsoft account to combine text and media to create a presentable website. Users can pull content locally from the device in use, or from internet sources such as Bing, Facebook, OneDrive, and YouTube. Sway is distinguished from Microsoft FrontPage and Microsoft Expression Web – unrelated web design programs previously developed by Microsoft – in that Sway includes a method for hosting sites. Sway sites are stored on Microsoft's servers and are tied to the user's Microsoft account. They can be viewed and edited from any web browser through Office on the web. There is no offline editing or viewing function, but sites can be accessed using the app for Windows, and formerly iOS. == History == Sway was developed internally by Microsoft. In late 2014, the company announced an invite-only preview version of Sway and announced that Sway would not require an Office 365 subscription. An iOS app was released as a preview on 31 October 2014, but was discontinued on 17 December 2018 due to low usage. As of July 17, 2021, the Sway iOS app's discontinuance in 2018 was the last piece of news posted in the Sway tech blog. The Sway feature blog has not received an update since April 2017. The Microsoft Office Roadmap did not include any items related to Sway ever since. The iOS application is no longer under active development, and is not available for download. Since 2023, Microsoft has been consolidating the domains of its Microsoft 365 apps and services under cloud.microsoft. By 2025, the vast majority of services, including Sway, have already migrated to the cloud.microsoft domain. == Features == Users are able to add content from various sources into their Sway presentations. Some of the integrated services are owned by Microsoft, including OneNote, Bing, and other Sway sites. The program also provides native integration with other services, including YouTube, Facebook, Twitter, Mixcloud, and Infogram.
Semantic analysis (machine learning)
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans. Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning. For the restricted domain of spatial analysis, a computer-based language understanding system was demonstrated. Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA). Latent Dirichlet allocation, which involves attributing document terms to topics. n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms. == Stochastic semantic analysis ==
TheFWA
FWA (Favourite Website Awards) is an international award platform that honors and rewards web designers, developers and agencies around the world for excellence within the field of web design and development. The FWA was founded in May 2000 by Rob Ford. In November 2012, The FWA was the most visited website award program in the history of the internet, with over 170 millions site visits. == Jury == The FWA jury is composed of more than 500 web professionals (200 women + 200 men) from 35 countries. == Awards granted == FWA of the Day (FOTD) : Every day, the FWA jury selects the best project, FWA of the Month (FOTM): Every month, the FWA jury selects the best project, People's Choice Award (PCA) : Every year, a public vote selects the people's favourite project, FWA of the Year (FOTY) : Every year, the FWA jury selects the best project. == Hall Of Fame == The FWA Hall of Fame was established in May 2007 (to celebrate the seventh anniversary of the FWA), as a recognition of web's greatest individuals and companies.
Electronics (journal)
Electronics is a peer-reviewed, scientific journal that covers the study of electronics, including the design, development, and application of electronic devices, systems, and circuits. The journal is published by MDPI and was established in 2012. The editor-in-chief is Flavio Canavero 'Politecnico di Torino). The journal covers a wide range of topics related to electronics, including: electronic devices, electronic materials, electronic circuits, electronic systems, communication electronics, power electronics, and biomedical electronics. The journal also includes articles on the application of electronics in various fields, such as consumer electronics, industrial electronics, automotive electronics, and military electronics. The journal publishes original research articles, review articles, and short communications. == Abstracting and indexing == EBSCO databases ProQuest databases Scopus According to the Journal Citation Reports, the journal has a 2021 impact factor of 2.690.
Fifth Estate
The Fifth Estate is a socio-cultural reference to groupings of outlier viewpoints in contemporary society, and is most associated with bloggers, journalists publishing in non-mainstream media outlets, and online social networks. The "Fifth" Estate extends the sequence of the three classical estates (clergy (first), nobility (second), commoners (third)) and the preceding Fourth Estate, essentially the common press. The use of "fifth estate" dates to the 1960s counterculture, and in particular the influential Fifth Estate, an underground newspaper first published in Detroit in 1965. Web-based technologies have enhanced the scope and power of the Fifth Estate far beyond the modest and boutique conditions of its beginnings. Nimmo and Combs asserted in 1992 that political pundits constitute a Fifth Estate. Media researcher Stephen D. Cooper argued in 2006 that bloggers are the Fifth Estate. In 2009, William Dutton argued that the Fifth Estate is not just the blogging community, nor an extension of the media, but "networked individuals" enabled by the Internet, e.g. social media, in ways that can hold the other estates accountable.
Automated decision-making
Automated decision-making (ADM) is the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM may involve large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences. == Overview == There are different definitions of ADM based on the level of automation involved. Some definitions suggests ADM involves decisions made through purely technological means without human input, such as the EU's General Data Protection Regulation (Article 22). However, ADM technologies and applications can take many forms ranging from decision-support systems that make recommendations for human decision-makers to act on, sometimes known as augmented intelligence or 'shared decision-making', to fully automated decision-making processes that make decisions on behalf of individuals or organizations without human involvement. Models used in automated decision-making systems can be as simple as checklists and decision trees through to artificial intelligence and deep neural networks (DNN). Since the 1950s computers have gone from being able to do basic processing to having the capacity to undertake complex, ambiguous and highly skilled tasks such as image and speech recognition, gameplay, scientific and medical analysis and inferencing across multiple data sources. ADM is now being increasingly deployed across all sectors of society and many diverse domains from entertainment to transport. An ADM system (ADMS) may involve multiple decision points, data sets, and technologies (ADMT) and may sit within a larger administrative or technical system such as a criminal justice system or business process. == Data == Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and financial data for public administration, medical records in health, criminal records in law. This can sometimes involve vast amounts of data and computing power. === Data quality === The quality of the available data and its ability to be used in ADM systems is fundamental to the outcomes. It is often highly problematic for many reasons. Datasets are often highly variable; corporations or governments may control large-scale data, restricted for privacy or security reasons, incomplete, biased, limited in terms of time or coverage, measuring and describing terms in different ways, and many other issues. For machines to learn from data, large corpora are often required, which can be challenging to obtain or compute; however, where available, they have provided significant breakthroughs, for example, in diagnosing chest X-rays. == ADM technologies == Automated decision-making technologies (ADMT) are software-coded digital tools that automate the translation of input data to output data, contributing to the function of automated decision-making systems. There are a wide range of technologies in use across ADM applications and systems. ADMTs involving basic computational operations Search (includes 1-2-1, 1-2-many, data matching/merge) Matching (two different things) Mathematical Calculation (formula) ADMTs for assessment and grouping: User profiling Recommender systems Clustering Classification Feature learning Predictive analytics (includes forecasting) ADMTs relating to space and flows: Social network analysis (includes link prediction) Mapping Routing ADMTs for processing of complex data formats Image processing Audio processing Natural Language Processing (NLP) Other ADMT Business rules management systems Time series analysis Anomaly detection Modelling/Simulation === Machine learning === Machine learning (ML) involves training computer programs through exposure to large data sets and examples to learn from experience and solve problems. Machine learning can be used to generate and analyse data as well as make algorithmic calculations and has been applied to image and speech recognition, translations, text, data and simulations. While machine learning has been around for some time, it is becoming increasingly powerful due to recent breakthroughs in training deep neural networks (DNNs), and dramatic increases in data storage capacity and computational power with GPU coprocessors and cloud computing. Machine learning systems based on foundation models run on deep neural networks and use pattern matching to train a single huge system on large amounts of general data such as text and images. Early models tended to start from scratch for each new problem however since the early 2020s many are able to be adapted to new problems. Examples of these technologies include Open AI's DALL-E (an image creation program) and their various GPT language models, and Google's PaLM language model program. == Applications == ADM is being used to replace or augment human decision-making by both public and private-sector organisations for a range of reasons including to help increase consistency, improve efficiency, reduce costs and enable new solutions to complex problems. === Debate === Research and development are underway into uses of technology to assess argument quality, assess argumentative essays and judge debates. Potential applications of these argument technologies span education and society. Scenarios to consider, in these regards, include those involving the assessment and evaluation of conversational, mathematical, scientific, interpretive, legal, and political argumentation and debate. === Law === In legal systems around the world, algorithmic tools such as risk assessment instruments (RAI), are being used to supplement or replace the human judgment of judges, civil servants and police officers in many contexts. In the United States RAI are being used to generate scores to predict the risk of recidivism in pre-trial detention and sentencing decisions, evaluate parole for prisoners and to predict "hot spots" for future crime. These scores may result in automatic effects or may be used to inform decisions made by officials within the justice system. In Canada ADM has been used since 2014 to automate certain activities conducted by immigration officials and to support the evaluation of some immigrant and visitor applications. === Economics === Automated decision-making systems are used in certain computer programs to create buy and sell orders related to specific financial transactions and automatically submit the orders in the international markets. Computer programs can automatically generate orders based on predefined set of rules using trading strategies which are based on technical analyses, advanced statistical and mathematical computations, or inputs from other electronic sources. === Business === ==== Continuous auditing ==== Continuous auditing uses advanced analytical tools to automate auditing processes. It can be utilized in the private sector by business enterprises and in the public sector by governmental organizations and municipalities. As artificial intelligence and machine learning continue to advance, accountants and auditors may make use of increasingly sophisticated algorithms which make decisions such as those involving determining what is anomalous, whether to notify personnel, and how to prioritize those tasks assigned to personnel. === Media and entertainment === Digital media, entertainment platforms, and information services increasingly provide content to audiences via automated recommender systems based on demographic information, previous selections, collaborative filtering or content-based filtering. This includes music and video platforms, publishing, health information, product databases and search engines. Many recommender systems also provide some agency to users in accepting recommendations and incorporate data-driven algorithmic feedback loops based on the actions of the system user. Large-scale machine learning language models and image creation programs being developed by companies such as OpenAI and Google in the 2020s have restricted access however they are likely to have widespread application in fields such as advertising, copywriting, stock imagery and gra
Europa-Lehrmittel
The Verlag Europa-Lehrmittel Nourney, Vollmer GmbH & Co. KG (English: European educational media), based in Haan (district Gruiten) near Düsseldorf, Germany, develops educational media for vocational training and further education as used in vocational schools, universities, companies and further education institutions for industrial-technical, business, social, health as well as gastronomic professions. The contents are delivered in print as well as in digital formats, including offered as apps, simulations, software and through an exam preparation portal. The publisher has its own digital learning platform, the Europathek. General partner is Nourney, Vollmer & Co. GmbH, a school and specialist book publisher. == History == The publishing house was founded in Wuppertal in 1948, when the first books were published. In Eislingen/Fils a construction office was set up to create the technical drawings for the textbooks - today the drawing office of the publisher in Ostfildern. A little over 40 years later, in 1989, a new company building was built in Haan-Gruiten. In 1997, Europa-Lehrmittel acquired the Fachbuchverlag Pfanneberg with its program aimed at gastronomic professions. Further program expansions in vocational training were realized by adding titles of the publishing houses Lau, Gerber, Pluspunkt, parts of the Verlag Handwerk und Technik program and that of the Gildebuchverlag. In 2013, the scientific and technical university titles of Verlag Harri Deutsch were taken over, including the bestselling "Handbook of Mathematics" by Ilya Nikolaevich Bronstein and Konstantin Adolfovic Semendyayev. In addition, Europa-Lehrmittel's first exam apps appeared. In 2017, the publisher introduced its own digital learning platform named Europathek. The Prüfungsdoc (English: Examination Doc) platform, which enables targeted online preparation for exams, went online the same year. In 2018, the titles of the Düsseldorf publishing house SOL for self-organized learning were incorporated into Europa-Lehrmittel's program. == Program == The program meanwhile includes well over 2000 print and digital publications, including specialist titles on metal technology, automotive and electrical engineering (like "Tabellenbuch Metall" (English: Metal Book of Tables)), on business administration, as well as works for the gastronomic profession (e.g. "Der junge Koch" (English: The Young Cook)). Many titles also appear as licensed editions in over 20 different languages on all continents. All contents are presented in an annual catalog, on the website, in schools, in companies, and at trade fairs and congresses. The publisher is a member of the Deutscher Hauswirtschaftsrat. == Digital educational media == Europathek: In addition to digital books, Europathek also provides media packages, additional materials and e-learning contents. The online contents of the 'media shelf' can be used per web browser. There are also software versions and apps for offline use, with the help of which the obtained titles can be viewed after downloading. Prüfungsdoc (English: Examination Doc): Prüfungsdoc offers web-based learning, practice and repetition to prepare for the intermediate and final examinations. This includes simulations of the exams with direct evaluation of the results.