Comparison of user features of operating systems refers to a comparison of the general user features of major operating systems in a narrative format. It does not encompass a full exhaustive comparison or description of all technical details of all operating systems. It is a comparison of basic roles and the most prominent features. It also includes the most important features of the operating system's origins, historical development, and role. == Overview == An operating system (OS) is system software that manages computer hardware, software resources, and provides common services for computer programs. Time-sharing operating systems schedule tasks for efficient use of the system and may also include accounting software for cost allocation of processor time, mass storage, printing, and other resources. For hardware functions such as input and output and memory allocation, the operating system acts as an intermediary between programs and the computer hardware, although the application code is usually executed directly by the hardware and frequently makes system calls to an OS function or is interrupted by it. Operating systems are found on many devices that contain a computer – from cellular phones and video game consoles to web servers and supercomputers. As of June 2024, the dominant general-purpose desktop operating system is Microsoft Windows with a market share of around 72.91%. macOS by Apple Inc. is in second place (14.93%), and the varieties of Linux are collectively in third place (4.04%). In the mobile sector, including both smartphones and tablets, Android is dominant with a market share of 71%, followed by Apple's iOS with 28%; for smartphones alone, Android has 72% and iOS has 28%. Linux distributions are dominant in the server and supercomputing sectors. Other specialized classes of operating systems (special-purpose operating systems)), such as embedded and real-time systems, exist for many applications. Security-focused operating systems also exist. Some operating systems have low system requirements (i.e. light-weight Linux distribution). Others may have higher system requirements. Some operating systems require installation or may come pre-installed with purchased computers (OEM-installation), whereas others may run directly from media (i.e. live cd) or flash memory (i.e. USB stick). == MS-DOS == === Overview === MS-DOS (acronym for Microsoft Disk Operating System) is an operating system for x86-based personal computers mostly developed by Microsoft. Collectively, MS-DOS, its rebranding as IBM PC DOS, and some operating systems attempting to be compatible with MS-DOS, are sometimes referred to as "DOS" (which is also the generic acronym for disk operating system). MS-DOS was the main operating system for IBM PC compatible personal computers during the 1980s, from which point it was gradually superseded by operating systems offering a graphical user interface (GUI), in various generations of the graphical Microsoft Windows operating system. IBM licensed and re-released it in 1981 as PC DOS 1.0 for use in its PCs. Although MS-DOS and PC DOS were initially developed in parallel by Microsoft and IBM, the two products diverged after twelve years, in 1993, with recognizable differences in compatibility, syntax, and capabilities. During its lifetime, several competing products were released for the x86 platform, and MS-DOS went through eight versions, until development ceased in 2000. Initially, MS-DOS was targeted at Intel 8086 processors running on computer hardware using floppy disks to store and access not only the operating system, but application software and user data as well. Progressive version releases delivered support for other mass storage media in ever greater sizes and formats, along with added feature support for newer processors and rapidly evolving computer architectures. Ultimately, it was the key product in Microsoft's development from a programming language company to a diverse software development firm, providing the company with essential revenue and marketing resources. It was also the underlying basic operating system on which early versions of Windows ran as a GUI. == Microsoft Windows == === Overview === Microsoft Windows, commonly referred to as Windows, is a group of several proprietary graphical operating system families, all of which are developed and marketed by Microsoft. Each family caters to a certain sector of the computing industry. Active Microsoft Windows families include Windows NT and Windows IoT; these may encompass subfamilies, (e.g. Windows Server or Windows Embedded Compact) (Windows CE). Defunct Microsoft Windows families include Windows 9x, Windows Mobile, and Windows Phone. Microsoft announced an operating environment named Windows on 10 November 1983, as a graphical operating system shell for MS-DOS in response to the growing interest in graphical user interfaces (GUIs); Windows 1.0 first shipped on 20 November 1985. Microsoft Windows came to dominate the world's personal computer (PC) market with over 90% market share, overtaking Mac OS, which had been introduced in 1984, while Microsoft has in 2020 lost its dominance of the consumer operating system market, with Windows down to 30%, lower than Apple's 31% mobile-only share (65% for desktop operating systems only, i.e. "PCs" vs. Apple's 28% desktop share) in its home market, the US, and 32% globally (77% for desktops), where Google's Android leads. Apple came to see Windows as an unfair encroachment on their innovation in GUI development as implemented on products such as the Lisa and Macintosh (eventually settled in court in Microsoft's favor in 1993). As of January 2023, on PCs, Windows is still the most popular operating system in all countries. However, in 2014, Microsoft admitted losing the majority of the overall operating system market to Android, because of the massive growth in sales of Android smartphones. In 2014, the number of Windows devices sold was less than 25% that of Android devices sold. This comparison, however, may not be fully relevant, as the two operating systems traditionally target different platforms. Still, numbers for server use of Windows (that are comparable to competitors) show one third market share, similar to that for end user use. As of October 2020, the most recent version of Windows for PCs, tablets and embedded devices is Windows 10, version 20H2. The most recent version for server computers is Windows Server, version 20H2. A specialized version of Windows also runs on the Xbox One video game console. === Windows 95 === Windows 95 introduced a redesigned shell based around a desktop metaphor; File shortcuts (also known as shell links) were introduced and the desktop was re-purposed to hold shortcuts to applications, files and folders, reminiscent of Mac OS. In Windows 3.1 the desktop was used to display icons of running applications. In Windows 95, the currently running applications were displayed as buttons on a taskbar across the bottom of the screen. The taskbar also contained a notification area used to display icons for background applications, a volume control and the current time. The Start menu, invoked by clicking the "Start" button on the taskbar or by pressing the Windows key, was introduced as an additional means of launching applications or opening documents. While maintaining the program groups used by its predecessor Program Manager, it also displayed applications within cascading sub-menus. The previous File Manager program was replaced by Windows Explorer and the Explorer-based Control Panel and several other special folders were added such as My Computer, Dial Up Networking, Recycle Bin, Network Neighborhood, My Documents, Recent documents, Fonts, Printers, and My Briefcase among others. AutoRun was introduced for CD drives. The user interface looked dramatically different from prior versions of Windows, but its design language did not have a special name like Metro, Aqua or Material Design. Internally it was called "the new shell" and later simply "the shell". The subproject within Microsoft to develop the new shell was internally known as "Stimpy". In 1994, Microsoft designers Mark Malamud and Erik Gavriluk approached Brian Eno to compose music for the Windows 95 project. The result was the six-second start-up music-sound of the Windows 95 operating system, The Microsoft Sound and it was first released as a startup sound in May 1995 on Windows 95 May Test Release build 468. When released for Windows 95 and Windows NT 4.0, Internet Explorer 4 came with an optional Windows Desktop Update, which modified the shell to provide several additional updates to Windows Explorer, including a Quick Launch toolbar, and new features integrated with Internet Explorer, such as Active Desktop (which allowed Internet content to be displayed directly on the desktop). Some of the user interface elements introduced in Windows 95, such as the desktop, taskbar, Start menu and Windows
AZFinText
Arizona Financial Text System (AZFinText) is a textual-based quantitative financial prediction system written by Robert P. Schumaker of University of Texas at Tyler and Hsinchun Chen of the University of Arizona. == System == This system differs from other systems in that it uses financial text as one of its key means of predicting stock price movement. This reduces the information lag-time problem evident in many similar systems where new information must be transcribed (e.g., such as losing a costly court battle or having a product recall), before the quant can react appropriately. AZFinText overcomes these limitations by utilizing the terms used in financial news articles to predict future stock prices twenty minutes after the news article has been released. It is believed that certain article terms can move stocks more than others. Terms such as factory exploded or workers strike will have a depressing effect on stock prices whereas terms such as earnings rose will tend to increase stock prices. The AZFinText system analyzes financial news to identify the patterns in how investors react to such specific information. It uses methods like sentiment analysis and term weighting to examine the text of news articles. This system is designed to find price differences that occur when the market responds to news stories. This approach provides an alternative and easier method for predicting stock market movements. == Overview of research == The foundation of AZFinText can be found in the ACM TOIS article. Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article was released was the most effective model and using proper nouns was the most effective textual representation technique. Combining the two, AZFinText netted a 2.84% trading return over the five-week study period. AZFinText was then extended to study what combination of peer organizations help to best train the system. Using the premise that IBM has more in common with Microsoft than GM, AZFinText studied the effect of varying peer-based training sets. To do this, AZFinText trained on the various levels of GICS and evaluated the results. It was found that sector-based training was most effective, netting an 8.50% trading return, outperforming Jim Cramer, Jim Jubak and DayTraders.com during the study period. AZFinText was also compared against the top 10 quantitative systems and outperformed 6 of them. A third study investigated the role of portfolio building in a textual financial prediction system. From this study, Momentum and Contrarian stock portfolios were created and tested. Using the premise that past winning stocks will continue to win and past losing stocks will continue to lose, AZFinText netted a 20.79% return during the study period. It was also noted that traders were generally overreacting to news events, creating the opportunity of abnormal returns. A fourth study looked into using author sentiment as an added predictive variable. Using the premise that an author can unwittingly influence market trades simply by the terms they use, AZFinText was tested using tone and polarity features. It was found that Contrarian activity was occurring within the market, where articles of a positive tone would decrease in price and articles of a negative tone would increase in price. A further study investigated what article verbs have the most influence on stock price movement. From this work, it was found that planted, announcing, front, smaller and crude had the highest positive impact on stock price. == Notable publicity == AZFinText has been the topic of discussion by numerous media outlets. Some of the more notable ones include The Wall Street Journal, MIT's Technology Review, Dow Jones Newswire, WBIR in Knoxville, TN, Slashdot and other media outlets.
Lesk algorithm
The Lesk algorithm is a classical algorithm for word sense disambiguation introduced by Michael E. Lesk in 1986. It operates on the premise that words within a given context are likely to share a common meaning. This algorithm compares the dictionary definitions of an ambiguous word with the words in its surrounding context to determine the most appropriate sense. Variations, such as the Simplified Lesk algorithm, have demonstrated improved precision and efficiency. However, the Lesk algorithm has faced criticism for its sensitivity to definition wording and its reliance on brief glosses. Researchers have sought to enhance its accuracy by incorporating additional resources like thesauruses and syntactic models. == Overview == The Lesk algorithm is based on the assumption that words in a given "neighborhood" (section of text) will tend to share a common topic. A simplified version of the Lesk algorithm is to compare the dictionary definition of an ambiguous word with the terms contained in its neighborhood. Versions have been adapted to use WordNet. An implementation might look like this: for every sense of the word being disambiguated one should count the number of words that are in both the neighborhood of that word and in the dictionary definition of that sense the sense that is to be chosen is the sense that has the largest number of this count. A frequently used example illustrating this algorithm is for the context "pine cone". The following dictionary definitions are used: PINE 1. kinds of evergreen tree with needle-shaped leaves 2. waste away through sorrow or illness CONE 1. solid body which narrows to a point 2. something of this shape whether solid or hollow 3. fruit of certain evergreen trees As can be seen, the best intersection is Pine #1 ⋂ Cone #3 = 2. == Simplified Lesk algorithm == In Simplified Lesk algorithm, the correct meaning of each word in a given context is determined individually by locating the sense that overlaps the most between its dictionary definition and the given context. Rather than simultaneously determining the meanings of all words in a given context, this approach tackles each word individually, independent of the meaning of the other words occurring in the same context. "A comparative evaluation performed by Vasilescu et al. (2004) has shown that the simplified Lesk algorithm can significantly outperform the original definition of the algorithm, both in terms of precision and efficiency. By evaluating the disambiguation algorithms on the Senseval-2 English all words data, they measure a 58% precision using the simplified Lesk algorithm compared to the only 42% under the original algorithm. Note: Vasilescu et al. implementation considers a back-off strategy for words not covered by the algorithm, consisting of the most frequent sense defined in WordNet. This means that words for which all their possible meanings lead to zero overlap with current context or with other word definitions are by default assigned sense number one in WordNet." Simplified LESK Algorithm with smart default word sense (Vasilescu et al., 2004) The COMPUTEOVERLAP function returns the number of words in common between two sets, ignoring function words or other words on a stop list. The original Lesk algorithm defines the context in a more complex way. == Criticisms == Unfortunately, Lesk’s approach is very sensitive to the exact wording of definitions, so the absence of a certain word can radically change the results. Further, the algorithm determines overlaps only among the glosses of the senses being considered. This is a significant limitation in that dictionary glosses tend to be fairly short and do not provide sufficient vocabulary to relate fine-grained sense distinctions. A lot of work has appeared offering different modifications of this algorithm. These works use other resources for analysis (thesauruses, synonyms dictionaries or morphological and syntactic models): for instance, it may use such information as synonyms, different derivatives, or words from definitions of words from definitions. == Lesk variants == Original Lesk (Lesk, 1986) Adapted/Extended Lesk (Banerjee and Pederson, 2002/2003): In the adaptive lesk algorithm, a word vector is created corresponds to every content word in the wordnet gloss. Concatenating glosses of related concepts in WordNet can be used to augment this vector. The vector contains the co-occurrence counts of words co-occurring with w in a large corpus. Adding all the word vectors for all the content words in its gloss creates the Gloss vector g for a concept. Relatedness is determined by comparing the gloss vector using the Cosine similarity measure. There are a lot of studies concerning Lesk and its extensions: Wilks and Stevenson, 1998, 1999; Mahesh et al., 1997; Cowie et al., 1992; Yarowsky, 1992; Pook and Catlett, 1988; Kilgarriff and Rosensweig, 2000; Kwong, 2001; Nastase and Szpakowicz, 2001; Gelbukh and Sidorov, 2004.
Ultra Hal
Ultra Hal is a chatbot intended to function as a virtual assistant. It was developed by Zabaware, Inc. Ultra Hal uses a natural language interface with animated characters using speech synthesis. Users can communicate with the chatterbot via typing or via a speech recognition engine. It utilizes the WordNet lexical dictionary. Its name is an allusion to HAL 9000, the artificial intelligence from the movie 2001: A Space Odyssey. Ultra Hal won the 2007 Loebner Prize for "most human" chatterbot.
AUTINDEX
AUTINDEX is a commercial text mining software package based on sophisticated linguistics. AUTINDEX, resulting from research in information extraction, is a product of the Institute of Applied Information Sciences (IAI) which is a non-profit institute that has been researching and developing language technology since its foundation in 1985. IAI is an institute affiliated to Saarland University in Saarbrücken, Germany. AUTINDEX is the result of a number of research projects funded by the EU (Project BINDEX), by Deutsche Forschungsgemeinschaft and the German Ministry for Economy. Amongst the latter there are the projects LinSearch, and WISSMER, see also the reference to IAI-Website. The basic functionality of AUTINDEX is the extraction of key words from a document to represent the semantics of the document. Ideally the system is integrated with a thesaurus that defines the standardised terms to be used for key word assignment. AUTINDEX is used in library applications (e.g. integrated in dandelon.com) as well as in high quality (expert) information systems, and in document management and content management environments. Together with AUTINDEX a number of additional software comes along such as an integration with Apache Solr / Lucene to provide a complete information retrieval environment, a classification and categorisation system on the basis of a machine learning software that assigns domains to the document, and a system for searching with semantically similar terms that are collected in so called tag clouds.
WIPO GREEN
WIPO GREEN is a World Intellectual Property Organization program established in 2013 that supports global efforts to address climate change and food security through sharing of sustainable technology innovations. == WIPO GREEN database == The WIPO GREEN database is the foundation of the platform. The database is a free, solutions-oriented, global innovation catalog that connects needs for solving environmental or climate change problems with sustainable solutions from prototypes to marketable products available for sale, license, collaborations, knowledge transfer, joint ventures, or collaborations. Green technology innovators can promote their products, businesses, organizations, and governments looking for green technologies can explain their needs and seek collaboration with providers. As of July 2022, WIPO GREEN has over 120,000 technologies, needs and experts, more than 2000 users in 110 countries, and has recorded over 1000 connections made between technology providers and seekers. The database utilizes AI-assisted auto-matching, user uploads tracing and alerts, full-text search for solutions based on long need descriptions, and the Patent2Solution search function for finding commercial applications of a patent, which are some of the unique features of the database. Free registration is required for detailed record view and uploading. All technologies uploaded to the WIPO GREEN database remain the property of the rights holder. It is up to the rights holder and the collaborating parties to structure agreements in the manner they feel is most appropriate and effective. WIPO GREEN does not require that technologies or innovations uploaded to the database be patented or in the process of being patented. Therefore, technology providers can upload their technology while related patent applications are pending. Technology providers are encouraged to upload technology solutions on the WIPO GREEN database and connect with other users to explore partnerships, technology transfers, including funding and licensing opportunities. == Acceleration projects == Acceleration projects work with WIPO GREEN partners and local organizations to explore local challenges and green opportunities for particular environmental needs. These projects are organized annually in different countries or regions around and connect providers and seekers of green technologies. For example, the Latin America Acceleration Project explores innovative new technologies in the region and facilitates green technology exchange between providers and seekers in green opportunities in intensified crop rotation, soil re-carbonization, and forest management in Argentina; zero-till or conservation agriculture in Brazil; and wine production in Chile. In October 2021, a project in Indonesia on palm oil mill effluent (POME), a by-product of palm oil production that emits greenhouse gases and reportedly harms flora and fauna in local rivers, identified viable green solutions to turn the high organic content of POME wastewater into biogas and other environmentally friendly uses. Former projects took place in Cambodia, Indonesia, and the Philippines around wastewater treatment, agriculture, and water technologies. == The Green Technology Book == In November 2022 at UNFCCC COP27, WIPO introduced its new Flagship publication the Green Technology Book. This digital-first publication aims to put innovation, technology and intellectual property at the forefront in the fight against climate change. The inaugural edition of this annual publication focused on available solutions for climate-change adaptation to reduce vulnerability as well as to increase resilience to the impacts of climate change. The book was created in cooperation with the Climate Technology Center and Network (CTCN) and the Egyptian Academy of Scientific Research and Technology (ASTR). It features 200 adaptation technologies, which are also available in the WIPO GREEN database of innovative technologies and needs. == Partners Network == WIPO GREEN partners are public or private institutions that wish to collaborate to advance WIPO GREEN’s mission. The network is aimed at helping the implementation and diffusion of green technology innovations around the world. Partners include government institutions, intergovernmental organizations, academia, and businesses – from small and medium-sized enterprises to Fortune 500 companies. As of 2022, WIPO GREEN has a network of over 146 partner organizations involved in green technology.
Mistral Vibe
Mistral Vibe or Vibe (Le Chat until May 2026), is a chatbot that uses generative artificial intelligence developed in France by Mistral AI. Mistral Vibe is available in iOS and Android. Its services are operated on a freemium model. == History == In February 2024, Mistral AI released Le Chat. In January 2025, Mistral AI made a content deal with Agence France-Presse (AFP) that lets Le Chat query AFP's entire archive dating back to 1983. On 6 February 2025, a mobile app for Le Chat was released for iOS and Android, and a subscription tier, Pro, was introduced at a cost of $14.99 per month. In July 2025, Mistral AI released Voxtral, an open-source language model that understands and generates audio. Mistral introduced a voice mode for chatting that uses Voxtral, and projects, which allows grouping chats and files. In September 2025, Le Chat introduced the capability to remember previous conversations. In May 2026, Mistral AI announced the rebrand from Le Chat to Mistral Vibe and new features were introduced at the same time.