AI App Apple Watch

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  Text-to-video model

  A text-to-video model is a form of generative artificial intelligence that uses a natural language description as input to produce a video relevant to the input text. Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models. == Models == There are different models, including open source models. Chinese-language input CogVideo is the earliest text-to-video model "of 9.4 billion parameters" to be developed, with its demo version of open source codes first presented on GitHub in 2022. That year, Meta Platforms released a partial text-to-video model called "Make-A-Video", and Google's Brain (later Google DeepMind) introduced Imagen Video, a text-to-video model with 3D U-Net. === 2023 === In February 2023, Runway released Gen-1 and Gen-2, among the first commercially available text-to-video and video-to-video models accessible to the public through a web interface. Gen-1, initially released as a video-to-video model, allowed users to transform existing video footage using text or image prompts. Gen-2, introduced in March 2023 and made publicly available in June 2023, added text-to-video capabilities, enabling users to generate videos from text prompts alone. In March 2023, a research paper titled "VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation" was published, presenting a novel approach to video generation. The VideoFusion model decomposes the diffusion process into two components: base noise and residual noise, which are shared across frames to ensure temporal coherence. By utilizing a pre-trained image diffusion model as a base generator, the model efficiently generated high-quality and coherent videos. Fine-tuning the pre-trained model on video data addressed the domain gap between image and video data, enhancing the model's ability to produce realistic and consistent video sequences. In the same month, Adobe introduced Firefly AI as part of its features. === 2024 === In January 2024, Google announced development of a text-to-video model named Lumiere which is anticipated to integrate advanced video editing capabilities. Matthias Niessner and Lourdes Agapito at AI company Synthesia work on developing 3D neural rendering techniques that can synthesise realistic video by using 2D and 3D neural representations of shape, appearances, and motion for controllable video synthesis of avatars. In June 2024, Luma Labs launched its Dream Machine video tool. That same month, Kuaishou extended its Kling AI text-to-video model to international users. In July 2024, TikTok owner ByteDance released Jimeng AI in China, through its subsidiary, Faceu Technology. By September 2024, the Chinese AI company MiniMax debuted its video-01 model, joining other established AI model companies like Zhipu AI, Baichuan, and Moonshot AI, which contribute to China's involvement in AI technology. In December 2024 Lightricks launched LTX Video as an open source model. === 2025 === Alternative approaches to text-to-video models include Google's Phenaki, Hour One, Colossyan, Runway's Gen-3 Alpha, and OpenAI's Sora, Several additional text-to-video models, such as Plug-and-Play, Text2LIVE, and TuneAVideo, have emerged. FLUX.1 developer Black Forest Labs has announced its text-to-video model SOTA. Google was preparing to launch a video generation tool named Veo for YouTube Shorts in 2025. In May 2025, Google launched the Veo 3 iteration of the model. It was noted for its impressive audio generation capabilities, which were a previous limitation for text-to-video models. In July 2025 Lightricks released an update to LTX Video capable of generating clips reaching 60 seconds, and in October 2025 it released LTX-2, with audio capabilities built in. === 2026 === In February 2026, ByteDance released Seedance 2.0, it was noted for its impressive realistic generation, motion and camera control and 15 second generation, however the model faced huge critiscism from Motion Picture Association for copyright infringement. After viewing a viral clip of a fight between actors Brad Pitt and Tom Cruise, Rhett Reese, who is the co-writer of Deadpool & Wolverine and Zombieland announced that on social media "I hate to say it. It’s likely over for us," further stating that "In next to no time, one person is going to be able to sit at a computer and create a movie indistinguishable from what Hollywood now releases." == Architecture and training == There are several architectures that have been used to create text-to-video models. Similar to text-to-image models, these models can be trained using Recurrent Neural Networks (RNNs) such as long short-term memory (LSTM) networks, which has been used for Pixel Transformation Models and Stochastic Video Generation Models, which aid in consistency and realism respectively. An alternative for these include transformer models. Generative adversarial networks (GANs), Variational autoencoders (VAEs), — which can aid in the prediction of human motion — and diffusion models have also been used to develop the image generation aspects of the model. Text-video datasets used to train models include, but are not limited to, WebVid-10M, HDVILA-100M, CCV, ActivityNet, and Panda-70M. These datasets contain millions of original videos of interest, generated videos, captioned-videos, and textual information that help train models for accuracy. Text-video datasets used to train models include, but are not limited to PromptSource, DiffusionDB, and VidProM. These datasets provide the range of text inputs needed to teach models how to interpret a variety of textual prompts. The video generation process involves synchronizing the text inputs with video frames, ensuring alignment and consistency throughout the sequence. This predictive process is subject to decline in quality as the length of the video increases due to resource limitations. The Will Smith Eating Spaghetti test is a benchmark for models. == Limitations == Despite the rapid evolution of text-to-video models in their performance, a primary limitation is that they are very computationally heavy which limits its capacity to provide high quality and lengthy outputs. Additionally, these models require a large amount of specific training data to be able to generate high quality and coherent outputs, which brings about the issue of accessibility. Moreover, models may misinterpret textual prompts, resulting in video outputs that deviate from the intended meaning. This can occur due to limitations in capturing semantic context embedded in text, which affects the model's ability to align generated video with the user's intended message. Various models, including Make-A-Video, Imagen Video, Phenaki, CogVideo, GODIVA, and NUWA, are currently being tested and refined to enhance their alignment capabilities and overall performance in text-to-video generation. Another issue with the outputs is that text or fine details in AI-generated videos often appear garbled, a problem that stable diffusion models also struggle with. Examples include distorted hands and unreadable text. == Ethics == The deployment of text-to-video models raises ethical considerations related to content generation. These models have the potential to create inappropriate or unauthorized content, including explicit material, graphic violence, misinformation, and likenesses of real individuals without consent. Ensuring that AI-generated content complies with established standards for safe and ethical usage is essential, as content generated by these models may not always be easily identified as harmful or misleading. The ability of AI to recognize and filter out NSFW or copyrighted content remains an ongoing challenge, with implications for both creators and audiences. == Impacts and applications == Text-to-video models offer a broad range of applications that may benefit various fields, from educational and promotional to creative industries. These models can streamline content creation for training videos, movie previews, gaming assets, and visualizations, making it easier to generate content. During the Russo-Ukrainian war, fake videos made with artificial intelligence were created as part of a propaganda war against Ukraine and shared in social media. These included depictions of children in the Ukrainian Armed Forces, fake ads targeting children encouraging them to denounce critics of the Ukrainian government, or fictitious statements by Ukrainian President Volodymyr Zelenskyy about the country's surrender, among others. === Movies === Kaur vs Kore is the first Indian feature film made using generative AI which features dual role for the AI character of Sunny Leone, set to release in 2026. Chiranjeevi Hanuman – The Eternal is an Indian movie made entirely using Generative AI created by Vijay Subramaniam which is set for theatrical release in 2026. The movie was widely criticised by the Film makers in the Bollywood industr

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  Geo-replication

  Geo-replication systems are designed to provide improved availability and disaster tolerance by using geographically distributed data centers. This is intended to improve the response time for applications such as web portals. Geo-replication can be achieved using software, hardware or a combination of the two. == Software == Geo-replication software is a network performance-enhancing technology that is designed to provide improved access to portal or intranet content for users at the most remote parts of large organizations. It is based on the principle of storing complete replicas of portal content on local servers, and then keeping the content on those servers up-to-date using heavily compressed data updates. === Portal acceleration === Geo-replication technologies are used to provide replication of the content of portals, intranets, web applications, content and data between servers, across wide area networks WAN to allow users at remote sites to access central content at LAN speeds. Geo-replication software can improve the performance of data networks that suffer limited bandwidth, latency and periodic disconnection. Terabytes of data can be replicated over a wide area network, giving remote sites faster access to web applications. Geo-replication software uses a combination of data compression and content caching technologies. differencing technologies can also be employed to reduce the volume of data that has to be transmitted to keep portal content accurate across all servers. This update compression can reduce the load that portal traffic places on networks, and improve the response time of a portal. === Portal replication === Remote users of web portals and collaboration environments will frequently experience network bandwidth and latency problems which will slow down their experience of opening and closing files, and otherwise interacting with the portal. Geo-replication technology is deployed to accelerate the remote end user portal performance to be equivalent to that experienced by users locally accessing the portal in the central office. === Differencing engine technologies === To deliver this reduction in the size of the required data updates across a portal, geo-replication systems often use differencing engine technologies. These systems are able to difference the content of each portal server right down to the byte level. This knowledge of the content that is already on each server enables the system to rebuild any changes to the content on one server, across each of the other servers in the deployment from content already hosted on those other servers. This type of differencing system ensures that no content, at the byte level, is ever sent to a server twice. === Offline portal replication on laptops === Geo-replication systems are often extended to deliver local replication beyond the server and down to the laptop used by a single user. Server to laptop replication enables mobile users to have access to a local replica of their business portal on a standard laptop. This technology may be employed to provide in the field access to portal content by, for example, sales forces and combat forces. == Geo-replication systems ==

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  Douglas Parkhill

  Douglas F. Parkhill is a Canadian technologist and former research minister, best known for his pioneering work on what is now called cloud computing, and his work on Canada's Telidon videotex project. He started working at the Canadian ministry of Communications (now part of the Department of Trade and Industry) in 1969, having previously worked at the Mitre Corporation. He was responsible for many activities in communications satellites, computer communications, command and control systems and telecommunications. He was winner of the Treasury Board of Canada Secretariat's Outstanding Achievement award in 1982, the Conestoga shield for services to government and industry in computer communications research and development, the Touche Ross award for Telidon development. He was an author of several publications including the 1966 book, The Challenge of the Computer Utility. In the book, Parkhill thoroughly explored many of the modern-day characteristics of cloud computing (elastic provisioning through a utility service) as well as the comparison to the electricity industry and the use of public, private, government and community forms. The book won the McKinsey Foundation award for distinguished contributions to management literature. He worked with Dave Godfrey, the Canadian writer and novelist on a later book Gutenberg two about the social and political meaning of computer technology. He was in charge of research at the Federal Department of Communications at the time when the department was funding development of the Telidon videotext system, was heavily involved in promoting the system, and had overall control of the program. In a radio broadcast in 1980, he outlined some of the potential of the system, from financial information, to theatre reservations, with the ability to pay and print out tickets from the system. He later documented the history of the Telidon project, and the history of videotext in general. == Publications == The Challenge of the Computer Utility, Addison-Wesley, 1966, ISBN 0-201-05720-4 edited with Dave Godfrey, Gutenberg Two: The New Electronics and Social Change, Press Porcepic, 1979, ISBN 0-88878-191-1 The Beginning of a Beginning. Ottawa; Department of Communications, 1987. A history of the Telidon project.

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  Software diversity

  Software diversity is a research field about the comprehension and engineering of diversity in the context of software. == Areas == The different areas of software diversity are discussed in surveys on diversity for fault-tolerance or for security. The main areas are: design diversity, n-version programming, data diversity for fault tolerance randomization software variability == Techniques == === Code transformations === It is possible to amplify software diversity through automated transformation processes that create synthetic diversity. A "multicompiler" is compiler embedding a diversification engine. A multi-variant execution environment (MVEE) is responsible for selecting the variant to execute and compare the output. Fred Cohen was among the very early promoters of such an approach. He proposed a series of rewriting and code reordering transformations that aim at producing massive quantities of different versions of operating systems functions. These ideas have been developed over the years and have led to the construction of integrated obfuscation schemes to protect key functions in large software systems. Another approach to increase software diversity of protection consists in adding randomness in certain core processes, such as memory loading. Randomness implies that all versions of the same program run differently from each other, which in turn creates a diversity of program behaviors. This idea was initially proposed and experimented by Stephanie Forrest and her colleagues. Recent work on automatic software diversity explores different forms of program transformations that slightly vary the behavior of programs. The goal is to evolve one program into a population of diverse programs that all provide similar services to users, but with a different code. This diversity of code enhances the protection of users against one single attack that could crash all programs at the same time. Transformation operators include: code layout randomization: reorder functions in code globals layout randomization: reorder and pad globals stack variable randomization: reorder variables in each stack frame heap layout randomization === Natural software diversity === It is known that some functionalities are available in multiple interchangeable implementations. This natural diversity can be exploited, for example it has been shown valuable to increase security in cloud systems.

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  Galaksija BASIC

  Galaksija BASIC was the BASIC interpreter of the Galaksija build-it-yourself home computer from Yugoslavia. While being partially based on code taken from TRS-80 Level 1 BASIC, which the creator believed to have been a Microsoft BASIC, the extensive modifications of Galaksija BASIC—such as to include rudimentary array support, video generation code (as the CPU itself did it in absence of dedicated video circuitry) and generally improvements to the programming language—is said to have left not much more than flow-control and floating point code remaining from the original. The core implementation of the interpreter was fully contained in the 4 KiB ROM "A" or "1". The computer's original mainboard had a reserved slot for an extension ROM "B" or "2" that added more commands and features such as a built-in Zilog Z80 assembler. == ROM "A"/"1" symbols and keywords == The core implementation, in ROM "A" or "1", contained 3 special symbols and 32 keywords: ! begins a comment (equivalent of standard BASIC REM command) # Equivalent of standard BASIC DATA statement & prefix for hex numbers ARR$(n) Allocates an array of strings, like DIM, but can allocate only array with name A$ BYTE serves as PEEK when used as a function (e.g. PRINT BYTE(11123)) and POKE when used as a command (e.g. BYTE 11123,123). CALL n Calls BASIC subroutine as GOSUB in most other BASICs (e.g. CALL 100+4X) CHR$(n) converts an ASCII numeric code into a corresponding character (string) DOT x, y draws (command) or inspects (function) a pixel at given coordinates (0<=x<=63, 0<=y<=47). DOT displays the clock or time controlled by content of Y$ variable. Not in standard ROM EDIT n causes specified program line to be edited ELSE standard part of IF-ELSE construct (Galaksija did not use THEN) EQ compare alphanumeric values X$ and Y$ FOR standard FOR loop GOTO standard GOTO command HOME equivalent of standard BASIC CLS command - clears the screen HOME n protects n characters from the top of the screen from being scrolled away IF standard part of IF-ELSE construct (Galaksija did not use THEN) INPUT user entry of variable INT(n) a function that returns the greatest integer value equal to or lesser than n KEY(n) test whether a particular keyboard key is pressed LIST lists the program. Optional numeric argument specifies the first line number to begin listing with. MEM returns memory consumption data (need details here) NEW clears the current BASIC program NEW n clears BASIC program and moves beginning of BASIC area NEXT standard terminator of FOR loop OLD loads a program from tape OLD n loads program to different address PTR Returns address of the variable PRINT Printing numeric or string expression. RETURN Return from BASIC subroutine RND function (takes no arguments) that returns a random number between 0 and 1. RUN runs (executes) BASIC program. Optional numeric argument specifies the line number to begin execution with. SAVE saves a program to tape. Optional two arguments specify memory range to be saved (need details here). STEP standard part of FOR loop STOP stops execution of BASIC program TAKE replacement for READ and RESTORE. If the parameter is variable name, acts as READ, if it is number, acts as RESTORE UNDOT x, y "undraws" (resets) at specified coordinates (see DOT) UNDOT Stops the clock, not part of ROM USR Calls machine code subroutine WORD Double byte PEEK and POKE == ROM "B"/"2" additional symbols and keywords == The extended BASIC features, in ROM "B" or "2", contained one extra reserved symbol and 22 extra keywords: % /LABEL ABS(x) ARCTG(x) COS(x) COSD(x) DEL DUMP EXP(x) INP(x) LDUMP LLIST LN(x) LPRINT OUT PI POW(x,y) REN SIN(x), SIND(x) SQR(x) TG(x) TGD(x)

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  Distributed manufacturing

  Distributed manufacturing, also known as distributed production, cloud producing, distributed digital manufacturing, and local manufacturing, is a form of decentralized manufacturing practiced by enterprises using a network of geographically dispersed manufacturing facilities that are coordinated using information technology. It can also refer to local manufacture via the historic cottage industry model, or manufacturing that takes place in the homes of consumers. == Enterprise == In enterprise environments, the primary attribute of distributed manufacturing is the ability to create value at geographically dispersed locations. For example, shipping costs could be minimized when products are built geographically close to their intended markets. Also, products manufactured in a number of small facilities distributed over a wide area can be customized with details adapted to individual or regional tastes. Manufacturing components in different physical locations and then managing the supply chain to bring them together for final assembly of a product is also considered a form of distributed manufacturing. Digital networks combined with additive manufacturing allow companies a decentralized and geographically independent distributed production (cloud manufacturing). == Consumer == Within the maker movement and DIY culture, small scale production by consumers often using peer-to-peer resources is being referred to as distributed manufacturing. Consumers download digital designs from an open design repository website like Youmagine or Thingiverse and produce a product for low costs through a distributed network of 3D printing services such as 3D Hubs, Geomiq. In the most distributed form of distributed manufacturing the consumer becomes a prosumer and manufacturers products at home with an open-source 3-D printer such as the RepRap. In 2013 a desktop 3-D printer could be economically justified as a personal product fabricator and the number of free and open hardware designs were growing exponentially. Today there are millions of open hardware product designs at hundreds of repositories and there is some evidence consumers are 3-D printing to save money. For example, 2017 case studies probed the quality of: (1) six common complex toys; (2) Lego blocks; and (3) the customizability of open source board games and found that all filaments analyzed saved the prosumer over 75% of the cost of commercially available true alternative toys and over 90% for recyclebot filament. Overall, these results indicate a single 3D printing repository, MyMiniFactory, is saving consumers well over $60 million/year in offset purchases of only toys. These 3-D printers can now be used to make sophisticated high-value products like scientific instruments. Similarly, a study in 2022 found that 81% of open source designs provided economic savings and the total savings for the 3D printing community is more than $35 million from downloading only the top 100 products at YouMagine. In general, the savings are largest when compared to conventional products when prosumers use recycled materials in 'distributed recycling and additive manufacturing' (DRAM). == Emergency Distributed Manufacturing During COVID-19 Pandemic == Distributed manufacturing became far more visible during the COVID-19 pandemic because it offered a practical response to the breakdown of centralized global supply chains. As lock downs, border restrictions, and factory shutdowns disrupted conventional production, decentralized networks using local facilities such as Open Source Medical Supplies stepped in and manufactured over 48 million products. Additive manufacturing /3D printing were used to produce urgently needed items such as face shields, ventilators and their components, nasopharyngeal swabs, and other personal protective equipment. This demonstrated that distributed manufacturing could reduce lead times, improve responsiveness, and lessen dependence on distant suppliers during crisis conditions for a wide range of products. Peer-reviewed studies on pandemic-era manufacturing note that additive manufacturing was especially valuable because digital design files could be shared rapidly and produced close to the point of need, enabling hospitals, universities, small firms, and maker communities to supplement strained medical supply chains. The pandemic also helped shift distributed manufacturing from being seen as a niche or experimental model to a credible strategy for resilience, flexibility, and emergency response. At the same time, scholars caution that its wider adoption depends on solving issues related to quality assurance, regulation, material consistency, and coordination across distributed production sites. Overall, COVID-19 popularized distributed manufacturing by showing that localized, digitally enabled production could complement traditional manufacturing systems when speed, adaptability, and supply-chain resilience were critical. == Social change == Some call attention to the conjunction of commons-based peer production with distributed manufacturing techniques. The self-reinforced fantasy of a system of eternal growth can be overcome with the development of economies of scope, and here, the civil society can play an important role contributing to the raising of the whole productive structure to a higher plateau of more sustainable and customised productivity. Further, it is true that many issues, problems and threats rise due to the large democratization of the means of production, and especially regarding the physical ones. For instance, the recyclability of advanced nanomaterials is still questioned; weapons manufacturing could become easier; not to mention the implications on counterfeiting and on "intellectual property". It might be maintained that in contrast to the industrial paradigm whose competitive dynamics were about economies of scale, commons-based peer production and distributed manufacturing could develop economies of scope. While the advantages of scale rest on cheap global transportation, the economies of scope share infrastructure costs (intangible and tangible productive resources), taking advantage of the capabilities of the fabrication tools. And following Neil Gershenfeld in that "some of the least developed parts of the world need some of the most advanced technologies", commons-based peer production and distributed manufacturing may offer the necessary tools for thinking globally but act locally in response to certain problems and needs. As well as supporting individual personal manufacturing social and economic benefits are expected to result from the development of local production economies. In particular, the humanitarian and development sector are becoming increasingly interested in how distributed manufacturing can overcome the supply chain challenges of last mile distribution. Further, distributed manufacturing has been proposed as a key element in the Cosmopolitan localism or cosmolocalism framework to reconfigure production by prioritizing socio-ecological well-being over corporate profits, over-production and excess consumption. == Technology == By localizing manufacturing, distributed manufacturing may enable a balance between two opposite extreme qualities in technology development: Low technology and High tech. This balance is understood as an inclusive middle, a "mid-tech", that may go beyond the two polarities, incorporating them into a higher synthesis. Thus, in such an approach, low-tech and high-tech stop being mutually exclusive. They instead become a dialectic totality. Mid-tech may be abbreviated to "both…and…" instead of "neither…nor…". Mid-tech combines the efficiency and versatility of digital/automated technology with low-tech's potential for autonomy and resilience. == Contracting in Distributed Manufacturing == Research into contracting and order processing models tailored for distributed manufacturing has highlighted the need for flexible, role-based frameworks and advanced digital tools. These tools and frameworks are essential for addressing issues related to quality assurance, payment structures, legal compliance, and coordination among multiple actors. By addressing these challenges, contracting models for distributed manufacturing can unlock its potential for more localized, efficient, and sustainable production systems. A system prototype has been developed to simplify contracting for distributed manufacturing. This tool allows buyers to manage orders across multiple manufacturers using a single interface, automating workflows to ensure clarity and accountability for everyone involved. This research was led by the Internet of Production, as part of the mAkE project (African European Maker Innovation Ecosystem), funded by the European Horizon 2020 research and innovation programme.

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  Umple

  Umple is a language for both object-oriented programming and modelling with class diagrams and state diagrams. The name Umple is a portmanteau of "UML", "ample" and "Simple", indicating that it is designed to provide ample features to extend programming languages with UML capabilities. == History and philosophy == The design of Umple started in 2008 at the University of Ottawa. Umple was open-sourced and its development was moved to Google Code in early 2011 and to GitHub in 2015. Umple was developed, in part, to address certain problems observed in the modelling community. Most specifically, it was designed to bring modelling and programming into alignment, It was intended to help overcome inhibitions against modelling common in the programmer community. It was also intended to reduce some of the difficulties of model-driven development that arise from the need to use large, expensive or incomplete tools. One design objective is to enable programmers to model in a way they see as natural, by adding modelling constructs to programming languages. == Features and capabilities == Umple can be used to represent in a textual manner many UML modelling entities found in class diagrams and state diagrams. Umple can generate code for these in various programming languages. Currently Umple fully supports Java, C++ and PHP as target programming languages and has functional, but somewhat incomplete support for Ruby. Umple also incorporates various features not related to UML, such as the singleton pattern, keys, immutability, mixins and aspect-oriented code injection. The class diagram notations Umple supports includes classes, interfaces, attributes, associations, generalizations and operations. The code Umple generates for attributes include code in the constructor, 'get' methods and 'set' methods. The generated code differs considerably depending on whether the attribute has properties such as immutability, has a default value, or is part of a key. Umple generates many methods for manipulating, querying and navigating associations. It supports all combinations of UML multiplicity and enforces referential integrity. Umple supports the vast majority of UML state machine notation, including arbitrarily deep nested states, concurrent regions, actions on entry, exit and transition, plus long-lasting activities while in a state. A state machine is treated as an enumerated attribute where the value is controlled by events. Events encoded in the state machine can be methods written by the user, or else generated by the Umple compiler. Events are triggered by calling the method. An event can trigger transitions (subject to guards) in several different state machines. Since a program can be entirely written around one or more state machines, Umple enables automata-based programming. The bodies of methods are written in one of the target programming languages. The same is true for other imperative code such as state machine actions and guards, and code to be injected in an aspect-oriented manner. Such code can be injected before many of the methods in the code Umple generates, for example before or after setting or getting attributes and associations. The Umple notation for UML constructs can be embedded in any of its supported target programming languages. When this is done, Umple can be seen as a pre-processor: The Umple compiler expands the UML constructs into code of the target language. Code in a target language can be passed to the Umple compiler directly; if no Umple-specific notation is found, then the target-language code is emitted unchanged by the Umple compiler. Umple, combined with one of its target languages for imperative code, can be seen and used as a complete programming language. Umple plus Java can therefore be seen as an extension of Java. Alternatively, if imperative code and Umple-specific concepts are left out, Umple can be seen as a way of expressing a large subset of UML in a purely textual manner. Code in one of the supported programming languages can be added in the same manner as UML envisions adding action language code. == License == Umple is licensed under an MIT-style license. == Examples == Here is the classic Hello world program written in Umple (extending Java): This example looks just like Java, because Umple extends other programming languages. With the program saved in a file named HelloWorld.ump, it can be compiled from the command line: $ java -jar umple.jar HelloWorld.ump To run it: $ java HelloWorld The following is a fully executable example showing embedded Java methods and declaration of an association. The following example describes a state machine called status, with states Open, Closing, Closed, Opening and HalfOpen, and with various events that cause transitions from one state to another. class GarageDoor { status { Open { buttonOrObstacle -> Closing; } Closing { buttonOrObstacle -> Opening; reachBottom -> Closed; } Closed { buttonOrObstacle -> Opening; } Opening { buttonOrObstacle -> HalfOpen; reachTop -> Open; } HalfOpen { buttonOrObstacle -> Opening; } } } == Umple use in practice == The first version of the Umple compiler was written in Java, Antlr and Jet (Java Emitter Templates), but in a bootstrapping process, the Java code was converted to Umple following a technique called Umplification. The Antlr and Jet were also later converted to native Umple. Umple is therefore now written entirely in itself, in other words it is self-hosted and serves as its own largest test case. Umple and UmpleOnline have been used in the classroom by several instructors to teach UML and modelling. In one study it was found to help speed up the process of teaching UML, and was also found to improve the grades of students. == Tools == Umple is available as a Jar file so it can be run from the command line, and as an Eclipse plugin. There is also an online tool for Umple called UmpleOnline , which allows a developer to create an Umple system by drawing a UML class diagram, editing Umple code or both. Umple models created with UmpleOnline are stored in the cloud. Currently UmpleOnline only supports Umple programs consisting of a single input file. In addition to code, Umple's tools can generate a variety of other types of output, including user interfaces based on the Umple model.

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  Research software engineering

  Research software engineering is the application of software engineering practices, methods and techniques for research software, i.e. software that was made for and is mainly used within research projects. As usual for software engineering, this also includes knowledge of other (and in this case varying) research fields as well as open science that need to be incorporated into a software development process. The term was proposed in a research paper in 2010 in response to an empirical survey on tools used for software development in research projects. It started to be used in United Kingdom in 2012, when it was needed to define the type of software development needed in research. This focuses on reproducibility, reusability, and accuracy of data analysis and applications created for research. == Support == Various type of associations and organisations have been created around this role to support the creation of posts in universities and research institutes. In 2014 a Research Software Engineer Association was created in UK, which attracted 160 members in the first three months and which lead to the creation of the Society of Research Software Engineering in 2019. Other countries like the Netherlands, Germany, and the USA followed creating similar communities and there are similar efforts being pursued in Asia, Australia, Canada, New Zealand, the Nordic countries, and Belgium. In January 2021 the International Council of RSE Associations was introduced. UK counts over 40 universities and institutes with groups that provide access to software expertise to different areas of research. Additionally, the Engineering and Physical Sciences Research Council created a Research Software Engineer fellowship to promote this role and help the creation of RSE groups across UK, with calls in 2015, 2017, and 2020. The world first RSE conference took place in UK in September 2016 and it has been repeated annually (except for a gap in 2020) since. In 2019 the first national RSE conferences in Germany and the Netherlands were held, next editions were planned for 2020 and then cancelled. US-RSE held its first national conference in 2023. The Research Software Alliance was formed in 2019 to advance the global research software ecosystem by collaborating with decision makers and key influencers. The SORSE (A Series of Online Research Software Events) community was established in late‑2020 in response to the COVID-19 pandemic and ran its first online event in September 2020.

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  Programming tool

  A programming tool or software development tool is a computer program that is used to develop another computer program, usually by helping the developer manage computer files. For example, a programmer may use a tool called a source code editor to edit source code files, and then a compiler to convert the source code into machine code files. They may also use build tools that automatically package executable program and data files into shareable packages or install kits. A set of tools that are run one after another, with each tool feeding its output to the next one, is called a toolchain. An integrated development environment (IDE) integrates the function of several tools into a single program. Usually, an IDE provides a source code editor as well as other built-in or plug-in tools that help with compiling, debugging, and testing. Whether a program is considered a development tool can be subjective. Some programs, such as the GNU compiler collection, are used exclusively for software development while others, such as Notepad, are not meant specifically for development but are nevertheless often used for programming. == Categories == Notable categories of development tools: Assembler – Converts assembly language into machine code Bug tracking system – Software application that records software bugs Build automation – Building software via an unattended fashion Code review software – Activity where one or more people check a program's code Compiler – Software that translates code from one programming language to another Compiler-compiler – Program that generates parsers or compilers, a.k.a. parser generator Debugger – Software for debugging a computer program Decompiler – Program translating executable to source code Disassembler – Computer program to translate machine language into assembly language Documentation generator – Automation technology for creating software documentation Graphical user interface builder – Software development tool Linker – Program that combines intermediate build files into an executable file Loader – Loads executable files into memory and prepares them for execution by the CPU. Memory debugger – Software memory problem finder Minifier – Removal of unnecessary characters in code without changing its functionality Pretty-printer – Formatting to make code or markup easier to readPages displaying short descriptions of redirect targets Performance profiler – Measuring the time or resources used by a section of a computer program Static code analyzer – Analysis of computer programs without executing themPages displaying short descriptions of redirect targets Source code editor – Text editor specializing in software codePages displaying short descriptions of redirect targets Source code generation – Type of computer programmingPages displaying short descriptions of redirect targets Version control system – Stores and tracks versions of files

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  SIP (software)

  SIP is an open source software tool used to connect computer programs or libraries written in C or C++ with the scripting language Python. It is an alternative to SWIG. SIP was originally developed in 1998 for PyQt — the Python bindings for the Qt GUI toolkit — but is suitable for generating bindings for any C or C++ library. == Concept == SIP takes a set of specification (.sip) files describing the API and generates the required C++ code. This is then compiled to produce the Python extension modules. A .sip file is essentially the class header file with some things removed (because SIP does not include a full C++ parser) and some things added (because C++ does not always provide enough information about how the API works). For PyQt v4 I use an internal tool (written using PyQt of course) called metasip. This is sort of an IDE for SIP. It uses GCC-XML to parse the latest header files and saves the relevant data, as XML, in a metasip project. metasip then does the equivalent of a diff against the previous version of the API and flags up any changes that need to be looked at. Those changes are then made through the GUI and ticked off the TODO list. Generating the .sip files is just a button click. In my subversion repository, PyQt v4 is basically just a 20M XML file. Updating PyQt v4 for a minor release of Qt v4 is about half an hours work. In terms of how the generated code works then I don't think it's very different from how any other bindings generator works. Python has a very good C API for writing extension modules - it's one of the reasons why so many 3rd party tools have Python bindings. For every C++ class, the SIP generated code creates a corresponding Python class implemented in C. == Notable applications that use SIP == PyQt, a python port of the application framework and widget toolkit Qt QGIS, a free and open-source cross-platform desktop geographic information system (GIS) QtiPlot, a computer program to analyze and visualize scientific data calibre (software), a free and open-source cross-platform e-book manager Veusz, a free and open-source cross-platform program to visualize scientific data

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  Managed private cloud

  Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.

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  Circular thresholding

  Circular thresholding is an algorithm for automatic image threshold selection in image processing. Most threshold selection algorithms assume that the values (e.g. intensities) lie on a linear scale. However, some quantities such as hue and orientation are a circular quantity, and therefore require circular thresholding algorithms. The example shows that the standard linear version of Otsu's method when applied to the hue channel of an image of blood cells fails to correctly segment the large white blood cells (leukocytes). In contrast the white blood cells are correctly segmented by the circular version of Otsu's method. == Methods == There are a relatively small number of circular image threshold selection algorithms. The following examples are all based on Otsu's method for linear histograms: (Tseng, Li and Tung 1995) smooth the circular histogram, and apply Otsu's method. The histogram is cyclically rotated so that the selected threshold is shifted to zero. Otsu's method and histogram rotation are applied iteratively until several heuristics involving class size, threshold location, and class variance are satisfied. (Wu et al. 2006) smooth the circular histogram until it contains only two peaks. The histogram is cyclically rotated so that the midpoint between the peaks is shifted to zero. Otsu's method and histogram rotation are applied iteratively until convergence of the threshold. (Lai and Rosin 2014) applied Otsu's method to the circular histogram. For the two class circular thresholding task they showed that, for a histogram with an even number of bins, the optimal solution for Otsu's criterion of within-class variance is obtained when the histogram is split into two halves. Therefore the optimal solution can be efficiently obtained in linear rather than quadratic time. == References and further reading == D.-C. Tseng, Y.-F. Li, and C.-T. Tung, Circular histogram thresholding for color image segmentation in Proc. Int. Conf. Document Anal. Recognit., 1995, pp. 673–676. J. Wu, P. Zeng, Y. Zhou, and C. Olivier, A novel color image segmentation method and its application to white blood cell image analysis in Proc. Int. Conf. Signal Process., vol. 2. 2006, pp. 16–20. Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), 992–1001 (2014). doi:10.1109/TIP.2013.2297014

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  Buckeye Corpus

  The Buckeye Corpus of conversational speech is a speech corpus created by a team of linguists and psychologists at Ohio State University led by Prof. Mark Pitt. It contains high-quality recordings from 40 speakers in Columbus, Ohio conversing freely with an interviewer. The interviewer's voice is heard only faintly in the background of these recordings. The sessions were conducted as Sociolinguistics interviews, and are essentially monologues. The speech has been orthographically transcribed and phonetically labeled. The audio and text files, together with time-aligned phonetic labels, are stored in a format for use with speech analysis software (Xwaves and Wavesurfer). Software for searching the transcription files is also available at the project web site. The corpus is available to researchers in academia and industry. The project was funded by the National Institute on Deafness and Other Communication Disorders and the Office of Research at Ohio State University.

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  Peanut App

  Peanut, a product of Peanut App Ltd. is an online community for women who are planning to become pregnant, women who are pregnant, women who have had children, and women who are experiencing menopause. Profiles of potential friends are displayed to users who can swipe up to show intent to connect. Users can also connect via discussion threads, groups, and live audio conversations. The app allows users to select their stage of life (trying to conceive, pregnancy, motherhood, or menopause), so as to meet women at a similar life stage, and to discover relevant content. Peanut was founded by Michelle Kennedy shortly after she left Bumble, a female-first dating app. She has described Peanut as, "the app she wishes she had when she first became a mother". == History == Peanut was initially launched in 2017 for mothers and pregnant women. The app focuses on helping users find others with shared interests, such as spoken languages, occupations, and hobbies. It also displays a woman's life stage, such as the age of her children, or the stage of pregnancy. In 2018, it launched a community discussion feature that intended to give women an "alternative to other social platforms". In 2019, it started to serve women who are trying to conceive. In April 2021, it integrated live audio, in response to the COVID-19 pandemic, and the restrictions around in-person socializing. in September 2021, it started to include women who are navigating perimenopause, menopause, and postmenopausal. Although it had initially catered for younger women navigating into new families, a large number of users had undergone surgically or chemically induced menopause due to medical conditions. In July 2021, Peanut launched an investment micro fund, Peanut StartHER, focused on investing in women-owned businesses, as well as other historically excluded founders. == Operation == The Peanut app is a social network exclusively for women, focusing on topics of pregnancy, motherhood, fertility, and menopause. It is available on iOS and Android devices. Users must prove their identity, in keeping with the primary function of in-app safety, and then they can create a profile to interact with other users. For pregnant users, the “Bump Buddies” feature helps connect them with other Peanut users who have a similar due date, which aimed to help expecting mothers combat loneliness during the COVID-19 pandemic. Peanut users also have the option to join “Groups” ‒ sub-sections of users focused on specific topics, including (but not limited to) location, life stage, pregnancy due date, and interests or hobbies. The live voice chat feature “Pods”, enables Peanut users to socialize without the pressure of photos or video chat. It offers features such as a muted audience of listeners who need to virtually raise their hand to speak, emoji reactions, and hosts who can moderate the conversations and invite people to speak.

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  Process map

  Process map is a global-system process model that is used to outline the processes that make up the business system and how they interact with each other. Process map shows the processes as objects, which means it is a static and non-algorithmic view of the processes. It should be differentiated from a detailed process model, which shows a dynamic and algorithmic view of the processes, usually known as a process flow diagram. There are different notation standards that can be used for modelling process maps, but the most notable ones are TOGAF Event Diagram, Eriksson-Penker notation, and ARIS Value Added Chain. == Global process models == Global characteristics of the business system are captured by global or system models. Global process models are presented using different methodologies and sometimes under different names. Most notably, they are named process map in Visual Paradigm and MMABP, value-added chain in ARIS, and process diagram in Eriksson-Penker notation – which can easily lead to the confusion with process flow (detailed process model). Global models are mainly object-oriented and present a static view of the business system; they do not describe dynamic aspects of processes. A process map shows the presence of processes and their mutual relationships. The requirement for the global perspective of the system as a supplementary to the internal process logic description results from the necessity of taking into consideration not only the internal process logic but also its significant surroundings. The algorithmic process model cannot take the place of this perspective since it represents the system model of the process. The detailed process model and the global process model represent different perspectives on the same business system, so these models must be mutually consistent. A macro process map represents the major processes required to deliver a product or service to the customer. These macro process maps can be further detailed in sub-diagrams. It is often the case that process maps cross different functional areas of the organization. Process maps are used by many companies to have a holistic view of all processes and the connections between them. Maps help in navigating the sub-processes and make understanding of the organization's operations easier. The process map shows relationships and dependencies between processes and its focus should be on core business processes of the organization. A process map can be seen as the most abstract level of the process architecture, and it acts as the introduction to the more detailed levels. A process map that is correctly designed is able to provide a general understanding of a company's operations. Designing the process map is an important and strategic step for the organization, and it is followed by further business process modelling implementation. == Context == Methodology for Modelling and Analysis of Business Process (MMABP) is a business process modelling methodology developed at the Department of Information Technology, Faculty of Informatics and Statistics of the Prague University of Economics and Business. The methodology is defined as a “general methodology for modelling business systems using informatics methods and approaches”. Methodology is used to analyse business processes and to develop a comprehensive model of the system. The goal of developing a model is to be used for process optimization. The model should be created following the characteristics and specifics of the organization in question and following external influences that can affect the organization. The model should be optimal from an economic perspective, but it should also be optimal from a factual perspective, meaning that it should be as simple as possible while maintaining complete functionality. Business system modelling is based on a two-dimensional approach: Real World structure (substance) – set of objects and their relationships Real World behaviour – set of mutually connected business processes Additionally, there are also two views of the systems: Global view of the system Detailed view of the system's parts This results in the need to model the system from four different perspectives in order to achieve the complete and comprehensive view of the business system. MMABP also proposes which notation languages can be used for modelling each perspective, and it also suggests some improvements to the notation languages in order to fit the purpose. Global view of the objects – Conceptual model (Class diagram) Detailed view of the objects – Object life cycle (State Chart) Global view of the processes – Process map (Eriksson-Penker Diagram/TOGAF Event Diagram/ARIS VAC) Detailed view of the processes – Model of the process flow (BPMN Diagram) Data Flow Diagram (DFD) is additional diagram used for describing the required functionalities of the information system. == Notation standards == === Eriksson-Penker Diagram === Eriksson-Penker diagram is a tool used in business model analysis and design. It is named after Hans-Erik Eriksson and Magnus Penker, who developed the concept in their book "Business modelling with UML: Business Patterns at Work”. Eriksson-Penker diagrams are used to map out the key components of a business model and how they interact with one another. The diagrams typically consist of a series of boxes and lines that represent the different elements of the business model, such as the value proposition, customer segments, channels, revenue streams, and key resources. The lines between the boxes represent the relationships and dependencies between the different elements of the business model. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. It is possible to use Eriksson-Penker diagrams to create a global process view of a business. In this case, a diagram would be used to map out the key processes and activities that are involved in the business, as well as the relationships and dependencies between these processes. For example, an Eriksson-Penker diagram could be used to depict the various steps involved in the product development process, from concept development to market launch. It could also be used to show how different functions within the organization, such as marketing, sales, and production, interact and depend on one another to support the overall business. Eriksson-Penker diagram is one of the most popular de facto standards that can be used for an object-oriented global view of business processes. It is developed as an extension of the UML, and it is often used together with the BPMN to compensate for the lack of possibility to model the global view with this widely accepted standard. === TOGAF Event Diagram === TOGAF (The Open Group Architecture Framework) is a framework for enterprise architecture that provides a common language and set of standards for designing, planning, implementing, and governing an enterprise's IT architecture. TOGAF event diagrams are diagrams used in the TOGAF framework to represent the flow of events within a system or process. The TOGAF Event Diagram is a visual representation of the events within an organization or system. It can be used to show the sequence of events that occur in a particular process, as well as the relationships between the events and the stakeholders involved. TOGAF Event Diagrams can be useful in creating a global process view because they provide a visual representation of the events, which can be helpful in understanding how the process fits into the larger context of the organization. TOGAF Event Diagram is the most perspective standard for the system view of processes today. It is used to represent the system of processes as well as their connections to the functional organizational structure. === ARIS Value Added Chain === ARIS (Architecture of Integrated Information Systems) is a methodology and a set of tools for designing and managing business processes. It is based on the idea that business processes are the core of an organization and that they can be modelled and optimized to improve efficiency and effectiveness. The ARIS methodology provides a framework for understanding and analysing business processes, as well as for designing and implementing improvements to those processes. It includes a set of graphical modelling languages and tools for creating process models, as well as a database for storing and managing pr

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