AI Generator Of Trump

AI Generator Of Trump — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Patch management

  Patch management (or patch management policy or patch policy or patch management process) is concerned with the identification, acquisition, distribution, testing and installation of patches to systems. Proper patch management can be a net productivity boost for an organization. Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them. Problems can arise during patch management, including buggy patches that either fail to fix their problem or introduce new issues. Patch management tools help orchestrate all of the procedures involved in patch management. == Description == Patch management is defined as a sub-practice of various disciplines including vulnerability management (part of security management), lifecycle management (with further possible sub-classification into application lifecycle management and release management), change management, and systems management. The practice is broadly concerned with the identification, acquisition, distribution, and installation of patches to systems. Some definitions of patch management are as a software-level practice, while others are as a systems-level process: software, drivers, and firmware. == Cost–benefit analysis == While reserving time for patching takes up enterprise resources, there are balancing factors which can make proper patch management into a net productivity boost for an organization. Up-to-date systems often perform more efficiently, less costly, with less errors, less security risks, and better user workflow. Additionally, compliance with changing local and federal regulations are more likely to be satisfied. Patching security vulnerabilities has been one among many competing priorities for organizations, leading to longer periods before patching for some organizations. Equifax was too slow to implement its 2015 patch management plan to be able to mitigate or prevent the 2017 Equifax data breach, leading to scrutiny from regulators. == Relation to security management == Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them; therefore, patch management can be considered a sub-discipline of vulnerability management. Every patchable device in a system presents an attack surface that must be secured. === Time plan === Automatic updates are where the patch is applied automatically with little to know actions or planning required. This approach is recommended for many individuals and organizations. Some organizations also have to prioritize which patches to prioritize given limited resources. Patch Tuesday is the most common process when major companies like Microsoft and Adobe release patches on a known date so that companies can plan resources around implementing the patches more quickly. Linux is open-sourced and patches can be released at any time, leading some to rely on mailing lists or other ways to be alerted to updates. === Inventory === Taking an inventory of software and hardware, including versions can make it easier to correlate with bugs or patches as they become known. Taking stock of how much education and support others in an organization need to install their patches can also help for planning how to implement the patch or design systems to begin with. Streamlining the process by using tools that can communicate with each other can also help to reduce the time of exposure to known vulnerabilities. == Challenges == There are a multitude of problems that can arise during patch management. A common issue is buggy patches, which either fail to fix their problem or introduce new issues. Another issue is deployment synchronization, since various subsystems may receive instructions to update at different times. Similarly, the difficulty of patch management across many devices may grow at an uncontrollable rate depending on organizational size. One prominent demonstration of the challenges facing proper patch management was the buggy Falcon Sensor patch by CrowdStrike which caused one of the worst IT outages of all time. == Implementations == A patch management tool (alternatively patch manager, patch management system, patch management software, or centralized patch management) help orchestrate all of the procedures involved in patch management. Tools can be in-house (applied locally by local administrators), or external, as with managed service providers (applied externally by a provider). === Patch management software === Windows Update for Business, System Center Configuration Manager, and Windows Server Update Services offer control over patch deployment, with features enabling testing, scheduling updates, and setting custom configurations on Windows platforms. === Managed service providers === == Regulatory requirements (United States) == Timely patching of software vulnerabilities is a requirement under multiple regulatory frameworks in the United States. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect electronic protected health information by implementing security measures sufficient to reduce risks to a reasonable and appropriate level, which industry guidance has long interpreted to include timely patch management. A proposed new HIPAA Security Rule would make patch management requirements explicit, mandating that covered entities and business associates deploy security patches and updates within a defined risk-based timeline and maintain written procedures for prioritizing, testing, and applying patches to systems that store, process, or transmit ePHI. The 2025 proposal continues to receive industry pushback as of December 2025. HIPAA was last updated in 2013. The Payment Card Industry Data Security Standard (PCI DSS) requires organizations to protect system components from known vulnerabilities by installing applicable security patches within one month of release for critical patches. The Cybersecurity and Infrastructure Security Agency (CISA) maintains a Known Exploited Vulnerabilities (KEV) catalog that compels U.S. federal agencies to remediate listed vulnerabilities within specified timelines. Agencies are typically required to patch within 3 weeks, though some vulnerabilities must be fixed within 24 hours.

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  Tom's Planner

  Tom's Planner is a web-based tool and application service provider for project planning, management and collaboration. == History == Tom's Planner is based on Curaçao. In November 2009, it announced its public beta launch on TechCrunch and moved out of beta in August 2010. In 2013 Tom's Planner acquired its competitor Gantto. == Software == Tom's Planner is project management software that enables the creation of project schedules (Gantt charts) using a visual perspective. Tom's Planner uses the Freemium Business Model. Users can register for a free account or choose a paid version. Tom's Planner is available in five languages and is used by thousands of users on a daily basis in more than 100 countries worldwide. Customers range from fortune 500 companies to small mom-and-pop shops. == Reviews == Tom's Planner has been reviewed by PC World, TechCrunch, Lifehacker, and several other periodicals.

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  Directional cubic convolution interpolation

  Directional cubic convolution interpolation (DCCI) is an edge-directed image scaling algorithm created by Dengwen Zhou and Xiaoliu Shen. By taking into account the edges in an image, this scaling algorithm reduces artifacts common to other image scaling algorithms. For example, staircase artifacts on diagonal lines and curves are eliminated. The algorithm resizes an image to 2x its original dimensions, minus 1.

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  Box blur

  A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix 1 9 [ 1 1 1 1 1 1 1 1 1 ] . {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}}.} Due to its property of using equal weights, it can be implemented using a much simpler accumulation algorithm, which is significantly faster than using a sliding-window algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate a Gaussian blur. In the frequency domain, a box blur has zeros and negative components. That is, a sine wave with a period equal to the size of the box will be blurred away entirely, and wavelengths shorter than the size of the box may be phase-reversed, as seen when two bokeh circles touch to form a bright spot where there would be a dark spot between two bright spots in the original image. == Extensions == Gwosdek, et al. has extended Box blur to take a fractional radius: the edges of the 1-D filter are expanded with a fraction. It makes slightly better gaussian approximation possible due to the elimination of integer-rounding error. Mario Klingemann has a "stack blur" that tries to better emulate gaussian's look in one pass by stacking weights: 1 9 [ 1 2 3 2 1 ] {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&2&3&2&1\end{bmatrix}}} The triangular impulse response it forms decomposes to two rounds of box blur. Stacked Integral Image by Bhatia et al. takes the weighted average of a few box blurs to fit the gaussian response curve. == Implementation == The following pseudocode implements a 3x3 box blur. The example does not handle the edges of the image, which would not fit inside the kernel, so that these areas remain unblurred. In practice, the issue is better handled by: Introducing an alpha channel to represent the absence of colors; Extending the boundary by filling in values, ranked by quality: Fill in a mirrored image at the border Fill in a constant color extending from the last pixel Pad in a fixed color A number of optimizations can be applied when implementing the box blur of a radius r and N pixels: The box blur is a separable filter, so that only two 1D passes of averaging 2 r + 1 pixels will be needed, one horizontal and one vertical, for each pixel. This lowers the complexity from O(Nr2) to O(Nr). In digital signal processing terminology, each pass is a moving-average filter. Accumulation. Instead of discarding the sum for each pixel, the algorithm re-uses the previous sum, and updates it by subtracting away the old pixel and adding the new pixel in the blurring range. A summed-area table can be used similarly. This lowers the complexity from O(Nr) to O(N). When being used in multiple passes to approximate a Gaussian blur, the cascaded integrator–comb filter construction allows for doing the equivalent operation in a single pass.

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  Google Books Ngram Viewer

  The Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in printed sources published between 1500 and 2022 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. There are also some specialized English corpora, such as American English, British English, and English Fiction. The program can search for a word or a phrase. The n-grams are matched with the text within the selected corpus, and if found in 40 or more books, are then displayed as a graph. The program supports searches for parts of speech and wildcards. It is routinely used in research. == History == The Ngram Viewer was created by Google software engineers Will Brockman and Jon Orwant , who teamed up with Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden. The service was released on December 16, 2010. Before the release, it was difficult to quantify the rate of linguistic change because of the absence of a database that was designed for this purpose, said Steven Pinker, a well-known linguist who was one of the co-authors of the Science paper published on the same day. The Google Books Ngram Viewer was developed in the hope of opening a new window to quantitative research in the humanities field, and the database contained 500 billion words from 5.2 million books publicly available from the very beginning. The intended audience was scholarly, but the Google Books Ngram Viewer made it possible for anyone with a computer to see a graph that represents the diachronic change of the use of words and phrases with ease. Lieberman said in response to The New York Times that the developers aimed to provide even children with the ability to browse cultural trends throughout history. In the Science paper, Lieberman and his collaborators called the method of high-volume data analysis in digitized texts "culturomics". == Usage == Commas delimit user-entered search terms, where each comma-separated term is searched in the database as an n-gram (for example, "nursery school" is a 2-gram or bigram). The Ngram Viewer then returns a plotted line chart. Due to limitations on the size of the Ngram database, only matches found in at least 40 books are indexed. == Limitations == The data sets of the Ngram Viewer have been criticized for their reliance upon inaccurate optical character recognition (OCR) and for including large numbers of incorrectly dated and categorized texts. Because of these errors, and because they are uncontrolled for bias (such as the increasing amount of scientific literature, which causes other terms to appear to decline in popularity), care must be taken in using the corpora to study language or test theories. Furthermore, the data sets may not reflect general linguistic or cultural change and can only hint at such an effect because they do not involve any metadata like date published, author, length, or genre, to avoid any potential copyright infringements. Systemic errors like the confusion of s and f in pre-19th century texts (due to the use of ſ, the long s, which is similar in appearance to f) can cause systemic bias. Although the Google Books team claims that the results are reliable from 1800 onwards, poor OCR and insufficient data mean that frequencies given for languages such as Chinese may only be accurate from 1970 onward, with earlier parts of the corpus showing no results at all for common terms, and data for some years containing more than 50% noise. Guidelines for doing research with data from Google Ngram have been proposed that try to address some of the issues discussed above.

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  Caffe (software)

  Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. == History == Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub. == Features == Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. == Applications == Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework. == Caffe2 == In April 2017, Facebook announced Caffe2, which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch.

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  Microsoft Azure

  Microsoft Azure, sometimes stylized Azure, and formerly Windows Azure, is the cloud computing platform developed by Microsoft. It offers management, access and development of applications and services to individuals, companies, and governments through its global infrastructure. Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. Azure was first introduced at the Professional Developers Conference (PDC) in October 2008 under the codename "Project Red Dog". It was officially launched as Windows Azure in February 2010 and later renamed to Microsoft Azure on March 25, 2014. == Services == Microsoft Azure uses large-scale virtualization at Microsoft data centers worldwide and offers more than 600 services. Microsoft Azure offers a service level agreement (SLA) that guarantees 99.9% availability for applications and data hosted on its platform, subject to specific terms and conditions outlined in the SLA documentation. === Computer services === Virtual machines, infrastructure as a service (IaaS), allowing users to launch general-purpose Microsoft Windows and Linux virtual machines, software as a service (SaaS), as well as preconfigured machine images for popular software packages. Starting in 2022, these virtual machines are now powered by Ampere Cloud-native processors. Most users run Linux on Azure, some of the many Linux distributions offered, including Microsoft's own Linux-based Azure Sphere. App services, platform as a service (PaaS) environment, letting developers easily publish and manage websites. Azure Web Sites allows developers to build sites using ASP.NET, PHP, Node.js, Java, or Python, which can be deployed using FTP, Git, Mercurial, Azure DevOps, or uploaded through the user portal. This feature was announced in preview form in June 2012 at the Meet Microsoft Azure event. Customers can create websites in PHP, ASP.NET, Node.js, or Python, or select from several open-source applications from a gallery to deploy. This comprises one aspect of the platform as a service (PaaS) offerings for the Microsoft Azure Platform. It was renamed Web Apps in April 2015. Web Jobs are applications that can be deployed to an App Service environment to implement background processing that can be invoked on a schedule, on-demand, or run continuously. The Blob, Table, and Queue services can be used to communicate between Web Apps and Web Jobs and to provide state. Azure Kubernetes Service (AKS) provides the capability to deploy production-ready Kubernetes clusters in Azure. In July 2023, watermarking support on Azure Virtual Desktop was announced as an optional feature of Screen Capture to provide additional security against data leakage. === Identity === Entra ID connect is used to synchronize on-premises directories and enable SSO (Single Sign On). Entra ID B2C allows the use of consumer identity and access management in the cloud. Entra Domain Services is used to join Azure virtual machines to a domain without domain controllers. Azure information protection can be used to protect sensitive information. Entra ID External Identities is a set of capabilities that allow organizations to collaborate with external users, including customers and partners. On July 11, 2023, Microsoft announced the renaming of Azure AD to Microsoft Entra ID. The name change took place four days later. === Mobile services === Mobile Engagement collects real-time analytics that highlight users' behavior. It also provides push notifications to mobile devices. HockeyApp can be used to develop, distribute, and beta-test mobile apps. === Storage services === Storage Services provides REST and SDK APIs for storing and accessing data on the cloud. Table Service lets programs store structured text in partitioned collections of entities that are accessed by the partition key and primary key. Azure Table Service is a NoSQL non-relational database. Blob Service allows programs to store unstructured text and binary data as object storage blobs that can be accessed by an HTTP(S) path. Blob service also provides security mechanisms to control access to data. Queue Service lets programs communicate asynchronously by message using queues. File Service allows storing and access of data on the cloud using the REST APIs or the SMB protocol. === Communication services === Azure Communication Services offers an SDK for creating web and mobile communications applications that include SMS, video calling, VOIP and PSTN calling, and web-based chat. === Data management === Azure Data Explorer provides big data analytics and data-exploration capabilities. Azure Search provides text search and a subset of OData's structured filters using REST or SDK APIs. Cosmos DB is a NoSQL database service that implements a subset of the SQL SELECT statement on JSON documents. Azure Cache for Redis is a managed implementation of Redis. StorSimple manages storage tasks between on-premises devices and cloud storage. Azure SQL Database works to create, scale, and extend applications into the cloud using Microsoft SQL Server technology. It also integrates with Active Directory, Microsoft System Center, and Hadoop. Azure Synapse Analytics is a fully managed cloud data warehouse. Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. Azure Data Lake is a scalable data storage and analytic service for big data analytics workloads that require developers to run massively parallel queries. Azure HDInsight is a big data-relevant service that deploys Hortonworks Hadoop on Microsoft Azure and supports the creation of Hadoop clusters using Linux with Ubuntu. Azure Stream Analytics is a Serverless scalable event-processing engine that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, websites, social media, and other applications. === Messaging === The Microsoft Azure Service Bus allows applications running on Azure premises or off-premises devices to communicate with Azure. This helps to build scalable and reliable applications in a service-oriented architecture (SOA). The Azure service bus supports four different types of communication mechanisms: Event Hubs, which provides event and telemetry ingress to the cloud at a massive scale, with low latency and high reliability. For example, an event hub can be used to track data from cell phones such as coordinating with a GPS in real time. Queues, which allows one-directional communication. A sender application would send the message to the service bus queue and a receiver would read from the queue. Though there can be multiple readers for the queue, only one would process a single message. Topics, which provides one-directional communication using a subscriber pattern. It is similar to a queue; however, each subscriber will receive a copy of the message sent to a Topic. Optionally, the subscriber can filter out messages based on specific criteria defined by the subscriber. Relays, which provides bi-directional communication. Unlike queues and topics, a relay does not store in-flight messages in its memory; instead, it just passes them on to the destination application. === Media services === A PaaS offering that can be used for encoding, content protection, streaming, or analytics. === CDN === Azure has a worldwide content delivery network (CDN) designed to efficiently deliver audio, video, applications, images, and other static files. It improves the performance of websites by caching static files closer to users, based on their geographic location. Users can manage the network using a REST-based HTTP API. Azure has 118 point-of-presence locations across 100 cities worldwide (also known as Edge locations) as of January 2023. === Developer === Application Insights Azure DevOps === Management === With Azure Automation, users can easily automate repetitive and time-consuming tasks, often prone to cloud or enterprise setting errors. They can accomplish it using runbooks or desired state configurations for process automation. Microsoft SMA === Azure AI === Microsoft Azure Machine Learning (Azure ML) provides tools and frameworks for developers to create their own machine learning and artificial intelligence (AI) services. Azure AI Services by Microsoft comprises prebuilt APIs, SDKs, and services developers can customize. These services encompass perceptual and cognitive intelligence features such as speech recognition, speaker recognition, neural speech synthesis, face recognition, computer vision, OCR/form understanding, natural language processing, machine translation, and business decision services. Many AI characteristics in Microsoft's products and services, namely Bing, Office, Teams, Xbox, and Windows, are driven by Azure AI Services. Microsoft Foundry (formerly known as Azure AI Studio)

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  Radek Maneuver

  The Radek Maneuver is a scale-up-then-scale-down tactic used in the administration of web services, specifically those deployed under a cloud computing paradigm (by a provider e.g. Amazon Elastic Compute Cloud or Microsoft Azure). == History == Developed by Olivier "Radek" Dabrowski in the mid-2010s, the Radek Maneuver was originally conceived of in using and maintaining applications running on a PaaS system. == Execution == The Radek Maneuver consists of a series of steps, usually executed via the PaaS or web portal interface. The tactic should be used when a service is misbehaving or otherwise experiencing errors, and the suspected cause is the underlying cloud layer, rather than the application layer. This includes networking issues and other "bad box" problems. The steps are as follows: Identify the application or service which is misbehaving. Increase the compute resource (number of CPU cores, amount of ram) for the instance on which the application is running. This is also known as scaling up. Wait for the application to re-deploy and stabilize. Scale back down to the original instance size. == Principle of action == This scale-up-scale-down method is understood to shift the application to a different physical machine underlying the PaaS service or application virtual machine. While this layer of the cloud computing stack is generally out of the access of an application developer (instead in the hands of the cloud provider), the maneuver allows troubleshooting and dodging errors in that layer.

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  Microsoft Support Diagnostic Tool

  The Microsoft Support Diagnostic Tool (MSDT) is a legacy service in Microsoft Windows that allows Microsoft technical support agents to analyze diagnostic data remotely for troubleshooting purposes. In April 2022 it was observed to have a security vulnerability that allowed remote code execution which was being exploited to attack computers in Russia and Belarus, and later against the Tibetan government in exile. Microsoft advised a temporary workaround of disabling the MSDT by editing the Windows registry. == Use == When contacting support the user is told to run MSDT and given a unique "passkey" which they enter. They are also given an "incident number" to uniquely identify their case. The MSDT can also be run offline which will generate a .CAB file which can be uploaded from a computer with an internet connection. == Security vulnerabilities == === Follina === Follina is the name given to a remote code execution (RCE) vulnerability, a type of arbitrary code execution (ACE) exploit, in the Microsoft Support Diagnostic Tool (MSDT) which was first widely publicized on May 27, 2022, by a security research group called Nao Sec. This exploit allows a remote attacker to use a Microsoft Office document template to execute code via MSDT. This works by exploiting the ability of Microsoft Office document templates to download additional content from a remote server. If the size of the downloaded content is large enough it causes a buffer overflow allowing a payload of Powershell code to be executed without explicit notification to the user. On May 30 Microsoft issued CVE-2022-30190 with guidance that users should disable MSDT. Malicious actors have been observed exploiting the bug to attack computers in Russia and Belarus since April, and it is believed Chinese state actors had been exploiting it to attack the Tibetan government in exile based in India. Microsoft patched this vulnerability in its June 2022 patches. === DogWalk === The DogWalk vulnerability is a remote code execution (RCE) vulnerability in the Microsoft Support Diagnostic Tool (MSDT). It was first reported in January 2020, but Microsoft initially did not consider it to be a security issue. However, the vulnerability was later exploited in the wild, and Microsoft released a patch for it in August 2022. The vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. Originally discovered by Mitja Kolsek, the DogWalk vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. The vulnerability is exploited by creating a malicious diagcab file that contains a specially crafted path. This path contains a sequence of characters that is designed to exploit the path traversal vulnerability in the sdiageng.dll library. When the diagcab file is opened, the MSDT tool will attempt to follow the path. However, the path will contain characters that are not valid for a Windows path. This will cause the MSDT tool to crash. When the MSDT tool crashes, it will generate a memory dump. This memory dump will contain the malicious code that was executed by the MSDT tool. The attacker can then use this memory dump to extract the malicious code and execute it on their own computer. == Retirement == Microsoft will no longer be supporting the Windows legacy inbox Troubleshooters. In 2025, Microsoft will remove the MSDT platform entirely. Get Help is the replacement tool. == Windows versions == Windows 7 Windows 8.1 Windows 10 Windows 11 (up to 22H2) Future versions and feature upgrades will deprecate the MSDT after May 23, 2023.

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  Message queuing service

  A message queueing service is a message-oriented middleware or MOM deployed in a compute cloud using software as a service model. Service subscribers access queues and or topics to exchange data using point-to-point or publish and subscribe patterns. It's important to differentiate between event-driven and message-driven (aka queue driven) services: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers. Message queues can be a good buffer to handle spiky workloads but they have a finite capacity. According to Gregor Hohpe, message queues require proper mechanisms (aka flow controls) to avoid filling the queue beyond its manageable capacity and to keep the system stable. == Ordering Guarantees in Message Queues == Amazon SQS FIFO and Azure Service Bus sessions are queue-based messaging systems that provide ordering guarantees within a message group or session attempt but do not necessarily guarantee ordered delivery in cases of retries or failures. In SQS FIFO, messages in the same message group are processed in order, with subsequent messages held until the preceding message is successfully processed or moved to the dead-letter queue (DLQ). Once a message is placed in the DLQ, it is no longer retried, creating a gap in the sequence. However, the remaining messages continue to be delivered in order. Azure Service Bus sessions function similarly by maintaining ordering within a session, provided a single consumer processes messages sequentially. The implementation differs from SQS FIFO but follows the same fundamental ordering principle. In contrast, Apache Kafka is a distributed log-based messaging system that guarantees ordering within individual partitions rather than across the entire topic. Unlike queue-based systems, Kafka retains messages in a durable, append-only log, allowing multiple consumers to read at different offsets. Kafka uses manual offset management, giving consumers control over retries and failure handling. If a consumer fails to process a message, it can delay committing the offset, preventing further progress in that partition while other partitions remain unaffected. This partition-based design enables fault isolation and parallel processing while allowing ordering to be maintained within partitions, depending on consumer handling. == Vendors == Apache Kafka Apache Kafka is a distributed system consisting of servers that store and forward messages between producer client and consumer applications. IBM MQ IBM MQ offers a managed service that can be used on IBM Cloud and Amazon Web Services. Microsoft Azure Service Bus Service Bus offers queues, topics & subscriptions, and rules/actions in order to support publish-subscribe, temporal decoupling, and load balancing scenarios. Azure Service Bus is built on AMQP allowing any existing AMQP 1.0 client stack to interact with Service Bus directly or via existing .Net, Java, Node, and Python clients. Standard and Premium tiers allow for pay as you go or isolated resources at massive scale. Oracle Messaging Cloud Service This service provides a messaging solution for applications for asynchronous communication and is influenced by the Java Message Service (JMS) API specification. Any application platform that understands HTTP can also use Oracle Messaging Cloud Service through the REST interface. For Java applications, Oracle Messaging Cloud Service provides a Java library that implements and extends the JMS 1.1 interface. The Java library implements the JMS API by acting as a client of the REST API. Amazon Simple Queue Service Supports messages natively up to 256K, or up to 2GB by transmitting payload via S3. Highly scalable, durable and resilient. Provides loose-FIFO and 'at least once' delivery in order to provide massive scale. Supports REST API and optional Java Message Service client. Low latency. Utilizes Amazon Web Services. IronMQ Supports messages up to 64k; guarantees order; guarantees once only delivery; no delays retrieving messages. Supports REST API and beanstalkd open source protocol. Runs on multiple clouds including AWS and Rackspace. Scaling must be managed by user. RabbitMQ RabbitMQ is a reliable and mature messaging and streaming broker, which is easy to deploy on cloud environments, on-premises, and on your local machine. Supports AMQP, STOMP, MQTT StormMQ Open platform supports messages up to 50Mb. Uses AMQP to avoid vendor lock-in and provide language neutrality. Locate-It Option allows customers to audit the location of their data at all times and satisfy data protection principles. AnypointMQ An enterprise multi-tenant, cloud messaging service that performs advanced asynchronous messaging scenarios between applications. Anypoint MQ is fully integrated with Anypoint Platform, offering role based access control, client application management, and connectors.

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  Static program analysis

  In computer science, static program analysis (also known as static analysis or static simulation) is the analysis of computer programs performed without executing them, in contrast with dynamic program analysis, which is performed on programs during their execution in the integrated environment. The term is usually applied to analysis performed by an automated tool, with human analysis typically being called "program understanding", program comprehension, or code review. In the last of these, software inspection and software walkthroughs are also used. In most cases the analysis is performed on some version of a program's source code, and, in other cases, on some form of its object code. Two leading approaches to resource certification have been Static Analysis (SA) and Implicit Computational Complexity (ICC). SA is algorithmic in nature: it focuses on a broad programming language of choice, and seeks to determine by syntactic means whether given programs in that language are feasible. In contrast, ICC attempts to create from the outset specialized programming languages or methods that delineate a complexity class. Thus, SA's focus is on compile time, making no demand on the programmer; whereas ICC is a language-design discipline." The discipline of static analysis should not be confused with linting, which is the process of checking for coding style mistakes. == Rationale == The sophistication of the analysis performed by tools varies from those that only consider the behaviour of individual statements and declarations, to those that include the complete source code of a program in their analysis. The uses of the information obtained from the analysis vary from highlighting possible coding errors (e.g., the lint tool) to formal methods that mathematically prove properties about a given program (e.g., its behaviour matches that of its specification). Software metrics and reverse engineering can be described as forms of static analysis. Deriving software metrics and static analysis are increasingly deployed together, especially in creation of embedded systems, by defining so-called software quality objectives. A growing commercial use of static analysis is in the verification of properties of software used in safety-critical computer systems and locating potentially vulnerable code. For example, the following industries have identified the use of static code analysis as a means of improving the quality of increasingly sophisticated and complex software: Medical software: The US Food and Drug Administration (FDA) has identified the use of static analysis for medical devices. Nuclear software: In the UK the Office for Nuclear Regulation (ONR) recommends the use of static analysis on reactor protection systems. Aviation software (in combination with dynamic analysis). Automotive & Machines (functional safety features form an integral part of each automotive product development phase, ISO 26262, section 8). A study in 2012 by VDC Research reported that 28.7% of the embedded software engineers surveyed use static analysis tools and 39.7% expect to use them within 2 years. A study from 2010 found that 60% of the interviewed developers in European research projects made at least use of their basic IDE built-in static analyzers. However, only about 10% employed an additional other (and perhaps more advanced) analysis tool. In the application security industry the name static application security testing (SAST) is also used. SAST is an important part of Security Development Lifecycles (SDLs) such as the SDL defined by Microsoft and a common practice in software companies. == Tool types == The OMG (Object Management Group) published a study regarding the types of software analysis required for software quality measurement and assessment. This document on "How to Deliver Resilient, Secure, Efficient, and Easily Changed IT Systems in Line with CISQ Recommendations" describes three levels of software analysis. Unit Level Analysis that takes place within a specific program or subroutine, without connecting to the context of that program. Technology Level Analysis that takes into account interactions between unit programs to get a more holistic and semantic view of the overall program in order to find issues and avoid obvious false positives. System Level Analysis that takes into account the interactions between unit programs, but without being limited to one specific technology or programming language. A further level of software analysis can be defined. Mission/Business Level Analysis that takes into account the business/mission layer terms, rules and processes that are implemented within the software system for its operation as part of enterprise or program/mission layer activities. These elements are implemented without being limited to one specific technology or programming language and in many cases are distributed across multiple languages, but are statically extracted and analyzed for system understanding for mission assurance. == Formal methods == Formal methods is the term applied to the analysis of software (and computer hardware) whose results are obtained purely through the use of rigorous mathematical methods. The mathematical techniques used include denotational semantics, axiomatic semantics, operational semantics, and abstract interpretation. By a straightforward reduction to the halting problem, it is possible to prove that (for any Turing complete language), finding all possible run-time errors in an arbitrary program (or more generally any kind of violation of a specification on the final result of a program) is undecidable: there is no mechanical method that can always answer truthfully whether an arbitrary program may or may not exhibit runtime errors. This result dates from the works of Church, Gödel and Turing in the 1930s (see: Halting problem and Rice's theorem). As with many undecidable questions, one can still attempt to give useful approximate solutions. Some of the implementation techniques of formal static analysis include: Abstract interpretation, to model the effect that every statement has on the state of an abstract machine (i.e., it 'executes' the software based on the mathematical properties of each statement and declaration). This abstract machine over-approximates the behaviours of the system: the abstract system is thus made simpler to analyze, at the expense of incompleteness (not every property true of the original system is true of the abstract system). If properly done, though, abstract interpretation is sound (every property true of the abstract system can be mapped to a true property of the original system). Data-flow analysis, a lattice-based technique for gathering information about the possible set of values; Hoare logic, a formal system with a set of logical rules for reasoning rigorously about the correctness of computer programs. There is tool support for some programming languages (e.g., the SPARK programming language (a subset of Ada) and the Java Modeling Language—JML—using ESC/Java and ESC/Java2, Frama-C WP (weakest precondition) plugin for the C language extended with ACSL (ANSI/ISO C Specification Language) ). Model checking, considers systems that have finite state or may be reduced to finite state by abstraction; Symbolic execution, as used to derive mathematical expressions representing the value of mutated variables at particular points in the code. Nullable reference analysis == Data-driven static analysis == Data-driven static analysis leverages extensive codebases to infer coding rules and improve the accuracy of the analysis. For instance, one can use all Java open-source packages available on GitHub to learn good analysis strategies. The rule inference can use machine learning techniques. It is also possible to learn from a large amount of past fixes and warnings. == Remediation == Static analyzers produce warnings. For certain types of warnings, it is possible to design and implement automated remediation techniques. For example, Logozzo and Ball have proposed automated remediations for C# cccheck.

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  Pandas (software)

  Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals, as well as a play on the phrase "Python data analysis". Wes McKinney started building what would become Pandas at AQR Capital while he was a researcher there from 2007 to 2010. The development of Pandas introduced into Python many comparable features of working with DataFrames that were established in the R programming language. The library is built upon another library, NumPy. == History == Developer Wes McKinney started working on Pandas in 2008 while at AQR Capital Management out of the need for a high performance, flexible tool to perform quantitative analysis on financial data. Before leaving AQR, he was able to convince management to allow him to open source the library in 2009. Another AQR employee, Chang She, joined the effort in 2012 as the second major contributor to the library. In 2015, Pandas signed on as a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. == Data model == Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. === Series === A Series is a one-dimensional array-like object that stores a sequence of values together with an associated set of labels, called an index. It is built on top of NumPy's array and affords many similar functionalities, but instead of using implicit integer positions, a Series allows explicit index labels of many data types. A Series can be created from Python lists, dictionaries, or NumPy arrays. If no index is provided, pandas automatically assigns a default integer index ranging from 0 to n-1, where n is the number of items in the Series. A simple example with customized labels is: To access a value or list of values from a Series, use its index or list of indices: Series can be used arithmetically, as in the statement series_3 = series_1 + series_2. This will align data points with corresponding index values in series_1 and series_2 (similar to a join in relational algebra), then add them together to produce new values in series_3. A Series has various attributes, such as name (Series name), dtype (data type of values), shape (number of rows), values, and index. They can be used in many of the same operations as NumPy arrays, with additional methods for reindexing, label-based selection, and handling missing data. === DataFrame === A DataFrame is a two-dimensional, tabular data structure with labeled rows and columns. Each column is stored internally as a Series and may hold a different data type (numeric, string, boolean, etc.). DataFrames can be created by a variety of means, including dictionaries of lists, NumPy arrays, and external files such as CSV or Excel spreadsheets: To retrieve a DataFrame column as a Series, use either 1) the index (dict-like notation) or 2) the name of column if the name is a valid Python identifier (attribute-like access). DataFrames support operations such as column assignment, row and column deletion, label-based indexing with loc, position-based indexing with iloc, reshaping, grouping, and joining. Merge operations implement a subset of relational algebra and allow one-to-one, many-to-one, and many-to-many joins. Some common attributes of a DataFrame include dtypes (data type of each column), shape (dimensions of the DataFrame returned as a tuple with form (number of rows, number of columns)), index/columns (labels of the DataFrame's rows/columns, respectively, returned as an Index object), values (data in the DataFrame returned as a 2D array), and empty (returns True if the DataFrame is empty). === Index === Index objects hold metadata for Series and Dataframe objects, such as axis labels and names, and are automatically created from input data. By default, a pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can also use any NumPy data type, including floating point, timestamps, or strings. Indices are also immutable, which allows them to be safely shared across multiple objects. pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values. For example, if s is a Series, s['a'] will return the data point at index a. Unlike dictionary keys, index values are not guaranteed to be unique. If a Series uses the index value a for multiple data points, then s['a'] will instead return a new Series containing all matching values. A DataFrame's column names are stored and implemented identically to an index. As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it is actually defined. pandas also supports hierarchical indices with multiple values per data point through the "MultiIndex" class. MultiIndex objects allow a single DataFrame to represent multiple dimensions, similar to a pivot table in Microsoft Excel, where each level can optionally carry its own unique name. In practice, data with more than 2 dimensions is often represented using DataFrames with hierarchical indices, instead of the higher-dimension Panel and Panel4D data structures. == Functionality == pandas supports a variety of indexing and subsetting techniques, allowing data to be selected by label, index, or Boolean conditions. For example, df[df['col1'] > 5] will return all rows in the DataFrame df for which the value of the column col1 exceeds 5. The library also implements grouping operations based on the split-apply-combine approach, enabling users to aggregate, transform, or restructure data according to column values or functions applied to index labels. For example, df['col1'].groupby(df['col2']) groups the data in 'col1' by their values in 'col2', while df.groupby(lambda i: i % 2) groups all data in the whole DataFrame by whether their index is even. The library also provides extensive tools for transforming, filtering and summarizing data. Users may apply arbitrary functions to Series and DataFrames, and because the library is built on top of Numpy, most NumPy functions can be applied directly to pandas objects as well. The library also includes built-in operations for arithmetic operations, string processing, and descriptive statistics such as mean, median, and standard deviation. These built-in functions are designed to handle missing data, usually represented by the floating-point value NaN. In addition, pandas includes tools for reorganizing data into different structural formats, with methods that can reshape tabular data between "wide" and "long" formats and pivot values based on column labels. pandas also implements a flexible set of relational operations for combining datasets. For instance, merge() links row in DataFrames based on one or more shared keys or indices, supporting one-to-one, one-to-many, and many-to-many relationships in a manner analogous to join operations in relational databases like SQL. DataFrames can also be concatenated or stacked together along an axis through the concat() method, and overlapping data can be further spliced together using combine_first() to fill in missing values. Furthermore, the library includes specialized support for working with time-series data. Features include the ability to interpolate values and filter using a range of timestamps, such as data['1/1/2023':'2/2/2023'] , which will return all dates between January 1 and February 2. Missing values in time-series data are represented by a dedicated NaT (Not a Timestamp) object, instead of the NaN value it uses elsewhere. == Criticisms == Pandas has been criticized for its inefficiency. The entire dataset must be loaded in RAM, and the library does not optimize query plans or support parallel computing across multiple cores. Wes McKinney, the creator of Pandas, has recommended Apache Arrow as an alternative to address these performance concerns and ot

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  Automatic acquisition of sense-tagged corpora

  The knowledge acquisition bottleneck is perhaps the major impediment to solving the word-sense disambiguation (WSD) problem. Unsupervised learning methods rely on knowledge about word senses, which is barely formulated in dictionaries and lexical databases. Supervised learning methods depend heavily on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the Senseval exercises. == Existing methods == Therefore, one of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically. WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval (IR). In this case, however, the reverse is also true: Web search engines implement simple and robust IR techniques that can be successfully used when mining the Web for information to be employed in WSD. The most direct way of using the Web (and other corpora) to enhance WSD performance is the automatic acquisition of sense-tagged corpora, the fundamental resource to feed supervised WSD algorithms. Although this is far from being commonplace in the WSD literature, a number of different and effective strategies to achieve this goal have already been proposed. Some of these strategies are: acquisition by direct Web searching (searches for monosemous synonyms, hypernyms, hyponyms, parsed gloss' words, etc.), Yarowsky algorithm (bootstrapping), acquisition via Web directories, and acquisition via cross-language meaning evidences. == Summary == === Optimistic results === The automatic extraction of examples to train supervised learning algorithms reviewed has been, by far, the best explored approach to mine the web for word-sense disambiguation. Some results are certainly encouraging: In some experiments, the quality of the Web data for WSD equals that of human-tagged examples. This is the case of the monosemous relatives plus bootstrapping with Semcor seeds technique and the examples taken from the ODP Web directories. In the first case, however, Semcor-size example seeds are necessary (and only available for English), and it has only been tested with a very limited set of nouns; in the second case, the coverage is quite limited, and it is not yet clear whether it can be grown without compromising the quality of the examples retrieved. It has been shown that a mainstream supervised learning technique trained exclusively with web data can obtain better results than all unsupervised WSD systems which participated at Senseval-2. Web examples made a significant contribution to the best Senseval-2 English all-words system. === Difficulties === There are, however, several open research issues related to the use of Web examples in WSD: High precision in the retrieved examples (i.e., correct sense assignments for the examples) does not necessarily lead to good supervised WSD results (i.e., the examples are possibly not useful for training). The most complete evaluation of Web examples for supervised WSD indicates that learning with Web data improves over unsupervised techniques, but the results are nevertheless far from those obtained with hand-tagged data, and do not even beat the most-frequent-sense baseline. Results are not always reproducible; the same or similar techniques may lead to different results in different experiments. Compare, for instance, Mihalcea (2002) with Agirre and Martínez (2004), or Agirre and Martínez (2000) with Mihalcea and Moldovan (1999). Results with Web data seem to be very sensitive to small differences in the learning algorithm, to when the corpus was extracted (search engines change continuously), and on small heuristic issues (e.g., differences in filters to discard part of the retrieved examples). Results are strongly dependent on bias (i.e., on the relative frequencies of examples per word sense). It is unclear whether this is simply a problem of Web data, or an intrinsic problem of supervised learning techniques, or just a problem of how WSD systems are evaluated (indeed, testing with rather small Senseval data may overemphasize sense distributions compared to sense distributions obtained from the full Web as corpus). In any case, Web data has an intrinsic bias, because queries to search engines directly constrain the context of the examples retrieved. There are approaches that alleviate this problem, such as using several different seeds/queries per sense or assigning senses to Web directories and then scanning directories for examples; but this problem is nevertheless far from being solved. Once a Web corpus of examples is built, it is not entirely clear whether its distribution is safe from a legal perspective. === Future === Besides automatic acquisition of examples from the Web, there are some other WSD experiments that have profited from the Web: The Web as a social network has been successfully used for cooperative annotation of a corpus (OMWE, Open Mind Word Expert project), which has already been used in three Senseval-3 tasks (English, Romanian and Multilingual). The Web has been used to enrich WordNet senses with domain information: topic signatures and Web directories, which have in turn been successfully used for WSD. Also, some research benefited from the semantic information that the Wikipedia maintains on its disambiguation pages. It is clear, however, that most research opportunities remain largely unexplored. For instance, little is known about how to use lexical information extracted from the Web in knowledge-based WSD systems; and it is also hard to find systems that use Web-mined parallel corpora for WSD, even though there are already efficient algorithms that use parallel corpora in WSD.

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  Cloud-based quantum computing

  Cloud-based quantum computing refers to the remote access of quantum computing resources—such as quantum emulators, simulators, or processors—via the internet. Cloud access enables users to develop, test, and execute quantum algorithms without the need for direct interaction with specialized hardware, facilitating broader participation in quantum software development and experimentation. In 2016, IBM launched the IBM Quantum Experience, one of the first publicly accessible quantum processors connected to the cloud. In early 2017, researchers at Rigetti Computing demonstrated programmable quantum cloud access through their software platform Forest, which included the pyQuil Python library. Since the early-2020s, cloud-based quantum computing has grown significantly, with multiple providers offering access to a variety of quantum hardware modalities, including superconducting qubits, trapped ions, neutral atoms, and photonic systems. Major platforms such as Amazon Braket, Azure Quantum, and qBraid aggregate quantum devices from hardware developers like IonQ, Rigetti Computing, QuEra, Pasqal, Oxford Quantum Circuits, and IBM Quantum. These platforms provide unified interfaces for users to write and execute quantum algorithms across diverse backends, often supporting open-source SDKs such as Qiskit, Cirq, and PennyLane. The proliferation of cloud-based access has played a key role in accelerating quantum education, algorithm research, and early-stage application development by lowering the barrier to experimentation with real quantum hardware. Cloud-based quantum computing has expanded access to quantum hardware and tools beyond traditional research laboratories. These platforms support educational initiatives, algorithm development, and early-stage commercial applications. == Applications == Cloud-based quantum computing is used across education, research, and software development, offering remote access to quantum systems without the need for on-site infrastructure. === Education === Quantum cloud platforms have become valuable tools in education, allowing students and instructors to engage with real quantum processors through user-friendly interfaces. Educators use these platforms to teach foundational concepts in quantum mechanics and quantum computing, as well as to demonstrate and implement quantum algorithms in a classroom or laboratory setting. === Scientific Research === Cloud-based access to quantum hardware has enabled researchers to conduct experiments in quantum information, test quantum algorithms, and compare quantum hardware platforms. Experiments such as testing Bell's theorem or evaluating quantum teleportation protocols have been performed on publicly available quantum processors. === Software Development and Prototyping === Developers use cloud-based platforms to prototype quantum software applications across fields such as optimization, machine learning, and chemistry. These platforms offer SDKs and APIs that integrate classical and quantum workflows, enabling experimentation with quantum algorithms in real-world or simulated environments. === Public Engagement and Games === Quantum cloud tools have also been used to create educational games and interactive applications aimed at increasing public understanding of quantum concepts. These efforts help bridge the gap between theoretical content and intuitive learning. == Existing platforms == qBraid Lab by qBraid is a cloud-based platform for quantum computing. It provides software tools for researchers and developers in quantum, as well as access to quantum hardware. qBraid provides cloud based access to Microsoft Azure Quantum and Amazon Braket devices including IQM, QuEra, Pasqal, Rigetti, IonQ, QIR simulators, Amazon Braket simulators, and the NEC Vector Annealer, as of August 2025. qBraid's base version is free, where unlimited hardware and simulator access is available with the purchase of credits. Quandela Cloud by Quandela is the platform to access first cloud-accessible European photonic quantum computer. The computer is interfaced using the Perceval scripting language, with tutorials and documentation available online for free. Xanadu Quantum Cloud by Xanadu is a platform with cloud-based access to three fully programmable photonic quantum computers. Forest by Rigetti Computing is a tool suite for cloud-based quantum computing. It includes a programming language, development tools and example algorithms. LIQUi> by Microsoft is a software architecture and tool suite for quantum computing. It includes a programming language, example optimization and scheduling algorithms, and quantum simulators. Q#, a quantum programming language by Microsoft on the .NET Framework seen as a successor to LIQUi|>. IBM Quantum Platform by IBM, providing access to quantum hardware as well as HPC simulators. These can be accessed programmatically using the Python-based Qiskit framework, or via graphical interface with the IBM Q Experience GUI. Both are based on the OpenQASM standard for representing quantum operations. There is also a tutorial and online community. Quantum in the Cloud by The University of Bristol, which consists of a quantum simulator and a four qubit optical quantum system. Quantum Playground by Google is an educational resource which features a simulator with a simple interface, and a scripting language and 3D quantum state visualization. Quantum in the Cloud is an experimental quantum cloud platform for access to a four-qubit nuclear magnetic resonance-NMRCloudQ computer, managed by Tsinghua University. Quantum Inspire by Qutech is the first platform in Europe providing cloud-based quantum computing to two hardware chips. Next to a 5-qubit transmon processor, Quantum Inspire is the first platform in the world to provide online access to a fully programmable 2-qubit electron spin quantum processor. Amazon Braket is a cloud-based quantum computing platform hosted by AWS which, as of June 2025, provides access to quantum computers built by IonQ, Rigetti, IQM, and QuEra. Braket also provides a quantum algorithm development environment and simulator. Forge by QC Ware is a cloud-based quantum computing platform that provides access to D-Wave hardware, as well as Google and IBM simulators. The platform offers a 30-day free trial, including one minute of quantum computing time. Quantum-as-a-Service by Scaleway is a cloud-based platform created in 2022 to access to real quantum hardware from IQM Quantum Computers, Alpine Quantum Technologies, Quandela and Pasqal. It also include access to GPU-powered emulators such as Aer, Qsim and Quandela proprietary emulation.

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  Shopify

  Shopify Inc., stylized as shopify, is a Canadian multinational e-commerce company headquartered in Ottawa, Ontario that operates a platform for retail point-of-sale systems. The company has over 5 million customers and processed US$292.3 billion in transactions in 2024, of which 57% was in the United States. Major customers include Tesla, LVMH, Nestlé, PepsiCo, AB InBev, Kraft Heinz, Lindt, Whole Foods Market, Red Bull, and Hyatt. The company's software has been praised for its ease of use and reasonable fee structure. It has been described as the "go-to e-commerce platform for startups". However, the company has faced criticism for allegedly inflating their sales data and for associating with controversial sellers. == History == === 2006: Founding === Shopify was founded in 2006 by friends Tobias Lütke, Daniel Weinand and Scott Lake after launching Snowdevil, an online store for snowboarding equipment, in 2004. Dissatisfied with the existing e-commerce products on the market, Lütke, a computer programmer by trade, instead built his own. Lütke used the open source web application framework Ruby on Rails to build Snowdevil's online store and launched it after two months of development. The Snowdevil founders launched the platform as Shopify in June 2006. Shopify created an open-source template language called Liquid, which is written in Ruby and has been used since 2006. In June 2009, Shopify launched an application programming interface (API) platform and App Store. The API allows developers to create applications for Shopify online stores and then sell them on the Shopify App Store. === 2010s === In January 2010, Shopify started its Build-A-Business competition, in which participants create a business using its commerce platform. The winners of the competition received cash prizes and mentorship from entrepreneurs, such as Richard Branson, Eric Ries and others. In April of that year, Shopify launched a free mobile app on the Apple App Store. The app allows Shopify store owners to view and manage their stores from iOS mobile devices. In December 2010, Shopify raised $7 million from a series A round from Bessemer Venture Partners, FirstMark Capital, and Felicis Ventures at a $20 million pre-money valuation. At that time, the company had annualized transaction value of $132 million. In October 2011, it raised $15 million in a Series B round. In August 2013, Shopify launched Shopify Payments in partnership with Stripe. Shopify Payments allows merchants to accept payments without requiring a third-party payment gateway. The company also announced the launch of a point of sale system to enable in-person sales in addition to online. The company received $100 million in Series C funding in December 2013. Shopify earned $105 million in revenue in 2014, twice as much as it raised the previous year. In February 2014, Shopify released "Shopify Plus" for large e-commerce businesses seeking access to additional features and support. Shopify went public via an initial public offering on May 21, 2015 raising more than $131 million. In September 2015, Amazon.com closed its Amazon Webstore service for merchants and selected Shopify as the preferred migration provider; In April 2016, Shopify announced Shopify Capital, a cash advance product. Shopify Capital was initially piloted to merchants within the US and allowed merchants to receive an advance on future earnings processed through its payment gateway. Since its launch in 2016, Shopify Capital has provided more than $5.1 billion in funding to Shopify merchants, with a maximum advance of $2 million. On June 7, 2016, Shopify launched its Shopify Plus Partners Program, to help agencies connect with evolving businesses in ecommerce space. On October 3, 2016, Shopify acquired Boltmade. In November 2016, Shopify partnered with Paystack which allowed Nigerian online retailers to accept payments from customers around the world. On November 22, 2016, Shopify launched Frenzy, a mobile app that improves flash sales. In January 2017, Shopify announced integration with Amazon that would allow merchants to sell on Amazon from their Shopify stores. In April 2017, Shopify introduced its Chip & Swipe Reader, a Bluetooth enabled debit and credit card reader for brick and mortar retail purchases. The company has since released additional technology for brick and mortar retailers, including a point-of-sale system with a Dock and Retail Stand similar to that offered by Square, and a tappable chip card reader. Shopify announced a one-click accelerated checkout feature called Shopify Pay in April 2017 as an exclusive feature for merchants using Shopify Payments as their payment processor. Customers can save their shipping and payment information for future purchases from all participating Shopify stores. In November 2017 Shopify announced Arrive, a mobile application to help customers track packages from both Shopify merchants and other e-commerce websites. In September 2018, Shopify announced plans to expand its office space in Toronto's King West neighborhood in 2022 as part of "The Well" complex, jointly owned by Allied Properties REIT and RioCan REIT. In October 2018, Shopify opened its first flagship, a physical space for business owners in Los Angeles. The space offered educational classes, coworking space, a "genius bar" for companies that use Shopify software, and workshops. Online cannabis sales in Ontario, Canada, used Shopify's software when the drug was legalized in October 2018. Shopify's software is also used for in-person cannabis sales in Ontario since becoming legal in 2019. In January 2019, Shopify announced the launch of Shopify Studios, a full-service television and film content and production house. On March 22, 2019, Shopify and email marketing platform Mailchimp ended an integration agreement over disputes involving customer privacy and data collection. In April 2019, Shopify announced an integration with Snapchat to allow Shopify merchants to buy and manage Snapchat Story ads directly on the Shopify platform. The company had previously secured similar integration partnerships with Facebook and Google. On August 14, 2019, Shopify launched Shopify Chat, a new native chat function that allows merchants to have real-time conversations with customers visiting Shopify stores online. === 2020s === In January 2020, the company announced plans to hire in Vancouver, Canada. Additionally, the effects of the COVID-19 pandemic contributed to lifting stock prices. On February 21, 2020, Shopify announced plans to join the Diem Association, known as Libra Association at the time. Also that month, Shopify Pay was rebranded as Shop Pay. In April, Arrive was rebranded as Shop, combining both customer-facing features under a single brand. In May, during the COVID-19 pandemic, Shopify announced it would shift most of its global workforce to permanent remote work. It was reported that Shopify's valuation would likely rise on the back of options it had in the company Affirm that was expecting to go public shortly. In November 2020, Shopify announced a partnership with Alipay to support merchants with cross-border payments. Shopify also provided the opportunity for users to connect Alibaba and AliExpress to Shopify through a Alibaba Dropshipping app that could be purchased through the Shopify App Store. Multiple applications launched between 2021 and 2024 allowed customers to connect their Shopify store to their Alibaba account and then import and publish your products. The integration automatically syncs inventory and orders between both platforms so that Alibaba vendors can ship directly to dropshipping customers.As a result of Affirm's January 13, 2021 IPO, Shopify's 8% stake in Affirm was worth $2 billion. About half of Shopify's C-level executives left the company in early 2021. On June 29, 2021, Shopify removed the 20% revenue share for app developers that make less than US$1 million per year. On January 18, 2022, Shopify announced a partnership with JD.com to let U.S. merchants expand their operations in China, listing their products on JD's cross-border e-commerce platform JD Worldwide. On March 22, 2022, Shopify introduced Linkpop, a product to create a branded, social marketplace through which merchants can advertise and market their products via links to be added on social media channels. The following month, Shopify, Alphabet Inc., Meta Platforms, McKinsey & Company, and Stripe, Inc. announced a $925 million advance market commitment of carbon dioxide removal (CDR) from companies that are developing CDR technology over the next 9 years. In June 2022, Shopify partnered with Twitter. As a part of the deal, Twitter announced that it would launch a sales channel app for all of Shopify's U.S. merchants through its app store. Shopify also partnered with PayPal to offer Shopify Payments to merchants in France. On July 26, 2022, Lütke announced immediate layoffs totalling roughly 10 percent of its workforce. In

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