AI For Business Nci

AI For Business Nci — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

SAP NetWeaver Visual Composer

SAP NetWeaver Visual Composer is SAP’s web-based software modelling tool. It enables business process specialists and developers to create business application components, without coding. Visual Composer produces applications in a declarative form, enabling code-free execution mode for multiple runtime environments. It provides application lifecycle support by maintaining the connection between an application and its model throughout its lifecycle. Visual Composer is designed with an open architecture, which enables developers to extend its design-time environment and modelling language, as well as to integrate external data services. The tool aims to increase productivity by reducing development effort time, and narrowing the gap between application definition and implementation. Starting with a blank canvas, the Visual Composer user, typically a business process specialist, draws the application in Visual Composer Storyboard (workspace), without writing code, to prototype, design and produce applications. A typical workflow for creating, deploying and running an application using Visual Composer is: Create a model Discover data services and add them to the model Select necessary UI elements and add them to the model Connect model elements to define the model logic and data flow Edit the layout Arranging the UI elements and the controls of the application on forms and tables. Deploy the model This step includes compilation, validation and deployment to a selected environment. Run the application The application can run using different runtime environment (such as Adobe Flex and HTML). In 2014 a runtime environment was introduced that is utilizing HTML5 capabilities of SAPUI5.
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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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Deep image compositing

Deep image compositing is a way of compositing and rendering digital images that emerged in the mid-2010s. In addition to the usual color and opacity channels a notion of spatial depth is created. This allows multiple samples in the depth of the image to make up the final resulting color. This technique produces high quality results and removes artifacts around edges that could not be dealt with otherwise. == Deep data == Deep data is encoded by advanced 3D renderers into an image that samples information about the path each rendered pixel takes along the z axis extending outward from the virtual camera through space, including the color and opacity of every non-opaque surface or volume it passes through along the way, as well as neighboring samples. It might be considered somewhat analogous to the way ray tracing generates simulated photon paths through such mediums; however, ray tracing and other traditional rendering techniques generally produce images that contain only three or four channels of color and opacity values per pixel, flattened into a two dimensional frame. Depth maps, on the other hand, contain z axis information encoded in a grayscale image. Each level of gray represents a different slice of the z space. The "thickness" of each slice is determined at time of render, allowing for more or less depth fidelity depending on how deep the scene is. Depth maps have been a boon to compositors for blending 3D renders with live action and practical elements. To be useful, the map must have high enough bit depth to encode separation between close-to-camera objects and objects near infinity. Most 3D software packages are now capable of generating 16-bit and 32-bit depth maps, providing up to 2 billion depth levels. Depth maps do not however include transparency information about non-opaque surfaces or volumes and as such, objects beyond and viewed through these semi- or fully-transparent objects will have no depth information of their own and may not get composited or blurred correctly. Even the popular addition of cryptomattes to many post-production and VFX studios' pipelines, while providing separate color-coded ID shapes for individual elements in a rendered scene to further bridge the gap between CGI and compositing, don't allow for the nearly automated and fully non-linear workflows that deep data does. This is because deep images encapsulate enough 3D information that normally time-intensive tasks such as rotoscoping with numerous holdout mattes for complex interactions between moving characters and semi-transparent environmental volumes like smoke or water, are essentially trivial. Instead of going through that process, multiple mattes could easily be generated from a single set of deep images with no need to re-render every matte element and background for each case. In addition to that efficiency and flexibility, deep data images inherently provide much higher visual quality in common areas that have been difficult with traditional renders, such as the motion-blurred edges of characters with semi-transparent elements like hair. One downside to the use of deep images is their substantial file size, since they encode a relatively enormous amount of data per frame compared to even multichannel formats such as OpenEXR. === Function-based (integrated) === The data is stored as a function of depth. This results in a function curve that can be used to look up the data at any arbitrary depth. Manipulating the data is harder. === Sample-based (deintegrated) === Each sample is considered as an independent piece and can so be manipulated easily. To make sure the data is representing the right detail, an additional expand value needs to be introduced. == Generating deep data == 3D renderers produce the necessary data as a part of the rendering pipeline. Samples are gathered in depth and then combined. The deep data can be written out before this happens and so is nothing new to the process. Generating deep data from camera data needs a proper depth map. This is used in a couple of cases but still not accurate enough for detailed representation. For basic holdout task this can be sufficient though. == Compositing deep data images == Deep images can be composited like regular images. The depth component makes it easier to determine the layering order. Traditionally this had to be input by the user. Deep images have that information for themselves and need no user input. Edge artifacts are reduced as transparent pixels have more data to work with. == History == Deep Images have been around in 3D rendering packages for quite a while now. The use of them for holdouts was first done at several VFX houses in shaders. Holdout mattes can be generated at render time. Using them in a more interactive manner was started recently by several companies, SideFX integrated it in their Houdini software and facilities like Industrial Light & Magic, DreamWorks Animation, Weta, AnimalLogic and DRD studios have implemented interactive solutions. In 2014 the Academy of Motion Picture Arts and Sciences honored the technology with its annual SciTech awards. Dr. Peter Hillman for the long-term development and continued advancement of innovative, robust and complete toolsets for deep compositing and to Colin Doncaster, Johannes Saam, Areito Echevarria, Janne Kontkanen and Chris Cooper for the development, prototyping and promotion of technologies and workflows for deep compositing. == Resources == Pixar Paper Deep Image Paper Video tutorial of Deep Imaging as used on 2012 film Rise of the Planet of the Apes, Nuke compositing software Deep Compositing Course Deep Image File Format at Google Code Academy Award for the Technology Theory of Deep Pixels OpenEXR Deep Pixels
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Web development tools

Web development tools (often abbreviated to dev tools) allow web developers to test, modify and debug their websites. They are different from website builders and integrated development environments (IDEs) in that they do not assist in the direct creation of a webpage, rather they are tools used for testing the user interface of a website or web application. Web development tools come as browser add-ons or built-in features in modern web browsers. Browsers such as Google Chrome, Firefox, Safari, Microsoft Edge, and Opera have built-in tools to help web developers, and many additional add-ons can be found in their respective plugin download centers. Web development tools allow developers to work with a variety of web technologies, including HTML, CSS, the DOM, JavaScript, and other components that are handled by the web browser. == History and support == Early web developers manually debugged their websites by commenting out code and using JavaScript functions. One of the first browser debugging tools to exist was Mozilla's Firebug extension, which possessed many of the current core features of today's developer tools, leading to Firefox becoming popular with developers at the time. Safari's WebKit engine also introduced its integrated developer tools around that period, which eventually became the basis for both Safari and Chrome's current tooling. Microsoft released a developer toolbar for Internet Explorer 6 and 7; and then integrated them into the browser from version 8 onwards. In 2017, Mozilla discontinued Firebug in favour of integrated developer tools. Nowadays, all modern web browsers have support for web developer tools that allow web designers and developers to look at the make-up of their pages. These are all tools that are built into the browser and do not require additional modules or configuration. Firefox – F12 opens the Firefox DevTools. Google Chrome and Opera – Developer Tools (DevTools) Microsoft Edge – F12 opens Web Developer Tools. Microsoft incorporates additional features that are not included in mainline Chromium. Safari – The Safari Web Inspector has to be enabled from its settings pane. == Features == The built-in web developer tools in the browser are commonly accessed by hovering over an item on a webpage and selecting the "Inspect Element" or similar option from the context menu. Alternatively the F12 key tends to be another common shortcut. === HTML and the DOM === HTML and DOM viewer and editor is commonly included in the built-in web development tools. The difference between the HTML and DOM viewer, and the view source feature in web browsers is that the HTML and DOM viewer allows you to see the DOM as it was rendered in addition to allowing you to make changes to the HTML and DOM and see the change reflected in the page after the change is made. In addition to selecting and editing, the HTML elements panels will usually also display properties of the DOM object, such as display dimension, and CSS properties. Firefox, Safari, Chrome, and Edge all allow users to simulate the document on a mobile device by modifying the viewport dimensions and pixel density. Additionally, Firefox and Chrome both have the option to simulate colour blindness for the page. === Web page assets, resources and network information === Web pages typically load and require additional content in the form of images, scripts, font and other external files. Web development tools also allow developers to inspect resources that are loaded and available on the web page in a tree-structure listing, and the appearance of style sheets can be tested in real time. Web development tools also allow developers to view information about the network usage, such as viewing what the loading time and bandwidth usage are and which HTTP headers are being sent and received. Developers can manipulate and resend network requests. === Profiling and auditing === Profiling allows developers to capture information about the performance of a web page or web application. With this information developers can improve the performance of their scripts. Auditing features may provide developers suggestions, after analyzing a page, for optimizations to decrease page load time and increase responsiveness. Web development tools typically also provide a record of the time it takes to render the page, memory usage, and the types of events which are taking place. These features allow developers to optimize their web page or web application. ==== JavaScript debugging ==== JavaScript is commonly used in web browsers. Web development tools commonly include a debugger panel for scripts by allowing developers to add watch expressions, breakpoints, view the call stack, and pause, continue, and step while debugging JavaScript. A console is also often included, which allow developers to type in JavaScript commands and call functions, or view errors that may have been encountered during the execution of a script. === Extensions === The devtools API allows browser extensions to add their own features to developer tools.
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Ciscogate

Ciscogate, also known as the Black Hat Bug, is the name given to a legal incident that occurred at the Black Hat Briefings security conference in Las Vegas, Nevada, on July 27, 2005. On the morning of the first day of the conference, July 26, 2005, some attendees noticed that 30 pages of text had been physically ripped out of the extensive conference presentation booklet the night before at the request of Cisco Systems and the CD-ROM with presentation slides was not included. It was determined the pages covered a talk to be given by Michael Lynn, a security researcher with Atlanta-based IBM Internet Security Systems (ISS). Instead of the pages with the details, attendees found a photographed copy of a notice from Black Hat saying "Due to some last minute changes beyond Black Hat's control, and at the request of the presenter, the included materials aren't up to the standards Black Hat tries to meet. Black Hat will be the first to apologize. We hope the vendors involved will follow suit." According to Lynn's lawyer, his employer had approved of the talk leading up to the conference but changed their minds two days before the scheduled talk, forbidding him from presenting. Lynn's original presentation was to cover a vulnerability in Cisco routers. The presentation was one of four scheduled to follow Jeff Moss' keynote address on the first day of the conference, titled "Cisco IOS Security Architecture". After being told by his employer that he could not present on the topic, Lynn chose an alternate topic. Cisco and ISS had offered to give new joint presentation but this was turned down by Black Hat because the original speaking slot was given to Lynn, not Cisco. Lynn's presentation began by covering security issues in services that allow users to make Voice over IP telephone calls. Shortly after beginning the presentation Lynn changed back to his original topic and began disclosing some technical details of the vulnerability he found in Cisco routers stating that he would rather resign from his job at ISS than keep the details private. == Lawsuit == Shortly after Lynn concluded his talk he met Jennifer Granick, who would soon become his lawyer. During their initial meeting Lynn told Granick that he expected to be sued. Later in the evening Lynn had heard that Cisco and ISS had filed a lawsuit and requested a temporary restraining order against Black Hat but not himself. A public relations representative from Black Hat told Granick that the lawsuit was against both Black Hat and Lynn and that the companies had scheduled an Ex parte hearing in San Francisco the next morning to request the restraining order. That night, Andrew Valentine, an attorney for ISS and Cisco called Lynn who directed them to Granick. During the conversation Valentine explained the claims and accusations against Lynn, which included three things: 1) ISS claimed copyright over the presentation that Lynn gave, 2) Cisco claimed copyright over the decompiled machine code obtained from the router which was included in the presentation, and 3) Cisco claimed the presentation contained trade secrets. These complaints were outlined in a civil complaint at the U.S. Northern District of California and filed against both Lynn and Black Hat. According to Granick, she and Valentine were able agree to an injunction to settle the case without court proceedings. This deal was almost called off due to an inadvertent mistake by Black Hat in which they had restored Lynn's presentation on their web server. Black Hat, Granick, and the plaintiff's lawyers were able to resolve this problem and the deal stood. One condition of the settlement required Lynn to provide an image of all computer data he used in his research to be provided to a third party for forensic analysis before erasing his research and any Cisco data from his systems. The settlement also stipulated that Lynn was prohibited from talking about the vulnerability in the future. == FBI Investigation == Shortly after lawyers for Lynn and ISS / Cisco filed settlement papers, FBI agents from the Las Vegas office arrived at the conference to begin asking questions. According to Granick, they were there at the request of the Atlanta FBI office and Lynn was not of interest. Granick asserted the Fifth and Sixth amendment rights on behalf of her client, Lynn. Granick asserted his rights for the Atlanta office and asked if an arrest warrant had been issued for Lynn. Over the next 24 hours Granick was not able to ascertain the status of a warrant but ultimately determined no warrant was issued. When the FBI was asked about the case by a journalist, spokesman Paul Bresson declined to discuss the case saying "Our policy is to not make any comment on anything that is ongoing. That's not to confirm that something is, because I really don't know". Granick would only confirm to journalists that the "investigation has to do with the presentation". == Response == === Attendees === Attendees of Black Hat Briefings, as well as many that also attended DEF CON, were not happy with vendors threatening legal action over vulnerability disclosure. The term "Ciscogate" was coined quickly by an unknown person, but some attendees were quick to create shirts to commemorate the incident. === Cisco === Mojgan Khalili, a senior manager for corporate PR at Cisco, issued a statement to the press saying "It is important to note that the information Mr. Lynn presented was not a disclosure of a new vulnerability or a flaw with Cisco IOS software. Mr. Lynn's research explores possible ways to expand exploitations of existing security vulnerabilities impacting routers." === ISS === Kim Duffy, managing director of ISS Australia, was asked about ISS's response to the incident. Duffy responded that it was "business as usual" as the company handled the incident "strictly by the book". He gave a brief statement to ZDNet UK saying "ISS has published rules for disclosure and that is what we stick to. We didn't care to publish [the disclosure] because we were not ready. We had not completed the research to our satisfaction so it was not ready to be disclosed". ISS spokesperson Roger Fortier confirmed that Lynn was no longer employed with the company and that ISS was still working with Cisco on the matter. He gave a statement to the Washington Post saying "ISS and Cisco have been working on this in the background and didn't feel at this time that the material was ready for publication. The decision was made on Monday to pull the presentation because we wanted to make sure the research was fully baked."
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Write or Die

Write or Die is an online web application designed to combat writer's block by letting users of the application punish themselves if they slow down or stop typing in the application's window. How severe the punishments are depends on the mode the user chooses, which ranges from "Gentle" to "Kamikaze". It was reviewed by publications PCWorld, the Los Angeles Times and The Guardian, and it was most notably used by writers Helen Oyeyemi and David Nicholls. The creator, Jeff Printy, explained that he wrote the application because he wants "to be published and make a living as a writer."
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RCUDA

rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface (API), it allows the allocation of one or more CUDA-enabled GPUs to a single application. Each GPU can be part of a cluster or running inside of a virtual machine. The approach is aimed at improving performance in GPU clusters that are lacking full utilization. GPU virtualization reduces the number of GPUs needed in a cluster, and in turn, leads to a lower cost configuration – less energy, acquisition, and maintenance. The recommended distributed acceleration architecture is a high performance computing cluster with GPUs attached to only a few of the cluster nodes. When a node without a local GPU executes an application needing GPU resources, remote execution of the kernel is supported by data and code transfers between local system memory and remote GPU memory. rCUDA is designed to accommodate this client-server architecture. On one end, clients employ a library of wrappers to the high-level CUDA Runtime API, and on the other end, there is a network listening service that receives requests on a TCP port. Several nodes running different GPU-accelerated applications can concurrently make use of the whole set of accelerators installed in the cluster. The client forwards the request to one of the servers, which accesses the GPU installed in that computer and executes the request in it. Time-multiplexing the GPU, or in other words sharing it, is accomplished by spawning different server processes for each remote GPU execution request. == rCUDA v20.07 == The rCUDA middleware enables the concurrent usage of CUDA-compatible devices remotely. rCUDA employs either the InfiniBand network or the socket API for the communication between clients and servers. rCUDA can be useful in three different environments: Clusters. To reduce the number of GPUs installed in High Performance Clusters. This leads to energy savings, as well as other related savings like acquisition costs, maintenance, space, cooling, etc. Academia. In commodity networks, to offer access to a few high performance GPUs concurrently to many students. Virtual Machines. To enable the access to the CUDA facilities on the physical machine. The current version of rCUDA (v20.07) supports CUDA version 9.0, excluding graphics interoperability. rCUDA v20.07 targets the Linux OS (for 64-bit architectures) on both client and server sides. CUDA applications do not need any change in their source code in order to be executed with rCUDA.
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Stevens Award

The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque
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Elowan

Elowan is a plant-robot cyborg. Using its own internal bioelectrical signals, The plant has a robotic extension that makes it move towards light sources. Electrodes are inserted into the leaves, stem, and ground to detect the faint bioelectrical signals the plant produces. Then they are amplified so the robot can read them. So when the plant "wants" to go to light, the cyborg automatically goes to the nearest light source. Future extensions of the robot could provide: Protection, growth frameworks, and nutrients. Other factors that could make the cyborg move are temperature, soil, and gravity conditions Elowan is one in a series of plant-electronic hybrid experiments.
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Closest point method

The closest point method (CPM) is an embedding method for solving partial differential equations on surfaces. The closest point method uses standard numerical approaches such as finite differences, finite element or spectral methods in order to solve the embedding partial differential equation (PDE) which is equal to the original PDE on the surface. The solution is computed in a band surrounding the surface in order to be computationally efficient. In order to extend the data off the surface, the closest point method uses a closest point representation. This representation extends function values to be constant along directions normal to the surface. == Definitions == Closest Point function: Given a surface S , c p ( x ) {\displaystyle {\mathcal {S}},cp(\mathbf {x} )} refers to a (possibly non-unique) point belonging to S {\displaystyle {\mathcal {S}}} , which is closest to x {\displaystyle \mathbf {x} } [SE]. Closest point extension: Let S {\displaystyle {\mathcal {S}}} , be a smooth surface in R d {\displaystyle \mathbb {R} ^{d}} . The closest point extension of a function u : S → R {\displaystyle u:{\mathcal {S}}\rightarrow \mathbb {R} } , to a neighborhood Ω {\displaystyle \Omega } of S {\displaystyle {\mathcal {S}}} , is the function v : Ω → R {\displaystyle v:\Omega \rightarrow \mathbb {R} } , defined by v ( x ) = u ( c p ( x ) ) {\displaystyle v(\mathbf {x} )=u(cp(\mathbf {x} ))} . == Closest point method == Initialization consists of these steps [EW]: If it is not already given, a closest point representation of the surface is constructed. A computational domain is chosen. Typically this is a band around the surface. Replace surface gradients by standard gradients in R 3 {\displaystyle \mathbb {R} ^{3}} . Solution is initialized by extending the initial surface data on to the computational domain using the closest point function. After initialization, alternate between the following two steps: Using the closest point function, extend the solution off the surface to the computational domain. Compute the solution to the embedding PDE on a Cartesian mesh in the computational domain for one time step. == Banding == The surface PDE is extended into R 3 {\displaystyle \mathbb {R} ^{3}} however it is only necessary to solve this new PDE near the surface. Hence, we solve the PDE in a band surrounding the surface for efficient computational purposes. Ω c x : ‖ x − c p ( x ) ‖ 2 ≤ λ {\displaystyle \Omega _{c}{x:\|x-cp(x)\|_{2}\leq \lambda }} where λ {\displaystyle \lambda } is the bandwidth. == Example: Heat equation on a circle == Using initial profile u S ( θ , t ) = sin ⁡ ( θ ) {\displaystyle u_{S}(\theta ,t)=\sin(\theta )} leads to the solution u S ( θ , t ) = exp ⁡ ( − t ) sin ⁡ ( θ ) {\displaystyle u_{S}(\theta ,t)=\exp(-t)\sin(\theta )} for the heat equation. Forward Euler time-stepping is used with relation Δ t = 0.1 Δ x 2 {\displaystyle \Delta t=0.1\Delta x^{2}} and degree-four interpolation polynomials for the interpolations. Second-order centered differences are used for the spatial discretization. The CPM results in the expected second order error in the solution u {\displaystyle u} . == Applications == The closest point method can be applied to various PDEs on surfaces. Reaction–diffusion problems on point clouds [RD], eigenvalue problems [EV], and level set equations [LS] are a few examples.
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List of assembly software and tools

This is a list of assembly software and tools, including software used for assembly language programming, machine code generation, disassembly, debugging, binary analysis, reverse engineering, and instruction-set simulation. == Assemblers and machine-code generators == == Disassemblers and binary-analysis tools == == Debuggers with assembly-level features == == Educational IDEs, simulators and emulators == == Portable and intermediate assembly-like languages == == Assembly language families == Assembly language is not a single programming language, but a family of low-level languages associated with particular instruction set architectures and processor families. Examples include:
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Protocol engineering

Protocol engineering is the application of systematic methods to the development of communication protocols. It uses many of the principles of software engineering, but it is specific to the development of distributed systems. == History == When the first experimental and commercial computer networks were developed in the 1970s, the concept of protocols was not yet well developed. These were the first distributed systems. In the context of the newly adopted layered protocol architecture (see OSI model), the definition of the protocol of a specific layer should be such that any entity implementing that specification in one computer would be compatible with any other computer containing an entity implementing the same specification, and their interactions should be such that the desired communication service would be obtained. On the other hand, the protocol specification should be abstract enough to allow different choices for the implementation on different computers. It was recognized that a precise specification of the expected service provided by the given layer was important. It is important for the verification of the protocol, which should demonstrate that the communication service is provided if both protocol entities implement the protocol specification correctly. This principle was later followed during the standardization of the OSI protocol stack, in particular for the transport layer. It was also recognized that some kind of formalized protocol specification would be useful for the verification of the protocol and for developing implementations, as well as test cases for checking the conformance of an implementation against the specification. While initially mainly finite-state machine were used as (simplified) models of a protocol entity, in the 1980s three formal specification languages were standardized, two by ISO and one by ITU. The latter, called SDL, was later used in industry and has been merged with UML state machines. == Principles == The following are the most important principles for the development of protocols: Layered architecture: A protocol layer at the level n consists of two (or more) entities that have a service interface through which the service of the layer is provided to the users of the protocol, and which uses the service provided by a local entity of level (n-1). The service specification of a layer describes, in an abstract and global view, the behavior of the layer as visible at the service interfaces of the layer. The protocol specification defines the requirements that should be satisfied by each entity implementation. Protocol verification consists of showing that two (or more) entities satisfying the protocol specification will provide at their service interfaces the specified service of that layer. The (verified) protocol specification is used mainly for the following two activities: The development of an entity implementation. Note that the abstract properties of the service interface are defined by the service specification (and also used by the protocol specification), but the detailed nature of the interface can be chosen during the implementation process, separately for each entity. Test suite development for conformance testing. Protocol conformance testing checks that a given entity implementation conforms to the protocol specification. The conformance test cases are developed based on the protocol specification and are applicable to all entity implementations. Therefore standard conformance test suites have been developed for certain protocol standards. == Methods and tools == Tools for the activities of protocol verification, entity implementation and test suite development can be developed when the protocol specification is written in a formalized language which can be understood by the tool. As mentioned, formal specification languages have been proposed for protocol specification, and the first methods and tools where based on finite-state machine models. Reachability analysis was proposed to understand all possible behaviors of a distributed system, which is essential for protocol verification. This was later complemented with model checking. However, finite-state descriptions are not powerful enough to describe constraints between message parameters and the local variables in the entities. Such constraints can be described by the standardized formal specification languages mentioned above, for which powerful tools have been developed. It is in the field of protocol engineering that model-based development was used very early. These methods and tools have later been used for software engineering as well as hardware design, especially for distributed and real-time systems. On the other hand, many methods and tools developed in the more general context of software engineering can also be used of the development of protocols, for instance model checking for protocol verification, and agile methods for entity implementations. == Constructive methods for protocol design == Most protocols are designed by human intuition and discussions during the standardization process. However, some methods have been proposed for using constructive methods possibly supported by tools to automatically derive protocols that satisfy certain properties. The following are a few examples: Semi-automatic protocol synthesis: The user defines all message sending actions of the entities, and the tool derives all necessary reception actions (even if several messages are in transit). Synchronizing protocol: The state transitions of one protocol entity are given by the user, and the method derives the behavior of the other entity such that it remains in states that correspond to the former entity. Protocol derived from service specification: The service specification is given by the user and the method derives a suitable protocol for all entities. Protocol for control applications: The specification of one entity (called the plant - which must be controlled) is given, and the method derives a specification of the other entity such that certain fail states of the plant are never reached and certain given properties of the plant's service interactions are satisfied. This is a case of supervisory control. == Books == Ming T. Liu, Protocol Engineering, Advances in Computers, Volume 29, 1989, Pages 79–195. G.J. Holzmann, Design and Validation of Computer Protocols, Prentice Hall, 1991. H. König, Protocol Engineering, Springer, 2012. M. Popovic, Communication Protocol Engineering, CRC Press, 2nd Ed. 2018. P. Venkataram, S.S. Manvi, B.S. Babu, Communication Protocol Engineering, 2014.
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Synonym (database)

In databases, a synonym is an alias or alternate name for a table, view, sequence, or other schema object. They are used mainly to make it intuitive for users to access database objects owned by other users. They also hide the underlying object's identity and make it harder for a malicious program or user to target the underlying object (security through obscurity). Because a synonym is just an alternate name for an object, it requires no storage other than its definition. When an application uses a synonym, the DBMS forwards the request to the synonym's underlying base object. By coding your programs to use synonyms instead of database object names, you insulate yourself from any changes in the name, ownership, or object locations, at the cost of adding another layer that also needs to be maintained. Users can also have different needs, for example some may wish to use a shorter name to refer to database objects they often query, which can be done with aliases without having to rename the underlying object and alter the code referring to it. Synonyms are very powerful from the point of view of allowing users access to objects that do not lie within their schema. All synonyms have to be created explicitly with the CREATE SYNONYM command and the underlying objects can be located in the same database or in other databases that are connected by database links There are two major uses of synonyms: Object invisibility: Synonyms can be created to keep the original object hidden from the user. Location invisibility: Synonyms can be created as aliases for tables and other objects that are not part of the local database. When a table or a procedure is created, it is created in a particular schema, and other users can access it only by using that schema's name as a prefix to the object's name. The way around for this is for the schema owner creates a synonym with the same name as the table name. == Public synonyms == Public synonyms are owned by special schema in the Oracle Database called PUBLIC. As mentioned earlier, public synonyms can be referenced by all users in the database. Public synonyms are usually created by the application owner for the tables and other objects such as procedures and packages so the users of the application can see the objects The following code shows how to create a public synonym for the employee table: Now any user can see the table by just typing the original table name. If you wish, you could provide a different table name for that table in the CREATE SYNONYM statement. Remember that the DBA must create public synonyms. Just because you can see a table through public (or private) synonym doesn’t mean that you can also perform SELECT, INSERT, UPDATE or DELETE operations on the table. To be able to perform those operations, a user needs specific privileges for the underlying object, either directly or through roles from the application owner. == Private synonyms == A private synonym is a synonym within a database schema that a developer typically uses to mask the true name of a table, view stored procedure, or other database object in an application schema. Private synonyms, unlike public synonyms, can be referenced only by the schema that owns the table or object. You may want to create private synonyms when you want to refer to the same table by different contexts. Private synonym overrides public synonym definitions. You create private synonyms the same way you create public synonyms, but you omit the PUBLIC keyword in the CREATE statement. The following example shows how to create a private synonym called addresses for the locations table. Note that once you create the private synonym, you can refer to the synonym exactly as you would the original table name. == Drop a synonym == Synonyms, both private and public, are dropped in the same manner by using the DROP SYNONYM command, but there is one important difference. If you are dropping a public synonym; you need to add the keyword PUBLIC after the keyword DROP. The ALL_SYNONYMS (or DBA_SYNONYMS) view provides information on all synonyms in your database.
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Endomondo

Endomondo is a health and wellness website. It allows users to track their health statistics and provides insights on fitness trends. Originally launched in 2007, Endomondo was acquired by Under Armour in 2015. Under Armour shut down Endomondo in 2020, but, by 2024, Endomondo re-launched as its own entity. == History == Endomondo started in Denmark in 2007 by Mette Lykke, Christian Birk and Jakob Nordenhof Jønck. In 2011, the company opened an office in Silicon Valley, USA, but kept its research and development department in Denmark. In 2013, Endomondo LLC was listed in Red Herring as a European finalists for promising start-ups. The same year, Christian Birk and Jakob Nordenhof Jønck left the daily operation of the company, but kept co-ownership. In February 2015, Endomondo LLC was acquired by athletic apparel maker Under Armour for $85 million. Endomondo, at that time, had over 20 million users. In October 2020, Under Armour announced that Endomondo would be shutting down and selling off MyFitnessPal to the private equity firm Francisco Partners for $345 million. Service stopped on 31 December 2020, giving customers until 15 February 2021 to download an archive of their historic data. In 2024, Endomondo.com was brought back online as a professional fitness guidance website. == Features == Endomondo provides numerous workouts, guidance on exercises, performance-enhancing nutrition, and tips. Previously, Endomondo was able to track numerous fitness attributes such as running routes, distance, duration, and calories. The software helped analyze performance and recommend improvements. There was a free and a paid version available of Endomondo. The free version had advertisements. The paid Premium version was free of advertisements and included additional features such as the possibility to create one's own training plan. The offering of additional features was different between the Android, IOS and Windows platforms, and had significantly better features for tracking performance over time than UnderArmours suggested replacement. Endomondo offered challenges of various types to the user and allowed users to create their own challenges.
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Software bot

A software bot is a type of software agent in the service of software project management and software engineering. A software bot has an identity and potentially personified aspects in order to serve their stakeholders. Software bots often compose software services and provide an alternative user interface, which is sometimes, but not necessarily conversational. Software bots are typically used to execute tasks, suggest actions, engage in dialogue, and promote social and cultural aspects of a software project. The term bot is derived from robot. However, robots act in the physical world and software bots act only in digital spaces. Some software bots are designed and behave as chatbots, but not all chatbots are software bots. Discussions about the past and future of software bots show that software bots have been adopted for many years. == Usage == Software bots are used to support development activities, such as communication among software developers and automation of repetitive tasks. Software bots have been adopted by several communities related to software development, such as open-source communities on GitHub and Stack Overflow. GitHub bots have user accounts and can open, close, or comment on pull requests and issues. GitHub bots have been used to assign reviewers, ask contributors to sign the Contributor License Agreement, report continuous integration failures, review code and pull requests, welcome newcomers, run automated tests, merge pull requests, fix bugs and vulnerabilities, etc. The Slack tool includes an API for developing software bots. There are slack bots for keeping track of todo lists, coordinating standup meetings, and managing support tickets. The ChatBot company products further simplify the process of creating a custom Slack bot. On Wikipedia, Wikipedia bots automate a variety of tasks, such as creating stub articles, consistently updating the format of multiple articles, and so on. Bots like ClueBot NG are capable of recognizing vandalism and automatically remove disruptive content. == Taxonomies and Classification Frameworks == Lebeuf et al. provide a faceted taxonomy to characterize bots based on a literature review. It is composed of 3 main facets: (i) properties of the environment that the bot was created in; (ii) intrinsic properties of the bot itself; and (iii) the bot's interactions within its environment. They further detail the facets into sets of sub-facets under each of the main facets. Paikari and van der Hoek defined a set of dimensions to enable comparison of software bots, applied specifically to chatbots. It resulted in six dimensions: Type: the main purpose of the bot (information, collaboration, or automation) Direction of the "conversation" (input, output, or bi-directional) Guidance (human-mediated, or autonomous) Predictability (deterministic, or evolving) Interaction style (dull, alternate vocabulary, relationship-builder, human-like) Communication channel (text, voice, or both) Erlenhov et al. raised the question of the difference between a bot and simple automation, since much research done in the name of software bots uses the term bot to describe various different tools and sometimes things are "just" plain old development tools. After interviewing and surveying over 100 developers the authors found that not one, but three definitions dominated the community. They created three personas based on these definitions and the difference between what the three personas see as being a bot is mainly the association with a different set of human-like traits. The chat bot persona (Charlie) primarily thinks of bots as tools that communicates with the developer through a natural language interface (typically voice or chat), and caring little about what tasks the bot is used for or how it actually implements these tasks. The autonomous bot persona (Alex) thinks of bots as tools that work on their own (without requiring much input from a developer) on a task that would normally be done by a human. The smart bot persona (Sam) separates bots and plain old development tools through how smart (technically sophisticated) a tool is. Sam cares less about how the tool communicates, but more about if it is unusually good or adaptive at executing a task. The authors recommends that people doing research or writing about bots try to put their work in the context of one of the personas since the personas have different expectations and problems with the tools. == Example of notable bots == Dependabot and Renovatebot update software dependencies and detect vulnerabilities. (https://dependabot.com/) Probot is an organization that create and maintain bots for GitHub. The example bots using Probot are the following. Auto Assign (https://probot.github.io/apps/auto-assign/) license bot (https://probot.github.io/) Sentiment bot (https://probot.github.io/apps/sentiment-bot/) Untrivializer bot (https://probot.github.io/apps/untrivializer/) Refactoring-Bot (Refactoring-Bot): provides refactoring based on static code analysis Looks good to me bot (LGTM) is a Semmle product that inspects pull requests on GitHub for code style and unsafe code practices. == Issues and threats == Software bots may not be well accepted by humans. A study from the University of Antwerp has compared how developers active on Stack Overflow perceive answers generated by software bots. They find that developers perceive the quality of software bot-generated answers to be significantly worse if the identity of the software bot is made apparent. By contrast, answers from software bots with human-like identity were better received. In practice, when software bots are used on platforms like GitHub or Wikipedia, their username makes it clear that they are bots, e.g., DependaBot, RenovateBot, DatBot, SineBot. Bots may be subject to special rules. For instance, the GitHub terms of service does not allow 'bots' but accepts 'machine account', where a 'machine account' has two properties: 1) a human takes full responsibility of the bot's actions 2) it cannot create other accounts.
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