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Scrolling

In computer displays, filmmaking, television production, video games and other kinetic displays, scrolling is sliding text, images or video across a monitor or display, vertically or horizontally. "Scrolling," as such, does not change the layout of the text or pictures but moves (pans or tilts) the user's view across what is apparently a larger image that is not wholly seen. A common television and movie special effect is to scroll credits, while leaving the background stationary. Scrolling may take place completely without user intervention (as in film credits) or, on an interactive device, be triggered by touchscreen or a keypress and continue without further intervention until a further user action, or be entirely controlled by input devices. Scrolling may take place in discrete increments (perhaps one or a few lines of text at a time), or continuously (smooth scrolling). Frame rate is the speed at which an entire image is redisplayed. It is related to scrolling in that changes to text and image position can only happen as often as the image can be redisplayed. When frame rate is a limiting factor, one smooth scrolling technique is to blur images during movement that would otherwise appear to "jump". == Computing == === Implementation === Scrolling is often carried out on a computer by the CPU (software scrolling) or by a graphics processor. Some systems feature hardware scrolling, where an image may be offset as it is displayed, without any frame buffer manipulation (see also hardware windowing). This was especially common in 8 and 16bit video game consoles. === UI paradigms === In a WIMP-style graphical user interface (GUI), user-controlled scrolling is carried out by manipulating a scrollbar with a mouse, or using keyboard shortcuts, often the arrow keys. Scrolling is often supported by text user interfaces and command line interfaces. Older computer terminals changed the entire contents of the display one screenful ("page") at a time; this paging mode requires fewer resources than scrolling. Scrolling displays often also support page mode. Typically certain keys or key combinations page up or down; on PC-compatible keyboards the page up and page down keys or the space bar are used; earlier computers often used control key combinations. Some computer mice have a scroll wheel, which scrolls the display, often vertically, when rolled; others have scroll balls or tilt wheels which allow both vertical and horizontal scrolling. Some software supports other ways of scrolling. Adobe Reader has a mode identified by a small hand icon ("hand tool") on the document, which can then be dragged by clicking on it and moving the mouse as if sliding a large sheet of paper. When this feature is implemented on a touchscreen it is called kinetic scrolling. Touch-screens often use inertial scrolling, in which the scrolling motion of an object continues in a decaying fashion after release of the touch, simulating the appearance of an object with inertia. An early implementation of such behavior was in the "Star7" PDA of Sun Microsystems ca. 1991–1992. Scrolling can be controlled in other software-dependent ways by a PC mouse. Some scroll wheels can be pressed down, functioning like a button. Depending on the software, this allows both horizontal and vertical scrolling by dragging in the direction desired; when the mouse is moved to the original position, scrolling stops. A few scroll wheels can also be tilted, scrolling horizontally in one direction until released. On touchscreen devices, scrolling is a multi-touch gesture, done by swiping a finger on the screen vertically in the direction opposite to where the user wants to scroll to. If any content is too wide to fit on a display, horizontal scrolling is required to view all of it. In applications such as graphics and spreadsheets there is often more content than can fit either the width or the height of the screen at a comfortable scale, and scrolling in both directions is necessary. === Infinite scrolling === In contrast to material divided into discrete pages, the web design approach of infinite scrolling dynamically adds new material to the user display, leading to a continuous, apparently bottomless or endless scrolling experience. === Text === In languages written horizontally, such as most Western languages, text documents longer than will fit on the screen are often displayed wrapped and sized to fit the screen width, and scrolled vertically to bring desired content into view. It is possible to display lines too long to fit the display without wrapping, scrolling horizontally to view each entire line. However, this requires inconvenient constant line-by-line scrolling, while vertical scrolling is only needed after reading a full screenful. Software such as word processors and web browsers normally uses word-wrapping to display as many words in a single line as will fit the width of the screen or window or, for text organised in columns, each column. === Demos === Scrolling texts, also referred to as scrolltexts or scrollers, played an important part in the birth of the computer demo culture. The software crackers often used their deep knowledge of computer platforms to transform the information that accompanied their releases into crack intros. The sole role of these intros was to scroll the text on the screen in an impressive way. == Film and television == Scrolling is commonly used to display the credits at the end of films and television programs. Scrolling is often used in the form of a news ticker towards the bottom of the picture for content such as television news, scrolling sideways across the screen, delivering short-form content. In the dynamic layout of kinetic typography, scrolling typography can scroll across the flat screen, or can appear to recede or advance. An iconic example is the Star Wars opening crawl inspired by the Flash Gordon serials. == Video games == In computer and video games, scrolling of a playing field allows the player to control an object in a large contiguous area. Early examples of this method include Taito's 1974 vertical-scrolling racing video game Speed Race, Sega's 1976 forward-scrolling racing games Moto-Cross (Fonz) and Road Race, and Super Bug. Previously the flip-screen method was used to indicate moving backgrounds. The Namco Galaxian arcade system board introduced with Galaxian in 1979 pioneered a sprite system that animated pre-loaded sprites over a scrolling background, which became the basis for Nintendo's Radar Scope and Donkey Kong arcade hardware and home consoles such as the Nintendo Entertainment System. Parallax scrolling, which was first featured in Moon Patrol, involves several semi-transparent layers (called playfields), which scroll on top of each other at varying rates in order to give an early pseudo-3D illusion of depth. Belt scrolling is a method used in side-scrolling beat 'em up games with a downward camera angle where players can move up and down in addition to left and right. == Studies == A 1993 article by George Fitzmaurice studied spatially aware palmtop computers. These devices had a 3D sensor, and moving the device caused the contents to move as if the contents were fixed in place. This interaction could be referred to as “moving to scroll.” Also, if the user moved the device away from their body, they would zoom in; conversely, the device would zoom out if the user pulled the device closer to them. Smartphone cameras and “optical flow” image analysis utilize this technique nowadays. A 1996 research paper by Jun Rekimoto analyzed tilting operations as scrolling techniques on small screen interfaces. Users could not only tilt to scroll, but also tilt to select menu items. These techniques proved especially useful for field workers, since they only needed to hold and control the device with one hand. A study from 2013 by Selina Sharmin, Oleg Špakov, and Kari-Jouko Räihä explored the action of reading text on a screen while the text auto-scrolls based on the user's eye tracking patterns. The control group simply read text on a screen and manually scrolled. The study found that participants preferred to read primarily at the top of the screen, so the screen scrolled down whenever participants’ eyes began to look toward the bottom of the screen. This auto-scrolling caused no statistically significant difference in reading speed or performance. An undated study occurring during or after 2010 by Dede Frederick, James Mohler, Mihaela Vorvoreanu, and Ronald Glotzbach noted that parallax scrolling "may cause certain people to experience nausea."
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Radical trust

Radical trust is the confidence that any structured organization, such as a government, library, business, religion, or museum, has in collaboration and empowerment within online communities. Specifically, it pertains to the use of blogs, wiki and online social networking platforms by organizations to cultivate relationships with an online community that then can provide feedback and direction for the organization's interest. The organization 'trusts' and uses that input in its management. One of the first appearances of the notion of radical trust appears in an info graphic outlining the base principles of web 2.0 in Tim O'Reilly's weblog post "What is Web 2.0". Radical Trust is listed as the guiding example of trusting the validity of consumer generated media. This concept is considered to be an underlying assumption of Library 2.0. The adoption of radical trust by a library would require its management let go of some of its control over the library and building an organization without an end result in mind. The direction a library would take would be based on input provided by people through online communities. These changes in the organization may merely be anecdotal in nature, making this method of organization management dramatically distinct from data-based or evidence based management. In marketing, Collin Douma further describes the notion of radical trust as a key mindset required for marketers and advertisers to enter the social media marketing space. Conventional marketing dictates and maintains control of messages to cause the greatest persuasion in consumer decisions, but Douma argued that in the social media space, brands would need to cede that control in order to build brand loyalty.
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Intent-based network

Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.
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TikTokification

TikTokification (also written TikTok-ification) is a term used to describe the widespread adoption of TikTok's short-form, vertical video format and its algorithmic content-delivery model across the broader social media landscape. The phenomenon encompasses the strategic and cultural changes made by competing platforms such as Instagram, YouTube, Facebook, Snapchat, and LinkedIn in response to TikTok's global dominance. Beyond platform design, the term is also used more broadly to describe shifts in media consumption habits, advertising strategies, and, more critically, the potential cognitive and psychological effects associated with constant short-form video consumption. == Background == === Origins of short-form video === The short-form video format predates TikTok. Vine, launched in 2013, popularised six-second looping videos before shutting down in 2017. TikTok itself, known as Douyin in the Chinese market, was created by the Chinese technology company ByteDance in September 2016. Following its international expansion and its 2018 merger with Musical.ly, TikTok grew rapidly. By 2020, the application had surpassed two billion total downloads worldwide, with over 800 million monthly active users. A key driver of TikTok's success was its recommendation algorithm. The platform's "For You Page" (FYP) serves content to users based on behaviour rather than follower count, making it possible for unknown creators to achieve widespread reach organically. Analysts noted that TikTok serves "fast, visually engaging, and authentic videos that feel more like entertainment than advertising," fundamentally reshaping consumer expectations of digital content. TikTok has been described as "the center of the internet for young people," where users go for entertainment, news, trends, and shopping. As of the mid-2020s, TikTok had approximately 1.12 billion monthly active users. == Platform responses == TikTok's success compelled nearly every major social media platform to restructure its product around short-form video. In 2020, Instagram launched Reels and YouTube launched Shorts, both directly in response to TikTok's growth. Platforms like Meta's Instagram Reels and Google's YouTube Shorts subsequently expanded aggressively, launching new features, creator tools, and even considering separate standalone applications to compete. LinkedIn, traditionally a professional networking site, began experimenting with TikTok-style short-form vertical video feeds. Facebook launched a singular unified video feed combining Reels, long videos, and live videos, similar in structure to TikTok's feed. Snapchat redesigned its application to combine Stories and Spotlight into a unified entertainment feed. YouTube extended its Shorts format to allow videos up to three minutes in length, up from the previous limit of sixty seconds, as of October 2024. Despite these adaptations, experts noted that none of TikTok's rivals had matched its algorithmic precision as of mid-2025. == Societal and cultural impact == === Media and journalism === News organisations have also been affected by TikTokification. Short-form video grew rapidly as a format for news content, driven in large part by TikTok's popularity. According to Pew Research Center, 17% of adults in the United States reported regularly getting news from TikTok in 2024, with 63% of teenagers saying they used the platform as a news source. In response, major publishers began creating bespoke short-form content for TikTok's audience, with organisations such as the BBC building dedicated internal TikTok teams. === Advertising and commerce === TikTokification has had significant effects on the advertising industry. US social video advertising spending was projected to surpass linear television advertising spending for the first time in 2025. Global social commerce sales were projected to reach approximately $900 billion in 2025, with platforms like Douyin and TikTok driving a large share of that growth. TikTok itself generated an estimated $23.6 billion in advertising revenue in 2024. Short-form video has been described as bridging the gap between brand awareness and direct conversion. Surveys have found that consumers trust user-generated content 8.7 times more than influencer content and 6.6 times more than branded content, prompting brands to favour creator-led video formats. === Attention spans and cognitive effects === A growing body of research has examined the cognitive consequences of heavy short-form video consumption, a set of effects sometimes referred to as "TikTok Brain." A large systematic review and meta-analysis published in Psychological Bulletin, analysing data from 98,299 participants across 71 studies, found that the more short-form video content a person watches, the poorer their cognitive performance in attention and inhibitory control. The review also found that greater engagement with short-form video was associated with higher levels of anxiety, depression, and stress, as well as sleep disturbances. The platform's inherent demand for engaging content has resulted in the proliferation of sludge content, a genre of split screen video with the main video on the top and an unrelated attention-grabbing video on the bottom, typically repetitive gameplay (notably of the endless runner mobile game Subway Surfers) or oddly satisfying videos, designed to maximize viewer retention in cases where the main video may appear uninteresting and would normally cause the viewer to skip it. Sludge content is often described as overstimulating, reflecting and contributing to declining attention spans, though the scholarly evidence supporting such claims is not conclusive. Dr. Yann Poncin, associate professor at the Child Study Center at Yale University, noted that "infinite scrolling and short-form video are designed to capture your attention in short bursts," contrasting this with earlier entertainment formats that guided audiences through longer narratives. Research suggests that children and teenagers may be particularly vulnerable, with early exposure to rapid frame changes potentially conditioning the brain's neural pathways to require constant stimulation, making it more challenging to engage with slower-paced activities. A separate study published in Nature Communications by researchers at the Technical University of Denmark documented a notable decrease in collective attention span over time, attributing it in part to the increasing volume and pace of content production and consumption online. Researchers caution, however, that the majority of relevant studies are cross-sectional, meaning they capture data at a single point in time and cannot establish causality. It remains possible that individuals with pre-existing conditions such as anxiety or attention deficits may be more likely to engage heavily with these platforms as a coping mechanism. === Academic and sociological analysis === Scholars have framed TikTokification within the context of the attention economy. A 2024 academic analysis described TikTok as representing "a new paradigm of social media communication" shaped by youth culture, mobile technology, and the economics of attention, in which spectators become active contributors to a shared content pipeline. The same analysis noted that TikTok "reflects a new mode of communication influenced by avant-garde cinema, the use of mobile technology, and the social habits of particular social groups." US social media users were projected to spend 61.1% of their time on social networks watching videos in 2025, up from 33.3% in 2019, before TikTok became widely popular, underscoring the scale of the behavioural shift. == Monetisation challenges == Despite high engagement levels, monetising short-form video has remained difficult for platforms and creators alike. Unlike long-form YouTube content, short clips offer limited space for advertisers to insert advertisements. YouTube Shorts pays approximately four cents per 1,000 views, considerably less than its long-form counterpart. From 2025 onward, platforms began introducing creator funds, advertisements, and AI-driven content recommendations as part of broader efforts to make short-form video economically sustainable for creators.
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Report generator

A report generator is a computer program whose purpose is to take data from a source such as a database, XML stream or a spreadsheet, and use it to produce a document in a format which satisfies a particular human readership. Report generation functionality is almost always present in database systems, where the source of the data is the database itself. It can also be argued that report generation is part of the purpose of a spreadsheet. Standalone report generators may work with multiple data sources and export reports to different document formats. Information systems theory specifies that information delivered to a target human reader must be timely, accurate and relevant. Report generation software targets the final requirement by making sure that the information delivered is presented in the way most readily understood by the target reader. == History == An early report writer was part of NOMAD developed in the 1970s. The evolution of reporting software has a rich history dating back to the mid-20th century, driven by the increasing need for businesses to efficiently analyze and present data. Initially, manual extraction and tabulation were commonplace, but the advent of computers in the 1960s marked a transformative phase with the emergence of basic reporting tools. The 1980s saw the widespread adoption of database management systems, laying the groundwork for more sophisticated reporting capabilities. Notable dedicated reporting software, such as Crystal Reports and BusinessObjects, gained prominence in the 1990s amidst the growing demand for business intelligence. The 21st century witnessed a paradigm shift towards web-based reporting solutions and the rise of self-service BI tools, empowering users to create reports independently. Presently, reporting software continues to evolve with a focus on data visualization, integration of artificial intelligence, and the imperative for real-time analytics in decision-making.
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Client-side encryption

Client-side encryption is the cryptographic technique of encrypting data on the sender's side, before it is transmitted to a server such as a cloud storage service. Client-side encryption features an encryption key that is not available to the service provider, making it difficult or impossible for service providers to decrypt hosted data. Client-side encryption allows for the creation of applications whose providers cannot access the data its users have stored, thus offering a high level of privacy. Applications utilizing client-side encryption are sometimes marketed under the misleading or incorrect term "zero-knowledge", but this is a misnomer, as the term zero-knowledge describes something entirely different in the context of cryptography. == Details == Client-side encryption seeks to eliminate the potential for data to be viewed by service providers (or third parties that compel service providers to deliver access to data), client-side encryption ensures that data and files that are stored in the cloud can only be viewed on the client-side of the exchange. This prevents data loss and the unauthorized disclosure of private or personal files, providing increased peace of mind for its users. Current recommendations by industry professionals as well as academic scholars offer great vocal support for developers to include client-side encryption to protect the confidentiality and integrity of information. === Examples of services that use client-side encryption by default === Tresorit MEGA Cryptee Cryptomator === Examples of services that optionally support client-side encryption === Apple iCloud offers optional client-side encryption when "Advanced Data Protection for iCloud" is enabled. Google Drive, Google Docs, Google Meet, Google Calendar, and Gmail — However, as of Jul 2024, optional client-side encryption features are only available to paid users. === Examples of services that do not support client-side encryption === Dropbox === Examples of client-side encrypted services that no longer exist === SpiderOak Backup
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Omni-Path

Omni-Path Architecture (OPA) is a high-performance communication architecture developed by Intel. It aims for low communication latency, low power consumption and a high throughput. It directly competes with InfiniBand. Intel planned to develop technology based on this architecture for exascale computing. The current owner of Omni-Path is Cornelis Networks. == History == Production of Omni-Path products started in 2015 and delivery of these products started in the first quarter of 2016. In November 2015, adapters based on the 2-port "Wolf River" ASIC were announced, using QSFP28 connectors with channel speeds up to 100 Gbit/s. Simultaneously, switches based on the 48-port "Prairie River" ASIC were announced. First models of that series were available starting in 2015. In April 2016, implementation of the InfiniBand "verbs" interface for the Omni-Path fabric was discussed. In October 2016, IBM, Hewlett Packard Enterprise, Dell, Lenovo, Samsung, Seagate Technology, Micron Technology, Western Digital and SK Hynix announced a joint consortium called Gen-Z to develop an open specification and architecture for non-volatile storage and memory products—including Intel's 3D Xpoint technology—which might in part compete against Omni-Path. Intel offered their Omni-Path products and components via other (hardware) vendors. For example, Dell EMC offered Intel Omni-Path as Dell Networking H-series, following the naming-standard of Dell Networking in 2017. In July 2019, Intel announced it would not continue development of Omni-Path networks and canceled OPA 200 series (200-Gbps variant of Omni-Path). In September 2020, Intel announced that the Omni-Path network products and technology would be spun out into a new venture with Cornelis Networks. Intel would continue to maintain support for legacy Omni-Path products, while Cornelis Networks continues the product line, leveraging existing Intel intellectual property related to Omni-Path architecture. In 2021, Cornelis announced Omni-Path Express, which replaces PSM2-based drivers and middleware, which trace back to PathScale's PSM created in 2003, for the existing Omni-Path hardware, with a native libfabric provider.
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Simply Local

Simply Local is a decentralized community social networking and neighborhood broadcasting service developed by Simply Local, based in New Delhi. The app is used as a tool by residents to bridge the information gap and know what is happening in the locality. Simply Local creates private geo-fenced networks for people living in an area and provides social and community related services within that network. The user doesn’t post to a single person but broadcasts to a chosen community. One of its primary purposes is also to connect citizens to their elected representatives. Each community is independent of the other and information shared remains telescoped to that particular community. The app has been designed to maintain privacy and security of users and provides decentralized social networking in the sense that it forms an owner-independent, micro community, which is not connected with the world outside. Simply Local is available on Android Play and iOS App Store. It is available in two languages - English and Hindi. Simply Local’s founder and CEO is Nikhil Bapna. == History == 2020 May: Included as a Top 5 Useful App by Zee News. 2020: Used to connect candidates with local residents during the Delhi assembly elections. 2019: Renamed from Gadfly to its current name. 2018: Used for Karnataka State Elections to get detailed information on candidates. 2017: Launched under the name Gadfly as a tool to connect citizens with their elected representatives.
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Elastix (image registration)

Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for case-specific applications. It was first developed by Stefan Klein and Marius Staring under the supervision of Josien P.W. Pluim at Image Sciences Institute (ISI). Its first version was command-line based, allowing the final user to employ scripts to automatically process big data-sets and deploy multiple registration pipelines with few lines of code. Nowadays, to further widen its audience, a version called SimpleElastix is also available, developed by Kasper Marstal, which allows the integration of elastix with high level languages, such as Python, Java, and R. == Image registration fundamentals == Image registration is a well-known technique in digital image processing that searches for the geometric transformation that, applied to a moving image, obtains a one-to-one map with a target image. Generally, the images acquired from different sensors (multimodal), time instants (multitemporal), and points of view (multiview) should be correctly aligned to proceed with further processing and feature extraction. Even though there are a plethora of different approaches to image registration, the majority is composed of the same macro building blocks, namely the transformation, the interpolator, the metric, and the optimizer. Registering two or more images can be framed as an optimization problem that requires multiple iterations to converge to the best solution. Starting from an initial transformation computed from the image moments the optimization process searches for the best transformation parameters based on the value of the selected similarity metric. The figure on the right shows the high-level representation of the registration of two images, where the reference remains constant during the entire process, while the moving one will be transformed according to the transformation parameters. In other words, the registration ends when the similarity metric, which is a mathematical function with a certain number of parameters to be optimized, reaches the optimal value which is highly dependent on the specific application. == Main building blocks == Following the structure of the image registration workflow, the elastix toolbox proposes a modular solution that implements for each of the building blocks different algorithms, highly employed in medical image registration, and helps the final users to build their specific pipeline by selecting the most suitable algorithm for each of the main building blocks. Each block is easily configurable both by selecting pre-defined initialization values or by trying multiple sets of parameters and then choosing the most performing one. The registration is performed on images, and the elastix toolbox supports all the data formats supported by ITK, ranging from JPEG and PNG to medical standard formats such as DICOM and NIFTI. It also stores physical pixel spacing, the origin and the relative position to an external world reference system, when provided in the metadata, to facilitate the registration process, especially in medical field applications. === Transformation === The transformation is an essential building block, since it defines the allowable transformations. In image registration, the main distinction can be done between parallel-to-parallel and parallel-to-non parallel (deformable) line mapping transformations. In the elastix toolbox, the final users can select one transformation or compose more transformations either through addition or via composition. Below are reported the different transformation models in order of increasing flexibility, along with the corresponding elastix class names between brackets. Translation (TranslationTransform) allows only translations Rigid (EulerTransform) expands the translation adding rotations and the object is seen as a rigid body Similarity (SimilarityTransform) expands the rigid transformation by introducing isotropic scaling Affine (AffineTransform) expands the rigid transformation allowing both scaling and shear B-splines (BSplineTransform) is a deformable transformation usually preceded by a rigid or affine one Thin-plate splines (SplineKernelTransform) is a deformable transformation belonging to the class of kernel-based transformations that is a composition of and affine and a non-rigid part === Metric === The similarity metric is the mathematical function whose parameters should be optimized to reach the desired registration, and, during the process, it is computed multiple times. Below are reported the available metrics computed employing the reference and the transformed images and the corresponding elastix class names between brackets. Mean squared difference (AdvancedMeanSquares) to be used for mono-modal applications Normalized correlation coefficient (AdvancedNormalizedCorrelation) to be used for images that have an intensity linear relationship Mutual information (AdvancedMattesMutualInformation) to be used for both mono- and multi-modal applications and optimized to reach better performance compared to the normalized version Normalized mutual information (NormalizedMutualInformation) for both mono- and multi-modal applications Kappa statistic (AdvancedKappaStatistic) to be used only for binary images === Sampler === For the computation of the similarity metrics, it is not always necessary to consider all the voxels and, sometimes, it can be useful to use only a fraction of the voxels of the images, i.e. to reduce the execution time for big input images. Below are reported the available criteria for selecting a fraction of the voxels for the similarity metric computation and the corresponding elastix class names between brackets. Full (Full) to employ all the voxels Grid (Grid) to employ a regular grid defined by the user to downsample the image Random (Random) to randomly select a percentage of voxels defined by the users (all voxels have equal probability to be selected) Random coordinate (RandomCoordinate) like the random criterion, but in this case also off-grid positions can be selected to simplify the optimization process === Interpolator === After the application of the transformation, it may occur that the voxels used for the similarity metric computation are at non-voxel positions, so intensity interpolation should be performed to ensure the correctness of the computed values. Below are reported the implemented interpolators and the corresponding elastix class names between brackets. Nearest neighbor (NearestNeighborInterpolator) exploits little resources, but gives low quality results Linear (LinearInterpolator) is sufficient in general applications N-th order B-spline (BSplineInterpolator) can be used to increase the order N, increasing quality and computation time. N=0 and N=1 indicate the nearest neighbor and linear cases respectively. === Optimizer === The optimizer defines the strategy employed for searching the best transformation parameter to reach the correct registration, and it is commonly an iterative strategy. Below are reported some of the implemented optimization strategies. Gradient descent Robbins-Monro, similar to the gradient descent, but employing an approximation of the cost function derivatives A wider range of optimizers is also available, such as Quasi-Newton or evolutionary strategies. === Other features === The elastix software also offers other features that can be employed to speed up the registration procedure and to provide more advanced algorithms to the end-users. Some examples are the introduction of blur and Gaussian pyramid to reduce data complexity, and multi-image and multi-metric framework to deal with more complex applications. == Applications == Elastix has applications mainly in the medical field, where image registration is fundamental to get comprehensive information regarding the analysed anatomical region. It is widely employed in image-guided surgery, tumour monitoring, and treatment assessment. For example, in radiotherapy planning, image registration allows to correctly deliver the treatment and evaluate the obtained results. Thanks to the wide range of implemented algorithms, the use of the elastix software allows physicians and researchers to test different registration pipelines from the simplest to more complex ones, and to save the best one as a configuration file. This file and the fact that the software is completely open-source makes it easy to reproduce the work, that can help supporting the open science paradigm, and allows fast reuse on different patients data. In image-guided surgery, registration time and accuracy are critical points, considering that, during the registration, the patient is on the operating table, and the imag
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Airborne Networking

An Airborne Network (AN) is the infrastructure owned by the United States Air Force that provides communication transport services through at least one node that is on a platform capable of flight. == Background == === Definition === The intent of the US Air Force's Airborne Network is to expand the Global Information Grid (GIG) to connect the three major domains of warfare: Air, Space, and Terrestrial. The Transformational Satellite Communications System network currently provides connectivity for all communication through space assets. The Combat Information Transport System and Theater Deployable Communications provide terrestrial connectivity for theatre based operations. The Airborne Network is engineered to utilize all airborne assets to connect with space and surface networks building a seamless communications platform across all domains. === Capabilities === The capabilities identified by this type of system are vastly beyond that of our current military. This system will enable the Air Force to provide a transportable network, flexible enough to communicate with any air, space, or ground asset in the area. The network will provide a beyond line-of-sight (LoS) communications infrastructure that can be packed up and moved in and out of the designated battlespace, enabling the military to have a reliable and secure communications network that extends globally. The network is designed to be flexible enough to provide the right communication and network packages for a specific region, mission, or technology. Operationally, The AN is designed to be self-forming, self-organizing, and self-generating, with nodes joining and leaving the network as they enter and exit a specific region. The network consists of dedicated tactical links, wideband air-to-air links, and ad hoc networks constructed by the Joint Tactical Radio System (JTRS) networking services. JTRS is a software-defined radio that will work with many existing military and civilian radios. It includes integrated encryption and Wideband Networking Software to create mobile ad hoc networks. It also provides system performance analysis and fault diagnostics automatically, reducing the demand for human intervention and network maintenance. === Intended Use === The AN was designed as the cornerstone for the new military doctrine known as Network Centric Warfare. This doctrine was developed to use information superiority to equip warfighters with more precise information enabling commanders and shooters to make smarter decisions faster. The AN contributes to Network Centric Warfare by enabling commanders to provide real-time information to warfighters in the air and on the ground. Warfighters can then utilize more information and make more educated decisions about how to act in a particular situation. Once the act has been carried out commanders will have immediate information about the result and can make judgments on how to continue. All-in-all the AN was designed to reduce the time necessary to identify a target, make clear and educated decisions to pull or not to pull the trigger, and assess battle == Topologies == There are four main network topologies that will be deployed and vary based on the placement of backbone and subnet class networks. === Space, Air, Ground Tether === Establishing a direct connection to another aircraft or ground node, via a point-to-point link for nodes within LOS or via a Satellite Communications (SATCOM) link for nodes that are beyond line-of-sight is known as tethering. SATCOM links provide connectivity to a network ground entry point. Strike aircraft that accompany C2 aircraft such as an AWACS are tethered via point-to-point links. Finally, C2 or intelligence, surveillance, and reconnaissnce (ISR) aircraft may connect via a LOS link directly to a network ground entry point. Each of these tethered alternatives works exactly like a hub or switch that has an entry point to a larger network and allows their connected users access to that network. === Flat Ad Hoc === A flat ad hoc topology refers to establishing nonpersistent network connections as needed among AN nodes that are present at a given time. With this network the nodes dynamically “discover” other nodes to which they can interconnect and form the network. The specific interconnections between the nodes are not planned in advance, but are made as opportunities arise. The nodes join and leave the network at will, continually changing connections to neighbor nodes based upon their location and mobility characteristics. === Tiered Ad Hoc === Ad hoc networks can be flat in the sense that all nodes are peers of each other in a single network, as discussed above, or they can dynamically organize themselves into hierarchical tiers such that higher tiers are used to move data between more localized subnets. This network topology can be compared to any conventional deployed network that utilizes routers, switches, and hubs to temporarily connect users. === Persistent Backbone === A network topology characterized by a persistent backbone is established using relatively persistent wideband connections among high-value platforms flying relatively stable orbits. It provides the connectivity between the tactical subnets which are considered edge networks relative to the backbone. This provides concentration points for connectivity to the space backbone as well as to terrestrial networks. This type of network topology is comparable to a conventional permanent network with established data trunks, routers, switches, and hubs to connect users. == Architecture == === Network Management === The platform management system enables operators to manage all on-board network elements. It interfaces and interoperates with the Airborne Network management system to enable operators to manage remote network elements in the airborne network. The network management system monitors the health of the network by passively testing the network for faults and latency. The system will also actively troubleshoot faults with probes to identify and isolate faulty connections, and enables operators to apply network parameters and security changes to all systems based on the status of the network. === Routing/Switching === Routing and switching enables data to be dynamically transmitted over the network to other nodes. Routing protocols must be able to identify nodes transmitted within their own platform and data to be sent to other platforms regardless of the current topology. The routing protocol must also provide seamless roaming by ensuring that no routed packets are lost when a node changes its point of attachment to the network. Maintaining scalability is important in routing as the network is constantly changing. The network must be able to function with numerous levels of platforms, varying numbers of fast moving platforms, and varying amounts of traffic per platform. Routers and switches will use metrics to determine the best paths to take when routing data. The routing protocol utilized for the AN will be an Adaptive Quality of Service routing protocol. === Gateways/Proxies === Gateways and proxies enable the connection numerous technology types regardless of age to communicate across the IP-based network. Gateways and proxies are essential in the operation of this network because so many different technologies are used to communicate in each domain. These systems will facilitate the transition of the legacy on-board infrastructure, transmission systems, tactical data link systems, and user applications to the objective airborne network systems. Therefore, they are only temporary until all platforms use a standardized IP radio for transmission. === Performance Enhancing Proxies === Performance Enhancing Proxies improve the performance of user applications running across the Airborne Network by countering wireless network impairments, such as limited bandwidth, long delays, high loss rates, and disruptions in network connections. Proxy systems are implemented between the user application and the network and can be used to improve performance at the application and transport functional layers of the OSI model. Some techniques that can be employed include: Compression: Data compression or header compression can be used to minimize the number of bits sent over the network. Data bundling: Smaller data packets can be combined (bundled) into a single large packet for transmission over the network. Caching: A local cache can be used to save and provide data objects that are requested multiple times, reducing transmissions over the network (and improving response times). Store and forward: Message queuing can be used to ensure message delivery to users who become disconnected from the network or are unable to connect to the network for a period of time. Once the platform connects, the stored messages are sent. Pipelining: Rather than opening several separate network connections pipelining can be used to share a single networ
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SocialIQ

Social IQ (formerly Soovox Inc.) was a San Diego-based influencer marketing platform that measured users' online social influence and connected them with brands for word-of-mouth marketing campaigns. The company was founded in 2009 by Akram Benmbarek and was headquartered in San Diego, California. == History == Akram Benmbarek, who had previously worked in technology finance at Advanced Equities Financial Corp and in wealth management at Morgan Stanley, Merrill Lynch, and UBS, founded the company in mid-2009 under the name Soovox. In October 2011, Benmbarek rebranded the company as SocialIQ. At that time, the company was seeking a Series A round of venture capital, having raised under $1 million in angel seed funding. == Similar metrics == Klout PeerIndex
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Open Data-Link Interface

The Open Data-Link Interface (ODI) is an application programming interface (API) for network interface controllers (NICs) developed by Apple and Novell. The API serves the same function as Microsoft and 3COM's Network Driver Interface Specification (NDIS). Originally, ODI was written for NetWare and Macintosh environments. Like NDIS, ODI provides rules that establish a vendor-neutral interface between the protocol stack and the adapter driver. It resides in Layer 2, the Data Link layer, of the OSI model. This interface also enables one or more network drivers to support one or more protocol stacks.
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Circular convolution

Circular convolution, also known as cyclic convolution, is a special case of periodic convolution, which is the convolution of two periodic functions that have the same period. Periodic convolution arises, for example, in the context of the discrete-time Fourier transform (DTFT). In particular, the DTFT of the product of two discrete sequences is the periodic convolution of the DTFTs of the individual sequences. And each DTFT is a periodic summation of a continuous Fourier transform function (see Discrete-time Fourier transform § Relation to Fourier Transform). Although DTFTs are usually continuous functions of frequency, the concepts of periodic and circular convolution are also directly applicable to discrete sequences of data. In that context, circular convolution plays an important role in maximizing the efficiency of a certain kind of common filtering operation. == Definitions == The periodic convolution of two T-periodic functions, h T ( t ) {\displaystyle h_{_{T}}(t)} and x T ( t ) {\displaystyle x_{_{T}}(t)} can be defined as: ∫ t o t o + T h T ( τ ) ⋅ x T ( t − τ ) d τ , {\displaystyle \int _{t_{o}}^{t_{o}+T}h_{_{T}}(\tau )\cdot x_{_{T}}(t-\tau )\,d\tau ,} where t o {\displaystyle t_{o}} is an arbitrary parameter. An alternative definition, in terms of the notation of normal linear or aperiodic convolution, follows from expressing h T ( t ) {\displaystyle h_{_{T}}(t)} and x T ( t ) {\displaystyle x_{_{T}}(t)} as periodic summations of aperiodic components h {\displaystyle h} and x {\displaystyle x} , i.e.: h T ( t ) ≜ ∑ k = − ∞ ∞ h ( t − k T ) = ∑ k = − ∞ ∞ h ( t + k T ) . {\displaystyle h_{_{T}}(t)\ \triangleq \ \sum _{k=-\infty }^{\infty }h(t-kT)=\sum _{k=-\infty }^{\infty }h(t+kT).} Then: Both forms can be called periodic convolution. The term circular convolution arises from the important special case of constraining the non-zero portions of both h {\displaystyle h} and x {\displaystyle x} to the interval [ 0 , T ] . {\displaystyle [0,T].} Then the periodic summation becomes a periodic extension, which can also be expressed as a circular function: x T ( t ) = x ( t m o d T ) , t ∈ R {\displaystyle x_{_{T}}(t)=x(t_{\mathrm {mod} \ T}),\quad t\in \mathbb {R} \,} (any real number) And the limits of integration reduce to the length of function h {\displaystyle h} : ( h ∗ x T ) ( t ) = ∫ 0 T h ( τ ) ⋅ x ( ( t − τ ) m o d T ) d τ . {\displaystyle (hx_{_{T}})(t)=\int _{0}^{T}h(\tau )\cdot x((t-\tau )_{\mathrm {mod} \ T})\ d\tau .} == Discrete sequences == Similarly, for discrete sequences, and a parameter N, we can write a circular convolution of aperiodic functions h {\displaystyle h} and x {\displaystyle x} as: ( h ∗ x N ) [ n ] ≜ ∑ m = − ∞ ∞ h [ m ] ⋅ x N [ n − m ] ⏟ ∑ k = − ∞ ∞ x [ n − m − k N ] {\displaystyle (hx_{_{N}})[n]\ \triangleq \ \sum _{m=-\infty }^{\infty }h[m]\cdot \underbrace {x_{_{N}}[n-m]} _{\sum _{k=-\infty }^{\infty }x[n-m-kN]}} This function is N-periodic. It has at most N unique values. For the special case that the non-zero extent of both x and h are ≤ N, it is reducible to matrix multiplication where the kernel of the integral transform is a circulant matrix. == Example == A case of great practical interest is illustrated in the figure. The duration of the x sequence is N (or less), and the duration of the h sequence is significantly less. Then many of the values of the circular convolution are identical to values of x∗h, which is actually the desired result when the h sequence is a finite impulse response (FIR) filter. Furthermore, the circular convolution is very efficient to compute, using a fast Fourier transform (FFT) algorithm and the circular convolution theorem. There are also methods for dealing with an x sequence that is longer than a practical value for N. The sequence is divided into segments (blocks) and processed piecewise. Then the filtered segments are carefully pieced back together. Edge effects are eliminated by overlapping either the input blocks or the output blocks. To help explain and compare the methods, we discuss them both in the context of an h sequence of length 201 and an FFT size of N = 1024. === Overlapping input blocks === This method uses a block size equal to the FFT size (1024). We describe it first in terms of normal or linear convolution. When a normal convolution is performed on each block, there are start-up and decay transients at the block edges, due to the filter latency (200-samples). Only 824 of the convolution outputs are unaffected by edge effects. The others are discarded, or simply not computed. That would cause gaps in the output if the input blocks are contiguous. The gaps are avoided by overlapping the input blocks by 200 samples. In a sense, 200 elements from each input block are "saved" and carried over to the next block. This method is referred to as overlap-save, although the method we describe next requires a similar "save" with the output samples. When an FFT is used to compute the 824 unaffected DFT samples, we don't have the option of not computing the affected samples, but the leading and trailing edge-effects are overlapped and added because of circular convolution. Consequently, the 1024-point inverse FFT (IFFT) output contains only 200 samples of edge effects (which are discarded) and the 824 unaffected samples (which are kept). To illustrate this, the fourth frame of the figure at right depicts a block that has been periodically (or "circularly") extended, and the fifth frame depicts the individual components of a linear convolution performed on the entire sequence. The edge effects are where the contributions from the extended blocks overlap the contributions from the original block. The last frame is the composite output, and the section colored green represents the unaffected portion. === Overlapping output blocks === This method is known as overlap-add. In our example, it uses contiguous input blocks of size 824 and pads each one with 200 zero-valued samples. Then it overlaps and adds the 1024-element output blocks. Nothing is discarded, but 200 values of each output block must be "saved" for the addition with the next block. Both methods advance only 824 samples per 1024-point IFFT, but overlap-save avoids the initial zero-padding and final addition.
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Software token

A software token (a.k.a. soft token) is a piece of a two-factor authentication security device that may be used to authorize the use of computer services. Software tokens are stored on a general-purpose electronic device such as a desktop computer, laptop, PDA, or mobile phone and can be duplicated. (Contrast hardware tokens, where the credentials are stored on a dedicated hardware device and therefore cannot be duplicated — absent physical invasion of the device) Because software tokens are something one does not physically possess, they are exposed to unique threats based on duplication of the underlying cryptographic material - for example, computer viruses and software attacks. Both hardware and software tokens are vulnerable to bot-based man-in-the-middle attacks, or to simple phishing attacks in which the one-time password provided by the token is solicited, and then supplied to the genuine website in a timely manner. Software tokens do have benefits: there is no physical token to carry, they do not contain batteries that will run out, and they are cheaper than hardware tokens. == Security architecture == There are two primary architectures for software tokens: shared secret and public-key cryptography. For a shared secret, an administrator will typically generate a configuration file for each end-user. The file will contain a username, a personal identification number, and the secret. This configuration file is given to the user. The shared secret architecture is potentially vulnerable in a number of areas. The configuration file can be compromised if it is stolen and the token is copied. With time-based software tokens, it is possible to borrow an individual's PDA or laptop, set the clock forward, and generate codes that will be valid in the future. Any software token that uses shared secrets and stores the PIN alongside the shared secret in a software client can be stolen and subjected to offline attacks. Shared secret tokens can be difficult to distribute, since each token is essentially a different piece of software. Each user must receive a copy of the secret, which can create time constraints. Some newer software tokens rely on public-key cryptography, or asymmetric cryptography. This architecture eliminates some of the traditional weaknesses of software tokens, but does not affect their primary weakness (ability to duplicate). A PIN can be stored on a remote authentication server instead of with the token client, making a stolen software token no good unless the PIN is known as well. However, in the case of a virus infection, the cryptographic material can be duplicated and then the PIN can be captured (via keylogging or similar) the next time the user authenticates. If there are attempts made to guess the PIN, it can be detected and logged on the authentication server, which can disable the token. Using asymmetric cryptography also simplifies implementation, since the token client can generate its own key pair and exchange public keys with the server.
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SIPRNet

The Secret Internet Protocol Router Network (SIPRNet) is "a system of interconnected computer networks used by the U.S. Department of Defense and the U.S. Department of State to transmit classified information (up to and including information classified SECRET) by packet switching over the 'completely secure' environment". It also provides services such as hypertext document access and electronic mail. SIPRNet is a component of the Defense Information Systems Network. Other components handle communications with other security needs, such as the NIPRNet, which is used for nonsecure communications, and the Joint Worldwide Intelligence Communications System (JWICS), which is used for Top Secret communications. == Access == According to the U.S. Department of State Web Development Handbook, domain structure and naming conventions are the same as for the open internet, except for the addition of a second-level domain, like, e.g., "sgov" between state and gov: openforum.state.sgov.gov. Files originating from SIPRNet are marked by a header tag "SIPDIS" (SIPrnet DIStribution). A corresponding second-level domain smil.mil exists for DoD users. Access is also available to a "...small pool of trusted allies, including Australia, Canada, the United Kingdom and New Zealand...". This group (including the US) is known as the Five Eyes. SIPRNet was one of the networks accessed by Chelsea Manning, convicted of leaking the video used in WikiLeaks' "Collateral Murder" release as well as the source of the US diplomatic cables published by WikiLeaks in November 2010. == Alternate names == SIPRNet and NIPRNet are referred to colloquially as SIPPERnet and NIPPERnet (or simply sipper and nipper), respectively.
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